A systematic review of the literature on self-management interventions and discussion of their potential relevance for people living with HIV in sub-Saharan Africa.

A systematic review of the literature on self-management interventions and discussion of their potential relevance for people living with HIV in sub-Saharan Africa.

Patient Educ Couns. 2014 Jan 30;

Authors: Aantjes CJ, Ramerman L, Bunders JF


OBJECTIVE: This study systematically reviews the literature on self-management interventions provided by health care teams, community partners, patients and families and discusses the potential relevance of these interventions for people living with HIV in sub-Saharan Africa.

METHODS: We searched major databases for literature published between 1995 and 2012. 52 studies were included in this review.

RESULTS: The review found very few studies covering people living with HIV and generally inconclusive evidence to inform the development of chronic care policy and practice in sub-Saharan Africa.

CONCLUSION: Chronic care models and self-management interventions for sub-Saharan Africa has not been a research priority. Furthermore, the results question the applicability of these models and interventions in sub-Saharan Africa. There is a need for studies to fill this gap in view of the rapidly increasing number of people needing chronic care services in Africa.

PRACTICE IMPLICATIONS: The established practices for long-term support for HIV patients are still the most valid basis for promoting self-management. This will be the case until there are more studies which assess those practices and their effect on self-management outcomes and other studies which assess the utility and feasibility of applying chronic care models that have been developed in high-income countries.

PMID: 24560067 [PubMed - as supplied by publisher]

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Africa: Q&A – Omotade Akin Aina On Expanding PhDs in Africa

[SciDev.Net]The National Research Foundation in South Africa, in partnership with Carnegie Corporation of New York, is hosting a workshop on excellence in doctoral programmes in sub-Saharan Africa (27-29 October) in Gauteng, South Africa.
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Lunch with PayPal

imagesHave you experienced frustration in trying to pay or be paid for goods or services online? Come have lunch and discuss ecommerce and payments in Africa with Malvina Goldfeld, PayPal’s head of business development for Sub-Saharan Africa. Malvina will expand on PayPal’s activities in the region in general and their new partnership with Equity Bank […]
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Infographic: Why it’s Africa’s turn for an economic boom

Africa now has a $2 trillion economy, and it’s poised to keep growing–fast.

A click-through infographic in the Harvard Business Review blog shows seven reasons why Africa’s economy is growing faster than those of all other continents, and why its global economic spotlight will beginto shine brighter.

Here are some of our favorite, most promising factors driving the economic explosion:

Reason number 1: It’s a huge market opportunity. The map shows Africa’s relative size, but beyond that, it’s urban population and middle class keep growing, meaning more people are able to pay for goods and services than ever before.

Reason number 3: Internal trade is picking up. New companies popping up and trade barriers falling mean growing trade within the continent.

Reason number 4: Soon home to the world’s largest workforce. By 2050, one quarter of the world’s workers will be African.

Reason number 7: Most available cropland is African land. More than 60 percent of the world’s potential farmland is on the African continent. That, combined with other newfound resource supplies, will continue to drive foreign investment.

Click through all seven reasons and see the future of promising growth in Africa’s frontier markets.


An infographic from Harvard Business Review shows Africa’s potential for economic growth. Image: Harvard Business Review blog

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How will crops fare under climate change? Depends on how you ask (Global Change Biology)

By Morgan Kelly, Office of Communications: Princeton Journal Wtach

The damage scientists expect climate change to do to crop yields can differ greatly depending on which type of model was used to make those projections, according to research based at Princeton University. The problem is that the most dire scenarios can loom large in the minds of the public and policymakers, yet neither audience is usually aware of how the model itself influenced the outcome, the researchers said.

The report in the journal Global Change Biology is one of the first to compare the agricultural projections generated by empirical models — which rely largely on field observations — to those by mechanistic models, which draw on an understanding of how crop growth and development are affected by the environment. Building on similar studies from ecology, the researchers found yet more evidence that empirical models may show greater losses as a result of climate change, while mechanistic models may be overly optimistic.

