Decline in HIV prevalence among young women in Zambia: national-level estimates of trends mask geographical and socio-demographic differences.

Abstract

A decline in HIV incidence has been reported in Zambia and a number of other sub-Saharan countries. The trend of HIV prevalence among young people is a good marker of HIV incidence. In this study, different data sources are used to examine geographical and sub-population group differentials in HIV prevalence trends among men and women aged 15-24 years in Zambia.
We analysed ANC data for women aged 15-24 years from 22 sentinel sites consistently covered in the period 1994-2008, and HIV data for young men and women aged 15-24 years from the ZDHS 2001/2 and 2007. In addition, we systematically reviewed peer-reviewed articles that have reported findings on HIV prevalence and incidence among young people.
Overall trends of the ANC surveillance data indicated a substantial HIV prevalence decline among young women in both urban and rural areas. However, provincial declines differed substantially, i.e. between 10% and 68% among urban women, and from stability to 86% among rural women. Prevalence declines were steeper among those with the highest educational attainments than among the least educated. The ZDHS data indicated a significant reduction in prevalence between the two survey rounds among young women only. Provincial-level ZDHS changes were difficult to assess because the sample sizes were small. ANC-based trend patterns were consistent with those observed in PMTCT-based data (2002-2006), whereas population-based surveys in a selected urban community (1995-2003) suggested that the ANC-based data underestimated the prevalence declines in the general populations of both young both men and women.
The overall HIV prevalence declined substantially among young women in Zambia and this is interpreted as indicating a decline in HIV incidence. It is noteworthy that overall national trends masked substantial differences by place and by educational attainment, demonstrating critical limitations in the current focus on overall country-level trends in epidemiological reports.

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Decline in HIV Prevalence among Young Women in
Zambia: National-Level Estimates of Trends Mask
Geographical and Socio-Demographic Differences
Nkomba Kayeyi1,2*, Knut Fylkesnes1, Charles Michelo2, Mpundu Makasa1,3, Ingvild Sandøy1
1Centre for International Health, University of Bergen, Bergen, Norway, 2Department of Public Health, School of Medicine, University of Zambia, Lusaka, Zambia,
3Ministry of Health, Lusaka, Zambia
Abstract
Background: A decline in HIV incidence has been reported in Zambia and a number of other sub-Saharan countries. The
trend of HIV prevalence among young people is a good marker of HIV incidence. In this study, different data sources are
used to examine geographical and sub-population group differentials in HIV prevalence trends among men and women
aged 15–24 years in Zambia.
Design and Methods: We analysed ANC data for women aged 15–24 years from 22 sentinel sites consistently covered in the
period 1994–2008, and HIV data for young men and women aged 15–24 years from the ZDHS 2001/2 and 2007. In addition,
we systematically reviewed peer-reviewed articles that have reported findings on HIV prevalence and incidence among
young people.
Findings: Overall trends of the ANC surveillance data indicated a substantial HIV prevalence decline among young women
in both urban and rural areas. However, provincial declines differed substantially, i.e. between 10% and 68% among urban
women, and from stability to 86% among rural women. Prevalence declines were steeper among those with the highest
educational attainments than among the least educated. The ZDHS data indicated a significant reduction in prevalence
between the two survey rounds among young women only. Provincial-level ZDHS changes were difficult to assess because
the sample sizes were small. ANC-based trend patterns were consistent with those observed in PMTCT-based data (2002–
2006), whereas population-based surveys in a selected urban community (1995–2003) suggested that the ANC-based data
underestimated the prevalence declines in the general populations of both young both men and women.
Conclusion: The overall HIV prevalence declined substantially among young women in Zambia and this is interpreted as
indicating a decline in HIV incidence. It is noteworthy that overall national trends masked substantial differences by place
and by educational attainment, demonstrating critical limitations in the current focus on overall country-level trends in
epidemiological reports.
Citation: Kayeyi N, Fylkesnes K, Michelo C, Makasa M, Sandøy I (2012) Decline in HIV Prevalence among Young Women in Zambia: National-Level Estimates of
Trends Mask Geographical and Socio-Demographic Differences. PLoS ONE 7(4): e33652. doi:10.1371/journal.pone.0033652
Editor: Gilda Tachedjian, Burnet Institute, Australia
Received September 15, 2011; Accepted February 17, 2012; Published April 4, 2012
Copyright: ? 2012 Kayeyi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors would like to acknowledge the Norwegian programme for development, research and education (NUFU) for financing this study. Further
acknowledgement is made to the funders of the surveys, in particular the ANC sentinel surveillance, which has been funded over the years by the Norwegian
Agency for Development Cooperation (NORAD), the Swedish International Cooperation Agency (SIDA) and the Centers for Disease Control and Prevention (CDC).
Other organisations include MEASURE DHS, which funds the Demographic and Health Survey (DHS). The funders of this study had no role in the study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: nkayeyi@yahoo.co.uk
Introduction
According to the 2011 report of The Joint United Nations
Programme on HIV/AIDS (UNAIDS), a number of countries
with generalised HIV epidemics, including Zambia, have
experienced incidence declines since the year 2001 [1]. This
conclusion is based on modelling of prevalence data from
antenatal clinic sentinel surveillance (ANC) and national popula-
tion-based surveys [2]. However, ANC-based sentinel surveillance
has been and continues to be an instrumental data source for
estimating HIV prevalence trends over time, although the extent
to which these estimates are representative of the general
population has been questioned [3]. Population-based survey
designs are the gold standard in the provision of valid HIV
estimates, but can be used as reference for validating ANC-based
estimates only if biases due to non-participation or other sources
have been carefully considered. Nationally representative demo-
graphic and health surveys (DHS) have included HIV testing in a
number of sub-Saharan African countries. However, there are still
too few DHS measurement points for trend estimation in most of
these countries.
