Poverty, endemic Disease, and HIV

Transkrypt

Department of Economics
Working Paper Series
Understanding the Southern African
‘Anomaly’: Poverty, endemic Disease, and
HIV
by
Larry Sawers and Eileen Stillwaggon
No. 2009-09
July 2009
http://www.american.edu/cas/economics/research/papers.cfm
Copyright © 2009 by Larry Sawers and Eileen Stillwaggon. All rights reserved. Readers may make
verbatim copies of this document for non-commercial purposes by any means, provided that this
copyright notice appears on all such copies.
Development and Change
UNDERSTANDING THE SOUTHERN AFRICAN ‘ANOMALY’:
POVERTY, ENDEMIC DISEASE, AND HIV
Journal:
Manuscript ID:
Manuscript Type:
DECH-08-327.R1
Original Article
Date Submitted by the
Author:
Complete List of Authors:
Keywords:
Sawers, Larry; American University, Economics
Stillwaggon, Eileen; Gettysburg College, Economics
Acquired Immune Deficiency Syndrome (AIDS) < General,
Migration < General, Southern Africa < Regions, Poverty < General,
Health < General, HIV < General, Developing countries < General,
Tropical Disease
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1
UNDERSTANDING THE SOUTHERN AFRICAN ‘ANOMALY’
POVERTY, ENDEMIC DISEASE, AND HIV
Larry Sawers and Eileen Stillwaggon
Key Words: HIV/AIDS, migration, southern Africa, tropical disease, poverty
Word count: ABSTRACT
Background: Adult HIV prevalence in southern Africa is many times greater than
prevalence in other low- and middle-income countries. Previous studies argue that the
intensity of the HIV epidemic in southern Africa results from regional characteristics,
such as apartheid labour regulations and mineral wealth, which contributed to circular
migration patterns and highly skewed income distribution, both thought to promote
risky sexual behaviour. This study emphasizes the importance of common infectious
and parasitic diseases that increase the likelihood of HIV transmission by making HIVinfected persons more contagious and by making uninfected persons more vulnerable.
Method: The study uses multiple regression analysis on country-level data with HIV
prevalence as the dependent variable. Regressors include socio-economic variables used
in previous cross-national analyses of HIV to which we add a measure of cross-border
migration and five cofactor infections. Results: The socio-economic variables explain
statistically only one-fourth of the difference in HIV prevalence between southern
Africa and other low- and middle-income countries. Adding the cofactor infection
variables to the model allows us to explain statistically about two-thirds of the southern
Africa difference in HIV prevalence. Conclusion: The relative affluence of southern
Africa and historical migration patterns have tended to mask the vulnerability of the
majority of the population who are poor and who have very high prevalence of
infectious and parasitic diseases. Those diseases replicate a cycle of poverty that
produces not just social vulnerability to HIV through risky behaviours but also
biological vulnerability through coinfections. An important implication of this research
is that integrating treatment of endemic diseases with other HIV-prevention policies
2
may be necessary to slow the spread of HIV. Treatment of cofactor infections is a lowcost, policy-sensitive, and potentially high-impact variable.
INTRODUCTION
Average adult HIV prevalence in 2007 was 18 percent in the nine countries of southern
Africa (South Africa, Swaziland, Lesotho, Zimbabwe, Botswana, Mozambique, Namibia,
Zambia, and Malawi). Elsewhere in sub-Saharan Africa, HIV prevalence averaged 2.7 percent in
2007; in other low- and middle-income countries, HIV prevalence averaged about 0.5 percent.
The epicentre of the global HIV epidemic is southern Africa. Understanding the nature of HIV
requires that we understand the region where the epidemic is most intense.
HIV is an opportunist virus that may have found in southern Africa a laboratory with the
most propitious combination of factors promoting transmission. Various explanations have been
proposed for the region’s vulnerability to HIV, but the present study is the first to attempt a
comprehensive account of the southern Africa anomaly and to test it statistically. Explaining
HIV in southern Africa may give us a key to understanding how social, biological, and
ecological factors interact to produce catastrophic results. That is particularly instructive for
understanding a pathogen that has had relatively little impact in most other parts of the world.
We examine the relevance of numerous factors considered in the literature and then construct
statistical models of HIV prevalence. We find that, controlling for a combination of socioeconomic and biological factors, the levels of HIV infection in southern Africa are not
anomalous. They conform to what is known about HIV and reveal important vulnerabilities
generally overlooked in discussions of southern Africa.
BACKGROUND: HISTORY, ECONOMICS, AND HEALTH IN SOUTHERN AFRICA
Some of southern Africa’s historical features are unique, but many of its socio-economic,
ecological, and health characteristics are similar to those in the rest of Africa or in other low- and
middle-income countries. The severity of the HIV epidemic in southern Africa is most probably
the result of a combination of attributes it shares with other poor countries, especially in Africa,
as well as features unique to southern Africa. The literature on HIV and southern Africa suggests
a number of factors that might have aggravated the spread of HIV in the region.
Labour migration and apartheid: Perhaps the most obvious characteristic of southern
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Africa that sets it apart from the rest of the world is its history of apartheid and the earlier system
of internal and international labour migration that it reinforced. Even before the National Party
decreed strict residential segregation in South Africa in 1948, cross-border and internal labour
migration supplied the labour for the country’s mines and factories. An extensive literature
dating to the 1940s addressed the health effects of migration in southern Africa, including the
spread of tuberculosis and sexually transmitted infections (STIs) from migrants to their home
communities (see Horwitz, 2001 for a summary of the early literature).
There is also a substantial literature that points to the role of migration in the spread of HIV,
and many of those works focus on southern Africa in particular (Brummer, 2002; Clark et al.,
2007; Crush et al., 2005; Gillespie et al., 2007b; Hargreaves et al., 2007; Horwitz, 2001; Hunter,
2002; Hunter, 2007; International Labour Office, 1998; Kanyenze, 2004; Lurie, 2004; Lurie et
al., 2002; Marks, 2002; Thahane, 1991; Zuma et al., 2003). High prevalence among migrants
seems to support the assumption that long separation of spouses in circular migrant streams,
single-sex hostels, and higher levels of commercial sex work around mines and factories promote
the spread of HIV (Lurie, 2004; see also Brummer, 2002; Clark et al., 2007; and Kanyenze,
2004, although without data).
Migration has also been associated with HIV transmission in other parts of the world. In
Ecuador, for example, over 80 percent of the people infected with HIV in the first decade of the
epidemic had worked in the United States or were partners of migrants to the United States.
Circular migration between the United States and Dominican Republic has also been associated
with the spread of HIV in the Dominican Republic (Stillwaggon, 2006). Not all studies, however,
have found that labour migrants are more likely to be HIV-infected. In Southeast Asia,
Oppenheimer and colleagues found that labour migrants were more risk-averse and less likely to
be infected (Oppenheimer et al., 1998, cited in Skeldon, 2002, 3).
