Abt Associates Inc.  4800 Montgomery Lane, Suite 600, Bethesda, MD 20814 
Tel: 301/913-0500  Fax: 301/652-3916
Prepared by
Ali Arbaji, MD, MPA
In collaboration with:
University of Colorado  Initiatives, Inc.  TransCentury Associates
Funded by:
United States Agency for International Development
Primary Health Care
Initiatives (PHCI)
Evaluation of Utilization
of Health Services
Delivery and Health Status
February 2005
b
Evaluation of Utilization of Health Services Delivery
and Health Status
Contract No: 278-C-99-00-00059-00
Project No.: MAARD No. OUTNMS 106
Submitted to: USAID/JORDAN
Ministry of Health of Jordan
February 2005
c
Abstract
This report presents evaluation of Primary Health Care Initiatives (PHCI) project
activities according to a set of selected indicators. The USAID funded project was
implemented in cooperation of the Ministry of Health (MoH) over 5 years
(September1999-December 2004). PHCI aimed at improving quality of primary
health care and reproductive health services at MoH. The evaluation followed quasi￾experimental design. Focal health centers (HCs) received most of the PHCI
interventions, while non-focal centers received either few or no interventions. Users
of MoH non-focal HCs served as the control (comparison) group while clients of
MoH focal health centers served as the intervention group. A representative sample
was selected using stratified two stage cluster sampling approach. A set of utilization
of services and proxy health status indicators were chosen for evaluation. The
indicators were based on timeliness of vaccination, growth and development
monitoring visits, antenatal- postnatal visits, screening children and pregnant women
for anemia, screening of adults for hypertension, contraceptive use, anemia among
children and pregnant women, and status of control of diabetes and hypertension.
Some variables were collected from records and others through cross-sectional
surveys. Data for pretest was collected during October 2000, while posttest was
carried out during June 2004. Comparing pretest and posttest figures, the findings
showed that overall timeliness of vaccination improved insignificantly from 64.5% to
68.2% with no differences between focal and non-focal health centers. Appropriate
growth and monitoring visits of 3-year old children dropped significantly from 21.6%
to 16%. Deterioration was noticed for both focal and non-focal health centers.
Appropriate number of antenatal visits did not change (57.7% to 57.3%), while
attendance of postnatal care increased form 29.6% to 36.1%. Improvement in
utilization of postnatal care was significant only for focal HCs. Family planning
counseling during postnatal visits improved from 34.7% to 77.2% and was significant
for both focal and non-focal HCs. The prevalence of modern contraceptives increased
from 52.9% to 70%, while use of traditional methods dropped from 20.6% to 13%.
Changes in contraceptive use were consistent across focal and non-focal centers. The
seemingly high figures of contraceptive prevalence are due to excluding pregnant
women and provision of MCH services at study HCs. Screening adults aged 40 years
and older for hypertension changed insignificantly from 37% to 38.5%. Screening of
children aged 6-24 months for anemia did not change over 4.5 year period (37.9% to
37.4%), while prevalence of anemia improved only insignificantly from 24.3% to
21.4%. Screening of pregnant women for anemia changed insignificantly from 88.2 to
90.5%, while prevalence of anemia was significantly decreased from about 25% to
about 21%. The prevalence of uncontrolled diabetics increased insignificantly from
61.4% to 63.4%. ANCOVA showed significantly better figures of HbA1c in focal
versus non-focal health centers. Controlled hypertensive patients showed increase
from 11% to 22.3%. The improvement was statistically significant for both focal and
non-focal HCs. Short maturity of PHCI interventions and absence of effective
monitoring systems have contributed to the above findings that showed low impact of
interventions.
i
Table of Contents
Abstract .....................................................................................................................................c
List of Tables........................................................................................................................... iii
Acknowledgment ..................................................................................................................... v
Document Layout ................................................................................................................... vi
Executive Summary............................................................................................................... vii
Introduction..............................................................................................................vii
Methodology............................................................................................................vii
Findings.....................................................................................................................ix
Timeliness of Vaccination........................................................................................ix
Growth and Development Visits and Anemia of Children...........................................ix
Antenatal-Postnatal Visits and Anemia of Pregnancy .................................................. x
Use of Contraceptive Methods..................................................................................xi
Screening for Hypertension .....................................................................................xii
Status of Diabetes Control.......................................................................................xii
Status of Hypertension Control...............................................................................xiii
Recommendations.................................................................................................xiii
1. Introduction ......................................................................................................................... 1
1.1 Background ..........................................................................................................1
1.2 Purpose & Significance........................................................................................1
1.3 Objectives ............................................................................................................2
2. Methodology......................................................................................................................... 3
2.1 Study Design........................................................................................................3
2.2 Sampling Design..................................................................................................4
2.2.1 Introduction .................................................................................................... 4
2.2.2 Sampling Frame .............................................................................................. 5
2.2.3 Sample Size ..................................................................................................... 5
2.2.4 Calculating Weights......................................................................................... 7
2.3 Main Variables and Indicators .............................................................................8
2.4 Data Collection Methods ...................................................................................12
2.4.1 Data Collection Techniques ........................................................................... 12
2.4.2 Data Collection Tools.................................................................................... 12
2.4.3 Data Collection Plan...................................................................................... 17
2.5 Data Analysis ....................................................................................................19
3. Results................................................................................................................................. 20
3.1 Timeliness of Vaccination ............................................................................................... 21
3.1.1 Description of the sample ...............................................................................21
3.1.2 Analysis of Timeliness of Vaccination ...........................................................22
3.2 Growth Monitoring and Anemia of Children............................................................... 25
3.2.1 Description of the sample ...............................................................................25
3.2.2 Appropriateness of Growth and Development Monitoring Visits..................27
3.2.3 Anemia of Children Aged 6-24 Months .........................................................28
3.3 Antenatal Care................................................................................................................. 31
3.3.1 Sample Description.........................................................................................31
3.3.2 Appropriateness of Antenatal and Postnatal Visits.........................................32
3.3.3 Anemia of Pregnancy......................................................................................36
3.4 Use of Contraceptive Methods........................................................................................ 39
ii
3.4.1 Sample Description.........................................................................................39
3.4.2 Family Planning Use.......................................................................................41
3.4.3 Source of Family Planning Methods...............................................................43
3.4.4 Prediction of Contraceptive Use .....................................................................44
3.5 Screening for Hypertension ............................................................................................ 47
3.5.1 Sample Description.........................................................................................47
3.5.2 Screening for Hypertension ............................................................................48
3.5.3 Prediction of Screening for Hypertension.......................................................49
3.6 Status of Control of Diabetes.......................................................................................... 51
3.6.1 Sample Description.........................................................................................51
3.6.2 Status of Control of Diabetes and Obesity......................................................53
3.6.3 Prediction of the Status of Control of Diabetes ..............................................54
3.6.4 Analysis of Paired Observations for Diabetes Control ...................................55
3.7 Status of Control of Hypertension ................................................................................. 58
3.7.1 Sample Description.........................................................................................58
3.7.2 Status of Control of Hypertension and Obesity ..............................................60
3.7.3 Prediction of the Status of Control of Hypertension.......................................61
3.7.4 Analysis of Paired Observations for Hypertension Control............................62
4. Conclusions and Recommendations ................................................................................ 66
5. Annexes............................................................................................................................... 69
iii
List of Tables
Table 2.1: Sampling frame for PSUs......................................................................................... 5
Table 2.2: Selection of Primary Sampling Units with Probability Proportionate to Size ......... 6
Table 2.3: Main Study Variables and Indicators ....................................................................... 8
Table 2.4: Definition of Timeliness of Vaccination for Different Doses................................ 13
Table 2.5: Definition of BMI Categories................................................................................ 16
Table 2.6: Definition of Blood Pressure Levels ...................................................................... 17
Table 3.1.1: Distribution of Valid and Missing Cases by Vaccine Dose ................................ 21
Table 3.1.2: Overall Sample Characteristics ........................................................................... 22
Table 3.1.3: Distribution of Timeliness of Different Vaccine Doses by Study Phase ............ 23
Table 3.1.4: Distribution of Timeliness of Different Vaccine Doses by Study Phase and
Intervention ................................................................................................................... 23
Table 3.1.5: Logistic Regression of Timeliness of Vaccination for All Doses Combined*.... 24
Table 3.2.1: Distribution of Valid and Missing Cases for Main Variables ............................. 25
Table 3.2.2: Overall Sample Characteristics ........................................................................... 26
Table 3.2.3: Distribution of Appropriateness of Growth and Development Monitoring Visits
by Study Phase .............................................................................................................. 27
Table 3.2.4: Distribution of Appropriateness of Growth and Development Monitoring Visits
by Study Phase and Intervention................................................................................... 27
Table 3.2.5: Logistic Regression of Appropriateness of Growth and Development Monitoring
Visits ............................................................................................................................. 28
Table 3.2.6: Distribution of Screening for Anemia by Study Phase ....................................... 29
Table 3.2.7: Distribution of Appropriateness of Growth and Monitoring Visits by Screening
for Anemia .................................................................................................................... 29
Table 3.2.8: Distribution of Anemia Among Children by Study Phase .................................. 30
Table 3.3.1: Distribution of Valid and Missing Cases For Main Variables ............................ 31
Table 3.3.2: Overall Sample Characteristics ........................................................................... 31
Table 3.3.3: Distribution of Main Variables by Study Phase.................................................. 33
Table 3.3.4: Distribution of Main Variables by Study Phase and Intervention....................... 33
Table 3.3.5: Logistic Regression of Appropriateness of Number of Antenatal Visits ............ 34
Table 3.3.6: Logistic Regression of Appropriateness of Number of Postnatal Visits ............. 35
Table 3.3.7: Logistic Regression of Family Planning Counseling .......................................... 36
Table 3.2.8: Distribution of Screening for Anemia by Study Phase ....................................... 36
Table 3.3.9: Distribution of Anemia Among Pregnant Study Phase....................................... 37
Table 3.3.10: Distribution of Anemia by Trimester During the Posttest................................. 37
Table 3.3.11: Logistic Regression of Anemia ......................................................................... 38
Table 3.4.1: Distribution of Valid and Missing Cases For Main Variables ............................ 39
Table 3.4.2: Overall Sample Characteristics ........................................................................... 40
Table 3.4.3: Distribution of Main Variables by Phase of the Study........................................ 41
Table 3.4.4: Distribution of Main Variables by Study Phase and Intervention...................... 42
Table 3.4.5: Distribution of Family Planning Source by Study Phase .................................... 43
Table 3.4.6: Distribution of Source of Selected Family Planning Methods by Study Phase .. 43
Table 3.4.7: Distribution of Difficulties Getting or Using Family Planning Methods by Study
Phase ............................................................................................................................. 44
Table 3.4.8: Distribution of Type Difficulties Getting or Using Family Planning Methods by
Study Phase ................................................................................................................... 44
Table 3.4.9: Logistic Regression of Use of Modern Contraceptive Methods ......................... 45
Table 3.4.10: Logistic Regression of Use of Natural Contraceptive Methods ........................ 46
Table 3.5.1: Distribution of Valid and Missing Cases For Main Variables ............................ 47
Table 3.5.2: Overall Sample Characteristics ........................................................................... 48
Table 3.5.3: Distribution of Main Variables by Study Phase.................................................. 49
Table 3.5.4: Distribution of Main Variables by Study Phase and Intervention Group........... 49
iv
Table 3.5.6: Logistic Regression of Screening for Hypertension............................................ 50
Table 3.6.1: Distribution of Valid and Missing Cases For Main Variables ............................ 51
Table 3.6.2: Overall Sample Characteristics ........................................................................... 52
Table 3.6.3: Distribution of the Status of Control of Diabetes and BMI by Study Phase ....... 53
Table 3.6.4: Distribution of the Status of Control of Diabetes and BMI by Study Phase and
Intervention ................................................................................................................... 54
Table 3.6.5: Logistic Regression of Status of Control of Diabetes ......................................... 55
Table 3.6.6 Tests of Between Subjects Effects for the Posttest HbA1c As Dependent Variable
and Focal-Non-Focal as Intervention............................................................................ 56
Table 3.6.7: Parameter Estimates for the Posttest Readings of HbA1c as Dependent Variable
for Focal and Non-Focal Health Centers....................................................................... 56
Table 3.6.8: Estimated Marginal Means of Posttest HbA1c..................................................... 57
Table 3.7.1: Distribution of Valid and Missing Cases For Main Variables ............................ 58
Table 3.7.2: Overall Sample Characteristics ........................................................................... 59
Table 3.7.3: Distribution of the Status of Control of Hypertension by Study Phase............... 60
Table 3.7.4: Distribution of Obesity Status by Study Phase ................................................... 60
Table 3.7.5: Distribution of the Status of Control of Hypertension and by Study Phase and
Intervention ................................................................................................................... 61
Table 3.7.6: Logistic Regression of Status of Control of Hypertension.................................. 62
Table 3.7.7: Parameter Estimates for the Posttest Readings of HbA1c as Dependent Variable
for Focal and Non-Focal Health Centers....................................................................... 63
Table 3.7.8: Estimated Marginal Means of Posttest Systolic BP ............................................ 64
Table 3.7.9 Tests of Between Subjects Effects for the Posttest HbA1c As Dependent Variable
and Focal-Non-Focal as Intervention............................................................................ 64
Table 3.7.10: Parameter Estimates for the Posttest Readings of Diastolic BP as Dependent
Variable for Focal and Non-Focal Health Centers........................................................ 65
Table 3.7.11: Estimated Marginal Means of Posttest Diastolic BP Readings......................... 65
v
Acknowledgment
PHCI would like to extend deep appreciation to the Ministry of Health and USAID
mission for support and facilitation of the study. We would like to thank all
individuals who in some way contributed to this study. Special thanks go to Engineer
Manal Anani and to Dr. Qasem Al-Rabie from the Directorate of Planning and Project
Administration. Appreciation is also extended to research counterparts and to the data
collection team from the Ministry of Health. PHCI management, technical and
support staff played instrumental role in realization of the study.
vi
Document Layout
 All numbers and proportions are weighted values to fit the multistage stratified
cluster sampling design.
 This report starts with an abstract followed by an executive summary covering
the introduction, methodology, results and recommendations.
 Section 1 describes the introduction covering background information,
purpose and objectives of the study.
 Section 2 describes methodology covering the study design, sampling
procedures, main variables, data collection techniques and tools, data
collection plan and data analysis procedures.
 Section 3 describes the findings of the study. This section is organized in
seven subsections (3.1-3.7) each describing one of the indicators or a group of
related indicators based on the relevant variables. Each results subsection
describes the relevant indicator, looks for possible effect of PHCI
interventions, and provides predication of the main variable by the available
independent variables.
 Section 4 offers the main conclusions and recommendations based on study
findings.
 Annexes include data collection tools.
vii
Executive Summary
Introduction
Primary Health Care Initiatives (PHCI) is a USAID funded project that has been
implemented throughout the Hashemite Kingdom of Jordan by the international
consulting firm Abt Associates, Inc. in cooperation with Ministry of Health (MoH).
The lifetime of the project was 5 years (September1999-June 2004). The technical
components of the project were extended for six months till December of 2004. It was
designed to improve primary and reproductive healthcare through provision of an
integrated package of services. The project had six main components namely; (a)
quality assurance, (b) training, (c) reproductive health (d) health communication and
marketing, (e) management information systems, (f) applied research, and (g)
renovation and equipment. One of the main objectives of the Research component
was overall project evaluation.
The purpose of this study is to evaluate the impact of various PHCI project activities
on utilization of health services and health status of clients using MoH primary health
care facilities.
Methodology
This study follows the “quasi-experimental design” in which there is random selection
of study subjects as well as a pretest and posttest with a comparison group, but lacks
the random allocation of subjects to either comparison or intervention groups.
The 200 primary and comprehensive heath centers (PHCs and CHCs) that received
most of PHCI interventions were labeled as focal and considered as the intervention
group. The rest of PHCs and CHCs that had hitherto received either few or none of
PHCI interventions were labeled as non-focal and were considered as a comparison
group. The selected indicators were measured at the pretest phase during October￾November 2000 and re-measured during June-July 2004 as a posttest phase. It is
worth mentioning that the study largely followed a separate pretest posttest design.
This type of design carries the risk of having nonequivalence within each group since
the same subjects were not followed up from pretest to posttest.
A stratified two-stage cluster sampling design was used. The three geographic regions
of Jordan (north, center and south) and the two types of health centers (CHCs and
PHCs) served as the basis for stratifying the sample into six strata. Health centers
viii
were the Primary Sampling Units (PSUs) representing the first level cluster. Study
subjects were chosen at random from the selected health care centers. For certain
centers with expected low patient load, the first arrivals were selected to ensure
finding a sufficient number of study subjects over the 2-4 day period of data
collection. This issue was further dealt with by weighting.
Sampling frame for PSUs consisted of a total of 306 PHCs and CHCs that offer MCH
services. The final number of selected health centers was 89 and 10 subjects were
supposed to be selected in each health center. For two variables (diabetes and
hypertension control) where paired observations on the same individuals were
planned to be collected in the pretest and posttest, the number of subjects per cluster
was increased to 13 during pretest instead of 10 to compensate for the expected
attrition over a 4 year period. The 89 health centers selected at the pretest phase were
divided into intervention and comparison centers as the only available choice without
having separate samples. Relative weight was used to reflect the population from
which the sample was drawn while keeping the sample size close to the original value.
All numbers and proportions in the results sections represent the weighted values.
The study variables reflect important health issues such as the status of diabetes and
hypertension control as proxy health indicators that the project activities were
intended to improve. The study further examines other important utilization indicators
like contraceptive use and screening for hypertension. Although contraceptive use has
been widely researched in Jordan, studies examining contraceptive use by MoH users
are not available. Finally, the study looks at some record based indicators of health
status and utilization such as anemia of children and pregnant women, timeliness of
vaccination, appropriateness of growth and monitoring visits for children and
appropriateness of antenatal-postnatal care.
Three main Techniques of data collection were used in the study: 1) using available
information for record based surveys on timely vaccination, growth and development
visits, antenatal-postnatal visits, anemia of pregnancy, anemia of children and partly
screening for hypertension, 2) interviewing study subjects using questionnaires was
applied to get data on contraceptive use and partly for screening of hypertension,
diabetes and hypertension control status, and 3) measurements (observations) that
applies to measuring glycosylated hemoglobin, and blood pressure in diabetes and
hypertension.
ix
Findings
Timeliness of Vaccination
Timeliness for all doses combined increased insignificantly by 5.7% from 64.5% in
the pretest to 68.2% in the posttest. There were some significant variations between
the pre and posttest data for some individual doses but the difference was not
consistent in favor of one stage. No statistically significant difference was noted for
intervention focal health centers. This finding can be attributed to the fact that PHCI
did not implement direct interventions that aimed at improving the timeliness of
vaccination.
