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

Measurement of Catastrophic Health Expenditure in India: A Systematic Review and Meta-Analysis

INTRODUCTION

The escalating burden of catastrophic health expenditure (CHE) poses a significant threat to individuals and households in India, where out-of-pocket expenditure (OOP) constitutes a substantial portion of healthcare financing. With rising OOP in India, a proper measurement to track and monitor CHE due to health expenditure is of utmost important. This study focuses on synthesizing findings, understanding measurement variations, and estimating the pooled incidence of CHE by health services, reported diseases, and survey types.

METHOD

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a thorough search strategy was employed across multiple databases, between 2010 and 2023. Inclusion criteria encompassed observational or interventional studies reporting CHE incidence, while exclusion criteria screened out studies with unclear definitions, pharmacy revenue-based spending, or non-representative health facility surveys. A meta-analysis, utilizing a random-effects model, assessed the pooled CHE incidence. Sensitivity analysis and subgroup analyses were conducted to explore heterogeneity.

RESULTS

Out of 501 initially relevant articles, 36 studies met inclusion criteria. The review identified significant variations in CHE measurements, with incidence ranging from 5.1% to 69.9%. Meta-analysis indicated the estimated incidence of CHE at a 10% threshold is 0.30 [0.25-0.35], indicating a significant prevalence of financial hardship due to health expenses. The pooled incidence is estimated by considering different sub-groups. No statistical differences were found between inpatient and outpatient CHE. However, disease-specific estimates were significantly higher (52%) compared to combined diseases (21%). Notably, surveys focusing on health reported higher CHE (33%) than consumption surveys (14%).

DISCUSSION

The study highlights the intricate challenges in measuring CHE, emphasizing variations in recall periods, components considered in out-of-pocket expenditure, and diverse methods for defining capacity to pay. Notably, the findings underscore the need for standardized definitions and measurements across studies. The lack of uniformity in reporting exacerbates the challenge of comparing and comprehensively understanding the financial burden on households.

Relying on the French territorial offer of thermal spa therapies to build a care pathway for long COVID-19 patients

BACKGROUND

Work on long COVID-19 has mainly focused on clinical care in hospitals. Thermal spa therapies represent a therapeutic offer outside of health care institutions that are nationally or even internationally attractive. Unlike local care (hospital care, general medicine, para-medical care), their integration in the care pathways of long COVID-19 patients seems little studied. The aim of this article is to determine what place french thermal spa therapies can take in the care pathway of long COVID-19 patients.

METHODS

Based on the case of France, we carry out a geographic mapping analysis of the potential care pathways for long COVID-19 patients by cross-referencing, over the period 2020-2022, the available official data on COVID-19 contamination, hospitalisations in intensive care units and the national offer of spa treatments. This first analysis allows us, by using the method for evaluating the attractiveness of an area defined by David Huff, to evaluate the accessibility of each French department to thermal spas.

RESULTS

Using dynamic geographical mapping, this study describes two essential criteria for the integration of the thermal spa therapies offer in the care pathways of long COVID-19 patients (attractiveness of spa areas and accessibility to thermal spas) and three fundamental elements for the success of these pathways (continuity of the care pathways; clinical collaborations; adaptation of the financing modalities to each patient). Using a spatial attractiveness method, we make this type of geographical analysis more dynamic by showing the extent to which a thermal spa is accessible to long COVID-19 patients.

CONCLUSION

Based on the example of the French spa offer, this study makes it possible to place the care pathways of long COVID-19 patients in a wider area (at least national), rather than limiting them to clinical and local management in a hospital setting. The identification and operationalization of two geographical criteria for integrating a type of treatment such as a spa cure into a care pathway contributes to a finer conceptualization of the construction of healthcare pathways.

What are the factors associated with catastrophic health expenditure in Colombia? A multi-level analysis

INTRODUCTION

Target 3.8 of the Sustainable Development Goals calls for the guaranteeing of universal health service coverage without generating financial risks for households and individuals. In Colombia, there is no up-to-date information on the proportion of households that suffer catastrophic health expenditure (CHE), nor about how these expenses are associated with the place of residence. To contribute to an understanding of these issues, this study analyses the differences in the levels of CHE among Colombian households, and their association with the province and area (urban or rural) of residence.

METHODS

This is a descriptive and analytical cross-sectional study using the 2016-2017 National Household Budget Survey, the household and population Census 2018, and the Register of Health Providers 2017. We used the definition of CHE proposed by the World Health Organization, with a threshold of 20%. We estimated the percentage of households facing CHE, and its intensity, and estimated a multi-level logistic regression model, using as the dependent variable the question of whether a household experienced CHE, and the province as a second level, where explanatory variables related to the province were included.

RESULTS

We found differences in CHE levels according to the province of residence. At the national level, 1.77% of households experienced CHE, and households in the provinces of Boyacá (5.04%), Nariño (4.04%), Cauca (3.82%), and Chocó (3.78%) faced the highest CHE. For most households with CHE in these provinces, spending on medicines and medical consultations represented close to 50% of their out-of-pocket spending. The multi-level logistic regression model indicated that there are significant variations in CHE attributed to the provinces under study, where the contextual variables of hospital-bed density (AOR = 0.91; 95% CI 0.86-0.96) and incidence of multi-dimensional poverty (AOR = 1.13; 95% CI 1.01-1.30) were factors associated with CHE. For an urban household, 6.58% of the CHE variation is attributed to the province in question, while for a rural household the corresponding variation is 1.56%.

CONCLUSIONS

The geographical location of the household is a key factor when studying CHE in Colombia, where rural households present higher levels of CHE, mainly in the delivery of medicines and medical consultations. The findings reveal the need to analyse financial protection at the local level and establish policies to protect households, especially poor households, from CHE.

