Impact of the health insurance scheme for stateless people on inpatient utilization in Kraburi Hospital, Thailand.

OBJECTIVES: This study sought to investigate the impact of the Thai "Health Insurance for People with Citizenship Problems" (HI-PCP) on access to care for stateless patients, compared to Universal Coverage Scheme patients and the uninsured, using inpatient utilization as a proxy for impact. METHODS: Secondary data analysis of inpatient records of Kraburi Hospital, Ranong province, between 2009 (pre-policy) and 2012 (post-policy) was employed. Descriptive statistics and multivariate analysis by difference-in-difference model were performed. RESULTS: The volume of inpatient service utilization by stateless patients expanded after the introduction of the HI-PCP. However, this increase did not appear to stem from the HI-PCP per se. After controlling for key covariates, including patients' characteristics, disease condition, and domicile, there was only a weak positive association between the HI-PCP and utilization. Critical factors contributing significantly to increased utilization were older age, proximity to the hospital, and presence of catastrophic illness. CONCLUSION: A potential explanation for the insignificant impact of the HI-PCP on access to inpatient care of stateless patients is likely to be a lack of awareness of the existence of the scheme among the stateless population and local health staff. This problem is likely to have been accentuated by operational constraints in policy implementation, including the poor performance of local offices in registering stateless people. A key limitation of this study is a lack of data on patients who did not visit the health facility at the first opportunity. Further study of health-seeking behavior of stateless people at the household level is recommended

Economic Impact of Illness with Health Insurance but without Income Insurance

__Abstract__ We examine economic vulnerability to illness when, as for informal sector workers in Thailand, there is universal coverage for health care but earnings losses are uninsured. Even with comprehensive health care entitlement, severe illness that strikes an initially healthy worker is found to raise out-of-pocket medical expenses by around two thirds and increase the probability that medical spending absorbs more than a tenth of the household budget by nine percentage points. Moreover, severe illness reduces the probability of remaining in employment by 18 points and precipitates a reduction in household labor income of almost one third. Despite the rise in medical expenses and fall in earnings, households are able to maintain expenditure on goods and services other than medical care by drawing on remittances and informal transfers, cutting back on saving, and by borrowing. In the short term, informal insurance fills gaps left uncovered by formal insurance but there is likely to be subst antial exposure to economic risks associated with long-term illness

How to do (or not to do) … a health financing incidence analysis.

Financing incidence analysis (FIA) assesses how the burden of health financing is distributed in relation to household ability to pay (ATP). In a progressive financing system, poorer households contribute a smaller proportion of their ATP to finance health services compared to richer households. A system is regressive when the poor contribute proportionately more. Equitable health financing is often associated with progressivity. To conduct a comprehensive FIA, detailed household survey data containing reliable information on both a cardinal measure of household ATP and variables for extracting contributions to health services via taxes, health insurance and out-of-pocket (OOP) payments are required. Further, data on health financing mix are needed to assess overall FIA. Two major approaches to conducting FIA described in this article include the structural progressivity approach that assesses how the share of ATP (e.g. income) spent on health services varies by quantiles, and the effective progressivity approach that uses indices of progressivity such as the Kakwani index. This article provides some detailed practical steps for analysts to conduct FIA. This includes the data requirements, data sources, how to extract or estimate health payments from survey data and the methods for assessing FIA. It also discusses data deficiencies that are common in many low- and middle-income countries (LMICs). The results of FIA are useful in designing policies to achieve an equitable health system

Universal Coverage on a Budget: Impacts on Health Care Utilization and Out-Of-Pocket Expenditures in Thailand

We estimate the impact on health care utilization and out-of-pocket (OOP) expenditures of a major reform in Thailand that extended health insurance to one-quarter of the population to achieve universal coverage while keeping health spending below 4% of GDP. Identification is through comparison of changes in outcomes of groups to whom coverage was extended with those of public sector employees and their dependents whose coverage was not affected. The reform is estimated to have reduced the probability that a sick person goes without formal treatment by 3.2 percentage points (11%). It increased the probability of receiving public ambulatory care by 2.7 ppt (5%) and of admission to a public hospital by 1 ppt (18%). OOP expenditures were reduced by one-third on average, as was the probability of spending more than 10% of the household budget on health care, while spending at the very top of the OOP distribution was reduced by one-half representing substantial reductio ns in exposure to medical expenditure risk. Supply-side measures implemented with the coverage extension are likely to have helped realize these effects from an increased, but still very tight, budget

Good Health at Low Cost 25 years on: lessons for the future of health systems strengthening.

In 1985, the Rockefeller Foundation published Good health at low cost to discuss why some countries or regions achieve better health and social outcomes than do others at a similar level of income and to show the role of political will and socially progressive policies. 25 years on, the Good Health at Low Cost project revisited these places but looked anew at Bangladesh, Ethiopia, Kyrgyzstan, Thailand, and the Indian state of Tamil Nadu, which have all either achieved substantial improvements in health or access to services or implemented innovative health policies relative to their neighbours. A series of comparative case studies (2009-11) looked at how and why each region accomplished these changes. Attributes of success included good governance and political commitment, effective bureaucracies that preserve institutional memory and can learn from experience, and the ability to innovate and adapt to resource limitations. Furthermore, the capacity to respond to population needs and build resilience into health systems in the face of political unrest, economic crises, and natural disasters was important. Transport infrastructure, female empowerment, and education also played a part. Health systems are complex and no simple recipe exists for success. Yet in the countries and regions studied, progress has been assisted by institutional stability, with continuity of reforms despite political and economic turmoil, learning lessons from experience, seizing windows of opportunity, and ensuring sensitivity to context. These experiences show that improvements in health can still be achieved in countries with relatively few resources, though strategic investment is necessary to address new challenges such as complex chronic diseases and growing population expectations

Private-Public Mix in Woman and Child Health in Low-Income Countries: An Analysis of Demographic and Health Surveys

This paper provides an analysis of demographic and health surveys of Private-Public Mix in Women and Health in Low-Income countries. As always the Demographic and Health Survey is a very valuable source of data that can be used to understand health-seeking behaviour in developing countries by teasing out the sources of health care used by households. [Technical Partner Paper 1]Demographic, Health, Surveys, Public, Private, household, valuable, care