The majority of systems examining patterns and trends of the HIV epidemic within the main African HIV belt are based on sentinel surveillance amongst antenatal clinic attenders (ANC). Pregnant women are a selected group and various types of selection biases might be involved when extrapolating to the general population. The literature presents two publications on the validity of this key sentinel group in terms of HIV prevalence estimation [1,2], and both are based on data collected in 1990/1991 from the Mwanza region, Tanzania. The main conclusion was that ANC (urban) underestimate the overall HIV prevalence rate of women in the general population (rate ratio, 0.75). In an Ugandan population-based cohort, self-reported pregnancy status was used for particular comparisons of trends in HIV prevalence . The serial prevalence measures for 1990–1992 showed a more pronounced decline among pregnant women than in the general population or in all women of reproductive age, a finding that was suggested to partly be explained by the observed negative impact of HIV infection on fertility [3,4].
It is likely that the degree of representativeness of ANC relative to the general population varies during the various stages of a HIV epidemic. In this regard, validation efforts have left much to be desired. The first Zambian population-based HIV survey was conducted in 1995/1996, and one of the major motives was to provide data suitable for assessing the validity of ANC- based data derived from the national HIV surveillance system [5,6]. The catchment areas of two sentinel sites included in the national HIV surveillance system were selected as survey populations. Furthermore, the ANC- based overall rates of these areas were approximately at the national level for urban and rural areas, respectively. We present major findings from the population-based survey, and assess the validity of extrapolations from sentinel data to the general population. The available data on the Zambian HIV epidemic seem to indicate a rapid increase of HIV prevalence quite early in the 1980s [7–10], apparently followed by a stabilization during the first years of 1990s [5,6]. Thus, our observations are assumed to reflect the dynamics typical of a matured epidemic.
Population-based HIV survey
The sample was selected using a stratified random cluster sampling method from Chelston residential area, urban Lusaka, and from Kapiri Mposhi district. Chelston is a suburb of the capital city that has a fairly static core population and relatively high population density (urban I). Kapiri Mposhi district has low population density with a rural population that is village-based and widespread, contrasting with a densely populated small-town population with many transients (urban II). The Zambian census of population mapping system was used to establish the sampling frame consisting of all the 24 standard enumeration areas (SEA) in Chelston (2786 households) and all the 26 SEA in Kapiri Mposhi (5225 households). The sampling design to select SEA was ‘probability proportional to the measure of size’ using the number of households in each area, derived from the 1990 census figures, as the measure of size. In Chelston, the sample size was fixed at 10 SEA (44% of all households) and in Kapiri Mposhi district at six rural SEA (29% of all households) and two urban SEA (14% of all households). In the sampled SEA, all households and household members aged ≥ 15 years were listed and contacted.
The data were collected at the household level employing (i) a structured interview with the head of household for listing of household members aged above 14 years; (ii) a structured personal interview; and (iii) collection of a saliva sample after the interview. Trained enumerators and research assistants operated in five teams (one man and one woman in each), and the data collection tools were tested in a pilot study including 20 households. Apart from sociodemographic background variables, the interview covered questions on health status, health-care use, sexual behaviour/condom protection, and some particular questions to women on pregnancy, births and type of antenatal care services used. The final part of the interview was related to previous HIV testing and the willingness to be tested. Each individual was informed about the general strategy in Zambia related to HIV testing (i.e., that testing is based on blood and linked to counselling services). Furthermore, respondents were informed that the result of the saliva-based HIV test was strictly for research purposes and to be handled anonymously. Those indicating an interest in being counselled/tested were provided with a letter of invitation for contacting a counsellor, and arrangements for a blood sample to be collected.
The Omni-SAL device (Saliva Diagnostic Systems, Inc., Singapore) was used for saliva collection. The specimens were transported to the national reference laboratory (Lusaka University Teaching Hospital) once weekly and screened using Wellcozyme HIV-1&2 GACELISA (Wellcome Diagnostics, Dartford, Kent, UK). A total of 450 samples were randomly selected and retested using the BIONOR HIV-1&2 (BIONOR AS, Skien, Norway) magnetic particle assay following modifications for saliva. Due to the high agreement in test results (99.8%), no further retesting was performed.
