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Bull World Health Organ. Author manuscript; available in PMC 2007 March 2.
Published in final edited form as:
PMCID: PMC1808347
EMSID: UKMS13544

Assessing adult mortality in HIV-1 afflicted Zimbabwe: Manicaland 1998 – 2003

Abstract

Introduction

The countries most severely affected by the HIV epidemic have incomplete vital registration systems and therefore must use alternative data sources to assess the epidemic's impacts on mortality.

Objectives

To (1) compare alternative methods for estimating adult mortality and (2) describe contemporary patterns of mortality in Manicaland, Zimbabwe.

Methods

We compared estimates of adult mortality calculated from (a) a single question on household mortality (b) repeated household censuses and (c) an adult cohort study with linked HIV testing from Manicaland in Eastern Zimbabwe with (d) a mathematical model fitted to local age-specific HIV prevalence (1998 to 2000).

Findings

The crude death rate was found to be roughly consistent when derived from the single question (29 per 1000 person years (pyrs)) and the mathematical model (22 to 25 per 1000 pyrs) but much lower from the household censuses (12 per 1000 pyrs). Adult mortality (45q15) as measured from the household censuses (M: 0.65; F: 0.51) was lower than in the cohort study (M: 0.77; F: 0.57) while mathematical models gave a substantially higher estimate, especially for females (M: 0.80 to 0.83; F: 0.75 to 0.80). The population attributable fraction of adult deaths due to HIV was 0.61 for men and 0.70 for women, with life expectancy estimated to be 34.3 for males and 38.2 for females.

Conclusions

Each method for estimating adult mortality had limitations in terms of loss to follow-up (cohort study) under-ascertainment (household censuses), transparency of underlying processes (single question) and sensitivity to parameterisation (mathematical model). However, these analyses make clear the advantages of longitudinal cohort data, which (a) provide more complete ascertainment than household censuses, (b) serve to highlight possible inaccuracies in model assumptions, and (c) allow direct quantification of the impact of HIV.

Introduction

Southern Africa is the region of the world most severely affected by the HIV epidemic. In Zimbabwe, prevalence among 15 to 49 year olds is approximately 25%. (1) The epidemic may have stabilised since the late 1990s but this should not obscure the fact that the peak in prevalence will precede the peak in mortality by nearly a decade due to the long incubation of HIV/AIDS.(2) Of course, a stable epidemic is a result of equal numbers of deaths and new infections – both of which were estimated to be approximately 180,000 in Zimbabwe in 2003. (3)

In most Southern African countries, vital registration systems suffer from substantial under-reporting (4) and hospital records may be biased, especially in rural areas, since many individuals die at home and AIDS deaths may be under-reported to protect surviving kin from stigma. Despite these problems, empirical studies have demonstrated the impact that the HIV epidemic is having on mortality in sub-Saharan Africa. Between 1990 and 1995, at a time when seroprevalence of HIV was eight percent in Masaka district, Uganda, 40% of all adults deaths were due to HIV infection, with the percentage as high as 70% in 25 to 44 year olds.(5) In the mid 1990's, half of all adult deaths in Mwanza, Tanzania and two-thirds of adult deaths in Rakai, Uganda could be attributed to HIV.(6, 7) Mortality rates trebled from 1991 to 2003 in Namibia.(8) In South Africa, AIDS related deaths are now the leading cause of mortality. (9)

Various study designs are used to assess the impact of HIV on levels and patterns of mortality. These include demographic and health surveys (DHS), national census, sub-national household census, cohort studies, indirect estimation based on sibling and orphan survival, mathematical projection models and even examination of parish registries and cemetery records. (8-13) The population under observation in the Manicaland HIV/STD Prevention Project provide a unique opportunity to analyse trends and to compare different indicators of mortality. Using these data sources, this paper aims to (1) compare alternative methods for estimating adult mortality and (2) describe contemporary patterns of mortality in Manicaland, Zimbabwe.

Methods

Study population

The Manicaland HIV/STD Prevention Project is an ongoing population-based open cohort study, details of which can be found elsewhere.(14, 15) The study population were resident in 4 subsistence farming areas, 2 roadside trading centres, 4 forestry, tea and coffee estates, and 2 small towns in the rural province of Manicaland in eastern Zimbabwe. All local residents were enumerated in an initial household census (conducted between July 1998 and Febrary 2000) which was repeated 3 yrs later in each site. Males aged 17 to 54 and females aged 15 to 44 were considered eligible for a concurrent cohort study of HIV transmission. The different age ranges were chosen to reflect the different age patterns of HIV infection, in Zimbabwe.

