DHS ANALYTICAL 

STUDIES 47

Contraceptive Dynamics following 

HIVTesting

SEPTEMBER 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Kerry MacQuarrie, Sarah Bradley, Alison Gemmill, and Sarah Staveteig.

DHS Analytical Studies No. 47 

Contraceptive Dynamics Following HIV Testing 

Kerry L.D. MacQuarrie 1

Sarah E.K. Bradley 1, 2

Alison Gemmill 2 

Sarah Staveteig 1 

ICF International 

Rockville, Maryland, USA 

September 2014 

1

 The DHS Program 

2

 University of California, Berkeley 

Corresponding author: Kerry L.D. MacQuarrie, ICF International, 530 Gaither Road, Rockville, 

Maryland, USA; phone: 301-572-0282; fax: 301–407-6501; email: kerry.macquarrie@icfi.com 
Acknowledgments: The authors would like to thank Dr. Sanyukta Mathur for insightful comments on an 

earlier draft of this report. In addition, we thank Bryant Robey for editing the report and Yuan Cheng for 

formatting it. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about the DHS Program contact: 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 301-

407-6500, fax: 301-407-6501, email: reports@dhsprogram.com, Internet: www.dhsprogram.com. 

Recommended citation: 

MacQuarrie, Kerry L.D., Sarah E.K. Bradley, Alison Gemmill, and Sarah Staveteig. 2014. Contraceptive 

Dynamics Following HIV Testing. DHS Analytical Studies No. 47. Rockville, Maryland, USA: ICF 

International. 
iii 

Contents 

Tables ........................................................................................................................................................... v 

Figures .......................................................................................................................................................... v 

Preface ........................................................................................................................................................ vii 

Abstract ....................................................................................................................................................... ix 

Executive Summary ................................................................................................................................... xi 

1. Introduction ............................................................................................................................................. 1 

2. Methods .................................................................................................................................................... 5 

2.1. Data ............................................................................................................................................... 5 

2.2. Analytical Strategy ......................................................................................................................... 8 

2.3. Measures ........................................................................................................................................ 9 

3. Results .................................................................................................................................................... 13 

3.1. Descriptive Results ...................................................................................................................... 13 

3.2. Model 1—Postpartum Adoption of Contraception among Women with a Recent Birth .............. 23 

3.3. Model 2—Adoption of Contraception among Women without a Recent Birth ............................ 26 

3.4. Model 3—Discontinuation of Contraception ............................................................................... 28 

4. Limitations ............................................................................................................................................. 31 

5. Conclusions and Discussion ................................................................................................................. 33 

References .................................................................................................................................................. 35 

v 

Tables 

Table 1. Analytic samples ............................................................................................................................. 7 

Table 2. Current use of contraception among all women age 15-49 in study countries ............................. 14 

Table 3. Percentage of women ever tested for HIV by timing of most recent test and receipt of results ... 17 

Table 4. HIV testing during ANC among women who had a birth in the past 24 months ......................... 17 

Table 5. Profile of women age 15-49 and of analytic samples: Percent distribution and means of 

selected characteristics ................................................................................................................. 20 

Table 6. Model 1: Time ratios predicting adoption of contraception following birth, among women 

age 15-49 who have had a birth in the previous 24 months ......................................................... 24 

Table 7. Model 2: Time ratios predicting adoption of contraception among women age 15-49 who 

have not had a birth in the previous 24 months ............................................................................ 27 

Table 8. Model 3: Hazard ratios predicting discontinuation of contraception among women age 15-49 

who have not had a birth in the previous 24 months .................................................................... 29 

Figures 

Figure 1. HIV prevalence among women age 15-49 .................................................................................. 15 

Figure 2. Percent of women 15-49 ever tested for HIV and received results ............................................. 16 

Figure 3. Current fertility intentions among women age 15-49, by HIV status .......................................... 18 

Figure 4. Current fertility intentions among women age 15-49, by history of HIV testing ........................ 19 

vii 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be 

useful to policymakers and program managers in low- and middle-income countries. DHS Analytical 

Studies serve this objective by providing in-depth research on a wide range of topics, typically including 

several countries and applying multivariate statistical tools and models. These reports are also intended to 

illustrate research methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with 

the U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

This study uses Demographic and Health Survey (DHS) data to investigate how receipt of HIV test 

results influences subsequent contraceptive behavior. In five countries (Kenya, Lesotho, Malawi, Zambia, 

and Zimbabwe), we analyze the effect of HIV testing during antenatal care on adoption of contraception 

following birth. In two of the countries (Lesotho and Zimbabwe), we also examine (a) adoption and (b) 

discontinuation of contraception among women who did not experience a birth in the past 24 months and 

for whom the option of an HIV test occurs outside of the context of antenatal care. We use longitudinal 

contraceptive calendar data to estimate hazard models of a change in contraceptive behavior, using as 

covariates (1) HIV status as determined by biomarker testing at the time of the survey and (2) women’s 

self-reported HIV testing experience. 

In Kenya only, HIV-positive women have a longer duration to adopting contraception following birth 

compared with HIV-negative women. This relationship is not found elsewhere, nor is HIV status 

associated with the hazard of adopting or discontinuing contraception among women without a birth in 

the past 24 months. 

In Lesotho, Zambia, and Zimbabwe, women who receive HIV test results during antenatal care have a 

shorter expected time to adopting contraception, regardless of HIV status, but not in Kenya or Malawi. 

Among women without a birth in the past 24 months, those who receive HIV test results have a shorter 

expected time to adopting contraception in Zimbabwe, but there is no statistical difference in Lesotho. 

Experience with HIV testing does not influence discontinuation of contraception. 

xi 

Executive Summary 

Policies and programs addressing the reproductive health needs of HIV-positive women encourage 

contraceptive use as a key intervention for reducing unintended pregnancy and preventing vertical 

transmission of the virus. Women living in countries with high HIV prevalence, however, have some of 

the highest levels of unmet need for family planning, suggesting that access to and adoption of 

contraception following HIV diagnosis remain important global health priorities. 

A large body of literature focusing on contraceptive use dynamics among HIV-positive women suggests 

extensive heterogeneity across regional, cultural, and programmatic contexts. Nonetheless, many of these 

studies indicate differences in contraceptive use between HIV-positive and HIV-negative women. One 

area of research that remains less well explored is how learning one’s status alters contraceptive use 

patterns. To date, few studies have investigated the causal impact of receipt of test results on 

contraceptive use dynamics, such as uptake, method mix, discontinuation, and failure. 

This study uses Demographic and Health Survey (DHS) data from five countries (Kenya, Lesotho, 

Malawi, Zambia, and Zimbabwe) to investigate how receipt of HIV test results during antenatal care 

(ANC) influences adoption of contraception following birth. In Lesotho and Zimbabwe this study also 

examines (a) adoption and (b) discontinuation of contraception among women who did not experience a 

birth in the past 24 months and for whom the option of an HIV test occurs outside of the context of ANC. 

We use longitudinal contraceptive calendar data to estimate hazard models of a change in contraceptive 

behavior, using as covariates (1) HIV status as determined by biomarker testing at the time of the survey 

and (2) women’s self-reported HIV testing experience. 

Using this longitudinal study design, the only significant relationship between contraceptive use and HIV 

status found in multivariate analysis is among women in Kenya who gave birth in the 24 months 

preceding the survey. Among these, HIV-positive women have a 53 percent greater expected time to 

adopting a contraceptive method following birth compared with HIV-negative women, net of other 

factors. This relationship is not found elsewhere, nor is HIV status associated with adopting or 

discontinuing contraception among women without a birth in the past 24 months. 

There is more evidence from these analyses to suggest that exposure to HIV testing is associated with 

shorter expected durations to contraceptive adoption among women with a recent birth and among other 

non-users of contraception at the time of HIV testing. In Lesotho, Zambia, and Zimbabwe, women who 

have had an HIV test and received results during ANC have shorter expected times to adopting a 

contraceptive method following birth. In addition, in Zimbabwe, but not Lesotho, having had an HIV test 

and received results is also associated with a shorter time to adopting contraception among women who 

did not have a birth in the previous 24 months. 

As with HIV status, HIV testing experience is not associated with discontinuing contraception among 

women who were using contraception at the start of the observation period, net of other factors. 

For the most part the results presented here confirm and extend those of the only previous study that 

assessed differences by HIV status in how knowing one’s HIV status affects contraceptive practices. We 

found almost no significant differences between HIV-positive and HIV-negative women in contraceptive 

patterns following HIV testing. 

The new finding that in four of the five study countries HIV testing experience is associated with quicker 

adoption of contraception offers tentative support for the conclusion that interaction with health services 

appears to facilitate contraceptive use, rather than HIV status or knowledge of HIV status per se. 
xii 

However, further exploration is warranted of how HIV status and HIV testing experience are related to 

interactions with other health services, fertility intentions, and attitudes toward contraception, as well as 

contraceptive behavior. 
1 

1. Introduction 

Policies and programs addressing the reproductive health needs of HIV-positive women encourage 

contraceptive use as a key intervention for reducing unintended pregnancy and preventing vertical 

transmission of the virus (Halperin et al. 2009; Mazzeo et al. 2012; Reynolds et al. 2006). Women living 

in countries with high prevalence of HIV, however, have some of the highest levels of unmet need for 

family planning (Wilcher et al. 2009), suggesting that access to and adoption of contraception following 

HIV diagnosis remain important global health priorities. 

A large body of literature focusing on contraceptive use dynamics among HIV-positive women suggests 

extensive heterogeneity across regional, cultural, and programmatic contexts. Nonetheless, many of these 

studies indicate differences in contraceptive use between HIV-positive and HIV-negative women. One 

area of research that remains less well explored is how learning one’s HIV status impacts or alters 

contraceptive use patterns. This study employs Demographic and Health Survey (DHS) data from five 

countries to investigate how receipt of HIV status may alter contraceptive use dynamics. 