Mechanistic (top row) and empirical (bottom row) simulations compared recent, or “baseline,” maize production in South Africa (1979-99) to projected future production under climate change (2046-65). While both models showed a reduction in output, the third column shows that the empirical model estimated a widespread yield loss of around 10 percent (in yellow), while the mechanistic model showed several areas of increased production (in green). (Image by Lyndon Estes)

The researchers ran an empirical and a mechanistic model to see how maize and wheat crops in South Africa — the world’s ninth largest maize producer, and sub-Saharan Africa’s second largest source of wheat — would fare under climate change in the years 2046 to 2065. Under the hotter, wetter conditions projected by the climate scenarios they used, the empirical model estimated that maize production could drop by 3.6 percent, while wheat output could increase by 6.2 percent. Meanwhile, the mechanistic model calculated that maize and wheat yields might go up by 6.5 and 15.2 percent, respectively.

In addition, the empirical model estimated that suitable land for growing wheat would drop by 10 percent, while the mechanistic model found that it would expand by 9 percent. The empirical model projected a 48 percent expansion in wheat-growing areas, but the mechanistic reported only 20 percent growth. In regions where the two models overlapped, the empirical model showed declining yields while the mechanistic model showed increases. These wheat models were less accurate, but still indicative of the vastly different estimates empirical and mechanistic can produce, the researchers wrote.

Disparities such as these aren’t just a concern for climate-change researchers, said first author Lyndon Estes, an associate research scholar in the Program in Science, Technology and Environmental Policy in Princeton’s Woodrow Wilson School of Public and International Affairs. Impact projections are crucial as people and governments work to understand and address climate change, but it also is important that people understand how they are generated and the biases inherent in them, Estes said. The researchers cite previous studies that suggest climate change will reduce South African maize and wheat yields by 28 to 30 percent — according to empirical studies. Mechanistic models project a more modest 10 to 19 percent loss. What’s a farmer or government minister to believe?

“A yield projection based only on empirical models is likely to show larger yield losses than one made only with mechanistic models. Neither should be considered more right or wrong, but people should be aware of these differences,” Estes said. “People who are interested in climate-change science should be aware of all the sources of uncertainty inherent in projections, and should be aware that scenarios based on a single model — or single class of models — are not accounting for one of the major sources of uncertainty.”

The researchers’ work relates to a broader effort in recent years to examine the biases introduced into climate estimates by the models and data scientists use, Estes said. For instance, a paper posted Aug. 7 by Global Change Biology — and includes second author and 2011 Princeton graduate Ryan Huynh — challenges predictions that higher global temperatures will result in the widespread extinction of cold-blooded forest creatures, particularly lizards. These researchers say that a finer temperature scale than existing projections use suggests that many cold-blooded species would indeed thrive on a hotter Earth.

Scientists are aware of the differences between empirical and mechanistic models, said Estes, who was prompted by a similar comparison that showed an empirical-mechanistic divergence in tree-growth models. Yet, only one empirical-to-mechanistic comparison (of which Estes also was first author) has been published in relation to agriculture — and it didn’t even examine the impact of climate change.

The solution would be to use both model classes so that researchers could identify each class’s biases and correct for it, Estes said. Each model has different strengths and weaknesses that can be complementary when combined.

For wheat, the mechanistic model (top row) projected greater wheat yields, while the empirical model (bottom row) suggested that wheat-growing areas would expand by almost 50 percent. (Image by Lyndon Estes)

Simply put, empirical models are built by finding the relationship between observed crop yields and historical environmental conditions, while mechanistic models are built on the physiological understanding of how the plant grows and reproduces in response to a range of conditions. Empirical models, which are simpler and require fewer inputs, are a staple in studying the possible effects of climate change on ecological systems, where the data and knowledge about most species is largely unavailable. Mechanistic models are more common in studying agriculture because there is a much greater wealth of data and knowledge that has accumulated over several thousand years of agricultural development, Estes said.