Labour migration has played a very important role in the economies of southern Africa
and very likely has aggravated the HIV epidemic. But migration is not unique to the region and
may be insufficient to explain the difference in prevalence between southern Africa and West
Africa, the Caribbean, the Middle East, or Southeast Asia. In combination with other
characteristics of southern Africa, however, migration may have been an important factor in the
complex aetiology of the epidemic and should be included in a model of HIV transmission.
4
Post-apartheid migration and economic reform: In the post-apartheid years, there have
been significant changes in the southern African economies. Migration within South Africa and
to South Africa from most neighbouring countries has increased, but the composition of the
migrant labour force has changed significantly. Women are migrating in increasing numbers,
both with and without male partners. Rising levels of male unemployment have changed the
support relationships in the region (Posel and Casale, 2003). Mark Hunter argues that the rapid
increase and enormous scale of the HIV epidemic in South Africa (and one might extend that
analysis to other countries in the region) was not an inevitable result of a history of apartheid,
although he acknowledges that labour migration and the enforcement of family separation in
apartheid did set the stage. Rather, the decision to institute market-oriented reforms and austerity
measures in the 1990s was disastrous for a large proportion of the population. The
unemployment rate has risen to over 40 percent, marital rates have fallen sharply, and inequality
is substantially higher in the last two decades (Hunter, 2007). Women’s migration has increased
and informal settlements have mushroomed around both large and small cities (Crush et al.,
2005; Posel, 2003; Posel and Casale, 2003). Hunter draws much-needed attention to the gap in
health care provision that mirrors the stark inequality in income and wealth in South Africa
(Hunter, 2007). (For more on inequality in health care, see Booysen, 2002; Booysen, 2003).
Cross-border and internal migration is socially disruptive and affects health in numerous
ways. The literature on HIV and migration in southern Africa, however, focuses exclusively on
the effect of migration on risky sexual behaviour, often without data, to the exclusion of other
health-related factors (Brummer, 2002; Kanyenze, 2004). While the literature of social medicine
in South Africa of the last half century has always included some mention of the miserable
working and living conditions and inadequate nutrition of migrants (Horwitz, 2001; Marks,
2002), the sound bite that has emerged in AIDS policy discourse emphasizes “the constant
movement of large numbers of sexually active men from town to countryside and back” (Marks,
2002, 18). Certainly the behavioural implications of separating men from their families and
communities were very important in the spread of STIs, and later HIV, in southern Africa.
Nevertheless, that focus on sexual behaviour reinforces the prevailing explanation for high HIV
prevalence in sub-Saharan Africa that focuses almost exclusively on risky sexual behaviour.
The literature on HIV and migration in southern Africa overlooks the direct biological
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impact of deprivation on migrant health and its role in accelerating the HIV epidemics in the
region. There is a substantial body of evidence that poor sanitation,1 nutrition,2 and inadequate
health care for people living in single-sex barracks and squatter settlements3 can increase the
1
Poor sanitation spreads numerous bacterial, viral, and parasitic infections. Borkow and Bentwich (Borkow
and Bentwich, 2006) survey a large number of studies and trials investigating the connection between helminth
infections and cell-mediated immunity. They argue that the majority of observational studies provide evidence for
interaction between HIV and helminth infection, but there are many studies that do not show a connection and a few
that suggest a protective effect of certain helminth infections. Almost all of those studies, however, are observational
studies. Walson et al., 2009 in an unpublished survey of over 7000 studies of HIV patients treated with antihelminthics find only three randomised controlled trials, all three of which confirm the HIV-helminth connection.
(See also Bentwich et al., 2008; Fawzi and Hunter, 1998; Friis and Michaelsen, 1998; John et al., 1997; Landers,
1996; Montresor et al., 2001; Nacher, 2002; PPC (Partnership for Parasite Control), 2002; Semba et al., 1994;
Walson et al., 2008; Wolday et al., 2002; Woodward, 1998. Additional studies are cited in Stillwaggon, 2006.) In
contrast to other helminths, the hypothesized link between HIV and S. hematobium has to do with its effect on
epithelial integrity and not only on its effect on cell-mediated immunity. See the in-text discussion of S. hematobium
below. In our statistical analysis presented below, we do include any measure of helminth infection other than
schistosomiasis.
2
Protein, fats, calories, and most of the 40-plus micronutrients necessary for human health affect or
modulate the immune system in some way and thus affect a person’s vulnerability to infectious disease. There is a
“vast” scientific literature showing that malnutrition undermines the immune system and that vitamin
supplementation improves immune response (Mehta and Fawzi, 2007, page 358). HIV is an infection and thus poor
nutrition is likely to make people more susceptible to HIV infection. Malnutrition may also shorten the lives of those
already infected with HIV and make them more contagious (Gillespie and Suneetha, 2005; Stillwaggon, 2006). The
most comprehensive and up-to-date survey of studies and trials that examine the nutrition-HIV interaction (Mehta
and Fawzi, 2007) sums up the evidence as follows: Vitamin A supplementation reduces morbidity and delays
mortality for HIV-infected children. Multivitamin supplementation reduces adverse pregnancy outcomes in HIVinfected women, reduces mother-to-child transmission for poorly nourished mothers, and slows the progression to
AIDS for HIV patients. On the other hand, the evidence from trials is that vitamin A supplementation alone does not
protect against mother-to-child transmission of HIV except for preterm babies (see Coutsoudis et al., 1999 page
1522; Fawzi et al., 2001 provides corroborating evidence) and may even increase transmission (possibly due to
dosing with massive amounts of the wrong form of the vitamin). The evidence about whether vitamin A protects
adults from contracting HIV is mixed. In the statistical analysis below, we do not include any measure of nutrition
because of the lack of suitable measures at the national level.
3
Poor housing cannot provide adequate protection from disease vectors such as mosquitoes that spread
malaria and flies that spread trachoma. The connections between HIV and malaria, and HIV and trachoma are
6
transmission of HIV. A fully specified model of the HIV epidemics in southern Africa must
include variables that reflect what is known about the interactions between HIV and other
infectious and parasitic diseases.
Poverty in poor countries: From the beginning of the HIV epidemics in sub-Saharan
Africa, researchers noted that in many countries, HIV prevalence was higher in upper income
and wealth brackets. (Gillespie et al., 2007b is the most comprehensive survey of that literature.
Other reviews of the literature include Gillespie and Suneetha, 2005; Greener, 2004.) Most
research on the connection between income/wealth and HIV looks at a single country or district
within a country, but Mishra et al., 2007 use nationally representative surveys from eight subSaharan African countries to examine the issue. They find that HIV prevalence is higher in the
wealthiest quintile than in the poorest quintile in all eight countries, usually by a substantial
margin. Since antiretroviral treatment in poor countries is more readily available for the affluent
than for poor people, wealthy people infected with HIV live longer on average and that biases
comparisons of prevalence across wealth levels. Mishra et al. 2007 were not able to control for
this effect, so the higher HIV prevalence of the wealthy is at least in part a result of the greater
longevity of the wealthy rather than a reliable indicator of cumulative incidence. Moreover,
wealthier people are more likely to live in cities and be better educated, both of which are
associated with higher HIV prevalence. Wealthier men in the eight countries were also more
likely to report two or more sexual partners in the previous year (except in Tanzania), more
lifetime sexual partners, and sex with nonregular partners. On the other hand, the wealthy in the
surveys are more likely to report having information about HIV and using condoms. Using a
multiple regression analysis and controlling for these and other factors, Mishra et al. 2007 find
that the wealth difference in HIV prevalence becomes statistically insignificant for both men and
women in all but one country. Furthermore, there is some evidence that HIV incidence in the
region is falling among the wealthy and better educated (Gillespie et al., 2007b, page S12;
Lopman et al., 2007). Nevertheless, we do not make a case that HIV is primarily a disease of
poverty since, even with the above adjustments to the data, it is clear that it is not mostly the
poor who contract HIV in poor countries.