Written informed consent was obtained from all cohort participants, who were offered free HIV counselling and testing in addition to free treatment of other sexually transmitted diseases. A maximum of one member of each marital group was selected. HIV serological testing was performed on dried blood spots using a highly sensitive and specific antibody dipstick assay.(16) Information on demographic, socioeconomic and sexual behaviour data were collected from each individual through an interviewer-led questionnaire. Responses to sensitive questions about sexual behaviour were collected using an informal confidential system of voting.(17)

Here, we use data from the first 2 rounds. HIV prevalence was 15% for males (17 to 44 yrs) and 21% for females (15 to 44 yrs) at baseline.(18)

In this analysis, we use three empirical data sources (a single question on household mortality, repeated household censuses, and an individual-based cohort with linked HIV testing) and a deterministic mathematical model.(19)

1) Single household question

As part of the round 2 household census, respondents to the household questionnaire were asked “how many deaths have there been in the household in the last 12 months”. In other words, respondents were asked to sum all household deaths within a recall period of 12 months. The crude death rate was calculated by dividing the total number of deaths in the last 12 months by the total number resident at the time of the census plus the total number of deaths. Recent in-migrants were included in both the numerator (deaths) and denominator (population).

2) Household censuses

Longitudinal household censuses were performed at three-yearly intervals for each of the 12 study sites. A total of 28986 household members were enumerated at round 1 and 5270 new household members or in-migrants were additionally enumerated at round 2. If a household member died in the course of follow-up, or in-migrated and died before follow-up, the month and year of death were recorded by interview with the household head. The overall household follow-up rate was 82%. Households where all members out-migrated (n = 736, 8.6%) or were lost to follow-up for other reasons (n = 832, 9.7%) were not included in the analysis.

3) Individual cohort study

Males and females local residents who were considered eligible for the cohort study were administered a structured questionnaire. HIV serological testing was performed on dried blood spots using a highly sensitive and specific antibody dipstick assay. (16) Follow-up was contemporaneous with the household censuses. Again, out-migrants and losses to follow-up were not included. Outmigrants had similar HIV prevalence to non-migrants.(20) so their exclusion may not have introduced major bias. Overall, follow-up in the individual closed cohort was 60.8% (n = 5776).

4) Mathematical model

A mathematical model was constructed in order to compare observed patterns of mortality to projected mortality, based on prevalence of HIV. Based on a previously described model,(19) we simulate the heterosexual transmission of HIV in an age and sexual-activity stratified population. Age specific mortality in the absence of HIV was set at level 17 of the West model life table, as this reflected most closely observed mortality in the HIV negative cohort. To give a range of plausible estimates of mortality rates, we created two model scenarios to give an upper and lower bound. The upper bound (higher mortality) assumed a declining epidemic (higher HIV prevalence before baseline) and used estimates for survival with HIV from an observational cohort study in rural Tanzania, as such data do not exist for Zimbabwean populations.(21) The lower bound assumed that the epidemic stabilised before baseline and extracted estimates for survival with HIV from a meta-analysis of cohorts in industrialised countries (22) and applied these estimates to the demographic context of Zimbabwe, as described in the appendix.

In each scenario, the model was fitted to the observed baseline prevalence from the cohort study (year 20 in the model). Mortality estimates were examined for years 23 to 25.

To compare the crude death rate (CDR) from model predictions to the cohort and household censuses, age specific mortality rates (ASMRs) were standardised to the appropriate population and summarised using the life table method described below.

Life table methods for adult mortality indicators

A period life table for all age groups was constructed, first using the household census data. Infant mortality (1q0) estimates were derived from maternal birth history data obtained in the cohort study for the inter-survey period and were decremented based on mother's HIV serostatus at baseline. The same technique could not be applied for child mortality (4q1) because linked data were not collected from mothers on survival of children born before the first survey round. Therefore, 4q1 was estimated indirectly using the Brass method – a procedure for estimating child mortality based on child survivorship as reported by women in age groups 15-19, 20-24, etc.(23) Coale and Demeny's ‘West’ model life table was used since as it is the most widely applied when little is known about the age pattern of mortality.(24-26)

Using the cohort data and mathematical model projection of mortality rates, a period life table decremented by HIV status was constructed. Data from the cohort were only available for ages 17–54 for males and 15-44 years for females, so the rest of the cohort life table was completed with household census data. This, in effect, assumed the same mortality for HIV positive and negative groups in ages not captured by the cohort study.