Pooled data showed that region, health center type and mothers education are
significant predictors of timeliness of vaccination. North and central regions were 2
and 1.7 times more likely to have appropriate timing of vaccination than the south.
The results were in favor of the CHCs, where the records of timeliness were 41%
more likely to be higher than the PHCs. As for mothers’ education, the illiterate
women were 3.1 times less likely to get their children timely vaccinated than those
with higher education. Other factors such as sex of the child, family income and
father’s education did not show significant prediction.
Growth and Development Visits and Anemia of Children
Overall, the appropriateness of growth and development visits made at MoH health
centers decreased significantly from 21.6% to only 16%. The trend was also noted for
the appropriateness of first and second year visits. The data for the first and second
years went down from 63.4% and 37.1% during the pretest to respectively 55.4% and
27.7% during the posttest. The deterioration in the appropriateness of growth and
development visits was noted for both focal and non-focal health centers, yet less
pronounced in the focal centers. Logistic regression analysis did not reveal any worth
mentioning results.
Screening for anemia among children aged 6-24 months showed no change during the
two phases of the study (37.9% and 37.4%). Hemoglobin testing is compulsory at the
age of one year for all children utilizing MoH facilities. Nevertheless, figures are still
profoundly low with no change over time. Those screened for anemia showed about
2.3 times more appropriate visits for growth and development monitoring than those
not screened. As for anemia an insignificant decreased was noted for children aged 6-
24 months (24.3% to 21.4%). These figures are close to those available in the MoH
x
database as far as they have the same source. It is worthwhile mentioning that anemia
results should be interpreted with caution as representation of children is considered
low at less than 38%.
Antenatal-Postnatal Visits and Anemia of Pregnancy
Percentage of women with appropriate number of antenatal visits made at the same
MoH health center did not change over the intervention period. The figure changed
insignificantly from 57.7% during the pretest to 57.3% during the posttest.
The prevalence of appropriate postnatal visits improved significantly from 29.6%
during the pretest to 36.1% during the posttest. The changes were significant only for
users of focal health centers. Family planning counseling during postnatal visits
improved from 34.7% during the pretest to 77.2% during the posttest (p=<0.005). The
improvement was noted for both focal and non-focal health centers. Decision to use
family planning methods based on counseling did not change significantly between
the pre and posttest phases.
Study phase, appropriateness of antenatal visits, age, health center type and woman’s
educational level were shown to be significant predictors of appropriateness of
postnatal visits. Regional differences were absent. Paying an appropriate number of
antenatal visits was the most predictive factor of coming to at least one postnatal visit.
If a pregnant woman attended 4 or more antenatal visits she is about 2.8 times more
likely to be seen at the postnatal clinic than those women paying less visits. Pregnant
women were 1.38 times more likely to pay at least one postnatal visit after delivery in
the posttest than in the pretest (p = 0.003). CHCs were 1.4 times less likely to attract
pregnant women to have postnatal care than the PHCs. Each year increase in age
makes pregnant women 2% less likely to attend postnatal care after delivery.
Out of the available variables, family planning counseling was best predicted by the
study phase and region. During the posttest pregnant women were 8.4 times more
likely to be counseled for family planning during a postnatal care visit than the
pretest. Women attending postnatal clinics in the north and central region were 5.4
and 2 times respectively more likely to be counseled for family planning than women
attending clinics in the south.
Screening of pregnant women for anemia has improved from 88.2% to 90.5% during
the pre and posttest respectively. Anemia among pregnant women has significantly
xi
decreased by 20% from about 25% in the pretest to about 21% in the posttest
(p=0.04). The logistic model did not show the study phase as a significant predictor.
Anemia progressed from 9.8 g% during the first trimester to 18.5% during the second
and reaching 27.6% during the third trimester for the posttest. These findings are
consistent with course of pregnancy.
Use of Contraceptive Methods
The prevalence of use of various family planning methods was calculated for all
married women of reproductive age visiting MoH health centers with MCH services
excluding pregnant women. It is estimated that about 20% of women visiting health
centers with MCH services are pregnant. Therefore, contraceptive prevalence rate
among non-pregnant is expected to be higher than figures reported by DHS or other
studies that include all women.
The use of modern methods increased from 52.9% during the pretest to 70% during
the posttest. The prevalence increased significantly by 36% among users of focal
health centers compared to 24% increase in non-focal. Parallel to the increase of use
in modern methods, about 37% drop in the use of traditional methods from 20.6%
during the pretest to 13% during the posttest was noted. As for individual methods use
of condoms and injectables increased by almost 100% followed by use of pills by
51%, while use of IUDs remained unchanged.
Over the two phases of the study there was a noticeable increase by 30% of the source
of family planning method being the local health center. The dependence on sources
other than MoH centers was reduced by about 30% between the pretest and posttest.
The results of logistic regression for the pooled pre-posttest data showed that study
phase, region, age, employment, education and number of male and female children
were significant predictors of modern methods use. Modern contraceptive use was
about twice more likely in the posttest compared to the pretest. Women in central and
north regions were respectively 69% and 33% more likely to use modern methods
than women in the south region. Women in the age group of less than 30 years were
about 2.3 times more likely to use modern methods than those older than 40 years.
Employed women were found to be about 1.8 times more likely to use modern
methods as compared to the unemployed. Illiterate women were twice less likely to
use modern methods than those with higher education. Women married to illiterate
husbands were 2.8 times less likely to use modern methods when compared to those
married to husbands with higher education.
xii
Screening for Hypertension
This variable measures screening for hypertension among those aged 40 years and
above. The overall screening took into consideration BP recordings in the medical file
over the last year including the day in which the survey was conducted. It was found
that screening for hypertension did not change significantly over more than 4 years.
Screening increased by only 4% from 37% in the pretest to reach 38.5% in the
posttest. No pre-posttest difference was noted for focal versus non-focal health
centers.
Age and sex showed significant prediction for the hypertension screening variable. A
male patient was 1.37 times less likely to have his blood pressure checked than a
female patient. For each one year increase in age there was 1.2% more likelihood that
the patient is screened for hypertension. The study phase, health center type, region
and years of schooling did now show any significant prediction.
Status of Diabetes Control
Subjects with HbA1c readings below 7% were considered as controlled diabetics. The
prevalence of uncontrolled diabetics increased insignificantly from 61.4% during the
pretest to 63.4% during the posttest. The change of the status of control of diabetes over
the project lifetime was insignificant for both focal and non-focal health centers. Body
Mass Index (BMI) showed that obese had also insignificantly decreased from 47.9% during
the pretest to 43.8% during the posttest. The change in BMI for the focal centers between
the two phases of the study was significant.
Region, age, years of schooling, disease duration and obesity had some prediction to
diabetes control while study phase, health center type, sex and employment had no
significant prediction. North was not different from the south while respondents from
the central region were 29% more likely to have their diabetes controlled compared to
respondents from the south. An increase of one year of schooling improved control of
diabetes by 6%. With each year of increase in disease duration the possibility that a
diabetic patient becomes controlled is about 6% less. Non-obese subjects are 1.34
times more likely to be controlled than obese subjects.
Overall, only 446 diabetic subjects out of the 1190 recruited in the pretest (37.5%)
were followed in the posttest. The paired observations constituted about 39% of the
posttest respondents. The final results of analysis of co-variance (ANCOVA) showed
xiii
that the marginal mean value of HbA1c for diabetics using focal health centers was
significantly less at 7.97% as compared to non-focal health centers at 8.81%
irrespective of the differences in the pretest readings.
Status of Hypertension Control
Controlled hypertensive patients showed significant increase from 11% during the
pretest to 22.3% during the posttest. Improvement was noted also among the three
grades of hypertension with more patients appearing in the first grade and less in the
third grade during the posttest. The improvement was significant for focal and non￾focal health centers. Prevalence of obesity had decreased insignificantly from 58%
during the pretest to 55.6% during the posttest. The obesity results were consistent for
focal and non-focal health centers.
Prediction of the status of control of hypertension was limited to study phase, years of
schooling and obesity. The odds of hypertension control during the posttest were 2.2
that of the odds of the pretest indicating that hypertensive patients were over two
times more likely to be controlled in the posttest than in the pretest. With each year of
increase in schooling, a hypertensive patient was 5% more likely to be controlled.
Normal weight hypertensive patients were significantly about 1.5 times more likely to
be controlled than their obese counterparts.
Overall, only 371 hypertensive patients out of the 1148 recruited in the pretest
(32.3%) could be followed in the posttest. The paired observations constituted about
34% of the posttest respondents. The final results of analysis of co-variance
(ANCOVA) for systolic BP measurements showed that the marginal mean was 149.2
mm/Hg for focal health centers compared to 153.8 mm/Hg for non-focal health
centers. The prediction formula showed better results among focal health centers only
if pretest systolic BP exceeded 140 mm/Hg. The marginal mean for diastolic BP
readings was 89.6 mm/Hg for the focal health centers compared to 90.3 mm/Hg for
non-focal health centers when the pretest value at its mean of 94 mm/Hg.
Conclusions and Recommendations
1. PHCI as a large project with multiple diverse components reflecting a
mixture of software and hardware activities had a relatively prolonged
preparatory phase. During the first quarter of 2003 only five health
centers had all the six PHCI components completed. Over the last two
xiv
years of the project most of the PHC related activities at health centers
were accomplished with different periods of maturation. Even
activities in some health centers did not start yet when this study was
done. With such short period of interventions it was expected that
PHCI activities would not affect most of the set impact indicators back
in early 2000.
2. PHCI activities started its technical and non-technical components
without the availability of satisfactory systems to sustain those
activities. The absence of effective supervisory system at the MoH and
engrossment of PHCI with completion of the delayed planned activity
had led to few if any changes over the last two years of project
implementation of PHCI activities. Furthermore, PHCI project was
more output oriented without clear measurable outcome indicators
related to various activities. The PHCI vague monitoring and
evaluation plan had contributed to weak impact of project
interventions.
3. Ways to improve the postnatal care at MCH facilities should be
considered including outreach programs. Furthermore, missed
opportunities for family planning during postnatal visits have to be
considered seriously.
4. Improve the utilization of growth and development monitoring visits
for children during second and third year of life. This can be achieved
by improving health awareness of the community towards growth
monitoring needs and benefits. Developing the outreach program at the
MOH can add considerable value to this particular intent.
5. Improve the quality of maternal and child health care services in order
to ensure high quality care delivery. Developing follow up mechanisms
is a necessary step for modifying maternal and child health services.
6. Review and institute policies and procedures necessary for early
detection of anemia both during pregnancy and early childhood.
Developing procedures and protocols to be used for correct diagnosis
and treatment of anemia and its underlying causes is recommended.
7. Record keeping systems should have clear evaluation schemes in order
to facilitate correct monitoring of health problems. Documentation of
procedures and findings in patient’s medical records has to be
improved. Failure of recording BP in 43% of cases screened for
xv
hypertension shows the negligence of physicians that might be
occurring with other procedures.
8. Create a management system whereby a set of standards is provided
and ensured. Standards that cover all areas of primary health care
service delivery should be reviewed and updated as needed. These
standards should be made available to all health care providers and
used in monitoring service provision.
9. A national strategy for chronic non-communicable diseases is urgently
needed to improve the status of awareness, counseling, treatment, and
control levels among hypertensive and diabetic populations
10. Screening mechanisms for hypertension among those aged 25 years
and above have to be established with no delay. Screening is a simple
procedure that can be applied to a prevalent disease in order to enable
the prevention of serious complications. Effective treatment schedules
can be made readily available once the disease is discovered.
11. Assist the MOH in developing a health promotion schemes that target
common health problem such as anemia, diabetes, hypertension and
low use of pills in face of almost 100% availability.
1
1. Introduction
1.1 Background
In cooperation with the Hashemite Kingdom of Jordan, USAID/Jordan has developed
a program to improve basic primary health care through an integrated package of
family health services in which reproductive health, child health, adult health and
health prevention and promotion that was delivered by a health provider teams at
primary health care settings. This project, called the Primary Health Care Initiatives
(PHCI), had been implemented throughout the country by the international consulting
firm Abt Associates, Inc. in cooperation with Ministry of Health. The original life
time of the project was 5 years (September1999-June 2004). The technical
components of the project were extended for six months till December of 2004.
The project had seven interventions and included: (a) quality assurance, (b) clinical
training, (c) reproductive health (d) health communication and marketing, (e) health
management information systems, (f) applied research, and (g) renovation and
equipment. One of the main objectives of the research component of PHCI was the
overall project evaluation. The combination of the various inputs that were designed
to increase the quality of health care services in MoH based primary health care
facilities in Jordan namely, primary and comprehensive health care centers (CHCs
and PHCs). The five-year life span of this project presented an opportunity to
empirically test the validity of this assumption. This evaluation study uses mainly
outcome measures to help identify gaps in the current system and to evaluate the
quality and impact of the various PHCI programs. Furthermore, information from the
pretest phase of the evaluation process was used to refine proposed PHCI activities.
For more details on PHCI activities, please refer to the end of project situation
analysis report.
1.2 Purpose & Significance
The purpose of this study is to evaluate the impact of the various PHCI project
activities on utilization of services and health status. Methodologically, the study uses
quasi-experimental design with pretest, posttest and control group. The pretest phase
took place in October-November 2000, while the posttest was conducted in June-July
of 2004.
2
The indicators of utilization and health status used in the study were selected with
involvement of various stakeholders over two roundtable sessions during the first
quarter of 2000. The two roundtable workshops were attended by specialists from
MoH, PHCI, Universities, USAID and some visiting Abt consultants.
The purpose of this report is to inform MoH and USAID in addition to other
stakeholders of the change of some primary health care utilization and proxy health
status indicators that came about over the period of evaluation of about 4.5 years .
The study covered users of MoH primary health care system. It looked for such
important health issues as the status of diabetes and hypertension control as proxy
health indicators that the project activities were intended to improve. The status of
control of these two major chronic diseases that lead to significant morbidity and
mortality has never been done in MoH facilities on a national scale. The determinants
of the above indicators extend well beyond the traditional boundaries of the health
care system such as socio-economic status. Nevertheless, it is believed that these
indicators provided a good appraisal of quality health care.
The study further examines some utilization indicators like screening for hypertension
and contraceptive use rate. Contraceptive use has been well researched in Jordan but
no figures were available for MoH users. Finally, the study looks at some record
based indicators of health status and utilization such as anemia of children and
pregnant women, timeliness of vaccination doses, appropriateness of growth and
development monitoring visits for children and appropriateness of antenatal-postnatal
care. The current report provides a strong foundation for decision making and activity
planning that can positively affect future projects and programs in Jordan.
1.3 Objectives
In light of the above background and purpose, the overall primary objectives of this
evaluation study are the following:
 To measure and assess change in a set of selected utilization
of services indicators in PHCs and CHCS over the period
from October 2000 to June 2004.
 To measure and assess change in a set of selected proxy
health status indicators in PHCs and CHCS over the period
from October of 2000 to June of 2004
3
2. Methodology
2.1 Study Design
This study follows the “quasi-experimental design” in which there is a pretest and
posttest, a set of interventions and a comparison group with random selection of study
subjects but lacks the random allocation of subjects to either control or intervention
groups. This is illustrated as follows:
Study Groups Assignment October
2000 Intervention
June
2004
Focal MoH facilities
(intervention group) [N] O1 X O2
Non-focal MoH facilities
(comparison group) [N] O1 O2
where,
N Non-random assignment of the intervention to
O1 =
The pretest measurements of the selected utilization and proxy health
status indicators for intervention and comparison groups .
X PHCI interventions
O2 =
The posttest measurements of the selected utilization and proxy health
status indicators for intervention and comparison groups.
It is worth mentioning that during the early design stages, clients using all MoH PHCs
and CHCs were considered as intervention group while clients attending United
Nations Relief Works Agency for Palestinian Refugees (UNRWA) clinics represented
the comparison group. Users of UNRWA did not represent a close match to MoH
users by all means, nevertheless they were the best available at that stage. The pretest
was carried out in 2000 with users of UNRWA health centers as intervention group.
UNRWA had only 13 health centers serving the refugee camps mainly in the central
region as compared to the ministry with over 350 primary and comprehensive health
centers distributed all over the country. After completion of the pretest UNRWA
launched programs to improve MCH and non-communicable diseases services. In
4
September of 2001 the project was amended and the mandate was reduced from all
MoH PHCs and CHCs to only 200 health centers. Those 200 health centers were
called "focal-health centers" in contrast to the rest of the health centers that were
labeled as "non-focal". During the posttest, it was decided to consider non-focal
health centers as the comparison group while the focal as the intervention group.
Nevertheless, the new comparison group was not a perfect choice but rather the best
available. The 200 focal health centers represented 80% of the workload at the
ministry. Users of the comparison group were contaminated with various PHCI
interventions such as renovation, training and quality assurance. Mass media
campaigns implemented by the communication and marketing component of the
project were designed nationally and for all sectors.
This stresses the fact that the proposed design is a non-equivalent groups design. It is
worth mentioning that all tools but the status of control of diabetes and hypertension,
the design is a separate pretest posttest. This kind of design carries the risk of having
nonequivalence within each group since the same subjects are not followed up from
pretest to posttest. With all the above mentioned pitfalls, the design was still the best
that fitted the situation as described..
2.2 Sampling Design
2.2.1 Introduction
A stratified two-stage cluster sampling design was used. Since the study aimed at
generalizing results according to the type of health care center and regional as well as
national levels, three samples of PHCs and three samples of CHCs were selected
proportionate to size from the three regions of Jordan, namely; north, central and
south. Health centers in each stratum were then selected at random.
The primary and comprehensive health care centers constituted the primary Sampling
units (PSUs) representing the first level clusters. Study subjects visiting the health
centers were considered the secondary sampling units. Depending on indicator, either
patients’ medical files were randomly chosen or cross-sectional surveys were applied
with random selection of subjects. For certain centers with expected low load of
patients, the first arrivals were selected to ensure finding sufficient number of study
subjects over the 2-3 day data collection period. This issue was further dealt with by
weighting since centers with low load will definitely get lower weights.
5
2.2.2 Sampling Frame
Sampling frame for PSUs
consisted of a total of 306 PHCs
and CHCs that offer MCH
services. All centers (about 70)
that do not offer MCH services
were excluded because three of
the instruments used were
designed to collect data on MCH
related indicators. The sampling
frame covered all 12
Governorates as well as the 20
health directorates. Table 2.1
summarizes the sampling frame.