The argument that this paper makes, however, is that HIV is a disease of poor countries
discussed below.
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because widespread poverty increases prevalence for both poor and wealthy people. It is widely
believed—though there is little direct evidence—that poverty can force women into transactional
sex and into sexual relations where they do not have the power to negotiate safe sexual practices
(Gillespie et al., 2007b page S6). Moreover, affluent men can afford to pay for the sexual
services of poor and desperate women. The evidence from Mishra et al. 2007 is that the wealthy
are more likely to engage in sexual relations outside of monogamous unions. In countries with
more desperately poor women, both well-to-do men and poor women may be more likely to
become infected with HIV. Members of both groups can then pass HIV on to their regular sex
partners. In that case, we would expect poor countries to have higher HIV prevalence than
wealthier countries.
Poor countries are also less likely to provide safe and effective medical care than higher
income countries. It is higher income people (especially women), better-educated people, and
people living in cities who have greater access to physicians, clinics, and hospitals, possibly
explaining the higher HIV prevalence among those groups. There is substantial evidence that
effective infection-control procedures (such as sterilizing syringes or specula) are not followed as
consistently in poor countries as in higher income countries.4 In the countries that Mishra et al.
2007 studied other than Lesotho, wealthy men were more likely to be circumcised that poor men.
Circumcision is thought to reduce HIV transmission, but in a poor country where unsafe medical
practices are widespread, circumcision can spread HIV rather than protect against it. In sum,
iatrogenic/nosocomial transmission of HIV will be high among the poor and perhaps even higher
among the affluent within poor countries when compared with wealthy countries.
Poverty may push some people into risky sexual behaviour or into contact with unsafe
and inadequate medical practices, but it also forces people into other behaviours that pose risks
4
There is substantial evidence that an important proportion of the HIV infections in sub-Saharan Africa and
elsewhere result from iatrogenic/nosocomial transmission. (For example, see Deuchert and Brody, 2006; Deuchert
and Brody, 2007a; Gisselquist, 2008.) Southern Africa seems to be unusually derelict in providing safe medical care,
even when compared with other sub-Saharan African countries. Five of the nine countries in southern Africa use no
autodisable syringes in child vaccination programs. Elsewhere in sub-Saharan Africa, only 15 percent of countries
fail to do so. See Table 5. Another important reason for an association between poor health care and HIV is that
diseases that may promote HIV transmission, such as schistosomiasis, malaria, and STIs, are less likely to be treated
or treated effectively and safely in countries without good health care.
8
for their health. Poor countries on average have a share of the population that is poorly
nourished, lives in dwellings that do not protect against disease vectors, and resides in
communities with inadequate sanitation. Poor people thus are burdened with high rates of
infectious and parasitic diseases. The health of the affluent in poor countries is also affected
since they are surrounded by sick people and thus more likely to become ill than in countries
where disease is less prevalent. For example, malaria cases exceeded 25 percent of the
population in 27 African countries in 2006; schistosomiasis prevalence was 25 percent or more
in 22 African countries (WHO (World Health Organization), 2008b; WHO (World Health
Organization), 2009). Living in a country where one is surrounded with disease raises the
likelihood that any mosquito bite or contact with ground water will bring an infection. To the
extent that some of those diseases promote the transmission of HIV, HIV is a disease of poor
countries.
Inequality: All southern African countries except Malawi have above average income
inequality. Namibia, Lesotho, and Botswana have the highest income inequality in the world
(measured by Gini coefficient), and South Africa, Swaziland, Zambia, and Zimbabwe are well
above the world average. There is a widely noted correlation across countries between income
inequality and HIV prevalence (Gillespie et al., 2007a; Mahal, 2002; Over, 1998; Stillwaggon,
2000; Stillwaggon, 2002; Tsafack and Bassolé, 2006), which appears to be related to the
foregoing arguments about the connection between poverty and HIV. For a country at any given
level of income, a higher level of income inequality means that the poor are a greater share of the
population. If a large proportion of poor people in a country is an important driver of HIV
epidemics, then a country with high income inequality should have a higher HIV prevalence.
South Africa, Botswana, Namibia, and Swaziland are middle-income countries (and Lesotho is
an enclave surrounded by South Africa) and those countries have the highest HIV prevalence in
the world. At the most fundamental level, in the four middle-income countries of southern
Africa, stark inequality means that the relatively high per capita GDP masks the profound
poverty of the majority of the population and the direct impact of that poverty on health.
Gender inequality: Another dimension of inequality is gender inequality. Many
researchers argue—though with little direct evidence—that the oppression of women, by
reducing their incomes, assets, and power, makes it difficult for them to negotiate safe sexual
practices and encourages them to engage in transactional sex (Brummer, 2002; Gillespie et al.,
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2007b; Hunter, 2007; Over, 1998; Tsafack, 2006). In that case, a country with greater gender
inequality would have higher rates of HIV. On the other hand, greater gender equality could
mean fewer traditional restrictions for women on sexual behaviours, some of which might be
risky. In a statistical analysis using cross-national data, therefore, it is difficult to predict whether
measures of gender inequality would be directly or inversely correlated with HIV prevalence.
Risky sexual behaviours: The previous sections of the paper argue that long-distance
labour migration, poverty, income inequality, and gender inequality could all lead to higher
prevalence of risky sexual behaviours. HIV prevalence is highest in southern Africa and in
neighbouring countries. It is widely assumed in scholarly and popular discussions that the HIV
epidemic in sub-Saharan Africa is transmitted largely through heterosexual activity and therefore
people in the region must have high rates of risky sexual behaviour. Much of the early research
on the causes of the epidemic asserted that conjecture as if it were fact, though providing only
anecdotal evidence.5 Survey data, however, show that by almost all measures of risky sexual
behaviour, people in the United States, Canada, Britain, France, and other affluent countries far
outdo the average African, including in southern Africa. (See Stillwaggon, 2006 and Wellings et
al., 2006 for extensive data on sexual behaviour worldwide.)
