Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia.

BACKGROUND: Financial protection is a challenge for low- and middle-income countries, where the fiscal space is limited, and majority of the population is engaged in the informal economy. This study developed and validated household consumption predictive models for Cambodia to collect contributions according to one's ability to pay. METHODS: This study used nationally representative survey data collected annually between 2010 and 2017, involving 38,472 households. We developed four alternative models: the manually selected linear model, the linear model with stepwise technique, the mixed effects linear model, and the model with regularisation technique. Subsequently, we performed out-of-sample cross-validation for each model, and evaluated the model prediction performance. RESULTS: Overall, observed and predicted household consumptions were linearly related in all four models. While the prediction performance of the models did not substantially differ, the stepwise linear model showed the best performance. The regularisation and the mixed effects were not particularly effective in these regressions. The household consumption was better predicted for those with lower consumption, and the predictivity declined as the consumption level increased. CONCLUSIONS: This study suggests the possibility of predicting household consumption at a reasonable level. This would maximise the contribution revenue, optimise the government subsidy, and ensure equity in healthcare access.

Hospital admission for type 2 diabetes mellitus under the Universal Coverage Scheme in Thailand: A time- and geographical-trend analysis, 2009-2016.

BACKGROUND: Descriptive analyses of 2009-2016 were performed using the data of the Universal Coverage Scheme (UCS) which covers nearly 70 percent of the Thai population. The analyses described the time and geographical trends of nationwide admission rates of type 2 diabetes mellitus (T2DM) and its complications, including chronic kidney disease (CKD), myocardial infarction, cerebrovascular diseases, retinopathy, cataract, and diabetic foot amputation. METHODS AND FINDINGS: The database of T2DM patients aged 15-100 years who were admitted between 2009 and 2016 under the UCS and that of the UCS population were retrieved for the analyses. The admitted cases of T2DM were extracted from the database using disease codes of principal and secondary diagnoses defined by the International Classification of Diseases 9th and 10th Revisions. The T2DM admission rates in 2009-2016 were the number of admissions divided by the number of the UCS population. The standardized admission rates (SARs)were further estimated in contrast to the expected number of admissions considering age and sex composition of the UCS population in each region. A linearly increased trend was found in T2DM admission rates from 2009 to 2016. Female admission rates were persistently higher than that of males. In 2016, an increase in the T2DM admission rates was observed among the older ages relative to that in 2009. Although the SARs of T2DM were generally higher in Bangkok and central regions in 2009, except that with CKD and foot amputation which had higher trends in northeastern regions, the geographical inequalities were fairly reduced by 2016. CONCLUSION: Admission rates of T2DM and its major complications increased in Thailand from 2009 to 2016. Although the overall geographical inequalities in the SARs of T2DM were reduced in the country, further efforts are required to improve the health system and policies focusing on risk factors and regions to manage the increasing T2DM.

Tracking Japan's development assistance for health, 2012-2016.

BACKGROUND: Development assistance for health (DAH) is one of the most important means for Japan to promote diplomacy with developing countries and contribute to the international community. This study, for the first time, estimated the gross disbursement of Japan's DAH from 2012 to 2016 and clarified its flows, including source, aid type, channel, target region, and target health focus area. METHODS: Data on Japan Tracker, the first data platform of Japan's DAH, were used. The DAH definition was based on the Organisation for Economic Co-operation and Development's (OECD) sector classification. Regarding core funding to non-health-specific multilateral agencies, we estimated DAH and its flows based on the OECD methodology for calculating imputed multilateral official development assistance (ODA). RESULTS: Japan's DAH was estimated at 1472.94 (2012), 823.15 (2013), 832.06 (2014), 701.98 (2015), and 894.57 million USD (2016) in constant prices of 2016. Multilateral agencies received the largest DAH share of 44.96-57.01% in these periods, followed by bilateral grants (34.59-53.08%) and bilateral loans (1.96-15.04%). Ministry of Foreign Affairs (MOFA) was the largest contributors to the DAH (76.26-82.68%), followed by Ministry of Finance (MOF) (10.86-16.25%). Japan's DAH was most heavily distributed in the African region with 41.64-53.48% share. The channel through which the most DAH went was Global Fund to Fight AIDS, Tuberculosis, and Malaria (20.04-34.89%). Between 2012 and 2016, approximately 70% was allocated to primary health care and the rest to health system strengthening. CONCLUSIONS: With many major high-level health related meetings ahead, coming years will play a powerful opportunity to reevaluate DAH and shape the future of DAH for Japan. We hope that the results of this study will enhance the social debate for and contribute to the implementation of Japan's DAH with a more efficient and effective strategy.