One of the primary reasons contraceptive use might differ between HIV-positive and HIV-negative 

women following testing and knowledge of their HIV status is that HIV-positive women may, at least 

temporarily, revise their fertility preferences downward. Alternately, women who learn that they are HIVpositive may revise their fertility preferences upward if they: are concerned with avoiding stigma, by 

demonstrating their continued ability to bear a child; become concerned with their legacy; seek to secure a 

relationship that has become vulnerable to dissolution; or would like to fulfill childbearing desires before 

their health deteriorates (Cooper et al. 2007; Erhabor et al. 2012; Nattabi et al. 2009; Yeatman 2009a). 

Longitudinal studies allowing for comparison of fertility preferences pre- and post-diagnosis generally 

support the former notion, that HIV-positive women are more likely to revise their fertility preferences 

downward (Bonnenfant et al. 2012; Hoffman et al. 2008; Keogh et al. 2012; Yeatman 2009b). For 

example, among women receiving HIV voluntary counseling and testing (VCT) in Malawi, the proportion 

of women reporting a desire for future children declined one week after diagnosis (from 33 percent to 15 

percent), and then remained constant over the course of the year (Hoffman et al. 2008). In a longitudinal 

study of women receiving antenatal care in Tanzania, Keogh and colleagues (2012) found no change in 

short-term postpartum fertility preferences, but a downward adjustment measured 15 months after HIV 

diagnosis. In Malawi, Yeatman (2009b) found a downward revision of fertility preferences following HIV 

diagnosis, but only among women who perceived a low likelihood of infection. 

Other studies comparing fertility desires between HIV-positive and HIV-negative women provide further 

support for differences by HIV status after receiving test results. For example, among those receiving 

results from VCT clinics in Rwanda, HIV-positive men and women were six times more likely to report 

wanting to stop childbearing compared with HIV-negative individuals (Heys et al. 2009). Likewise, 

among couple VCT clients in Ethiopia, women in serodiscordant relationships were 10 to 11 times more 

likely to no longer desire children following diagnosis compared with women in HIV-negative 

concordant relationships (Bonnenfant et al. 2012). Cross-sectional studies also provide some evidence of 

differences by HIV status. A multi-country analysis using nationally representative DHS data found that, 

in five of the eight countries studied, HIV-positive women who knew their status were significantly less 

likely to want no more children compared with HIV-negative women who knew their status (Bankole et 

al. 2011). 

HIV-positive women face common family planning barriers shared by all women of reproductive age. For 

example, women enrolled in a prevention of mother-to-child HIV transmission trial in Western Kenya 

often cited partner’s approval and support as key determinants of intention to use family planning (Akelo 
2 

et al. 2013). In addition, in India women not using contraception following HIV diagnosis cited lack of 

contraceptive counseling, low acceptability of non-condom contraception, and partner’s involvement 

(Chakrapani et al. 2011). In Soweto, South Africa, qualitative evidence suggests that HIV-positive 

women may not use contraceptives because of concerns about side effects, body image, and being 

amenorrheic (Laher et al. 2009). Similarly, in a qualitative study of people living with HIV in two Uganda 

clinics, patients indicated concerns about complications and the cost of some family planning methods 

(Wanyenze et al. 2013). 

HIV-positive women may face even greater barriers to family planning use than other women of 

reproductive age. HIV-positive women may avoid health services as a way to avoid HIV stigma and to 

limit inadvertent disclosure of their HIV status (Church and Mayhew 2009). New expenses related to the 

cost of HIV treatment, care, and support could divert resources away from spending on contraceptive 

methods or visits to family planning providers, although this remains less well-explored in the literature. 

HIV-positive women may also avoid use of hormonal contraception because of fears related to 

interactions with antiretroviral therapies or disease progression (Mbonye et al. 2012; Todd et al. 2011). 

Alternatively, women who access HIV testing services may be more likely to use contraception because 

they access services that supply it. This effect may represent a self-selection process to the extent that 

women who are more likely to access HIV testing or use ANC services that offer HIV testing are the 

same women who are more likely to access other types of health care, including family planning services. 

HIV testing may also facilitate interactions with family planning services if contraception is promoted in 

the course of HIV test counseling, if direct referrals are made for family planning services, or if HIV 

testing and family planning services are integrated. This effect may be related to the act of testing for HIV 

and thus hold true for women regardless of HIV status, or it may be accentuated for women who test 

positive for HIV and are referred for ART treatment and general preventative health services, and thus 

have more engagement with health services, including family planning. 

To date, few studies have investigated the causal impact of receipt of test results on contraceptive use 

dynamics, such as uptake, method mix, discontinuation, and failure. Using data collected prospectively as 

part of a randomized control trial of the diaphragm for HIV prevention in South Africa and Zimbabwe, 

Blanchard and colleagues found that, among newly diagnosed women, a majority (65 percent) had no 

change in contraceptive status, while 17 percent switched to more effective methods (e.g. from condoms 

to injectables) and 18 percent switched to less effective ones (Blanchard et al. 2011). These patterns, 

however, did not significantly differ by HIV status. 

In a prospective cohort study of women presenting to family planning, sexually transmitted infection, or 

voluntary counseling and testing clinics in Malawi, Hoffman et al. (2008) found that contraceptive use 

increased from 38 percent before HIV diagnosis to 52 percent one week later, but declined to 46 percent 

by 12 months. The study did not include HIV-negative women for comparison. Lastly, a subanalysis of a 

prospective study on hormonal contraception and HIV acquisition in Uganda, Zimbabwe, and Thailand 

found an uptake of hormonal contraception six months after diagnosis among women not using 

contraception at baseline (i.e. 0 to 25 percent), and low discontinuation (1.5 percent) among those who 

were using hormonal contraception at the time of diagnosis (Nanda et al. 2011). This study also did not 

include a comparison group of HIV-negative women. 

Some studies have also tried to assess changes in contraceptive use via structured questionnaires, which 

capture HIV-positive individuals’ contraceptive use prior to their diagnosis. Using this study design, 

Chakrapani et al. (2011) found that contraceptive uptake, including dual use, increased significantly 

among recently diagnosed Indian women, with condom use increasing from 11 percent to 92 percent and 

dual use increasing from 5 percent to 30 percent. Likewise, among HIV-positive women in Vietnam, 
3 

overall contraceptive use increased from 63 percent before diagnosis to 89 percent afterward, and 

reported condom use increased from 39 percent to 87 percent (Chi et al. 2012). 

The literature reviewed above is limited to studies using non-representative cohorts of women. Thus, it is 

unclear if the observed patterns are representative of the general population. However, cross-sectional 

evidence from representative population surveys investigating the relationship between HIV status and 

contraceptive use indicates different patterns across countries. In the multi-country study reviewed above, 

the authors found no statistically significant association in current contraceptive use by HIV status in six 

of the eight countries (Bankole et al. 2011). In the two countries with significant findings, HIV-positive 

women in Lesotho were less likely to use contraception, but in Rwanda were more likely to use it. The 

observed patterns of contraceptive use are also likely to interact with demand for contraception. Using 

DHS data, Johnson and colleagues (2009) found that in three out of the four countries studied, HIVpositive women who likely knew their status were more likely to want to limit childbearing by using 

contraception. Also, among women using contraception, HIV-positive women were more likely to use 

condoms. 

These studies reveal existing gaps in our understanding of the relationship between HIV status and 

contraceptive use, and highlight the need for analyses using nationally representative data. The aim of this 

study, therefore, is to assess contraceptive use dynamics following receipt of test results, using nationally 

representative data from recent DHS surveys in five countries in sub-Saharan Africa—Kenya, Lesotho, 

Malawi, Zambia, and Zimbabwe. Specifically, we use a semi-longitudinal design and contraceptive 

calendar data to compare contraceptive uptake and discontinuation between recently diagnosed HIVpositive and HIV-negative women. 

5 

2. Methods 

2.1. Data 

DHS surveys are nationally representative, population-based household surveys that employ standardized 

questionnaires and protocols for the collection of biomarker data, increasingly including HIV serostatus. 

This study uses DHS survey data from five countries in which HIV prevalence among women is greater 

than 4 percent and for which the most recent survey includes HIV testing and the contraceptive calendar. 

The included surveys are: Kenya 2008-09, Lesotho 2009, Malawi 2010, Zambia 2007, and Zimbabwe 

2010-11. All five surveys interviewed women of reproductive age (15-49), regardless of marital status 

(currently, formerly, or never-married). Response rates among eligible women range from 93.3 percent in 

Zimbabwe to 97.9 percent in Lesotho. 

We use two HIV-related measures: first, HIV status at the time of the survey bio-medically confirmed 

through DHS biomarker testing; and second, a self-reported measure of testing and receipt of results. We 

use self-reported date of most recent HIV test as an assumed date of learning one’s current HIV status. 

DHS contraceptive calendar data provide a longitudinal window in which to examine use and 

discontinuation of contraception over time. We analyze changes in contraceptive behavior within two 

groups of women: (1) women who have had a birth in the 24 months preceding the survey, for whom the 

opportunity to have an HIV test occurs during pregnancy; and (2) women who have neither had a birth in 

the 24 months preceding the survey nor were pregnant 24 months before the survey, and thus for whom 

the option of having an HIV test occurs outside the context of pregnancy-related care. For the first group, 

we examine uptake of a contraceptive method following the most recent birth. We examine the influence 

on adopting a contraceptive method of HIV status and of having had an HIV test and received the results 

during pregnancy. 

We investigate two aspects of contraceptive dynamics among the second group. First, we examine 

adopting a contraceptive method among women who were not initially using contraception 24 months 

preceding the survey. Next, we examine discontinuation of contraception among women who were using 

a contraceptive method 24 months preceding the survey. For each, we compare the contraceptive behavior 

of women who are HIV-positive with those who are HIV-negative, and those who have had an HIV test 

and received the results with those who have not. 

2.1.1. Contraceptive calendar 

Calendar data are complete month-by-month retrospective histories of episodes of contraceptive use or 

non-use, as well as pregnancies, births, and terminations, that occur in the five years preceding the survey. 

In several surveys additional columns collect data on reason for discontinuation of contraception or 

changes in marital status. Data are recorded hierarchically in case of two events occurring in the same 

month. Terminations supersede pregnancies, which supersede contraceptive use. Methods of 

contraception are recorded according to their level of efficacy (ICF International 2012; Trussell 2011; 

WHO/RHR and JHU/CCP 2011). As such, dual method use (of a barrier and a hormonal method) is not 

recorded; only the most effective method is captured. 