In the last few years, some have tried to explain the high rates of HIV in sub-Saharan
Africa by emphasizing the importance of concurrent sexual relationships, though again without
much empirical evidence. A recent report based on nationally representative surveys in 22
developing countries shows that few men and even fewer women have concurrent sexual
relationships (defined as having multiple overlapping partners in the previous year) (Mishra and
Bignami-Van Assche, 2009). In the 18 sub-Saharan African countries surveyed, 1.9 percent of
women reported having more than one sexual partner in the previous year, only some of whom
overlapped (Tables 5 and 6). Only 13.4 percent of men had more than one sexual partner—fewer
than two-thirds of whom were concurrent. A substantial fraction of those concurrent
relationships were polygamous marriages. Respondents with more than one partner, especially if
their sexual relationships overlapped, were more likely to be infected with HIV, after controlling
for other variables, but rates of concurrent relationships across countries in the surveys do not
5
See Stillwaggon, 2003 for an explanation of why social scientists and policy makers assumed, without
evidence, that African sexuality was exceptional.
10
correlate with HIV prevalence.
Risky sexual behaviour increases the likelihood that the individual will become infected
with HIV, but differences in sexual behaviour between countries do not explain differences in
HIV prevalence because sexual contact is a necessary, but not sufficient condition for the sexual
transmission of HIV. Between otherwise healthy adults in developed countries, HIV has very
low rates of heterosexual transmission (World Bank, 1997). In the developing world, including
southern Africa, however, the majority of the population is not “otherwise healthy”.
Cofactor infections: Field studies and trials as well as laboratory research have found
substantial evidence for a connection between several diseases highly endemic in southern Africa
and the transmission of HIV. The relative affluence of southern Africa and the emphasis on risky
sexual behaviour have led most policy makers to overlook the biological vulnerability of the
majority of southern Africa’s population to infectious disease.
There seems to be little doubt that sexually transmitted infections (STIs) increase
transmission of HIV. (See Fleming and Wasserheit, 1999, for their review of more than 80 trials.
See also Boulton and Gray-Owen, 2002; Corbett et al., 2002; Fleming and Wasserheit, 1999;
Grosskurth et al., 1995; Hayes et al., 1995; Laga et al., 1993; Wang et al., 2001; Wolday et al.,
2004.) Some STIs lead to genital ulceration creating entry points for the HIV virus, thereby
making those who are not infected with HIV more vulnerable to infection and making infected
persons more likely to transmit HIV. Both ulcerative and non-ulcerative STIs also make those
who are not infected with HIV more vulnerable to HIV infection by producing genital
inflammation, which draws CD4+ cells to the infected tissues. Those cells are the channel
through which HIV infects the body. Furthermore, both ulcerative and non-ulcerative STIs make
those who are infected with HIV more contagious by increasing viral shedding, which is the
discharge of virus in the genital tract. In poor populations, bacterial STIs are more likely to be
untreated because of lack of access to health care (Sturm et al., 1998). The health burden of
untreated STIs is far higher in African countries than in other low- and middle-income countries.
(See Table 5.) Together, these facts suggest that one of the reasons for the high prevalence of
HIV in southern Africa is the high prevalence of untreated STIs.6
6
Trachoma might seem unrelated to HIV since it is an eye infection, but trachoma is caused by the same
bacteria (C. trachomatis) as the STI, chlamydia. As much as 70 percent of women and 25 percent of men with
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Urinary schistosomiasis (S. hematobium), which afflicts more than 100 million people in
sub-Saharan Africa (and almost nowhere else) [WHO (World Health Organization), 2008a], also
can act as a cofactor of HIV transmission much in the same way as do STIs. Worms and ova of
S. hematobium infect the reproductive tract of both men and women, producing lesions and
inflammation of the genital area (Attili et al., 1983; Feldmeier et al., 1995; Leutscher et al., 1998;
Marble and Key, 1995). The genital ulcers of S. hematobium are indistinguishable without
biopsy from those of ulcerative STIs. A recent trial in Zimbabwe found that genital lesions of
schistosomiasis increased HIV risk in women three-fold compared to women in the same
villages without genital lesions of schistosomiasis (Kjetland et al., 2006).7 The WHO Ad Hoc
Strategic and Advisory Group on Neglected Tropical Diseases recently identified the interactions
of HIV with endemic parasitic and infectious diseases as a priority for research, but AIDS policy
organizations have not adopted protocols for treating coinfections of these locally endemic
diseases.
Malaria, which is especially prevalent in sub-Saharan Africa, interacts with HIV,
increasing viral load up to ten-fold. Viral load remains elevated in HIV-infected persons for six
weeks after malarial episodes (Abu-Raddad et al., 2006; Hoffman et al., 1999; Kublin et al.,
chlamydia are asymptomatic (Webster et al., 1993). That suggests the possibility that trachoma sufferers are
coinfected genitally without the knowledge of the patient or medical personnel. Alternatively, high prevalence of
trachoma may be merely a proxy for poor health care, since it is easily treated in its early stages and extracts a very
high toll in blindness that would not be tolerated in a country with an adequate health system. Consequently, we
include the trachoma variable in some of the regressions presented below because we think it suggests some
important factor worthy of further investigation.
7
A recent study shows that rhesus monkeys infected with S. mansoni are more susceptible to SIV (simian
immunodeficiency virus) infection and have elevated viral load compared to monkeys without schistosomiasis
(Chenine et al., 2008). Human trials with S. mansoni have not looked for differential incidence (whether coinfection
with schistosomiasis makes one more vulnerable to HIV) but have examined viral load and CD4+ counts of
coinfected HIV patients (that is, whether HIV patients are more contagious) and how they are affected by treatment
with antihelminthics. Those trials have produced mixed results. Kallestrup et al., 2005 support the hypothesis that
schistosomiasis coinfection makes HIV patients more contagious by raising viral load, but others do not (Brown et
al., 2005; Elliott et al., 2003; Lawn et al., 2000; Mwinzi et al., 2001). There are 54 million people in sub-Saharan
Africa afflicted with S. mansoni. The argument of this paper is that the genital manifestations of S. hematobium
make it different from other species of schistosomiasis as well as other helminths.
