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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.

ABSTRACT: Policy Points: 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. 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. Contextual factors at macro and micro political units are important to reduce health spending and CHS in India. CONTEXT: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. METHODS: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. FINDINGS: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%). CONCLUSIONS: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.

SUBMITTER: Mohanty SK

PROVIDER: S-EPMC5835682 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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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.

Mohanty Sanjay K SK Kim Rockli R Khan Pijush Kanti PK Subramanian S V SV

The Milbank quarterly 20180301 1

Policy Points: 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. 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. Contextual factors at ma ...[more]

PMID: 29504205

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Project description:ObjectiveTo understand what patterns of health care use are associated with higher post-hospitalization spending.Data sourcesMedicare hospital, skilled nursing, inpatient rehabilitation, and home health agency claims, and Medicare enrollment data from 2007 and 2008.Study designFor 10 common inpatient conditions, we calculated variation across hospitals in price-standardized and case mix-adjusted Medicare spending in the 30 days following hospital discharge. We estimated the fraction of spending differences between low- and high-spending hospitals attributable to readmissions versus post-acute care, and within post-acute care between inpatient rehabilitation facility (IRF) versus skilled nursing facility (SNF) use. For each service, we distinguished between differences in probability of use and spending conditional on use.Data extraction methodsWe identified index hospital claims and examined hospital and post-acute care occurring within a 30-day period following hospital discharge. For each Medicare Severity Diagnosis-Related Group (MS-DRG) at each hospital, we calculated average price-standardized Medicare payments for readmissions, SNFs, IRFs, and post-acute care overall (also including home health agencies and long-term care hospitals).Principal findingsThere was extensive variation across hospitals in Medicare spending in the 30 days following hospital discharge. For example, the interquartile range across hospitals ranged from $1,245 for chronic obstructive pulmonary disease to over $4,000 for myocardial infarction MS-DRGs. The proportion of differences attributable to readmissions versus post-acute care differed across conditions. For myocardial infarction, 74 to 93 percent of the variation was due to readmissions. For hip and femur procedures and joint replacement, 72 to 92 percent of the variation was due to differences in post-acute care spending. There was also variation in the relative importance of the type of post-acute spending. For hip and femur procedures, joint replacement, and stroke, whether patients received IRF was the key driver of variation in post-acute care spending In contrast, for pneumonia and heart failure, whether patients received SNF care was the key driver of variation in post-acute spending.ConclusionsThrough initiatives such as bundled payment, hospitals are financially responsible for spending in the post-hospitalization period. The key driver of variation in post-hospitalization spending varied greatly across conditions. For some conditions, the key driver was having a readmission, for others it was whether patients receive any post-acute care, and for others the key driver was the type of post-acute care. These findings may help hospitals implement strategies to reduce post-discharge spending.

Project description:BackgroundHousehold food insecurity is a potent social determinant of health and health care costs in Canada, but understanding of the social and economic conditions that underlie households' vulnerability to food insecurity is limited.MethodsData from the 2011-12 Canadian Community Health Survey were used to determine predictors of household food insecurity among a nationally-representative sample of 120,909 households. Household food insecurity over the past 12 months was assessed using the 18-item Household Food Security Survey Module. Households were classified as food secure or marginally, moderately, or severely food insecure based on the number of affirmative responses. Multivariable binary and multinomial logistic regression analyses were used to determine geographic and socio-demographic predictors of presence and severity of household food insecurity.ResultsThe prevalence of household food insecurity ranged from 11.8% in Ontario to 41.0% in Nunavut. After adjusting for socio-demographic factors, households' odds of food insecurity were lower in Quebec and higher in the Maritimes, territories, and Alberta, compared to Ontario. The adjusted odds of food insecurity were also higher among households reliant on social assistance, Employment Insurance or workers' compensation, those without a university degree, those with children under 18, unattached individuals, renters, and those with an Aboriginal respondent. Higher income, immigration, and reliance on seniors' income sources were protective against food insecurity. Living in Nunavut and relying on social assistance were the strongest predictors of severe food insecurity, but severity was also associated with income, education, household composition, Aboriginal status, immigration status, and place of residence. The relation between income and food insecurity status was graded, with every $1000 increase in income associated with 2% lower odds of marginal food insecurity, 4% lower odds of moderate food insecurity, and 5% lower odds of severe food insecurity.ConclusionsThe probability of household food insecurity in Canada and the severity of the experience depends on a household's province or territory of residence, income, main source of income, housing tenure, education, Aboriginal status, and household structure. Our findings highlight the intersection of household food insecurity with public policy decisions in Canada and the disproportionate burden of food insecurity among Indigenous peoples.

Dataset Information

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

Publications

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

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