Click a topic below for an index of articles:

 

New-Material

Home

Alternative-Treatments

Financial or Socio-Economic Issues

Forum

Health Insurance

Hepatitis

HIV/AIDS

Institutional Issues

International Reports

Legal Concerns

Math Models or Methods to Predict Trends

Medical Issues

Our Sponsors

Occupational Concerns

Our Board

Religion and infectious diseases

State Governments

Stigma or Discrimination Issues

 

If you would like to submit an article to this website, email us at info@heart-intl.net for a review of this paper
info@heart-intl.net

any words all words
Results per page:

“The only thing necessary for these diseases to the triumph is for good people and governments to do nothing.”


     

HIV, AIDS, and the Changing Burden of Disease in Southern Africa: A brief note on the evidence and implications.
Amar Hamoudi

June 12, 2000

Discussion around President Mbeki’s recent AIDS panel has focused on his decision to include the views of “HIV dissidents” including Peter Duesberg and others, who maintain that HIV is benign.  “African AIDS,” according to Duesberg, “is caused by malnutrition, parasitic infection and poor sanitation. . . [T]here is no scientific evidence for the correlation between HIV and African AIDS, only guesses.” (“Peter Duesberg on AIDS” website, http://www.duesberg.org).  Implicit in this claim, of course, is the contention that the AIDS epidemic has not had a qualitative impact on the burden of disease in sub-Saharan Africa.

Even in the absence of long-term, comparable data, however, empirical evidence on burden of disease in sub-Saharan Africa directly contradicts this claim.  As the AIDS epidemic has matured, it has clearly had devastating and appreciable demographic and epidemiological effects.  Here, three pieces of evidence are cited which demonstrate that higher levels of HIV transmission result in a qualitative difference in the burden of disease.  First, life expectancies in the countries within Africa which are hardest hit by HIV are significantly lower than life expectancies in the rest of the continent and the rest of the world even after accounting for standard determinants of longevity including sanitation, nutrition, and the risk of infection with parasitic diseases.  Second, these countries over the past half-decade have shown dramatic increases in population-adjusted crude death rates, significantly greater than the rest of the continent.  Finally, local areas within South Africa have seen dramatic increases in morbidity and mortality in just the past few years, which are unlikely to have been accounted for by infrastructural deterioration or increases in the burden of parasitic diseases. 

    

Within Africa, the countries in the southern region are by far the hardest hit by HIV.  Ranking the countries in the world with population of one million or more in order by HIV seroprevalence rates as of 1998, the top seven are in the southern African region— Zimbabwe, Botswana, Namibia, Zambia, Malawi, South Africa, and Mozambique.  Lesotho, the only other country in this region, is 16th (prevalence data are from UNAIDS, Report on the Global HIV/AIDS Epidemic June 1998, http://www.UNAIDS.org, and are based on direct serosurveys).  Table 1 shows the results of regressions of log life expectancy on standard determinants of longevity.  The regression in column 1 includes all countries of population one million or more for which data are available.  Life expectancies in the regions of Africa hardest hit by HIV are significantly shorter than the rest of the world (by about two years), and the rest of Africa (by about one year)[2].  Column 3 includes only countries in Africa south of the Sahara.  Life expectancies in the region hardest hit by HIV remain statistically significantly lower than elsewhere on the continent.  Finally, column 4 examines log life expectancy in 1980— before the HIV/AIDS epidemic had begun to have a generalized effect.  In this regression, the eight countries in the southern African region showed no statistically significant differences in life expectancy from the rest of the continent.[3]  Can factors other than HIV prevalence fully explain this difference?  Incidence of malnutrition and the availability of sanitation are not significantly different between the southern African region and the rest of the continent (t-test results not shown here).  The most devastating parasitic disease in the world is malaria, which causes over a million deaths each year (World Health Organization, World Health Report 1999, http://www.who.int).  However, for well-documented ecological and geographical reasons, malaria is much more widely transmitted in tropical Africa than in much of southern Africa (particularly Botswana and South Africa, where conditions are particularly adverse for the malaria parasite and its vector mosquitoes).[4]  Furthermore, the mortality burden of conditions like malnutrition, diarrheal diseases attendant on poor sanitation, and parasitic diseases— including malaria— is borne primarily among infants and young children (see UNICEF Annual Report on the State of the World’s Children 1998).  However, the observed differences in life expectancy between southern Africa and the rest of the continent remain statistically significant at the 5% level, even after adding under-5 mortality rates or log under-5 mortality rates to the regressions in table 1, column 3 (regression results not shown here).  Factors hypothesized to account for differences in life expectancy, therefore, must explain differences in mortality rates among older children, young adults, and adults. 

