KEY POINTS
Question What are the costs and risks of impoverishment for patients with acute myocardial infarction and their families in Kerala, India, and how do these factors vary by health insurance status?
Findings In this prespecified cross-sectional substudy of a randomized clinical trial, 2114 respondents reported $480 international dollars in out-of-pocket costs per acute myocardial infarction, with $400 international dollars in higher costs among individuals without insurance vs those with insurance. Catastrophic health spending was also higher among individuals without insurance (58.1% vs 39.9%) as was distress financing (9.7% vs 3.1%).
Meaning Survivors of acute myocardial infarction in Kerala may face high costs and risk for impoverishment, and expansion of insurance access and coverage should be considered for financial risk protection.
ABSTRACT
IMPORTANCE Ischemic heart disease is the leading cause of death in India, and treatment can be costly.OBJECTIVE To evaluate individual- and household-level costs and impoverishing effects of acute myocardial infarction among patients in Kerala, India.
DESIGN, SETTING, AND PARTICIPANTS This investigation was a prespecified substudy of the Acute Coronary Syndrome Quality Improvement in Kerala study, a stepped-wedge, cluster randomized clinical trial conducted between November 2014 and November 2016 across 63 hospitals in Kerala, India. In this cross-sectional substudy, individual- and household- level cost data were collected 30 days after hospital discharge from a sample of 2114 respondents from November 2014 to July 2016. Data were analyzed from July through October 2018 and in March 2019.
EXPOSURES Health insurance status.
MAIN OUTCOMES AND MEASURES The primary outcomes were detailed direct and indirect cost data associated with acute myocardial infarction and respondent ability to pay as well as catastrophic health spending and distress financing. Catastrophic health spending was defined as 40% or more of household expenditures minus food costs spent on health, and distress financing was defined as borrowing money or selling assets to cover health costs. Hierarchical regression models were used to evaluate the association between health insurance and measures of financial risk. Costs were converted from Indian rupees to international dollars (represented herein as “$”).
RESULTS Among 2114 respondents, the mean (SD) age was 62.3 (12.7) years, 1521 (71.9%) were men, 1144 (54.1%) presented with an ST-segment elevation myocardial infarction, and 1600 (75.7%) had no health insurance. The median (interquartile range) expenditure among respondents was $480.4 ($112.5-$1733.0) per acute myocardial infarction encounter, largely driven by in-hospital expenditures. There was greater than 15-fold variability between the 25th and 75th percentiles. Individuals with or without health insurance had similar monthly incomes and annual household expenditures, yet individuals without health insurance had approximately $400 higher out-of-pocket cardiovascular health care costs (median [interquartile range] total cardiovascular expenditures among uninsured, $560.3 [$134.1-$1733.6] vs insured, $161.4 [$23.2-$1726.9]; P < .001). Individuals without health insurance also had a 24% higher risk of catastrophic health spending (adjusted risk ratio, 1.24; 95% CI, 1.07- 1.43) and 3-fold higher risk of distress financing (adjusted risk ratio; 3.05; 95% CI, 1.45-6.44).
CONCLUSIONS AND RELEVANCE The results of this study indicate that acute myocardial infarction carries substantial financial risk for patients in Kerala. Expansion of health insurance may be an important strategy for financial risk protection to disrupt the poverty cycle associated with cardiovascular diseases in India.
INTRODUCTION
Ischemic heart disease is the leading cause of death and disability in India,1 and treatment of acute and chronic manifestations of ischemic heart disease can be costly for patients and their families.2 Although the stepwise, inverse relationship between baseline socioeconomic position and incident cardiovascular disease (CVD) has been well described,3 including in Asia,4 the impoverishing effects of CVD have not been as widely reported. High-functioning health systems need to not only be accessible and responsive to patients’ needs but also provide financial risk protection for short- and long-term treatment. As a result, the United Nations Sustainable Development Goals includes target 3.8: “Achieve universal health coverage, including financial risk protection, access to quality essential health care services and access to safe, effective, quality, and affordable essential medicines and vaccines for all.”5
Individuals’ abilities to pay for health care expenditures are estimated by comparing out-of-pocket health care expenditures with household expenditures after accounting for subsistence, or food, expenditures. Catastrophic health spending occurs when individuals spend a high proportion of overall expenditures on health care with variable thresholds set, ranging from 10% to 40%,6 with subsequent poverty incidence rates ranging from 0.5% to 14%.6 Distress financing occurs when individuals try to buffer spending shocks by selling or mortgaging assets or borrowing money to cover health expenditures. A 2011 report from the south Indian state of Kerala (500 respondents) demonstrated a more than 60% prevalence rate of catastrophic health spending and distress financing among individuals with a recent acute myocardial infarction or stroke.2 India has subsequently enacted and scaled a social health insurance program, Rashtriya Swasthya Bima Yojana, to 36 million households to cover hospital expenses, with 2.1 million families covered in Kerala.7 However, this program does not seem to have reduced out-of-pocket spending.8
The Acute Coronary Syndrome Quality Improvement in Kerala (ACS QUIK) study was a stepped-wedge, cluster randomized clinical trial evaluating the effect of a quality improvement toolkit on clinical outcomes.9,10 As a prespecified substudy of ACS QUIK, the present study collected data on individual- and household-level expenditures associated with acute myocardial infarction. We sought to describe the individual and household costs associated with acute myocardial infarction; to assess how these costs compare with an individual’s ability to pay; to explore other patient-, hospital-, and treatment-level factors; and to evaluate the degree to which insurance mitigates risk for catastrophic health spending, distress financing, or both.
