Spread of HIV infection in a rural area of Tanzania

Jt, Boerma; Urassa, Mark; Senkoro, K.P.; Klokke, A. H.; Jz, Ng'weshemi

doi:10.1097/00002030-199907090-00013

Cited by 91 publications

(57 citation statements)

References 17 publications

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“…This is echoed in research from elsewhere in sub-Saharan Africa examining geographic differences in HIV infection rates. For example, numerous surveillance sites have reported four to six fold variations in HIV prevalence within the same local area (Barongo et al, 1992;Bloom et al, 2002;Boerma et al, 1999;Killewo et al, 1994;Kipp et al, 1995;Soderberg et al, 1994). This pattern captures disparities between low prevalence isolated rural areas and higher prevalence more accessible trading centres.…”

Section: Insert Table 4 Here Discussionmentioning

confidence: 99%

Is social capital associated with HIV risk in rural South Africa?

Pronyk

Harpham

Morison

et al. 2008

Social Science & Medicine

118

113

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Section: Insert Table 4 Here Discussionmentioning

confidence: 99%

Is social capital associated with HIV risk in rural South Africa?

Pronyk

Harpham

Morison

et al. 2008

Social Science & Medicine

118

113

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“…The idea that the projections overestimate AIDS mortality in Zimbabwe is confirmed by the analysis of cause-specific mortality in 1995 (Figure 3). In the case of Zimbabwe, the most recent surveillance data suggest However, in most SSA countries median age at infection among women is significantly younger than 25 years (Boerma et al 1999;Gouws et al 2002;Mbulaiteye et al 2002). Since age at infection and survival are correlated, with infection one year earlier being associated with an increase in median survival of just under two months, most women may survive longer than 9.4 years (Collaborative Finally, if the assumed ratio of female-to-male HIV prevalence of 1.2 in a mature epidemic is too high, then levels of AIDS mortality among women would be overestimated.…”

Section: Discussionmentioning

confidence: 99%

Comparison of household-survey estimates with projections of mortality and orphan numbers in sub-Saharan Africa in the era of HIV/AIDS

et al. 2004

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The United Nations publish estimates of HIV prevalence, AIDS mortality and orphan numbers for all countries of the world. It is important to assess the validity of these model-based estimates since they underpin much care and prevention policy.Household surveys that ask questions about the survival of children"s parents (orphanhood) offer an independent source of data with which these estimates can be compared. Survey estimates of maternal and paternal orphans are significantly lower than model estimates for 40 surveys in 36 countries of sub-Saharan Africa (p<0.001, p=0.002). This is probably because adult mortality due to causes other than AIDS is lower than assumed in the models, although under-reporting of orphanhood in surveys may also play a role. Reducing adult mortality due to causes other than AIDS brings the model estimates into close agreement with the surveys. This suggests that the fraction of orphans due to AIDS is greater than estimated previously.

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“…Second, no such default values are available from the literature for the sex and age patterns of incidence. A few studies have produced estimates of incidence by age (e.g., Boerma et al 1999;Kamali et al 2000;Kengeya-Kayondo et al 1996;Wawer et al 1994), but these estimates are derived from a small number of seroconversions. A more robust pattern is estimated next by pooling a large number of observations from different East African populations that are related to age-specific incidence.…”

Section: Empirical Regularities and Model Reductionmentioning

confidence: 99%

HIV and Population Dynamics: A General Model and Maximum-Likelihood Standards for East Africa

Heuveline¹

2003

Demography

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In high-prevalence populations, the HIV epidemic undermines the validity of past empirical models and related demographic techniques. A parsimonious model of HIV and population dynamics is presented here and fit to 46,000 observations, gathered from 11 East African populations. The fitted model simulates HIV and population dynamics with standard demographic inputs and only two additional parameters for the onset and scale of the epidemic. The underestimation of the general prevalence of HIV in samples of pregnant women and the fertility impact of HIV are examples of the dynamic interactions that demographic models must reproduce and are shown here to increase over time even with constant prevalence levels. As a result, the impact of HIV on population growth appears to have been underestimated by current population projections that ignore this dynamic.National population forecasts are essential instruments of development planning. Since the mid-1950s, nearly all national and international agencies producing such forecasts have relied on a population-dynamics model known to demographers as the cohort-component model of population projection (hereafter, CCMPP). For a number of African countries, the main challenge to demographic forecasting has been to meet even the relatively modest data requirements of this model. Toward this end, international donors sponsored the first censuses and fertility surveys in a number of countries. Concurrently, demographers developed a series of ingenious "indirect" techniques (e.g., Brass 1975; United Nations (UN) 1967) and empirical models (e.g., model life tables; Coale and Demeny 1983; UN 1982) which allowed them to extract, correct, and supplement the required information from incomplete or inaccurate data.HIV has quickly rendered obsolete these contributions to demographic estimation and forecasting in the populations that have been the hardest hit by the epidemic. The new challenge to population forecasting for these countries is not merely the uncertainty about the future level of mortality. Because the age pattern of AIDS-related mortality is so different from the existing J-shaped model age patterns of mortality, no existing model accurately represents the age structure of mortality when AIDS accounts for a significant proportion of all deaths. The epidemic also invalidates many indirect techniques that rely on p-heuveline@uchicago.edu.

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Spread of HIV infection in a rural area of Tanzania

Cited by 91 publications

References 17 publications

Is social capital associated with HIV risk in rural South Africa?

Is social capital associated with HIV risk in rural South Africa?

Comparison of household-survey estimates with projections of mortality and orphan numbers in sub-Saharan Africa in the era of HIV/AIDS

HIV and Population Dynamics: A General Model and Maximum-Likelihood Standards for East Africa

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