Although HIV incidence provides a more direct measure of the
impact of HIV preventive and treatment programmes, it is more
difficult to determine than prevalence [4]; cohort studies [5,6] and
biological assays that distinguish recent from established HIV
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infections [5,7,8] are very expensive and logistically difficult to
conduct on more representative population samples [5]. Many
studies have continued to use HIV prevalence among young
people aged 15–24 years as a marker or indicator for HIV
incidence [9,10]. This age group has been found appropriate for
estimating HIV incidence because it is less affected by AIDS-
related mortality [11,12] and people in this age group are assumed
to have engaged recently in sexual debut.
Both ANC-based data and population-based data from Zambia
have revealed great differentials in the magnitude and trends of
HIV by place and socio-economic status [13,14,15,16]. Accord-
ingly, trend data at provincial and sub-group levels are critical for
providing the details necessary for proper monitoring, evaluation
and programming. This article examines and compares data from
the national ANC-based HIV surveillance system (1994–2008) and
two rounds of the nationally representative population-based
Zambia Demographic and Health Survey (ZDHS) (2001/2–2007)
by urban-rural residence, province, age-group and education
among young people (15–24 years). Several other studies on HIV
prevalence have been conducted in Zambia, and this paper also
provides a review of published peer-reviewed articles to compare
the ANC- and DHS-derived HIV prevalence and incidence
estimates with estimates from other data sources.
Methods
National ANC-based Sentinel Surveillance
The ANC sentinel surveillance is the main source of national
HIV and syphilis trend data in Zambia. Detailed descriptions of
the methodology of the ANC sentinel surveillance system and
developments over the period 1994–2008 have been published
[13,14,17,18,19,20]. In brief, this paper focuses on the 22 sites that
have consistently been part of the surveillance system since 1994.
The target sample of pregnant women was 500 in most sites, but in
Ndola and the four sites in Lusaka (and in Livingstone and Kapiri
Mposhi in 1994 and 1998) the target was 800. The focus for this
paper was young women aged 15–24 years who participated in the
surveys, giving a total sample of 39,064 (5542 in 1994, 7106 in
1998, 7092 in 2002, 6606 in 2004, 6575 in 2006 and 6143 in
2008). Women were enrolled consecutively in the study and socio-
demographic data were collected through routine interviews. The
data collection period was four months.
Blood specimens obtained from eligible women were tested for
both syphilis and HIV in the local laboratory. The specimens for
HIV testing were anonymous and unlinked to the women.
Capillus HIV-1/HIV-2 (Cambridge Diagnosis Ltd., UK) was
the first test for screening of HIV in the 1994–2002 surveys, and all
reactive specimens were re-tested using a confirmatory test,
Wellcozyme HIV (Murex Diagnosis Ltd., UK). Subsequently,
the screening test was Abbot Determine HIV1/HIV2 (Abbot
Laboratories, USA) and all reactive specimens were re-tested using
Murex ICE HIV (Murex Diagnosis Ltd., UK). Discordant
specimen results were re-tested using a tie-breaker, Bionor
HIV1&2 (BIONOR AS, Norway). For quality control, 10% (5%
each in 1994 and 1998) of the negative specimens and all positive
specimens were re-tested using Abbot Determine. If one of the
negative specimens was found positive, then an additional 40–50%
of the negative results were re-tested. If more false negatives were
discovered, then the entire bunch of negative specimens was re-
tested. Two national reference laboratories performed the quality
control and confirmatory testing: Tropical Diseases Research
Centre (TDRC) in Ndola catered for the northern region of
Zambia, and University Teaching Hospital Virology Laboratory
in Lusaka catered for the southern region.
Zambia Demographic and Health Surveys (ZDHS)
The ZDHS is a nationally representative population-based
survey, and HIV testing was included in selected households in its
2001/2002 and 2007 rounds [21,22,23]. A two-stage cluster
(proportional to size) sampling procedure was followed using the
2000 Population Census as the sampling frame. In the selected
clusters, all households were listed and a systematic random
sampling of households was undertaken. Women aged 15–49 years
and men aged 15–59 years were eligible to participate in the
survey. In the 2001/02 round, men were only eligible for interview
in one-third of the selected households, and men and women in
these households were asked to consent to HIV testing. In the
2007 survey, men in all selected households were eligible for
interview and all eligible men and women who consented were
tested for HIV infection. The general response rates to the
interview were 95% (89% among men and 98% among women)
and 94% (91% among men and 96% among women) in the 2001/
02 and 2007 rounds, respectively. Of those eligible, 76% (2001/
02) and 77% (2007) consented to the HIV test. The total sample of
participants aged 15–24 years comprised 3146 males (669 in
2001/02 and 2477 in 2007) and 3960 females (957 in 2001/02
and 3003 in 2007). Other details have been presented elsewhere
[21,22,23].
HIV testing in the 2001/2002 survey was anonymous, and only
sex, age, province and urban/rural residence could be linked to
the HIV data. Venous blood was collected on filter paper cards as
dried blood spots (DBS) [21,24]. However, in the 2007 survey,
capillary blood from finger pricks was used for the DBS [22], and
new protocols were followed that allowed HIV data to be linked to
household and individual information while maintaining the
anonymity of the participants. Wellcozyme HIV-1&2 GACELISA
was used to test for HIV antibodies in the 2001/2002 survey. All
positive specimens and 10% of the negative specimens were re-
tested using Bionor HIV 1&2, and discordant specimens were
tested with Western blot [21]. For the 2007 survey, the screening
test was Vironostika HIV & Biomerieux, and all positive
specimens were re-tested with a confirmatory test, Enzygnost
Anti-HIV1/2 Plus (Dade Behring). Discordant specimens were
tested using Western blot [22]. Ten percent of the specimens (both
positive and negative) collected in both surveys were sent to the
Global Clinical Viral Laboratory (GCVL) in Durban, South Africa
for external quality assessment and the results had a 99%
agreement rate with TDRC [21,22,23].
Review of past literature
Published peer-reviewed articles on HIV prevalence and
incidence in Zambia were identified through a computer search
of the databases PubMed, Web-of-Science, EMBASE, Google-
scholar, and African Journal on-line. The search was done with
the following words: ‘‘HIV prevalence’’ OR ‘‘HIV incidence’’ OR
‘‘HIV trend’’ AND ‘‘Zambia’’. Studies were selected on the basis
of the following criteria: (1) must have been undertaken in Zambia,
(2) must be an original publication of HIV data from a sample
likely to be representative of the general population, and (3) must
present HIV prevalence or incidence data. The search identified
22 articles that met the criteria and these were based on four data
sources, apart from the ANC and DHS data.