2.1.2 HIV biomarker testing in the DHS 

DHS surveys follow a standard DHS protocol for anonymous, informed, and voluntary HIV testing of 

survey respondents. In some surveys, HIV testing is conducted in every household (Zambia, Zimbabwe). 

In others, it is collected in every second household (Kenya, Lesotho) or third household (Malawi). Blood 
6 

spots for HIV testing are collected on filter paper from a finger prick and are then transported to a 

laboratory. The laboratory protocol is typically based on an initial ELISA test and a retest of all positive 

samples with a second ELISA. Between 5 to 10 percent of samples that are negative on the first ELISA 

test are retested. For samples with discordant results on the two ELISA tests, a third ELISA or a Western 

Blot is performed. Respondents do not receive the results of the survey’s blood test, but are instead 

referred to available voluntary testing and counseling services. The protocol for HIV testing undergoes 

ethical reviews in survey countries and in the United States. Response rates for HIV testing in study 

countries range from 77.1 percent in Zambia to 93.6 percent in Lesotho. In all countries, our analytic 

samples are restricted to those women who were offered and consented to HIV biomarker testing and for 

whom valid testing of dried blood spots yielded valid (not indeterminate) results. 

2.1.3. Sampling 

DHS surveys use a two-stage clustered sampling technique. In the first sampling stage, the country is 

stratified into regions from which census-based enumeration areas are selected with probability 

proportional to size. Urban areas and less populous areas are typically oversampled in this first stage. A 

mapping and household listing exercise is carried out in each selected enumeration area. In the second 

sampling stage, households are randomly selected from the household list within each selected 

enumeration area. 

A sampling weight is applied, which accounts for both sampling probability and non-response. In this 

case, we apply the weight that accounts for sample selection and non-response for HIV biomarker testing. 

Additionally, we use the complex survey (svy) commands available within Stata 13 to account for the 

clustered sampling design and estimate robust standard errors. 

The sample of women interviewed in study countries ranges from 7,146 in Zambia to 23,020 in Malawi, 

and the sample of women with valid HIV biomarker test results ranges from 3,641 in Kenya to 7,313 in 

Zimbabwe. This study conducts separate analyses on two segments of this population. The first analyses 

are restricted to women who have had a birth in the 24 months preceding the survey and consented to 

survey HIV biomarker testing, in all five study countries (analytic sample #1). For these women, we 

examine contraceptive behavior following their most recent birth. The second set of analyses is restricted 

to women who have not had a birth in the 24 months preceding the survey nor were pregnant 24 months 

preceding the survey and who consented to an HIV biomarker test, in the Lesotho and Zimbabwe surveys 

(analytic sample #2). Further restricting our analyses to women for whom data are complete on all 

variables in our analytical models, our analytical samples are as described below. 
7 

Table 1. Analytic samples

 

Weighted number of women age 15-49 

Interviewed 

HIV 

biomarker 

tested1

Analytic sample #1 Analytic sample #2 

Who had a birth 

in the previous 

24 months 

and HIV 

biomarker tested 

Who did not have a birth in the previous 

24 months and were not pregnant 

24 months prior to the survey 

and HIV biomarker tested 

 

Not using 

contraception2 Using 

contraception2 Total 

Kenya 2008-09 8,444 3,641 1,006 na na na 

Lesotho 2009 7,624 3,778 794 2,076 904 2,980 

Malawi 2010 23,020 7,091 2,437 na na na 

Zambia 2007 7,146 5,502 2,023 na na na 

Zimbabwe 2010-11 9,171 7,313 2,003 3,320 1,973 5,293 

1

 Consented to HIV biomarker testing via dried blood spot during the DHS survey 

2

 At 24 months prior to the survey 

2.1.4. Structure of the data set 

In the contraceptive calendar women may report multiple episodes of contraceptive use or non-use, or 

other reproductive events. We create a contraceptive events dataset wherein each episode is composed of 

person-months from start of observation until conclusion of the episode (failure) or time of interview 

(censoring). 

The period of observation for this study is the 24 months preceding the survey, for both analytical 

samples. We limit our analysis to this period because the closer to the time of the survey, the more likely 

the results of the HIV biomarker test conducted as a part of the DHS survey are to be the same as the 

results a woman has received at her most recent HIV test. In all five countries a majority of women who 

had an HIV test in the DHS also have had an HIV test within the preceding 24 months. 

In the first analytic sample of women who gave birth in the past 24 months, women enter observation in 

the month of their most recent birth, with all women being non-users of contraception. The period of 

observation continues until the woman takes up a contraceptive method, or until the date of interview 

(censoring). We link this episode of observation with data on the woman’s preceding pregnancy. 

Specifically, we link information on whether the woman had antenatal care (ANC) and whether she had 

an HIV test and received results during ANC1

. 

For the second analytic sample, we examine the episode of contraceptive use or non-use occurring at the 

start of the observation period 24 months preceding the survey. Women enter observation exactly 24 

months before the survey, and continue until their contraceptive status (use or non-use) changes, or until 

the date of interview (censoring). For Lesotho and Zimbabwe only, we are able to link to this episode data 

on the timing, in months, of the most recent HIV test. Thus, for each woman, we observe not only 

whether or not she had an HIV test during that episode of contraceptive use or non-use, but also when she 

had an HIV test and by how many months it precedes a change, if any, in her contraceptive behavior. 

 

1

 In Zimbabwe, the measure captures women who had an HIV test during ANC or during delivery. 
8 

2.2. Analytical Strategy 

After presenting descriptive data in the next section (Results), we take advantage of the longitudinal 

nature of the calendar data to conduct a series of hazard models on the two analytic samples. With the 

first analytic sample of women who have had a birth in the 24 months preceding the survey, we analyze 

the hazard of adopting a method of contraception following their most recent birth, using HIV status and 

receipt of results of an HIV test during ANC or delivery as our key covariates in Model 1. 

We divide the second analytic sample of women who have not had a birth in the 24 months preceding the 

survey nor were pregnant at the start of the period of observation into two categories: (1) women who are 

not using contraception at the start of the observation period and (2) women who are contraceptive users 

at the start of the observation period. For women in the first category, our analytical model (Model 2) is 

similar to the group of women who have had a birth (Model 1). We analyze the relative hazard of 

adopting a method of contraception in the observation period, using HIV status and whether or not the 

women received results from an HIV test prior to adopting contraception (or censoring) as key covariates. 

For women in the second category, we analyze in Model 3 the relative hazard of discontinuing 

contraception during the observation, given women’s HIV status and whether or not women received 

results from an HIV test prior to discontinuation (or censoring). 

The first two hazard models (Models 1 and 2) predicting the adoption of contraception are lognormal 

accelerated failure time (AFT) hazard models with the baseline survival function (tj) estimated by: 

ܵ଴൫ݐ௝൯ = 1 − Ф	 ቆln ݐ ௝−	ߚ଴

ቇ ߪ

Where Ф represents the cumulative distribution function with a Gaussian (normal) distribution and where 

tj is measured in months (Cleves et al. 2010). As with other accelerated failure time models, the 

covariates’ effects accelerate or decelerate the time to failure by a factor of exp(-xjβx) such that: 

ൟ௝ݐ(௫ߚ௝ݔ−)	exp଴൛ܵ =	൯௝ݔห௝ݐ൫ܵ

Therefore, results are reported as the time ratios, or exponentiated coefficients, to ease interpretation 

(Allison 1995; Box-Steffensmeier and Jones 2004). Lognormal hazard models are fully parametric, 

maximum likelihood models in which the scale of the lognormal-distributed baseline hazard is estimated 

with σ (Allison 1995; Box-Steffensmeier and Jones 2004). Hazards are not assumed to be proportional. 

One benefit of the lognormal model is that the hazard function increases and then decreases (Cleves et al. 

2010), which reflects the process identified in other research on adoption of contraception (e.g., 

Blanchard et al. 2011). This model is selected as the best fit for the baseline hazard from among other 

AFT and proportional hazard models on the basis of the Akaike Information Criterion (AIC) and 

Bayesian Information Criterion (BIC), which is consistently lowest in all study countries. 

The final model (Model 3), which models a different process than that in the first two models 

(contraceptive discontinuation rather than adoption), is estimated using a Gompertz hazard model of the 

form: 

(௫ߚ௝ݔ)	exp)ݐ)଴h௝) =	ݔ|ݐ)h

We also report results of this model as the exponentiated coefficients, or hazard ratios, to ease 

interpretation. The Gompertz model assumes a baseline hazard as follows: 

(଴ߚ)	exp) ݐߛ)exp) = ݐ)଴h
9 

In which the shape parameter, γ, indicates an increasing hazard over time if positive and a hazard that 

decreases over time if negative. Gompertz hazard models are fully parametric proportional models 

estimated using maximum likelihood procedures and assume hazards are proportional across groups 

(Allison 1995; Cleves et al. 2010). All covariates are tested for violations of the proportional hazards 

property using Schoenfeld’s residuals (Grambsch and Therneau 1994; Schoenfeld 1982). We selected this 

model among other parametric models on the basis of its low AIC and BIC and find that a Gompertzdistributed hazard represents the process of discontinuation, which differs from the process of adoption of 

contraception, better than does the lognormal baseline hazard. 

Ties in failure times in all three models are handled using Breslow’s approximation (Breslow 1974). All 

models are conducted for each study country separately. A pooled model, in which each survey was 

equally weighted, was tested. However, a variable denoting country was significant and non-proportional 

in all models, suggesting that both the baseline hazard and relative hazard of failure (adopting or 

discontinuing contraception) differs in each survey. The results of the separate hazard models presented in 

the next section confirm that a pooled model is not appropriate, as they indicate that the significance, 

magnitude, and, in some cases, direction of the coefficients vary by survey. 

2.3. Measures 

As mentioned above, the outcome of our analytical models is a change in contraceptive behavior (from 

non-user to user or from user to non-user) and our principal covariates of interest are women’s HIV status 

and whether and when women have received the results of a recent HIV test, indicating likely knowledge 

of her current HIV status. 