Provincial variations in catastrophic health expenditure and medical impoverishment in China: a nationwide population-based study

Background

Financial protection, as a key dimension of Universal Health Coverage (UHC), has been under increasing attention in recent years. A series of studies have examined the nationwide extent of catastrophic health expenditure (CHE) and medical impoverishment (MI) in China. However, disparities in financial protection at the province level have rarely been studied. The aim of this study was to investigate provincial variations in financial protection as well as its inequality across provinces.

Methods

Using data from the 2017 China Household Finance Survey (CHFS), this study estimated the incidence and intensity of CHE and MI for 28 Chinese provinces. Ordinary least square (OLS) estimation, using robust standard errors, was used to explore the factors associated with financial protection at the province level. Moreover, this study examined the urban-rural differences in financial protection within each province, and calculated the concentration index of CHE and MI indicators for each province using household income per capita.

Findings

The study revealed large provincial variations in financial protection within the nation. The nationwide CHE incidence was 11.0% (95% CI: 10.7%, 11.3%), ranging from 6.3% (95% CI: 5.0%, 7.6%) in Beijing to 16.0% (95% CI: 14.0%, 18.0%) in Heilongjiang; the national MI incidence was 2.0% (95% CI: 1.8%, 2.1%), ranging from 0.03% (95% CI: 0.00%, 0.06%) in Shanghai to 4.6% (95% CI: 3.3%, 5.9%) in Anhui province. We also found similar patterns for provincial variations in intensity of CHE and MI. Moreover, substantial provincial variations in income-related inequality and urban-rural gap existed across provinces. Eastern developed provinces in general had much lower inequality within them, compared with central and western provinces.

Interpretation

Despite the great advances towards UHC in China, substantial provincial variations exist in financial protection across provinces. Policymakers should pay special attention to low-income households in central and western provinces. Provision of better financial protection for these vulnerable groups will be key to achieving UHC in China.

Funding

This research was supported by the National Natural Science Foundation of China (Grant Number: 72074049) and the Shanghai Pujiang Program (2020PJC013).

Associations between Indonesia's national health insurance, effective coverage in maternal health and neonatal mortality: a multilevel interrupted time-series analysis 2000-2017

BACKGROUND

We assessed the effect of Indonesia's national health insurance programme (Jaminan Kesehatan Nasional (JKN)) on effective coverage for maternal and child health across geographical regions and population groups.

METHODS

We used four waves of the Indonesia Demographic and Health Survey from 2000 to 2017, which included 38 880 women aged 15-49 years and 144 000 birth records. Key outcomes included antenatal and delivery care, caesarean section and neonatal and infant mortality. We used multilevel interrupted time-series regression to examine changes in outcomes after the introduction of the JKN in January 2014.

FINDINGS

JKN introduction was associated with significant level increases in (1) antenatal care (ANC) crude coverage (adjusted OR (aOR) 1.81, 95% CI 1.44 to 2.27); (2) ANC quality-adjusted coverage (aOR 1.66, 95% CI 1.38 to 1.98); (3) ANC user-adherence-adjusted coverage (aOR 1.80, 95% CI 1.45 to 2.25); (4) safe delivery service contact (aOR 1.83, 95% CI 1.42 to 2.36); and (5) safe delivery crude coverage (aOR 1.45, 95% CI 1.20 to 1.75). We did not find any significant level increase in ANC service contact or caesarean section. Interestingly, increases in ANC service contact and crude coverage, and safe delivery crude coverage were larger among the poorest compared with the most affluent. No statistically significant associations were found between JKN introduction and neonatal and infant mortality (p>0.05) in the first 3 years following implementation.

INTERPRETATION

Expansion of social health insurance led to substantial improvements in quality of care for maternal health services but not in child mortality. Concerted efforts are required to equitably improve service quality and child mortality across the population in Indonesia.

Small area variation in severe, moderate, and mild anemia among women and children: A multilevel analysis of 707 districts in India

India is home to the highest global number of women and children suffering from anemia, with one in every two women impacted. India's current strategy for targeting areas with a high anemia burden is based on district-level averages, yet this fails to capture the substantial small area variation in micro-geographical (small area) units such as villages. We conducted statistical and econometric analyses to quantify the extent of small area variation in the three grades of anemia (severe, moderate, and mild) among women and children across 36 states/union territories and 707 districts of India. We utilized data from the fifth round of the National Family Health Survey conducted in 2019-21. The final analytic sample for analyses was 183,883 children aged 6-59 months and 690,153 women aged 15-49 years. The primary outcome variable for the analysis was the three anemia grades among women and children. We adopted a three-level and four-level logistic regression model to compute variance partitioning of anemia among women and children. We also computed precision-weighted prevalence estimates of women and childhood anemia across 707 districts and within-district, between-cluster variation using standard deviation (SD). For severe anemia among women, small area (villages or urban blocks) account for highest share (46.1%; Var: 0.494; SE: 0.150) in total variation followed by states (39.4%; Var: 0.422; SE: 0.134) and districts (12.8%; Var: 0.156; SE: 0.012). Similarly, clusters account for the highest share in the variation in severe (61.3%; Var: 0.899; SE: 0.069) and moderate (46.4%: Var: 0.398; SE: 0.011) anemia among children. For mild and moderate anemia among women, however, states were the highest source of variation. Additionally, we found a high and positive correlation between mean prevalence and inter-cluster SD of moderate and severe anemia among women and children. In contrast, the correlation was weaker for mild anemia among women (r = 0.61) and children (0.66). In this analysis, we are positing the critical importance of small area variation within districts when designing strategies for targeting high burden areas for anemia interventions.