The Chelstone and Kapiri Mposhi population-based surveys (PBS) were
undertaken in the township of Chelstone in Lusaka and in rural
Kapiri Mposhi district. Data were collected serially in 1995, 1999
and 2003. Random cluster sampling was used and all adults aged
15–49 years in all households in the selected clusters were eligible
for the interview and the HIV test. Saliva samples were collected
and tested for HIV using Gacelisa HIV 1&2 (in the 1995 survey)
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and Bionor HIV 1&2 (1999 and 2003 surveys). Further details of
thesurvey methodologyhave
[14,15,17,20].
The prevention of mother to child transmission (PMTCT)-surveys were
undertaken between 2002 and 2006. Their main objective was to
examine trends in HIV seroprevalence among pregnant and
parturient women in Lusaka district. Two data sources were used:
(1) Routine data on number of women counselled and tested for
HIV, number of women with positive test results, and number of
women and infants on antiretroviral (ARV) prophylaxis from 24
public health facilities with PMTCT programmes (between July
2002 and December 2006); (2) Two surveillance rounds (June–
August 2003 and October 2005–January 2006) with testing for
maternal IgG HIV antibodies in umbilical cord-blood from 10,194
discarded placentas (from all facility-based live-births in Lusaka
during the specified periods). Seropositivity of the umbilical-cord
blood indicated maternal HIV infection. Specimens were tested
for HIV using Determine HIV1&2. Full details of the study are
provided elsewhere [25].
The ‘‘Four cities’’ multicentre study was conducted between 1997 and
1998 in four African countries; in Zambia, Ndola was selected.
Two-stage random cluster sampling was used to select households
in which all adults (15–49 years) were eligible for interview and
blood specimen collection. The target sample size was 1000 men
and 1000 women, of which 367 men and 510 women aged 15–29
years provided blood for HIV testing. Blood specimens were tested
for HIV, syphilis and herpes simplex virus type two. The clusters
were selected in the catchment areas of five ANC clinics, resulting
in 40% of all women seen in these antenatal clinics being eligible
for the population survey. In addition, 824 antenatal attendees
aged 15–29 years were interviewed and tested for syphilis and
HIV. The HIV test was performed using an enzyme-linked
immunosorbent assay. Further details on the study have been
published elsewhere [26,27].
The Microbicide clinical preparedness study was a cohort study
conducted in Chilenje and Kamwala health centres in Lusaka.
The target population was women aged 16–49 years who were
recruited through community meetings and local family planning
meetings. All consenting women were screened for HIV and STIs
and those found negative were enrolled into the study, giving a
total of 239 women. These women were followed up for one year
with monthly study visits (June 2003–October 2004). HIV
incidence was estimated from venous blood specimens collected
every three months. The testing was done using an ELISA test for
screening and Western blot as a confirmatory test [28,29].
beenreportedelsewhere
Statistical analysis
Analyses in this paper were restricted to young people aged 15–
24 years [9,10]. ANC and ZDHS data were analysed with SPSS
18 (IBM SPSS, Chicago, Illinois) and STATA Intercooled version
11.0 (StataCorp LP, College Station, Texas). We estimated HIV
prevalence trends by individual ANC sites and socio-demographic
characteristics using the chi-square (x2) linear-by-linear trend test in
SPSS. Trends by province and urban-rural residence (urban and
rural were defined according to the Zambian Central Statistical
Office standards) were examined by age-adjusted risk ratios (aRR)
using log-binomial regression of the generalized linear model in STATA.
The direction and magnitude of changes in HIV prevalence
among women were compared between 2002 and 2008 in the
ANC data and between the ZDHS 2001/2 and 2007. Both data
sources were stratified by age and province, and the changes in
prevalence estimates were measured by age-adjusted risk ratios.
Analyses of ZDHS data were adjusted for clustering and weighted
to account for differential sampling and response probabilities.
Prevalence changes for young men were also estimated, stratifying
by urban/rural residence and age, but not by province since this
yielded subgroups that were too small. Furthermore, we conducted
a sensitivity analysis of the ZDHS estimates to assess the extent to
which different scenarios of bias due to non-participation (refusals
and absence) affected the prevalence estimate. We assessed three
different scenarios, each assuming the HIV prevalence ratio to be
somewhat higher among people who refused than among those
who were absent in comparison with participants. The prevalence
ratios tested were as follows: Scenario 1: Refusals/participations
ratio 1:3 and absentees/participants ratio 1:1; the respective ratios
in scenario 2 were 1:5 and 1:3; and in scenario 3, 2:1 and 1:5
(Table S1). The percentages of refusals and absences were
obtained from the reports of the two surveys [21,22].
Ethics
The Ethics and Research sub-committee of the National AIDS
Surveillance Committee approved the HIV sentinel surveillance in
Zambia in 1989. Procedures were instituted to fulfil the
requirements for unlinked anonymous testing of blood specimens
collected as part of routine antenatal care. For the ZDHS, the
Ethical Review Committee of the University of Zambia and the
Institutional Review Board of ORC Macro of USA approved the
protocols for the surveys. Informed consent was sought using oral
and written methods from eligible participants and from the
parent or guardian if the respondent was less than 18 years old.
For HIV testing, separate consent was sought. The participants
were not told their HIV results, but those who wanted to know
their status were referred to VCT centres for counselling and
testing. Details of ethical approvals for the other studies reviewed
are provided elsewhere [15,17,20,25,26,27,28,30].