Contraceptive use (failure variable)—Model 1 (among women who have had a birth in the previous 24 

months) and Model 2 (among women who have not had a birth in the previous 24 months and are not 

pregnant at the start of observation) both model the adoption of a contraceptive method among women 

who are initially non-users of contraception. Therefore, the failure variable is defined as use of a 

contraceptive method. 

Conversely, Model 3 models the discontinuation of contraception among women who are initially users 

of contraception at the start of the observation period and so the failure variable is defined as the cessation 

of contraceptive use. Contraceptive users who stop using one method of contraception in order to switch 

to another method of contraception are not considered to have discontinued use. Sequential episodes of 

two or more different methods of contraception are measured as a single episode as a contraceptive user. 

Up to a one-month interruption between methods is permitted. Therefore, discontinuation is defined as 

discontinuing the use of a contraceptive method without adopting another method of contraception for 

two or more successive months. 

For all models, contraceptive use is defined as using any method of contraception, whether traditional or 

modern, and non-use of contraception is defined as using neither a traditional nor a modern method. 

Time variable—Analytical time (t) is measured linearly in single-month increments and extends from 24 

months preceding the survey until failure or interview in Models 2 and 3 and from the month of most 

recent birth until failure or interview in Model 1. The time of interview (censoring) and of the end of an 

episode of contraceptive use/non-use (failure) are recorded in century month codes (CMC), such that 

analytical time is calculated as the difference between these and the CMC for the start of the observation 

period. 

HIV status—Current HIV status is determined by the biomarker testing performed as part of the DHS 

survey and is coded 0 to indicate HIV-negative and 1 to indicate HIV-positive. Women who consent to 
10 

survey biomarker testing do not receive the results of this test. HIV status refers to women’s HIV status at 

the time of the survey, which may or may not be the same as the results of an HIV test received earlier. 

HIV testing experience—DHS surveys collect data from all women on the most recent HIV test and, from 

women who report a birth in the five2 years preceding the survey only, on HIV testing through ANC 

during the most recent pregnancy as well as any HIV test they may have had since the time they were 

tested during pregnancy. Although women may test for HIV multiple times in their lives, the DHS does 

not collect data on any earlier testing experience beyond the most recent HIV test, with the exception 

related to testing during and since ANC, described above. 

A dichotomous variable is created from two questions regarding (1) whether women have had an HIV test 

and (2) whether they received the results. Women who meet both conditions are coded 1 on this variable. 

Women who have either not had an HIV test or have had an HIV test but did not receive the results are 

coded 0. Two variations of this measure—a fixed-time version and a time-varying version—are 

developed for use with each of our two analytic samples. 

HIV testing experience during ANC (fixed-time)—For the first analytical sample (women who have had a 

birth in the previous 24 months) used in Model 1, this indicator variable refers to women who have had 

an HIV test during ANC3

. There are no women in the analysis who have had an HIV test since their most 

recent birth. As such, this is a fixed-time variable that precedes the observation period. 

HIV testing experience (time-varying)—For women in the second analytic sample, this variable refers to 

testing during the period of observation. This variable, and therefore this analytic sample, is developed 

only for the Lesotho and Zimbabwe surveys as these are the only surveys that record the timing of most 

recent HIV test in discrete months4

, which is necessary to link the time of the HIV test with the timing of 

the episode of contraceptive behavior. The variable is time-varying and coded as 0 for each month a 

woman does not have an HIV test and receive results in the time between the start of the observation 

period (24 months preceding the survey) and the month in which her episode of contraceptive behavior 

ends in failure5

 or, if no failure is observed, by the time of the survey. It is coded as 1 in the month a 

woman has an HIV test and receives the results. The variable is then coded 1 for each subsequent month 

in the observation period until failure or time of interview. Women who have had an HIV test and 

received results, but after the conclusion of her episode of contraceptive behavior, are coded as 0, as 

temporal sequencing prevents this test from influencing her adoption or discontinuation of contraception. 

Knowledge of family planning—A score is calculated to represent the woman’s level of knowledge of 

family planning. This variable is an additive index, ranging from 0 to 3, based on the woman’s knowledge 

at the time of the survey of male condoms, oral contraceptive pills, and injectables. Pills and injectables 

are among the three most commonly used contraceptive methods in four of five study countries. In 

Malawi, injectables are the most commonly used method, but use of pills lags behind female sterilization. 

 

2

 We restrict our analysis to women who have a birth in the 24 months, not five years, preceding the survey or, for 

women not reporting a birth in this period, to the 24 months preceding the survey. 

3

 In Zimbabwe, this variable refers to women who were tested for HIV during ANC or during delivery. 

4

 The other three surveys ask when was the last time the respondent was tested, but records the response in three 

broad categories: less than 12 months ago, 12-23 months ago, or two or more years ago. 

5

 Failure is defined as adoption of a contraceptive method among initial non-users of contraception in Model 2, and 

as discontinuation of contraception among initial contraceptive users in Model 3. 
11 

Place of delivery—This categorical variable is used only in Model 1. It captures whether the place of 

delivery for the woman’s most recent birth was a home, a medical facility, or “other”, with home delivery 

being the reference category. 

Age—Woman’s age at the start of the observation period is included in all models as a covariate. This 

variable is calculated as the difference between the CMC for the start of the observation period (i.e., date 

of each woman’s most recent birth for analytic sample #1 and 24 months preceding the date of interview 

for analytic sample #2) and CMC for date of birth. It is expressed in whole and partial years. 

Education—Woman’s education is measured as the number of years of schooling completed at the time 

of the survey. 

Parity—A continuous variable captures the number of births a woman has had by the start of the 

observation period. For women in Model 1, this includes the most recent birth that immediately precedes 

the episode under analysis. 

Place of residence—A dichotomous variable captures whether the woman lives in a rural area or urban 

area at the time of the survey. 

Household wealth quintile—DHS calculates standard household wealth quintiles based on factor analysis 

of material asset items (Filmer and Pritchett 2001; Rutstein 2008; Rutstein and Johnson 2004). This 

measure captures relative wealth within the country at the time of the survey for the household in which 

the woman resides. While wealth quintile is measured at the time of the survey and not at the start of the 

observation period, wealth, particularly when measured by assets, is a more stable measure of 

socioeconomic status than measures based on income or consumption. 

Marital status—A marital status variable measures whether the woman has ever been married (currently 

married or formerly married) or has never been married at the time of the survey. 

 

13 

3. Results 

3.1. Descriptive Results 

3.1.1. Contraceptive use and HIV prevalence in study countries 

Table 2 describes contraceptive prevalence, by method type and specific method, among all women age 

15-49 in the study countries. In all five countries, the majority of women do not currently use any method 

of contraception. Current use of contraception ranges from 30 percent in Zambia to 41 percent in 

Zimbabwe. Current use of modern methods follows a similar pattern and makes up the majority of all 

contraceptive use in the study countries. The pill is the most commonly used method of contraception in 

Zimbabwe (27 percent) and Zambia (7 percent). Injectables are the most commonly used method of 

contraception in Malawi (19 percent), Kenya (15 percent), and Lesotho (13 percent). The male condom is 

the second most commonly used method in Lesotho (10 percent), the only country in which use exceeds 5 

percent. Female sterilization is used less frequently than pills or injectables, except in Malawi, where 

female sterilization is the second most commonly used method (8 percent). Other modern methods are 

even less frequently used. 

Current use of traditional methods, while low overall, is highest in Zambia (5 percent). In Kenya the 

dominant traditional method is periodic abstinence, and withdrawal in the other four study countries.
Table 2. Current use of contraception among all women age 15-49 in study countries 

Not 

currently 

using 

Any 

method 

Modern methods Traditional methods 

Number 

of 

women 

Any 

modern 

method

Female 

sterilization Pill Inject- ables Male condom

Other 

modern 

methods1

Any 

traditional 

method 

Periodic 

Abstinence Withdrawal

Other 

traditional 

methods 

Kenya 2008-09 68.0 32.0 28.0 3.2 4.7 14.8 2.6 2.7 4.1 3.2 0.4 0.4 8,444 Lesotho 2009 64.1 35.9 34.9 1.7 7.8 13.4 10.4 1.5 1.1 0.1 0.5 0.5 7,624 Malawi 2010 64.6 35.4 32.6 7.5 1.9 19.2 2.7 1.3 2.8 0.6 1.2 0.9 23,020 

Zambia 2007 70.1 29.9 24.5 1.4 7.4 6.2 5.0 4.5 5.3 0.9 3.6 0.9 7,146 

Zimbabwe 2010-11 58.7 41.3 40.5 0.9 27.3 6.1 3.5 2.8 0.8 0.1 0.6 0.1 9,171 

1 Includes: male sterilization, IUD, implant, female condom, and lactational amenorrhea method (LAM) 

14 
15 

Figure 1 shows estimates of HIV prevalence among women age 15-49 in the study countries. HIV 

prevalence ranges from 8 percent in Kenya to 27 percent in Lesotho. 

Figure 1. HIV prevalence among women age 15-49 

3.1.2. Patterns of HIV testing during ANC and outside of ANC 

Figure 2 presents the proportion of women who have ever had an HIV test, either during ANC or outside 

of ANC. In Zambia, approximately 35 percent of women have ever had an HIV test, while in Malawi 

about double that proportion have had an HIV test. Lesotho has the highest proportion of women 

obtaining an HIV test outside of ANC and the lowest proportion having an HIV test during ANC. Kenya 

and Zimbabwe are similar to one another in overall levels of women ever testing for HIV and in the 

proportions testing during ANC and outside of ANC. 

26.7

17.7

16.1

12.9

8.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

Lesotho 2009

Zimbabwe 2010-11

Zambia 2007

Malawi 2010

Kenya 2008-09
16 

Figure 2. Percent of women 15-49 ever tested for HIV and received results 

3.1.3. Timing of HIV testing 

Table 3 shows the patterns of HIV testing in greater detail, for all women (analytic samples 1 and 2 

combined). In every study country a substantially larger proportion of women have had an HIV test 

within the past 24 months than were tested more than 24 months ago. In Zimbabwe the proportion 

recently receiving a test is two and a half times the proportion receiving a test more than 24 months ago, 

and in Lesotho more than ten-fold. 