Effect of Socioeconomic Factors on the Choice of Health care Institutions for Delivery Care

During the past two decades, Pakistan witnessed a significant progress in maternal health outcomes. However, there exist persistent urban-rural and socio-economic inequalities in access and utilization of maternal healthcare services across the country. The overall objective of this research was to identify the significant socio-economic factors determining the choice of healthcare institutions for delivery care. This was a cross-sectional study conducted in Rajan Pur, a predominantly rural district in Punjab province. Using a multi-stage random sampling technique, 368 mothers who had childbirths from 1st October to 31st December 2020 in different healthcare institutions were interviewed. Data for the study was collected through a validated study instrument used by earlier studies on maternal healthcare utilization. The results of logistic regression analysis showed that use of public healthcare facilities for delivery care increases with increasing maternal education, monthly household income, and distance to healthcare facilities. The findings and recommendations drawn from the research would provide some insights to health policymakers and planners in developing an integrated and viable maternal healthcare program in Pakistan.

Financial risk protection from out-of-pocket health spending in low- and middle-income countries: a scoping review of the literature

BACKGROUND

Financial risk protection (FRP), defined as households' access to needed healthcare services without experiencing undue financial hardship, is a critical health systems target, particularly in low- and middle-income countries (LMICs). Given the remarkable growth in FRP literature in recent times, we conducted a scoping review of the literature on FRP from out-of-pocket (OOP) health spending in LMICs. The objective was to review current knowledge, identify evidence gaps and propose future research directions.

METHODS

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to conduct this scoping review. We systematically searched PubMed, Scopus, ProQuest and Web of Science in July 2021 for literature published since 1 January 2015. We included empirical studies that used nationally representative data from household surveys to measure the incidence of at least one of the following indicators: catastrophic health expenditure (CHE), impoverishment, adoption of strategies to cope with OOP expenses, and forgone care for financial reasons. Our review covered 155 studies and analysed the geographical focus, data sources, methods and analytical rigour of the studies. We also examined the level of FRP by disease categories (all diseases, chronic illnesses, communicable diseases) and the effect of health insurance on FRP.

RESULTS

The extant literature primarily focused on India and China as research settings. Notably, no FRP study was available on chronic illness in any low-income country (LIC) or on communicable diseases in an upper-middle-income country (UMIC). Only one study comprehensively measured FRP by examining all four indicators. Most studies assessed (lack of) FRP as CHE incidence alone (37.4%) or as CHE and impoverishment incidence (39.4%). However, the LMIC literature did not incorporate the recent methodological advances to measure CHE and impoverishment that address the limitations of conventional methods. There were also gaps in utilizing available panel data to determine the length of the lack of FRP (e.g. duration of poverty caused by OOP expenses). The current estimates of FRP varied substantially among the LMICs, with some of the poorest countries in the world experiencing similar or even lower rates of CHE and impoverishment compared with the UMICs. Also, health insurance in LMICs did not consistently offer a higher degree of FRP.

CONCLUSION

The literature to date is unable to provide a reliable representation of the actual level of protection enjoyed by the LMIC population because of the lack of comprehensive measurement of FRP indicators coupled with the use of dated methodologies. Future research in LMICs should address the shortcomings identified in this review.

Hospitalization & health expenditure in Odisha: Evidence from National Sample Survey (1995-2014)

Background & objectives

Financing healthcare services through out-of-pocket payments is common in India. Household impoverishments due to health expenditure can be daunting, especially among the economically vulnerable households. This study investigated hospitalization and patient's health expenditure in Odisha State in India.

Methods

The national sample survey data were used to assess hospitalization and patient's health expenditure over two time periods (1995 and 2014). Disease classification was made following International Classification of Diseases 10th revision (ICD-10). The hospitalization rate and health expenditure were estimated for infectious, cardiovascular, non-communicable, disability and other diseases. Andersen model was used to examine the determinants of healthcare expenditure.

Results

Findings of the study revealed that hospitalization in Odisha increased nearly three folds and health expenditure by more than two times between 1995 to 2014. While the hospitalization for other diseases remained consistently higher, health expenditure for disability was the highest and it increased three times within the last two decades. The socio-economic and demographic divides in the hospitalization rate and health expenditure were evident.

Interpretation & conclusions

Our analysis indicated that predisposing factors such as age and marital status played an important role in hospitalization whereas, enabling factors likely determined the health expenditure. There is a need to recognize the unique vulnerabilities of older population, widowed and health financial mechanism for disability-related illness.

Small area variations in low birth weight and small size of births in India

The states and districts are the primary focal points for policy formulation and programme intervention in India. The within‐districts variation of key health indicators is not well understood and consequently underemphasised. This study aims to partition geographic variation in low birthweight (LBW) and small birth size (SBS) in India and geovisualize the distribution of small area estimates. Applying a four‐level logistic regression model to the latest round of the National Family Health Survey (2015–2016) covering 640 districts within 36 states and union territories of India, the variance partitioning coefficient and precision‐weighted prevalence of LBW (<2.5 kg) and SBS (mother's self‐report) were estimated. For each outcome, the spatial distribution by districts of mean prevalence and small area variation (as measured by standard deviation) and the correlation between them were computed. Of the total valid sample, 17.6% (out of 193,345 children) had LBW and 12.4% (out of 253,213 children) had SBS. The small areas contributed the highest share of total geographic variance in LBW (52%) and SBS (78%). The variance of LBW attributed to small areas was unevenly distributed across the regions of India. While a strong correlation between district‐wide percent and within‐district standard deviation was identified in both LBW (r = 0.88) and SBS (r = 0.87), they were not necessarily concentrated in the aspirational districts. We find the necessity of precise policy attention specifically to the small areas in the districts of India with a high prevalence of LBW and SBS in programme formulation and intervention that may be beneficial to improve childbirth outcomes.

The small areas contribute the highest share of the total geographic variance of low birth weight (LBW) and small birth size (SBS) in India.

A high burden of LBW is found mostly in the central‐western part of India and Odisha. The prevalence of SBS is high across the district of northern‐western regions and the north‐eastern regions of India.