Users of the above centers constituted the sampling frame for the selected subjects
2.2.3 Sample Size
Estimation of the sample size was based on the results of a study on contraceptive use
in Jordan carried out in 1997. The prevalence of contraceptive use was about 0.4 and
that would allow the maximum variability possible taking into consideration the
estimates for other main variables. The calculated sample size was used for all other
variables despite that some required a smaller sample size. The Coefficient of
Variation (CV) was found to be 0.02 while the variance within each cluster (S2w) was
0.041554 and the variance among clusters (S2
b) was 0.02726. The estimated variation
for proportion V (p) was calculated for CV% of 5% to be 0.000692. The following
formula was used to estimate the number of PSUs:
V (p) = S2
b/n + S2w/mn where m is the sample size for PSUs and n represents the
number of subjects to be selected in each cluster.
If “n” is considered 8 then we will end up with an m of 47 centers, if “n” is 10 then
we need 45 centers and when “n” is 12 then the expected number is about 44 centers.
It was decided to use 10 subjects per cluster, therefore a minimum of 45 centers were
needed (Using 10 subjects per cluster lead to selecting 45 clusters). For certain
variables (hypertension and diabetes control) where paired observations on the same
individuals are to be collected in pre and posttests, the number of subjects per cluster
was increased to 13 to compensate for the expected attrition over a 4.5 year period. As
Table 2.1: Sampling frame for PSUs
Health Center Type Number of Centers
Central CHC 20
Central PHC 97
Northern CHC 11
Northern PHC 115
Southern CHC 11
Southern PHC 52
TOTAL 306
Total CHC 42
Total PHC 264
6
a result, the minimum number of required subjects was 450 with 10 subjects from
each of the 45 centers.
Data on annual number of visits and number of employees for 1999 was obtained
from the MoH information centers. PSUs were selected with probability proportionate
to size (PPS) within each stratum.
The size of each health center was calculated according to the following formula:
Size of the center  capacity of center  number of annual visits
2
The capacity was calculated according to the following formula:
Capacity  No. of clients per stratum  No. of employees at a given center
Number of employees per stratum
Table 2.2 shows the distribution of health centers by size for each stratum that was
used to define the number of health centers in each stratum proportionate to size. The
fifth column shows the adjusted number after selection with probability proportionate
to size. As far as sampling from each stratum is separate the number of PSUs was
inflated to allow sufficient number of health centers in each stratum. The inflation was
done in an arbitrary way taking into account the number of the health centers in each
stratum and the number calculated by PPS. The adjusted final numbers used in the
sample are shown in the last column of Table 2.2. Within each stratum the PSUs were
selected at random.
Table 2.2: Selection of Primary Sampling Units with Probability Proportionate to
Size
Health Center Size
Number of
Centers
Rounded N
with PPS
Adjusted
Number
Central CHC 767883 20 7 11
Central PHC 1464292 97 13 24
Northern CHC 342858 11 3 5
Northern PHC 1503615 115 14 28
Southern CHC 330372 11 3 5
Southern PHC 526134 52 5 16
TOTALS 4935154 306 45 89
7
Finally, 89 (well distributed) health centers were selected over the six strata. The
overall sample size was expected to be 890 for all the tools with 10 subjects from each
health center. For diabetes and hypertension with 13 subjects from each selected
health center, 1160 individuals were expected at least during the pretest phase.
It is worth mentioning that the study lacked separate samples for the intervention and
comparison groups. This flaw happened because when pretest was conducted the
users of MoH HCs were considered as the intervention group and users of UNRWA
as the comparison group. During posttest, the original pretest sample of MoH users
was used and divided into the intervention (63 HCs) and comparison groups (26 HCs)
as the only available choice.
2.2.4 Calculating Weights
Weighting was done in the first place to reflect the population from which the sample
was drawn. Relative weight was used to fit the design in various conditions.
 Expansion weight was calculated for each study subject in all
tools according to the following formula: EW=W1W2 where,
EW is the expansion weight, W1
is the weight of a health center
in the stratum and W2
is the weight for the study subjects in the
health center.
W1 was calculated as a reciprocal of the probability of selecting the health
center in the stratum. Dividing the size of the health center by the total size
in the stratum and multiplying the product by the number of health centers
in the stratum calculated the probability of selecting a health center in that
stratum.
W2 was calculated as the reciprocal of the probability of selecting one
study subject in a given health center. Dividing the number of selected
subjects at the health center by the total number of clients visiting the
center during the study period equaled the probability of selecting a study
subject.
Expansion weight was used to calculate the relative weight.
8
 Relative weight was calculated by dividing the expansion weight for each
subject by the average weight. The average or mean weight was calculated
by dividing the total expansion weight for all subjects in the sample by the
total number of subjects in the sample.
The above mentioned expansion weight is suitable for inflation of the
small samples at the stratum level in order to mirror the population that
they represent. But when analysis at the national, regional or health center
type levels is needed the inflation resulting from using the expansion
weight will render the tests of statistical significance, with a standard
statistical package like SPSS, almost meaningless. This happens simply
because the computations do not reflect the actual number of observations
and become too exaggerated ending up mostly with statistically significant
relationships.
Relative weights just downsize the expansion weights to numbers that are
close to the actual sample size but still maintain the appropriate
distribution of cases as produced by the expansion weight.
2.3 Main Variables and Indicators
Main variables are those used for calculation of utilization of services and proxy
health status indicators. The variables were divided into two groups: a) utilization of
services; and b) proxy health status variables. Each of the above groups was further
divided into three categories. The first category deals with children up to three years
of life, the second deals with women and the third with the adult population.
Table 2.3 shows the main study variables and their relevant indicators.
Table 2.3: Main Study Variables and Indicators
Utilization Variables:
These are variables used to calculate some process and output indicators related to
utilization of services at health care centers.
Variables Indicators
Children aged three years or less:
9
Table 2.3: Main Study Variables and Indicators
Timeliness of Vaccination:
Dates of vaccination for 2-year-old
children.
Proportion of children aged 2 years who
were timely vaccinated.
Growth and Development Visits:
Number of growth and development
visits made by 3-year-old children.
Proportion of 3-year-old children with
appropriate number of growth and
development visits (5, 2 and 1 visits for 1st
,
2
nd and 3rd year respectively).
Screening Children for Anemia:
The presence of at least one
hemoglobin reading in the child’s
record that was performed at the age 6-
24 months.
Proportion of children aged 6-24 months
with hemoglobin test that was done and
recorded at least once.
Women:
Antenatal Visits:
Number of antenatal visits made by a
pregnant woman and recorded in her
medical file during her last completed
pregnancy.
Proportion of pregnant women with at least
4 antenatal visits made at the selected
health center at the end of pregnancy.
Postnatal Visits:
Number of postnatal visits made by a
pregnant woman after her last delivery.
Proportion of pregnant women with at least
one postnatal visit within the first 6 weeks
after delivery
Screening Pregnant Women for
Anemia:
The presence of at least one
hemoglobin reading during last
pregnancy in the antenatal record.
Proportion of pregnant women with
hemoglobin test that was done and
recorded.
Use of Contraceptive Methods:
The status of using contraceptive
methods by married women aged 15-
49 years.
Proportion of women of reproductive age
who were currently using any method of
contraception.
Adults
Screening for Hypertension:
The status of screening of non￾hypertensive adults aged 40 years and
above of both sexes during the last
year.
Proportion of non-hypertensive adults aged
40 years and above screened for
hypertension during last year.
10
Table 2.3: Main Study Variables and Indicators
Health Status Variables
Due to the relatively short lifetime of the project, measurable impact is not expected
on major health indicators like infant mortality, maternal mortality and life
expectancy nor on prevalence of main diseases like hypertension and diabetes.
Instead, PHCI interventions were evaluated against a group of proxy health
indicators an outcome measures.
Children aged 6-24 months:
Anemia of Children*:
Hemoglobin readings made at 6-24
months of life.
Proportion of anemic children at 6-24
months of age.
Women:
Anemia of Pregnancy*:
Hemoglobin readings of pregnant
women attending MCH centers.
Proportion of anemic pregnant women.
Adults
Control of Diabetes:
Glycosylated hemoglobin (HbA1c)
readings for diabetic patients.
Proportion of controlled diabetics.
Control of Hypertension:
Blood pressure measurements for
selected hypertensive subjects.
Proportion of controlled hypertensives.
*Anemia of children and pregnancy indicators were added as proxy health status indicators because of
the ease of getting data from the already surveyed medical records without anticipating that PHCI
intervention are going to affect them.
The vaccination coverage in Jordan is very high; figures above 90% for individual
vaccines are reported from different sources. Jordan is currently at the final stages of
poliomyelitis eradication and the early stages of measles elimination. Given the
population movement from other countries that are still behind Jordan in vaccination
coverage, the timeliness of vaccination seems to be very important
Regular growth assessment of children during their first years of life is the single
measurement that best defines the health and nutritional status. Certain socio￾economic factors are beyond the control of the health team providing the service.
11
Nevertheless, there is a long list of health conditions affecting growth that can be
corrected with appropriate growth monitoring visits to MCH centers including
anemia.
Antenatal-postnatal care addresses both the psychosocial and the medical needs of the
pregnant woman. Periodic health check-ups during the antenatal period are necessary
to establish confidence between the woman and her health care provider, and to
identify and manage any maternal complications or risk factors. Antenatal visits are
also used to provide essential services that are recommended for all pregnant women,
such as tetanus toxoid immunization and the prevention of anemia through nutrition
education and provision of iron/folic acid tablets. Postnatal care is also essential for
the early detection and adequate management of problems and disease emerging
during the first 6 weeks after delivery in addition to being a good opportunity for
offering family planning counseling.
Jordan has realized the discrepancy between the natural population growth rate and
economic growth that poses increasing pressure on the public sector regarding
education, health, employment and other aspects as well. Jordan’s National
Population Strategy calls for the expansion of family planning services throughout the
Kingdom and seeks to increase rates of family planning use.
Despite the fact that contraceptive prevalence has been widely studied in Jordan with
almost annual Jordan Population and Family Health Surveys over the last years, the
current study is designed to gather information on users of MoH as far as the PHCI
project is more facility based project. The results provided by nationwide household
surveys are expected to be different from facility based surveys depending on type of
facility under consideration. In our case the sample is biased towards more use of oral
contraceptive as far as only health centers with MCH services were chosen.
Hypertension is a highly prevalent disease in Jordan. Jordan Morbidity Survey of
MoH in 1996 pointed to an overall 32% prevalence of hypertension in those aged 25
years and above. The disease is the best example of secondary prevention. Screening
for hypertension is a simple procedure applied to a prevalent disease with serious
complications, easily prevented by the availability of very effective treatment
schedules once the disease is discovered.
A mixture of health problems that is common in both developing and industrialized
countries burdens the health care delivery system in Jordan. Hypertension occupies a
major role in the etiology and development of coronary heart disease and stroke. It
12
specifically poses a major public health challenge to public health authorities in
developing countries where the health system is already loaded with other more
evident health problems. The severity of elevated blood pressure is directly related to
coronary heart disease and stroke.
One of the most common chronic conditions prevailing in the Jordanian community is
Diabetes. In 1998, the National Center for Diabetes, Endocrine and Genetic Diseases
in Jordan reported a 13.4% prevalence rate for diabetes mellitus*
. Management and
control of diabetes is essential for delaying complications.
2.4 Data Collection Methods
2.4.1 Data Collection Techniques
Three main Techniques of data collection were used in the study:
 Using available information was utilized for record based surveys on
timely vaccination, growth and development visits, antenatal-postnatal
visits, anemia of pregnancy, anemia of children and partly screening
for hypertension. The necessary data was transcribed from existing
records to survey instruments. One form was used to fill out each
record.
 Interviewing study subjects using questionnaires was used to get
data on contraceptive use and partly for screening of hypertension,
diabetes and hypertension control status.
 Measurements (observations) that apply to measuring glycosylated
hemoglobin and blood pressure in diabetes and hypertension.
2.4.2 Data Collection Tools
2.4.2.1 Timeliness of Vaccination
Data for the timeliness of vaccination was obtained from records of MCH centers for
sampled subjects. Annex 1 shows the form used for data collection on timeliness of
* Ajlouni K, Jaddou H, Batieha A. Diabetes and impaired glucose tolerance in Jordan: prevalence and
associated risk factors. J Intern Med 1998 Oct;244(4):317-23.
13
vaccination. The tool was used to transfer data from records on dates of vaccination
and other available background variables of two-year-old children. The six categories
of parents’ education were brought down to four during data analysis by combining
elementary and secondary to become “less than secondary” and the last two categories
to become “higher education”.
Data was collected on vaccination dates for doses of hepatitis B, DTP, polio, measles
and MMR. Children who were registered for the first time during the period from 1/1-
30/4/1998 constituted the sampling universe for the pretest phase of the study. .
Children who were registered for the first time during the period from 1/1-30/4/2002
constituted the sampling universe for the posttest phase of the study.
The required number of records (10) was selected by systematic random sampling
from the total number of children who registered for the first time during the above￾specified dates. Children were expected to register when they were 2 months old and
vaccination records were traced for about two years after registration. Children were
expected to be 2 years of age by 1/4/2000 and 1/4/2004 for the pretest and posttest
phases respectively
A vaccination dose was considered timely if the child was brought to the clinic on
the scheduled date (Table 2.4). For the first three doses of hepatitis, DTP and polio an
additional one-month was allowed between doses. If the time between two subsequent
doses was less than 28 days, the visits were labeled as inappropriate. First measles
dose was considered appropriate even when given up to three months after the
proposed age of 9 months. The second dose of measles as well as the booster doses
was considered appropriate if given between 15 and up to 24 months of age.
Table 2.4: Definition of Timeliness of Vaccination for Different Doses
Vaccine Age of Children
Dose Hepatitis B DTP Poliomyelitis Measles MMR
1
st 8-12 weeks 8-12 weeks 8-12 weeks 9-12
months
15-24*
months
2
nd 30-60 days
from first
30-60 days
from first
30-60 days
from first
15-24*
months
3
rd 30-60 days
from Second
30-60 days
from Second
30-60 days
from Second
Booster 15-24 months 15-24 months
 The second dose of measles was looked for only if MMR was not
given.
14
2.4.2.2 Growth and development visits, and anemia of children
Data for these variables was obtained from MCH records of selected subjects. Annex
2 shows the instrument that was used for data collection. The data for growth and
development visits, screening for anemia and the anemia of children variables
appeared in the same tool as far as they are available in the same patient’s record.
Growth and development visits were collected from a sample of children who were
registered to get the service for the first time during the period from 1/1-30/4/1997 for
the pretest while it the period was from 1/1-30/4/2001 for the posttest. Children were
expected to register at 2 months of age; they were traced until the age of 3 years. The
number of growth and development visits was recorded for the first, second and third
years of life separately.
Appropriate was considered 5 or more visits during the first year of life, 3 or more
visits for the second year and 1 or more visits for the third year of life.
Anemia of children was calculated based on the hemoglobin test that is routinely done
at about one year of age. Children having hemoglobin (Hb) or packed cell volume
(PCV) readings any time between 6 and 24 months of age were considered screened
for anemia. Anemia was considered to be present when Hb was less than 11 g/dl
according to WHO criteria. Anemia was considered mild, moderate and severe when
Hb was 10 –11 g/dl, 7/10 g/dl and less 7 g/dl respectively
2.4.2.3 Antenatal, postnatal visits, and anemia of pregnancy
Data for the above three main variables was obtained from the records of subjects of
selected sample of health centers. Annex 3 shows the instrument for data collection
for the main variables as well as some background and control variables.
Antenatal care was measured by noting the number of antenatal care visits made by a
pregnant woman in the selected sample whose registration date lied within the period
from 1/1/-30/4/1999 for the pretest and 1/1-30/4/2003 for the posttest. Any notes
found to indicate incomplete pregnancy disqualified the women from being included
in the study. All pregnancies labeled as “risk pregnancies” were excluded from the
sample to reduce the bias of frequent visits in such situations. Risk pregnancies as
defined by MoH are those with essential hypertension, diabetes, proteinuria, heart
disease and abnormal fetal positions. Visits not related to pregnancy were not
counted.
15
Paying 4 or more antenatal visits during the period of a completed pregnancy was
considered appropriate for normal uncomplicated pregnancy.
Attending a postnatal clinic once within the first 6 weeks after delivery was
considered appropriate.
Screening for anemia of pregnancy in the same sample for antenatal-postnatal visits
was considered appropriate if at least one Hb reading was available in the record.
Anemia was calculated based on the last available Hb or PCV readings as described
under anemia of children.
2.4.2.4 Use of Contraceptive Methods
Data on the current use of contraceptives was collected through an exit interview at
the selected health care center for a sample of women in the age group 15-49. Annex
4.1 shows the questionnaire on the use of contraceptives.
Variables related to the use of any method whether modern or traditional were
included in the questionnaire. Some questions on the source of contraceptive methods
as well as problems related to the use of contraceptive methods were also included.
2.4.2.5 Screening for Hypertension
Data for screening hypertension was collected through an exit interview using the
questionnaire shown in annex 5. Data was collected on a sample of non-hypertensive
adults aged 40 years and above of both sexes during the study period.
The questionnaire contains variables that test the screening practice for hypertension
on the day of the survey as well as over the period of the last year from the date of the
survey.
The patient was considered screened for hypertension when the medical file showed
that blood pressure was recorded at least once over the last year including the day of
the survey. To look for the discrepancy between checking BP and recording the result
in the patient’s file, the data collected on the day of the survey was used. The patient
was first asked about checking his/her BP and the response was compared to what
was recorded in the medical file.
16
2.4.2.6 Status of Diabetes Control
Data on Diabetes control was collected using the questionnaire shown in annexes 6.1
and 6.2. Blood specimens were obtained for a sample of diabetic subjects for
measuring glycosylated hemoglobin (HbA1c). The American Diabetic Association
criteria were used to determine the status of control of diabetes*
. Only readings of
HbA1c below 7 were considered controlled. For purpose of standardization, the test
was done at the Central Laboratories at the MoH during the pre and posttest phases.
It is worth mentioning that in the pretest report different less stringent criteria were
used.
As far as this study is not intended to look in depth for factors affecting diabetes
control, only few independent variables were collected such as weight, height and
disease duration.
Data was collected on weight and height to calculate the body mass index (BMI).
Known for its simplicity, the index correlates to
fatness and can be applied to both men and
women. BMI was calculated using the
conventional formula (weight*10,000 /height2
)
where weight is in kilograms and height in
centimeters. BMI of 30 Kg/m2 was considered the
cutoff point between obesity and non-obesity. BMI
of 25 Kg/m2 was considered the cutoff point between normal and overweight. BMI
was calculated for those who were above 17 years of age.
As mentioned earlier 13 patients were selected in each health center to allow for the
expected attrition and deaths in 4 years from the pretest. Patient’s name, address and
phone number were collected to facilitate locating them at the posttest stage. Patients
were selected as for all other tools using systematic random sampling depending on
the load during the 2-4 days of the survey in the target health centers.