Results
Trends in HIV prevalence at ANC surveillance sites
Overall, the five rounds of antenatal surveillance from 22 sites
conducted during the period 1994–2008 show that HIV
prevalence among young women aged 15–24 years has decreased
substantially in both urban (median 27.4% to 15.5%) and rural
(median 11.4% to 6.4%) sites. All urban sites except Kalingalinga
and Matero had significantly falling trends; whereas among rural
sites, only four (Macha, Isoka, Kasaba and Mukinge) had
significantly decreasing trends. The rest of the rural sites showed
no clear trend or had a non-significant increase in HIV prevalence
(Kabompo) (Table 1).
HIV prevalence trends and relative risk ratios by urban-
rural residence and province
Table 2 showed that HIV prevalence proportionally decreased
in the five rounds of the ANC, by 39% among young urban
women and by 27% among young rural women. Overall, HIV
prevalence among urban women was almost twice that of rural
women at baseline. However, significant and gradual declines in
HIV prevalence trends were more prominent among young urban
women than among young rural women. In fact, significant
declines among rural residents occurred only between 2004 and
2008.
At provincial level, the most consistent and significant declines
were among young urban women in Lusaka, Northern and North-
Western provinces. Further, urban residents of Eastern province
and rural residents of Luapula province had significant declines
between the 2002 and the 2008 surveys, whereas in Southern
(urban women) and Northern provinces (rural women) there were
consistent and substantial declines in the period between 2004 and
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2008. Otherwise, most provinces showed either a stable prevalence
or fluctuations in prevalence over time (Table 2).
ANC data vs. ZDHS data
While significant declines were observed among both urban and
rural women in the ANC surveys, the decreases observed in the
ZDHS among urban and rural young women were non-
significant. A comparison of pregnant women and women in the
general population showed that the directions and magnitudes of
change in the period evaluated were largely similar (30% vs. 22%
among urban residents and 20% vs. 22% among rural residents,
respectively). Despite a tendency towards decline in HIV
prevalence among urban ANC participants in most provinces,
there were no significant changes at province level for young
urban female ZDHS participants. The provincial level changes for
rural ANC participants and female ZDHS participants were
mostly non-significant (Table 3). There were a non-significant
increase in HIV prevalence among urban men aged 15–24 years
(from 3.7% to 5.0%, aRR 1.67 CI 0.75–3.69) and among younger
rural men aged 15–19 years (from 2.6% to 3.0%, aRR 1.86 CI
0.75–4.64).
HIV prevalence trends and socio-demographic
characteristics
ANC data further indicated a significant decrease in HIV
prevalence irrespective of marital status. Among both urban and
rural married women, significantly declining HIV prevalence
trends were observed (from 27.8% to 17.1% and from 11.6% to
7.4%, respectively, p,0.001). Similar declines were observed
among urban single women (27.1% to 15.6%, p,0.001) and rural
(10.7% to 9.4%, p=0.030). Furthermore, clearly marked declines
were also seen among young urban residents with more than five
years of schooling and among young rural residents with more
than six years of schooling (p,0.001) (Figure 1). HIV prevalence
has been consistently lower among less educated young women
than among those with more education, but only a non-significant
decline occurred during the five rounds of the ANC in both urban
(p=0.159) and rural (p=0.245) areas.
Table 1. HIV prevalence among young people (15–24 year) for ANC sites with data from 1994 to 2008 by location of sites.
Urban Rural
1994 19982002200420062008
P-value
199419982002200420062008
P-
value
Site name
nnnnnn
Site namen
nnnnn
(%)(%) (%)(%)(%) (%)(%) (%)(%)(%)(%)(%)
Mongu 276291 311279 250289Kalabo149 217 249274300297
(30.1)(27.1) (30.2) (23.3)(14.8)(25.6) 0.005 (8.1) (13.4)(14.5)(14.2) (9.7)(12.1) 0.730
Livingstone337437315165291275Macha280285268271264173
(32.0)(29.5)(29.8)(27.9)(21.6)(22.2)0.001(7.9)(7.0)(6.3)(5.9) (3.4) (1.2)0.001
Chelstone268474447 411460377Kapiri Mp284498312239244226
(25.0)(22.8)(19.9) (17.8) (15.0)(11.1)
, ,0.001 (13.4) (16.5)(22.8)(20.1)(9.4) (17.7)0.587
Chilenje 273288426427 345400Minga 287293298288284202
(34.8)(21.9) (26.8) (18.0) (19.1)(15.8)
, ,0.001(8.0) (10.6)(7.7) (19.4)(6.0) (3.5) 0.537
Kalingalinga 280286 338403427404Isoka274316296269254273
(20.0)(23.4)(20.1) (19.9) (19.7)(15.1)0.061 (11.3) (10.1)(7.1) (11.5)(3.1)(5.1)0.001
Matero 248297487 482424 376Nchelenge 262301283274274257
(28.2)(22.9)(21.6) (26.3) (25.5)(19.9)0.195 (13.7) (13.0)(18.4) (15.3)(9.9) (14.8)0.906
Kabwe 275306280263 277254Kasaba 261276 158205142167
(28.4)(24.8) (22.1)(23.6) (16.2)(24.4)0.019 (11.5)(5.1)(4.4)(3.4)(2.8)(3.0)
, ,0.001
Chipata261 289283280290268 Ibenga218263 223217175179
(27.6)(24.2)(21.9)(8.9) (19.0)(14.6)
, ,0.001(11.0) (8.0)(8.1) (7.8)(10.3) (7.3)0.387
Kasama251 308299 306282280Mukinge 205206 281 250269 143
(21.9)(12.3) (12.0)(13.4)(18.8) (8.6)0.007(9.8)(6.8)(4.3)(5.2)(5.9)(2.1)0.005
Mansa 268346299249284236Kabompo159164219302336295
(23.5) (20.8)(21.4) (24.5)(15.1) (16.9)0.037(1.9)(9.8) (5.9)(7.9)(5.4)(8.8)0.112
Ndola 288612578450394475
(27.1) (25.8) (21.6)(22.2)(18.0) (15.2)
, ,0.001
Solwezi120295310302289297
(20.8) (16.6)(11.9) (12.6)(15.2) (11.4)0.018
Total 31454229 4373401740133931 237928192587 258925422212
(Mean)(27.0)(23.1)(21.7) (19.8)(18.4) (16.5)
, ,0.001(10.0)(10.6)(10.4)(11.3)(6.6)(8.3)0.001
(Median) (27.4)(23.2) (21.6) (21.0)(18.4)(15.5) (11.4)(10.0) (7.4)(9.7)(6.0) (6.4)
X2linear trend tests. The highlighted p-values are statistically significant at 0.05 level. ‘‘n’’ is number, % is percent. Rural/urban refers to the location of the sites.
doi:10.1371/journal.pone.0033652.t001
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Table 2. ANC HIV prevalence and age-adjusted risk ratios for changes in HIV prevalence by province and residence (urban/rural)
among young women 15–24 years.