The mean prevalence and standard deviation are strongly correlated in the case of both LBW (r = 0.88) and SBS (r = 0.87) in India. It indicates that the districts which have a higher prevalence of LBW and SBS also have a higher between small area disparity within the districts.

We find a similar pattern of distribution in LBW and SBS between the policy‐focused aspirational districts and other districts of India.

Findings indicate reprioritizing the policy intervention, focusing on the small areas of India for better childbirth outcomes.

Healthcare provider selection for elderly patients suffering from NCD's: an analysis with the combined approach of DEMATEL and AHP

Purpose

Elderly patients suffering from non-communicable disease face a dilemma in the selection of healthcare providers. This study attempts to identify the key variables playing a crucial role and identify the appropriate healthcare destination with the help of a combination of Decision-making Trial and Evaluation Laboratory (DEMATEL) and analytic hierarchy process (AHP) techniques. The primary objective is to introduce the DEMATEL and AHP as efficient decision-making methods to choose the right healthcare provider for elderly patients suffering from non-communicable diseases.

Design/methodology/approach

An integrative approach utilizing DEMATEL and AHP is used to reach the ideal solution for healthcare provider selection decisions. The DEMATEL approach is used to segregate the cause and effect variables. Similarly, the AHP is used to identify the weights of the top five cause-inducing variables, and the paired comparison method is used to select the healthcare provider.

Findings

The variables such as dependency on family members, easily accessible services, and patient autonomy play a vital role in the selection decision of healthcare providers in elderly patients suffering from non-communicable diseases.

Practical implications

In terms of priority, home healthcare should be considered the preferred provider for elderly patients suffering from non-communicable diseases followed by neighbourhood registered medical practitioners and hospitals.

Originality/value

This is the first of its kind study which has attempted to solve the healthcare provider selection decision with the combined approach of DEMATEL and AHP.

The relative importance of households as a source of variation in child malnutrition: a multilevel analysis in India

Background

Child malnutrition remains a major public health issue in India. Along with myriad upstream and social determinants of these adverse outcomes, recent studies have highlighted regional differences in mean child malnutrition rates. This research helps policy makers look between urban and rural communities and states to take a population-level approach to addressing the root causes of child malnutrition. However, one gap in this between-population approach has been the omission of households as a unit of analysis. Households could represent important sources of variation in child malnutrition within communities, districts, and states.

Methods

Using the fourth round of India’s National Family Health Survey from 2015 to 2016, we analyzed four and five-level multilevel models to estimate the proportion of variation in child malnutrition attributable to states, districts, communities, households, and children.

Results

Overall, we found that of the four levels that children were nested in (households, communities, districts, and states), the greatest proportion of variation in child height-for-age Z score, weight-for-age Z score, weight-for-height Z score, hemoglobin, birthweight, stunting, underweight, wasting, anemia, and low birthweight was attributable to households. Furthermore, we found that when the household level is omitted from models, the variance estimates for communities and children are overestimated.

Conclusions

These findings highlight the importance of households as an important source of clustering and variation in child malnutrition outcomes. As such, policies and interventions should address household-level social determinants, such as asset and social deprivations, in order to prevent poor child growth outcomes among the most vulnerable households in India.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01563-7.

Multilevel population and socioeconomic variation in health insurance coverage in India

OBJECTIVES

This study explores population-level variation in different types of health insurance coverage in India. We aimed to estimate the extent to which contextual factors at community, district, and state levels may contribute to place-based inequalities in coverage after accounting for household-level socioeconomic factors.

METHODS

We used data from the 2015-2016 National Family Health Survey in India, which provides the most recent and comprehensive information available on reports of different types of household health insurance coverage. We used multilevel regression models to estimate the relative contribution of different population levels to variation in coverage by national, state, and private health insurance schemes.

RESULTS

Among 601,509 households in India, 29% reported having coverage in 2015-2016. Variation in each type of coverage existed between population levels before and after adjusting for differences in the distribution of household socioeconomic and demographic factors. For example, the state level accounted for 36% of variation in national scheme coverage and 41% of variation in state scheme coverage after adjusting for household characteristics. In contrast, the community level was the largest contextual source of variation in private insurance coverage (accounting for 24%). Each type of coverage was associated with higher socioeconomic status and urban location.

CONCLUSIONS

Contextual factors at community, district, and state levels contribute to variation in household health insurance coverage even after accounting for socioeconomic and demographic factors. Opportunities exist to reduce disparities in coverage by focusing on drivers of place-based differences at multiple population levels. Future research should assess whether new insurance schemes exacerbate or reduce place-based disparities in coverage.

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Prior research has identified a number of risk factors ranging from inadequate household sanitation to maternal characteristics as important determinants of child malnutrition and health in India. What is less known is the extent to which these individual‐level risk factors are geographically distributed. Assessing the geographic distribution, especially at multiple levels, matters as it can inform where, and at what level, interventions should be targeted. The three levels of significance in the Indian context are villages, districts, and states. Thus, the purpose of this paper was to (a) examine what proportion of the variation in 21 risk factors is attributable to villages, districts, and states in India and (b) elucidate the specific states where these risk factors are clustered within India. Using the fourth National Family Health Survey dataset, from 2015 to 2016, we found that the proportion of variation attributable to villages ranged from 14% to 63%, 10% to 29% for districts and 17% to 62% for states. Furthermore, we found that Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh were in the highest risk quintile for more than 10 of the risk factors included in our study. This is an indication of geographic clustering of risk factors. The risk factors that are clustered in states such as Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh underscore the need for policies and interventions that address a broader set of child malnutrition determinants beyond those that are nutrition specific.

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004-18

Background

Estimates of catastrophic health expenditure (CHE) are counterintuitive to researchers, policy makers, and developmental partners due to data and methodological limitation. While inferences drawn from use of capacity-to-pay (CTP) and budget share (BS) approaches are inconsistent, the non-availability of data on food expenditure in the health survey in India is an added limitation.

