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Over the past few decades, knowledge management (KM) has become well-established in many fields, particularly in business. Several KM models have been at the forefront of promoting KM in businesses and organisations. However, the applicability of these traditional KM models to the global health field is limited by their focus on KM processes and activities with few linkages to intended outcomes. This paper presents the new Knowledge Management for Global Health (KM4GH) Logic Model, a practical tool that helps global health professionals plan ways in which resources and specific KM activities can work together to achieve desired health program outcomes. We test the validity of this model through three case studies of global and field-level health initiatives: an SMS-based mobile phone network among community health workers (CHWs) and their supervisors in Malawi, a global electronic Toolkits platform that provides health professionals access to health information resources, and a netbook-based eHealth pilot among CHWs and their clients in Bangladesh. The case studies demonstrate the flexibility of the KM4GH Logic Model in designing various KM activities while defining a common set of metrics to measure their outcomes, providing global health organisations with a tool to select the most appropriate KM activities to meet specific knowledge needs of an audience. The three levels of outcomes depicted in the model, which are grounded in behavioural theory, show the progression in the behaviour change process, or in this case, the knowledge use process, from raising awareness of and using the new knowledge to contributing to better health systems and behaviours of the public, and ultimately to improving the health status of communities and individuals. The KM4GH Logic Model makes a unique contribution to the global health field by helping health professionals plan KM activities with the end goal in mind.

Many of the studies on households and people’s welfare dynamics in Uganda have been preoccupied with poverty lines and welfare indices estimation, particularly at the macro level. Little attention has been paid to the impact of livelihood diversification strategies on household welfare and health outcomes (i.e. child nutrition) for both rural and urban dwellers. Thus, this study examines the impact of livelihood (i.e. income) diversification strategies on household welfare and child nutrition. Eight waves of the Uganda National Panel Survey (2005/06–2019/20) dataset collected by the Uganda Bureau of Statistics (UBOS) with the support of the World Bank are utilised for this purpose. An instrumental variable approach implemented through a two-stage residual inclusion (2SRI) technique is applied in which alternative correlated random effects (CRE) and fixed effects models are estimated, these two techniques enabled us to address the endogeneity problem associated with livelihood diversification by including a generalised residual among the regressors and also including appropriate list of instruments in the latter. The findings from the study show that indeed diversified households enjoy higher levels of consumption and thus livelihood diversification strategies are welfare-improving. Second, livelihood diversification is also found to improve child nutrition status. By and large, the results point to the fact that households should at least run a diversified means of livelihood since this offers a feasible way of shielding households against risks and also coping and adapting to shocks among rural and urban poor households.

The environment plays an important role in mediating human health. In this session we consider research addressing ways to overcome the challenges associated with studying the multifaceted and ever-changing environment. Environmental health research has a need for technological and methodological advances which will further our knowledge of how exposures precipitate complex phenotypes and exacerbate disease.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.

Despite widespread efforts to expand health insurance in developing countries, there is scant evidence as to whether doing so actually improves people’s health. This paper aims to fill this gap by evaluating the impact of Rural Mutual Health Care (RMHC), a community-based health insurance scheme, on enrollees’ health outcomes. RMHC is a social experiment that was conducted in one of China’s western provinces from 2003 to 2006. The RMHC experiment adopted a pre–post treatment-control study design. This study used panel data collected in 2002, 1 year prior to the intervention, and followed up in 2005, 2 years after the intervention, both in the intervention and control sites. We measured health status using both a 5-point Categorical Rating Scale and the EQ-5D instruments. The estimation method used here is difference-in-difference combined propensity score matching. The results show that RMHC has a positive effect on the health status of participants. Among the five dimensions of EQ-5D, RMHC significantly reduces pain/discomfort and anxiety/depression for the general population, and has a positive impact on mobility and usual activity for those over 55-years old. Our study provides useful policy information on the development of health insurance in developing countries, and also identifies areas where further research is needed.