HIV sentinel surveillance among ANC
The HIV sentinel surveillance system in Zambia has been gradually developed since 1990, and in 1994 a total of 27 areas were included covering all provinces in which both urban and rural populations were represented. Details on design, methods of data collection and quality assurance of HIV testing employed in this system are given elsewhere [5,6]. All first attenders for antenatal care were enrolled until reaching the required and maximum sample size of 500. Procedures were instituted to fulfil the requirement of unlinked anonymous HIV testing of blood samples collected for syphilis screening as part of the routine standards of care. Information was collected from personal interviews on age (single years), marital status (married, unmarried, divorced/widowed), place of residence (urban versus rural, according to the definition employed in the Zambian census of population mapping system), residence outside the catchment area (yes/no), educational level (number of years in school), and number of children born. Similar information was collected in the population-based HIV survey.
Validation strategy and statistical analyses
The population survey was conducted during the period October–December 1995 in Chelston and during January-March 1996 in Kapiri Mposhi. The comparisons between ANC- and population-based data had to be restricted to Chelston and Kapiri Mposhi rural areas. In both these areas the antenatal clinic serves the same geographic populations that were sampled for the population-based survey. Kapiri Mposhi urban area was excluded from validation due to the small number of urban ANC represented in the surveillance data. Chelston was included in surveillance system in 1992, but only the data collected in 1993, 1994 and 1996 contained information on age. The surveillance data from Kapiri Mposhi were collected in the last quarter of 1994 and were compared with the respective population-based data collected 12–15 months later. The analyses were restricted to persons aged 15–39 years due to small numbers of pregnant women aged ≥ 40 years. The rationale behind the inclusion of births was the assumption that patterns of biases due to lower pregnancy rates in infected than uninfected women can be picked reasonably well in cross-sectional data by examining how the relationship between HIV status and births differs between women in the general population versus ANC. A particular analysis of HIV prevalence by single year of age of teenagers (15–19 years) was performed. Chelston is one of four sentinel sites in Lusaka, and has been revealed as a proxy of the four sites both in terms of HIV prevalence, level of education, and age structure. Accordingly, the most feasible strategy for detailed analyses of teenagers was to compare population-based data from Chelston with ANC-based data from all the four Lusaka sites.
The analyses were performed using EpiInfo (Centers for Disease Control and Prevention, Atlanta, Georgia, USA) and the Statistical Package for Social Sciences (SPSS for Windows; SPSS, Chicago, Illinois, USA). For the population-based data, 95% confidence intervals (CI) of HIV prevalence and ratios were calculated by taking into account the design effect of the cluster sampling effect (CSAMPLE, EpiInfo). Rates were standardized for age using the Zambian census of population as standard population (1990 census projected for 1995), and 95% CI were determined using an approximation of crude intervals. In the analyses of the association between HIV infection and fertility, a linear regression model of number of the children born was used, with the inclusion of age (age-groups included as dummies), marital status, educational attainment (years in school grouped and included as dummies), and HIV status as independent variables.
Participation in the population survey
A total of 5847 adults (46.8% men) were listed as being currently resident household members, and 4920 (84.1%) of these were found at home (de facto eligible population). The number of individuals refusing to be interviewed was 108 (2.2%), and 4812 (40.5% men) were successfully interviewed. Men in all populations were less likely to be found at home (71.3% of listed men interviewed) than the women (92.0%). Amongst the individuals asked to provide a saliva sample, 93.5% consented. Whereas only minor sex differences appeared on the saliva refusal, the main difference in this regard was between Lusaka (8.3% refused) and Kapiri Mposhi district (3.4%). A full dataset linking information from a successful interview with the saliva based test result was obtained from 4195 individuals (40.1% men), representing 85.3% of the de facto eligible population. A total of 262 samples were excluded due to questionable labelling of samples or damaged samples. During the interview, 36.5% indicated an interest in being HIV-tested. A total of 174 (3.6%) actually attended counselling and testing, representing 9.4% of those initially willing. The proportion having been previously HIV-tested was 6.5%.
Population-based HIV prevalence by age, sex and residence
The age- and sex-adjusted overall prevalence rate for adults aged 15–49 years was 16.7% (95% CI, 13.0–20.0) among rural residents compared with 26.5% (95% CI, 24.1–28.8) and 32.9% (95% CI, 24.8–41.1) in the two urban populations (Table 1). Women appeared with higher overall prevalence rates than men when considering age-adjusted rates within the 15–49-year age-group. The difference by sex measured as female : male rate ratio varied between 1.36 (95% CI, 1.10–1.68) and 1.30 (95% CI, 1.11–1.53) in the two urban populations, but less prominent and not statistically significant in the rural population (rate ratio, 1.17; 95% CI, 0.73–1.87). HIV prevalence by age and sex revealed a consistently higher infection rate among young women than among young men, whereas in higher age-groups (≥ 30 years) men were found with the highest levels (urban) or at comparable levels to women (rural). The infection rates among men in the 15–19-year age-group were relatively low in both urban and rural populations, whereas in the highest age-group rates were still high, particularly amongst urban men. Table 1 shows urban residents with clearly higher infection rates than rural residents (district urban : rural rate ratio, 1.99; 95% CI, 1.42–2.82). The proportion of women aged 15–49 years that were reported to be pregnant was 10% among rural residents, 7% in Chelston, and 14% in urban Kapiri Mposhi. The small numbers made detailed comparisons of pregnant women versus the general population of rather limited value.