Methods

Using data from the health and consumption surveys of National Sample Surveys over 14 years, we have overcome these limitations and estimated the incidence and intensity of CHE and impoverishment using the CTP approach.

Results

The incidence of CHE for health services in India was 12.5% in 2004, 13.4% in 2014 and 9.1% by 2018. Among those households incurring CHE, they spent 1.25 times of their capacity to pay in 2004 (intensity of CHE), 1.71 times in 2014 and 1.31 times by 2018. The impoverishment due to health spending was 4.8% in 2004, 5.1% in 2014 and 3.3% in 2018. The state variations in incidence and intensity of CHE and incidence of impoverishment is large. The concentration index (CI) of CHE was − 0.16 in 2004, − 0.18 in 2014 and − 0.22 in 2018 suggesting increasing inequality over time. The concentration curves based on CTP approach suggests that the CHE was concentrated among poor. The odds of incurring CHE were lowest among the richest households [OR 0.22; 95% CI: 0.21, 0.24], households with elderly members [OR 1.20; 95% CI:1.12, 1.18] and households using both inpatient and outpatient services [OR 2.80, 95% CI 2.66, 2.95]. Access to health insurance reduced the chance of CHE and impoverishment among the richest households. The pattern of impoverishment was similar to that of CHE.

Conclusion

In the last 14 years, the CHE and impoverishment in India has declined while inequality in CHE has increased.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01421-6.

Small area variation in child undernutrition across 640 districts and 543 parliamentary constituencies in India

In India, districts serve as central policy unit for program development, administration and implementation. The one-size-fits-all approach based on average prevalence estimates at the district level fails to capture the substantial small area variation. In addition to district average, heterogeneity within districts should be considered in policy design. The objective of this study was to quantify the extent of small area variation in child stunting, underweight and wasting across 36 states/Union Territories (UTs), 640 districts (and 543 PCs), and villages/blocks in India. We utilized the 4th round of Indian National Family Health Survey (NFHS-4) conducted in 2015–2016. The study population included 225,002 children aged 0–59 months whose height and weight information were available. Stunting was defined as height-for-age z-score below 2 SD from the World Health Organization child growth reference standards. Similarly, underweight and wasting were each defined as weight-for-age < -2 SD and weight-for-height < -2 SD from the age- and sex-specific medians. We adopted a four-level logistic regression model to partition the total variation in stunting, underweight and wasting. We computed precision-weighted prevalence of child anthropometric failures across districts and PCs as well as within-district/PC variation using standard deviation (SD) measures. For stunting, 56.4% (var: 0.237; SE: 0.008) of the total variation was attributed to villages/blocks, followed by 25.8% (var: 0.109; SE: 0.030) to states/UTs, and 17.7% (Var: 0.074; SE: 0.006) to districts. For underweight and wasting, villages/blocks accounted for 38.4% (var: 0.224; SE: 0.007) and 50% (var: 0.285; SE: 0.009), respectively, of the total contextual variance in India. Similar findings were shown in multilevel models incorporating PC as a geographical unit instead of districts. We found high positive correlations between mean prevalence and SD for stunting (r = 0.780, p < 0.001), underweight (r = 0.860, p < 0.001), and wasting (r = 0.857, p < 0.001) across all districts in India. A similar pattern of correlation was found for PCs. Within-district and within-PC variation are the primary source of variation for child malnutrition in India. Our results suggest the importance of considering heterogeneity within districts and PCs when planning and administering child nutrition policies.

Understanding equity of institutional delivery in public health centre by level of care in India: an assessment using benefit incidence analysis

BACKGROUND

The National Health Mission (NHM), the largest ever publicly funded health programme worldwide, used over half of the national health budget in India and primarily aimed to improve maternal and child health in the country. Though large scale public health investment has improved the health care utilization and health outcomes across states and socio-economic groups in India, little is known on the equity concern of NHM. In this context, this paper examines the utilization pattern and net benefit of public subsidy for institutional delivery by the level of care in India.

METHODS

Data from the most recent round of the National Family Health Survey (NFHS 4), conducted during 2015-16, was used in the study. A total of 148,645 last birth delivered in a health centre during the 5 years preceding the survey were used for the analyses. Out-of-pocket (OOP) payment on delivery care was taken as the dependent variable and was analysed by primary care and secondary level of care. Benefits Incidence Analysis (BIA), descriptive statistics, concentration index (CI), and concentration curve (CC) were used to do the analysis.

RESULTS

Institutional delivery from the public health centres in India is pro-poor and has a strong economic gradient. However, about 28% mothers from richest wealth quintile did not pay for delivery in public health centres compared to 16% among the poorest wealth quintile. Benefit incidence analyses suggests a pro-poor distribution of institutional delivery both at primary and secondary level of care. In 2015-16, at the primary level, about 32.29% of subsidies were used by the poorest, 27.22% by poorer, 20.39% by middle, 13.36% by richer and 6.73% by the richest wealth quintile. The pattern at the secondary level was similar, though the magnitude was lower. The concentration index of institutional delivery in public health centres was - 0.161 [95% CI, - 0.158, - 0.165] compared to 0.296 [95% CI, 0.289, 0.303] from private health centres.

CONCLUSION

Provision and use of public subsidy for institutional delivery in public health centres is pro-poor in India. Improving the quality of service in primary health centres is recommended to increase utilisation and reduce OOP payment for health care in India.

