DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2015 No. 122

Kerry L.D. MacQuarrie

October 2015

This document was produced for review by the United States Agency for 

International Development.

HIV/AIDS and Unmet Need 

for Family Planning

HIV/AIDS and Unmet Need for Family Planning 

Kerry L.D. MacQuarrie 

ICF International 

Rockville, Maryland, USA 

October 2015 

Corresponding author: Kerry L.D. MacQuarrie, Senior Research Associate, The DHS Program; 

Telephone: 1-301-572-0282; Email: Kerry.MacQuarrie@icfi.com
Editor: Bryant Robey 

Document Production: Natalie La Roche 

The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support provided 

by the United States Agency for International Development (USAID) through The DHS Program (#AIDOAA-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: 

+1 301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: www. 

dhsprogram.com. 

Recommended citation: 

MacQuarrie, Kerry L.D. 2015. HIV/AIDS and Unmet Need for Family Planning. DHS Working Papers No. 

122. Rockville, Maryland, USA: ICF International. 
1 

BACKGROUND 

It is as yet unclear the extent to which women who are HIV-positive may have needs for 

contraception that differ from those who are HIV-negative. HIV-positive women may, at least 

temporarily after diagnosis, adjust their fertility preferences in favor of delaying or limiting a 

(subsequent) birth (Hoffman et al. 2008; Heys et al. 2009; Yeatman 2009; Bonnenfant et al. 2012; 

Keogh et al. 2012). For example, a multi-country analysis using nationally representative data from 

the Demographic and Health Surveys (DHS) found that in five of the eight countries studied HIVpositive women who likely knew their status were significantly less likely to want more children 

compared with HIV-negative women who likely knew their status (Bankole et al. 2011). In 

keeping with differences in women’s fertility intentions by their HIV status, Johnson and 

colleagues (2009) found that in three out of the four countries studied HIV-positive women who 

likely knew their status were more likely to want to limit childbearing by using contraception. 

Several longitudinal studies of HIV-positive women found an increase in contraceptive use among 

women newly diagnosed with HIV (Hoffman et al. 2008; 2011; Nanda et al. 2011; Chi et al. 2012), 

although their contraceptive use was not necessarily sustained (Hoffman et al. 2008). In contrast, 

a recent study using DHS data found no difference by HIV status in the rate at which initially noncontracepting women adopted contraception, although in three of the five countries studied women 

who had had an HIV test—regardless of their HIV status—were quicker to adopt contraception 

(MacQuarrie et al. 2014). 

Wilcher and colleagues have noted that countries with a high prevalence of HIV also have some 

of the highest levels of unmet need for family planning (2009). HIV-positive women may face 

even greater barriers to using family planning than other women of reproductive age if they avoid 

health services to limit inadvertent disclosure of their HIV status (Church and Mayhew 2009) or 

if they avoid hormonal contraception because of fears related to interactions with antiretroviral 

therapies or disease progression (Todd et al. 2011; Mbonye et al. 2012). HIV-positive women also 

face the barriers to using family planning shared by other women of reproductive age. In India, for 

example, women who were not using contraception following HIV diagnosis cited lack of 

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

involvement as reasons (Chakrapani et al. 2011). Qualitative evidence from South Africa and 
2 

Uganda suggests that HIV-positive women may not use contraceptives because of concerns about 

side effects and complications, cost, and body image (Laher et al. 2009; Wanyenze et al. 2013). 

This research literature reveals gaps in our understanding of the relationship between HIV status 

and women’s need for and use of contraception. In response to a special request from USAID, the 

tables described herein present recent DHS data on the need and unmet need for family planning 

and contraceptive use among HIV-positive and HIV-negative women. 

METHODS 

This analysis presents findings from 12 countries with HIV prevalence among women age 15-49 

greater than 3 percent. Table 1 presents the HIV prevalence and survey characteristics of the 

surveys included in the analysis. In these countries, HIV prevalence ranges from 3 percent in Togo 

to 31 percent in Swaziland. 

We restrict our analysis to women age 15-49 who have a valid HIV test result. DHS surveys follow 

a standard protocol for anonymous, informed, and voluntary HIV testing of survey respondents, 

which undergoes ethical review in the surveyed countries and in the United States. In some 

surveys, HIV testing is conducted in every household (Swaziland, Zambia, Zimbabwe). In others, 

it is conducted in every second household (Cameroon, Côte d’Ivoire, Kenya, Lesotho, Namibia, 

Rwanda, Togo) or every third household (Malawi). In Gabon, HIV testing is conducted in twothirds of the surveyed households. 

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

samples with a second ELISA. Between 5 and 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. Response rates for HIV testing in the study countries range from 

86 percent in Zimbabwe to nearly 100 percent in Rwanda. Women with an indeterminate test result 

are excluded from analysis. Analytic sample sizes range from 3,640 women in Kenya to 14,706 in 

Zambia. 