12
2005). Elevated viral load not only increases individual contagiousness of HIV-infected persons,
it also affects the dynamics of the epidemic at a population level (Abu-Raddad et al., 2006;
Bloland et al., 1995a; Bloland et al., 1995b; Corbett et al., 2002; Hoffman et al., 1999;
Whitworth et al., 2000; Xiao et al., 1998 Herrero et al., 2007; for additional discussion and
sources, see Stillwaggon, 2006). Individuals in malaria-endemic areas have a higher probability
of sexual contact with persons who have high viral load due to coinfection with malaria and who
are therefore more contagious. In the AIDS literature, there is increasing and warranted attention
to the first few months after infection with HIV because people have high viral loads at that time
and are thus more likely to transmit HIV to their partners (Brenner et al., 2007; Cohen and
Pilcher, 2005; Pilcher et al., 2004; Quinn et al., 2000). What also needs to be considered is that
malaria and other coinfections produce recurring elevation of viral loads over the lifetime of the
HIV-infected person, not just in the first few months following HIV infection.8
METHOD
This research extends the work of authors who use cross-national data and multiple
8
We have provided a rationale for a long list of independent variables in our model, but we should point
out a variable that we are not including. Numerous colleagues have suggested we add clade to our analysis. The
subtype of HIV-1 that is dominant in southern Africa is clade C, but that subtype is relatively rare in most other
countries (the most important exceptions being Bangladesh, Papua New Guinea, India, and Nepal). That fact
suggests to many observers that clade C is more fit for transmission than other clades and several contributors to the
virological literature have speculated just that (Ping et al., 1999; Rainwater et al., 2005; Spira et al., 2003; Tebit et
al., 2007). There is, however, little evidence to confirm that suspicion. One study finds that vaginal shedding in
pregnant women is greater with clade C (John-Stewart et al., 2005), but another study found no difference in
mother-to-child transmissibility of the virus among different clades (Tapia et al., 2003). A third study finds no
evidence of different fitness for heterosexual transmission of the different clades (Morison et al., 2001). The most
complete data on HIV clades are from the HIV Sequence Database provided by the Los Alamos National Laboratory
at http://www.hiv.lanl. gov/content/sequence/HIV/mainpage.html, from which one can obtain the percent of HIV
infections of clade C for 72 of the 88 countries in our dataset. That variable is strongly and positively correlated with
both HIV prevalence and the southern Africa variable in our dataset, but when the variables of the basic model are
added to the regression, the regression coefficient on clade C becomes negative and significant at the 94 percent
level. If one then adds the cofactor infections variables to the regression, the regression coefficient on the clade C
variable loses its statistical significance (the t statistic is only 0.28, significant at the 0.778 level). Accordingly, our
research provides no support for the notion that differences in HIV sequences between countries can explain the
higher HIV prevalence in southern Africa using multivariate analysis.
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regression analysis to estimate the effect of factors thought to drive the HIV epidemic (Bonnel,
2000; Deuchert and Brody, 2007b; Drain et al., 2004; Mahal, 2002; Over, 1998; Stillwaggon,
2000; Stillwaggon, 2002; Tsafack, 2008). All but one of those studies focus on a specific issue
that the author(s) believe especially important: risky sexual behaviours, gender discrimination,
human development, income inequality, economic growth, iatrogenic transmission of HIV, and
nutrition. Those authors all use the same or similar socio-economic control variables and then
add other variables relevant to the specific issue addressed in their studies. The most common
control variables used in those studies are income inequality, per capita income (level or growth
rate), and urbanization (level or growth rate), proportion of the population that is Muslim, age of
the epidemic, a measure of gender discrimination, literacy or school enrolment rates, and one or
more binary variables for region. Deuchert and Brody examined the iatrogenic or nosocomial
spread of HIV. Their study found a measure of unsafe injection practices to be positively and
significantly correlated with HIV prevalence, so we also include that variable in our equations.
To those control variables we add one not used in previous studies. We include a measure
of cross-border migration because of the special importance of migration in the discussion of
HIV in southern Africa. We call all of these control variables ‘the basic model’. Finally, in the
present study, which extends our previous research (Sawers et al., 2008), we add to the basic
model measures of cofactor infections that are thought to promote the transmission of HIV.
The regional binary variable in our model takes the value of one for the nine countries in
southern Africa and zero for other countries. A positive and significant coefficient on the
southern Africa variable tells us that HIV prevalence is higher in southern Africa than elsewhere,
but does not tell us why. Our objective is to explain differences in HIV prevalence among lowand middle-income countries (not just find statistical associations and a high R2). Location is not
a policy-sensitive variable. Although using a regional binary variable raises the predictive value
of the model in a technical sense, it begs the question of what is causing high prevalence in the
region. Accordingly, we measure our success in explaining the variation in HIV among countries
by how far we can reduce the size of the regression coefficient on the southern Africa variable.
Using data for 88 developing and transition countries,9 we estimate a number of ordinary
9
Countries with per capita income in 2003 of less than US$12,000 on a purchasing power parity basis were
included in the analysis. Rather than imputing missing values, we dropped countries with missing data from the
14
least squares multivariate regressions on the log of adult HIV prevalence in 2007.10 All of the
regressions reported here include the variables of the basic model: a binary variable for the nine
countries of southern Africa, the Gini coefficient (the most widely used measure of income
inequality), log of per capita income on a purchasing power parity basis, percent of adults who
are literate,11 percent of the population living in urban areas, age of the epidemic, a measure of
gender discrimination (one minus the ratio of female economic participation to male economic
participation), percent of the population who are Muslim, workers’ remittances as a percent of
GDP, and a binary variable that takes the value of one if the country used no autodisable syringes
in child vaccination programs. Table 1 lists the source for each of the variables. The first step is
to regress only the variables of the basic model on HIV prevalence. Next, we add each cofactor
infection variable, one at a time, to a regression with the basic model variables. Lastly, we
regress all of the cofactor infections together with the basic model variables. Estimation of all of
the regressions is robust to heteroskedasticity.
RESULTS
In the equation presented in Table 2, only the variables of the basic model are regressed
on HIV prevalence. The R2, that is, the percent of the variance in the log of adult HIV prevalence
associated with the independent variables taken together, is 66.1 percent. Five of the ten
independent variables are significant at the 96 percent level or better and another is significant at
analysis, leaving a data set of 88 countries. See Table 6 for a list of countries in the dataset. The present study
follows established practice by using the log form of HIV prevalence, per capita income, and some measures of
cofactor infections, which reduces the influence of outliers and generally leads to more robust and efficient estimates
when analysing highly skewed variables such as these.
10
It would have been preferable to use HIV incidence to describe how the epidemic is unfolding.
Prevalence includes historical information and is confounded by greater survival in countries with effective
antiretroviral treatment programs, but incidence data are generally not available.
11
All measures of literacy and school enrolment for our sample are highly correlated and perform similarly
as regressors. In our sample, the adult literacy rate and the female-to-male literacy ratio are virtually identical in a
statistical sense (with a simple correlation of 0.93). Using the latter variable, however, gives the misleading
impression that we are using it to measure gender discrimination; hence the present study uses the more
straightforward variable, adult literacy, to avoid that confusion.
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15
the 92 percent level. The southern Africa variable, the Gini coefficient, and the age of the
epidemic are positively correlated with HIV prevalence. The Muslim percent, adult literacy, the
gender discrimination index, and income per capita variables are negatively associated with HIV
prevalence. The regression coefficient on each independent variable in the model tells us the
change in the log of HIV prevalence—after accounting for the effects of the other independent
variables—when the value of the independent variable changes by 1. Since the southern Africa
variable takes the value of either 1 or 0, its coefficient tells us the difference in the log of HIV
prevalence between southern Africa and elsewhere after taking into account the effects of the
other basic model variables.
Average HIV prevalence in southern Africa is about 18 times the average prevalence in
other low- and middle-income countries in our dataset. The regression coefficient on the
southern Africa variable is 2.78 in the regression using only the variables in the basic model. To
find the ratio of HIV prevalence in southern Africa and elsewhere that is not explained by the
basic model variables, one takes the antilog (with an adjustment for heteroskedasticity) of the
coefficient on the southern Africa variable.12 The regression coefficient on southern Africa tells
us that HIV prevalence in southern Africa not explained by the model is 13.2 times the level in
other low- and middle-income countries. In other words, the basic model “explains” in a
statistical sense just over one-quarter of the nearly 18-fold differential in HIV prevalence
between the southern African and other low- and middle-income countries.
