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      Geographic Variation in Household and Catastrophic Health Spending in India: Assessing the Relative Importance of Villages, Districts, and States, 2011-2012 : Geographies of Household and Catastrophic Health Spending in India

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          Abstract

          <div class="section"> <a class="named-anchor" id="milq12315-sec-0010"> <!-- named anchor --> </a> <p class="first" id="d7193666e199"> <div class="boxed-text panel" id="d7193666e201"> <a class="named-anchor" id="d7193666e201"> <!-- named anchor --> </a> <div class="panel-content"> <div class="caption" id="d7193666e202"> <strong> <span class="fig-title">Policy Points:</span> </strong> </div> <p id="d7193666e205"> <div class="list"> <a class="named-anchor" id="milq12315-list-0001"> <!-- named anchor --> </a> <ul class="so-custom-list"> <li id="d7193666e208"> <div class="so-custom-list-content so-ol"> <p class="first" id="d7193666e209">Per‐capita household health spending was higher in economically developed states and was associated with ability to pay, but catastrophic health spending (CHS) was equally high in both poorer and more developed states in India. </p> </div> </li> <li id="d7193666e211"> <div class="so-custom-list-content so-ol"> <p class="first" id="d7193666e212">Based on multilevel modeling, we found that the largest geographic variation in health spending and CHS was at the state and village levels, reflecting wide inequality in the accessibility to and cost of health care at these levels. </p> </div> </li> <li id="d7193666e214"> <div class="so-custom-list-content so-ol"> <p class="first" id="d7193666e215">Contextual factors at macro and micro political units are important to reduce health spending and CHS in India. </p> </div> </li> </ul> </div> </p> </div> </div> </p> </div><div class="section"> <a class="named-anchor" id="milq12315-sec-0020"> <!-- named anchor --> </a> <h5 class="section-title" id="d7193666e220">Context</h5> <p id="d7193666e222">In India, health care is a local good, and households are the major source of financing it. Earlier studies have examined diverse determinants of health care spending, but no attempt has been made to understand the geographical variation in household and catastrophic health spending. We used multilevel modeling to assess the relative importance of villages, districts, and states to health spending in India. </p> </div><div class="section"> <a class="named-anchor" id="milq12315-sec-0030"> <!-- named anchor --> </a> <h5 class="section-title" id="d7193666e225">Methods</h5> <p id="d7193666e227">We used data on the health expenditures of 101,576 households collected in the consumption expenditure schedule (68th round) carried out by the National Sample Survey in 2011‐2012. We examined 4 dependent variables: per‐capita health spending (PHS), per‐capita institutional health spending (PIHS), per‐capita noninstitutional health spending (PNHS), and catastrophic health spending (CHS). CHS was defined as household health spending exceeding 40% of its capacity to pay. We used multilevel linear regression and logistic models to decompose the variation in each outcome by state, region, district, village, and household levels. </p> </div><div class="section"> <a class="named-anchor" id="milq12315-sec-0040"> <!-- named anchor --> </a> <h5 class="section-title" id="d7193666e230">Findings</h5> <p id="d7193666e232">The average PHS was 1,331 Indian rupees (INR), which varied by state‐level economic development. About one‐fourth of Indian households incurred CHS, which was equally high in both the economically developed and poorer states. After controlling for household level factors, 77.1% of the total variation in PHS was attributable to households, 10.1% to states, 9.5% to villages, 2.6% to districts, and 0.7% to regions. The pattern in variance partitioning was similar for PNHS. The largest interstate variation was found for CHS (15.9%), while the opposite was true for PIHS (3.2%). </p> </div><div class="section"> <a class="named-anchor" id="milq12315-sec-0050"> <!-- named anchor --> </a> <h5 class="section-title" id="d7193666e235">Conclusions</h5> <p id="d7193666e237">We observed substantial variations in household health spending at the state and village levels compared with India's districts and regions. The large variation in CHS attributable to states indicates interstate inequality in the accessibility to and cost of health care. Our findings suggest that contextual factors at the macro and micro political units are important to reduce India's household health spending and CHS. </p> </div>

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          Most cited references22

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          Health care and equity in India.

          In India, despite improvements in access to health care, inequalities are related to socioeconomic status, geography, and gender, and are compounded by high out-of-pocket expenditures, with more than three-quarters of the increasing financial burden of health care being met by households. Health-care expenditures exacerbate poverty, with about 39 million additional people falling into poverty every year as a result of such expenditures. We identify key challenges for the achievement of equity in service provision, and equity in financing and financial risk protection in India. These challenges include an imbalance in resource allocation, inadequate physical access to high-quality health services and human resources for health, high out-of-pocket health expenditures, inflation in health spending, and behavioural factors that affect the demand for appropriate health care. Use of equity metrics in monitoring, assessment, and strategic planning; investment in development of a rigorous knowledge base of health-systems research; development of a refined equity-focused process of deliberative decision making in health reform; and redefinition of the specific responsibilities and accountabilities of key actors are needed to try to achieve equity in health care in India. The implementation of these principles with strengthened public health and primary-care services will help to ensure a more equitable health care for India's population. Copyright © 2011 Elsevier Ltd. All rights reserved.
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            Partitioning Variation in Multilevel Models

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              Is Open Access

              Factors affecting catastrophic health expenditure and impoverishment from medical expenses in China: policy implications of universal health insurance

              OBJECTIVE: To assess the degree to which the Chinese people are protected from catastrophic household expenditure and impoverishment from medical expenses and to explore the health system and structural factors influencing the first of these outcomes. METHODS: Data were derived from the Fourth National Health Service Survey. An analysis of catastrophic health expenditure and impoverishment from medical expenses was undertaken with a sample of 55 556 households of different characteristics and located in rural and urban settings in different parts of the country. Logistic regression was used to identify the determinants of catastrophic health expenditure. FINDINGS: The rate of catastrophic health expenditure was 13.0%; that of impoverishment was 7.5%. Rates of catastrophic health expenditure were higher among households having members who were hospitalized, elderly, or chronically ill, as well as in households in rural or poorer regions. A combination of adverse factors increased the risk of catastrophic health expenditure. Families enrolled in the urban employee or resident insurance schemes had lower rates of catastrophic health expenditure than those enrolled in the new rural corporative scheme. The need for and use of health care, demographics, type of benefit package and type of provider payment method were the determinants of catastrophic health expenditure. CONCLUSION: Although China has greatly expanded health insurance coverage, financial protection remains insufficient. Policy-makers should focus on designing improved insurance plans by expanding the benefit package, redesigning cost sharing arrangements and provider payment methods and developing more effective expenditure control strategies.
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                Author and article information

                Journal
                The Milbank Quarterly
                The Milbank Quarterly
                Wiley
                0887378X
                March 2018
                March 2018
                March 05 2018
                : 96
                : 1
                : 167-206
                Affiliations
                [1 ]International Institute for Population Sciences
                [2 ]These authors contributed equally to this work
                [3 ]Harvard T.H. Chan School of Public Health
                [4 ]Harvard Center for Population & Development Studies
                Article
                10.1111/1468-0009.12315
                d4fb6833-7e8d-425d-97d3-346c670f9ae9
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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