Life expectancy

Life expectancy was calculated using life table methods.(26) A correction was made to subtract the baseline rate of death (nmx) from the HIV positive groups so that qxHIVn only reflected excess HIV-associated mortality. Life expectancy from birth in the population (e0) and life expectancy without the effect of HIV (e0HIV) were calculated. Inputs for child mortality (0 to 10 yrs) for HIV negative children were based on the West model life table, corresponding to a life expectancy of 50 years. Mortality in HIV positive children was based on models by Marston et al, which estimated survival of children born HIV at 67% at year 1, 39% at year 5 and 13% at year 10.(27) We assumed an HIV prevalence of 30.7% at birth amongst children born to HIV positive women. (28) Furthermore, we assumed that mortality was underestimated in the household censuses in the age groups not measured in the cohort study (10 to 14, 45+ for females; 10 to 16, 55+ for males) at the same level that it was underestimated in the observed age groups (12% for females, 19% for males). The difference between e0HIV and e0 should be taken as a conservative estimate of the mortality impact of HIV, since we assume no difference in HIV positive and negative mortality in the age ranges not captured in the cohort study.

Probability of dying from HIV

The cumulative probability of dying from HIV was calculated by dividing the number of people who died from HIV by the size of the model cohort alive at age 15. (26) Details of the calculation can be found in the appendix.

Results

There were a total of 817 deaths based on responses to the single question “how many deaths have there been in the household in the last 12 months?”. In the individual cohort study, there were 404 (M: 184; F: 220) deaths, with date of death missing in 37 (9%) of cases. One thousand one hundred and seven (M: 592; F: 515) deaths were reported in the 3 yr inter-censal period, with date of death missing in 85 (8%) cases.

Crude mortality

The CDR was substantially lower when measured by the household censuses (12 per 1000 pyrs) as compared to the single question on household mortality (29 per 1000 yrs), which was slightly higher than the range of model estimates (22 to 25 per 1000 pyrs)(Table 1).

Table 1
Summary of mortality indices from single household question, household censuses, individual cohort study and model predictions.

Adult mortality

Adult mortality (45q15) was also substantially lower as measured by the household censuses (M: 0.65; F: 0.51) compared to with the cohort study (M: 0.77; F: 0.57), which was slightly lower than the model estimates (M: 0.80 to 0.83; F: 0.75 to 0.80)(Table 1B). Despite there being a lower mortality rate measured by the household census, its level of statistical precision was higher given the larger size of the population (n = 28986 compared with 5776 in the cohort, note the width of the confidence intervals in Figure 1).

Figure 1
Annual mortality rates - from household censuses and cohort study - and proportion of deaths that were HIV positive in Manicaland, rural Zimbabwe: 1998 to 2003.

Gender

Female mortality was lower than male mortality (20 compared with 26 per 1000 pyrs) contrary to model predictions, when rates were standardised to the cohort age structure (see Table 1).

Over the whole study period, male adult mortality was observed to be higher than female adult mortality (Table 1). The age-adjusted mortality rate ratio (M: F) was 1.25 (95% CI: 1.1 – 1.4; p < 0.001) based on the household census data and 1.4 (95% CI: 1.1 – 1.7; p < 0.001) based on the individual cohort study (Table 1; Figure 1).

Temporal patterns

However, these figures mask different temporal patterns in men and women. No clear pattern was observed in men, but female adult mortality increased over the study period (Figure 1). Female mortality increased at an annual rate ratio of 1.19 (Poisson regression 95% CI: 1.06 to 1.35; p = 0.003; no evidence that a non-linear model provided better fit, likelihood ratio test p = 0.45). Levels of female mortality reached that of males by the last 2 years in the study period. Furthermore, the proportion of female deaths (from the cohort study) who were HIV positive at baseline increased from 70% in the 1st half of the study period (1998 to 2000: 69 HIV positive/98 deaths) to 82% in the 2nd half (2001 to 2003: 100 HIV positive / 122 deaths).

