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      Impact of community-based health insurance on utilisation of preventive health services in rural Uganda: a propensity score matching approach

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          Abstract

          The effect of voluntary health insurance on preventive health has received limited research attention in developing countries, even when they suffer immensely from easily preventable illnesses. This paper surveys households in rural south-western Uganda, which are geographically serviced by a voluntary Community-based health insurance scheme, and applied propensity score matching to assess the effect of enrolment on using mosquito nets and deworming under-five children. We find that enrolment in the scheme increased the probability of using a mosquito net by 26% and deworming by 18%. We postulate that these findings are partly mediated by information diffusion and social networks, financial protection, which gives households the capacity to save and use service more, especially curative services that are delivered alongside preventive services. This paper provides more insight into the broader effects of health insurance in developing countries, beyond financial protection and utilisation of hospital-based services.

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          Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies

          In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
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            Estimates of the global, regional, and national morbidity, mortality, and aetiologies of diarrhoea in 195 countries: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 provides an up-to-date analysis of the burden of diarrhoea in 195 countries. This study assesses cases, deaths, and aetiologies in 1990–2016 and assesses how the burden of diarrhoea has changed in people of all ages. Methods We modelled diarrhoea mortality with a Bayesian hierarchical modelling platform that evaluates a wide range of covariates and model types on the basis of vital registration and verbal autopsy data. We modelled diarrhoea incidence with a compartmental meta-regression tool that enforces an association between incidence and prevalence, and relies on scientific literature, population representative surveys, and health-care data. Diarrhoea deaths and episodes were attributed to 13 pathogens by use of a counterfactual population attributable fraction approach. Diarrhoea risk factors are also based on counterfactual estimates of risk exposure and the association between the risk and diarrhoea. Each modelled estimate accounted for uncertainty. Findings In 2016, diarrhoea was the eighth leading cause of death among all ages (1 655 944 deaths, 95% uncertainty interval [UI] 1 244 073–2 366 552) and the fifth leading cause of death among children younger than 5 years (446 000 deaths, 390 894–504 613). Rotavirus was the leading aetiology for diarrhoea mortality among children younger than 5 years (128 515 deaths, 105 138–155 133) and among all ages (228 047 deaths, 183 526–292 737). Childhood wasting (low weight-for-height score), unsafe water, and unsafe sanitation were the leading risk factors for diarrhoea, responsible for 80·4% (95% UI 68·2–85·0), 72·1% (34·0–91·4), and 56·4% (49·3–62·7) of diarrhoea deaths in children younger than 5 years, respectively. Prevention of wasting in 1762 children (95% UI 1521–2170) could avert one death from diarrhoea. Interpretation Substantial progress has been made globally in reducing the burden of diarrhoeal diseases, driven by decreases in several primary risk factors. However, this reduction has not been equal across locations, and burden among adults older than 70 years requires attention. Funding Bill & Melinda Gates Foundation.
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              Does matching overcome LaLonde's critique of nonexperimental estimators?

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                Author and article information

                Contributors
                erukundo@uni-bonn.de
                Journal
                Int J Health Econ Manag
                Int J Health Econ Manag
                International Journal of Health Economics and Management
                Springer US (New York )
                2199-9023
                2199-9031
                10 February 2021
                10 February 2021
                2021
                : 21
                : 2
                : 203-227
                Affiliations
                [1 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Center for Development Research (ZEF), , University of Bonn, ; Genscherallee 3, 53117 Bonn, Germany
                [2 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Institute for Food and Resource Economics, , University of Bonn, ; Nussallee 19, 53115 Bonn, Germany
                [3 ]GRID grid.59547.3a, ISNI 0000 0000 8539 4635, Department of Agriculture Economics, , University of Gondar, ; Gondar, Ethiopia
                [4 ]GRID grid.449527.9, ISNI 0000 0004 0534 1218, Kabale University, ; P.O. Box 317, Kabale, Uganda
                Author information
                http://orcid.org/0000-0002-2423-7120
                Article
                9294
                10.1007/s10754-021-09294-6
                8192361
                33566252
                0c26f560-61d8-43a7-90c9-3f08c55f6f78
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 July 2019
                : 25 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001655, Deutscher Akademischer Austauschdienst;
                Award ID: ST42 - 2014
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100011087, Stiftung fiat panis;
                Award ID: Awarded through the Center for Development Research
                Award Recipient :
                Funded by: Projekt DEAL
                Categories
                Research Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2021

                community-based health insurance,enrolment,preventive health,inverse probability weighting,rural uganda,i130,i150,i100

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