METHODS
Overview of ACS QUIK
The methods and results of ACS QUIK (ClinicalTrials. gov identifier, NCT02256657) have been reported.9,10 In brief, 21 374 eligible participants with acute myocardial infarction were recruited from 63 hospitals in Kerala, India, from November 2014 to November 2016. The trial used a stepped-wedge cluster randomized design to evaluate the effect of a locally adapted quality improvement toolkit on the rate of 30-day major adverse events, defined as death, reinfarction as defined by the Third Universal Definition of Myocardial Infarction,11 stroke, and major bleeding as defined by the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries criteria.12 The unadjusted difference between the intervention and control groups (5.3% vs 6.4%) was no longer observed after adjustment for clustering and temporal trends (adjusted risk difference, −0.09% [95% CI, −1.32% to 1.14%]; adjusted odds ratio, 0.98 [95% CI, 0.80-1.21]). Rates of reperfusion and in-hospital and discharge medication were higher in the intervention group compared with the control group, which persisted after adjustment. The present report adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. The ACS QUIK trial and this substudy received institutional review board approval from local, national, and international bodies and was approved by the Indian Health Ministry Screening Committee. All participants or their delegates provided writ- ten informed consent.
Sample Identification
We performed age-stratified (<50 years; 50-70 years; and >70 years) and sex-stratified random sampling of a subset of participants who presented for 30-day fol- low-up to evaluate microeconomic (ie, individual- and household-level) costs associated with acute myocardial infarction hospitalization. The initial recruitment target was 2200 respondents, which provided 95% power to detect differences in catastrophic health spending across age and sex strata, assuming a baseline catastrophic health spending prevalence rate of 60%.2 Recruitment for this substudy was planned to occur among sites and participants in the control period of the trial to minimize the potential influence of the toolkit intervention on costs, such as costs from higher rates of reperfusion and medications. However, slower-than-expected enrollment was identified during the first year of the trial owing to the length of the survey instrument and the time required from study personnel and participants. After consultation with the trial’s data and safety monitoring board, we revised our sampling frame in November 2015 to an unselected population within the overall trial to achieve our recruitment target by June 2016. Among all individuals who completed the microeconomic survey, we excluded 10 individuals who reported hospitalization outside the date range of the ACS QUIK trial (from November 2014 to November 2016). The most recent hospitalization was used for primary analysis to minimize recall bias. When hospitalizations were reported in the same month and year, we used the longer hospitalization.
Assessment of Microeconomic Costs and Insurance Status
The instrument used to collect self-reported information on health care–associated expenditures, reimbursement, individual and household income, and house- hold expenditures was developed in collaboration with experts from the World Bank and has been published.2 Health insurance was defined by self-report of having insurance at ACS QUIK admission or by any self-report of reimbursed expenditures from an insurer at the 30-day follow-up visit. Trained study personnel ad- ministered the survey in person to the participants, their delegates, or both during the 30-day follow-up research visit in the Malayalam or English language based on the participant’s preference. Costs were as- sessed in Indian rupees (Rs) and converted into 2015 international dollars based on purchasing power parities published by the World Bank (17.152 = inter- national $)13; thus, unless otherwise indicated, all costs reported herein as “$” represent international dollars.
Cardiovascular Hospitalization and Total Health Care Expenditures
Inpatient cardiovascular hospitalization costs were calculated as the sum of self-reported spending on hospital admission, diagnostic tests, emergency department, food, treatment, ambulance, surgery, medicine, attendants, travel, and other self-reported inpatient costs. Total health care costs additionally included posthospitalization expenses on physicians, rehabilitation, home care, diagnostic tests, food, medicine, transportation, and other self-reported post- hospitalization expenses. Out-of-pocket costs were calculated as the sum of all reported costs minus the total reimbursement by the insurance provider. A floor threshold of Rs 0 out-of-pocket costs was imposed where needed. Inpatient percentages were calculated only among patients with expenses reported.
Annual Household Expenses
Total annual household expenses were calculated as the sum of monthly expenses on rent, food, insurance, petrol, transportation, and educational expenses and annual expenses on goods, clothes, household fuel, health care, vehicle repair, property management, and any other self-reported household expenses. Nonfood expenses excluded monthly food expenses.
Proportionate Expenses
Cardiovascular disease expenditures as a proportion of annual household expenses were calculated as the out-of-pocket expenses on the most recent hospitalization as a proportion of the annual household expenditure. If Rs 0 total annual household expenditures were reported, then CVD expenses as a proportion of annual household expenditures were calculated as 100%.