Age specific mortality rates (ASMRs)

Figure 2 shows the ASMRs for all individuals (A&B) and with HIV positive individuals excluded (C&D). The household census data, coupled with the indirect Brass method based on child survivorship, produced lower estimates for child mortality and old age mortality compared to the model. The model reflects the observation of higher female than male mortality from ages 15 to 29, but produces substantially higher overall female mortality than is observed in the data. Figure 2C, which combines the household census-derived estimates for child and old-age mortality with HIV negative male adults from the cohort, fits closely to model predictions for adults. Figure 2D suggests that the level of female mortality due to AIDS predicted by the model may be too high. The higher mortality in males aged 55 to 69 and females aged 45 to 49 suggests that there is considerable AIDS mortality in these groups that is not captured in the cohort study due to the age-inclusion criteria.

Figure 2Figure 2
Age- and sex-specific mortality rates (per 1000 person-years) comparing estimates taken from the household censuses (thin line), the cohort study (bold line) and model predictions (grey shaded area). The shaded area illustrates the range of the model ...

Impact of HIV on adult mortality (based on cohort study)

Overall, HIV positive men and women experienced mortality rates 8.6 and 10.2 times higher, respectively, than HIV negative persons (from cohort study; Table 2). 61% of mortality in males and 70% of mortality in adult females can be attributed to HIV in this population. HIV-associated mortality in males was highest in the 45-54 age group (174 per 1000 person-years) whereas amongst females, the peak was in the 35-44 year age group (70 per 1000 person-years) (Table 2).

Table 2
Age-stratified adult mortality from the individual cohort: Rates, rate ratios and the population attributable fraction of HIV on adult mortality.

In the absence of HIV-associated mortality, 45q15 would be 0.34 for males and 0.27 for males (compared with 0.77 and 0.57, respectively, as observed with HIV-associated mortality).

The probability of a man or woman who survived to age 15 dying of HIV in by age 60 was 0.51 and 0.35, respectively (Figure 3).

Figure 3
Cumulative probability of death from HIV related causes between the ages of 15 and 60 for males and females.

Impact of HIV on life expectancy

The empirical estimate of e0 was calculated to be 34.3 years for males and 38.2 for females. Based on the decrement life table, life expectancy at birth in the absence of HIV (e0HIV) would be 48.8 for males (14.5 yr reduction) and 52.5 (14.3 yr reduction) for females. The model projections of e0 for men ranged from 36.6 to 37.8 and for women from 35.8 to 37.8, with HIV reducing life expectancy by a median of 19 yrs for men and 22 yrs for women (Table 1).

Discussion

By any measure, contemporary patterns of mortality in Manicaland, Zimbabwe are extremely troubling. HIV, the cause of 61% of adult male and 70% of adult female deaths, has reduced life expectancy to 34 yrs and 38 yrs, respectively. Given present HIV prevalence and mortality rates, males have a 51% chance and women have a 35% chance of dying from HIV between 15 and 60. Female mortality increased throughout the survey period and, in 2001, reached the level of male mortality.

Comparing the measures of mortality from different methods, certain consistencies as well as discrepancies emerge. Similar estimates of (i) crude mortality derived from the single question and the models and (ii) adult male mortality as measured by the cohort study and the model, suggested the accuracy of these measures.

First, in terms of inconsistencies, mortality rates derived from the household censuses were substantially lower than the other estimates. Because the respondents in household interviews are required to report on the survivorship of all members of a household, there may be a tendency to under-report, especially considering a long recall period of 3 years. Demographic surveillance systems typically rely on a 1 year recall period. Further exacerbating under-ascertainment, is the tendency of households to dissolve or relocate after a death occurs.(29) These biases in part explain the surprising finding that both the household census and the individual cohort give much lower estimates of crude mortality than the single question. One may expect the two household methods to give similar results, but the finding that the single household question was probably more accurate makes clear the importance of limiting the time frame in order to reduce recall period and for household dissolution to occur.