Year
UrbanRural
n% aRR (95% CI)n% aRR (95% CI)
Total (22 Sites)1994277027.91.00275411.5 1.00
1998 397423.3 0.85 (0.78–0.92)3042 11.30.99 (0.86–1.14)
2002 444721.8 0.79 (0.73–0.86)2471 10.10.88 (0.76–1.04)
20043862 20.2 0.71 (0.65–0.77)274411.3 0.95 (0.82–1.11)
2006377818.7 0.67 (0.61–0.73) 2677 7.00.60 (0.51–0.72)
20083683 16.90.59 (0.54–0.65)23818.30.72 (0.60–0.85)
Province
Central (two sites) 199427628.31.00 28313.41.00
199859922.9 0.83 (0.66–1.05)20410.30.77 (0.47–1.28)
2002 33321.90.80 (0.61–1.06)24823.81.77 (1.22–2.57)
200436222.90.83 (0.64–1.08)14019.31.41 (0.90–2.21)
200637215.60.58 (0.43–0.78)1317.60.57 (0.29–1.10)
2008393 22.90.82 (0.63–1.06)7812.80.95 (0.50–1.82)
Copperbelt (two sites)199433724.01.0016912.41.00
1998606 26.11.13 (0.90–1.42)2687.50.60 (0.34–1.08)
2002557 22.40.98 (0.77–1.25)2427.40.60 (0.33–1.09)
2004446 22.20.90 (0.70–1.17) 221 8.1 0.63 (0.35–1.15)
200638418.0 0.78 (0.58–1.03) 178 9.60.75 (0.41–1.37)
200845815.30.63 (0.48–0.84)1888.00.63 (0.33–1.18)
Eastern (two sites)1994 24628.91.003027.91.00
199822224.30.91 (0.68–1.24)36031.11.59 (1.00–2.53)
2002271 22.50.85 (0.63–1.13) 3097.80.96 (0.56–1.65)
2004 28719.5 0.70 (0.51–0.94)2818.91.08 (0.63–1.83)
2006268 18.70.68 (0.50–0.93) 295 6.10.72 (0.40–1.28)
2008 24113.30.46 (0.32–0.67) 2246.30.76 (0.40–1.43)
Luapula (three sites) 1994 6023.31.00 73115.71.00
1998 13222.00.99 (0.56–1.72) 78712.20.79 (0.62–1.02)
2002 25424.01.11 (0.67–1.84) 48412.80.86 (0.65–1.14)
2004 23824.41.03 (0.62–1.72) 49010.60.65 (0.48–0.89)
2006 12517.60.78 (0.43–1.41)5579.00.55 (0.40–0.75)
20088113.60.63 (0.31–1.28)56212.10.75 (0.57–0.99)
Lusaka (four sites)b
1994103227.6 1.00
1998118222.60.84 (0.73–0.97)
2002157922.2 0.80 (0.70–0.92)
2004156120.90.74 (0.64–0.85)
2006153420.20.72 (0.63–0.83)
2008 1522 15.70.54 (0.47–0.63)
Northern (two sites) 1994 14229.61.00 38311.51.00
199824314.40.51 (0.34–0.75) 3809.20.77 (0.51–1.17)
2002 35913.60.49 (0.34–0.70) 2343.40.29 (0.14–0.61)
2004 23413.20.48 (0.31–0.72)34112.0 0.98 (0.66–1.46)
200621120.40.69 (0.48–0.99)321 5.60.46 (0.27–0.78)
2008162 9.30.33 (0.19–0.57) 3845.70.48 (0.29–0.78)
North-Western (three sites)199410122.81.00 3836.51.00
199831516.80.75 (0.49–1.14) 3417.31.14 (0.67–1.94)
2002 37811.60.57 (0.36–0.89) 4294.20.66 (0.36–1.18)
2004 39610.40.46 (0.29–0.72) 4587.41.14 (0.69–1.88)
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ANC data vs. other surveys in Zambia
There is a general consensus among the trend studies that HIV
prevalence among young women is decreasing despite the use of
different methods and different populations to estimate the
prevalence. The population-based surveys from Chelstone showed
a 44% relative decrease in HIV prevalence among young
participants aged 15–24 years in the three rounds of the study
(from 16.5% to 8.5%), whereas among young ANC-attendees in
Chelstone the prevalence decreased proportionally by 20%
between 1994 and 2002 (from 25.0% to 19.9%) [3]. In the same
Chelstone study, it was found that HIV prevalence among less
educated young men and women was stable, but among the
educated young people, irrespective of gender, the trends
decreased significantly. During the same period, there was no
clear trend among young educated ANC attendees and a non-
significant decrease for the less educated ones. The HIV
prevalence among women (15–24 years) attending PMTCT-sites
in Lusaka decreased steadily from 24.8% (2002) to 21.6% (2006)
[25]. Within the same study, the umbilical cord blood surveillance
rounds also showed a reduction in HIV prevalence among Lusaka
women under the age of 25 years from 21.7% in 2003 to 17.3% in
2005/06 [25]. Further comparisons revealed that HIV prevalence
in the four Lusaka ANC surveillance sites (Chelstone, Chilenje,
Kalingalinga and Matero) decreased from 22.2% in 2002 to
20.2% in 2006.The ‘‘Microbicide preparedness study’’ found the
rate of new infections among young women (18–24 years) in
Chilenje and Kamwala residential areas in Lusaka to be 4.7 per
100 person-years [28,29].
