Dataset Information

Does Doing More Result in Doing Better? Exploring Synergies in an Integrated Population, Health and Environment Project in East Africa.

ABSTRACT: Population, health and environment (PHE) projects are an increasingly popular strategy for addressing lack of access to healthcare and livelihood opportunities in settings with threats to biodiversity loss. PHE projects integrate services and messaging from different development sectors, including health (particularly family planning), conservation and livelihoods. However, a question remains: do such projects produce value-added outcomes; that is, synergistic effects as a result of integration across sectors? Using qualitative data to explore value-added outcomes resulting from a PHE project serving communities along Lake Victoria in Kenya and Uganda, this study explores several theories about why this integrated project may be generating value-added outcomes, including changes in established gender roles, as well as substitution of time and investment of new income into sustainable livelihood activities, particularly among women. Integration led to several value-added benefits, particularly for women, although long-term sustainability of project outcomes remains a key concern.

SUBMITTER: Sellers S

PROVIDER: S-EPMC6400278 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Does Doing More Result in Doing Better? Exploring Synergies in an Integrated Population, Health and Environment Project in East Africa.

Sellers Samuel S

Environmental conservation 20180914 Spec Iss 1

Population, health and environment (PHE) projects are an increasingly popular strategy for addressing lack of access to healthcare and livelihood opportunities in settings with threats to biodiversity loss. PHE projects integrate services and messaging from different development sectors, including health (particularly family planning), conservation and livelihoods. However, a question remains: do such projects produce value-added outcomes; that is, synergistic effects as a result of integration ...[more]

PMID: 30853746

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Project description:Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel.Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP.In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

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Project description:BackgroundUpdated World Health Organization guidelines have amplified debate about how resource constraints should impact monitoring strategies for HIV-infected persons on combination antiretroviral therapy (cART). We estimated the incremental benefit and cost effectiveness of alternative monitoring strategies for east Africans with known HIV infection.MethodsUsing a validated HIV computer simulation based on resource-limited data (USAID and AMPATH) and circumstances (east Africa), we compared alternative monitoring strategies for HIV-infected persons newly started on cART. We evaluated clinical, immunologic and virologic monitoring strategies, including combinations and conditional logic (e.g., only perform virologic testing if immunologic testing is positive). We calculated incremental cost-effectiveness ratios (ICER) in units of cost per quality-adjusted life year (QALY), using a societal perspective and a lifetime horizon. Costs were measured in 2008 US dollars, and costs and benefits were discounted at 3%. We compared the ICER of monitoring strategies with those of other resource-constrained decisions, in particular earlier cART initiation (at CD4 counts of 350 cells/mm3 rather than 200 cells/mm3).ResultsMonitoring strategies employing routine CD4 testing without virologic testing never maximized health benefits, regardless of budget or societal willingness to pay for additional health benefits. Monitoring strategies employing virologic testing conditional upon particular CD4 results delivered the most benefit at willingness-to-pay levels similar to the cost of earlier cART initiation (approximately $2600/QALY). Monitoring strategies employing routine virologic testing alone only maximized health benefits at willingness-to-pay levels (> $4400/QALY) that greatly exceeded the ICER of earlier cART initiation.ConclusionsCD4 testing alone never maximized health benefits regardless of resource limitations. Programmes routinely performing virologic testing but deferring cART initiation may increase health benefits by reallocating monitoring resources towards earlier cART initiation.

Dataset Information

Does Doing More Result in Doing Better? Exploring Synergies in an Integrated Population, Health and Environment Project in East Africa.

Publications

Does Doing More Result in Doing Better? Exploring Synergies in an Integrated Population, Health and Environment Project in East Africa.

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