Catastrophic Health Spending and Distress Financing
Among patients who had out-of-pocket expenses and reported total nonfood annual household expenditures, catastrophic health spending was calculated. If out-of-pocket expenditures exceeded 40% of annual household expenditures minus food costs, then participants were considered to have experienced catastrophic health spending. Lower thresholds have been used to define catastrophic health spending (eg, 10% or 25%),14 but we reported results on the basis of our a priori conservative threshold that was based on previous reports.15 We also sought to compare prevalence rates in Kerala over time based on previous re- ports using the 40% threshold.2 Among patients who reported how health care costs were covered, participants were considered to have experienced distress financing if they reported covering their health care costs by borrowing money from friends, relatives, or a bank or they sold or mortgaged an asset.
Statistical Analysis
We compared baseline demographic, clinical, and socioeconomic characteristics by insurance status using the t test for continuous, normally distributed covariates and the χ2 test for categorical covariates. We used the Wilcoxon rank sum test and the Kruskal- Wallis test for nonparametric analyses. We similarly compared CVD expenditures, annual household expenditures, changes in income, and rates of catastrophic health spending and distress financing by insurance status. Similar analyses were performed by sex, hospital type (ie, government, nonprofit/charity, or private hospital), and ST-segment elevation myocardial infarction (STEMI) or non-STEMI status.
To evaluate the association between insurance status and catastrophic health spending and distress financing, we created hierarchical multivariable binomial regression models. We adjusted for patient-level factors, including Global Registry of Acute Coronary Events risk score variables (ie, age, sex, STEMI or non-STEMI status, baseline systolic blood pressure, and heart rate),16 and baseline household income. We did not include serum creatinine owing to the high frequency of missing data. We did not include in-hospital heart failure, cardiogenic shock, or cardiac arrest in these models owing to collinearity and a small number of events (35 total). We further adjusted for hospital- and study-level factors by creating a random effect to adjust for hospital-level clustering and adjusting for ACS QUIK randomization group (intervention vs control groups). Finally, we sought to adjust for treatment-level factors, including reperfusion (eg, coronary artery bypass graft surgery, percutaneous coronary intervention, thrombolysis, or a combination thereof) and optimal in-hospital and discharge medications—defined as aspirin, second antiplatelet agent, anticoagulant (in-hospital only), β-blocker, and statin17—but the degree of missing data was too high. Complete case analysis was thus used for derivation of all variables and models. Data were analyzed from July through October 2018 and in March 2019. We used a 2-sided P < .05 to define statistical significance and Stata, version 15 (StataCorp), SAS, version 9.4 (SAS Institute Inc), and R, version 3.5.1 (R Foundation) to conduct statistical analyses. Code files used for generation of results can be downloaded from the internet.
RESULTS
Baseline Characteristics
The flowchart of 2114 participants is given in eFigure 1 in the Supplement. Participant characteristics by insurance status are reported in Table 1 and by sex in eTable 1 in the Supplement. Mean (SD) age of participants was 62.3 (12.7) years, 1521 (71.9%) were men, and 1144 (54.1%) presented with STEMI. There were modest differences between 1600 uninsured participants (75.7%) and 514 insured participants (24.3%) in terms of mean (SD) age (62.9 [12.9] years vs 60.3 [11.7] years, respectively; P < .001), STEMI prevalence rates (838 of 1600 [52.4%] vs 306 of 514 [59.5%]; P = .005), ACS QUIK intervention group status (875 of 1600 [54.7%] vs 210 of 514 [40.9%]; P < .001), and educational level (median [interquartile range {IQR}] years of education, 10 [6-12] years for uninsured vs 8 [5-10] years for insured, P < .001). Of 2114 participants overall, most were married (1751, 82.8%) and from a rural setting (1595, 75.5%). The median (IQR) time to survey completion following hospitalization was 31 (30-33) days in the uninsured group compared with 34 (30-49) days in the insured group (P < .001).
Self-reported baseline monthly median (IQR) individual income was similar between uninsured and insured participants (uninsured, $583.0 [$87.5-$699.6] vs insured, $466.4 [$233.2-$583.0]; P = .56). By contrast, self-reported baseline monthly median (IQR) household income was higher among uninsured participants than insured participants ($874.5 [$583.0-$1166.0] vs $408.1 [$233.2-$606.3]; P < .001).
