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Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if CCVA is trained on non-local training data different from the local population of interest. This problem is a special case of transfer learning, i.e., improving classification within a target domain (e.g., a particular population) with the classifier trained in a source-domain. Most transfer learning approaches concern individual-level (e.g., a person's) classification. Social and health scientists such as epidemiologists are often more interested with understanding etiological distributions at the population-level. The sample sizes of their data sets are typically orders of magnitude smaller than those used for common transfer learning applications like image classification, document identification, etc. We present a parsimonious hierarchical Bayesian transfer learning framework to directly estimate population-level class probabilities in a target domain, using any baseline classifier trained on source-domain, and a small labeled target-domain dataset. To address small sample sizes, we introduce a novel shrinkage prior for the transfer error rates guaranteeing that, in absence of any labeled target-domain data or when the baseline classifier is perfectly accurate, our transfer learning agrees with direct aggregation of predictions from the baseline classifier, thereby subsuming the default practice as a special case. We then extend our approach to use an ensemble of baseline classifiers producing an unified estimate. Theoretical and empirical results demonstrate how the ensemble model favors the most accurate baseline classifier. We present data analyses demonstrating the utility of our approach. [ABSTRACT FROM AUTHOR]

Background: In 2010, Ethiopia began scaling up the integrated community case management (iCCM) of childhood illness strategy throughout the country allowing health extension workers (HEWs) to treat children in rural health posts. After 2 years of iCCM scale up, utilization of HEWs remains low. Little is known about factors related to the use of health services in this setting. This research aimed to elicit perceptions and experiences of caregivers to better understand reasons for low utilization of iCCM services.Methods: A rapid ethnographic assessment was conducted in eight rural health post catchment areas in two zones: Jimma and West Hararghe. In total, 16 focus group discussions and 78 in-depth interviews were completed with mothers, fathers, HEWs and community health volunteers.Results: In spite of the HEW being a core component of iCCM, we found that the lack of availability of HEWs at the health post was one of the most common barriers to the utilization of iCCM services mentioned by caregivers. Financial and geographic challenges continue to influence caregiver decisions despite extension of free child health services in communities. Acceptability of HEWs was often low due to a perceived lack of sensitivity of HEWs and concerns about medicines given at the health post. Social networks acted both to facilitate and hinder use of HEWs. Many mothers stated a preference for using the health post, but some were unable to do so due to objections or alternative care-seeking preferences of gatekeepers, often mothers-in-law and husbands.Conclusion: Caregivers in Ethiopia face many challenges in using HEWs at the health post, potentially resulting in low demand for iCCM services. Efforts to minimize barriers to care seeking and to improve demand should be incorporated into the iCCM strategy in order to achieve reductions in child mortality and promote equity in access and child health outcomes. [ABSTRACT FROM AUTHOR]

Objective: To assess the quality of care provided by Health Surveillance Assistants (HSAs)-a cadre of community-based health workers-as part of a national scale-up of community case management of childhood illness (CCM) in Malawi.Methods: Trained research teams visited a random sample of HSAs (n = 131) trained in CCM and provided with initial essential drug stocks in six districts, and observed the provision of sick child care. Trained clinicians conducted 'gold-standard' reassessments of the child. Members of the survey team also interviewed caregivers and HSAs and inspected drug stocks and patient registers.Findings: HSAs provided correct treatment with antimalarials to 79% of the 241 children presenting with uncomplicated fever, with oral rehydration salts to 69% of the 93 children presenting with uncomplicated diarrhoea and with antibiotics to 52% of 58 children presenting with suspected pneumonia (cough with fast breathing). About one in five children (18%) presented with danger signs. HSAs correctly assessed 37% of children for four danger signs by conducting a physical exam, and correctly referred 55% of children with danger signs.Conclusion: Malawi's CCM programme is a promising strategy for increasing coverage of sick child treatment, although there is much room for improvement, especially in the correct assessment and treatment of suspected pneumonia and the identification and referral of sick children with danger signs. However, HSAs provided sick child care at levels of quality similar to those provided in first-level health facilities in Malawi, and quality should improve if the Ministry of Health and partners act on the results of this assessment. [ABSTRACT FROM AUTHOR]

Background: In the absence of planned efforts to target the poor, child survival programs often favour the rich. Further evidence is needed urgently about which interventions and programme approaches are most effective in addressing inequities. The Lives Saved Tool (LiST) is available and can be used to model mortality levels across economic groups based on coverage levels for child survival interventions.Methods: We used LiST to model neonatal and under-5 mortality levels among the highest and the lowest wealth quintiles in Bangladesh based on national and wealth-quintile-specific coverage of child survival interventions. The cause-of-death structure among children under-5 was also modelled using the coverage levels. Modelled rates were compared to the rates measured directly from the 2004 Bangladesh Demographic and Health Survey and associated verbal autopsies.Results: Modelled estimates of mortality within wealth quintiles fell within the 95% confidence intervals of measured mortality for both neonatal and post-neonatal mortality. LiST also performed well in predicting the cause-of-death structure for these two age groups for the poorest quintile of the population, but less well for the richest quintile.Conclusions: LiST holds promise as a useful tool for assessing socio-economic inequities in child survival in low-income countries. [ABSTRACT FROM AUTHOR]