 
3 

Table 1. Sampling profile of Demographic and Health Surveys included in the analysis 

Country Year 

Eligibility of women 

interviewed 

Number of 

women 

interviewed 

Sampling for 

HIV testing 

through DHS 

Response rate 

for HIV testing, 

women age 

15-49 

Number of 

women age 

15-49 with a 

valid HIV test 

result1

HIV prevalence 

among women 

age 15-49 

HIV prevalence 

95% CI 

Cameroon 2011 all women age 15-49 15,426 eligible women in every 

second household 

97.2 7,253 5.6 4.9 – 6.3 

Côte d’Ivoire 2011-12 all women age 15-49 10,060 eligible women in every 

second household 

90.0 4,655 4.6 3.8 – 5.4 

Gabon 2012 all women age 15-49 8,422 eligible women in two of 

every three households 

97.3 5,489 5.8 4.6 – 7.0 

Kenya 2008-09 all women age 15-49 8,444 eligible women in every 

second household 

90.3 3,811 8.0 6.7 – 9.3 

Lesotho 2009 all women age 15-49 7,624 eligible women in every 

second household 

95.8 3,849 26.7 24.8 – 28.6 

Malawi 2010 all women age 15-49 23,020 eligible women in onethird of households 93.5 7,396 12.9 11.6 – 14.2 

Namibia 2013 all women age 15-49 9,176 women age 15-64 in 

every second household 

90.52 4,984 16.9 15.3 – 18.5 

Rwanda 2010 all women age 15-49 13,671 eligible women in every 

second household 

99.6 6,952 3.7 3.2 – 4.2 

Swaziland 2006-07 all women age 15-49; 

plus those age 12-14 

and age 50+ in onehalf of households 

 4,987 all eligible women 87.2 4,584 31.1 29.3 – 32.9 

Togo 2013-14 all women age 15-49 9,480 eligible women in every 

second household 

98.0 4,807 3.1 2.5 – 3.7 

Zambia 2013-14 all women age 15-49 16,411 all eligible women 94.0 15,433 15.1 14.0 – 16.1 

Zimbabwe 2010-11 all women age 15-49 9,171 all eligible women 86.1 7,852 17.7 16.6 – 18.8 

1 Women with an indeterminate test result are dropped from the analysis. 

2 Women age 15-64. Analysis uses women age 15-49. 

Source: Sampling eligibility, sample sizes, and response rates are from DHS Final Reports, available at: www.dhsprogram.com. 

HIV prevalence rates are from StatCompiler, accessed on October 9, 2015 and available at: www.statcompiler.com. 

Table 2 presents data on the need for family planning—with three categories: (1) the percentage 

of women with no need for family planning, (2) the percentage with a met need, and (3) the 

percentage with unmet need. Women with no need are defined as those who want a child within 

two years, are not sexually active, or are infecund. Women with a met need for family planning 

are defined as those who are currently using contraception, be it a modern or a traditional method, 

to avoid becoming pregnant. Women with an unmet need for family planning are defined as those 

who do not want to become pregnant in the next two years but are not using contraception. We do 

not distinguish between unmet need for spacing and for limiting births. The analysis uses the 

revised DHS definition of unmet need (Bradley et al. 2012). 
4 

Table 3 presents data on the percentage of women currently using contraception—both the 

proportion using any method and the proportion using a modern method. Modern contraceptive 

methods include female and male sterilization, IUD, implants, injectables, contraceptive pills, 

male condom, female condom, and emergency contraception. Traditional methods are periodic 

abstinence, withdrawal, and folkloric methods. 

Both Tables 2 and 3 present their data by women’s HIV status. For both HIV-positive and HIVnegative women, the tables further disaggregate the results by HIV testing experience—the 

percentage of women with an HIV test in the previous 12 months and the percentage without an 

HIV test in the previous 12 months. The latter group includes women who have never had an HIV 

test and those whose most recent HIV test occurred more than 12 months before the survey. 

The timing of the most recent HIV test is established by a direct question1

 to respondents in the 

survey. In Lesotho and in Malawi, however, some women whose most recent HIV test was 

conducted in the course of antenatal care were not asked this question about the timing of that HIV 

test. This affects 398 women in Lesotho and 1,951 women in Malawi. For these women, we 

estimate the time since HIV test based on the assumption that the HIV test occurred in the fifth 

month of pregnancy, as this pregnancy month is both the modal and median time of first antenatal 

care visit in both surveys. Because of this assumption, there may be some misclassification of 

women across the 12 months cut-off. 

All data are weighted to adjust for sampling probability and non-response, using the HIV sampling 

weight available in DHS datasets and the svy commands available in Stata 14 are used to account 

for the complex (stratified, clustered) sampling design. The analysis tables present both point 

estimates and 95 percent confidence intervals based on robust standard errors. In two surveys there 

are fewer than 50 unweighted cases in the category “HIV positive and tested in the last 12 months,” 

for which point estimates are presented in parentheses, indicating they should be interpreted with 

caution. The surveys with few cases in this category are Côte d’Ivoire (unweighted n=40) and 

Togo (unweighted n=28). 

 

1

 This question takes one of two forms: (1) “How many months ago was your most recent HIV test?” or 

(2) “When was the last time you were tested [for HIV]?”. 
5 

RESULTS 

Need for Family Planning 

The analysis shows that the need for family planning appears to be greater for HIV-positive women 

than for HIV-negative women. Table 2 shows that in 8 of the 12 surveys a greater proportion of 

HIV-negative women than HIV-positive women have no need for family planning, while in the 

other 4 surveyed countries—Gabon, Kenya, Malawi, and Togo—HIV-positive women are more 

likely to have no need for family planning. These differences are slight, at less than one percentage 

point, in Cameroon, Côte d’Ivoire, Kenya, and Zambia but are more sizable elsewhere. In 

Swaziland for example, 35 percent of HIV-positive women have no need for family compared 

with 50 percent of HIV-negative women. 