Expenditures, Catastrophic Health Spending, and Distress Financing
Health care expenditures and household expenditures are reported by insurance status in Table 2. The median (IQR) expenditure among respondents was $480.4 ($112.5-$1733.0) per acute myocardial infarction encounter, largely driven by in-hospital expenditures. There was greater than 15-fold variability between the 25th and 75th percentiles. Total median (IQR) CVD-associated expenditures were higher among uninsured participants ($560.3 [$134.1-$1733.6]) compared with insured participants ($161.4 [$23.3-$1726.9]; P < .001), with an approximately 6-fold higher out-of-pocket, CVD-associated expenditures among individuals without insurance despite similar annual household expenditures. Catastrophic health spending was thus higher among individuals without insurance (533 of 1600 [58.1%] vs 111 of 514 [39.9%]; P < .001) as was distress financing (155 of 1600 [9.7%] vs 16 of 514 [3.1%]; P < .001). Expenditures are also report- ed by hospital type in eTable 2 in the Supplement and by STEMI status in eTable 3 in the Supplement. Costs are disaggregated as medians with corresponding IQRs for each cost subtype by STEMI vs non-STEMI status and hospital type in the Figure and by payment source in eFigure 2 in the Supplement.
Insurance, Catastrophic Health Spending, and Distress Financing
Table 3 reports the results of binomial regression models evaluating the associations between insurance and catastrophic health spending, distress financing, or both. In unadjusted analyses, lack of health insurance was associated with a 45% higher rate of catastrophic health spending (risk ratio [RR], 1.45; 95% CI, 1.25-1.70), which remained similar after adjustment for patient-level factors, including base- line income (adjusted RR, 1.49; 95% CI, 1.28-1.73), but was attenuated to 24% higher when adjusting for ACS QUIK intervention group and hospital cluster (adjust- ed RR, 1.24; 95% CI, 1.07-1.43). Similarly, lack of health insurance was associated with a 3-fold higher rate of distress financing (unadjusted RR, 3.11; 95% CI, 1.88- 5.16), which was similarly attenuated after adjustment for patient-, study-, and hospital-level factors (adjusted RR, 3.05; 95% CI, 1.45-6.44).
DISCUSSION
Summary
In this study of 2114 survivors of acute myocardial infarction in Kerala, 24% of whom had baseline health insurance, we report detailed individual- and household-level income and expenditure data, including expenditures associated with hospitalization and subsequent cardiovascular care. Median expenditures were less than $500 per episode, largely driven by in-hospital expenditures, but there was greater than 15-fold variability in expenditures between the 25th and 75th percentiles. We also quantified expect- ed differences in expenditures by hospital type, with higher expenditures reported among respondents seeking care from nonprofit and private hospitals compared with public hospitals, which is concordant with previous reports from nationally representative samples.19 Individuals with or without health insurance had similar monthly incomes and similar annual household expenditures, yet individuals without baseline health insurance had approximately $400 higher out-of-pocket cardiovascular health care costs, a 45% greater risk of catastrophic health spending, and a 3-fold higher risk of distress financing.
Results in Context
Previous facility-based studies evaluating the costs of acute cardiovascular care in India have reported higher CVD expenditures and higher rates of catastrophic health spending than the present study.2,19,20 These differences may be attributable to sampling frames (by location, disease states, or both), definitions, or temporal changes in income, expenditures, or insurance or combinations thereof. Furthermore, one previous study used a lower threshold to define catastrophic health spending (30% of nonsubsistence spending vs 40% in the present report),20 which also partially explains the lower prevalence rate observed in our study. Nevertheless, the rate of catastrophic health spending remains high, especially when com- pared with the incidence of catastrophic spending us- ing global estimates aligned with the Sustainable De- velopment Goals and other community-based reports of out-of-pocket spending from India.21 For example, using a health spending threshold of 10% of total consumption, global incidence was 9.7% in 2000 and rose to 11.7% in 2010,14 or approximately 5 times lower than in our study. However, distress financing was 3 to 6 times less common and lower than in previous reports, including in India,2,19 which may be driven by temporal increases in income, savings, or a combina- tion thereof to better absorb health care–related cost shocks or greater wealth in Kerala compared with other states in India.19 Strategies to minimize the risk of “wealth shocks” will thus remain important not only to reduce the cycle of poverty from CVD but also to reduce associated mortality.22