“These two model classes characterize different portions of the environmental space, or niche, that crops and other species occupy,” Estes said. “Using them together gives us a better sense of the range of uncertainty in the projections and where the errors and limitations are in the data and models. Because the two model classes have such different structures and assumptions, they also can improve our confidence in scenarios where their findings agree.”

Read the abstract.

Estes, Lyndon D., Hein Beukes, Bethany A. Bradley, Stephanie R. Debats, Michael Oppenheimer, Alex C. Ruane, Roland Schulze and Mark Tadross. 2013. Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches. Global Change Biology. Accepted, unedited article first published online: July 17, 2013. DOI: 10.1111/gcb.12325

The work was funded by the Princeton Environmental Institute‘s Grand Challenges Program.

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By Morgan Kelly, Office of Communications


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UNC-Malawi cancer pathology laboratory is a model for Sub-Saharan Africa

Since 2011, the University of North Carolina has partnered with the government of Malawi to establish a pathology laboratory in the nation’s capital, building on an existing decades-long collaboration. The laboratory has provided an invaluable service to patients and has also built capacity at a national teaching hospital, according to an analysis of the first 20 months of operation published (date) online byPLOS ONE.

“A robust platform for cancer care and research now exists in a setting where it did not previously, and can serve as a model for similar interventions throughout sub-Saharan Africa,” said Dr. Satish Gopal, MD, MPH, study author and member of the UNC Lineberger Comprehensive Cancer Center’s Global Oncology Program.

In July of 2011, UNC and the Malawi Ministry of Health established a pathology laboratory at Kamuzu Central Hospital in Malawi’s capital city of Lilongwe. Relying on a largely Malawian staff (Fred Chimzimu, Coxcilly Kampani, Prof. George Liomba), supported by UNC collaborators, the lab has now assessed more than 3,600 specimens.

The lab became one of only two pathology labs in the nation of 16 million, helping to relieve diagnostic delays that contributed to late diagnoses and early deaths for Malawians suffering from cancer. Cancer has become a growing health problem in Sub-Saharan African countries like Malawi, with disease rates more than doubling since 1999 due to factors such as HIV which increases risk of many cancers, population aging, and the widespread adoption of Westernized lifestyles,.

Importantly, staffing of the Malawi laboratory relies on Malawians, including Prof. George Liomba, a senior Malawian pathologist formerly with the College of Medicine in Blantyre, who has analyzed more than 95 percent of specimens after joining UNC full-time in 2012. While telemedicine technology purchased by UNC Lineberger has allowed for weekly consultations between the Malawi lab and UNC pathologists and physicians in North Carolina, Dr. Gopal said that the early experience has shown that a sustainable laboratory must rely on Malawian health care workers.

“Telepathology has been an important tool for collaboration, rather than a primary mode by which diagnostic interpretation are rendered. Importantly, it cannot be a substitute for training a sufficient number of Malawian pathologists and laboratory technicians to provide essential diagnostic services,” said Dr. Gopal. In this regard, UNC has directly supported the training of technicians abroad who have now returned to Lilongwe to staff the lab.

Sustainability will be a major focus for the laboratory in the coming years, according to Dr. Gopal. Beyond the need for training local staff and physicians, the laboratory is developing locally appropriate fee schedules, and starting to provide diagnostic services to hospitals in surrounding areas outside Lilongwe. While external funds can provide the necessary support for training and equipping cancer facilities in the region, the ultimate goal must be independence from external support.

Additionally, the expansion of a cancer workforce in Africa is crucial, as the projected burden of cancer will continue to rise in the region. Pathology laboratories such as the UNC-Malawi partnership can begin the process of collecting data to allow governments to have a fuller picture of the prevalence of cancer in the region, in order to guide cancer control efforts at the local level.

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