Second, as a measure of crude mortality, the single question about deaths in the household in the last twelve months gave a fairly accurate CDR as with respect to the range of model projections. This is encouraging, considering the widespread use of this tool in surveys and censuses, although in the Manicaland data, it remains unclear whether there are multiple biases that combine to produce an accurate result. For example, under-reporting may occur for reasons stated above and over-reporting may occur if respondents included deaths that actually occurred more than 1 year ago.(4)

Thirdly, the cohort data produced estimates of adult mortality that were consistent with the model for men, but much lower than predicted for women. It is unlikely that female deaths would be ascertained more poorly than male deaths, which suggests inaccurate parameterisation of the model. Fitting the model to age-specific HIV prevalence at baseline may not have fully captured the extent to which generalisation of the epidemic occurred in males prior to females, or how behaviour or mixing patterns may have changed through time(19), or, perhaps, differential losses to follow-up of deaths.

So - did losses to follow-up contribute to the lower mortality estimates derived from the cohort study compared to the model? Overall, the HIV prevalence at baseline was lower amongst individuals who were not found at follow-up (n = 3646, 19%) than those who were found or were known to have died (n = 6162, 25%). However, this does not necessarily imply that individuals lost to follow-up had lower mortality. Losses to follow-up are a mixed group. Known out-migrants, who left the study sites for employment or other reasons, may be relatively healthy and truly have lower mortality rates but individuals who were not found at all may have migrated home or near a health centre in order to receive care for an eventually fatal illness. So it is possible this sub-group of out-migrants were at an advanced stage in their HIV illness and therefore had higher overall mortality despite their somewhat lower HIV prevalence.

Taking into consideration these limitations, our best estimates of life expectancy (M: 34.3 yrs; F: 38.2 yrs) were similar to model estimates (35.8 to 37.8 yrs) and WHO estimates (M: 37.1 yrs; F: 36.5 in 2001) (24)which were all higher than estimates from the National AIDS Council of Zimbabwe (32 years).(3) Our empirical measure of adult mortality (45q15), based mainly on the individual cohort study, is remarkably consistent with other estimates. Using sibling histories from Demographic and Health Surveys, Jasseh and Timæus calculated 45q15 in Zimbabwe to be 0.57 for women and 0.73 for men, compared with our measures of 0.57 and 0.77, respectively.(30) This represents a substantial rise from the early nineties when AIDS related deaths became prevalent in the Manicaland area (31) and in Zimbabwe as a whole. (4)

Our estimates that 61% of male and 70% of female adult mortality is attributable to HIV in this population (where prevalence is 15% for males and 21% for females) are higher than equivalent figures for Kisesa, Tanzania (45% for men and 54% for women) and the Masaka region, Uganda (40% for both sexes) from the mid 1990s where HIV prevalence was less than 10%. (5, 6) The higher PAF estimates from Rakai, Uganda at 66% for men and 80% for women (when HIV prevalence was 16%) can be attributed to lower mortality levels in the HIV-negative baseline group. (7)

This collection of diverse data has allowed the direct comparison of different estimates of mortality in the same population. Each of these methods has advantages and will be continued to be used in various study designs but these analyses make clear the advantages of longitudinal cohort data. These provided more complete ascertainment than repeated household censuses and served to highlight possible inaccuracies in model predictions.

The startling model based predictions made over 10 years ago, that HIV could be responsible for three-quarters of all deaths, (32) have proven to be true in this rural Zimbabwean population. However, the current results do suggest that reductions in HIV-associated mortality achieved through the use of anti-retroviral therapy, for example, would have a major impact on general mortality patterns.

Supplementary Material

Appendix

Acknowledgements

We thank the people of Manicaland, Zimbabwe for their participation in the study and the Biomedical Research and Training Institute field staff for their efforts in the data collection. Ethical approval for the study was granted by the Research Council of Zimbabwe (Number 02187) and the Applied and Qualitative Research Ethics Committee in Oxford, United Kingdom (N97.039).

Footnotes

We thank the Wellcome Trust, CDC Zimbabwe and UNAIDS for financial support.

Conflicts of interest

GPG has acted as a consultant for and/or received grants from GlaxoSmithKline, Aventis Pasteur, Merck, and Abbott Pharmaceuticals. GPG also chaired a meeting of the World Health Organization in 2003 to develop a consensus on the importance of unsafe injections in HIV epidemiology. SG owns shares in GlaxoSmithKlineBeecham and Astra Zeneca.

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