The next step in the analysis is to add each of the five cofactor infection variables—one
at a time—to a multiple regression in which the other independent variables are those in the basic
model. Schistosomiasis, chlamydia, syphilis, and gonorrhoea are measured by DALYs per
12
The coefficient on the southern Africa variable estimates the difference between the mean log of HIV
prevalence in southern Africa compared to other countries in the dataset. In order to convert this to a statement about
the difference in mean HIV prevalence (as opposed to mean log prevalence), one must take the antilog of that
coefficient, but also correct for the non-zero mean of the antilogged error terms. That correction takes into account
the heteroskedasticity in error terms between southern Africa and the other countries and yields a close
approximation of the actual ratio of means. The ratio of the predicted means is the antilog of [the regression
coefficient on the southern Africa variable plus (half the difference between the variance of the regression residuals
in southern Africa and the variance of the regression residuals outside of southern Africa)]. Thomas Hertz devised
this calculation.
16
100,000 population, and malaria is measured by the annual number of cases as a percent of the
population.13 The results of those five regressions are reported in Table 3.14 All of the cofactor
infection variables are positively correlated with HIV prevalence, and all of the cofactor
infection variables are significant at the 99.9 percent level or better.
The regressions reported in Table 3 do not measure the true importance of each cofactor
infection since they are subject to missing variable bias, that is, they do not include the other
cofactor infections that are correlated with HIV prevalence. On the other hand, the five cofactor
infections are highly collinear. (The simple correlation (R) between each pair of the five cofactor
infections ranges between 0.55 and 0.90—and exceeds .67 for all pairs excluding malaria.) We
entered a variety of different combinations of the five diseases into regressions with the basic
model variables, but they produced different results depending on which cofactor infections were
entered and how they were combined. Collinearity prevents us from determining the separate
effect of each infection. Nevertheless, the objective of our research is not to show the effect of
any particular disease, but to show that the burden of certain diseases taken together explains
statistically the variations in HIV prevalence across countries. Accordingly, we add together the
four cofactor infections that are measured the same way (in DALYs per 100,000 population) and
are thus additive. We call this variable “four-cofactor-infections variable”. (Malaria is entered
separately since it is measured differently.) Combining the four cofactor infections into a single
variable eliminates the collinearity problem, but also makes epidemiological sense since it
measures the combined burden of the four cofactor infections, all of which are thought to
promote HIV transmission through the same mechanisms. All southern African countries have
very high levels of chlamydia, gonorrhoea, and schistosomiasis (110 or more DALYs per
100,000 population) and only African countries have very high levels of those three infections.
Five southern African countries also have very high levels of syphilis and only three countries
13
Because the vast majority of deaths from malaria are in infants, the DALYs associated with malaria are
extremely high. That measure, however, does not reflect the burden of malaria among sexually active adults, unlike
the DALYs from other diseases. We therefore use malaria prevalence as our measure.
14
For the same reason (statistical efficiency) that we use the log of HIV prevalence and per capita income,
we also use the log of chlamydia, syphilis, and gonorrhoea in these regressions. There are many countries in the
dataset with no schistosomiasis or malaria and one cannot calculate a logarithm of zero.
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17
outside Africa have levels that high.
Accordingly, we estimate a regression with the basic model variables together with the
malaria prevalence and the four cofactor infections variable. That raises the R2 from 66.8 to 82.8
percent and raises the F statistic for the equation from 27 to 50. Both the four-cofactor-infections
variable and malaria prevalence are significant at the 99.9 percent level and the F statistic for
those two variables taken together is 29.6, significant at the 99.99 percent level. That test
confirms our hypothesis that cofactor infections are significantly correlated with HIV
prevalence.15 The beta coefficients for the cofactor infection variables are the largest of any
variable (.469 and .413) and substantially larger than either the southern Africa variable (.343) or
the percent Muslim variables (–.204). The beta coefficients measure which independent variables
have a greater statistical impact on the dependent variable after taking into account the different
units by which the variables are measured.
The regression coefficient on the southern Africa variable in the equation including the
cofactor infection variables is 1.99, which tells us that the HIV prevalence not “explained” by the
other variables in the equation is six times that of other low- and middle-income countries. In
other words, adding the cofactor infections to the model allows us to “explain” in a statistical
sense about two thirds of the 18-fold differential in HIV prevalence between southern Africa and
other low- and middle-income countries, substantially more than the model without the cofactor
diseases, which explains only about one-quarter of the southern Africa differential.
One can compare the regression coefficient and t statistic on the log of per capita income
in the regression with only the variables of the basic model (in Table 2) to those statistics in the
regression that includes the cofactor infections in Table 4. The coefficient on per capita income
in the regression without the cofactor infection variables is –0.459 (significant at the 92 percent
level), that is, lower income countries, other things equal, have higher HIV prevalence. Adding
15
As noted above, there is a substantial medical literature that supports our contention that STIs and S.
hematobium are cofactors with HIV, but there is no such literature connecting trachoma to HIV. Nevertheless, it is
plausible that persons infected with trachoma also have unrecognised genital infection with the same bacteria, that
is, they have chlamydia. We have reestimated the regression presented in Table 4, replacing the four-cofactorinfections variable with one that includes trachoma. The two regressions are almost identical except that when
trachoma is included, the coefficient on the southern Africa variable falls from 1.99 to 1.88. In other words,
including trachoma enhances our ability to “explain” the southern Africa differential.
18
the cofactor infection variables to the equation turns the coefficient on per capita income slightly
positive, but eliminates its statistical significance. The Gini coefficient is positively and
significantly correlated with HIV prevalence. Most researchers argue that in low- and middleincome countries, it is a proxy for widespread poverty. Adding the cofactor infection variables
reduces both the size of the regression coefficient on the Gini coefficient and its significance.
That is also consistent with our argument that what is important about the inverse association
between HIV prevalence and per capita income is the high disease burden of the poor.
LIMITATIONS
The statistical analysis presented above shows that after controlling for a number of
factors including the presence of cofactor infections, location by itself accounts for only a third
of the 18-fold difference in HIV prevalence between southern Africa and other low- and middleincome countries in our sample. Two points should be emphasized. That the coefficient on the
southern Africa variable remains significantly positive means that our model suffers from
missing variable bias. The southern Africa variable is without causal content. If it remains
statistically significant in the regression equations, it means that something else sets southern
Africa apart from the rest of the world that we have not yet discovered or found a way to
measure adequately. Some examples of variables that one would like to add to the equation
include condom use, a better measure of injection safety practices in medical and nonmedical
settings, various measures of risky sexual behaviour,16 prevalence of circumcision (not just
percent Muslim), and cross-border traffic with countries that have high HIV prevalence.17
16
The most widely available measure of risky sexual behaviour is Age at First Sex for Females, but that
variable has not been measured for almost half of the countries in our dataset. Twenty-two of the 46 countries for
which there are such data are in Africa, so those 46 countries are a representative sample of neither African
countries nor low- and middle-income countries. Sexual behaviour surveys reflect a bias in data collection common
in AIDS research. The assumption that the AIDS epidemics in sub-Saharan Africa result from something distinctive
about African sexuality means that there are more surveys about sexual behaviour in African countries than
elsewhere. (For a discussion of bias in data selection in Africa, see Stillwaggon, 2003).