2.4.2.7 Status of Hypertension Control
Data was collected using the questionnaire shown in annex 7. In addition to recording
systolic and diastolic blood pressure, data on some additional independent variables
* American Diabetes Association. Standards of Medical Care for patients with diabetes mellitus.
Diabetes Care [Suppl] 18/1/1995; 8-15
Table 2.5: Definition of BMI
Categories
Category Value (Kg/m2
)
Underweight <18.5
Normal 18.5-24.99
Overweight 25-29.99
Obesity 30
17
was collected similar to the previous tool on diabetes. Number of subjects selected at
each health center was 13 as for diabetes.
Using standard mercury sphygmomanometer, 2 seated blood pressure measurements
were recorded in both arms, and the higher measurement was recorded. Korotkoff
phases 1 and 5 established the levels of systolic and diastolic pressures, respectively.
Blood pressure readings below 140 and 90 for systolic and diastolic pressure
respectively were considered as controlled. All readings above the given figures were
labeled as uncontrolled. Further classification of degrees of uncontrolled hypertension
were done at the analysis stage, using the criteria shown in table 2.6 based on WHO
1999 guidelines*
.
Table 2.6: Definition of Blood Pressure Levels
BP Readings in mm/Hg
Systolic Diastolic
Category of Control
<140 <90 Controlled Disease
140-159 90-99 Mild Disease (Grade1)
160-179 100-109 Moderate Disease (Grade 2)
>179 >109 Severe Disease (Grade 3)
2.4.3 Data Collection Plan
2.4.3.1 Personnel and Logistics for Data Collection
Teams from MoH staff served as data collectors during both the pretest and posttest
phases with about 80% of the data collectors in the posttest being the same as in
pretest. Data collection was carried out by 15 teams consisting of three data collectors
each. A team consisted of one general practitioner, a midwife or a nurse who was
working in MCH facilities and a certified nurse, capable of drawing blood or a lab
technician capable of drawing blood as a substitute. In addition to his work as data
collector, the GP in the group was assigned as a team leader. Since the time needed to
fill in various forms and questionnaires was expected to vary greatly in different
facilities, the team leader was asked to assure equitable involvement of all team
members taking into consideration that annex 4 on contraceptive use was filled only
*
1999 World Health Organization-International Society of Hypertension Guidelines for the
Management of Hypertension
18
by a female nurse or midwife. Each of the 15 teams collected data from one health
care facility at a time and the average stay in one health center was 2-3 days.
To facilitate data collection, three teams collected data form the south, six teams from
the north and six teams from the central region. Team members were selected
exclusively from their relevant region. Each team of data collectors was assigned a
central supervisory team consisting provided guidance in addition to supervision.
Detailed tasks for each of the data collectors and their field supervisors were
described in a comprehensive training manual that covered all issues from greetings to
details in sampling patients and records to transporting blood and filled
questionnaires. Following final checking and pre-entry cleaning, a team of four
persons entered data at PHCI office using the data SPSS builder.
Transportation and cellular phones were provided to each team of data collectors and
supervisors to provide easy communication with the investigators as well as with
supervisors. Collected blood from diabetic patients was transported irrespective of the
closeness of the center to the central lab upon completion of data collection at the
health center. Working 6 days a week, data collection started on 28th of October and
finished on 22nd of November 2000 for the pretest and from 20th of June till the 17th
July for the posttest.
2.4.3.2 Ensuring quality of collected data
Ensuring both accuracy and reliability of the collected data was of prime concern
throughout the study. The following measures were carried out to ensure quality:
 The sampling plan detailed earlier was followed very strictly giving
minimal chance for deviation and after consulting with the investigators at
the pretest stage.
 Data collection tools were pre-tested on several occasions including
training of interviewers. Finally, all questions in the forms and
questionnaires raised no ambiguity and open-ended questions were set at
the minimum possible.
 About 5% of selected health facilities were revisited for validation of data
collection on tools that are record based.
 All sphygmomanometers for measuring BP and balances and heighteners
were new and from the same provider
19
 Glycosylated hemoglobin was done in one laboratory where quality
assurance methods were applied.
 A fieldwork-training manual was developed. It provided all the details
regarding the work to be done by data collection teams.
 Research teams received training before the actual data collection
including field-testing of all instruments.
 Adequate supervision was provided for all teams with double-checking for
quality control.
 Data entry started the third day of data collection and due efforts were
exercised to clean the data during the data entry stage.
2.5 Data Analysis
Data entry for SPSS was used to enter collected data. The program was used to create
forms (entry screens) that had almost the same design as the original questionnaires
with all necessary validation rules, checks and skips to minimize errors. The data
entry screens were largely devoid of coding. All coding was dealt with at the stage of
building the data entry forms, defining and labeling variables. Even multiple response
questions were imaged on the data entry screens as in the questionnaire or form. The
very few open-ended questions posed no problem later at the analysis stage.
SPSS 10 was used to analyze data taking into consideration that the above mentioned
data entry forms stored data directly in SPSS format.
Frequencies were calculated for simple descriptions of the results (means, medians,
95% confidence intervals etc.) Cross-tabulations showing relationships of main
variables with control and background variables were used with various types of χ2
.
Independent-sample t test was used to compare means of continuous numeric
variables for various groups. Logistic regression was used to study the predicting
ability of the available independent factors. Pooled data from the pre and posttest was
used to run logistic regression. Analysis of co-variance (ANCOVA) was used for
paired observations of diabetes and hypertension.
All counts and proportions are presented in the report as weighted numbers using the
relative weight.
20
3. Results
21
3.1 Timeliness of Vaccination
3.1.1 Description of the sample
Table 3.1.1 shows the distribution of missing values for the main vaccine doses. A
missing dose does not necessarily mean that the child missed the vaccine shot. It
rather indicates that the child was not brought to the respective clinic to receive the
dose. The child might have taken the dose at another MoH clinic or by other provider.
The appropriateness of the dose is calculated for the valid values only which brings
the number of the respondents down when combining doses.
In the pretest, data was collected form 878 records from the all sampled health
centers. In the posttest, 857 records were collected from 86 health centers with 3
centers showing no records for children less than 2 years of age.
Table 3.1.1: Distribution of Valid and Missing Cases by Vaccine Dose
Pretest Posttest
Vaccine Dose
Valid Missing Valid Missing
1st Dose of DPT, Polio and Hepatitis B 878 0 857 0
2nd Dose of DPT, Polio and Hepatitis 876 3 857 0
3rd Dose of DPT, Polio and Hepatitis 872 6 845 12
Primary Doses Combined 872 6 845 12
1st Dose of Measles 838 40 815 42
2nd Dose of Measles 728 150 737 120
Booster Dose of DTP and Polio 788 90 740 117
All Doses Combined 726 153 735 122
Table 3.1.2 summarizes the demographic variables available in the children’s records.
About 45% of the sample came from the central region while about 18% came from
the south. About 31% of the sampled children came from CHCs.
The male female ratio was almost 1:1. The mean monthly family income was 175 JDs
with almost 72% of the children coming from families with a reported income of less
than 200 JDs a month. Over 22% of both mothers and fathers of the selected children
had higher education with less than 6% illiteracy rate. Significant differences between
the pretest and posttest results of the demographic variables were noted only for
income and mothers’ education as judged by t test and logistic regression. The change
in mean income from 161.2 JDs in the pretest to 189.7 JDs in the posttest was also
reflected in the income categories. The difference was most probably due to expected
22
increase in income over 5-year period. As for mothers’ education, the significant
change was between secondary and higher educational categories with a notable
increase in the former category.
Table 3.1.2: Overall Sample Characteristics
Variable Pretest Posttest Pooled
N % N % N %
Total 878 100.0 857 100.0 1735 100.0
Region
North 314 35.7 336 39.2 650 37.4
Central 406 46.2 374 43.6 780 44.9
South 159 18.1 147 17.2 306 17.6
HC Type
CHCs 255 29.0 284 33.1 539 31.1
PHCs 623 71.0 573 66.9 1196 68.9
Sex
Male 454 51.7 430 50.2 884 51.0
Female 424 48.3 427 49.8 851 49.0
Income*
<100 62 8.4 43 6.1 105 7.3
100-199 516 70.1 411 58.4 927 64.4
200-299 108 14.7 142 20.2 250 17.4
300 50 6.8 108 15.3 158 11.0
Education (Mother)*
Illiterate 62 7.5 36 4.3 80 4.8
Less than Secondary 294 35.4 234 27.8 565 33.8
Secondary 297 35.7 379 45.0 658 39.3
Higher Education 178 21.4 194 23.0 371 22.2
Education (Father)
Illiterate 51 6.1 29 3.4 98 5.9
Less than Secondary 311 37.5 254 30.1 528 31.5
Secondary 300 36.1 358 42.4 676 40.4
Higher Education 168 20.2 203 24.1 372 22.2
* Statistically significant difference between the pre and posttest
3.1.2 Analysis of Timeliness of Vaccination
Table 3.1.3 summarizes results of timeliness of vaccination for the 13 vaccine doses
according to the phase of the study. Despite that timeliness for all doses combined
increased by 5.7% from 64.5% in the pretest to 68.2% in the posttest, the level of
increase was insignificant. There were some significant variations between the pre
and posttest for some individual doses but the difference was not consistent in favor
of one stage.
23
Despite that timeliness for all doses combined was relatively low, it was higher for
individual doses. Second dose of measles and booster doses had the highest
prevalence of timeliness (more than 94%) because of more loose criteria as opposed
to the more stringent criteria for the primary shots.
Table 3.1.3: Distribution of Timeliness of Different Vaccine Doses by Study Phase
Timeliness
Vaccine Dose Pretest Posttest
n % n %
p value
1st of DPT, Polio and Hepatitis B 870 82.0 729 85.1 0.086
2nd of DPT, Polio and Hepatitis 800 91.3 807 94.2 0.023
3rd of DPT, Polio and Hepatitis 784 89.9 761 90.1 0.917
Primary Doses Combined 616 70.6 639 75.6 0.02
1st Measles 741 88.3 729 89.4 0.446
2nd Measles 710 97.5 699 94.8 0.007
Booster of DTP and Polio 763 96.8 706 95.3 0.119
All Doses Combined 468 64.5 502 68.2 0.13
Table 3.1.4 displays at the distribution of timeliness of administering vaccine doses by
the study phase for focal and non-focal health centers. Surprisingly the non-focal
centers showed some significant improvements for the second and third primary doses
as well as for all doses combined. The only significant difference for the focal health
centers was observed for the second dose of measles, but with decreased prevalence.
Table 3.1.4: Distribution of Timeliness of Different Vaccine Doses by Study Phase and
Intervention
Timeliness
Vaccine Dose Focal % Non-Focal %
Pretest Posttest Pretest Posttest
1st of DPT, Polio and Hepatitis B 83.9 87.0 76.5 79.9
2nd of DPT, Polio and Hepatitis 91.9 93.9 89.5* 94.9
3rd of DPT, Polio and Hepatitis 92.0 89.0 83.6* 93.3
Primary Doses Combined 73.4 77.3 62.7 71.2
1st Measles 89.8 89.6 84.0 89.1
2nd Measles 97.8* 94.2 96.7 97.0
Booster of DTP and Polio 96.8 94.8 96.9 96.6
All Doses Combined 66.7 68.0 57.9* 68.8
* Statistically significant
The evident absence of any improvements among users of focal health centers as
compared to non-focal is mostly related to lack of clear intervention regarding this
24
indicator. Absence of emphasis on counseling regarding timeliness of vaccination and
short period of maturation of various interventions for most health centers should
have played a role leading to no change.
Table 3.1.5 shows the logistic
regression results for
timeliness of vaccination as the
dependent variable and the
phase of the study variable in
addition to demographic
variables as covariates.
Keeping all other variables
constant, the odds for
timeliness of vaccination were
11% higher in the posttest as
compared to the pretest. This
increase was not found to be
statistically significant with a
high p value at more than 0.4.
Overall, the timeliness of
vaccination was significantly
better in the northern and
central regions as compared to
the south. The results were in
favor of the CHCs, where the
records of timeliness were 41%
more likely to be higher than
the PHCs. As for mothers’
education, it seems that only
the lowest category had a
significant 3.1 times lower likelihood of getting their children timely vaccinated than
the highest education category.
Child’s gender, income and father’s education had no significant differences on
timeliness of vaccination. The regression results just hints to fact that PHCI
intervention did not affect the timeliness of vaccination even after controlling for
possible confounding factors.
Table 3.1.5: Logistic Regression of Timeliness of
Vaccination for All Doses Combined*
Variable Coefficient OR Sig.
Study Phase
Posttest 0.10 1.11 0.425
Pretest - - -
Region
North 0.70 2.01 <0.005
Central 0.53 1.69 0.003
South - - -
HC Type
CHCs 0.34 1.41 0.021
PHCs - - -
Sex
Male 0.20 1.22 0.114
Female - - -
Income 0.00 1.00 0.866
Education (Mother)
Illiterate -1.12 0.32 0.003
Less than Secondary -0.32 0.73 0.100
Secondary 0.20 1.22 0.263
Higher Education - - -
Education (Father)
Illiterate 0.00 1.00 0.992
Less than Secondary 0.36 1.43 0.066
Secondary 0.18 1.20 0.306
Higher Education - - -
*Dependent Variable - Comparison Group
25
3.2 Growth Monitoring and Anemia of Children
3.2.1 Description of the sample
The number of health centers with records on growth and development monitoring for
the three-year old children went down form 87 in the pretest to 80 in the posttest
(Table 3.2.1). This was reflected on the total records reviewed in the posttest (802)
compared to the pretest (867). It seems that inadequate supervision played a major
role for missing records in over 10% of the sampled health centers during the posttest.
In some health centers the staff blamed the PHCI renovation as the cause of
misplacing the records. It is worth mentioning that moving from and to the renovated
center was the responsibility of the MoH.
Table 3.2.1 shows the missing values for other variables. The growth and
development visits variables had no missing values. Anemia of children aged 6-24
months showed that only about 38% of the records in both pretest and posttest showed
valid values. This figure increased only by 2% when all children in the sample are
included. Despite that screening for anemia is compulsory at one year of age; the
figures are extremely low even when adopting a wider age definition. Absence of labs
in the health centers, lack of awareness from the child’s parents or negligence of the
provider especially in documenting the lab results could have contributed to this
outcome. This problem did not show any improvement over more than 4 years. It is
worth mentioning that the results of anemia will not be representative not only
because of the sample size but also because the socio-demographic attributes of non￾respondents might be different from those of respondents.
Table 3.2.1: Distribution of Valid and Missing Cases for Main Variables
Phase of the Study
Variables Pretest Posttest
Valid Missing Valid Missing
Number of Health Centers 87 2 80 9
First Growth and Development Visit 867 0 802 0
Second Growth and Development Visit 867 0 802 0
Third Growth and Development Visit 867 0 802 0
All Growth and Development Visits 867 0 802 0
Anemia of Children Aged 6-24 Months 329 538 300 502
Anemia of Children aged 3 Years or Less 352 515 320 481
26
Table 3.2.2 summarizes demographic variables available in the sampled records.
About 47% of the sample came from the central region, 35% from the north and about
18% came from the south. About 32% of the sampled children came from CHCs. The
male female ratio was 1.13:1. The mean monthly family income was 165 JDs with
over 77% of the children coming from families with a reported income of less than
200 JDs a month. Over 20% of both mothers and fathers of the selected children had
higher education with about 5% illiteracy rate. Significant differences between the
pretest and posttest results of the demographic variables were noted only for income
and fathers’ education as judged by t test and logistic regression. The income mean
changed from 158.9 JDs in the pretest to 172.2 JDs in the posttest (p=0.012). The
difference was most probably due to inconsistent reporting of income rather than real
increase. As for father’s education the significant change was between secondary and
higher educational categories with a notable increase in the former category.
Table 3.2.2: Overall Sample Characteristics
Variable Pretest Posttest Pooled
N % N % N %
Total 867 100 803 100 1669 100
Region
North 293 33.8 289 36.4 582 35.0
Central 429 49.5 356 44.8 785 47.3
South 145 16.7 149 18.8 294 17.7
HC Type
CHCs 280 32.3 257 32.3 537 32.3
PHCs 587 67.7 538 67.7 1125 67.7
Sex
Male 455 52.5 428 53.8 883 53.1
Female 412 47.5 367 46.2 779 46.9
Income*
<100 89 13.1 38 6.0 127 9.7
100-199 452 66.4 441 69.7 893 68.0
200-299 91 13.4 100 15.8 191 14.5
300 49 7.2 54 8.5 103 7.8
Education (Mother)
Illiterate 44 5.7 34 4.5 78 5.1
Less than Secondary 274 35.3 254 33.2 528 34.3
Secondary 297 38.3 293 38.4 590 38.3
Higher Education 161 20.7 183 24.0 344 22.3
Education (Father)*
Illiterate 39 5.0 32 4.2 71 4.6
Less than Secondary 298 38.3 247 32.3 545 35.3
Secondary 284 36.5 325 42.5 609 39.5
Higher Education 157 20.2 160 20.9 317 20.6
* Statistically significant difference between the pre and posttest
27
3.2.2 Appropriateness of Growth and Development Monitoring
Visits
Table 3.2.3 shows that readings for appropriateness of growth and monitoring
development visits were lower in the posttest as compared to the pretest. The
appropriateness of all visits combined went down by about 26% form 21.6% to only
16%. The negative change was significant for all but for the third visit.
Table 3.2.3: Distribution of Appropriateness of Growth and Development
Monitoring Visits by Study Phase
Appropriateness
Pretest Posttest Growth and Development Monitoring
Visits
n % n %
p
value
First Visit 550 63.4 444 55.4 0.001
Second Visit 322 37.1 222 27.7 <0.005
Third Visit 310 35.8 264 33.0 0.23
All Visits Combined 187 21.6 128 16.0 0.004
The above decrease in the appropriateness of growth and development visits was
noticed to be consistent for both focal and non-focal health centers (Table 3.2.4).
The evident absence of any improvements in focal health centers as compared to non￾focal is mostly related to absence of clear interventions regarding this indicator. Apart
from the effect of separate pretest posttest sample design, the negative change
observed over 4-year period hints to true deterioration in services provided to
children, poor documentation of provided services or to factors related to child’s
guardians. Most probably a combination of the above factors contributed to the
results.