Beyond broader initiatives to increase income and reduce health care–related expenditures, expanding access and enrollment in health insurance programs appears to be a central strategy to provide such finan- cial risk protection, especially given the consistent as- sociation between insurance status and catastrophic health spending. A 2018 systematic review of 66 stud- ies demonstrated that lack of health insurance was associated with a 2.7-fold higher risk of catastrophic health expenditures across a range of noncommu- nicable diseases, including CVD.23 Although health insurance should intuitively reduce out-of-pocket expenditures, not all studies show this association, especially among the lowest-income groups24 and through insurance obtained from private or non- profit institutions.14 Incomplete insurance will not eliminate financial risk associated with health care and may counterintuitively increase financial risk when costs of treatment are not transparent. Within India, the effect of the central government’s Rashtri- ya Swasthya Bima Yojana health insurance program on catastrophic inpatient spending appears limited based on the 2004 to 2012 survey data from the Na- tional Sample Survey Organization, but a longer time horizon for programmatic implementation may be needed.8 State-level government health insurance programs to provide financial support to low-income households, such as the lottery-based Kerala Karunya Benevolent Fund, could complement national pro- grams but have not yet been evaluated. Decreasing point-of-care fees for accessing health services and increasing access to credit or to disability insurance may be additional complementary strategies to re- duce financial risk associated with short-term care for cardiovascular or other conditions that warrant further research.6 India’s March 2018 announcement of the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana health insurance program, which would pro- vide up to Rs 500 000 (approximately US $7000) in coverage annually to 100 million families (500 million individuals), would be a major investment in health that could substantially improve financial risk protection.25
Strengths and Limitations
Our study has several strengths, including, to our knowledge, being the largest such study in India to report individual- and household-level detailed costs following hospitalization for an acute cardio- vascular condition. Administrative claims data from government-sponsored programs, such as Rashtriya Swasthya Bima Yojana, have been shown to be in- sufficient for this level of granularity by condition or cost.26 Thus, we also used infrastructure from the ACS QUIK trial to capture these complex data using trained personnel. However, our study also has poten- tial limitations. First, data on expenditures and costs were self-reported, which raises the risk of recall or social desirability bias. However, there is no central database on costs from which we could otherwise obtain these data, and other national reports relied on recall that was much further out from the period of hospitalization than our study. Further, we used a previously published instrument developed in collaboration with economists from the World Bank to collect detailed data to minimize these risks. Second, we changed our sampling frame during the study from an age- and sex-stratified random sampling frame to an unselected sampling frame given the low initial recruitment rates, which highlights the challenge of collecting these data. While the sample may not be representative of Kerala or of India, our study had suf- ficient power to demonstrate associations between patient-, hospital-, study-, and treatment-level factors with catastrophic health spending or distress financ- ing. Third, we collected data from individuals who survived acute myocardial infarction, and these in- dividuals may have different incomes, expenditures, and coping strategies than those who did not survive. Fourth, data were missing from some participants, including variables such as household expenditures, which were needed to estimate the prevalence of cat- astrophic health spending and may have influenced our results.
CONCLUSIONS
The present results indicate that acute myocardial infarction carries substantial financial risk for pa- tients in Kerala. Further expansion of health insurance access and coverage will be an important strategy for financial risk protection to disrupt the cycle of poverty from CVD in Kerala and in India.
ARTICLE INFORMATION
Accepted for Publication: March 26, 2019.
Published: May 17, 2019. doi:10.1001/jamanet- workopen.2019.3831
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Mohanan PP et al. JAMA Network Open.
Corresponding Author: Mark D. Huffman, MD, MPH, Northwestern University Feinberg School of Medicine, 680 N Lake Shore Dr, Ste 1400, Chicago, IL 60611 ([email protected] western.edu).
Author Contributions: Ms Baldridge and Dr Kondal had full ac- cess to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Mohanan and Huffman contributed equally to this work and are considered co–first authors.
Concept and design: Mohanan, Huffman, Joseph, Lloyd-Jones, Harikrishnan, Prabhakaran.
Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Huffman, Kondal, Ali, Thomas. Critical revision of the manuscript for important intellectual con- tent: Mohanan, Huffman, Baldridge, Devarajan, Zhao, Joseph, Eapen, Krishnan, Menon, Lloyd-Jones, Harikrishnan, Prabha- karan.
Statistical analysis: Baldridge, Kondal, Zhao.
Obtained funding: Huffman, Prabhakaran.
Administrative, technical, or material support: Mohanan, Huff- man, Devarajan, Krishnan, Thomas, Lloyd-Jones, Harikrishnan, Prabhakaran.
Supervision: Mohanan, Huffman, Eapen, Krishnan.
Conflict of Interest Disclosures: Dr Huffman reported receiving funding from the World Heart Federation to serve as its senior program advisor for the Emerging Leaders program, which is supported by Boehringer Ingelheim and Novartis with previous support from BUPA and AstraZeneca; receiving grant support from One Brave Idea, which is funded by the American Heart Association, Verily, and AstraZeneca outside the submitted work; and receiving personal fees from the American Medical Association and from The George Institute for Global Health for a secondary appointment outside the submitted work. George Health Enterprises, in association with The George Institute for Global Health, has received investment funds to develop fixed-dose combination products containing aspirin, statin, and blood pressure–lowering drugs and has submitted a patent for the treatment of hypertension. No other disclosures were reported.