H, Negi J, Nair A, Sadanandan R. Monitoring Universal Health Coverage reforms in primary health care facilities: Creating a framework, selecting and field-testing indicators in Kerala, India

In line with the Sustainable Development Goals (SDGs) and the target for achieving Universal Health Coverage (UHC), state level initiatives to promote health with "no-one left behind" are underway in India. In Kerala, reforms under the flagship Aardram mission include upgradation of Primary Health Centres (PHCs) to Family Health Centres (FHCs, similar to the national model of health and wellness centres (HWCs)), with the proactive provision of a package of primary care services for the population in an administrative area. We report on a component of Aardram's monitoring and evaluation framework for primary health care, where tracer input, output, and outcome indicators were selected using a modified Delphi process and field tested. A conceptual framework and indicator inventory were developed drawing upon literature review and stakeholder consultations, followed by mapping of manual registers currently used in PHCs to identify sources of data and processes of monitoring. The indicator inventory was reduced to a list using a modified Delphi method, followed by facility-level field testing across three districts. The modified Delphi comprised 25 participants in two rounds, who brought the list down to 23 approved and 12 recommended indicators. Three types of challenges in monitoring indicators were identified: appropriateness of indicators relative to local use, lack of clarity or procedural differences among those doing the reporting, and validity of data. Further field-testing of indicators, as well as the revision or removal of some may be required to support ongoing health systems reform, learning, monitoring and evaluation.

Long-term trend in socioeconomic inequalities and geographic variation in the utilization of antenatal care service in India between 1998 and 2015

OBJECTIVE

To investigate the temporal trend of socioeconomic and rural-urban disparities and geographical variation in the utilization of antenatal care (ANC) services in India before and throughout the Millennium Development Goals era.

DATA SOURCES/STUDY SETTING

For this temporal analysis, secondary data from the Indian National Family Health Surveys between 1998 and 2015 (Waves 2, 3, and 4) were used.

STUDY DESIGN

We analyzed the trend in inequality for at least one and four ANC visits to a health care professional (ANC1+ and ANC4+, respectively) by education, wealth, and residence type. Multilevel logistic regression models were used to assess the temporal trend and the relative contribution of communities and states to the overall variation in ANC1+ and ANC4+.

DATA COLLECTION/EXTRACTION METHODS

Data on utilization of ANC services for the last birth of women aged 15-49 years during the three or five years preceding the survey (depending on the survey year) were used.

PRINCIPAL FINDINGS

Educational and wealth inequality in ANC1+ and ANC4+ worsened between 1998 and 2005 and improved between 2005 and 2015 (for ANC4+, OR [95% CI] = 0.22 [0.19-0.25] in Wave 2; OR [95% CI] = 0.19 [0.17-0.22] in Wave 3; and OR [95% CI] = 0.38 [0.36-0.40] in Wave 4 for the poorest). Rural-urban inequality showed a consistent decline (for ANC4+, OR [95% CI] = 0.59 [0.54-0.64] in Wave 2; OR [95% CI] = 0.63 [0.59-0.68] in Wave 3; and OR [95% CI] = 0.82 [0.79-0.85] in Wave 4 for rural area). The relative contribution of the community to the total geographic variation in the utilization of ANC services increased more than four times during the study period.

CONCLUSIONS

The use of ANC services remains disproportionately lower among women with low socioeconomic status. Efforts to directly target these women are necessary to tackle inequality in ANC utilization in India.

Catastrophic health expenditure: A comparative analysis of smoking and non-smoking households in China

Introduction

Smoking is hazardous to health and places a heavy economic burden on individuals and their families. Clearly, smoking in China is prevalent since China is the largest consumer of tobacco in the world. Chinese smoking and nonsmoking households were compared in terms of the incidence and intensity of Catastrophic Health Expenditures (CHEs). The factors associated with catastrophic health expenditures were analyzed.

Methods

Data for this study were collected from two waves of panel data in 2011 and 2013 from the national China Health and Retirement Longitudinal Study (CHARLS). A total of 8073 households with at least one member aged above 45 were identified each year. Catastrophic health expenditure was measured by the ratio of a household’s out-of-pocket healthcare payments (OOP) to the household’s Capacity to Pay (CTP). A panel logit random-effects model was used to examine correlates with catastrophic health expenditure.

Results

The incidence of catastrophic health expenditures for Chinese households with members aged 45 and above in 2011 and 2013 were 12.99% and 15.56%, respectively. The mean gaps (MGs) were 3.16% and 4.88%, respectively, and the mean positive gaps (MPGs) were 24.36% and 31.40%, respectively. The incidences of catastrophic health expenditures were 17.41% and 20.03% in former smoking households, 12.10% and 15.09% in current smoking households, and 12.72% and 13.64% in nonsmoking households. In the panel logit regression model analysis, former smoking households (OR = 1.444, P<0.001) were more prone to catastrophic health expenditures than nonsmoking households. Risk factors for catastrophic health expenditures included members with chronic diseases (OR = 4.359, P<0.001), hospitalized patients (OR = 8.60, P<0.001), elderly people aged above 65 (OR = 1.577, P<0.001), or persons with disabilities (OR = 1.275, P<0.001). Protective factors for catastrophic health expenditures included being in an urban household, having a larger family size, and having a higher household income.

Conclusions

The incidence of catastrophic health expenditures in Chinese households is relatively high. Smoking is one of the primary risk factors for catastrophic health expenditures. Stronger interventions against smoking should be made in time to reduce the occurrence of health issues caused by smoking and the financial losses for individuals, families and society.

Progress on Catastrophic Health Expenditure in China: Evidence from China Family Panel Studies (CFPS) 2010 to 2016

Background: To provide an updated estimate of the level and change in catastrophic health expenditure in China and examine the association between catastrophic health expenditure and family net income, we obtained data from four waves of the China Family Panel Studies conducted between 2010 and 2016. Method: We defined catastrophic health expenditure as out-of-pocket payments equaling or exceeding 40% of the household’s capacity to pay. The Poisson regression with robust variance and generalized estimated equation (Poisson-GEE) model was used to quantify the level and change of catastrophic health expenditure, as well as the association between catastrophic heath expenditure and family net income. Result: Overall, the incidence of catastrophic expenditure in China experienced a 0.70-fold change between 2010 (12.57%) and 2016 (8.94%). The incidence of catastrophic health expenditure (CHE) decreased more in the poorest income quintile than the richest income quintile (annual decrease of 1.17% vs. 0.24% in urban areas, p < 0.001; 1.64% vs. −0.02% in rural areas, p < 0.001). Every 100% increase in income was associated with a 14% relative-risk reduction in CHE (RR = 0.86, 95% CI: 0.85–0.88) after adjusting for demographics, health needs, and health utilization characteristics; this association was weaker in recent years. Conclusion: Our analysis found that China made progress to reduce catastrophic health expenditure, especially for poorer groups. Income growth is strongly associated with this change.