In the Ndola part of the ‘Four cities’ multicentre study
conducted in 1997/1998, the crude HIV prevalence estimates
for youths aged 15–29 years in the general population was 28.3%,
(15.0% for men and 37.8% for women), whereas the ANC-data in
Ndola during the same year for young females (15–29 years) gave
an estimate of 28.2% (Table 4).
The three population-based surveys in Kapiri Mposhi found a
proportional decline in the HIV prevalence of 58% (from 16.1%
to 6.8%; P,0.001) among young females and a non-significant
decline among young men (from 5.7% to 3.2%; P=0.143). HIV
prevalence declines were more marked among educated men and
women than among less educated ones in Kapiri Mposhi. In
contrast, the ANC data from Kapiri Mposhi generally showed that
within the same period (1994, 1998, 2002) there was an increase in
HIV prevalence, as shown in table 4.
Discussion
The different data sources and methods used to estimate HIV
prevalence trends indicated overall falling HIV prevalence among
young women in Zambia but also highlighted geographical
differences. At provincial level, the ANC data showed substantial
geographical variations in the prevalence trends, with declines
ranging from between 10% and 68% among young urban women,
and from stability in three provinces to 86% decline among young
rural women. Although the point prevalence tended to differ
between ANC data and population-based data sources, the overall
results of the ANC were in agreement with the changes observed
between the two population-based ZDHS. Furthermore, the more
educated young pregnant women had substantial falling preva-
lence trends, whereas the less educated had almost stable HIV
prevalence. Similar results were reported in the population-based
studies in Chelstone and Kapiri Mposhi [15,30].
In our study we used HIV prevalence estimates among young
women aged 15–24 years as an indicator of HIV incidence. The
only study that has attempted to estimate incidence in Zambia
directly was the ‘‘Microbicide clinical preparedness study’’, which
drew its sample population from two communities in Lusaka [28].
The incidence estimates from this study (47 per 1000 person years)
was much higher than national incidence estimates derived from
mathematical modelling of pooled urban and rural ZDHS data
(approximately 17 per 1000 and 12 per 1000 person years among
15–19 and 20–24 year old women, respectively) [31]. This
difference is obviously due in part to the participation and selection
Table 2. Cont.
Year
UrbanRural
n%aRR (95% CI)n%aRR (95% CI)
200632115.30.67 (0.43–1.04)5585.0 0.79 (0.46–1.33)
200830012.0 0.54 (0.34–0.86)4326.3 0.98 (0.58–1.66)
Southern (two sites)1994 32732.11.002908.6 1.00
199841330.00.95 (0.77–1.17)3058.20.96 (0.57–1.63)
200230130.60.94 (0.75–1.18)2776.50.77 (0.43–1.38)
200416128.60.86 (0.65–1.14)2755.80.67 (0.36–1.22)
200627321.60.67 (0.51–0.88)2814.60.55 (0.29–1.06)
200827122.1 0.69 (0.53–0.90)1661.20.14 (0.03–0.59)
Western (two sites)199424929.7 1.0017611.9 1.00
1998 26226.70.90 (0.69–1.18) 24515.11.23 (0.75–2.02)
200241527.20.92 (0.72–1.17) 14212.0 1.01 (0.56–1.84)
200417722.0 0.72 (0.52–1.00)376 17.3 1.39 (0.88–2.19)
200629015.9 0.53 (0.38–0.73)2567.8 0.65 (0.36–1.16)
200825526.7 0.89 (0.68–1.17)32112.11.02 (0.62–1.67)
bA total of 600 women recorded as rural residents attending ANC sites in Lusaka were excluded from the analysis because all the sites in Lusaka were urban and Lusaka
is predominantly an urban district. Age adjustment was done using a continuous age variable. The highlighted p-values are statistically significant at 0.05 level, n is
number, % is percentage, aRR is age-adjusted risk ratio.
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criteria of participants, or to the differences in incidence between
urban and rural women. A comparison with the HIV prevalence
estimates from ANC surveillance and from the other studies
conducted in Lusaka thus indicates that women recruited for the
cohort study may have had a higher incidence than women in the
ANC surveillance or the general population. This was expected
since only sexually active women were included, and probably
women who joined the microbicide study were more likely to
perceive themselves as being at a heightened risk of HIV infection.
Our study also showed that declines in HIV prevalence varied
by urban-rural residence and educational attainment. The ANC
data indicated that HIV prevalence decline among young urban
women in Zambia had started by the mid-1990s, whereas declines
among young rural women became clearly evident only after
2004. The later change among rural residents may reflect
differences in intensity and outreach of prevention campaigns in
rural versus urban areas. For both urban and rural young women
with more than seven years of educational attainment, a sharp
drop in HIV prevalence can be observed from the mid or late
1990s. These results are in line with other studies on the
association between HIV prevalence and educational attainment
in sub-Saharan Africa [32,33], including Zambia [3,15]. In
addition, a similar pattern of marked decrease in syphilis
prevalence among educated women was observed in the same
ANC data [34]. A likely explanation for this change is that
educated people, once equipped with knowledge about HIV from
prevention campaigns, have been quicker in changing their sexual
behaviour [35,36,37,38,39,40,41].
Table 3. HIV prevalence and age-adjusted risk ratios estimates for ANC (2002 and 2008) and ZDHS (2001–2002 and 2007) by age-
group and province.