Among HIV-negative women, in all 12 surveys the proportion with no need for family planning 

is higher among women who have not tested for HIV in the previous 12 months compared with 

those who have tested for HIV in the last 12 months. The difference ranges from 1.8 percentage 

points in Gabon to 22.2 percentage points in Swaziland. Among HIV-positive women, the findings 

are more mixed. Five surveys (Cameroon, Côte d’Ivoire2

, Kenya, Malawi, and Rwanda) show the 

need for family planning to be higher among HIV-positive women who have not tested for HIV in 

the last 12 months, and seven surveys (Gabon, Lesotho, Namibia, Swaziland, Togo3, Zambia, and 

Zimbabwe) show the need for family planning to be higher among women who have tested for 

HIV. Where the need for family planning is greater among those who have not tested, the range is 

from a 0.7 percentage point difference in Cameroon to 8.5 percentage points in Côte d’Ivoire4

. 

Where the need for family is greater among those who have tested for HIV, the range is from a 0.8 

percentage point difference in Gabon to 9.5 percentage points in Zimbabwe. 

 

2

 Figure is based on 40 unweighted cases and should be interpreted with caution. 

3

 Figure is based on 28 unweighted cases and should be interpreted with caution. 4 Figure is based on 40 unweighted cases and should be interpreted with caution.
6 

Table 2. Need for family planning among women age 15-49 by HIV status and testing experience, percentages (95% confidence intervals in brackets) 

Cameroon 2011 Côte d’Ivoire 2011-12 Gabon 2012 Kenya 2008-09 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

HIV-positive 

Tested in last 12 months 60.1 22.5 17.3 128 (63.3) (13.7) (23.0) 36 48.5 22.9 28.7 112 55.7 31.8 12.5 119 

[50.8 - 68.8] [15.4 - 31.8] [11.4 -25.6] [42.6 - 80.1] [4.04 - 37.5] [10.8 - 42.3] [34.9 - 62.2] [13.4 - 36.2] [17.1 - 44.0] [42.3 - 68.3] [21.1 - 45.0] [7.1 - 21.1] 

Did not test in last 59.4 24.2 16.4 270 54.8 25.3 19.9 171 49.3 41.9 8.8 205 48.1 26.3 25.6 172 

12 months [53.3 - 65.2] [19.1 - 30.3] [12.2 - 21.7] [46.3 - 63.0] [17.2 - 35.6] [13.7 - 28.2] [38.0 - 60.7] [31.2 - 53.5] [4.7 - 15.8] [36.5 - 59.8] [18.2 - 36.5] [16.9 - 36.8] 

Subtotal 59.4 23.9 16.7 402 56.2 23.4 20.4 207 49.0 35.2 15.8 317 51.2 28.6 20.2 291 

[54.3 - 64.4] [19.5 - 28.9] [13.3 - 20.7] [48.2 - 63.9] [16.5 - 32.1] [14.7 - 27.6] [40.7 - 57.4] [26.4 - 45.1] [11.1 - 22.0] [42.4 - 59.8] [21.8 - 36.5] [14.4 - 27.6]

HIV-negative 

Tested in last 12 months 50.1 31.7 18.2 1,553 51.2 24.0 24.7 650 42.2 30.6 27.2 1,677 40.3 36.8 22.9 987 

[46.6 - 53.6] [28.7 - 35.0] [15.9 - 20.7] [46.4 - 56.0] [19.6 - 29.14 [20.4 - 29.6] [38.3 - 46.2] [26.3 - 35.2] [24.1 - 30.6] [35.8 - 45.0] [31.5 - 42.4] [19.0 - 27.4]

Did not test in last 63.5 19.7 16.9 5,165 58.4 19.5 22.1 3,579 44.0 36.7 19.3 3,414 52.9 30.8 16.4 2,318 

12 months [61.7 - 65.2] [18.3 - 21.2] [15.7 - 18.1] [56.0 - 60.7] [17.4 - 21.8] [19.9 - 24.5] [41.2 - 46.9] [33.2 - 40.3] [17.3 - 21.6] [50.3 - 55.4] [28.4 - 33.3] [14.4 - 18.6] 

Subtotal 60.3 22.5 17.2 6,796 57.1 20.1 22.8 4,293 43.5 34.6 22.0 5,121 49.2 32.4 18.3 3,349 

[58.6 - 61.9] [21.1 - 24.1] [16.1 - 18.3] [55.0 - 59.1] [18.2 - 22.2] [20.8 - 25.0] [40.9 - 46.0] [31.4 - 38.0] [20.1 - 24.0] [47.0 - 51.5] [30.1 - 34.8] [16.4 - 20.5]

Total 60.2 22.6 17.2 7,198 57.0 20.2 22.7 4,500 43.8 34.6 21.6 5,438 49.4 32.1 18.5 3,640 

[58.6 - 61.8] [21.2 - 24.1] [16.1 - 18.2] [55.0 - 59.0] [18.3 - 22.3] [20.8 - 24.8] [41.3 - 46.3] [31.3 - 38.1] [19.8 - 23.6] [47.1 - 51.7] [29.9 - 34.4] [16.5 - 20.6] 

