Articles tagged as "Mali"

An integrated investment approach for women’s and children’s health

Advancing social and economic development by investing in women's and children's health: a new Global Investment Framework.

Stenberg K, Axelson H, Sheehan P, Anderson I, Gülmezoglu AM, Temmerman M, Mason E, Friedman HS, Bhutta ZA, Lawn JE, Sweeny K, Tulloch J, Hansen P, Chopra M, Gupta A, Vogel JP, Ostergren M, Rasmussen B, Levin C, Boyle C, Kuruvilla S, Koblinsky M, Walker N, de Francisco A, Novcic N, Presern C, Jamison D, Bustreo F; on behalf of the Study Group for the Global Investment Framework for Women's Children's Health. Lancet. 2013 Nov 18. doi: S0140-6736(13)62231-X. pii: 10.1016/S0140-6736(13)62231-X. [Epub ahead of print]

A new Global Investment Framework for Women's and Children's Health demonstrates how investment in women's and children's health will secure high health, social, and economic returns. We costed health systems strengthening and six investment packages for: maternal and newborn health, child health, immunisation, family planning, HIV/AIDS, and malaria. Nutrition is a cross-cutting theme. We then used simulation modelling to estimate the health and socioeconomic returns of these investments. Increasing health expenditure by just $5 per person per year up to 2035 in 74 high-burden countries could yield up to nine times that value in economic and social benefits. These returns include greater gross domestic product (GDP) growth through improved productivity, and prevention of the needless deaths of 147 million children, 32 million stillbirths, and 5 million women by 2035. These gains could be achieved by an additional investment of $30 billion per year, equivalent to a 2% increase above current spending.

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Editor’s notes: Over the past 20 years there have been substantial gains in maternal and child health (MCH). However, much still needs to be done – assuming a continuation of current rates of progress, there would nevertheless be shortfalls in the achievement of MDG 4 and 5 targets. Especially in sub-Saharan Africa, HIV is an important underlying cause of maternal and child ill health. This paper models the costs and benefits of an accelerated action on MCH, including for HIV, the prevention of mother to child HIV transmission; first line treatment for pregnant women; cotrimoxazole for children, and the provision of paediatric antiretroviral therapy (ART). These HIV services are complemented by health systems strengthening; increased family planning provision; and packages for malaria, immunisation, and child health. The paper is interesting for many reasons, including both the breadth of its intervention focus, and the detailed modelling of the likely health, social and economic benefits of such investments.

Although the direct HIV related benefits are not described in detail in the main paper, it is likely that these result both from increased contraceptive use (prong 2 for preventing vertical HIV transmission), as well as ART and cotrimoxazole provision. It also illustrates the potential value of developing a cross-disease investment approach, as a means to ensure that services effectively respond to the breadth of women’s and children’s health needs. This more ‘joined up’, integrated perspective on strategies for health investment can support core investments in health systems strengthening. It can also potentially achieve important cross-disease synergies, e.g., ensuring that a child who has not acquired HIV at birth does not then die from malaria. 

Africa, Asia, Latin America, Oceania
Afghanistan, Angola, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, China, Comoros, Congo, Côte d'Ivoire, Democratic People's Republic of Korea, Democratic Republic of the Congo, Djibouti, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, India, Indonesia, Iraq, Kenya, Kyrgyzstan, Lao People's Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mexico, Morocco, Mozambique, Myanmar, Nepal, Niger, Nigeria, Pakistan, Papua New Guinea, Peru, Philippines, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, South Africa, Sudan, Swaziland, Tajikistan, Togo, Turkmenistan, Uganda, United Republic of Tanzania, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe
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Disproportionately high HIV risk and gender disparity in prevalence among urban poor in Sub-Saharan Africa

The disproportionate high risk of HIV infection among the urban poor in sub-Saharan Africa.

Magadi MA. AIDS Behav. 2013 Jun;17(5):1645-54. doi: 10.1007/s10461-012-0217-y.

The link between HIV infection and poverty in sub-Saharan Africa (SSA) is rather complex and findings from previous studies remain inconsistent. While some argue that poverty increases vulnerability, existing empirical evidence largely support the view that wealthier men and women have higher prevalence of HIV. In this paper, we examine the association between HIV infection and urban poverty in SSA, paying particular attention to differences in risk factors of HIV infection between the urban poor and non-poor. The study is based on secondary analysis of data from the Demographic and Health Surveys from 20 countries in SSA, conducted during 2003-2008. We apply multilevel logistic regression models, allowing the urban poverty risk factor to vary across countries to establish the extent to which the observed patterns are generalizable across countries in the SSA region. The results reveal that the urban poor in SSA have significantly higher odds of HIV infection than urban non-poor counterparts, despite poverty being associated with a significantly lower risk among rural residents. Furthermore, the gender disparity in HIV infection (i.e. the disproportionate higher risk among women) is amplified among the urban poor. The paper confirms that the public health consequence of urban poverty that has been well documented in previous studies with respect to maternal and child health outcomes does apply to the risk of HIV infection. The positive association between household wealth and HIV prevalence observed in previous studies largely reflects the situation in the rural areas where the majority of the SSA populations reside.

