Y. Natalia Alfonso

Success factors for reducing maternal and child mortality

Reducing maternal and child mortality is a priority in the Millennium Development Goals (MDGs), and will likely remain so after 2015. Evidence exists on the investments, interventions and enabling policies required. Less is understood about why some countries achieve faster progress than other comparable countries. The Success Factors for Womens and Childrens Health studies sought to address this knowledge gap using statistical and econometric analyses of data from 144 low- and middle-income countries (LMICs) over 20 years; Boolean, qualitative comparative analysis; a literature review; and country-specific reviews in 10 fast-track countries for MDGs 4 and 5a. There is no standard formula--fast-track countries deploy tailored strategies and adapt quickly to change. However, fast-track countries share some effective approaches in addressing three main areas to reduce maternal and child mortality. First, these countries engage multiple sectors to address crucial health determinants. Around half the reduction in child mortality in LMICs since 1990 is the result of health sector investments, the other half is attributed to investments made in sectors outside health. Second, these countries use strategies to mobilize partners across society, using timely, robust evidence for decision-making and accountability and a triple planning approach to consider immediate needs, long-term vision and adaptation to change. Third, the countries establish guiding principles that orient progress, align stakeholder action and achieve results over time. This evidence synthesis contributes to global learning on accelerating improvements in womens and childrens health towards 2015 and beyond.

Cost-effectiveness analysis of a voucher scheme combined with obstetrical quality improvements: quasi experimental results from Uganda

The maternal mortality ratio (MMR) in Uganda has declined significantly during the last 20 years, but Uganda is not on track to reach the millennium development goal of reducing MMR by 75% by 2015. More evidence on the cost-effectiveness of supply- and demand-side financing programs to reduce maternal mortality could inform future strategies. This study analyses the cost-effectiveness of a voucher scheme (VS) combined with health system strengthening in rural Uganda against the status quo. The VS, implemented in 2010, provided vouchers for delivery services at public and private health facilities (HF), as well as round-trip transportation provided by private sector workers (bicycles or motorcycles generally). The VS was part of a quasi-experimental non-randomized control trial. Improvements in institutional delivery coverage (IDC) rates can be estimated using a difference-in-difference impact evaluation method and the number of maternal lives saved is modelled using the evidence-based Lives Saved Tool. Costs were estimated from primary and secondary data. Results show that the demand for births at HFs enrolled in the VS increased by 52.3 percentage points. Out of this value, conservative estimates indicate that at least 9.4 percentage points are new HF users. This 9.4% bump in IDC implies 20 deaths averted, which is equivalent to 1356 disability-adjusted-life years (DALYs) averted. Cost-effectiveness analysis comparing the status quo and VSs most conservative effectiveness estimates shows that the VS had an incremental cost-effectiveness ratio per DALY averted of US

Factors contributing to maternal and child mortality reductions in 146 low- and middle-income countries between 1990 and 2010.

302 and per death averted of US

Post-2015 health goals: could country-specific targets supplement global ones?

20 756. Although there are limitations in the data measures, a favourable cost-effectiveness ratio persists even under extreme assumptions. Demand-side vouchers combined with supply-side financing programs can increase attended deliveries and reduce maternal mortality at a cost that is acceptable.

Medicaid Cost Savings of a Preventive Home Visit Program for Disabled Older Adults

Introduction From 1990–2010, worldwide child mortality declined by 43%, and maternal mortality declined by 40%. This paper compares two sources of progress: improvements in societal coverage of health determinants versus improvements in the impact of health determinants as a result of technical change. Methods This paper decomposes the progress made by 146 low- and middle-income countries (LMICs) in lowering childhood and maternal mortality into one component due to better health determinants like literacy, income, and health coverage and a second component due to changes in the impact of these health determinants. Health determinants were selected from eight distinct health-impacting sectors. Health determinants were selected from eight distinct health-impacting sectors. Regression models are used to estimate impact size in 1990 and again in 2010. Changes in the levels of health determinants were measured using secondary data. Findings The model shows that respectively 100% and 89% of the reductions in maternal and child mortality since 1990 were due to improvements in nationwide coverage of health determinants. The relative share of overall improvement attributable to any single determinant varies by country and by model specification. However, in aggregate, approximately 50% of the mortality reductions were due to improvements in the health sector, and the other 50% of the mortality reductions were due to gains outside the health sector. Conclusions Overall, countries improved maternal and child health (MCH) from 1990 to 2010 mainly through improvements in the societal coverage of a broad array of health system, social, economic and environmental determinants of child health. These findings vindicate efforts by the global community to obtain such improvements, and align with the post-2015 development agenda that builds on the lessons from the MDGs and highlights the importance of promoting health and sustainable development in a more integrated manner across sectors.

Machine-Learning Algorithms to Code Public Health Spending Accounts

This article discusses how country-specific targets could supplement the proposed global post-2015 targets in a way that maintains their strengths and minimises their drawbacks.

679 Economic impact and care-seeking patterns of injuries in Bangladesh

Little is known about cost savings of programs that reduce disability in older adults. The objective was to determine whether the Community Aging in Place, Advancing Better Living for Elders (CAPABLE) program saves Medicaid more money than it costs to provide.

Factores de éxito para reducir la mortalidad materna e infantil

Objectives: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. Methods: We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Results: Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Conclusions: Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

Facteurs de réussite pour la réduction de la mortalité maternelle et infantile

This study aims to provide an understanding of the economic hardship of individuals with unintentional injuries and economic recovery options in rural Bangladesh by assessing the variation in mortality and morbidity due to injuries and estimating the economic burden of injuries by type of injury. Data were obtained from an annual demographic and injury surveillance system conducted in 7 sub-districts in rural Bangladesh during fiscal year 2014–2015. We tabulated injury prevalence and care-seeking patterns by injury type, age group and socioeconomic status (SES) and applied Chi square tests. A two part model of spending applied a generalised linear model to estimate the probability of any spending and amount of out-of-pocket costs per injury type. Lastly, a Markov model was developed to estimate the probability and cost for each type of injury. There were 1,163,290 individuals and 119,669 self-reported injuries. The most common injuries were from falls (38%), cuts (22%), blunt objects (10%), and transport (9%). Drownings and violence injuries were more common among low SES, while electrocution were more common among high SES. Most injuries (88%) sought treatment, 81% used village doctors, 3% were hospitalised for a median of 5 days, and 25% of the hospitalised had surgery. Of those treated, 4% reported no improvement in health. The mean and median cost for treated injuries, in 2015 BDT, was

Income Elasticity of Vaccines Spending versus General Healthcare Spending

1,302 and