Table 3.2.4: Distribution of Appropriateness of Growth and Development Monitoring
Visits by Study Phase and Intervention
Appropriateness
Vaccine Dose Focal % Non-Focal %
Pretest Posttest Pretest Posttest
First Visit 64.6* 58.5 59.9* 47.3
Second Visit 37.4* 29.2 36.2* 24.1
Third Visit 37.4 29.2 36.2* 24.1
All Visits Combined 21.8* 17.3 20.8* 12.5
* Statistically significant
The pretest-posttest results of appropriateness of growth and development monitoring
visits did not change when controlling for the available demographic variables using
28
logistic regression. The odds of appropriateness decreased in posttest by 1.6 times
compared to the pretest (Table 3.2.5). The rate of appropriateness was found to be 2.2
times less likely in the north as compared to the south and 1.14 times more in the
central region than the south. Data presented in table 3.2.5 should be considered with
caution as far as Hosmer and Lemeshow goodness of fit test showed significant
differences between the observed and predicted values of the appropriateness of visits
variable.
Table 3.2.5: Logistic Regression of Appropriateness of Growth and Development
Monitoring Visits
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 0.63 0.47 0.85 0.002 Study Phase
Pretest - - - -
CHCs 0.76 0.56 1.05 0.098 HC Type
PHCs - - - -
North 0.46 0.30 0.71 0.001
Region Central 1.14 0.78 1.66 0.501
South - - - -
Male 1.06 0.80 1.42 0.679 Sex
Female - - - -
Illiterate 0.77 0.26 2.30 0.639
Less than Secondary 1.86 1.14 3.03 0.013
Secondary 1.43 0.92 2.22 0.114
Education
(Mother)
Higher Education - - - -
Illiterate 1.62 0.69 3.79 0.267
Less than Secondary 1.09 0.68 1.76 0.713
Secondary 1.44 0.95 2.19 0.087
Education
(Father)
Higher Education - - -
Income Income 1.00 1.00 1.00 0.023
- Comparison Group Hosmer and Lemeshow Test: p =0.027
3.2.3 Anemia of Children Aged 6-24 Months
As mentioned earlier screening for anemia was shown to be very low at both pre and
posttest. For children aged 6-24 months the figure was almost identical for both
phases of the study at 37.9% and 37.4% (Table 3.2.6). Even increasing the age range
to include all children age 3 years and less the figures did not change much. As far as
the above results of screening for anemia among children are representative of MoH
29
data, one should be cautious about interpreting the anemia data available at MoH
database. The latter conclusion is due to the fact of lacking detailed characteristics of
over 60% of children who were not screened for anemia.
Table 3.2.6: Distribution of Screening for Anemia by Study Phase
Phase of the Study
Variables Pretest Posttest
n % n %
Anemia of Children Aged 6-24 Months 329 37.9 300 37.4
Anemia of Children aged 3 Years or Less 352 40.6 320 40.0
Table 3.2.7 shows that
the prevalence of the
appropriateness of
growth visits was 2.3
times higher among
those screened for
anemia compared to
those not screened. This finding hints to the fact that screening a child for anemia is
related to the pattern of utilization of MoH services by child’s guardians rather than to
providers.
Complying with the screening period for anemia at around one year of age will end in
less than 200 cases for both pretest and posttest phases of the study. Table 3.2.8 shows
anemia categories for all children in the sample who had hemoglobin test done and
documented and for a subset of children aged 6-24 months. The mean age of children
tested for anemia was 12.5 months with a minimum of one and a maximum of 35.5
months. Mean hemoglobin increased from 11.4 g% in the pretest to 11.6 g% in the
posttest. The increase was significant at p = 0.038. Nevertheless, no significant
changes were noted for anemia categories as shown in table 3.2.8. The study phase
variable as well as other demographic variables did not show any statistically
significant predictive ability for the anemia variable.
Table 3.2.7: Distribution of Appropriateness of Growth
and Monitoring Visits by Screening for Anemia
Screening
Yes No Appropriateness
of Growth Visits
n % n %
Yes 193 28.64 122 12.26
No 481 71.36 873 87.74
30
Table 3.2.8: Distribution of Anemia Among Children by Study Phase
Study Phase
Anemia of Children < 36 Months Old Pretest Posttest Sig.
n % n %
Anemic 89 25.3 66 20.6
Non-Anemic 263 74.7 254 79.4
Total 352 100.0 320 100.0
0.152
Anemia of Children Aged 6-24 Months
Anemic 80 24.3 64 21.4
Non-Anemic 249 75.7 235 78.6
Total 329 100.0 299 100.0
0.386
31
3.3 Antenatal Care
3.3.1 Sample Description
Missing values were absent for all main variables but screening for anemia. Valid
values for screening for anemia minimally increased from 88.2% in the pretest to
91.5% in the posttest. Screening of pregnant women for anemia is much higher than
that of children mostly because less attention is paid to healthy babies by both parents
and providers.
Table 3.3.1: Distribution of Valid and Missing Cases For Main Variables
Pretest Posttest Variable
Valid Missing Valid Missing
Number of Sampled Health Centers 88 1 87 2
Antenatal Care 840 0 861 0
Postnatal Care 840 0 861 0
Counseling for Family Planning* 248 0 311 0
Decision for Family Planning** 86 0 240 0
Screening for Anemia 741 99 779 82
* Number of those who attended postnatal care
* Number of those who were given counseling for family planning
Table 3.3.2 shoes that about 45% of the sample came form the north, 40.2 % from the
central region and 14.7 from the south. About one third of sample came from CHCs
and the rest form PHCs. The mean of reported income rose from 154 JDs to 174 JDs
(p = <0.005). This finding was consistent for vaccination, growth monitoring and
antenatal care. The true increase in income over about 5 years is possible but not
definite.
Mean age for pretest and posttest phases was 26.3 and 27 years respectively with
almost two thirds of the sample being in the age group 20-29 years. Illiteracy rate as
judged by zero years of schooling for pregnant women and their husbands was found
to be less than 5%, while the higher education rate was over 18% (Table 3.3.2).
Table 3.3.2: Overall Sample Characteristics
Variable Pretest Posttest Pooled
N % N % N %
Total 840 100 861 100 1701 100
Region
North 384 45.7 384 44.6 768 45.1
32
Table 3.3.2: Overall Sample Characteristics
Variable Pretest Posttest Pooled
N % N % N %
Central 339 40.4 344 40.0 683 40.2
South 117 13.9 133 15.4 250 14.7
HC Type
CHCs 266 31.7 285 33.1 551 32.4
PHCs 574.0 68.3 577 66.9 1151 67.6
Income*
<100 47 7.3 42 5.7 89 7.3
100-199 498 77.1 457 62.2 955 77.1
200-299 70 10.8 191 26.0 261 10.8
300 31 4.8 45 6.1 76 4.8
Age Groups in Years*
<20 74 8.9 58 6.7 132 7.8
20-29 550 65.9 540 62.7 1090 64.3
30-39 198 23.7 240 27.9 438 25.8
=>40 13 1.6 23 2.7 36 2.1
Education (Pregnant)
Illiterate 39 4.7 32 3.7 71 4.2
Less than Secondary 255 31.0 224 26.1 479 28.5
Secondary 365 44.4 440 51.3 805 47.9
Higher Education 163 19.8 161 18.8 324 19.3
Education (Husband)*
Illiterate 28 3.4 23 2.7 51 3.0
Less than Secondary 325 39.6 344 40.1 669 39.9
Secondary 298 36.3 352 41.1 650 38.7
Higher Education 170 20.7 138 16.1 308 18.4
* Statistically significant difference between the pre and posttest
3.3.2 Appropriateness of Antenatal and Postnatal Visits
The average number of antenatal visits in the pretest of 4.56 visits was almost similar
to the average number of visits in the posttest phase at 4.35 visits (p=0.117). Table
3.3.3 shows similar results with appropriate number of antenatal visits being 57.7%
and 57.3% for the pretest and posttest respectively. The appropriate postnatal visits
showed a statistically significant improvement over the intervention period. It
increased by over 22% from 29.6% at the pretest to 36.1% in the posttest. The
percentage of offering family planning counseling during the postnatal visit increased
by 123% in posttest compared to the posttest. Nevertheless, the decision on use of
family planning methods did not change.
33
Table 3.3.3: Distribution of Main Variables by Study Phase
Phase
Variable Pretest Posttest
n % n %
p
value
Appropriate Number of Antenatal Visits 485 57.7 493 57.3 0.842
Appropriate Number of Postnatal Visits 248 29.6 311 36.1 0.004
Family Planning Counseling 86 34.7 240 77.2 <0.005
Decision to Use of Family Planning 62 71.3 152 63.6 0.197
When pretest-posttest results are broken further into focal and non-focal to reflect the
effect of the more focused interventions, the appropriateness of antenatal visits did not
show significant change. Despite that the postnatal care increased for both focal and
non-focal health centers, the increase was significant only for focal health centers
(Table 3.3.4). Family planning counseling showed significant increase for both focal
and non-focal heath centers. Decision for family planning was shown to be
consistently insignificant across focal and non-focal health centers. The above results
point to a possibility of an effect caused by PHCI interventions on the provision of
postnatal care.
Table 3.3.4: Distribution of Main Variables by Study Phase and Intervention
Intervention
Variable Focal % Non-Focal %
Pretest Posttest Pretest Posttest
Appropriate Number of Antenatal
Visits 58.3 58.5 55.7 53
Appropriate Number of Postnatal
Visits 28.7* 35.5 32.4 34
Family Planning Counseling 37.4* 74.5 26.7* 85.5
Decision to Use of Family
Planning 71.4 63.4 68.8 64.6
* Statistically significant
Table 3.3.5 shows logistic regression for the available demographic variables in
addition to the study phase variable. The study phase variable has no predication of
the appropriateness of the number of antenatal visits where the significance test is
very close to unity.
Aside from monthly income, other available demographic variables showed no
prediction ability of the main variable. Pregnant women coming from lower income
categories were more likely to have appropriate number of antenatal visits. Higher
34
income pregnant women are more likely to use other providers including private
sectors, thus making less visits to MoH health centers.
Table 3.3.5: Logistic Regression of Appropriateness of Number of Antenatal Visits
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 0.99 0.79 1.24 0.942 Study Phase
Pretest - - - -
CHCs 0.83 0.65 1.05 0.119 HC Type
PHCs - - - -
North 1.29 0.93 1.81 0.130
Region Central 1.00 0.72 1.40 0.996
South - - - -
<100 1.36 0.71 2.61 0.361
100-199 2.16 1.30 3.58 0.003
200-299 1.87 1.09 3.18 0.022
Income
300 - - - -
Illiterate 1.30 0.63 2.70 0.483
Less than Secondary 1.16 0.82 1.66 0.403
Secondary 1.03 0.76 1.40 0.833
Education
(Pregnant)
Higher Education - - - -
Illiterate 1.50 0.66 3.39 0.336
Less than Secondary 1.40 0.99 1.97 0.057
Secondary 1.04 0.75 1.43 0.829
Education
(Husband)
Higher Education - - -
Age Age 1.00 0.98 1.02 0.716
- Comparison Group Hosmer and Lemeshow Test: p =0. 313
Table 3.3.6 shows logistic regression for the postnatal care. Income was removed
from the equation because of 321 missing values, which will affect the model. Income
was kept in table 3.3.5, as the results did not change after its removal.
Pregnant women were 1.38 times more likely to pay at least one postnatal visit after
delivery in the posttest than in the pretest (p = 0.003). The same table shows that out
of all covariates in the model, paying appropriate number of antenatal visits was the
most predictive of coming to at least one postnatal visit. If pregnant women attended
4 or more antenatal visits she was about 2.8 times more likely to be seen at the
postnatal clinic (p<0.005).
Ironically, CHCs that are supposed to provide better primary health care were 1.4
times less likely to attract pregnant women to have postnatal care than the PHCs
35
(p=0.006). Each year increase in age makes pregnant women 2% less likely to attend
postnatal care after delivery.
Table 3.3.6: Logistic Regression of Appropriateness of Number of Postnatal Visits
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 1.38 1.11 1.71 0.003 Study Phase
Pretest - - - -
CHCs 0.71 0.56 0.91 0.006 HC Type
PHCs - - - -
No. of AntenatalAppropriate 2.75 2.19 3.44 <0.005
Visits Inappropriate - - - -
North 1.19 0.86 1.65 0.302
Region Central 1.09 0.78 1.52 0.598
South - - - -
Illiterate 0.65 0.34 1.26 0.200
Less than Secondary 0.70 0.49 0.98 0.040
Secondary 0.83 0.62 1.12 0.227
Education
(Pregnant)
Higher Education - - - -
Illiterate 1.17 0.56 2.44 0.667
Less than Secondary 1.09 0.78 1.52 0.608
Secondary 0.86 0.63 1.19 0.371
Education
(Husband)
Higher Education - - -
Age Age 0.98 0.96 1.00 0.039
- Comparison Group Hosmer and Lemeshow Test: p =0. 401
Table 3.3.7 shows logistic regression for family planning counseling. During the
posttest pregnant women were 8.4 times more likely to be counseled for family
planning during a postnatal care visit than during the pretest.
Women attending postnatal clinics in the north and central region were 5.4 and 2
times respectively more likely to be counseled for family planning than women
attending clinics in the south. Overall, despite that the number of antenatal visits paid
to MoH health centers did not change over the intervention period, the postnatal care
has improved. A more prominent change was noticed with provision of family
planning counseling.
36
Table 3.3.7: Logistic Regression of Family Planning Counseling
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 8.40 5.17 13.65 <0.005 Study Phase
Pretest - - - -
CHCs 1.48 0.87 2.52 0.145 HC Type
PHCs - - - -
North 5.43 2.58 11.43 <0.005
Region Central 2.06 0.99 4.26 0.052
South - - - -
<100 0.26 0.06 1.15 0.076
100-199 0.76 0.23 2.47 0.643
200-299 1.26 0.36 4.49 0.718
Income
300 - - - -
Illiterate 0.55 0.11 2.75 0.469
Less than Secondary 1.60 0.77 3.36 0.211
Secondary 1.61 0.86 3.00 0.137
Education
(Pregnant)
Higher Education - - - -
Illiterate 1.30 0.25 6.74 0.753
Less than Secondary 0.67 0.33 1.37 0.271
Secondary 1.36 0.69 2.69 0.373
Education
(Husband)
Higher Education - - -
Age Age 1.00 0.95 1.04 0.911
- Comparison Group Hosmer and Lemeshow Test: p =0. 585
3.3.3 Anemia of Pregnancy
Table 3.2.8 shows that screening for anemia increased from 88.2% during the pretest
to 90.5% during the posttest. The increase was insignificant with a p = 0.13. The same
trend was noted for both focal and non-focal health centers.
Table 3.2.8: Distribution of Screening for Anemia by Study Phase
Phase of the Study
Pretest Posttest Screening for Anemia
of Pregnancy
n % n %
Yes 741 88.2 779 90.5
No 99 11.8 82 9.5
Mean hemoglobin increased insignificantly from 11.6 g% in the pretest to 11.7 g% in
the posttest (p=0.122). Table 3.3.9 shows that anemia among pregnant women has
37
significantly decreased by 20% from about 25% in the pretest to about 21% in the
posttest (p=0.04). Analyzing anemia status by intervention rendered the improvement
insignificant for both focal and non-focal health centers.
Most of the anemia was mild in both pretest and posttest. Severe anemia was absent.
The prevalence of mild and moderate anemia decreased during the posttest. Breaking
down anemia into mild moderate and severe rendered the changes between the pre
and posttest insignificant (p=0.069).
Table 3.3.9: Distribution of Anemia Among Pregnant Study Phase
Study Phase
Pretest Posttest Anemia of Pregnant
Women
n % %Cum. n % %Cum.
Mild Anemia 147 19.9 19.9 136 17.5 17.5
Moderate 40 5.4 25.4 27 3.4 20.9
No Anemia 553 74.7 616 79.1
Table 3.3.10 shows the highly significant progression of anemia from 9.8 g% during
the first trimester to 18.5% during the second and reaching 27.6% during the third
trimester for the posttest. The pretest data was not shown because the trimester of
screening for anemia was not available. The observed progression is consistent with
findings from other studies.
Table 3.3.10: Distribution of Anemia by Trimester During the Posttest
Trimester
1
st Trimester 2ed Trimester 3rd Trimester
Total
Anemia
n % n % n % n %
Anemia 14 9.8 53 18.5 95 27.6 162 20.9
No Anemia 129 90.2 234 81.5 249 72.4 612 79.1
Total 143 100 287 100 344 100 774 100
Table 3.3.11 shows that pregnant women were about 1.3 times less likely to have
anemia in the posttest as compared to the posttest. This change was found to be
insignificant. Pregnant women in the north were over 2 times more likely to be
anemic than those in the south while women in the south and central regions were
more or less similar. These results were not different from those of the MoH database.
The observed 2% increase in likelihood of having anemia for every one year increase
in pregnant woman’s age was found to be insignificant. Women receiving their
38
antenatal care from CHCs were about 1.7 times less likely to be anemia than those
attending PHCs. Women’s education can predict anemia significantly. Illiterate
pregnant women were about 3 times more likely to have anemia as compared to those
with higher education.
Table 3.3.11: Logistic Regression of Anemia
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 0.78 0.59 1.04 0.090 Study Phase
Pretest - - - -
CHCs 0.59 0.43 0.81 0.001 HC Type
PHCs - - - -
North 2.03 1.28 3.24 0.003
Region Central 1.31 0.81 2.12 0.266
South - - - -
<100 0.84 0.35 2.01 0.687
100-199 1.12 0.56 2.25 0.753
200-299 0.79 0.37 1.67 0.530
Income
300 - - - -
Illiterate 2.84 1.19 6.77 0.018
Less than Secondary 2.59 1.62 4.15 <0.005
Secondary 1.39 0.91 2.12 0.131
Education
(Pregnant)
Higher Education - - - -
Illiterate 0.84 0.35 2.01 0.687
Less than Secondary 1.12 0.56 2.25 0.753
Secondary 0.79 0.37 1.67 0.530
Education
(Husband)
Higher Education - - -
Age Age 1.02 1.00 1.05 0.089
- Comparison Group Hosmer and Lemeshow Test: p =0.039
39
3.4 Use of Contraceptive Methods
3.4.1 Sample Description
Table 3.4.1 shows that data was collected from all the 89 health centers in both the
pretest and posttest. The same table shows absence of missing values for the main
variables with very few missing for the demographic variables.
Table 3.4.1: Distribution of Valid and Missing Cases For Main Variables
Pretest Posttest Variable
Valid Missing Valid Missing
Number of Sampled Health Centers 89 0 89 0
Use of Family Planning Methods 892 0 889 0
Source of Contraceptive Method* 506 0 595 0
Age 888 4 887 1
Male Children 892 0 889 0
Female Children 892 0 889 0
Employment Status 884 8 884 4
Woman's Education 892 0 889 0
Husband's Education 892 0 888 1
* Only for those using modern methods of family planning
Table 3.4.2 summarized the sample description where data was collected from 1781
non-pregnant women visiting MoH health centers that offer primary health care
including maternity and childhood services.