Lesotho 2009 Malawi 2010 Namibia 2013 Rwanda 2010 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

HIV-positive 

Tested in last 12 months 40.5 46.9 12.6 465 48.1 29.0 22.8 303 26.8 59.6 13.6 342 54.0 35.0 11.0 113 

[35.2 - 46.1] [41.3 - 52.6] [9.8 - 16.0] [40.6 - 55.7] [22.6 - 36.4] [15.4 - 32.5] [21.9 - 32.4] [53.8 - 65.3] [9.9 - 18.3] [44.6 - 63.1] [27.0 - 44.0] [6.5 - 18.1] 

Did not test in last 42.7 40.2 17.1 546 44.7 37.6 17.7 606 34.2 58.5 7.2 330 53.1 39.0 7.9 142 12 months [37.7 - 47.8] [35.2 - 45.5] [13.9 - 20.7] [40.0 - 49.6] [32.8 - 42.7] [14.1 - 22.0] [28.2 - 40.9] [52.1 - 64.7] [4.8 - 10.8] [44.3 - 61.7] [30.8 - 47.8] [4.6 - 13.2] 

Subtotal 41.7 43.3 15.0 1,010 45.8 34.7 19.5 911 31.0 58.6 10.4 683 53.7 37.1 9.3 257 

[38.2 - 45.3] [39.8 - 46.9] [12.8 - 17.5] [41.9 - 49.7] [30.8 - 38.9] [16.1 - 23.5] [26.7 - 35.7] [54.1 - 63.0] [8.2 - 13.3] [47.3 - 59.9] [31.2 - 43.3] [6.3 - 13.3] 

HIV-negative 

Tested in last 12 months 42.0 36.2 21.7 1,234 40.1 36.4 23.5 2,330 31.9 51.9 16.3 1,708 56.7 30.0 13.3 2,566 

[38.7 - 45.4] [33.29 - 39.4] [19.0 - 24.7] [37.6 - 42.5] [33.9 - 39.1] [21.3 - 25.9] [29.4 - 34.5] [49.1 - 54.6] [14.4 - 18.4] [54.7 - 58.7] [28.1 - 31.9] [12.0 - 14.7]

Did not test in last 58.6 29.0 12.5 1,534 46.9 35.9 17.3 3,808 46.8 44.9 8.3 1,608 62.8 27.3 9.9 4,056 12 months [55.5 - 61.6] [26.5 - 31.5] [10.7 - 14.5] [44.8 - 49.0] [33.9 - 37.9] [15.8 - 18.9] [43.4 - 50.1] [41.5 - 48.3] [6.9 - 10.0] [61.1 - 64.5] [25.8 - 28.9] [9.0 - 11.0] 

Subtotal 51.2 32.2 16.6 2,768 44.2 36.2 19.6 6,168 38.8 48.7 12.5 3,367 60.3 28.5 11.2 6,658 

[48.6 - 53.8] [30.2 - 34.3] [14.8 - 18.6] [42.7 - 45.8] [34.5 - 37.8] [18.4 - 20.9] [36.8 - 40.9] [46.6 - 50.8] [11.2 - 14.0] [59.1 - 61.6] [27.3 - 29.7] [10.4 - 12.0]

Total 48.7 35.2 16.2 3,778 44.4 36.0 19.6 7,079 37.5 50.4 12.2 4,051 60.1 28.8 11.1 6,915 

[46.5 - 50.8] [33.3 - 37.1] [14.7 - 17.8] [43.0 - 46.0] [34.4 - 37.6] [18.4 - 20.8] [35.6 - 39.4] [48.5 - 52.2] [10.9 - 13.5] [58.8 - 61.4] [27.6 - 30.0] [10.3 - 12.0] 

(Continued)
7 

Table 2. − (Continued)

Swaziland 2006-07 Togo 2013-14 Zambia 2013-14 Zimbabwe 2010-11 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

No 

need 

Met 

need 

Unmet 

Need 

Weighted 

n 

HIV-positive 

Tested in last 12 months 33.6 45.9 20.5 402 (56.4) (23.7) (19.9) 30 46.0 38.7 15.2 1,029 38.4 47.9 13.8 477 

[29.3 - 38.2] [41.1 - 50.7] [16.3 - 25.50] [36.7 - 74.3] [11.0 - 43.7] [8.8 - 39.0] [42.5 - 49.6] [35.0 - 42.6] [12.9 - 17.8] [33.8 - 43.1] [43.0 - 52.8] [11.0 - 17.2]

Did not test in last 36.1 45.5 18.4 957 62.2 14.5 23.3 116 49.2 36.7 14.1 1,165 47.9 42.5 9.6 800 12 months [32.9 - 39.5] [41.9 - 49.2] [16.0 - 21.0] [51.1 - 72.2] [8.9 - 22.9] [14.8 - 34.7] [45.7 - 52.7] [33.3 - 40.2] [11.7 - 16.9] [44.4 - 51.5] [39.2 - 46.0] [7.7 - 11.9] 

Subtotal 35.3 45.7 18.9 1,378 60.5 16.2 23.2 147 47.4 38.0 14.6 2,216 44.4 44.4 11.3 1,295 

[32.7 - 38.0] [42.9 - 49.0] [16.9 - 21.2] [50.5 - 69.8] [11.1 - 23.2] [15.7 - 32.8] [45.1 - 49.9] [35.7 - 40.4] [12.9 - 16.5] [41.5 - 47.3] [41.5 - 47.2] [9.6 - 13.3] 