Nearly all women who have tested for HIV received the test results. In Zambia a cumulative 4 percent of 

women have ever had an HIV test but not received the results; this proportion is even lower in the other 

study countries. The proportion of women who have ever had an HIV test and received their results 

ranges from 35 percent in Zambia to 72 percent in Malawi. The proportion of women who have had an 

HIV test and received their results in the 24 months preceding the survey ranges from 28 percent in 

Zambia to 52 percent in Lesotho. 

29.1

9.1

19.9

19.8

14.8

42.6

56.5

37.5

36.7

20.4

71.6

65.6

57.4

56.5

35.3

0.0 20.0 40.0 60.0 80.0 100.0

Malawi 2010

Lesotho 2009

Zimbabwe 2010-11

Kenya 2008-09

Zambia 2007

During ANC Not during ANC
17 

Table 3. Percentage of women ever tested for HIV by timing of most recent test and receipt of 

results

Ever tested 

Never 

tested 

Number 

of 

women

Last tested in past 

24 months 

Last tested 24+ 

months ago Timing unknown1

Percent 

never 

tested 

 for HIV 

Received 

results of 

last test 

Did not 

receive 

results 

Received 

results of 

last test 

Did not 

receive 

results 

Received 

results of 

last test 

Did not 

receive 

results 

Kenya 2008-09 43.2 1.4 13.3 0.4 0.0 0.2 41.6 8,444 

Lesotho 2009 51.9 1.8 4.9 0.3 8.8 0.8 31.4 7,624 

Malawi 2010 39.5 0.4 6.9 0.2 25.2 0.8 26.9 23,020 

Zambia 2007 28.0 3.1 7.2 0.9 0.1 0.1 60.6 7,146 

Zimbabwe 2010-11 42.4 1.6 15.0 0.7 0.0 0.0 40.3 9,171 

1

 In Lesotho and Malawi, this category includes all women who gave birth in the past 2 years and whose most recent 

test was during ANC. These women were not asked about the timing of their HIV test. 

Table 4 presents patterns of ANC and of HIV testing during ANC among women who have had a birth in 

the 24 months preceding the survey (analytic sample 2). The proportion of women who received no ANC 

during their most recent pregnancy ranges from 2 percent in Malawi to 13 percent in Zimbabwe. The 

proportion of women with a birth in the previous 24 months who did not have an HIV test as a part of 

ANC exceeds 45 percent in Lesotho and Zambia, but is less than 20 percent in the other three countries. 

In Lesotho and Zambia a little more than 40 percent of women who have had a birth in the previous 24 

months have had an HIV test and received the results during ANC. This figure approaches three quarters 

of women with a recent birth in Kenya and Zimbabwe, and is highest in Malawi (87 percent). As with the 

general population, the majority of these women have received the results of their HIV test. The 

proportion of women in the sample group who have had an HIV test but not received the results during 

ANC reaches 5 percent in Zambia, and is approximately 2 percent in the other four study countries, levels 

slightly higher than among the general population. 

Table 4. HIV testing during ANC among women who had a birth in the past 24 months

 

Received results 

of last test 

during ANC1

Did not receive 

results of last 

test during ANC

Not tested 

during ANC 

Did not receive 

ANC 

Number of 

women2

Kenya 2008-09 74.0 2.0 16.5 7.4 2,264 

Lesotho 2009 43.0 1.9 46.9 8.1 1,606 

Malawi 2010 86.6 2.1 9.5 1.8 7,724 

Zambia 2007 40.3 5.2 51.8 2.7 2,631 

Zimbabwe 2010-11 73.2 2.1 12.3 12.5 2,448 

1

 In Zimbabwe, this includes women tested during ANC or during delivery 

2

 This number differs from the analytical sample because not all women who had a birth in the past 24 months were 

offered or consented to HIV biomarker testing during the DHS survey 

 
18 

3.1.4. Fertility intentions by HIV status and HIV testing experience 

Women learning their HIV status may change their contraceptive behavior because their fertility 

intentions change. Fertility intentions are not included in this study’s multivariate hazard models because 

they lie on the causal pathway between the explanatory variables of interest and the outcomes of interest. 

Their relationship is explored in bivariate analysis: Figure 3 presents current fertility intentions among 

women age 15-49 in the study countries, by HIV status, and Figure 4 presents fertility intentions by 

history of HIV testing. 

Figure 3. Current fertility intentions among women age 15-49, by HIV status 

As Figure 3 shows, in no study country are there are meaningful differences between women who are 

HIV-positive and HIV-negative in the proportion who are infecund or have been sterilized, and 

differences in the proportion who want a child within 24 months are small. However, there are differences 

by HIV status in the distribution of women who want a child after 24 months or more and in the 

distribution of women who want no more children. In all five countries the proportion of HIV-positive 

women who want a child after two or more years is smaller, and the proportion of women who want no 

more children is larger, than among HIV-negative women. These differences are largest in Zimbabwe, 

where 60 percent of HIV-positive women want no more children, nearly double the 32 percent among 

HIV-negative women. 

Similarly, Figure 4 indicates that the proportions who are infecund or sterilized and who want a child 

within 24 months do not differ by prior HIV testing experience. However, there are differences in the 

proportion of women who want a child after 24 months or more and in the proportion who want no more 

children. Similar to differences by HIV status, a greater proportion of women who have had an HIV test 

32.1

59.9

27.9

41.6

29.7

52.5

51.3

64.3

34.6

53.8

52.0

23.3

58.8

39.1

51.2

23.0

36.6

16.7

49.4

27.4

13.9

13.9

10.4

15.8

9.4

12.8

10.5

16.1

11.4

14.2

2.0

2.9

2.8

3.5

9.6

11.7

1.6

3.0

4.6

4.7

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Zimbabwe (HIV-)

Zimbabwe (HIV+)

Zambia (HIV-)

Zambia (HIV+)

Malawi (HIV-)

Malawi (HIV+)

Lesotho (HIV-)

Lesotho (HIV+)

Kenya (HIV-)

Kenya (HIV+)

wants no more wants after 2+ years wants within 2 years infecund/sterilized
19 

in the past want no more children, and a smaller proportion want to delay having a child for 24 months or 

more, compared with women who have never had an HIV test. Again, these differences are greatest in 

Zimbabwe. 

Figure 4. Current fertility intentions among women age 15-49, by history of HIV testing 

3.1.5. Sample profile 

Table 5 provides a descriptive profile of all interviewed women in study countries as well as the specific 

analytic samples used in this study. Compared with the full sample of women (and, in Lesotho and 

Zimbabwe, compared with the second analytic sample), prevalence of HIV is lower among women in the 

first analytic sample, that is, women who have had a birth in the previous 24 months. These differences 

are virtually undetectable in Kenya. 

A larger proportion of women in the first analytic sample have had an HIV test and received the results 

compared either with women in the full sample or with the analytic sample of women who have not had a 

birth in the past 24 months. The difference is roughly 20 percentage points in all of the study countries 

except Zambia, where the difference is 9 percentage points. This finding suggests the important 

contribution of receiving ANC to the provision of HIV testing in the study countries. 

 

27.6

43.3

27.1

33.2

26.4

36.6

50.3

56.9

28.7

40.6

54.3

41.4

57.9

53.1

52.3

45.1

34.9

29.3

54.0

43.8

15.8

13.1

12.0

11.0

11.8

9.2

13.0

11.7

12.1

11.7

2.2

2.2

3.1

2.7

9.5

9.2

1.7

2.1

5.2

3.9

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Zimbabwe--Never tested

Zimbabwe--Tested

Zambia--Never tested

Zambia--Tested

Malawi--Never tested

Malawi--Tested

Lesotho--Never tested

Lesotho--Tested

Kenya--Never tested

Kenya--Tested

wants no more wants after 2+ years wants within 2 years infecund/sterilized
Table 5. Profile of women age 15-49 and of analytic samples: Percent distribution and means of selected characteristics

Kenya Lesotho Malawi Zambia Zimbabwe 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

All 

women

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

n=8,444 n=1,006 n=7,624 n=794 n=2,980 n=23,020 n=2,427 n=7,146 n=2,023 n=9,171 n=2,003 n=5,293 

HIV status1

HIV-positive 8.1 8.1 26.7 24.6 27.3 12.8 9.7 15.9 12.3 17.7 13.6 19.2 HIV-negative 91.9 91.9 73.3 75.4 72.7 87.2 90.3 84.1 87.7 82.3 86.4 80.8 HIV testing Tested and received results 56.5 79.9 65.6 84.6 59.6 71.6 90.5 35.3 43.6 57.4 80.3 50.3 

Knowledge of family planning Mean score (0-3) 2.66 2.76 2.75 2.83 2.72 2.81 2.92 2.70 2.84 2.79 2.92 2.76 

Place of delivery for most recent birth2

Home 53.1 53.1 36.9 39.5 na 21.4 18.9 49.0 51.3 32.4 35.1 na 

Medical facility 46.8 46.6 61.1 57.7 na 76.1 78.0 50.5 48.3 66.3 63.4 na Other 0.2 0.3 2.0 2.8 na 2.5 3.1 0.5 0.4 1.3 1.5 na Marital status Never married 31.2 11.0 34.3 14.5 39.8 19.7 2.8 26.0 8.8 24.0 4.9 30.1 Ever married 68.8 89.0 65.7 85.5 60.2 80.3 97.2 74.0 91.2 76.0 95.1 69.9 Currently married 54.2 76.9 52.3 78.1 46.0 58.7 76.1 60.9 82.5 59.4 82.5 50.9 Currently living together 4.1 5.2 0.8 0.4 0.8 8.7 11.2 0.7 0.8 2.8 3.8 2.5 Divorced 4.4 1.8 7.5 3.9 8.4 3.6 1.2 4.4 1.6 6.1 1.4 8.3 Widowed 1.3 0.7 0.9 0.3 1.0 4.9 4.7 6.0 4.0 3.7 2.9 4.1 Separated 4.8 4.4 4.2 2.7 4.0 4.4 4.0 2.0 2.2 4.1 4.6 4.0 

(Continued...)