Over 54% of the respondents came from the central region, about 35% from the north
and less than 11% from the south. Over one third of the sample was from CHCs. The
weighted distribution by region inflated the central region from about 39% to 54%
while the north was deflated from 37 to 35% and south more drastically from 24% to
about 11%. This reflects the reality of having more married women of reproductive
age visiting the clinics in the central region. The same explanation applies to CHCs
where the un-weighted proportion was 25% as opposed to about 36%.
Mean age in the pretest was 30.5 compared to 30.9 years in the posttest (p=. 0.198).
Sampled women were almost equally distributed across the age groups below 30
years and from 30-40 years while only about 7% were in the age group above 40
years.
40
Mean years of schooling for respondents were 10.5 and 10.7 years in the pretest and
posttest respectively (p=0.215). Husbands’ mean years of schooling was 11.1 and
10.9 years in the pretest and posttest respectively (p=0.204) About 27% of both
respondents and their husbands had higher education and only about 3% had zero
years of schooling. The employment rate of the sampled women was about 16%
increasing from 13.9% in the pretest to 17.6% in the posttest.
Average number of children per women was about 4 in both the pretest and posttest
(p=0.245) with almost 2 males and 2 females. About 50% of women had 1-3 children
and over 14% had more than 7 children.
Table 3.4.2: Overall Sample Characteristics
Pretest Posttest Pooled Variable N % N % N %
Total 892 100 889 100 1781 100
Region
North 314 35.2 308 34.7 622 34.9
Central 490 54.9 479 53.9 969 54.4
South 88 9.9 101 11.4 189 10.6
HC Type*
CHCs 298 33.4 338 38.0 636 35.7
PHCs 594 66.6 551 62.0 1145 64.3
Age Groups in Years
<30 419 47.2 400 45.0 819 46.1
30-40 410 46.2 423 47.6 833 46.9
>40 59 6.6 65 7.3 124 7.0
Education (Respondent)*
Illiterate 28 3.1 30 3.4 58 3.3
Basic 304 34.0 243 27.4 547 30.7
Secondary 331 37.1 369 41.6 700 39.3
Higher 230 25.8 246 27.7 476 26.7
Education (Husband)
Illiterate 19 2.1 22 2.5 41 2.3
Basic 295 33.1 261 29.4 556 31.2
Secondary 324 36.3 375 42.2 699 39.3
Higher 254 28.5 230 25.9 484 27.2
Employment *
Employed 123 13.9 156 17.6 279 15.8
Not Employed 754 85.2 726 82.1 1480 83.7
Retired 8 0.9 2 0.2 10 0.6
No of Live Children
0 2 0.2 3 0.3 5 0.3
1-3 428 48.0 420 47.3 848 47.6
4-6 315 35.3 355 40.0 670 37.6
=>7 147 16.5 110 12.4 257 14.4
* Statistically significant difference between the pre and posttest
41
3.4.2 Family Planning Use
Table 3.4.3 shows the distribution of main variables by the study phase. It is worth
mentioning that the prevalence of use of various family planning methods was
calculated for married women of reproductive age visiting MoH health centers with
MCH services excluding pregnant women. Knowing that about 20-25% of women
visiting health centers with MCH services are pregnant, the contraceptive prevalence
rate among non-pregnant is expected to be higher than figures reported by DHS or
other studies that include all women. Furthermore, in a facility based surveys where
MCH services are provided the prevalence of contraceptive use is expected to be
higher than in household surveys.
Overall, use of any method increased significantly by about 13% over a 4.5-year
period from 73.5% in the pretest to 82.8% in the posttest. A more drastic increase was
noted by about 38% for using any modern method from about 53% in the pretest to
70% in the posttest. Parallel to the increase of use in modern methods, about 37%
drop in the use of traditional methods from 20.6% to 13% was noted (Table 3.4.3).
Table 3.4.3: Distribution of Main Variables by Phase of the Study
Phase
Variable Pretest Posttest
n % n %
p
value
Any Family Planning Method 656 73.5 735 82.8 <0.005
Any Modern Method 472 52.9 622 70.0 <0.005
Pills 132 14.8 199 22.4 <0.005
IUDs 264 29.6 260 29.2 0.871
Condoms 49 5.5 97 10.9 <0.005
Injectables 16 1.8 32 3.6 0.018
Female Sterilization 11 1.2 17 1.9 0.249
Use of LAM NA NA 16 1.8 NA
Male Sterilization 0 0 0 0 0
Norplant 0 0 0 0 0
Any Traditional Method 184 20.6 116 13.0 <0.005
Breastfeeding 106 11.9 59 6.6 <0.005
Withdrawal 50 5.6 31 3.5 0.032
Abstinence 40 4.5 25 2.8 0.06
Diaphragm, Jell or Foam 2 0.2 1 0.1 0.595
42
Of the modern methods, use of injectables increased the most by about 100%
followed by condoms, which increased by 98% in the posttest compared to the pretest.
Use of pills increased by over 51% from (14.8% in the pretest to 22.4% in the
posttest), while IUDs did not change over the intervention period. There was an
insignificant increase in female sterilization. Data collected during the pretest did not
allow calculating the prevalence of LAM among users, while it was 1.8% in the
posttest. The decrease in use of traditional methods was observed for all methods. Use
of breastfeeding as a contraceptive method decreased by about 80% from 11.9% to
only 6.6% while abstinence and withdrawal decreased by 61% and 60% respectively.
Table 3.4.4 shows the breakdown of use of various family planning methods by study
phase and intervention group. Overall, there was a significant increase in the use of
any family planning method by 16% among users of the focal health centers while the
increase of 3% among users of the non-focal health centers was insignificant. Using
any modern method showed significantly increasing prevalence in the posttest as
compared to the pretest among users of both focal and non-focal health centers.
Nevertheless, the increase among users of focal was higher at 36% than users of non￾focal at 24%.
Table 3.4.4: Distribution of Main Variables by Study Phase and Intervention
Intervention
Variable Focal Non-Focal
Pretest Posttest Pretest Posttest
Any Family Planning Method 71.5* 82.9 80.0 82.1
Any Modern Method 51.8* 70.2 56.1* 69.4
Pills 13.5* 21.2 19.1* 27.0
IUDs 30.3 30.6 27.3 24.5
Condoms 5.1* 11.5 6.9 8.7
Injectables 2.0 3.3 1.0* 4.6
Female Sterilization 1.2 1.9 1.5 2.0
Use of LAM 0.0 1.9 0.0 2.0
Male Sterilization 0 0 0 0
Norplant 0 0 0 0
Any Traditional Method 19.7* 13.1 24.0* 12.8
Breastfeeding 11.1* 7.1 14.7* 5.6
Withdrawal 5.2* 3.0 6.8 5.1
Abstinence 4.9* 3.0 2.9 2.0
Diaphragm, Jell or Foam 0.3 0.0 0.0 0.5
43
The use of pills among users of focal health centers increased by 57% as compared to
only 41% among users of non-focal. The most noticeable significant increase among
users of focal health centers was in the prevalence of using condoms at 127% while
the change was insignificant for users of non-focal health centers.
Overall, there was an increase in the prevalence of use of modern contraceptives over
the PHCI lifetime. Despite of the presence of some support in favor of PHCI activities
leading to improvement in contraceptive use, the evidence was not consistent. There
are several country-wide initiatives supported by USAID and other donors aiming at
improving the use of contraceptive prevalence in addition to the efforts exercised by
the MCH directorate of MoH.
3.4.3 Source of Family Planning Methods
Table 3.4.5 shows about 30% increase in the prevalence of getting the method from
the surveyed health center. The dependence on sources other than the ministry of
health decreased by about one third from 34.2% during the pretest compared to 23.8%
during the posttest.
Table 3.4.5: Distribution of Family Planning Source by Study Phase
Pretest Posttest Total Source
n % n % n %
This Health Center 219 43.3 336 56.4 555 50.4
Another MoH Health Center 114 22.5 118 19.8 232 21.1
Non-MoH Health Center 173 34.2 142 23.8 315 28.6
Total 506 100.0 596 100.0 1102 100.0
p<0.005
The positive change in the source of family planning was noticed for the main three
modern contraceptive methods. Current health centers served as a source for getting
pills in about 86% of pill users during the posttest compared to only 66% for the
pretest. This was accompanied by a noticeable decrease in outside sources from
25.8% in the pretest to 10.1%. The same trend but to a lesser degree was noted for
both condoms and IUDs.
Table 3.4.6: Distribution of Source of Selected Family Planning Methods by Study
Phase
Pills % Condoms % IUDs % Source
Pretest Posttest Pretest Posttest Pretest Posttest
This Health Center 65.9 85.9 76.0 84.4 19.0 25.5
Another MoH Health Center 8.3 4.0 10.0 4.2 34.6 34.4
Non-MoH Health Center 25.8 10.1 14.0 11.5 46.4 40.2
44
Facing difficulties in getting or using family planning methods was mentioned by
about 12% of users during the pretest. The figure went down significantly by over
45% during the posttest to reach only 6.5% (Table 3.4.7).
Table 3.4.8 shows that
complications and side
effects were the main type
of difficulties identified by
users. Figures of 5.4% and
5.2% of complications and
side effects for pretest and
posttest were very close
and were seemingly not responsible for the overall reduction in the prevalence of
difficulties among users. Non-availability of the service at the local health center and
lack of provision for some services on daily basis were the kind of difficulties that
were reduced during the posttest. It is worth mentioning that the latter types of
difficulties were mainly related to IUD insertion. The “others” category included a
variety of answers ranging from male provider, not knowing that the service is
available and is free, far distance, long waiting time and method inconvenience.
Table 3.4.8: Distribution of Type Difficulties Getting or Using Family Planning
Methods by Study Phase
Study Phase Type of Difficulty Frequency Percent
Complications and side effects 36 5.4
Service is not provided daily 19 2.9
Not Availability in local HC 16 2
Pretest
Others 7 1.1
Complications and side effects 38 5.2
Service is not provided daily 4 0.5
Not Availability in local HC 4 0.5
Posttest
Others 1 0.1
3.4.4 Prediction of Contraceptive Use
As shown in table 3.4.9 women tended to use the modern contraceptive methods
about twice more likely in the posttest compared to the pretest. Women in central and
northern regions were respectively 69% and 33% more likely to use modern methods
than women in the south region. Women users of CHCs were only insignificantly 2%
more likely to use modern contraceptive methods. Women in the younger age groups
were more likely to use modern methods. Women in the age group of less than 30
years were about 2.3 times more likely to use modern methods than those older than
40 years. While those in the age group of 30-40 years were about 1.9 times more
Table 3.4.7: Distribution of Difficulties Getting or
Using Family Planning Methods by Study Phase
Study Phase Response Frequency Percent
Yes 78 11.9
No 574 87.6
Not sure 3 0.5 Pretest
Total 656 100.0
Yes 48 6.5
No 686 93.2
Not sure 2 0.2 Posttest
Total 735 100.0
45
likely to use modern methods than the oldest age group. Employment was also found
to be a significant predictor of modern family planning use. Employed women were
found to be about 1.8 times more likely to use modern methods as compared to the
unemployed.
Clearly, both pregnant woman’s and husband’s education had some predictive ability
for the use of modern methods. Illiterate women were twice less likely to use modern
methods than those with higher education. Women married to illiterate husbands were
2.8 times less likely to use modern methods when compared to those married to
husbands with higher education.
With every additional male child, women were about 30% more likely to use modern
contraceptive methods, while only 11% more likely to use such methods with every
additional female child.
Table 3.4.9: Logistic Regression of Use of Modern Contraceptive Methods
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 2.10 1.72 2.58 <0.005 Study Phase
Pretest - - - -
CHCs 1.02 0.83 1.27 0.824 HC Type
PHCs - - - -
North 1.33 1.06 1.66 0.013
Region Central 1.69 1.16 2.46 0.006
South - - - -
<30 2.29 1.40 3.74 0.001
Age Groups 30-40 1.91 1.24 2.93 0.003
>40 - - - -
Employed 1.80 1.29 2.52 0.001
Employment
Not Employed - - - -
Illiterate 0.49 0.25 0.95 0.035
Basic 0.79 0.56 1.11 0.166
Secondary 0.87 0.65 1.17 0.351
Education
(Pregnant)
Higher Education - - - -
Illiterate 0.36 0.17 0.75 0.007
Basic 0.95 0.70 1.28 0.731
Secondary 1.07 0.81 1.40 0.644
Education
(Husband)
Higher Education - - -
Male Children No. of Male Children 1.29 1.19 1.41 <0.005
Female ChildrenNo. of Female Children 1.11 1.03 1.20 0.007
- Comparison Group Hosmer and Lemeshow Test: p =0.025
46
Table 3.4.10 shows that women in the posttest were about 1.7 times less likely to use
natural methods compared to women in the pretest. Despite that the use of natural
methods were more likely prevalent among the younger age groups, age did not seem
to be a significant predicator for using natural methods. Furthermore, having one
more male child made the women 1.2 less likely to rely on natural methods for family
planning.
Employed women were about 1.9 less likely to use a natural method than their
unemployed counterparts. Illiterate women and women married to illiterate husbands
were more likely to use natural methods compared to those with higher education.
Table 3.4.10: Logistic Regression of Use of Natural Contraceptive Methods
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 0.60 0.46 0.78 <<0.0055 Study Phase
Pretest - - - -
CHCs 0.69 0.52 0.91 0.009 HC Type
PHCs - - - -
<30 1.13 0.57 2.22 0.730
Age Groups 30-40 1.46 0.79 2.67 0.225
>40 - - - -
Employed 0.53 0.33 0.84 0.007
Employment
Not Employed - - - -
Illiterate 1.92 0.90 4.10 0.091
Basic 1.01 0.65 1.56 0.960
Secondary 1.14 0.78 1.66 0.510
Education
(Pregnant)
Higher Education - - - -
Illiterate 3.10 1.42 6.76 0.004
Basic 1.14 0.77 1.69 0.498
Secondary 1.15 0.81 1.63 0.450
Education
(Husband)
Higher Education - - -
Male Children No. of Male Children 0.83 0.74 0.92 0.001
Female ChildrenNo. of Female Children 1.03 0.94 1.13 0.514
- Comparison Group Hosmer and Lemeshow Test: p =0.139
47
3.5 Screening for Hypertension
3.5.1 Sample Description
Table 3.5.1 shows that data for hypertension screening was collected from all the 89
health centers in both the pretest and posttest.
Aside from very few records with missing data on age, sex and years of schooling
during the posttest phase, all main variables had completely valid values.
Table 3.5.1: Distribution of Valid and Missing Cases For Main Variables
Pretest Posttest Variable
Valid Missing Valid Missing
Number of Sampled Health Centers 89 0 89 0
Age 884 0 917 4
Sex 884 0 918 3
Years of Schooling 884 0 917 4
BP Checking During the Survey Day 884 0 921 0
BP Recording During the Survey Day* 884 0 276 0
Total Number of Visits Over the Last Year 884 0 921 0
Number of Times BP Was Recorded Last Year 884 0 921 0
Final Screening for Hypertension 884 0 921 0
* Only for those reporting their blood pressure checked
Table 3.5.2 shows that over 48% of the sample came from the central region, 36%
from the north and 16% from the south. About 32% of the respondents came from the
CHCs.
The male female ratio was 0.72:1 reflecting the expected sex differential of users of
MoH health centers. The mean age during the pretest and posttest phases was almost
identical at 52.69 and 52.64 years respectively (p = 0.92). While the majority of
respondents (44%) were belonging to the youngest age group 40-49 years, less than
8% were in age group above 70 years of age. There were some significant differences
in the proportion of age categories of respondents between the pre and posttest data.
The mean number of years of schooling was identical at 5 years for the pre and
posttest. The low education is probably related to the age structure of the sample.
About 41% of the sample had zero years of schooling compared to 10.3% with higher
education.
48
Table 3.5.2: Overall Sample Characteristics
Variable Pretest Posttest Pooled
N % N % N %
Total 884 100 921 100 1805 100
Region*
North 338 38.2 314 34.1 652 36.1
Central 398 45.0 470 51.0 868 48.1
South 148 16.7 137 14.9 285 15.8
HC Type
CHCs 270 30.5 306 33.2 576 31.9
PHCs 614 69.5 615 66.8 1229 68.1
Sex
Male 351 39.7 403 43.9 754 41.8
Female 533 60.3 515 56.1 1048 58.2
Age Groups in Years*
40-49 412 46.7 384 41.9 796 44.2
50-59 234 26.5 293 32.0 527 29.3
60-69 147 16.6 188 20.5 335 18.6
=>70 90 10.2 52 5.7 142 7.9
Education
Illiterate 385 43.5 362 39.4 747 41.4
1-6 174 19.7 238 25.9 412 22.9
7-12 229 25.9 229 24.9 458 25.4
Higher Education 97 11.0 89 9.7 186 10.3
* Statistically significant difference between the pre and posttest
3.5.2 Screening for Hypertension
Table 3.5.3 shows that 30% of respondents in the posttest compared to 26.4% in the
pretest reported having their blood pressure checked. The 14% of the observed
improvement in the posttest was not shown to be significant. For those reporting their
BP was checked, medical files showed that BP readings were recorded only in 63% in
the posttest compared to 57.5% in the pretest. Again the 10% observed improvement
in the posttest did not show statistical significance.
Screening for hypertension among respondents on the survey day was noted to be
18.9% in the posttest compared to 15.2% in the pretest. The 24% increase between pre
and posttest was significant. Finally, the overall screening that takes into
consideration BP recordings in the medical file over the last year including the survey
day did not change significantly over more than 4 years. Screening increased by only
4% from 37% in the pretest to reach 38.5% in the posttest.
49
Table 3.5.3: Distribution of Main Variables by Study Phase
Phase
Variable Pretest Posttest
n % n %
p
value
BP Checking During the Survey Day 233 26.4 276 30.0 0.088
BP Recording During the Survey Day* 134 57.5 174 63.0 0.203
Screening During the Survey Day** 134 15.2 174 18.9 0.035
Final Screening Over the Last Year 327 37.0 355 38.5 0.496
*Among those reporting their BP was checked
** Among the overall sample
Examining screening for hypertension on the survey day across the focal and non￾focal health centers showed some significant changes in favor of focal health centers
Focal health centers improved by about 36% in the posttest. Unfortunately this change
did not hold true for the year around screening for hypertension where there were no
significant changes between the pre and posttest (Table 3.5.4).
One can conclude that the efforts exercised through PHCI activities such as clinical
training and quality assurance did not materialize into improvement in screening for
hypertension irrespective of the procedure simplicity. Absence of active supervision is
thought to be the major player of absence of improvement for this indicator.