ANC surveillance compared with the general population
ANC surveillance tended to somewhat underestimate the overall HIV prevalence in the general population of both men and women, but the difference was not statistically significant in either of the areas (Table 2). In the urban area, the adjusted overall HIV prevalence rate of ANC was 24.4% (95% CI, 20.9–28.0) compared with 26.0% (95% CI, 23.4–28.6) in the general population (ANC : general population rate ratio, 0.94). The respective rural estimates were 12.5% (95% CI, 9.3–15.6) versus 16.4% (95% CI, 12.1–20.6) and an estimated rate ratio of 0.76. In both areas, ANC surveillance underestimated the overall prevalence in the general female population (Table 2). HIV prevalence by age amongst ANC differed from the general population: in the 15–19-year age-group, ANC had about twice the prevalence rate of the general population, with a reversed relationship when comparing the 30–39-year age-group, and had comparable levels regarding the 20–29-year age-group.
Fig. 1 illustrates the results of further analysis of HIV prevalence by age and sex of urban teenagers (aged 15–19 years). First, prevalence rates increased steeply by age, at comparable increment levels among women in both samples, whereas men were found to have stable prevalence rates by age. Second, the prevalence rates across ages were consistently higher in ANC than in women in general. A particular feature in this regard was the different age structure of the two samples, which was 66% of ANC aged 18–19 years and 34% in the general population of women (Fig. 1). Thus, the mean HIV prevalence of ANC aged 15–19 years will be skewed towards the higher infection rates of those aged 18–19 years.
Level of education strata (women)
Women with higher level of education were under-represented in the ANC-based sample relative to the general female population. This was regardless of age-group, but more prominent in the urban than the rural setting. For example, amongst urban women in the general population aged 15–19 years, 56% had achieved ≥ 8 years of education compared with 27% amongst urban ANC. The respective rural proportions were 28 and 19%. Among women in the population survey responding to a question on previous use of antenatal care services, more than 90% were reported to have used the public services and less than 2% to have used private or traditional services. The population-based data showed for both areas no variation in HIV prevalence by level of education within the two youngest age-groups (15–24 years). In the 25–39-year age-band, however, prevalence increased markedly by education level [i.e., urban residents with a ratio of approximately 2.0 between low and high (P < 0.001), rural residents by a ratio of about 3.0 (P < 0.005)]. When using the ANC-based data, the same analyses revealed closely similar patterns to the general female population, except that within the 20–24-year age-group HIV prevalence increased with education at a level comparable to that found within the 25–39-year age-group. The combined effect of these two phenomena (i.e., the selection bias linked to education and the association between HIV and education) was examined by adjusting ANC-based rates using the educational structure of women in the general population as standard. Compared with unadjusted overall ANC prevalence rates, the adjusted rates were 14 and 12% higher in urban and rural areas, respectively.
HIV infection and number of births
The way in which the two samples differed with regard to the age-specific relationship between number of births and HIV status is indicated in Fig. 2, showing an association mainly among women aged ≥ 25 years. The association was more prominent among women in the general population than in ANC, and in particular in the highest age-group. The multiple regression analyses of births revealed the impact of infection within the 25–39-year age-group to be 0.78 less children among women in the population sample compared with 0.49 less children in the ANC-based sample (Table 3).
This Zambian population-based HIV survey has revealed patterns of HIV prevalence by age and sex similar to other countries within the main African HIV belt [11–14]. It has confirmed findings from the national surveillance system of extremely high HIV prevalence levels, although the prevalence in pregnant women may somewhat underestimate prevalence in the general female population. Furthermore, when employing the general population of men and women as a comparison, the urban surveillance-based overall estimate matched, although it tended to underestimate prevalence in rural areas. The further validity assessments of extrapolations showed, however, that surveillance might in many ways draw a rather distorted picture of the current dynamics of the HIV epidemic, as seen in the population at large. First was the marked divergence in the pattern of HIV prevalence by age (and sex). Second, women with higher educational achievement are likely to be underrepresented in ANC-based data, mainly because of their lower pregnancy rates. Use of antenatal care services elsewhere, for example within the traditional or private sector, was not found to have contributed in this regard. Finally, an introduced marked selection bias in higher age-groups due to lower pregnancy rates in HIV-positive than in negative women appears to be the most important contributing factor explaining the revealed relatively low infection rates amongst ANC aged ≥ 30 years.