Funding/Support: This project was funded by the National Heart, Lung, and Blood Institute (R00 HL107749) with support from the Cardiological Society of India, Kerala chapter; Centre for Chronic Disease Control; Northwestern University Global Health Initiative Fund; and Northwestern University Clinical and Translational Science Institute (UL1TR001422).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Acute Coronary Syndrome Quality Improvement in Kerala (ACS QUIK) Investigators:Cardiological Society of India Kera- la chapter advisory committee: G. Vijayaraghavan, Kerala Institute of Medical Sciences; Ashokan Nambiar, Baby Memorial Hospital; Geevar Zachariah, Mother Hospital; Rajan J. Manjooran, Pushpagiri Medical College; and S. Sivasankaran, Sree Chitra Tirunal Institute of Medical Science and Technology. Trial steering committee: Dorai- raj Prabhakaran (co-chair), Centre for Chronic Disease Control/Public Health Foundation of India; Donald M. Lloyd-Jones (co-chair), North- western University; K. Srinath Reddy, Public Health Foundation of India; S. Harikrishnan, Sree Chitra Tirunal Institute of Medical Science and Technology; Robert O. Bonow, Northwestern University; Darwin R. Labarthe, Northwestern University; and Sidney C. Smith Jr, University of North Carolina. Data and safety monitoring board: Brahmajee Nallamothu (chair), University of Michigan; Thomas Alexander, Kovai Medical Center and Hospital; Karla Hemming, University of Birmingham; Simon Thom, Imperial College London; K. R. Sundaram, Amrita Institute of Medical Sciences; and Lawton Cooper, National Heart, Lung, and Blood Institute. Operations committee: Mark D. Huffman, Northwestern University; Padinhare Mohanan, Westfort Hi- Tech Hospital Ltd; Raji Devarajan, Centre for Chronic Disease Control; Abigail S. Baldridge, Northwestern University; Dimple Kondal, Cen- tre for Chronic Disease Control; Lihui Zhao, Northwestern University; and Mumtaj Ali, Public Health Foundation of India. Zonal project coordinators: Divin Davies, WestFort Hi-Tech Hospital Ltd; Prasad Arumugan, Caritas Hospi- tal; and Theckethottathil Chacko Aneesh, PRS Hospital. Trial sites, investigators, and coordina- tors: Amala Medical College, Thrissur: Dr Rajesh, Mr Anoop; Amrita Institute of Medical Sciences & Research Center, Ernakulam: Dr Natarajan, Mr Sujith Raj; Anathapurai Hospital, Thiruva- nanthapuram: Dr Bahuleyan, Mr Jinbert; Aswini Hospital, Thrissur: Dr Pramod, Mr Vineesh Varghese; Baby Memorial Hospital, Kozhikode: Dr Nambiar, Dr Bindu; Bharath Heart Institute, Kottayam: Dr Kathalankal, Mrs Susamma; Bishop Benziger Hospital, Kollam: Dr Renga, Ms Alphonsa; CH Memorial Hospital, Valanchery: Dr Ibrahimkutty; Caritas Hospitals: Dr Joseph, Mr Tony; Daya Specialty Hospital, Thrissur: Dr Ullas, Mr Ajmal; District Hospital, Palakkad: Dr Siar, Ms Pravya; District Hospital, Kollam: Dr Syam, Mrs Sajitha; Dr Damodaran Memorial Hospital, Kollam: Dr Robby, Mrs Prasanna; Elite Mission Hospital, Thrissur: Dr Manikandan, Ms Lekha; EMS Hospital Perinthalmana: Dr Som- anathan; Gokulam Medical College, Thiruvanan- thapuram: Dr Abilash, Dr Binu, Mr Aneesh; Gov- ernment Medical College, Thrissur: Dr Mathew, Dr Andrews, Mr Arun Gopi; Holy Cross Hospital, Kottayam: Dr Chacko, Mr Libin; Indira Gandhi Cooperative Hospital, Ernakulam: Dr Abraham, Ms Alphonsa Rony; Irinjalakuda Co-operative Hospital: Dr Ullas, Mr Midhun George; Jubilee Mission Hospital, Thrissur: Dr Govindanunny, Mr Lance Frank William; Kannur Medical Col- lege, Kannur: Dr Raveendran, Ms Athira; Kerala Institute of Medical Science, Thiruvanantha- puram: Dr Vijayaraghavan, Mrs Kavitha; KMCT Heart Institute, Manassery: Dr Saleem, Mr Joshy; Koyili Hospital, Kannur: Dr Kumar, Ms Alpha; KVM Hospital, Cherthala: Dr Venugopal, Mr Vipin; Lakshmi Hospital, Palakkad: Dr Jayago- pal, Ms Devaki; Lakshmi Hospital, Ernakulam: Dr Sasikumar; Lal Memorial Hospital, Irinjal- akuda: Dr Madhu; Lisie Hospital, Ernakulam: Dr Abdullakutty, Dr Mathew, Ms Serrin; Little Flower