ANC ZDHS
Women WomenMen
2002 20082001/200220072001/20022007
%%aRR (95% CI)%%aRR (95% CI)%%aRR (95% CI)
Urban21.8 16.8 0.72 (0.66–0.80) 15.2 12.50.79 (0.58–1.08)3.7 5.01.67 (0.75–3.69)
Age-Group
15–19 16.611.70.70 (0.58–0.85) 9.16.50.61 (0.34–1.12)1.9 3.51.78 (0.41–7.65)
20–24 25.319.40.76 (0.69–0.85)22.419.80.82 (0.57–1.19)5.47.21.57 (0.60–4.08)
Province
Central21.922.91.03 (0.78–1.35) 15.9 15.31.09 (0.47–2.51) 4.310.4 2.41 (0.32–18.0)
Copperbelt22.4 15.3 0.64 (0.49–0.84)12.3 8.9 0.76 (0.40–1.44)2.85.2 1.90 (0.42–8.52)
Eastern22.513.3 0.53 (0.36–0.78)0.0 11.1-0.0 2.9-
Luapula24.013.6 0.56 (0.32–1.01)11.1 10.5 0.84 (0.17–4.23)4.2 2.2-
Lusaka22.215.70.68 (0.58–0.78) 20.810.80.63 (0.37–1.07) 0.01.6-
Northern13.69.30.68 (0.40–1.17) 11.88.80.66 (0.16–2.79)6.0 7.3-
N/Western 11.612.00.93 (0.62–1.40) 5.9 9.41.50 (0.20–11.2)0.0 1.0-
Southern 30.622.10.73 (0.55–0.96) 21.719.70.94 (0.40–2.19)0.05.2-
Western27.226.70.97 (0.76–1.25)18.2 21.11.07 (0.34–3.33)16.79.40.25 (0.02–2.68)
Rural 10.08.3 0.80 (0.67–0.96)7.86.40.74 (0.52–1.07)3.12.91.12 (0.57–2.22)
Age-Group
15–197.75.90.76 (0.55–1.06)
4.36.01.26 (0.68–2.33)2.6 3.01.86 (0.75–4.64)
20–24 12.09.90.83 (0.67–1.02)11.6 6.80.55 (0.34–0.87) 3.9 2.80.72 (0.28–1.89)
Province
Central23.812.80.54 (0.29–0.99) 7.515.01.75 (0.72–4.25) 3.43.00.91 (0.19–4.39)
Copperbelt 7.4 8.01.01 (0.53–1.96)11.1 4.10.15 (0.04–0.57) 0.02.0-
Eastern7.86.30.80 (0.42–1.50)10.4 2.90.31 (0.11–0.86) 4.2 2.20.41 (0.09–1.88)
Luapula12.812.10.86 (0.62–1.18)6.58.21.29 (0.43–3.83)0.012.0-
Lusakab
---5.08.31.13 (0.18–7.01)0.0 2.2-
Northern3.45.71.58 (0.72–3.48)5.5 3.90.65 (0.22–1.90)1.1 2.42.10 (0.22–19.9)
N/Western4.26.31.49 (0.83–2.66)6.53.10.48 (0.13–1.73)6.60.0-
Southern 6.5 1.20.18 (0.04–0.77) 6.9 6.50.93 (0.33–2.61) 3.82.60.72 (015–3.50)
Western12.0 12.11.01 (0.60–1.73)12.7 8.40.65 (0.28–1.49) 2.41.10.55 (0.03–11.4)
bA total of 600 women recorded as rural residents attending ANC sites in Lusaka were excluded from the analysis because all the sites in Lusaka were urban and Lusaka
is predominantly an urban district. The highlighted p-values are statistically significant at 0.05 level, n is number and % is percentage, aRR is age- adjusted risk ratio. The
dash (–) represent missing cases.
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Provincial ANC data suggest substantial inter-provincial
differences in magnitude and trends of HIV prevalence. Most
provinces in Zambia had either a stable or a decreasing HIV
prevalence among both urban and rural residents. Lusaka,
Northern and North-Western provinces recorded consistent and
significant declines in HIV prevalence among urban ANC
Figure 1. ANC-based trends in HIV prevalence by educational attainment 1994–2008.
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attendees during the period. Multiple factors are likely to explain
the geographical differentials in magnitude and trends of HIV
infection in Zambia. Variation in coverage and intensity of
preventive efforts is an example. Furthermore, cultural and socio-
economic factors are likely to have contributed substantially
[16,37]. For example, the interplay of structural and individual
factors in Lusaka (i.e. relatively higher intensity of HIV preventive
programmes and educational attainment) may have fostered
sexual behaviour change, resulting in the decline reported here.
Better understanding of the factors underlining these differentials
in magnitude and trends might be critical for proper guidance of
future preventive efforts.
Comparison of ANC and ZDHS data reveals that the provincial
estimates for young urban women were similar in terms of
direction (except in three provinces) but slightly different in terms
of magnitude of change. Among young rural women, the
provincial estimates of change differed both in direction and
magnitude. It is likely that the differences seen were partially
attributable to the small sample sizes of young people at provincial
level in the ZDHS, since the provincial estimates were at least
more in line with the magnitude and direction of change among
urban women aged 15–49 years. HIV prevalence trends for men
and women tend to be parallel [42], as seen in the population-
based trends in selected urban and rural population [30] and other
data sources, indicating that sexual risk taking among men in
Zambia has declined during the same period (paper in progress);
we believe that the apparent increase in HIV prevalence among
young men in the ZDHS is also likely to be an artefact due to small
sample sizes. However, if another round of the DHS shows
differences in trend estimates for men and women, improved HIV
surveillance of men should be considered.
In the Ndola data, the ANC-based estimate was similar to the
crude HIV estimate of the overall general population but it
underestimated the prevalence of females aged 15–29 years by
25%, possibly indicating that HIV infection may affect fertility at a
young age [26]. Another factor influencing fertility is contraceptive
use. Modern contraceptive use among women in Zambia’s general
population has increased gradually from 8.9% in 1992, to 14.4%
in 1996, to 22.6% in 2001/02 and to 32.7% in 2007 [22]. In a
situation where use of contraceptives in the general population is
very high, the use of HIV prevalence of young women as a proxy
of incidence can be less reliable because women may be sexually
active for many years without becoming pregnant, leading to
selection biases in the antenatal surveillance data. Hormonal
contraceptive use has been linked to increased risk of HIV
infection [43]. However, those who take contraceptives to prevent
pregnancy might also take other precautions that put them at
lower risk of HIV (e.g., condom use or having fewer sexual
partners) than those who are sexually active and become pregnant.
In line with this, educated women have been found to be both
Table 4. HIV prevalence and incidence among young women and men aged 15–24 years from surveys in Zambia by residence
between 1994 and 2008.