3.5.3 Prediction of Screening for Hypertension
Table 3.5.6 shows prediction of the screening for hypertension for the available
variables. As expected the phase of the study did not show any prediction for the
appropriateness of screening for hypertension. Age and sex showed significant
prediction for the screening variable. A male patient aged 40 years and above was
1.37 times less likely to have his blood pressure checked than a female patient.
Table 3.5.4: Distribution of Main Variables by Study Phase and Intervention Group
Focal % Non-Focal % Variable
Pretest Posttest Pretest Posttest
BP Checking During the Survey Day 28.6 32.2 21.1 23.9
BP Recording During the Survey Day 56.7* 68.2 58.2 44.1
Screening During the Survey Day 16.2* 22.0 12.3 10.5
Final Screening Over the Last Year 38.5 40.5 33.3 33.2
* Statistically significant
50
For each one year increase in age there is 1.2% more likelihood that the patient was
screened for hypertension.
Table 3.5.6: Logistic Regression of Screening for Hypertension
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 1.07 0.88 1.29 0.507 Study Phase
Pretest - - - -
CHCs 0.99 0.80 1.23 0.914 HC Type
PHCs - - - -
North 0.79 0.58 1.06 0.118
Region Central 0.92 0.70 1.21 0.559
South - - - -
Male 0.73 0.58 0.90 0.004 Sex
Female - - - -
Age Age 1.01 1.00 1.03 0.012
Years of
Schooling Years of Schooling 1.01 0.99 1.03 0.324
- Comparison Group Hosmer and Lemeshow Test: p =0.002
51
3.6 Status of Control of Diabetes
3.6.1 Sample Description
Data was collected from all of the 89 selected health centers during both phases of the
study. All blood samples were delivered in good shape to the Central Lab where
HbA1c was performed with no single missing value (Table 3.6.1). The same table
shows few missing values for age, years of schooling and disease duration. The
highest missing values were reported for employment during the pretest at about
2.4%.
Table 3.6.1: Distribution of Valid and Missing Cases For Main Variables
Pretest Posttest Variable
Valid Missing Valid Missing
Number of Sampled Health Centers 89 0 89 0
Region 1190 0 1150 0
Health Center Type 1190 0 1150 0
Age 1188 2 1150 0
Sex 1190 0 1150 0
Years of Schooling 1188 2 1146 4
Employment 1161 29 1140 10
Duration of the Disease in Years 1181 9 1146 5
HbA1c 1190 0 1150 0
BMI 1174 16 1142 9
The pooled sample was 2340 respondents with 1190 from the pretest and 1150 from
the posttest (Table 3.6.2). About 44% of the sample came from the central region
followed by about 39% from the north and 17.3% from the south. More respondents
from the south and less from the central region were noted during the posttest as
compared to the pretest mainly due to higher responses from the south. Over 70% of
the respondents were users of PHCs.
The male female ratio was 1:1.4 which reflects the gender structure of clients in the
targeted age groups. The mean age of respondents was statistically younger in the
pretest at 55.1 years compared to 57.7 years in the posttest (p<0.005). The age
difference is clearly reflected in the age groups shown in table 3.6.2. The shift in age
is partly explained by follow up of the same respondents in about 38% of cases
making them more than 4 years older.
52
Overall, about 23% of the respondents were employed, 13% retired and 64%
unemployed. There were more retired in the posttest as compared to the pretest, which
might be partly due to follow up issue.
The differences in the educational level of respondents for the two study phases were
statistically insignificant. The mean years of schooling was 4.4 and 4.8 years at the
pretest and posttest respectively (p=0.036). The mean disease duration increased
significantly from 5.7 in the pretest to 6.3 years in the posttest. The disease duration
categories show similar changes with more respondents in the higher categories.
Again this can be explained by following up about 38% of the same participants.
Table 3.6.2: Overall Sample Characteristics
Pretest Posttest Pooled Variable N % N % N %
Total 1190 100 1150 100 2340 100
Region*
North 455 38.2 454 39.5 909 38.8
Central 555 46.6 472 41.0 1027 43.9
South 180 15.1 224 19.5 404 17.3
HC Type
CHCs 351 29.5 338 29.4 689 29.4
PHCs 839 70.5 812 70.6 1651 70.6
Sex
Male 480 40.3 472 41.0 952 40.7
Female 710 59.7 679 59.0 1389 59.3
Age Groups in Years*
<30 30 2.5 17 1.5 47 2.0
30-49 299 25.2 217 18.9 516 22.1
50-59 394 33.2 350 30.4 744 31.8
60-69 315 26.5 387 33.7 702 30.0
=>70 149 12.6 179 15.6 328 14.0
Employment*
Employed 374 32.2 145 12.7 519 22.6
Retired 90 7.8 218 19.1 308 13.4
Not Employed 697 60.0 777 68.2 1474 64.1
Education
Illiterate 567 47.7 510 44.5 1077 46.1
1-6 255 21.5 252 22.0 507 21.7
7-12 273 23.0 284 24.8 557 23.9
Higher Education 93 7.8 101 8.8 194 8.3
Disease Duration in Years
0-3 361 30.6 205 17.9 566 24.3
4-6 279 23.6 296 25.8 575 24.7
7-10 277 23.5 287 25.0 564 24.2
>10 264 22.4 358 31.2 622 26.7
* Statistically significant difference between the pre and posttest
53
3.6.2 Status of Control of Diabetes and Obesity
The diabetes control figures are different from those reported in the pretest report as
far as more stringent criteria of the American Diabetes Association were applied.
Readings of HbA1c less than 7% were considered controlled diabetes.
The mean HbA1c increased significantly from 7.55% during the pretest to reach
7.98% during the posttest. This difference is mainly due to the presence of high values
in the upper 5% of the distribution during the posttest. Nevertheless, the controlled -
uncontrolled categories of diabetic patients were statistically similar during both
phases of the study despite the observed mild increase of uncontrolled from about
61% to about 63%.
The mean of the body mass index (BMI) decreased insignificantly from 30.1 Kg/m2
in the pretest to 29.7 Kg/m2 (p=0.06). Examining the BMI categories further supports
the no change between the two study phases.
Table 3.6.3: Distribution of the Status of Control of Diabetes and BMI by Study
Phase
Phase
Variable Pretest Posttest
n % n %
p
value
Status of Control ofControlled 459 38.6 421 36.6
Diabetes Uncontrolled 731 61.4 729 63.4
0.327
Normal 210 17.9 231 20.2
Body Mass Index Overweight 402 34.2 411 36.0
Obese 562 47.9 501 43.8
0.123
Table 3.6.4 looks at the main variables taking into account the intervention dimension
in addition to study phase. The change in the status of control of diabetes over the
project lifetime was insignificant for both focal and non-focal health centers with a p
value of 0.9 and 0.14 respectively.
The body mass index figures were somewhat better in focal health centers than non￾focal with more respondents falling in the normal group and less in the obesity group.
The change in BMI for the focal centers between the two phases of the study was
significant.
The above findings indicate that training of health workers, introduction of standards
and protocols and presence of performance improvement review teams in the health
centers were insufficient to lead to significant improvement in diabetes control. This
54
might have happened due to a combined effect of short maturation of interventions
and absence of an effective supervision and follow up system.
Table 3.6.4: Distribution of the Status of Control of Diabetes and BMI by Study Phase
and Intervention
Focal % Non-Focal % Variable
Pretest Posttest Pretest Posttest
Status of Control Controlled 39.7 39.4 39.5 36.5
of Diabetes Uncontrolled 60.3 60.6 60.5 63.5
Normal 15.4* 17.8 16.6 22.6
Body Mass Index Overweight 34.0 37.6 35.7 34.6
Obese 50.6 44.7 47.7 42.8
*Statistically significant at p = 0.07
3.6.3 Prediction of the Status of Control of Diabetes
As expected table 3.6.5 shows that the study phase had no role in predicting the state
of control of diabetes. Similarly, type of health center did not seem to play any role in
the prediction of the status of control of diabetes despite the fact that CHCs had
internists offering a supposedly better management of diabetics. Region wise, the
north was not different from the south while respondents from the central region were
29% more likely to have their diabetes controlled compared to respondents from the
south.
The sex of the patient behaved indifferently to predicting disease control status. The
age was a significant predictor of control for diabetes where with each year of age
increase the diabetes was 2.2% more likely to be brought under control. Educational
level showed that with the increase of one year of schooling, the control of diabetes
becomes 6% significantly more likely to happen. Conversely, employment did not
show significant prediction of diabetes control. Disease duration showed a somewhat
negative relationship with control of diabetes. With each year of increase in disease
duration the possibility that a diabetic patient becomes controlled is about 6% less.
As expected, obesity also proved to be a significant predicator for the control of
diabetes. Non-obese subjects were 1.34 times more likely to be controlled than obese
subjects. Finally, one should note that this study did not aim at looking at a detailed
list of factors affecting diabetes control but rather reporting the indicators as is.
55
Table 3.6.5: Logistic Regression of Status of Control of Diabetes
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 0.98 0.82 1.17 0.834 Study Phase
Pretest - - - -
CHCs 0.97 0.79 1.19 0.759 HC Type
PHCs - - - -
North 0.95 0.73 1.24 0.703
Region Central 1.29 1.01 1.66 0.044
South - - - -
Male 1.07 0.86 1.32 0.556 Sex
Female - - - -
Age Age 1.02 1.01 1.03 <0.005
Years of
Schooling Years of Schooling 1.06 1.04 1.08 <0.005
Employed 0.82 0.65 1.33 0.678
Employment
Not Employed - - - -
Disease Duration Disease Duration 0.94 0.93 0.96 <0.005
Not Obese 1.34 1.12 1.60 0.002 Obesity
Obese - - - -
- Comparison Group Hosmer and Lemeshow Test: p =0.46
3.6.4 Analysis of Paired Observations for Diabetes Control
Overall, only 446 participants out of the 1190 recruited in the pretest (37.5%) were
followed in the posttest. The paired observations constituted about 39% of the posttest
respondents. The highest response was from the north at 47.9% followed by the south
region at 37.6% and the central region at 30.5%. Paired observations were obtained
from all health centers but one. Almost 54% of the respondents came from the focal
centers while the remaining 46% from non-focal health centers.
As far as the sample was designed to get data at the stratum level (region and health
center type), one can proceed with the analysis of the 446 paired observations at the
national level without going to lower levels of stratifications.
Analysis of co-variance (ANCOVA) was used to test the effect of PHCI interventions
as judged by focal versus non-focal health centers on the status of control of diabetes.
One should keep in mind that non-focal health centers were not a perfect comparison
56
due to unavoidable contamination especially regarding training component. Testing
the homogeneity of regression slopes revealed an F value of 1.35 corresponding to a p
value of 0.245. This finding indicates that the main assumption for ANCOVA of
having the same regression slopes for the first and second readings of HbA1c for
respondents coming from focal and non-focal centers was met.
Table 3.6.6 shows that the intervention as judged by focal-non-focal health centers
had a statistically significant effect on the level of posttest HbA1c with an F value of
13.4 and a p value of less than <0.0055.
Table 3.6.6 Tests of Between Subjects Effects for the Posttest HbA1cAs Dependent
Variable and Focal-Non-Focal as Intervention
Source Type III Sum of
Squares df Mean
Square F Sig. Observed
Power*
Corrected Model 394.5** 2 197.3 32.3 <<0.0055 1.000
Intercept 253.1 1 253.1 41.5 <<0.0055 1.000
Pretest HbA1c 304.33 1 3.4.3 49.9 <<0.0055 1.000
Intervention 81.6 1 81.6 13.4 <<0.0055 0.954
Error 2782.7 456 6.1
Total 35337.8 459
Corrected Total 3177.2 458
*Computed using alpha = .05
**R Squared = 0.124 (adjusted R squared = 0.120)
Table 3.6.7 shows parameter estimates of the regression of posttest HbA1c on pretest
HbA1c. The B coefficients of the regression are used to construct the estimated
marginal means shown in table 3.3.8 according to the formula: Estimated marginal
mean = intercept coefficient + coefficient corresponding to the level of intervention +
(intercept for the pretest HbA1c  mean of pretest HbA1c)
Table 3.6.7: Parameter Estimates for the Posttest Readings of HbA1c as Dependent
Variable for Focal and Non-Focal Health Centers
95% CI Parameter B Std.
Error t
p
value Lower Upper
Observed
Power*
Intercept 3.616 0.634 5.707 <0.005 2.371 4.862 1.000
Pretest HbA1c 0.566 0.080 7.062 <0.005 0.408 0.724 1.000
Non-Focal Health Centers 0.844 0.231 3.657 <0.005 0.391 1.298 0.954
Focal Health Centers 0** - - - - - -
*Computed using alpha = .05
**This parameter is set to zero because it is redundant (comparison parameter).
57
Table 3.6.8 summarizes the estimated marginal means with 95% confidence interval
while keeping the value of pretest HbA1c at its mean level. The HbA1c mean value
for diabetics using focal health centers was significantly less at 7.97% as compared to
non-focal health centers at 8.81% irrespective of the differences in the pretest
readings.
Table 3.6.8: Estimated Marginal Means of Posttest HbA1c
95% Confidence Interval Intervention Mean Std. Error
Lower Upper
Non-Focal Health Centers 8.81 0.17 8.48 9.14
Focal Health Centers 7.97 0.16 7.65 8.28
Evaluated at pretest HbA1c = 7.688.
58
3.7 Status of Control of Hypertension
3.7.1 Sample Description
Data was collected from all the 89 sampled health centers during both phases of the
study (Table 3.7.1). The same table shows very few missing values for age, years of
schooling and disease duration.
Table 3.7.1: Distribution of Valid and Missing Cases For Main Variables
Pretest Posttest Variable
Valid Missing Valid Missing
Number of Sampled Health Centers 89 0 89 0
Region 1148 0 1089 0
Health Center Type 1148 0 1089 0
Age 1144 4 1087 2
Sex 1148 0 1088 1
Years of Schooling 1148 0 1085 4
Employment 1148 0 1087 2
Duration of the Disease in Years 1145 3 1085 4
Systolic and Diastolic BP variables 1148 0 1089 0
BMI 1147 1 1089 0
The pooled sample consisted of 2237 respondents with 1148 from the pretest and
1089 from the posttest (Table 3.7.2). About 46% of the sample came from the central
region followed by about 37% from the north and 17% from the south. More
respondents from the south and less from the central region were noted during the
posttest as compared to the pretest. Sixty nine percent of the respondents were users
of PHCs while the rest were users of CHCs.
The male female ratio was 1:1.7 which reflects the gender structure of clients in the
targeted age groups. The mean age of respondents was statistically younger in the
pretest at 57.3 years compared to 59.5 years in the posttest (p<0.005). The age
difference is clearly reflected in the age groups shown in table 3.7.2. The shift in age
is partly explained by follow up of the same respondents in about 32.3% of cases
making them more than 4 years older.
Overall, about 23% of the respondents were employed, 11% retired and 66%
unemployed. There were less employed in the posttest compared to the pretest. The
mean years of schooling was 3.7 and 4.3 years at the pretest and posttest respectively
(p=0.004). The significant differences were reflected in the educational categories of
59
respondents for the two phases of the study. Overall, 52% of the sample had zero
years of schooling, while less than 8% had higher education than school.
The mean disease duration increased significantly from 6.4 in the pretest to 7.7 years
in the posttest. The disease duration categories show similar changes with more
respondents in the higher categories. Again, this is mainly explained by following up
about over 32% of the same participants.
Table 3.7.2: Overall Sample Characteristics
Pretest Posttest Pooled Variable N % N % N %
Total 1148 100 1189 100 2237 100
Region*
North 421 36.7 399 36.6 820 36.7
Central 565 49.2 471 43.3 1036 46.3
South 162 14.1 219 20.1 381 17.0
HC Type
CHCs 344 30.0 350 32.1 694 31.0
PHCs 804 70.0 739 67.9 1543 69.0
Sex*
Male 398 34.7 424 39.0 822 36.8
Female 750 65.3 664 61.0 1414 63.2
Age Groups in Years*
<50 245 21.4 165 15.2 410 18.4
50-59 372 32.5 325 29.9 697 31.2
60-69 348 30.4 398 36.6 746 33.4
=>70 180 15.7 199 18.3 379 17.0
Employment*
Employed 319 27.8 190 17.5 509 22.8
Retired 132 11.5 115 10.6 247 11.0
Not Employed 697 60.7 783 72.0 1480 66.2
Education *
Illiterate 630 54.9 533 49.2 1163 52.1
1-6 234 20.4 222 20.5 456 20.4
7-12 209 18.2 233 21.5 442 19.8
Higher Education 75 6.5 96 8.9 171 7.7
Disease Duration in
Years*
0-3 424 37.0 276 25.4 700 31.4
4-6 320 27.9 313 28.8 633 28.4
7-10 234 20.4 248 22.9 482 21.6
>10 168 14.7 248 22.9 416 18.6
* Statistically significant difference between the pre and posttest
60
3.7.2 Status of Control of Hypertension and Obesity
Table 3.7.3 shows that over 100% improvement in the status of control of
hypertension was noted during the posttest at 22.3% compared to the pretest at 11%.
The improvement was consistent across the six categories of the level of control of
hypertension. There was an increase in the percentage of the first three categories of
controlled blood pressure in the posttest compared to pretest. As for the uncontrolled
categories there was a drastic decrease in grade III hypertension and mild decrease in
grade II in favor of grade I disease. The observed differences were statistically
significant.
Table 3.7.3: Distribution of the Status of Control of Hypertension by Study Phase
Status of Control of Pretest Posttest
Hypertension N % N %
Optimal 25 2.2 47 4.3
Normal 42 3.7 86 7.9
High Normal 59 5.1 110 10.1
Controlled 126 11.0 243 22.3
Grade I Hypertension 327 28.5 429 39.4
Grade II Hypertension 380 33.1 310 28.5
Grade III Hypertension 314 27.4 107 9.8
Uncontrolled 1021 89.0 846 77.7
The mean BMI changed from 31.6 Kg/m2
during the pretest to 31.2 Kg/m2
during the
posttest with a p value of 0.048. Table 3.7.4 shows the distribution of BMI categories
by study phase. During the posttest, only less than 14% were enjoying normal BMI
while about 31% were overweight and the majority (55.6%) was obese. The results of
the pretest were not statistically different from the posttest (p=0.312).