Hospital, Angamaly: Dr Joseph, Mr Rajesh; Lourdes Hospital, Ernakulam: Dr Sujith Kumar, Mrs Ria Sandeep; Medical College Hospi- tal, Kozhikode: Dr Haridas, Mrs Deepa; Medical College Hospital, Alappuzha: Dr Viswanathan, Dr Sivaprasad, Dr Sreenivas, Dr Gagan; Medi- cal College, Thiruvananthapuram: Dr Koshy, Dr Raji; Medical College, Kottayam: Dr Jayaprakash, Dr Brijesh; Metro International Cardiac Centre, Kozhikode: Dr Mustafa, Ms Anooja; MIMS Heart, Kottakkal, Malappuram: Dr Tahsin, Mr Pradeesh; Modern Hospital, Kodungallur: Dr Ukken, Ms Teena Sudheer; MOSCM Hospital, Ernakulam: Dr Punnoose, Mr Binoy Kurian; Mother Hospital Limited, Thrissur: Dr K. J. James; NIMS Hospital, Thiruvananthapuram: Dr Sreedharan, Mrs Anju Mohan; Pariyaram Medical College, Kannur: Dr Sebastian, Mr Robin; PRS Hospital, Thiruvanan- thapuram: Dr Nair, Mr Aneesh; Pushpagiri Medical College, Thiruvilla: Dr Manjooran, Mr Jacob; PVS Memorial Hospital, Ernakulam: Dr Blessan, Mrs Nisha; Rajah Hospital, Guruvayoor: Dr Showjad, Dr Dhamoadharan; Ramdas Nursing Home, Perinthalmana: Dr Ramadas, Mr Jobson; SK Hospital, Thiruvananthapuram: Dr Suresh, Mrs Divy; SH Medical Centre Hospital, Kottayam: Dr Chacko, Mrs Saranya; Samaritan Hospital, Pazhangad: Dr Eapen, Mrs Sindhu; Santhi Nurs- ing Home, Punnayoorkulam: Dr Shaji, Dr Krish- nan; Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram: Dr Harikrishnan, Dr Ajit, Mr Suresh; Sree Naray- ana Institute of Medical Science, Ernakulam: Dr Menon, Mrs Nisha; St James Hospital, Chalakudy: Dr Jubil Mathew; St Joseph Hospital, Dharmagiri: Dr Thomas, Sr Betto; St Martin De Porres Hos- pital, Cherukunnu: Dr Muralidharan; St Mary’s Hospital, Thodupuzha: Dr Mathew Abraham; Sukuppuram Hospital, Edappal: Dr Narayanan; Tellicherry Co-operative Hospital, Thalassery: Dr Kumar, Dr Manoj; Thangam Hospital, Palakkad: Dr Jayakumar, Mr Sagar Thampy; Travancore Medical College, Thiruvananthapuram: Dr Abh- ilash, Mr Santhosh; and WestFort Hi-Tech Hospi- tal Ltd, Thrissur: Dr E. B. Manoj, Mr Divin Davies. Additional Information: Deidentified data, code, and the corresponding data dictionary from the ACS QUIK trial, including data included herein, are available through the Biologic Specimen and Data Repository Information Coordinating Center of the National Heart, Lung, and Blood Institute.
REFERENCES
Dandona L, Dandona R, Kumar GA, et al; India State-Level Disease Burden Initiative Collabo- rators. Nations within a nation: variations in epidemiological transition across the states of India, 1990-2016 in the Global Burden of Dis- ease Study. Lancet. 2017;390(10111):2437-2460. doi:10.1016/S0140-6736(17)32804-0PubMed- Google ScholarCrossref
Huffman MD, Rao KD, Pichon-Riviere A, et al. A cross-sectional study of the microeconomic impact of cardiovascular disease hospitalization in four low- and middle-income countries. PLoS One. 2011;6(6):e20821. doi:10.1371/journal. pone.0020821PubMedGoogle ScholarCrossref
Marmot M, Brunner E. Cohort profile: the Whitehall II study. Int J Epidemiol. 2005;34(2):251-256. doi:10.1093/ije/dyh372PubMedGoogle ScholarCrossref
Vathesatogkit P, Batty GD, Woodward M. So- cioeconomic disadvantage and disease- specific mortality in Asia: systematic review with meta-analysis of population-based cohort studies. J Epidemiol Community Health. 2014;68(4):375-383. doi:10.1136/jech- 2013-203053PubMedGoogle ScholarCrossref
United Nations General Assembly. Transform- ing our world: the 2030 agenda for sustainable development. United Nations Population Fund website. https://www.unfpa.org/re- sources/transforming-our-world-2030-agen- da-sustainable-development. Published Sep- tember 2015. Accessed April 8, 2019.
Alam K, Mahal A. Economic impacts of health shocks on households in low- and middle-income countries: a review of the literature. Global Health. 2014;10(1):21. doi:10.1186/1744-8603-10-21PubMedGoogle ScholarCrossref
Government of India. Rashtriya Swasthya Bima Yojana. http://www.rsby.gov.in/state_ level.html. Accessed September 6, 2018.