HIV-negative 

Tested in last 12 months 32.9 45.2 21.9 646 52.7 20.0 27.4 761 40.1 40.2 19.7 5,913 35.3 50.5 14.3 2,144 

[29.1 - 36.9] [41.1 - 49.5] [18.9 - 25.2] [48.3 - 57.0] [16.9 - 23.5] [24.0 - 31.0] [38.5 - 41.8] [38.6 - 41.9] [18.4 - 21.0] [32.9 - 37.7] [48.0 - 53.0] [12.6 - 16.1]

Did not test in last 55.1 31.2 13.7 2,384 55.4 19.8 24.8 3,789 55.1 30.3 14.6 6,467 54.2 36.9 8.9 3,739 12 months [52.9 - 57.3] [29.2 - 33.3] [12.4 - 15.2] [53.4 - 57.4] [18.1 - 21.6] [23.1 - 26.6] [53.6 - 56.6] [28.9 - 31.8] [13.6 - 15.7] [52.4 - 56.1] [35.1 - 38.7] [8.0 - 9.9] 

Subtotal 50.3 34.2 15.6 3,045 54.9 19.8 25.3 4,584 47.8 35.1 17.1 12,490 47.7 41.5 10.9 6,018 

[48.3 - 52.2] [32.3 - 36.1] [14.2 - 17.0] [53.1 - 56.6] [18.3 - 21.5] [23.8 - 26.9] [46.6 - 49.0] [33.9 - 36.3] [16.2 - 18.0] [46.1 - 49.2] [39.9 - 43.0] [10.0 - 11.8]

Total 45.6 37.8 16.6 4,423 55.0 19.7 25.3 4,731 47.7 35.5 16.7 14,706 47.1 42.0 10.9 7,313 

[44.0 - 47.2] [36.1 - 40.0] [15.4 - 17.9] [53.3 - 56.8] [18.2 - 21.4] [23.8 - 26.8] [46.7 - 48.8] [34.5 - 36.6] [15.9 - 17.5] [45.7 - 48.5] [40.6 - 43.4] [10.1 - 11.8] 

Notes: Figures in parentheses are based on 25-49 unweighted cases and should be interpreted with caution. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Figures presented here are based on the subsample of women who have a valid HIV test result and will deviate slightly from those published in DHS Final Reports. Figures are based on the revised definition of unmet need (Bradley et al. 2012). 
8 

Unmet Need for Family Planning 

Among HIV-positive women, unmet need for family planning ranges from 9 percent in Rwanda 

to 23 percent in Togo. Among HIV-negative women, the range is from 11 percent in Zimbabwe 

to 25 percent in Togo. In six of the eight surveys in which the total need for family planning is 

greater among HIV-positive women, met need is higher and unmet need is lower among HIVpositive women than among HIV-negative women (Cameroon, Côte d’Ivoire, Lesotho, Namibia, 

Rwanda, and Zambia). In the other two surveys in this category, Swaziland and Zimbabwe, levels 

of unmet need and met need both are higher for HIV-positive women than for HIV-negative 

women. 

Among the four surveys in which the total need for family planning is higher among HIV-negative 

women, in two of them—Malawi and Togo—levels of both unmet need and met need are higher 

among HIV-negative women than HIV-positive women. Among the other two surveys in this 

category, in Gabon unmet need is higher and met need is lower among HIV-negative women than 

HIV-positive women, while Kenya demonstrates the converse pattern: unmet need is lower and 

met need is higher among HIV-negative women than HIV-positive women. 

These data indicate that in most surveys, while HIV-positive women have greater levels of need 

for family planning compared with HIV-negative women, a substantial part of this need is being 

met. In 11 of the 12 surveys, met need exceeds unmet need among HIV-positive women. Among 

HIV-positive women, only in Togo is unmet need, at 23 percent, greater than met need, at 16 

percent. Among HIV-negative women, met need also exceeds unmet need, except in Togo and 

Côte d’Ivoire. 

Among HIV-positive women, in 9 of the 12 surveys unmet need is higher among women who have 

had an HIV test in the last 12 months compared with women who have not had a recent HIV test. 

The difference is greatest in Gabon, where 29 percent of HIV-positive women who have tested for 

HIV in the last 12 months have an unmet need for family planning compared with 9 percent of 

HIV-positive women who have not had a recent HIV test. The difference is much smaller in 

Cameroon and Zambia, at approximately one percentage point. The three surveyed countries in 

which the level of unmet need among HIV-positive women who have not tested recently for HIV 
9 

is higher than among those who have tested are Kenya, Lesotho, and Togo5

, with the difference 

ranging from 3 to 13 percentage points. Among HIV-negative women, unmet need is consistently 

higher among women who have tested for HIV in the past 12 months compared with those who 

have not. 

Use of Contraception 

As Table 3 shows, current contraceptive use among HIV-positive women ranges from 16 percent 

in Togo to 59 percent in Namibia. Among HIV-negative women, the range is from 20 percent in 

Togo and Côte d’Ivoire to 49 percent in Namibia. In 9 of the 12 surveys, contraceptive prevalence 

is higher among HIV-positive women than among HIV-negative women, from a 0.7 percentage 

point difference in Gabon to 11.5 percentage points in Swaziland. In the three surveys in which 

contraceptive prevalence is higher among HIV-negative women (Kenya, Malawi, and Togo), the 

differences are generally smaller. 

