Government Health Care Spending and Child Mortality

Maruthappu, Mahiben; Ng, Ka Ying Bonnie; Williams, Cheyenne; Atun, Rifat; Zeltner, Thomas

doi:10.1542/peds.2014-1600

Cited by 28 publications

(29 citation statements)

References 30 publications

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“…For example, in low–income countries a 1% decrease in government health spending is associated with an increase of 18 neonatal deaths for every 100 000 live births and 98 deaths before the age of five [21]. From 1999–2004, a 10% increase in per capita total health expenditure was associated with a 22% reduction in infant mortality rate and 10% increase in per capita public health expenditure was associated with a 21% reduction in infant mortality rate [22].…”

Section: Outcomes From Health Spendingmentioning

confidence: 99%

Aligning values and outcomes in priority–setting for health

Seidman¹,

Atun²

2016

Journal of Global Health

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Section: Outcomes From Health Spendingmentioning

confidence: 99%

Aligning values and outcomes in priority–setting for health

Seidman¹,

Atun²

2016

Journal of Global Health

Self Cite

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“…It is necessary to aim for efficient, equitable expenditure, a single collective pool, the creation or strengthen comprehensive and integrated health systems, resolutive capacity at the first level of care, coordinated networks, and a primary health care approach that not only offers curative services, but also health promotion and prevention. Evidence shows that increasing health expenditure for this approach can improve the health of the population (41,42).…”

Section: An Agenda For Research and Actionmentioning

confidence: 99%

Fiscal space for sustainable financing of health systems and universal health

Cid¹,

Pagano²,

Pescetto³

et al. 2019

Rev Panam Salud Publica

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The articles published in this series show that it is possible to create fiscal space for health in countries. This requires specific decisions since economic growth is not enough to generate the additional resources needed. The studies analyze the benefits of reviewing tax expenditures to identify exemptions, which are generally outdated and of low benefit for the countries; arguments in favor of increasing taxes on products harmful to health; and credits and donations, which do not result in a viable source of income for governments. Fiscal efforts must be accompanied by an improvement in efficiency, and the progressive creation of new revenues is key to improving equity. It is necessary to improve the collection of fiscal resources. It is necessary to develop a research and action agenda that includes the analysis of fiscal space within the processes of health system transformation and reform, including technical aspects not yet addressed and studies of the social efficiency of fiscal space for major development objectives such as the Sustainable Development Goals 2030. In the countries of the Region of the Americas it is essential to have efficient management to do more and better with more resources, even during adverse economic cycles. This should be evident at all levels, including solidarity for achieving better health outcomes, the strategic purchase of goods and services for health, and the strengthening of budget planning systems.

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“…The framework of distal and proximate social, environmental and biological determinants was first described by Mosley and colleagues (5). Unfortunately, many developing countries are constrained by limited finances and limited health budgets, and are unable to intervene on all of the determinants of child health at the same time (3). It is therefore increasingly important to identify the most important determinants to be prioritise and to determine the most pressing socio-economic issues that can serve as a starting point for government and policy makers to focus intervention strategy.…”

Section: Introductionmentioning

confidence: 99%

Predicting Under-five mortality across 21 Low and Middle-Income Countries using Deep Learning Methods

Adegbosin

Stantić

Sun

2019

Preprint

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Objectives: To explore the efficacy of Machine Learning (ML) techniques in predicting under-five mortality in LMICs and to identify significant predictors of under-five mortality (U5M). Design: This is a cross-sectional, proof-of-concept study. Settings and participants We analysed data from the Demographic and Health Survey (DHS). The data was drawn from 21 Low-and-Middle Income Countries (LMICs) countries (N = 1,048,575). Eligible mothers in each household were asked information about their children and the reproductive care they received during the pregnancy. Primary and secondary outcome measures: The primary outcome measure was under-five mortality; secondary outcome was comparing the efficacy of deep learning algorithms: Deep Neural Network (DNN); Convolution Neural Network (CNN); Hybrid CNN-DNN with Logistic Regression (LR) for the prediction of child survival. Results: We found that duration of breast feeding, household wealth index and the level of maternal education are the most important predictors of under-five mortality. We found that deep learning techniques are superior to LR for the classification of child survival: LR sensitivity = 0.47, specificity = 0.53; DNN sensitivity = 0.69, specificity = 0.83; CNN sensitivity = 0.68, specificity = 0.83; CNN-DNN sensitivity = 0.71, specificity = 0.83. Conclusion: Our findings provide an understanding of interventions that needs to be prioritized, in order to reduce levels of U5M in LMICs. It also demonstrates that deep learning models are more efficacious than a traditional analytical approach.

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Government Health Care Spending and Child Mortality

Abstract: BACKGROUND: Government health care spending (GHS) is of increasing importance to child health. Our study determined the relationship between reductions in GHS and child mortality rates in high-and low-income countries.

Cited by 28 publications

References 30 publications

Aligning values and outcomes in priority–setting for health

Aligning values and outcomes in priority–setting for health

Fiscal space for sustainable financing of health systems and universal health

Predicting Under-five mortality across 21 Low and Middle-Income Countries using Deep Learning Methods

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