Implementing a sustainable health insurance system in Cambodia: a study protocol for developing and validating an efficient household income-level assessment model for equitable premium collection.

BACKGROUND: As elsewhere in low- and middle-income countries, due to limited fiscal resources, universal health coverage (UHC) remains a challenge in Cambodia. Since 2016, the National Social Security Fund (NSSF) has implemented a social health insurance scheme with a contributory approach for formal sector workers. However, informal sector workers and dependents of formal sector workers are still not covered by this insurance because it is difficult to set an optimal amount of contribution for such individuals as their income levels are inestimable. The present study aims to develop and validate an efficient household income-level assessment model for Cambodia. We aim to help the country implement a financially sustainable social health insurance system in which the insured can pay contributions according to their ability. METHODS: This study will use nationally representative data collected by the Cambodia Socio-Economic Survey (CSES), covering the period from 2009 to 2019, and involving a total of 50,016 households. We will employ elastic net regression analysis, with per capita disposable income based on purchasing power parity as the dependent variable, and individual and community-level socioeconomic and demographic characteristics as independent variables. These analyses aim to create efficient income-level assessment models for health insurance contribution estimation. To fully capture socioeconomic heterogeneity, sub-group analyses will be conducted to develop separate income-level assessment models for urban and rural areas, as well as for each province. DISCUSSION: This research will help Cambodia implement a sustainable social health insurance system by collecting optimal amount of contributions from each socioeconomic group of the society. Incorporation of this approach into existing NSSF schemes will enhance the country's current efforts to prevent impoverishing health expenditure and to achieve UHC.

Limited alignment of publicly competitive disease funding with disease burden in Japan.

OBJECTIVE: The need to align investments in health research and development (R&D) with public health needs is one of the most important public health challenges in Japan. We examined the alignment of disease-specific publicly competitive R&D funding to the disease burden in the country. METHODS: We analyzed publicly available data on competitive public funding for health in 2015 and 2016 and compared it to disability-adjusted life year (DALYs) in 2016, which were obtained from the Global Burden of Disease (GBD) 2017 study. Their alignment was assessed as a percentage distribution among 22 GBD disease groups. Funding was allocated to the 22 disease groups based on natural language processing, using textual information such as project title and abstract for each research project, while considering for the frequency of information. RESULTS: Total publicly competitive funding in health R&D in 2015 and 2016 reached 344.1 billion JPY (about 3.0 billion USD) for 32,204 awarded projects. About 49.5% of the funding was classifiable for disease-specific projects. Five GDB disease groups were significantly and relatively well-funded compared to their contributions to Japan's DALY, including neglected tropical diseases and malaria (funding vs DALY = 1.7% vs 0.0%, p<0.01) and neoplasms (28.5% vs 19.2%, p<0.001). In contrast, four GDB disease groups were significantly under-funded, including cardiovascular diseases (8.0% vs 14.8%, p<0.001) and musculoskeletal disorders (1.0% vs 11.9%, p<0.001). These percentages do not include unclassifiable funding. CONCLUSIONS: While caution is necessary as this study was not able to consider public in-house funding and the methodological uncertainties could not be ruled out, the analysis may provide a snapshot of the limited alignment between publicly competitive disease-specific funding and the disease burden in the country. The results call for greater management over the allocation of scarce resources on health R&D. DALYs will serve as a crucial, but not the only, consideration in aligning Japan's research priorities with the public health needs. In addition, the algorithms for natural language processing used in this study require continued efforts to improve accuracy.