17
We know that HIV can spread from country to country across land borders. Truck routes are frequently
mentioned as conduits for the spread of HIV in sub-Saharan Africa and prevalence is higher along highways than in
more remote parts of the same country. In an attempt to capture those neighbourhood effects, we measure, for every
country in the sample, the average HIV prevalence of all countries with which it shares a land border. Adding that
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19
DISCUSSION
In retrospect, it is clear that, in southern Africa, HIV was an epidemic “waiting to
happen” (Marks, 2002). The factors that made southern Africa an ideal setting for HIV to
flourish are numerous. What follows is a discussion of how southern Africa compares with the
rest of sub-Saharan Africa and with other low- and middle-come countries. Table 5 presents
means of the variables used in this analysis for four groups of countries—southern Africa,
central Africa (the belt of a dozen contiguous countries that stretches across Africa from Angola
to Kenya), the rest of sub-Saharan Africa, and other low- and middle-income countries outside
sub-Saharan Africa.
Southern African shares many characteristics with the rest of sub-Saharan Africa that set
them apart from other low- and middle-income countries. Excluding South Africa, the proportion
of the population who live on less than two dollars a day (not shown in Table 5 because it was
not used in the regressions) is the same in southern Africa (71 percent) as elsewhere in subSaharan Africa.18 That compares with an average of 40 percent in other low- and middle-income
countries. Consequently, the cofactor infections analysed in this study (either diseases of poverty
or diseases less likely to be treated because of poverty) are found at roughly similar levels
throughout most of sub-Saharan Africa, but at far lower levels outside Africa. STIs are epidemic
around the world, but their burden in sub-Saharan Africa is more than triple the level in other
variable to the regression in Table 4 reduces the coefficient on southern Africa to insignificance (the t statistic is
0.20 and the p is 0.845) while leaving the coefficients on the cofactor infection variables significant at the 98 percent
level. In that sense, we have completely explained the southern Africa differential in HIV prevalence. Nevertheless,
we have merely shifted our ignorance back one step: in a statistical sense, we know why each country in southern
Africa has a high level of HIV, but we have not explained why all of its neighbours have high prevalence.
Furthermore, the southern Africa variable and the HIV prevalence in neighbouring countries variable are so highly
correlated (R = 0.93) that they are practically the same variable. That collinearity makes it difficult to tease out the
separate effects of the two locational variables. In short, there are some very complicated spatial dynamics to the
spread of HIV across sub-Saharan Africa and around the world that cannot be usefully analysed with an OLS
multiple regression on cross-section data.
18
The proportion of the population living on $2 a day would be an appropriate variable to use in the
regression analysis except for missing data. That measure was not available for 29 out of the 88 countries in the data
set.
20
low- and middle-income countries. Moreover, the difference for the other cofactor diseases is far
greater. The burden of schistosomiasis, for example, is between 17 and 20 times as great in subSaharan Africa as it is in other low- and middle-income countries. Recall that S. hematobium,
which affects the genital tract by creating lesions, inflammation, and viral shedding in ways
likely to promote HIV transmission, is found almost exclusively in Africa. To the extent that
poverty and the diseases that poverty promotes lead to high HIV prevalence, then southern
Africa is similar to the rest of sub-Saharan Africa and much more vulnerable than other low- and
middle-income countries.
Other factors that could promote the spread of HIV set southern Africa apart from the rest
of sub-Saharan Africa, but make it more similar to other developing and transition countries. A
number of authors have noted the positive correlation of educational attainment and literacy with
HIV (for example, Brent, 2006). Adult literacy (and the ratio of female to male literacy, which is
not shown in Table 5) in southern Africa is a little lower than in low- and middle-income
countries outside of sub-Saharan Africa and a little higher than in central Africa, but is much
higher than in the rest of sub-Saharan Africa. In addition, the level of migration (as measured by
remittances as a percent of GDP) is the same in southern Africa as it is in low- and middleincome countries outside Africa and more than twice the level in the remainder of sub-Saharan
Africa.
Income inequality puts southern Africa in a category by itself. Gini coefficients in
southern Africa are the highest in the world, but in the rest of sub-Saharan Africa, Gini
coefficients average about the same as in low- and middle-income countries elsewhere.
Furthermore, southern Africa on average has the lowest percentage of the population who are
Muslim among the four groups of low- and middle-income countries in Table 5.
In sum, why has HIV reached its highest levels in southern Africa—one-quarter of the
adult population in the most gravely affected countries? Perhaps what is unique about southern
Africa is that it faces higher risk from so many of the factors that promote HIV epidemics. With
regard to the level of poverty and the burden of cofactor infections, it is more like the rest of subSaharan Africa than countries elsewhere. With regard to literacy and migration, southern Africa
is more like low- and middle-income countries outside of Africa than those within Africa. With
regard to high levels of income inequality and low proportion of Muslims, southern Africa sets
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21
itself apart from the rest of the world. All of these factors come together in southern Africa to
produce the most intense epidemics of HIV in the world.
CONCLUSIONS
AIDS prevention policy has failed to stem the spread of HIV in southern Africa and
neighbouring countries. The research presented here suggests that policy makers have targeted
too few factors in their attempts to slow or reverse the epidemics. The socio-economic variables
in our basic model that are statistically associated with HIV prevalence, however, are not easily
affected by changes in policy. Levels of inequality have been shown to be highly resistant in the
short run to changes in the policy regime. Even the most successful efforts to promote economic
development produce single digit annual increases in per capita income. Migration patterns,
literacy, or female participation in the economy are slow to change. Moreover, after decades of
trying to convince people to avoid risky sexual behaviour, there appears to be only scattered
success.
In contrast, the variables correlated with HIV prevalence in this study that are the
cheapest and easiest to change are the cofactor infections. Dramatic reductions in all of the
cofactor infections can be produced rapidly and at low cost. Some of the infections can be cured
by medicines that cost pennies per patient and can be distributed by schoolteachers or health care
providers with little training. They are highly effective, can be easily tolerated, and are heatstable with long shelf lives. Arguably, the easiest and fastest way to promote economic
development, to alleviate poverty, and even to slow or reverse the spread of HIV is to attack the
cofactor infections analysed in this study. The present research provides support for integrating
HIV prevention policy with efforts to address the multitude of health issues that beset Africa.
Treating or preventing diseases endemic in developing countries with antihelminthics,
antibiotics, and other interventions is a pragmatic, safe, ethically sound, and relatively
inexpensive strategy. Such interventions have substantial beneficial outcomes on their own, in
better health, better school performance, and higher productivity on the job. Moreover, the
improvement in overall well-being in itself promotes healthier behaviours in healthier people.