20 
Table 5. – Continued

Kenya Lesotho Malawi Zambia Zimbabwe 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

All 

women

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

n=8,444 n=1,006 n=7,624 n=794 n=2,980 n=23,020 n=2,427 n=7,146 n=2,023 n=9,171 n=2,003 n=5,293 

Parity 

Mean number of children born 2.7 3.4 1.8 2.4 1.7 3.1 3.6 3.0 3.9 2.1 2.6 2.0 Age 15-19 20.8 8.8 23.4 15.6 25.4 21.7 11.1 22.0 11.6 21.2 12.9 24.4 20-24 20.3 31.3 20.4 35.0 17.2 19.8 30.0 19.2 26.2 20.1 31.3 15.7 25-29 17.2 27.0 16.3 22.1 13.6 19.1 27.1 19.1 27.0 18.4 28.3 14.8 30-34 14.3 19.2 12.9 15.2 12.2 14.1 15.0 14.8 18.7 14.1 15.1 13.3 35-39 10.4 9.0 10.0 9.0 11.0 11.0 10.9 10.5 11.1 11.5 8.8 12.5 40-44 9.1 3.9 8.6 2.5 10.3 7.5 4.8 7.9 4.5 8.0 2.9 10.0 45-49 7.8 0.9 8.4 0.7 10.2 6.8 1.0 6.6 0.9 6.8 0.7 9.2 Education Mean years of schooling completed 7.9 7.4 8.1 7.6 8.1 5.4 5.0 6.4 5.6 9.0 8.8 8.9 Place of residence Urban 25.4 20.9 33.7 23.2 35.2 18.7 15.7 42.1 29.5 38.7 23.2 34.5 Rural 74.6 79.1 66.3 76.8 64.8 81.3 84.3 57.9 70.5 61.3 76.8 65.5 Household wealth quintile Lowest 16.5 25.2 14.1 22.0 12.7 18.5 20.3 17.4 23.3 16.9 24.6 16.6 

Second 17.6 20.9 15.6 19.8 15.7 18.8 21.8 18.0 22.5 17.4 22.5 18.1 Middle 19.1 14.3 17.4 18.8 17.7 19.6 22.2 17.9 21.0 18.3 20.4 19.6 Fourth 20.6 18.8 24.9 23.2 25.4 19.6 16.5 21.9 18.5 22.6 19.3 21.5 Highest 26.3 20.7 28.0 16.2 28.4 23.4 19.1 24.9 14.6 24.8 13.2 24.1 

(Continued...)

21 
Table 5. – Continued

Kenya Lesotho Malawi Zambia Zimbabwe 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

All 

women 

Analytic 

sample 

#1 

All 

women 

Analytic 

sample 

#1 

All 

women

Analytic 

sample 

#1 

Analytic 

Sample 

#2 

n=8,444 n=1,006 n=7,624 n=794 n=2,980 n=23,020 n=2,427 n=7,146 n=2,023 n=9,171 n=2,003 n=5,293 

Employment status 

Currently employed 58.9 57.2 45.7 41.3 46.4 72.8 72.5 54.3 56.4 42.4 36.8 43.5 Not currently employed 41.1 42.8 54.3 58.7 53.6 27.2 27.5 45.7 43.6 57.6 63.2 56.5 1 Calculated on the subsample of women who underwent HIV biomarker testing during the DHS survey. Weighted n's are: Kenya 3,641, Lesotho 3,778, Malawi 

7,090, Zambia 5,499, and Zimbabwe 7,313 

2 Calculated on the subsample of women who have had a birth in the past five years. Weighted n's are: Kenya 3,973, Lesotho 2,984, Malawi 13,664, Zambia 

4,136, Zimbabwe 4,426 

3 Analytic sample #1 consists of women age 15-49 who had a birth in the previous 24 months and were offered and consented to HIV biomarker testing during 

the DHS survey 

4 Analytic sample #2 consists of women age 15-49 who did not have a birth in the previous 24 months, were not pregnant 24 months prior to the survey and 

who consented to HIV biomarker testing during the DHS survey 

22 
23 

The analytic samples differ from the full sample in other ways as well. Women who have had a birth in 

the past 24 months score higher on family planning knowledge and have more children, on average, but 

have fewer years of schooling than among all women age 15-49. A higher proportion of women with a 

birth in the past 24 months are currently married and a higher proportion live in rural areas. In all 

countries except Zambia, a lower proportion of women with a birth in the past 24 months are currently 

employed compared with the full sample. Also, a higher proportion of women with a birth in the past 24 

months are age 20-34, and a higher proportion are in the lowest two and middle household wealth 

quintiles. These patterns are remarkably consistent across all study countries. 

In Lesotho and Zimbabwe the second analytic sample (women who have not had a birth in the past 24 

months and were not pregnant 24 months preceding the survey) more closely resembles the full sample of 

women than does the first analytic sample. 

3.2. Model 1—Postpartum Adoption of Contraception among Women with a Recent Birth 

Table 6 presents results of a multivariate model predicting expected time to adopting a contraceptive 

method among women who have had a birth in the 24 months preceding the survey (Model 1). Results are 

presented in time ratios, such that a positive time ratio (TR>1) corresponds to greater expected time to 

adopting contraception, while a negative time ratio (TR<1) implies a shorter expected time. 

For each of the five countries, a number of women adopted postpartum contraception during the study 

observation period. In Kenya, 1,006 women contributed 9,842 person-months of observation, during 

which 403 began using contraception. In Lesotho, 794 women contributed 7,332 person-months, and 406 

of these women adopted a contraceptive method during the observation period. Among Malawi’s 2,427 

women who contributed 21,300 person-months of observation, 1,312 women began using a postpartum 

contraceptive method in the observation period. In Zambia, during the 17,699 person-months of 

observation contributed by 2,023 women, 1,088 women adopted some form of contraception. In 

Zimbabwe 2,003 women contributed 12,445 months of observation, during which 1,498 women adopted 

a postpartum contraceptive method. 
Table 6. Model 1: Time ratios predicting adoption of contraception following birth, among women age 15-49 who have had a birth in the 

previous 24 months

Kenya Lesotho Malawi Zambia Zimbabwe 

n=1,006 women n=794 women n=2,427 women n=2,023 women n=2,003 women 

(9,842 personmonths) (7.332 person- months) (21,300 person- months) (17,699 person- months) (12,445 person- months) 

 Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI 

HIV status (ref=HIVnegative) HIV-positive 1.53 * (1.06, 2.21) 0.85 (0.68, 1.05) 1.20 (0.98, 1.47) 1.16 (0.97, 1.39) 1.10 (0.98, 1.24) 

HIV test (ref=no test/results) Had HIV test and received results during ANC1 0.87 0.64,1.18 0.68 ** (0.54, 0.86) 0.90 (0.77, 1.06) 0.84 * (0.73, 0.97) 0.79 *** (0.69, 0.90) 

Knowledge of family planning index 0.70 ** (0.54, 0.90) 0.84 (0.62, 1.14) 0.70 ** (0.56, 0.87) 0.76 *** (0.66, 0.88) 0.73 *** (0.63, 0.86) Place of delivery (ref=home delivery) Medical facility 0.90 (0.68, 1.19) 0.82 (0.64, 1.04) 1.02 (0.90, 1.17) 0.83 * (0.72, 0.97) 0.90 * (0.80, 1.00) 

Other 0.91 (0.14, 6.01) 1.11 (0.53, 2.32) 1.16 (0.87, 1.56) 1.14 (0.39, 3.31) 1.41 * (1.00, 1.99) 

Married (ref=never married) Ever-married 0.67 (0.41, 1.08) 0.64 (0.40, 1.02) 0.54 *** (0.38, 0.76) 0.48 *** (0.38, 0.62) 0.43 *** (0.34, 0.55) Parity 1.10 (1.00, 1.22) 1.07 (0.98, 1.18) 0.99 (0.95, 1.04) 0.97 (0.92, 1.02) 1.02 (0.97, 1.08) Age (at start of observation period) 0.98 (0.95, 1.02) 1.00 (0.97, 1.02) 1.00 (0.98, 1.01) 1.02 * (1.00, 1.04) 1.00 (0.98, 1.01) Completed years of schooling 0.95 * (0.91, 0.99) 1.00 (0.96, 1.04) 0.96 *** (0.94, 0.97) 1.01 (0.99, 1.04) 0.99 (0.97, 1.01) Residence (ref=urban) Rural 1.04 (0.80, 1.34) 1.07 (0.79, 1.46) 1.16 (0.95, 1.43) 0.93 (0.77, 1.14) 0.96 (0.84, 1.10) 

(Continued...)

24 
Table 6. – Continued

Kenya Lesotho Malawi Zambia Zimbabwe 

n=1,006 women n=794 women n=2,427 women n=2,023 women n=2,003 women 

(9,842 personmonths) (7.332 person- months) (21,300 person- months) (17,699 person- months) (12,445 person- months) 

 Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI Time ratio 95% CI 

Household wealth index (ref=lowest) Second 0.83 (0.57, 1.22) 0.84 (0.57, 1.24) 1.00 (0.85, 1.18) 1.52 *** (1.25, 1.84) 0.96 (0.83, 1.11) Middle 0.73 (0.48, 1.09) 0.77 (0.54, 1.09) 0.98 (0.84, 1.14) 1.56 *** (1.28, 1.90) 1.00 (0.87, 1.15) Fourth 0.82 (0.53, 1.25) 0.65 * (0.44, 0.95) 0.94 (0.79, 1.12) 1.32 * (1.06, 1.65) 0.92 (0.78, 1.08) 

Highest 0.61 (0.37, 1.02) 0.67 (0.42, 1.08) 1.10 (0.90, 1.34) 1.17 (0.89, 1.53) 0.88 (0.74, 1.04) Employment status (ref=not currently employed) Currently Employed 0.74 ** (0.60, 0.92) 0.89 (0.73, 1.08) 0.94 (0.82, 1.07) 0.94 (0.83, 1.06) 1.02 (0.92, 1.12) constant 144.21 *** (45.35, 458.60) 37.13 *** (10.92, 126.27) 58.14 *** (23.73, 142.48) 27.13 *** (12.99, 56.66) 42.30 *** (23.04, 77.70)σ (sigma) 1.06 (0.96, 1.16) 1.08 (1.01, 1.16) 0.93 (0.89, 0.97) 1.04 (0.99, 1.08) 0.89 (0.86, 0.93) 

Note: Results are from lognormal accelerated failure time hazard models. Data are weighted and adjusted to account for the complex survey design and robust 

standard errors estimated. Figures are time ratios. Numbers in parentheses are 95% confidence intervals. 