This evidence suggests that the burden of disease in southern Africa, where HIV transmission is highest, is qualitatively distinct from the burden in the rest of the continent and the rest of the world.  This difference has emerged within the past two decades, after the onset of a generalized HIV epidemic.  Furthermore, the claim that AIDS is caused by malnutrition and parasitic disease is in contradiction with this evidence.  In fact, the prevalence of malnutrition (measured as the prevalence of severely low weight for height among children under 5 years of age) and availability of sanitation infrastructure are poor predictors of overall life expectancy— although they are vital to infant and child survival.  Furthermore, southern Africa is not significantly different from the rest of the continent in terms of these metrics (t-test results not shown here).  The evidence further indicates that differences in life expectancy cannot be attributed simply to differences in conditions that have measurable effects primarily on infant or child mortality, like malnutrition or malaria.

    

AIDS has had a devastating and appreciable demographic impact throughout the southern African region.  In Botswana, one of the countries hardest hit by HIV, crude death rates nearly doubled over the decade between 1992 and 1997, increasing from eight deaths per 1000 population to over 15.  On average, crude death rates in the region have increased by about 18 percent over this period, compared with a six percent decline in the rest of the continent.  Unfortunately, detailed time-series data on sanitation, nutrition, and the incidence of parasitic disease over this period are sorely lacking, so direct quantitative assessment of the role of sanitation and nutrition is impossible.  However, any hypothesis to explain the rapid jump in the population-adjusted number of deaths in the region must include some factors unique to the region.  Infrastructural or economic collapse, famine, and changing age structure (e.g., aging of the population) are untenable as explanations of this change.  Southern Africa is not distinct from the rest of the continent on any of these metrics (t-test results not shown here).  Rather, this change is most likely accounted for by the fact that the region has seen the longest and widest transmission of HIV in the world. 

Finally, somewhat more specific data are available within South Africa which testify to an increased and changing burden of disease.  For example, figure 1 shows the incidence of workers who retire early due to ill health at one large South African parastatal.[5]  A steady rise is evident, with a dramatic increase between 1997 and 1998.  This burden of absenteeism has been distributed among firm offices across the country—in malarious as well as non-malarious areas.  The data indicate a dramatic rise in morbidity among formal sector wage earners in urban areas of South Africa, for whom well established sanitation and primary health care infrastructure are fully accessible.  Similarly, figure 2 shows the steady and dramatic increase in burials and cremations in the city of Durban over the past several years (data collected by Professor A.N. Smith, Department of Virology, University of Natal).  The observed 247 percent increase in less than half a decade is suggestive of a massive increase in the burden of mortality in metropolitan Durban, in the absence of a massive epidemic of malnutrition or parasitic and diarrheal disease.  In addition, a survey in rural KwaZulu Natal province in 1999 showed an 81% rise in hospital attendance at Hlabisa hospital between 1991 and 1998.  Much of this increase was attributable to increased incidence of tuberculosis; TB admissions were up 400% over the period (appraisal by Sean Drysdale and others of the state of HIV/AIDS in rural KwaZulu Natal).  The positive correlation between tuberculosis disease and HIV infection has been well documented around the world[6].  Furthermore, in a regression of TB incidence against log GDP/capita, urbanization, malnutrition, sanitation, and BCG vaccine coverage, southern African incidence rates are significantly higher than the rest of the world and the rest of the continent.  Adding HIV seroprevalence rates to the regression eliminates this “southern Africa effect” (regression results not shown here).