There is no clear pattern of contraceptive use by testing experience among HIV-positive women: 

in 7 of the 12 surveys, current use of contraception is higher among women who have tested for 

HIV in the last 12 months, while in the others it is higher among women who have not tested. 

Among the former surveys, the difference is greatest in Côte d’Ivoire and Togo, where the small 

number of HIV-positive women testing for HIV in the last 12 months requires that we interpret 

this comparison with caution. Among the latter surveys, the difference is greatest in Gabon and 

Malawi. With the exception of Gabon, contraceptive use is higher among HIV-negative women 

who have tested for HIV in the last 12 months than among HIV-negative women who have not 

tested. 

 

5 Figure is based on 28 unweighted cases and should be interpreted with caution.
10

Table 3. Current use of contraception among women age 15-49 by HIV status and testing experience, percentages (95% confidence intervals in brackets) 

Cameroon 2011 Côte d’Ivoire 2011-12 Gabon 2012 Kenya 2008-09 Lesotho 2009 Malawi 2010 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

HIV-positive 

Tested in last 12 months 22.5 15.1 128 (13.7) (12.4) 36 22.8 13.3 112 31.8 28.9 119 46.9 45.6 465 28.8 27.3 305 

[15.4 - 31.7] [9.3 - 23.6] [4.0 - 37.5] [3.2 - 37.3] [13.4 - 36.0] [6.6 - 25.1] [21.1 - 45.0] [18.5 - 42.1] [41.3 - 52.6] [40.1 - 51.2] [22.5 - 36.1] [21.2 - 34.5]

Did not test in last 24.2 13.5 270 25.3 20.7 171 41.9 29.3 205 26.3 23.5 172 40.2 39.1 546 37.6 35.5 606 12 months [19.0 - 30.2] [10.1 - 17.7] [17.2 - 35.6] [13.3 - 30.7] [31.2 - 53.5] [19.0 - 42.3] [18.2 - 36.5] [15.7 - 33.5] [35.2 - 45.5] [34.0 - 44.5] [32.8 - 42.7] [30.8 - 40.4]Subtotal 23.9 14.1 402 23.4 19.4 207 35.1 23.6 317 28.6 25.7 291 43.3 42.1 1,010 34.6 32.7 913 

[19.5 - 28.9] [11.0 - 18.0] [16.5 - 32.1] [13.1 - 27.8] [26.3 - 4.5] [16.0 - 33.5] [21.8 - 36.5] [19.1 - 33.7] [39.8 - 46.9] [38.5 - 45.7] [30.7 - 38.8] [28.9 - 36.8]

HIV-negative 

Tested in last 12 months 31.7 23.2 1,556 24.0 17.7 650 30.4 23.0 1,684 36.8 33.2 987 36.2 35.1 1,234 36.4 33.3 2,333 

[28.6 - 0.34.9] [20.6 - 26.0] [19.6 - 29.1] [14.1 - 22.1] [26.2 - 35.1] [19.5 - 27.0] [31.5 - 42.4] [27.9 - 39.0] [33.2 - 39.4] [32.1 - 38.3] [33.9 - 39.0] [30.7 - 35.9]

Did not test in last 19.6 13.5 5,182 19.5 13.5 3,587 36.5 25.1 3,427 30.8 26.2 2,319 29.0 28.0 1,534 35.8 32.8 3,814 12 months [18.2 - 21.1] [12.4 - 14.7] [17.4 - 21.7] [12.0 - 15.2] [33.0 - 40.1] [22.4 - 28.0] [28.4 - 33.3] [23.9 - 28.6] [26.5 - 31.5] [25.6 - 30.6] [33.9 - 37.8] [30.9 - 34.8]Subtotal 22.5 15.8 6,817 20.1 14.2 4,301 34.4 24.4 5,142 32.4 28.2 3,350 32.2 31.2 2,768 36.1 33.0 6,177 

[21.0 - 24.0] [14.7 - 17.1] [18.1 - 22.1] [12.7 - 15.7] [31.2 - 37.8] [21.9 - 27.0] [30.1 - 34.8] [26. - 30.5] [30.2 - 34.3] [29.2 - 33.3] [34.5 - 37.8] [31.4 - 34.7]

Total 22.6 15.7 7,219 20.2 14.4 4,509 34.5 24.3 5,459 32.1 28.0 3,641 35.2 34.1 3,778 35.9 33.0 7,090 

 [21.1 - 24.1] [14.6 - 16.9] [18.3 - 22.3] [13.0 - 15.9] [31.2 - 38.0] [21.8 - 27.0] [29.9 - 34.4] [25.9 - 30.1] [33.3 - 37.1] [32.2 - 36.0] [34.4 - 37.5] [31.5 - 34.6]

Namibia 2013 Rwanda 2010 Swaziland 2006-07 Togo 2013-14 Zambia 2013-14 Zimbabwe 2010-11 

Any 

method 

Any modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any 

modern 

method 

Weighted 

n 

Any 

method 

Any modern 

method 

Weighted 

n 

HIV-positive 

Tested in last 12 months 59.6 59.6 342 35.0 34.4 113 45.9 44.6 402 (23.7) (16.0) 30 38.7 37.2 1,029 47.9 47.2 477 