Residence
Type of Survey and
locationYear
Crude HIV Prevalence (%)
WomenMen
ANC prevalence estimates from
corresponding geographical area
UrbanChelstone PBS 199522.56.9 25.0
1999 18.37.4 22.8
200312.53.219.9
Ndola PBS**
1997/9837.815.028.2
PMTCT Lusaka200224.822.1
200324.0
200423.0 20.7
2005 22.3
200621.619.7
Cord-blood Surveillance Lusaka200321.7
2005/06 17.319.7
Microbicide HIV incidence#
2003/04 47
Rural Kapiri Mposhi PBS 199516.15.7 13.4
199910.37.5 16.5
20036.8 3.2 22.8
HIV prevalence estimated based on assumed stable incidence between age 15 and 24.
**Ndola PBS age range used is 15–29 years.
#HIV Incidence was measured per 1000 person years. Blank spaces - no HIV data
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more likely to use contraceptives [22,35] and to postpone sexual
debut, leading to a strong association between educational
attainment and reduced fertility [35]. Since HIV prevalence
declines were biased towards higher educational attainment in
Chelstone, postponement of first pregnancy among women with
high education seems the most plausible explanation for the
finding that ANC-based HIV prevalence trends substantially
underestimated the actual declines in HIV prevalence in the
general population [3]. The prevalence declines were also clearly
biased towards higher education groups in the national ANC-
based data presented here, so it is likely that the presented
estimates underestimate actual trends in the population. Another
potential bias affecting the trend estimates at ANC site level is
changes in the coverage of rural clinics over time, leading to
fluctuations in the number of urban and rural residents included at
individual sites. Such changes in the outreach to rural residents are
likely to explain the discrepancy observed in Kapiri Mposhi
between the trend estimates obtained from ANC and population-
based data [13].
Furthermore, we could not rule out the possibility that non-
participating respondents or excluded non-household populations
had a different risk of HIV infection from those who participated
in the DHS. The sensitivity analyses showed that point prevalence
estimates only increased by 1–2 percentage points in the most
extreme scenario and that the magnitude of change between the
2001/02 and 2007 ZDHS was similar. We assumed that people
who were absent had a lower risk of HIV than those who refused,
and this is reasonable since data from the population-based
surveys in Kapiri Mposhi and Chelstone indicated that the most
common reason for absence among young people was school
attendance. Being in school tends to be protective against HIV
infection [17,30]. Our sensitivity analysis is admittedly less
sophisticated than that conducted by Ba ¨rnighausen et al using a
Heckman-type selection model on the ZDHS 2007 data [44].
However, the estimates calculated in the latter paper indicate a
prevalence ratio of 3:7 among non-responders to participants, and
this is highly unlikely. Our assumptions and results are likely to be
more plausible.
Using PMTCT data to estimate HIV prevalence has inherent
biases since women agreeing to participate in the PMTCT
programme may be different from those refusing to participate
[10]. The differences may relate to the risk of infection and the
quality of counselling in the programme. However, a study
conducted in Uganda found that this bias was only important in
the initial period of PMTCT; after a couple of months there was
no significant difference between accepting and refusing women
[45]. This is consistent with the findings of the PMTCT study in
Lusaka, since the acceptance rate for women attending ANC in
the PMTCT sites increased (from 71% to 94% between July 2002
to December 2006) [25]. Furthermore, the PMTCT- and ANC-
based estimates from Lusaka were closer in 2006 than 2002. It has
been suggested that in situations where ANC and PMTCT
coverage are very high and routinely collected PMTCT data are
complete and accurate, routine PMTCT may replace the ANC
surveillance system [46]. However, in most of sub-Saharan Africa,
PMTCT data are still of poor quality [46,47].
Bias in HIV surveys may also arise as a result of the
antiretroviral therapy (ART) programme, stigma and migration.
Scaling-up of the ART programme to most parts of Zambia has
resulted in increased survival time among those accessing the
therapy, and this might in the long run have implications for the
reliability of using HIV prevalence among young people to
estimate incidence. This is because HIV-positive children may
survive into their teens [48] and become pregnant [49], thus
distorting the assumption of recent infections among young
people. However, since the ART programme was only imple-
mented nationally in 2003 in Zambia, no such effect could yet be
seen in 2008. HIV-related stigma could increase participation bias
because some people may fear that others will discover their HIV
status [50,51]. Selection bias due to migration is possible in HIV
trend studies since migrants who are at high risk of infection are
less likely to participate, and this warrants further investigation
[52].
Conclusion
In conclusion, the findings suggest that although there are
convincing HIV incidence declines in Zambia, the overall
prevalence trend estimates have masked differential trends by
place and by educational attainment. This might not only suggest
differential and dynamic sub-population epidemics but also the
need for tailored prevention programmes. Focusing on country-
level trends in epidemiological reports therefore seems to have
critical limitations and may even be directly misleading for policy
makers and local programme managers who should base their
efforts on comprehensive knowledge of the different epidemiolog-
ical contexts within Zambia.
Supporting Information
Table S1
(XLS)
Sensitivity Analysis.
Acknowledgments
We are indebted to the Central Statistical Office - Zambia and MEASURE
DHS - USA for allowing us access to the DHS data. We further
acknowledge the ANC team, which comprised staff at the different sentinel
sites and the two reference centre laboratories at the University Teaching
Hospital, Lusaka, and the Immunology Unit at the Tropical Disease
Research Centre, Ndola. We also acknowledge the important work done
by the group that registered and cleaned the data. Overall, we thank the
Ministry of Health Zambia for their leadership and guidance of both
surveys.
Author Contributions
Analyzed the data: NK KF IS. Wrote the paper: NK KF CM MM IS.
Reviewed all available data and literature, analysed and interpreted data,
and wrote the manuscript: NK. Provided guidance on the analysis,
interpretation and editorial comments, and was central in designing and
establishing the national HIV/Syphilis surveillance system in Zambia: KF.
Provided editorial comments: CM MM. Provided editorial comments on
the manuscript as well as technical advice and guidance on the analysis: IS.
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