Therefore, a massive increase in southern Africa’s burden of disease, and changes in the structure of this burden, are readily observable even despite the paucity of detailed and intertemporally comparable data.  Furthermore, southern Africa’s burden of mortality is qualitatively different from the burden on the rest of the continent and the rest of the world, and this difference has only emerged in the past two decades.  The hypothesis that this burden is solely determined by malnutrition, poor sanitation, and parasitic diseases like malaria fails to account for these observations.  These observations are most likely the early manifestations of a generalized, advanced and advancing AIDS epidemic.  More detailed data are urgently needed in order to assess and understand the changing burden of disease in southern Africa in the face of this epidemic.  For example, the evidence cited here indicate that the burden of mortality in the region is disproportionately greater among older children, young adults, and adults compared to the rest of the world.  However, this burden is likely to shift increasingly toward infants as the epidemic progresses.  Furthermore, only the broadest and most catastrophic effects of any epidemic are manifested by changes in crude death rates and life expectancy.  Other demographic effects, including changes in age structure, population distribution, and household dependency ratios have massive social, economic, and welfare repercussions, although they cannot be assessed using crude national-level measures.


Log life expectancy

1

All countries, 1995

2

All countries, 1995

3

Africa only, 1995

4

All countries, 1980

Log, GDP per capita 1995 (1980 for column 4)

0.0288

(1.75)*

0.0371

(4.37)***

0.0238

(0.630)

0.0311

(2.73)***

Log, per capita calorie supply, 1995 (1980 for column 4)

0.128

(2.99)***

0.0279

(0.726)

0.158

(1.27)

0.132

(2.48)**

Literacy rate (%) among adult females, 1995 (latest year available 1977-1985 for column 4)

0.00209

(6.32)***

0.00178

(6.55)***

0.00316

(4.91)***

0.00270

(9.51)***

Prevalence (%) of stunting, children under 5, latest year available 1990-1998

-0.000242

(0.336)

 

0.0000457

(0.025)

 

Proportion (%) of population with access to sanitation infrastructure, latest year available 1987-1995

-0.000247

(0.738)

 

-0.000254

(0.410)

 

Proportion of population living within 100 km of coast or navigable river (%)

.0610

(2.40)**

0.0411

(2.63)***

0.697

(0.70)

0.00468

(0.213)

Index: risk of infection with falciparum malaria (0 no risk, 3 high risk), 1994 (1982 for column 4)

-0.110

(4.80)***

-0.124

(5.98)***

-0.105

(2.01)*

-0.0612

(1.81)*

Dummy: sub-Saharan Africa

-0.0602

(3.03)***

-0.0767

(3.47)***

 

-0.0871

(2.74)***

Dummy: southern Africa

-0.0623

(2.54)**

-0.0632

(2.61)**

-0.0814

(2.63)**

-0.00981

(0.321)

Constant

2.82

(8.72)***

3.55

(13.47)***

2.51

(2.63)**

2.68

(7.23)***

N

81

137

35

92

R-squared

0.90

0.90

0.67

0.92

*- significant at 10% level; **- significant at 5% level; ***- significant at 1% level

Absolute value of robust t-statistics in parentheses



 



[1] Comments may be directed to: hamoudi@ksg.harvard.edu.

[2] For discussion on the functional form and regressors employed, as well as detailed references of data sources, see Hamoudi, Amar and Jeffrey D. Sachs, “Understanding the Economic Consequences of Health Status,” Center for International Development at Harvard University working paper number 30 (an update is available).

[3] Sanitation infrastructure data and malnutrition indicators are not available for 1980, and therefore these variables are omitted from the regression.  However, in recent years there is no statistically significant relationship between the southern Africa dummy and the residual regressions of either of these variables on log national income.  Therefore, it is unlikely that these estimates are biased due to the omission of these variables.

[4] For review of the geographical and epidemiological determinants of fatal malaria, see Hamoudi, Amar and Jeffrey D. Sachs, “The Changing Global Distribution of Malaria,” Center for International Development at Harvard University, working paper number 2; John Luke Gallup and Jeffrey D. Sachs, “Malaria and Economic Development,” CID working paper number 1; and the data and models compiled by the Mapping Malaria Risk in Africa project.

[5] Data collected by A.N. Smith, Department of Virology, University of Natal, South Africa and by the Harvard Institute for International Development (HIID).  For obvious reasons, the parastatal involved provided these and other data only on condition of anonymity; in publishing its analysis of the costs of HIV seroprevalence to the firm, HIID will refer to it as “company A.” 

[6] For reviews of the relationship between HIV infection and tuberculosis incidence, see Perlman, DC, P El-Helou, and N Salomon, “Tuberculosis in Patients with HIV Infection,” Seminars in Respiratory Infections, 14(4):344-352 and Rosen, MJ “Epidemiology and Risk of Pulmonary Disease,” Seminars in Respiratory Infections, 14(4):301-308.