***p≤ 0.001; **p≤ 0.01; *p≤ 0.05; † p≤0.10 1HIV test occurred during ANC. For Zimbabwe, HIV test occurred during ANC or delivery. 

25 
26 

3.2.1. HIV status, experience of HIV testing during ANC, and the postpartum adoption of 

contraception 

For four of the five countries studied (Lesotho, Malawi, Zambia, and Zimbabwe), there is no significant 

relationship between HIV serostatus and the expected time of adopting a postpartum contraceptive 

method, after adjusting for other covariates. In Kenya however, HIV-positive women have a 53 percent 

longer expected time to adopting a contraceptive method following birth compared with HIV-negative 

women. 

In Lesotho, Zambia, and Zimbabwe, women who have had an HIV test and received results during ANC 

or delivery have a shorter expected time of adopting a contraceptive, regardless of HIV serostatus. In 

Kenya and Malawi, having had an HIV test and receiving results during ANC does not influence the 

expected time to adopting a contraceptive method. 

3.2.2. Other covariates and the postpartum adoption of contraception 

The most consistent association with expected time of adopting a contraceptive method after birth appears 

to be with the knowledge of family planning index. For all countries except Lesotho, a one-unit increase 

in the family planning knowledge index score is associated with a 24 to 30 percent shorter expected time 

to contraceptive adoption in the postpartum period. Marital status is also significant in three out of the 

five countries studied (Malawi, Zambia, and Zimbabwe), such that ever married women have 

significantly shorter expected times to contraceptive adoption compared with never married women. 

There are no strong patterns across countries with other covariates in the model. Women who delivered at 

a medical facility have shorter expected contraceptive adoption times in Zambia and Zimbabwe compared 

with women who delivered at home, but there are no significant differences in Kenya, Lesotho, and 

Malawi. Completed years of schooling are related to timing of contraceptive adoption in two countries—

Kenya and Malawi—with each additional year of schooling associated with a decrease in the time to 

adopting a postpartum method. 

In Zambia age is related to timing of contraceptive use following birth such that a one-unit increase in age 

at the start of the observation period is associated with 2 percent longer expected time to adopting a 

contraceptive method. Wealth quintile is highly predictive of time to contraceptive adoption in Zambia, 

with the middle three wealth quintiles (i.e. second, middle, fourth) all associated with greater expected 

time to contraceptive use. In Lesotho, however, women in the fourth wealth quintile have a shorter than 

expected time to contraceptive use. In Kenya currently employed women also have shorter times to 

contraceptive adoption following birth. Parity and residence are not significantly associated with duration 

of adopting a contraceptive in any of the countries. 

3.3. Model 2—Adoption of Contraception among Women without a Recent Birth 

Table 7 presents the results of a similar analysis to Model 1, but the sample has changed: the analytic 

sample for this model is women who have not had a birth in the 24 months preceding the survey and who 

were not pregnant at 24 months preceding the survey (Model 2). Because the women in this sample have 

not given birth in the two-year observation period, any HIV test they may have had occurred outside of 

the context of antenatal care, which is the context of HIV testing for women included in Model 1. 

Of the 2,980 women analyzed in Lesotho, 2,076 were not using contraception at the start of the 

observation period and 904 were contraceptive users. In Zimbabwe, 3,320 of the 5,293 women in the 

analytic sample were not initially using contraception, while 1,973 women were using contraception 

when the observation period began. 
27 

For those women who were not contraceptive users at the start of the observation period, Model 2 (Table 

7) presents the results of a lognormal accelerated failure time model predicting the hazard of adopting a 

method of contraception between this time point and the time of the survey. Results are reported in time 

ratios. In Lesotho, 2,076 women contributed a cumulative 46,681 person-months of observation. During 

this time, 392 of the 2,076 women adopted some form of contraception. In Zimbabwe, 3,320 women 

contributed a total 81,587 person-months of observation and 386 women adopted a method of 

contraception during the observation period. 

Table 7. Model 2: Time ratios predicting adoption of contraception among women age 15-49 who 

have not had a birth in the previous 24 months

Lesotho Zimbabwe 

n=2,076 n=3,320 

(46,681 person-months) (81,587 person-months) 

 Time ratio 95% CI Time ratio 95% CI 

HIV status (ref=HIV-negative) 

HIV-positive 0.90 (0.69, 1.17) 1.00 (0.80, 1.26) 

HIV test (ref=no test/results) 

Had HIV test and received results1

 0.86 (0.57, 1.29) 0.42 *** (0.28, 0.62) 

Knowledge of family planning index 0.54 *** (0.44, 0.66) 0.46 *** (0.34, 0.62) 

Married (ref=never married) 

Ever-married 0.43 *** (0.31, 0.59) 0.22 *** (0.17, 0.29) 

Parity 0.84 *** (0.76, 0.92) 0.73 *** (0.68, 0.79) 

Age (at start of observation period) 1.06 *** (1.04, 1.09) 1.08 *** (1.06, 1.10) 

Completed years of schooling 0.98 (0.93, 1.03) 0.96 * (0.92, 0.99) 

Residence (ref=urban) 

Rural 1.07 (0.74, 1.56) 1.24 (0.92, 1.68) 

Household wealth index (ref=lowest) 

Second 0.83 (0.60, 1.15) 1.31 (0.97, 1.76) 

Middle 0.76 (0.54, 1.05) 1.26 (0.92, 1.72) 

Fourth 0.80 (0.54, 1.16) 0.99 (0.70, 1.39) 

Highest 0.64 * (0.42, 0.97) 1.31 (0.87, 1.96) 

Employment status (ref=not currently 

employed) 

Currently Employed 0.68 ** (0.54, 0.86) 0.82 * (0.68, 1.00) 

constant 339.93 *** (102.39, 1,128.62) 790.58 *** (254.67, 2,454.29)

σ (sigma) 1.48 (1.35, 1.64) 1.49 (1.37, 1.64) 

Note: Results are from lognormal accelerated failure time hazard models. Data are weighted and adjusted to 

account for the complex survey design and robust standard errors estimated. Figures are time ratios. Numbers in 

parentheses are 95% confidence intervals. 

***p≤ 0.001; **p≤ 0.01; *p≤ 0.05; † p≤0.10 

1

HIV test occurred during the period of observation. 
28 

3.3.1. HIV status, experience of HIV testing, and adoption of contraception 

After controlling for other covariates, HIV status does not influence the expected time to adopting 

contraception in either Lesotho or Zimbabwe. This finding is consistent with results of Model 1 

predicting adoption of contraception among postpartum women in these countries. 

In Zimbabwe, women who have an HIV test and receive the results during the observation period have a 

58 percent shorter expected time to adopting contraception than women who do not, but there is no 

statistical difference in Lesotho. These results are consistent with Model 1 results in Zimbabwe, but not in 

Lesotho, where testing experience has a significant association with the earlier adoption of contraception 

among postpartum women. 

3.3.2. Other covariates and the adoption of contraception 

In both Lesotho and Zimbabwe, knowledge of family planning, ever being married, higher parity, and 

current employment are all associated with a shorter time to adopting contraception, after controlling for 

other covariates. Each unit increase in the family planning knowledge index reduces the expected time to 

adoption of contraception by roughly half, while ever having been married is associated with a time to 

adoption that is 57 percent lower in Lesotho and 73 percent lower in Zimbabwe. Each one-year increase 

in age is associated with a six to eight percent longer expected time to adopting contraception. The pattern 

with respect to household wealth and education is not clear, with only women in the highest wealth 

quintile having a shorter expected time to adopting contraception compared with women in the lowest 

wealth quintile in Lesotho, but not Zimbabwe, and each additional year of schooling contributing to a four 

percent shorter duration to adoption of contraception in Zimbabwe only. 

3.4. Model 3—Discontinuation of Contraception 

As in Model 2, the analysis presented in Model 3 examines women who have not had a birth in the 

previous 24 months nor who were pregnant at the start of the observation period. Unlike Model 2, 

however, Model 3 examines the subset of this sample of women who were using contraception at the start 

of the observation period and uses a hazard model predicting discontinuation of contraception. A 

Gompertz hazard model, in which hazards are proportional across groups, is used for this purpose, as the 

process of discontinuation is better represented by a Gompertz-distributed baseline hazard than by a 

lognormal baseline hazard. Table 8 reports the results as hazard ratios. 