Variation in Chronic Diseases Across Households, Communities, Districts, and States in India

INTRODUCTION

Globally, chronic noncommunicable diseases are the leading cause of death and accounted for 6 million deaths in India in 2016. However, the extent to which variation in chronic disease can be attributed to different population levels in India is unknown, as is whether variation in individual-level factors explains outcome variation at different population levels.

METHODS

Cross-sectional data from the District Level Household and Facility Survey 2012-2013 conducted across 21 states, 275 districts, 14,235 villages, 378,487 households, and 1,098,940 individuals aged ≥18 years in India were analyzed in 2018‒2019. Multilevel logistic models were used to partition variation in outcomes and attribute it to individual, household, village, district and state population levels. Outcomes included experiencing respiratory, cardiovascular, musculoskeletal, or eye symptoms; reporting a positive diagnosis by a doctor for chronic heart disease, hypertension, diabetes, or vision problems; and objectively assessed real-time measures of hypertension and diabetes.

RESULTS

For reported diagnosis of hypertension or diabetes, a much larger percentage of variation in these outcomes was attributed to differences among households as compared to differences among units within other population levels. However, for objectively measured hypertension and diabetes, variation in these outcomes was important at the village level, followed by variation at the household level. Wealth status was positively associated with respiratory and cardiovascular symptoms, as well as all reported diagnoses and real-time measurements except for vision problems. Inclusion of individual-level sociodemographic variables explained 0%-30% of variation attributed to the household level for most chronic disease symptoms and diagnoses, but almost none at the higher levels.

CONCLUSIONS

These findings imply that household- and village-level factors explain substantial variation in the prevalence of chronic disease symptoms and reported diagnoses in India.

Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016

Background

The complex etiology of child growth failure and anemia—commonly used indicators of child undernutrition—involving proximate and distal risk factors at multiple levels is generally recognized. However, their independent and joint effects are often assessed with no clear conceptualization of inferential targets.

Methods

We utilized hierarchical linear modeling and a nationally representative sample of 139,116 children aged 6–59 months from India (2015–2016) to estimate the extent to which a comprehensive set of 27 covariates explained the within- and between-population variation in height-for-age, weight-for-age, weight-for-height, and hemoglobin level.

Results

Most of the variation in child anthropometry and hemoglobin measures was attributable to within-population differences (80–85%), whereas between-population differences (including communities, districts, and states) accounted for only 15–20%. The proximate and distal covariates explained 0.2–7.5% of within-population variation and 2.1–34.0% of between-population variation, depending on the indicator of interest. Substantial heterogeneity was observed in the magnitude of within-population variation, and the fraction explained, in child anthropometry and hemoglobin measures across the 36 states/union territories of India.

Conclusions

Policies and interventions aimed at reducing between-population inequalities in child undernutrition may require a different set of components than those concerned with within-population inequalities. Both are needed to promote the health of the general population, as well as that of high-risk children.

Do poor people in the poorer states pay more for healthcare in India?

BACKGROUND

Rising health spending is associated with high out-of-pocket expenditure (OOPE), catastrophic health spending (CHS), increasing poverty, and impoverishment. Though studies have examined poverty and impoverishment effect of health spending in India, there is limited research on the regional patterns of health spending by type of health centers. This paper tests the hypothesis that the poor people from the poorer states of India pay significantly more for hospitalization in public health centers than those in the richer states of India.

METHODS

Data from the Social Consumption of Health Survey (71st round, 2014), carried out by the National Sample Survey (NSS) is used in the analyses. Descriptive statistics, log-linear regression model and tobit model were used to examine the determinants and variations in health spending.

RESULTS

Inter-state variations in the utilization of public health services and the OOPE on hospitalization are high in India. States with high levels of poverty make higher use of the public health centers and yet incur high OOPE. In 2014, the mean OOPE per episode of hospitalization in public health centers in India was ₹5688 and ₹4264 for the economically poor households. It was lowest in the economically developed state of Tamil Nadu and highest in the economically poorer state of Bihar. The OOPE per episode of hospitalization in public health centers among the poor in the poorer states was at least twice that in Tamil Nadu. Among the poor using public health centers, the share of direct cost account 24% in Tamil Nadu compared to over 80% in Bihar, Odisha and other poorer states. Adjusting for socio-economic correlates, the cost of hospitalization per episode (CHPE) among the poor using public health centers was 51% lower than for the non-poor using private health centers in India.

CONCLUSION

The poor people in the poorer states in India pay significantly more to avail hospitalization in public health centers than those in the developed states. Provision of free medicines, surgery and free diagnostic tests in public health centers may reduce the high OOPE and medical poverty in India.

Out-of-pocket expenditure and distress financing on institutional delivery in India

BACKGROUND

Despite large investment in central and state sponsored schemes for maternal care, out-of-pocket expenditure (OOPE) and catastrophic health spending (CHS) on institutional delivery remain high over time, across states and across socio-economic groups. Though many studies have examined the OOPE and CHS, few studies have examined the nature and extent of distress financing on institutional delivery in India.

DATA

Data from the fourth round of National Family Health Survey (NFHS 4), 2015-16 was used for the analysis. Distress financing was defined as borrowing money or selling assets to meet the OOPE on delivery care. Composite variables, descriptive analyses, concentration index (CI), concentration curve (CC) and predicted probability were used to estimate the extent of distress financing for institutional delivery in India.