Abstract   Full-text [free] access 

Editor’s notes: Evidence on the association between socio-economic position and HIV incidence in sub-Saharan Africa (SSA) has been mixed and appears to be changing over time. Although wealth was previously a predictor of HIV infection, it has recently been suggested that poverty is increasingly driving new infections in mature epidemics, especially in rural areas, where the majority of the population in SSA resides. With high rates of urbanisation both in SSA and globally (according to UNAIDS 2 of every 3 people living with HIV will be living in urban areas by 2030), this article provides important disaggregated evidence of the higher risk of HIV infection among the urban poor as well, and particularly among poor urban women. Even after controlling for sexual behaviour, the results suggest that other structural factors that characterise the environment, in which the urban poor live, such as unemployment, discrimination and violence, may be playing a key role. Interestingly, higher educational attainment was found to be associated with higher HIV risk among the urban poor, while it appeared to be protective among the better-off urban population. This may be pointing towards the ‘inverse equity hypothesis’, discussed in another paper this month (Hargreaves et al.), whereby groups with higher socio-economic position (wealth and/or education) are expected to benefit first from HIV/health interventions, thereby initially widening the gap in health outcomes until the poor catch up. 

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Identifying hotspots of HIV infection in sub-Saharan Africa

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa.

Cuadros DF, Awad SF, Abu-Raddad LJ. Int J Health Geogr. 2013 May 22;12:28.

Background: The geographical structure of an epidemic is ultimately a consequence of the drivers of the epidemic and the population susceptible to the infection. The know your epidemicconcept recognizes this geographical feature as a key element for identifying populations at higher risk of HIV infection where prevention interventions should be targeted. In an effort to clarify specific drivers of HIV transmission and identify priority populations for HIV prevention interventions, we conducted a comprehensive mapping of the spatial distribution of HIV infection across sub-Saharan Africa (SSA).

Methods: The main source of data for our study was the Demographic and Health Survey conducted in 20 countries from SSA. We identified and compared spatial clusters with high and low numbers of HIV infections in each country using Kulldorff spatial scan test. The test locates areas with higher and lower numbers of HIV infections than expected under spatial randomness. For each identified cluster, a likelihood ratio test was computed. A P-value was determined through Monte Carlo simulations to evaluate the statistical significance of each cluster.

Results: Our results suggest stark geographic variations in HIV transmission patterns within and across countries of SSA. About 14% of the population in SSA is located in areas of intense HIV epidemics. Meanwhile, another 16% of the population is located in areas of low HIV prevalence, where some behavioral or biological protective factors appear to have slowed HIV transmission.

Conclusions: Our study provides direct evidence for strong geographic clustering of HIV infection across SSA. This striking pattern of heterogeneity at the micro-geographical scale might reflect the fact that most HIV epidemics in the general population in SSA are not far from their epidemic threshold. Our findings identify priority geographic areas for HIV programming, and support the need for spatially targeted interventions in order to maximize the impact on the epidemic in SSA.

 Abstract   Full-text [free] access

Editor’s notes: This novel study used DHS data to map the clustering of HIV at a local level in 20 sub-Saharan African countries. The method identifies ‘hotspots’ and ‘cool spots’ of HIV infection within each country, mapping the results in a visually striking way.  The data show marked geographical variation within countries. For example, in Senegal, where overall prevalence is 0.75%, a hotspot with general population prevalence of 4.35% was identified. Conversely, within some countries with substantial HIV epidemics (Tanzania, Kenya, Malawi), the study identified settings with very low HIV prevalence. The authors present a ‘relative risk’ (ratio of HIV prevalence within the cluster to that outside the cluster) and, not surprisingly, find that this was higher in low prevalence countries.  It may also be interesting to see an absolute risk, and estimated excess number of cases. The authors hypothesize that the spatial variation may be less to do with variation in behavioural and biological factors than to the fact that HIV infection transmission in SSA is close to the epidemic (or sustainability) threshold – which means that small changes in risk factors can generate substantial changes in HIV prevalence. The implication of this is that, by focusing on the HIV ‘hotspots’, even modest intervention-driven changes in risk behaviour may have considerable impact in reducing HIV prevalence.

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