In Lesotho, the sample is composed of 904 women, who contribute a total of 18,074 person-months of 

observation. Of the 904 initial contraceptive users, 238 discontinued using contraception over the 

observation period. In Zimbabwe, 1,973 women contribute 45,215 person-months of observation, and 634 

discontinued contraception at some point during the observation period. 
29 

Table 8. Model 3: Hazard ratios predicting discontinuation of contraception among women age 15-

49 who have not had a birth in the previous 24 months

Lesotho Zimbabwe 

n=904 women n=1,973 women 

(18,074 person-months) (45,215 person-months) 

 Hazard ratio 95% CI Hazard ratio 95% CI 

HIV status (ref=HIV-negative) 

HIV-positive 0.85 (0.63, 1.16) 0.90 (0.73, 1.12) 

HIV test (ref=no test/results) 

Had HIV test and received results1

 0.94 (0.66, 1.33) 0.99 (0.77, 1.26) 

Knowledge of family planning index 0.80 (0.62, 1.03) 1.12 (0.81, 1.55) 

Married (ref=never married) 

Ever-married 1.02 (0.66, 1.56) 1.24 (0.81, 1.89) 

Parity 0.96 (0.81, 1.14) 0.93 (0.86, 1.01) 

Age (at start of observation period) 0.96 ** (0.93, 0.99) 0.97 *** (0.95, 0.98) 

Completed years of schooling 1.01 (0.95, 1.07) 1.00 (0.97, 1.03) 

Residence (ref=urban) 

Rural 1.15 (0.76, 1.73) 1.05 (0.84, 1.30) 

Household wealth index (ref=lowest) 

Second 0.80 (0.41, 1.54) 0.89 (0.69, 1.15) 

Middle 0.55 * (0.33, 0.93) 0.96 (0.74, 1.24) 

Fourth 0.77 (0.45, 1.32) 0.71 * (0.51, 0.98) 

Highest 0.66 (0.36, 1.21) 0.67 * (0.47, 0.94) 

Employment status (ref=not currently employed)

Currently Employed 1.42 * (1.01, 2.01) 0.95 (0.80, 1.13) 

constant 0.01 *** (.003, .055) 0.004 *** (.001, .013) 

γ (gamma) 0.13 (0.10, 0.16) 0.15 (0.14, 0.17) 

Note: Results are from Gompertz proportional hazard models. Data are weighted and adjusted to account for the 

complex survey design and robust standard errors estimated. Figures are hazard ratios. Numbers in parentheses 

are 95% confidence intervals. 

***p≤ 0.001; **p≤ 0.01; *p≤ 0.05; † p≤0.10 

1

HIV test occurred during the period of observation. 
30 

3.4.1. Factors influencing discontinuation of contraception 

As with Model 2, any experience with HIV testing among women in Model 3 occurs outside of the 

context of antenatal care and is measured between the onset of the observation period and the time of 

discontinuation (failure) or time of interview (censoring). Neither women’s HIV status nor their 

experience with HIV testing seems to influence discontinuation of contraception among women who were 

using contraception at the start of the observation period in Lesotho or Zimbabwe. 

In both countries, age has a negative association with the hazard of discontinuation: in Lesotho, with each 

one year increase in age at the start of observation, women’s hazard of discontinuation is reduced by 4 

percent, and in Zimbabwe by 3 percent. Employment status also is associated with discontinuation in 

Lesotho only, such that women who are currently employed have a 42 percent greater hazard of 

discontinuing contraception. As with adoption of contraception, the role of wealth in discontinuation is 

not straightforward. In Lesotho, women in the middle household wealth quintile only, and in Zimbabwe, 

women in the two highest quintiles, have lower hazards of discontinuing contraception compared with 

women in the lowest household wealth quintile. 
31 

4. Limitations 

This study uses monthly calendar data on HIV testing, births, and use (or non-use) of contraception to 

place each of these events in proper temporal sequence and to analyze time to event. By taking a 

longitudinal perspective, rather than the cross-sectional approach to which researchers are typically 

restricted, we are able to gain additional insight into the relationships between testing and learning one’s 

HIV status on the one hand and contraceptive behavior on the other. Nevertheless, this study is not 

without weaknesses that limit the conclusions from these analyses. 

First and foremost among the study’s limitations is the assumption we make about women’s HIV status. 

We are forced to assume that the HIV status at the time women have an HIV test and receive the results is 

the same HIV status as determined by biomarker testing at the time of DHS survey. However, it is 

possible that a small proportion of women who were HIV-negative at the time of their most recent HIV 

test may have seroconverted by the time of the survey and are, therefore, mistakenly classified as HIVpositive. We restrict the period of analysis to the 24 months preceding the survey to reduce this error, as 

the likelihood of divergent HIV status at the time of the most recent HIV test and the time of the survey 

increases the longer the time between the two measures. This type of misclassification error would lead us 

to underestimate the true effect of women’s HIV status on their contraceptive behavior. 

Second, certain covariates that vary over time are only measured at the time of the survey and are treated 

in the analysis as if they are fixed-time covariates. We do not have retrospective values on employment 

status, household wealth quintile, residence, and level of family planning knowledge, all of which may 

have changed since the start of the observation period. Some of these measures may be relatively stable 

over short periods of time, such as household wealth quintile, while others may fluctuate more over time. 

In this analysis, we assume that the date of most recent HIV test is equivalent to the date of learning one’s 

HIV status. This assumption may be violated if women knew their status prior to their most recent HIV 

test. This assumption is most likely to be violated in Model 1 in countries where HIV testing is part of the 

standard ANC protocol, as is increasingly common6

. It is possible that some women who already know 

their HIV status, regardless of whether that status is HIV-positive or HIV-negative, were retested for HIV 

during ANC as part of standard ANC care. It is also possible that some women included in Models 2 and 

3 had tested for HIV prior to their most recent test. If the most recent HIV test result is the same as a 

previous test result, the date of the most recent HIV test would not necessarily represent the date of 

women’s learning their HIV status. 

If our assumption is violated, we may misestimate strength of any associations between learning one’s 

HIV status and subsequent changes in contraceptive behavior. The extent to which and even the direction 

in which we misestimate this effect may further depend on the HIV status of repeat testers. For example, 

some women who were diagnosed as HIV-negative on an earlier HIV test may be motivated to retest if 

they have become dubious of their continued HIV-negative status out of self-perceived risk of infection. 

For these women, receiving even confirmatory results on the most recent HIV test could be akin to 

learning one’s HIV status for the first time, and thus this assumption would not be violated. For women 

who are found to be HIV-positive on an earlier test and on the most recent HIV test, however, this 

assumption would most certainly be violated. The degree of the misestimation of the effect of learning 

one’s HIV status on contraceptive behavior would depend both on how many women receive concordant 

results upon retesting and on the proportion who are HIV-positive. 

 

6

 See Staveteig et al. (2013) for a discussion of trends in HIV testing, generally, and HIV testing in ANC, 

specifically. 
32 

Finally, we use an HIV testing variable that is time-varying, capturing its reported value at each month 

during the period of observation and preceding failure (adoption of contraception in Model 2 and its 

discontinuation in Model 3) or censoring. However, our measurement of these values is imperfect. Data 

on the time of the HIV test, in Lesotho and Zimbabwe, come from a survey question worded, “How many 

months ago was your most recent HIV test?” and recorded in months. A frequency distribution of this 

measure shows heaping on months 12 and 24, suggestive of displacement from adjacent months. The full 

extent of this measurement error and the implications for conclusions from these analyses are unknown. 
33 

5. Conclusions and Discussion 

Using a longitudinal study design, we find limited evidence of an association between women’s HIV 

serostatus and subsequent contraceptive use dynamics. The only significant relationship with HIV status 

that emerges in multivariate analysis is among women in Kenya who gave birth in the 24 months 

preceding the survey, with HIV-positive women having a 53 percent longer expected time to adopting a 

contraceptive method following birth compared with HIV-negative women. 

For the most part, the results presented here confirm and extend those of the only previous study that 

assessed differences by HIV status in the causal impact of knowing one’s status on contraceptive 

practices (Blanchard et al. 2011). As with the paper by Blanchard et al., we find almost no significant 

differences in contraceptive patterns following HIV testing between HIV-positive and HIV-negative 

women. Direct comparisons with other previous studies that that have analyzed changes in contraceptive 

use following receipt of results are not suitable due to differences in study design; these studies typically 

employ within-individual study designs, essentially comparing contraceptive use before and after 

diagnosis, and do not include HIV-negative individuals (Hoffman et al. 2008; Nanda et al. 2011). 

There is more evidence from these analyses to suggest that exposure to HIV testing is associated with 

shorter expected durations to contraceptive adoption among women who have recently had a birth and 

among other non-users of contraception at the time of HIV testing. In Lesotho, Zambia, and Zimbabwe, 

women who have had an HIV test and received results during ANC have shorter expected times to 

adopting a contraceptive method following birth. In addition, in Zimbabwe but not Lesotho, having had 

an HIV test and receiving results is also associated with a shorter time to adopting contraception among 

women who did not have a birth in the previous 24 months (Model 2). 

Taken together, the relatively weak evidence with regard to HIV status and somewhat stronger evidence 

for HIV testing experience offer tentative support for the conclusion that it is possibly interaction with 

health services that facilitates contraceptive use, rather than HIV status or knowledge of that status per se. 

This conclusion would be consistent with the rather lackluster findings on discontinuation of 

contraception: for women who have already made use of some form of health services to obtain 

contraception initially, interaction with HIV testing services may not offer additional support beyond that 

received from family planning services to improve adherence to their contraceptive method over time. 

However, further investigation into this question is warranted. 

This study presents novel findings using nationally representative data from five countries in sub-Saharan 

Africa. Nonetheless, the present data, taken together with other nationally representative studies, do not 

yet fully explain the HIV-contraception relationship. A further exploration of fertility intentions, while 

not a primary focus of the current study, may shed light on the interrelated processes of contraceptive 

motivation and use. However, difficulties related to measurement of fertility intentions, especially the 

rarity of prospective measures in nationally representative data, may preclude a deeper understanding of 

the impact of HIV testing on fertility-related behavior. 

A further area of exploration is to consider access to and use of antiretroviral therapy (ART), as it may 

interact with fertility intentions and contraceptive use (Bankole et al. 2014). The directionality of these 

possible interactions remains poorly understood. For example, in a study among women in Soweto, South 

Africa, use of ART was associated with contraceptive use (Kaida et al. 2010). On the contrary, in one 

study of women on ART in peri-urban Uganda, contraceptives were not used because of perceived drug 

interactions, along with fear of side effects and partner disapproval (Mbonye et al. 2012). Fears of 

interactions with ART as well as general disease progression were also found in a multi-country 

qualitative study (Todd et al. 2011). The increased provision and adoption of ART across several 
34 

programmatic and regional contexts may influence future contraceptive use patterns following HIV 

testing. Therefore, future studies should consider the effects of ART on the HIV-contraception 

relationship. Lastly, another area of inquiry, related to both changing fertility intentions and new concerns 

about side effects or drug interactions, is to explore how desired attributes of contraception and method 

mix may change upon learning that one is HIV-positive. 
35 

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