[53.8 - 65.3] [53.8 - 65.3] [27.0 - 44.0] [26.4 - 43.4] [41.1 - 50.7] [39.8 - 49.6] [11.0 - 43.7] [6.5 - 34.3] [35.0 - 42.6] [33.5 - 41.0] [43.0 - 52.8] [42.3 - 52.1]

Did not test in last 58.5 58.5 330 39.0 36.0 142 45.5 43.5 957 14.5 11.6 116 36.7 35.5 1,165 42.5 41.9 800 12 months [52.1 - 64.7] [52.1 - 64.7] [30.8 - 47.8] [28.1 - 44.8] [41.9 - 49.2] [40.0 - 47.1] [8.9 - 22.9] [6.7 - 19.1] [33.3 - 40.2] [32.1 - 39.1] [39.2 - 45.9] [38.7 - 45.2]Subtotal 58.6 58.6 683 37.1 35.2 257 45.7 44.0 1,378 16.2 12.4 147 38.0 36.5 2,216 44.4 43.7 1,295 

[54.1 - 63.0] [54.1 - 63.0] [31.2 - 43.3] [29.4 - 41.5] [42.9 - 48.7] [41.1 - 46.9] [11.1 - 23.2] [7.8 - 19.1] [35.7 - 40.4] [34.2 - 38.9] [41.5 - 47.2] [40.9 - 46.5]

HIV-negative 

Tested in last 12 months 51.9 51.4 1,708 30.0 26.7 2,566 45.2 43.8 646 19.9 16.4 763 40.2 37.6 5,920 50.5 49.9 2,144 

[49.1 - 54.6] [48.6 - 54.1] [28.1 - 31.9] [25.0 - 28.6] [41.1 - 49.5] [39.7 - 48.0] [16.9 - 23.4] [13.7 - 19.7] [38.5 - 41.8] [36.0 - 39.3] [48.0 - 53.0] [47.4 - 52.4]

Did not test in last 44.9 44.4 1,608 27.3 23.5 4,057 31.2 29.7 2,385 19.8 17.2 3,794 30.3 27.0 6,474 36.9 36.2 3,739 12 months [41.5 - 48.3] [41.0 - 47.8] [25.8 - 28.9] [22.0 - 25.1] [29.2 - 33.2] [27.8 - 31.7] [18.1 - 21.6] [15.8 - 18.8] [28.8 - 31.7] [25.6 - 28.5] [35.1 - 38.7] [34.4 - 38.0]Subtotal 48.7 48.2 3,367 28.5 24.9 6,660 34.2 32.7 3,046 19.8 17.1 4,590 35.1 32.2 12,503 41.5 40.8 6,018 

[46.6 - 50.8] [46.1 - 50.3] [27.3 - 29.7] [23.8 - 26.1] [32.3 - 36.1] [30.9 - 34.6] [18.3 - 21.5] [15.8 - 18.6] [33.9 - 36.3] [31.0 - 33.4] [39.9 - 43.0] [39.2 - 42.4]

Total 50.4 49.9 4,051 28.8 25.3 6,917 37.8 36.2 4,424 19.7 17.0 4,737 35.5 32.8 14,719 42.0 41.3 7,313 

 [48.5 - 52.2] [48.1 - 51.8] [27.6 - 30.0] [24.1 - 26.5] [36.1 - 39.6] [34.5 - 38.0] [18.1 - 21.3] [15.6 - 18.5] [34.5 - 36.6] [31.8 - 33.9] [40.6 - 43.4] [39.9 - 42.8] 

Notes: Figures in parentheses are based on 25-49 unweighted cases and should be interpreted with caution. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 

Figures presented here are based on the subsample of women who have a valid HIV test result and will deviate slightly from those published in DHS Final Reports.
11 

Modern methods comprise the majority of contraceptive use. Overall, patterns of modern method 

use are similar to those for any contraceptive use, although in Gabon, Cameroon, and Côte d’Ivoire 

a substantial portion of contraceptive use appears to be traditional methods. In nearly all surveys 

where use of any contraception is higher among HIV-positive women, use of a modern method is 

also higher among HIV-positive women. Similarly, in the surveys where any contraceptive use is 

higher among HIV-negative women, so too is modern method use. The two exceptions are 

Cameroon and Gabon. In these surveys, a slightly larger proportion of HIV-positive women than 

HIV-negative women use contraception, but a slightly smaller proportion of HIV-positive women 

use a modern method compared with HIV-negative women. 

The pattern of modern method use by women’s HIV testing experience mirrors that of any method 

use, for HIV-positive and HIV-negative women alike, but with two exceptions. In Cameroon, use 

of a modern method is slightly higher among HIV-positive women who have tested in the last 12 

months compared with those who have not tested (15 percent versus 14 percent), and in Togo 

modern method use is higher among HIV-negative women who have not tested for HIV in the last 

12 months compared with HIV-negative women who have tested (17 percent versus 16 percent)—

in contrast to the patterns for any contraceptive use. In both cases, the differences are small and 

unlikely to be statistically significant, given their overlapping confidence intervals. Overall, the 

results in Table 3 indicate higher levels of contraceptive use among HIV-positive women in nine 

surveys, and higher use of modern method use among HIV-positive women in seven surveys, with 

mixed patterns of contraceptive use by testing experience. 
12 

REFERENCES 

Bankole, A., A.E. Biddlecom, and K. Dzekedzeke. 2011. “Women’s and Men’s Fertility 

Preferences and Contraceptive Behaviors by HIV Status in 10 Sub-Saharan African 

Countries.” AIDS Education and Prevention 23 (4):313-328. 