RESULTS

The OOPE on institutional delivery has strong economic and educational gradient. One in four mothers resorted to borrowing or selling to meet the OOPE on institutional delivery. The extent of distress financing on institutional delivery was high in poorer state of Bihar and Odisha and in the state of Telangana that had highest prevalence of caesarean delivery. Savings was more prevalent among mothers compared to those who met the OOPE by borrowing/selling of assets. Finding are robust across the states of India. The predicted probability of incurring distress financing was 0.31 among mothers belonging to the poorest wealth quintile compared to 0.09 in the richest quintile, and 0.40 for those who incurred OOPE of more than INR 20,000. The probability of incurring distress financing was higher for mothers who had caesarean birth, delivered in private health centers and incurred high OOPE on institutional delivery.

CONCLUSION

Distress financing on institutional delivery was higher among the less educated, poor and in private health centers. Increasing use of public health centers, reducing caesarean births, improving the availability of medicine and diagnostic services can reduce the extent of distress financing in India.

Micro-geographic targeting for precision public policy: Analysis of child sex ratio across 587,043 census villages in India, 2011

Child sex ratio (CSR) is a marker of disproportionate sex ratio at birth and discriminatory practices that lead to differential survival in early childhood by sex. We used the 2011 Census on rural India to present the first local analysis of CSR across 587,043 villages. In our multilevel analysis considering villages, tehsils, districts, and states/union territories, we found 96% of the total variation in CSR to be attributed to villages. About 39% of the villages were 'boy' areas (CSR≤88 girls per 100 boys) and another 12% had deficits in girls (88 < CSR≤93), while 11% fell in the normal range of CSR (93<CSR≤98), another 10% had 98 < CSR≤103, and the remaining 28% were 'girl' villages (CSR>103). The magnitude of local variation in CSR was heterogeneous across states/union territories and districts. Our findings provide timely evidence to inform localized programmes like Beti Bachao, Beti Padhao to be implemented with greater precision.

Inequity & burden of out-of-pocket health spending: District level evidences from India

Background & objectives:

Numerous studies have highlighted the regressive and immiserating impact of out-of-pocket (OOP) health spending in India. However, most of these studies have explored this issue at the national or up to the State level, with an associated risk of overlooking intra-State diversities in the health system and health-seeking behaviour and their implication on the financial burden of healthcare. This study was aimed to address this issue by analyzing district level diversities in inequity, financial burden and impoverishing impact of OOP health spending.

Methods:

A household survey of 62,335 individuals from 12,134 households, covering eight districts across three States, namely Gujarat, Haryana and Rajasthan was conducted during 2014-2015. Other than general household characteristics, the survey collected information on household OOP [sum total of expenditure on doctor consultation, drugs, diagnostic tests etc. on inpatient depatment (IPD), outpatient depatment (OPD) or chronic ailments] and household monthly consumption expenditure [sum total of monthly expenditure on food, clothing, education, healthcare (OOP) and others]. Gini index of consumption expenditure, concentration index and Kakwani index (KI) of progressivity of OOP, catastrophic burden (at 20% threshold) and poverty impact (using district-level poverty thresholds) were computed, for these eight districts using the survey data. The concentration curve (of OOP expenditure) and Lorenz curve (of consumption expenditure) for the eight districts were also drawn.

Results:

The distribution of OOP was found to be regressive in all the districts, with significant inter-district variations in equity parameters within a State (KI ranges from −0.062 to −0.353). Chhota Udepur, the only tribal district within the sample was found to have the most regressive distribution (KI of −0.353) of OOP. Furthermore, the economic burden of OOP was more pronounced among the rural sample (CB of 19.2% and IM of 8.9%) compared to the urban sample (CB of 9.4% and IM of 3.7%).

Interpretation & conclusions:

The results indicate that greater decentralized planning taking into account district-level health financing patterns could be an effective way to tackle inequity and financial vulnerability emerging out of OOP expenses on healthcare.

Variations in catastrophic health expenditure across the states of India: 2004 to 2014

BACKGROUND

Financial protection is a key dimension of universal health coverage. Catastrophic health expenditure (CHE) has increased in India over time. The overall figures mask the subnational heterogeneity crucial for designing insurance coverage for 1.3 billion population across India. We estimated CHE in every state of India and the changes over a decade.

METHODS

We used National Sample Survey data on health care utilisation in 2004 and 2014. The states were placed in four groups based on epidemiological transition level (ETL), defined on the basis of ratio of disability-adjusted life-years from communicable diseases to those from non-communicable diseases and injuries combined, with a low ratio denoting high ETL state group. CHE was defined as the proportion of households that had out-of-pocket payments for health care equalling or exceeding 10% of the household expenditure. We assessed variation in the magnitude and distribution of CHE between ETL state groups and between states of India.

RESULTS

In 2014, CHE was higher in the high (30.3%, 95% confidence interval: 28.5 to 32.1) and higher-middle (27.4%, 26.3 to 28.6) ETL state groups than the low (21.8%, 20.8 to 22.8) and lower-middle (19.0%, 17.1 to 21.0) groups. From 2004 to 2014, CHE increased only in the high and higher-middle ETL groups (1.19 and 1.34 times, respectively). However, the individual states with substantial increase in CHE were spread across all ETL groups. The gap between the highest CHE of an individual state and the lowest was 8-fold in 2014. CHE was disproportionately concentrated among the rich in 2004 for most of India, but in 2014 CHE was distributed equally among the rich and poor because of the substantial increase in CHE among the poor over time.

CONCLUSIONS

Better provision of quality health care should be accompanied by financial protection measures to safeguard the poor from increasing CHE in India. The state-specific CHE trends can provide useful input for the planning of the recently launched National Health Protection Mission such that it meets the requirement of each state.