Karan A, Yip W, Mahal A. Extending health insurance to the poor in India: an impact eval- uation of Rashtriya Swasthya Bima Yojana on out of pocket spending for healthcare. Soc Sci Med. 2017; 181:83-92. doi:10.1016/j.socscimed.2 017.03.053PubMedGoogle ScholarCrossref
Huffman MD, Mohanan PP, Devarajan R, et al. Acute Coronary Syndrome Quality Im- provement in Kerala (ACS QUIK): rationale
and design for a cluster-randomized stepped- wedge trial. Am Heart J. 2017; 185:154-160. doi:10.1016/j.ahj.2016.10.026PubMedGoogle ScholarCrossref
Huffman MD, Mohanan PP, Devarajan R, et al; Acute Coronary Syndrome Qual- ity Improvement in Kerala (ACS QUIK)Investigators. Effect of a quality improve- ment intervention on clinical outcomes in patients in India with acute myocardial infarction: the ACS QUIK randomized clinical trial. JAMA. 2018;319(6):567-578. doi:10.1001/
jama.2017.21906 ArticlePubMedGoogle Schol- arCrossref
Thygesen K, Alpert JS, Jaffe AS, et al; Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarc- tion. Third universal definition of myocardial infarction. Circulation. 2012;126(16):2020- 2035. doi:10.1161/CIR.0b013e31826e1058PubMe
dGoogle ScholarCrossref
GUSTO Investigators. An international ran- domized trial comparing four thrombolytic strategies for acute myocardial infarction. N Engl J Med. 1993;329(10):673-682. doi:10.1056/ NEJM199309023291001PubMedGoogle Schol- arCrossref
World Bank. PPP conversion factor, GDP (LCU per international $). https:// data.worldbank.org/indicator/PA.NUS.
PPP?end=2017&start=1990. Accessed April 11, 2019.
Wagstaff A, Flores G, Hsu J, et al. Progress on catastrophic health spending in 133 countries: a retrospective observational study. Lancet Glob Health. 2018;6(2): e169-e179. doi:10.1016/ S2214-109X(17)30429-1PubMedGoogle Schol- arCrossref
Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analy- sis. Lancet. 2003;362(9378):111-117. doi:10.1016/ S0140-6736(03)13861-5PubMedGoogle Schol- arCrossref
Granger CB, Goldberg RJ, Dabbous O, et al; Global Registry of Acute Coronary Events Investigators. Predictors of hospital mortal- ity in the Global Registry of Acute Coronary Events. Arch Intern Med. 2003;163(19):2345- 2353. doi:10.1001/archinte.163.19.2345 ArticlePubMedGoogle ScholarCrossref
Huffman MD, Prabhakaran D, Abraham AK, Krishnan MN, Nambiar AC, Mohanan PP; Kerala Acute Coronary Syndrome Registry Investigators. Optimal in-hospital and dis- charge medical therapy in acute coronary syndromes in Kerala: results from the Kerala Acute Coronary Syndrome Registry. Circ Cardiovasc Qual Outcomes. 2013;6(4):436-443. doi:10.1161/CIRCOUTCOMES.113.000189PubM edGoogle ScholarCrossref
Baldridge A. ACS QUIK: code for the Acute Coronary Syndrome Quality Improvement in Kerala trial. GitHub website. https://github. com/abigailbaldridge/ACS-QUIK. Accessed April 11, 2019.
Kastor A, Mohanty SK. Disease-specific out- of-pocket and catastrophic health expenditure on hospitalization in India: do Indian house- holds face distress health financing? PLoS One. 2018;13(5): e0196106-e0196118. doi:10.1371/ journal.pone.0196106PubMedGoogle Schol- arCrossref
Jan S, Lee SW-L, Sawhney JP, et al. Cata- strophic health expenditure on acute coro- nary events in Asia: a prospective study. Bull World Health Organ. 2016;94(3):193-200. doi:10.2471/BLT.15.158303PubMedGoogle ScholarCrossref
Karan A, Selvaraj S, Mahal A. Moving to universal coverage? trends in the burden of out-of-pocket payments for health care across social groups in India, 1999-2000 to 2011- 12. PLoS One. 2014;9(8): e105162. doi:10.1371/ journal.pone.0105162PubMedGoogle Schol- arCrossref
Pool LR, Burgard SA, Needham BL, Elliott MR, Langa KM, Mendes de Leon CF. Associa- tion of a negative wealth shock with all-cause mortality in middle-aged and older adults in the United States. JAMA. 2018;319(13):1341- 1350. doi:10.1001/jama.2018.2055 ArticlePubMedGoogle ScholarCrossref
Jan S, Laba T-L, Essue BM, et al. Action to address the household economic burden of non-communicable diseases. Lancet.
2018;391(10134):2047-2058. doi:10.1016/S0140- 6736(18)30323- 4PubMedGoogle ScholarCross- ref
Acharya A, Vellakkal S, Taylor F, et al. The impact of health insurance schemes for the informal sector in low- and middle-income countries: a systematic review. World Bank Res Obs. 2013;28(2):236-266. doi:10.1093/wbro/ lks009Google ScholarCrossref
Angell BJ, Prinja S, Gupt A, Jha V, Jan S. The Ayushman Bharat Pradhan Mantri Jan Aro- gya Yojana and the path to universal health coverage in India: overcoming the challenges of stewardship and governance. PLoS Med. 2019;16(3):e1002759. doi:10.1371/journal. pmed.1002759PubMedGoogle ScholarCrossref
Morton M, Nagpal S, Sadanandan R, Bauhoff S. India’s largest hospital insur- ance program faces challenges in using claims data to measure quality. Health Aff (Millwood). 2016;35(10):1792-1799. doi:10.1377/ hlthaff.2016.0588PubMedGoogle Scholar- Crossref