Table 3.7.4: Distribution of Obesity Status by Study Phase
Pretest Posttest Obesity Status N % N %
Normal 132 11.5 147 13.5
Overweight 350 30.5 336 30.9
Obese 665 58.0 606 55.6
Table 3.7.5 shows that improvement in hypertension control occurred among users of
both focal and non-focal health centers. The figures of controlled blood pressure
among hypertensive patients was 1.9 times better in the posttest as compared to the
pretest in the focal health centers, while the improvement was 2.3 times in the non-
61
focal. The changes were highly significant for both focal and non-focal health centers
with p value less than 0.005. It is worth mentioning that clinical training was carried
out at both focal and non-focal health centers. Furthermore, some external factors
other than PHCI interventions might have affected the better control of hypertensive
patients such as the availability of more effective drugs. Table 3.7.5 also shows that
the status of BMI did not change for both focal and non-focal over the period of 4.5
years. The p value was 0.223 for focal and 0.373 for non-focal health centers.
Table 3.7.5: Distribution of the Status of Control of Hypertension and by Study Phase
and Intervention
Focal % Non-Focal % Variable
Pretest Posttest Pretest Posttest
Status of Control Controlled 12.0* 23.2 9.2* 20.9
of Hypertension Uncontrolled 88.0 76.8 90.8 79.1
Normal 9.8 11.5 14.4 16.7
Body Mass Index Overweight 27.5 30.2 36.0 31.8
Obese 62.7 58.3 49.6 51.5
*Statistically significant
3.7.3 Prediction of the Status of Control of Hypertension
Table 3.7.6 shows that the study phase was a significant predictor of hypertension
control. The odds of hypertension control during the posttest were 2.22 that of the
odds of the pretest indicating that hypertensive patients were over two times more
likely to be controlled in the posttest than in the pretest.
The level of education was shown to be another significant predicator for
hypertension control, as with each year of increase in schooling, a hypertensive
patient was 5% more likely to be controlled. Finally, there is some association
between the control of hypertension and obesity. Normal weight hypertensive patients
were significantly about 1.5 times more likely to be controlled than obese
counterparts. Overweight hypertensives were 1.24 times more likely to be controlled
than obese, yet the figure was not statistically significant. Type of health center,
region, sex, age, employment and disease duration did not show significant prediction
of the controlled status of hypertension.
62
Table 3.7.6: Logistic Regression of Status of Control of Hypertension
OR 95% CI Variable Odds Ratio
Upper Lower
Sig.
Posttest 2.22 1.73 2.85 <0.005 Study Phase
Pretest - - - -
CHCs 1.25 0.97 1.62 0.088 HC Type
PHCs - - - -
North 0.75 0.52 1.08 0.123
Region Central 1.29 0.94 1.78 0.116
South - - - -
Male 0.85 0.63 1.16 0.311 Sex
Female - - - -
<50 0.65 0.41 1.03 0.066
50-59 1.09 0.76 1.58 0.635
60-69 0.77 0.54 1.10 0.153
Age in Years
=>70 - - - -
Years of
Schooling Years of Schooling 1.05 1.02 1.08 0.001
Employed 0.94 0.67 1.32 0.723
Employment
Not Employed - - - -
Disease Duration Disease Duration 1.01 0.99 1.03 0.188
Normal 1.49 1.06 2.11 0.023
Obesity Overweight 1.24 0.95 1.62 0.113
Obese - - - -
- Comparison Group Hosmer and Lemeshow Test: p =0.682
3.7.4 Analysis of Paired Observations for Hypertension Control
Overall, only 371 participants out of the 1148 recruited in the pretest (32.3%) could
be followed in the posttest. The paired observations constituted about 34% of the
posttest respondents. The highest response was from the north at 49.2 % followed by
the south region at 33.9% and the central region at only 17.3%. The distribution is
explained by the more population movement in the central region and the more
difficult identification of study subjects in urban areas. Paired observations were
obtained from 82 health centers out the sampled 89 centers. Almost 55.6% of the
respondents came from the focal centers while the remaining 44.4% from non-focal
health centers.
63
The original sample was designed to get data at the stratum level (region and health
center type); so that one can proceed with the analysis of the 371-paired observations
at the national level without going to lower levels of stratifications.
Analysis of co-variance (ANCOVA) was used to test the effect of PHCI interventions
as judged by focal versus non-focal health centers on systolic and diastolic BP
readings. One should keep in mind that non-focal health centers were not a perfect
comparison due to unavoidable contamination especially regarding training and mass
media campaigns of the health communication and marketing components.
Testing the homogeneity of regression slopes for systolic blood pressure revealed an F
value of 6.36 corresponding to a p value of 0.01. This finding indicates that the main
assumption for ANCOVA of having the same regression slopes for the first and
second readings of systolic blood pressure for respondents coming from focal and
non-focal centers was violated. Accordingly, nested ANCOVA was used to estimate a
model having separate slopes.
Table 3.7.7 shows the parameter estimates of the regression of posttest on pretest BP
readings. The B coefficients of the regression are used to construct the estimated
prediction formula for both focal and non-focal health centers:
Predicted posttest reading of BP for non-focal = coefficient for non-focal health
centers + (coefficient corresponding to interaction between non-focal and the pretest
reading of BP  pretest reading of the BP). The formula for the focal health centers is
similar with substitution of non-focal for focal.
The formula shows that predicted posttest systolic BP readings when pretest readings
were above 140 mm of mercury were lower in the focal health centers compared to
non-focal.
Table 3.7.7: Parameter Estimates for the Posttest Readings of HbA1c as Dependent
Variable for Focal and Non-Focal Health Centers
95% CI Parameter B
Std.
Error t
p
value Lower Upper
Observed
Power1
Non-Focal Health Centers 81.95 12.35 6.64 <0.005 57.66 106.23 1.00
Focal Health Centers 120.27 11.48 10.47 <0.005 97.68 142.86 1.00
Non-Focal*Pretest Reading 0.46 0.08 5.97 <0.005 0.31 0.61 1.00
Focal*Pretest Reading 0.19 0.07 2.52 0.012 0.04 0.33 0.71
1-Computed using alpha = .05
64
Using the above formula, Table 3.7.8 shows the estimated marginal means while
keeping the value of pretest systolic BP readings at it mean level of about 156
mm/Hg. The mean posttest systolic BP was shown to be slightly lower for users of
focal health centers at 149.2 compared to non-focal health centers at 153.8 mm of
mercury.
Table 3.7.8: Estimated Marginal Means of Posttest Systolic BP
95% Confidence Interval Intervention Mean Std. Error
Lower Upper
Non-Focal Health Centers 153.82 1.75 150.38 157.27
Focal Health Centers 149.23 1.52 146.25 152.21
Evaluated at pretest systolic BP = 156.03
Testing homogeneity of regression slopes for diastolic BP readings revealed an F
value of 0.164 corresponding to a p value of 0.686. This finding indicates that the
main assumption for ANCOVA of having the same regression slopes for the pretest
and posttest readings of diastolic BP for respondents coming from focal and non-focal
centers was met.
Table 3.7.9 shows that PHCI interventions as judged by focal and non-focal health
centers have a statistically insignificant effect on the level of posttest HbA1c with an
F value of 0.32 and a p value of less than 0.57.
Table 3.7.9 Tests of Between Subjects Effects for the Posttest HbA1c As Dependent
Variable and Focal-Non-Focal as Intervention
Source Type III Sum
of Squares df Mean
Square F Sig. Observed
Power*
Corrected Model 5950.57 2 2975.29 27.51 <0.005 1.00
Intercept 13193.73 1 13193.73 121.97 <0.005 1.00
Pretest Diastolic BP 5716.76 1 5716.76 52.85 <0.005 1.00
Intervention 34.98 1 34.98 0.32 0.570 0.09
Error 36777.25 340 108.17
Total 2815091.00 343
Corrected Total 42727.83 342
*Computed using alpha = .05
**R Squared = 0.139 (adjusted R squared = 0.134)
Table 3.7.10 shows parameter estimates of the regression of posttest on pretest
diastolic BP values. The B coefficients of the regression are used to construct the
estimated marginal means shown in table 3.7.11 according to the formula:
65
Estimated marginal mean equals intercept coefficient + coefficient corresponding to
the level of intervention + (intercept for the pretest diastolic BP  mean of pretest
diastolic BP)
Table 3.7.10: Parameter Estimates for the Posttest Readings of Diastolic BP as
Dependent Variable for Focal and Non-Focal Health Centers
95% CI Parameter B Std.
Error t
p
value Lower Upper
Observed
Power*
Intercept 54.11 4.88 11.08 <0.005 44.50 63.71 1.00
Pretest HbA1c 0.38 0.05 7.27 <0.005 0.28 0.48 1.00
Non-Focal Health Centers 0.65 1.14 0.57 0.570 -1.60 2.90 0.09
Focal Health Centers 0** - - - - - -
*Computed using alpha = .05
**This parameter is set to zero because it is redundant (comparison parameter).
Table 3.7.11 summarizes the estimated marginal means with 95% confidence interval
while keeping the value of pretest diastolic BP at its mean level of about 94 mm/Hg.
The very close figures of 90.3 and 89.6 mm/Hg for non-focal and focal respectively
reflect the no effect as judged by the significance level in table 3.7.10.
Table 3.7.11: Estimated Marginal Means of Posttest Diastolic BP Readings
95% Confidence Interval Intervention Mean Std. Error
Lower Upper
Non-Focal Health Centers 90.28 0.86 88.58 91.97
Focal Health Centers 89.63 0.75 88.16 91.09
Evaluated at pretest diastolic BP = 94.04.
66
4. Conclusions and Recommendations
1. PHCI, as a large project with multiple diverse components reflecting a mixture
of software and hardware activities, had a relatively prolonged preparatory
phase. During the first quarter of 2003 only five health centers had all the six
PHCI components completed. Over the last two years of the project most of
the PHC related activities at health centers were accomplished with different
periods of maturation. Even activities in some health centers did not start yet
when this study was implemented. With such short period of interventions it
was expected that PHCI activities would not affect most of the indicators that
were set back in early 2000.
2. PHCI activities started its technical and non-technical components without the
availability of satisfactory systems to sustain these activities. Of outmost
importance was the absence of effective supervisory system that helps at early
stages enforce application of the new activities and maintain them over a long
period of time. Standards and protocols of care at the health centers were
developed, care providers were trained and PHCI developed some tools to
help observe the adherence to those standards. The absence of effective
supervisory system at the MoH and engrossment of PHCI with completion of
the planned activities have negatively affected the adherence to standards and
protocols during the last two years. Furthermore, PHCI project was more
output oriented without clear measurable outcome indicators related to various
activities. The PHCI vague monitoring and evaluation plan had contributed to
weak impact of project interventions
3. Evaluation of interventions that are expected to affect the primary health care
services should be done after at least 4-5 years of effective implementation.
Monitoring all indicators related to implementation is essential before
proceeding to evaluating impacts. Strengthening systems and policies should
precede efforts aiming at improving service utilization. Trying to improve
service utilization without well established systems and policies to support the
expected positive change would undermine sustainability.
4. Ways to improve the postnatal care at MCH facilities should be considered
including outreach programs. Furthermore, missed opportunities for family
planning during postnatal visits have to be considered seriously.
67
5. Improve the quality of maternal and child health care services in order to
ensure high quality care delivery. Performed at regular intervals, evaluation of
maternal and child health services should be considered as part of assuring
high quality care. Defining criteria and developing methods for assessing the
quality of maternal and child health services are necessary. Developing follow
up mechanisms is a necessary step for modifying maternal and child health
services.
6. Improve the utilization of growth and development monitoring visits for
children during second and third year of life. This can be achieved by
improving health awareness of the community towards growth monitoring
needs and benefits. Developing the outreach program at the MOH can add
considerable value to this particular intent.
7. Review and institute policies and procedures necessary for early detection of
anemia both during pregnancy and early childhood. Developing procedures
and protocols to be used for correct diagnosis and treatment of anemia and its
underlying causes is recommended. Anemia control and prevention programs
should focus on high-risk groups. Maternal and child health programs should
include a management component that can ensure monitoring of procedures
and protocols pertaining to anemia control. Further efforts should be exercised
to improve screening procedures for anemia among children and pregnant
women. Screening of children at one year of age and pregnant women for the
presence of anemia has to be enforced and closely monitored. Increasing
awareness of both professionals and parents of children toward the importance
of screening is essential.
8. Record keeping systems should have clear evaluation schemes in order to
facilitate correct monitoring of health problems. Monitoring recording systems
can assist in producing accurate prevalence figures of health problems.
Accuracy in reporting is essential for revealing changes and patterns of health
problems. Training health workers in data management and in effective use of
information is essential. Documentation of procedures and findings in
patient’s medical records has to be improved. Failure of recording BP in 43%
of cases screened for hypertension shows the negligence of physicians that
might be occurring with other procedures. Again failure to record background
information such as income and education for women and children with
multiple visits to the MCH clinic is another example of poor documentation.
68
9. Create a management system whereby a set of standards is provided and
ensured. Standards that cover all areas of primary health care service delivery
should be reviewed and updated as needed. These standards should be made
available to all health care providers and used in monitoring service provision.
10. The national strategy for chronic non-communicable diseases urgently needs
revision to improve awareness, counseling, treatment, and control levels
among the hypertensive and diabetic populations. The status of control of
diabetes which is considered very common disease in Jordan showed alarming
figures. Both diseases are associated with significant morbidity and mortality
related to complications. Improved control of the two diseases can prevent or
delay complications. The strategy must establish a comprehensive network of
public, private, professional, and voluntary groups involved in blood pressure
and diabetes control activities, including screening and follow-up services, as
well as public, patient, and professional education.
11. Screening mechanisms for hypertension among those aged 25 years and above
have to be established with no delay. Screening is a simple procedure that can
be applied to a prevalent disease in order to enable the prevention of serious
complications. Effective treatment schedules can be made readily available
once the disease is discovered.
12. Assist the MOH in developing a health education scheme that targets common
health problems. When working on this recommendation, it is suggested to
allocate considerable attention to the problem of anemia, diabetes and
hypertension. Furthermore, the low use of contraceptive pills in face of almost
100% availability at health centers should prompt a wider and more
comprehensive marketing of such pills.
69
5. Annexes
70
Timely Vaccination
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample
This cell is for office use only
Section II- Control Variables
10. Date of Birth 11. Gender Male Female 12. Family Monthly Income in JDs
13. Mother's
Education Illiterate Elementary Preparatory Secondary College University
14. Father's Education Illiterate Elementary Preparatory Secondary College University
Section III- Dates of Vaccination Total Number of Children
15. Dates of Vaccination
Vaccination Dose Hepatitis B DTP Poliomyelitis Measles MMR
1
st
2
ed
3
rd
4
th
Booster
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 1
71
Growth and Development Monitoring and Anemia
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample
This cell is for office use only
Section II- Control Variables
10. Date
of Birth 11. Gender Male Female 12. Family Monthly Income in JDs
13. Mother's Education Illiterate Elementary Preparatory Secondary College University
14. Father's Education Illiterate Elementary Preparatory Secondary College University
Section III- Growth Visits Total Number of Children
15. Number of Growth and Monitoring Visits
First Year of Life Second Year of Life Third Year of Life
16. Hemoglobin at the age of one year 17. PCV 18. Date
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 2
72
Antenatal, Postnatal Visits and Anemia of Pregnancy
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample
This cell is for office use only
Section II- Control Variables
10. Age 11. Family Income in JDs
12. Women's Education Illiterate Elementary Preparatory Secondary College University
13. Husband's Education Illiterate Elementary Preparatory Secondary College University
Section III- Antenatal Care Total Number of Women
14. Total Number of Antenatal Visits
Section IV- Postnatal Care
15. Postnatal Care Yes No 16. Family Planning Yes No 17. Decision Made Yes No
Section V- Anemia of Pregnancy
18. Last Hemoglobin Reading 19. Last reading of PCV
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 3
73
Use of Contraceptive Methods
NOTE: Please do not forget that your first question to the selected subject is about her marital and pregnancy status.
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample
This cell is for office use only
Section II- Control Variables Estimated Daily Load of MWRA
10. Age 11. Number of Male Children 12. Number of Female Children
13. Employment Status Employed Unemployed Retired Housewife
15. Women’s Years of Schooling 16. Husband’s Years of Schooling
Section III- Contraceptive Use
17. Do You Currently Use Any Contraceptive Method? Yes No If yes,
Pills Norplant Abstinence
IUD Diaphragm, foam,
ll
Withdrawal
Condom ♀ Sterilization Breastfeeding
18. What Method Of The
Following Do You Currently
Use
Injectables ♂ Sterilization Others:
19. What is the source of your contraceptive? This HC Other MoH HC Non-MoH HC
20. Do you have problems getting contraceptives? Yes No Not Sure
Non-availability Adverse Reactions
21. If Yes, specify the problem Male Provider Others Specify:
No Daily Provision
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 4
74
Screening for Hypertension
NOTE: You can proceed filling the questionnaire only if the patient is not known to be hypertensive and he/she
is over the age of 40
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample
This cell is for office use only
Section II- Control Variables
10. Age 11Gender: Male Female 12. Years of Schooling
Section II- Hypertension Screening Estimated Load of >40 Years of Age
13. Has your BP been checked during today's visit? Yes No
14. Today’s BP reading in patient’s medical record Yes No
15. Number of visits documented over the last year
16. Number of times the BP was checked over the same period of time
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 5
75
Status of Control of Diabetes
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample (office use only)
10. Name of the Patient 11. Phone Number
12. Address
Section II- Control Variables Expected Number of Diabetics During
the Data Collection Period
13. Age 14Gender: Male Female 15. Years of Schooling
16. Weight in Kg 17. Height in cm 18. Duration of Diabetes in Years
19. Employment Status Employed Unemployed Retired
Section III- Glycosylated Hemoglobin
21. HbA1c Reading
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 6.1
76
Blood Collection and Lab Form for Diabetes
Health Center Name Code of Health Center
Governorate Directorate of Health
Serial
Number
Name Date of Blood
Collection
Name of & Signature
of Physician
HBA1c
Reading
Date of the
Test
Name & signature
of lab technician
Annex Number 6.2
77
Status of Control of Hypertension
Section I. Identification Variables
1. Name of Health Center 2. Code of Health Center
3. Type of Health Center Comprehensive Primary 4. Governorate
5. Health Directorate 6. Location Urban Rural
7. Region North Middle South
8. Subject ID for Health Center 9. Subject ID for Sample (office use only)
10. Name of the Patient 11. Phone Number
12. Address
Section II- Control Variables Expected Number of Hypertensives
During the Period of Data Collection
13. Age 14. Gender: Male Female 15. Years of Schooling
16. Weight in Kg 17. Height in cm 18. Duration of Hypertension in Years
19. Employment Status Employed Unemployed Retired
Section III- Blood Pressure Readings
20. Systolic BP 21. Diastolic BP
Date Name of Data Collector Signature
Name Field Supervisor Signature
Name Office Supervisor Date Signature
Annex Number 7