Bonnenfant, Y.-T., M.J. Hindin, and D. Gillespie. 2012. “HIV Diagnosis and Fertility Intentions 

among Couple VCT Clients in Ethiopia.” AIDS Care 24 (11):1407-1415. 

Bradley, S.E., T.N. Croft, J.D. Fishel, and C.F. Westoff. 2012. Revising Unmet Need for Family 

Planning, DHS Analytical Studies No. 25. Calverton, MD: ICF International. 

Chakrapani, V., T. Kershaw, M. Shunmugam, P.A. Newman, D.H. Cornman, and R. Dubrow. 

2011. “Prevalence of and Barriers to Dual-Contraceptive Methods Use among Married 

Men and Women Living with HIV in India.” Infectious Diseases in Obstetrics and 

Gynecology 2011:376432. 

Chi, B.K., T. Gammeltoft, N.T.T. Hanh, and V. Rasch. 2012. “Contraceptive Use among HIV‐

Positive Women in Quang Ninh Province, Vietnam.” Tropical Medicine & International 

Health 17 (10):1227-1234. 

Church, K., and S.H. Mayhew. 2009. “Integration of STI and HIV Prevention, Care, and Treatment 

into Family Planning Services: A Review of the Literature.” Studies in Family Planning

40 (3):171-186. 

Heys, J., W. Kipp, G.S. Jhangri, A. Alibhai, and T. Rubaale. 2009. “Fertility Desires and Infection 

with HIV: Results from a Survey in Rural Uganda.” AIDS 23:S37-S45. 

Hoffman, I.F., F.E. Martinson, K.A. Powers, D.A. Chilongozi, E.D. Msiska, E.I. Kachipapa, C.D. 

Mphande, M.C. Hosseinipour, H.C. Chanza, and R. Stephenson. 2008. “The Year-Long 

Effect of HIV-Positive Test Results on Pregnancy Intentions, Contraceptive Use, and 

Pregnancy Incidence among Malawian Women.” JAIDS 47 (4):477-483. 

Johnson, K.B., P. Akwara, S.O. Rutstein, and S. Bernstein. 2009. “Fertility Preferences and the 

Need for Contraception among Women Living with HIV: The Basis for a Joint Action 

Agenda.” AIDS 23:S7-S17. 

Keogh, S.C., M. Urassa, M. Roura, Y. Kumogola, S. Kalongoji, D. Kimaro, J. Changalucha, and 

B. Zaba. 2012. “The Impact of Antenatal HIV Diagnosis on Postpartum Childbearing 

Desires in Northern Tanzania: A Mixed Methods Study.” Reproductive Health Matters 20 

(39):39-49. 

Laher, F., C.S. Todd, M.A. Stibich, R. Phofa, X. Behane, L. Mohapi, and G. Gray. 2009. “A 

Qualitative Assessment of Decisions Affecting Contraceptive Utilization and Fertility 

Intentions among HIV-Positive Women in Soweto, South Africa.” AIDS and Behavior 13 

(1):47-54. 
13 

MacQuarrie, K.L.D., S.E.K. Bradley, A. Gemmill, and S. Staveteig. 2014. Contraceptive 

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

USA: ICF International. Available at http://dhsprogram.com/pubs/pdf/AS47/AS47.pdf. 

Mbonye, A., K. Hansen, F. Wamono, and P. Magnussen. 2012. “Barriers to Contraception among 

HIV-Positive Women in a Periurban District of Uganda.” International Journal of STD & 

AIDS 23 (9):661-666. 

Nanda, K., C.S. Morrison, C. Kwok, J. Byamugisha, L. Jones, S. Sriplienchan, and T. Magwali. 

2011. “Discontinuation of Oral Contraceptives and Depot Medroxyprogesterone Acetate 

among Women with and without HIV in Uganda, Zimbabwe and Thailand.” Contraception

83 (6):542-548. 

Todd, C.S., M.A. Stibich, F. Laher, M.S. Malta, F.I. Bastos, K. Imbuki, D.N. Shaffer, S.K. Sinei, 

and G.E. Gray. 2011. “Influence of Culture on Contraceptive Utilization among HIVPositive Women in Brazil, Kenya, and South Africa.” AIDS and Behavior 15 (2):454-468. 

Wanyenze, R.K., G.J. Wagner, N.M. Tumwesigye, M. Nannyonga, F. Wabwire-Mangen, and 

M.R. Kamya. 2013. “Fertility and Contraceptive Decision-making and Support for HIV 

Infected Individuals: Client and Provider Experiences and Perceptions at Two HIV Clinics 

in Uganda.” BMC Public Health 13 (1):98. 

Wilcher, R., W. Cates Jr, and S. Gregson. 2009. “Family Planning and HIV: Strange Bedfellows 

No Longer.” AIDS (London, England) 23 (Suppl 1):S1-S6. 

Yeatman, S.E. 2009. “The Impact of HIV Status and Perceived Status on Fertility Desires in Rural 

Malawi.” AIDS and Behavior 13 (1):12-19.