The accuracy of saliva for detection of HIV antibodies has been shown to be comparable to serum-based tests [15–22]. For epidemiological purposes in particular, use of saliva seems to offer several advantages over serum. An important motivation for using saliva in our population-based survey was the assumption that a non-invasive method would contribute significantly in reducing selection bias due to non-consent. The high consent rate to provide a sample of saliva was very promising in this regard, but we lacked data for making a sound evaluation of the ways in which saliva and serum compete with regard to acceptability. The most apparent possible selection bias in the population survey seems to be related to the loss of male participants due to absence (only 71.3% were found at home compared with 92% of women). Reports from the data collection teams indicated that a significant proportion of persons reported to be residing household members, but not found at home, were apparently temporarily staying outside the area. The data on migration status of the participants in the population survey indicated substantial mobility within all the populations: 18% reported less than 12 months and 32% less than 2 years living continuously in the present household. With particular reference to the previously observed higher risk of infection by mobility [23,24], the most likely scenario is that absentees are found with a higher HIV prevalence than survey participants. Accordingly, the survey might somewhat underestimate male HIV prevalence and thus overestimate the male-female differentials. The high mobility was of particular concern with regard to follow-up of the population study.
Zambian data based on the national HIV surveillance system indicate an overall stable prevalence level in both urban and rural areas . However, the trends need to be verified through longer time intervals between surveys , and we lacked information for trend assessment from our rural study populations. Thus, it was rather unfortunate that in the rural areas the population survey was conducted 12–15 months later than the sentinel surveillance. This type of uncertainty did not apply to our urban study populations because surveillance data comprised repeated measurements from 1993 to 1996 showing stable overall prevalence levels. Interestingly, the data suggest a possibility that the revealed trend in overall prevalence mask an ongoing dynamic evolution of the epidemic. Prevalence appeared to decline both in young pregnant women aged 15–19 years (from 28.6 to 17.1%, a decline not statistically significant due to small numbers) and of those aged ≥ 30 years. The striking contrasts in HIV prevalence by age (and sex) comparing ANC with the general population appeared consistent in both urban and rural settings (i.e., ANC surveillance clearly overestimated infection in teenagers aged 15–19 years, and underestimated infection in the ≥ 30-year age-group). These data are consistent with previously documented lower pregnancy and birth rates in HIV-infected individuals than in uninfected individuals found both in a population cohort study and in clinic-based observations [4,25,26]. The negative impact of infection on fertility might have contributed significantly to the observed substantial decline in the total fertility rate in Zambia the last 5 years (from 6.7 to 6.1) . An indication of this impact appeared in our cross-sectional data on the association of lower total births and HIV infection in the ≥ 25-year age-group, but more pronounced in the general female population compared with pregnant women. Thus, lower pregnancy rates due to infection might lead to substantial bias that needs careful consideration when interpreting ANC-based findings.
Population-based cohort data suggest that prevalence trends measured in matured epidemics should be interpreted with great caution . Sentinel surveillance amongst ANC remains the key instrument for assessing the continued evolution of the HIV epidemic, and our results suggest that several inherited biases are likely to be introduced when extrapolating any finding to the population at large. However, the accuracy of extrapolations may be considerably improved by the type of ‘calibration’ efforts presented, and the demonstrated high acceptability and simple application of the saliva-based technology represent a facilitating factor in conducting population surveys . Given that HIV dynamics may change rapidly, calibrations need to be conducted on a regular basis, particularly in new or maturing epidemics. Our results suggest that some particular changes in the design of sentinel surveillance systems might improve the usefulness of the data. First, information on single year of age, level of education, and residence should be collected in order to allow appropriate stratification. Second, the sample size should be increased when this is feasible. Since the prevalence in young people may be the most reliable marker of changes in incidence, the best strategy would be to increase the sample size in the 15–19-year age-group (and possibly the 20–24-year age-group) in order to allow a more detailed age-stratification.
The authors appreciated the assistance of the staff of Central Statistical Office, Lusaka, Zambia regarding sampling/listing procedures, the research assistants, counsellors and drivers involved in the data collection, the Zambia National AIDS/STD/TB and Leprosy Programme staff involved in various tasks, and the staff of the two national reference laboratories at University Teaching Hospital, Lusaka and at the Tropical Disease Research Centre, Ndola, for handling the laboratory tasks.
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