Some HIV treatment protocols now include food for people taking antiretrovirals and treatment
for tuberculosis because failing to do so wastes lives and resources. Nutritional supplementation
for HIV-infected individuals may also reduce viral loads (making them less contagious),
22
postpone the onset of AIDS, prevent opportunistic infections, and prolong the efficacy of
antiretrovirals. Similarly, our research reinforces assertions by responsible researchers who argue
that redirecting some of the funds designated for HIV prevention to deworming, sanitation, STI
treatment, mosquito control, and safe water supports rather than undermines HIV prevention,
care, and treatment. Treating locally endemic infections and the conditions that cause those
infections in order to control HIV is not mission creep, but may be essential to the central
mission of AIDS programming.
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29
TABLES
Table 1. Sources of Data
Variable
Definition
Source
Adult HIV prevalence, 2006
Percent of persons aged 15 to 49 with HIV
UNAIDS, 2008 Report on the Global Epidemic
Gini coefficient
Measure of income inequality, 2004
World Development Indicators
Income per capita
Per capita GDP (PPP basis), 2003
Urbanization
Percent of population in urban areas, 2003
Age of the epidemic
Years since first diagnosis of HIV
World Bank, Confronting AIDS, 1997, 318B24
Adult literacy
Percent of adult population who are literate, 2003
UNDP, Human Development Indicators, 2005
Female participation ratio
UNDP, Human Development Indicators, 2007
Muslim percent
One minus ratio of female economic activity rate
to male economic activity rate, 2005
Percent of the population who are Muslim
Migrants
International migrants as a percent of population
Remittances
Workers’ remittances as a percent of GDP
Autodisable syringe use
Did not use autodisable syringes in child
vaccination programs
DALYs (Disability-adjusted life years) for each
disease per 100,000 people, 2002
Cases as a percent of population
WHO, World Health Report 2005, Annex Table 7
STIs, schistosomiasis,
trachoma
Malaria prevalence
CIA World Factbook
WHO, Global Burden of Disease Estimates, 2004
WHO, World Malaria Report, 2008
Page 30 of 33
30
Table 2.
Multiple Regression Analysis of HIV prevalence with basic model and without cofactor infections (N = 88).
Variable
coefficient
t statistic
probability
beta coefficient
Southern Africa
2.7080
6.81
0.000
.4792
Gini coefficient
.0316
2.11
0.038
.1774
–.4951
– 1.77
0.082
–.2350
Urbanization
.0022
0.24
0.809
.0251
Age of epidemic
.1180
3.08
0.003
.2128
Adult literacy
–.0119
–1.48
0.143
–.1627
–.0191
–2.43
0.017
–.2008
Muslim
–.0062
– 2.14
0.036
–.1363
Remittances
.0060
0.37
0.709
.0215
.1590
0.61
0.542
.0452
–1.2055
–0.62
0.539
Income per capita (log)
Constant
Page 31 of 33
31
Table 3. Multiple Regression Analysis of HIV prevalence with basic model and each cofactor infection
separately (N = 88).
Variable
coefficient
t statistic
probability
Chlamydia (log)
.7765
3.56
0.001
Gonorrhoea (log)
.7994
4.97
0.000
Syphilis (log)
.4135
4.96
0.000
STIs (log)
.9105
5.94
0.000
Schistosomiasis
.0061
3.50
0.001
Malaria
.0628
4.88
0.000
Table 4. Multiple Regression Analysis of HIV prevalence with basic model and cofactor infections (N = 88).
Variable
coefficient
t statistic
probability
beta coefficient
Four cofactor infections
.6756
4.27
0.000
.4689
Malaria prevalence
.0469
3.35
0.001
.4133
Southern Africa
1.9927
6.39
0.000
.3435
Gini coefficient
.0239
1.88
0.064
.1341
Income per capita (log)
.2544
1.26
0.212
.1302
–.0026
–0.29
0.771
–.0294
Age of epidemic
.0395
1.25
0.216
.0711
Adult literacy
.0139
1.80
0.077
.1902
–.0076
–0.95
0.344
–.0795
Muslim
–.0092
–3.31
0.001
–.2036
Remittances
.0411
2.48
0.015
.1472
.2667
1.16
0.248
.0758
–10.0337
–5.60
0.000
Urbanization
Constant
Page 32 of 33
32
Table 5. Means of variables in low- and middle-income countries by region (N = 88).
Variable
a
sub-Saharan Africa
other low- and
middle- income
countries
southern
central
Chlamydia DALYs per 100,000
120
121
other subSaharan Africa
119
Gonorrhoea DALYs per 100,000
156
163
168
42
Syphilis DALYs per 100,000
120
354
546
29
Schistosomiasis DALYs per 100,000
238
247
282
14
Trachoma DALYs per 100,000
148
192
190
10
STDs + Schistosomiasis DALYs
634
817
894
144
Malaria prevalence, % of population
12.8
32.2
32.2
0.9
HIV prevalence, % of adult population
18.3
4.6
1.60
0.56
Gini coefficient
55.3
42.9
43.2
42.0
Income per capita PPP in U$S
4391
1171
1321
5139
Urbanization, percent of population
33.8
30.5
36.0
51.9
Age of epidemic in years
21.6
21.7
20.2
19.6
Adult literacy, percent of population
73.2
67.1
35.5
84.6
Female economic participation ratio
.721
.824
.727
.649
Muslim, percent of population
9.8
20.2
61.9
24.8
Remittances as percent of GDP
5.9
2.8
2.8
5.9
Autodisable syringe, did not use percent
50.0
14.3
0.0
42.7
a
56
Angola, Gabon, Nigeria, Cameroon, Congo, Democratic Republic of the Congo, Central African Republic,
Rwanda, Burundi, Kenya, Uganda, and Tanzania
Page 33 of 33
33
Table 6. Countries in Sample
Albania
Ecuador
Latvia
Peru
Algeria
Egypt
Lesotho
Philippines
Armenia
El Salvador
Lithuania
Poland
Azerbaijan
Ethiopia
Madagascar
Romania
Bangladesh
Gambia
Malawi
Russian Federation
Belarus
Georgia
Malaysia
Rwanda
Benin
Ghana
Mali
Senegal
Bolivia
Guatemala
Mauritania
Sierra Leone
Botswana
Guinea
Mexico
South Africa
Brazil
Guinea-Bissau
Moldova
Sri Lanka
Bulgaria
Guyana
Mongolia
Swaziland
Burkina Faso
Haiti
Morocco
Tajikistan
Burundi
Honduras
Mozambique
Tanzania
Cambodia
India
Namibia
Thailand
Cameroon
Indonesia
Nepal
Trinidad and Tobago
Chile
Iran
Nicaragua
Tunisia
China
Jamaica
Niger
Turkey
Colombia
Jordan
Nigeria
Uganda
Costa Rica
Kazakhstan
Pakistan
Ukraine
Cote d'Ivoire
Kenya
Panama
Viet Nam
Croatia
Kyrgyzstan
Papua New Guinea
Yemen
Dominican Republic
Laos
Paraguay
Zambia

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