An analysis of government-sponsored health insurance enrolment and claims data from Meghalaya: Insights into the provision of health care in North East India.

INTRODUCTION: The Megha Health Insurance Scheme (MHIS) was launched in 2013 in the North-East Indian state of Meghalaya to reduce household out-of-pocket expenditure on health and provide access to high-quality essential healthcare. Despite substantial expansion of the MHIS since the scheme's inception, there is a lack of comprehensive documentation and evaluation of the scheme's performance against its Universal Health Care (UHC) objectives.
METHODS: We analysed six years of enrolment and claims data (2013-2018) covering three phases of the scheme to understand the pattern of enrolment, utilisation and care provision under the MHIS during this period. De-identified data files included information on age, sex, district of residence, the district of provider hospital, type of hospital, date of admission, status at discharge, claimed category of care, package codes, and amount claimed. Descriptive statistics were generated to investigate key trends in enrolment, service utilisation, and Government health spending under the MHIS.
RESULTS: Approximately 55% of the eligible population are currently enrolled in MHIS. Enrolment increased consistently from phase I through III and remained broadly stable across districts, gender, age group and occupation categories, with a small decline in males 19-60 years. Claims were disproportionately skewed towards private provision; 57% of all claims accrued to the 18 empanelled private hospitals and 39% to the 159 public sector facilities. The package 'General Ward Unspecified' was responsible for the highest volume of claims and highest financial dispensation across all three phases of the scheme. This likely indicates substantial administrative error and is potentially masking both true burden of disease and accurate financial provision for care under the MHIS. Anti-rabies injections for dog/cat bite contributed to 11% of total claims under MHIS III, and 1.6% of all claims under MHIS II. This warrants investigation to better understand the burden of animal bites on the Meghalayan population and inform the implementation of cost-effective strategies to reduce this burden.
CONCLUSIONS: This paper describes the first analysis of health insurance enrolment and claims data in the state of Meghalaya. The analysis has generated an important evidence base to inform future MHIS enrolment and care provision policies as the scheme expands to provide Universal Health Coverage to the state's entire population.

Introduction

India is home to approximately one sixth of the global population and has experienced rapid economic growth over the last decade [1]. Improvements in population health over this period have been less marked, with the largest number of cases of tuberculosis globally, and a growing burden of chronic non-communicable diseases, accompanied by high rates of child malnutrition, stunting and maternal and neonatal mortality across many states [2]. The Government of India presently spends roughly 1.3% of GDP on healthcare [3]—one of the lowest public expenditures on health in the world. Despite this, the Government is committed to achieving Universal Health Coverage (UHC) for the Indian population [4]. This means that every rupee spent on health must be spent wisely to maximise population health gains within the confines of limited resources.

Health is constitutionally a state responsibility within India’s federal structure; however, the central Government plays a major role in designing and implementing health programs and services in partnership with state Governments through the public healthcare network [5]. Both the central and various state Governments have experimented with a range of strategies to scale-up resources available for essential healthcare services, improve the accessibility and quality of care provided, and afford the poorest households with financial risk protection from catastrophic health expenditure [6].

In a country as vast as India, the success of Government-initiated health reforms in terms of expanding population coverage of essential health services, and reducing catastrophic out of pocket expenditure has varied widely. While some states have observed moderate improvements in healthcare access and quality with the implementation of progressive health reforms, such as the Aarogyasri social health insurance in Andhra Pradesh [7], or the similar Bhamashah Swasthya Bima Yojana (BSBY) in Rajasthan [8]. others have historically demonstrated significant difficulty in absorbing and diffusing the already limited Government health funding allocated towards improvements in health care delivery [9]. Launched in 2008, Rashtriya Swasthya Bima Yojana (RSBY) was India’s first centrally-administered national health insurance programme for the Indian poor [10]. The scheme aimed to provide health insurance coverage to those classified as working within the ‘unorganised’ sector and living below the poverty line. This scheme was succeeded in 2018 with the much-anticipated Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which expanded both the range of services provided and the pool of potential beneficiaries, and is intended to provide free access to healthcare for some 500 million people in the country [11]. The scheme was designed through a consultative process between central and state Governments, and implemented by the National Health Authority and nodal State Health Authorities, though states have the flexibility to modify terms of beneficiary eligibility and the package of services available to suit local needs.

The state of Meghalaya is situated in the north east of India, with a population of 3 million.(12) Approximately 20 percent of the Meghalayan population live in urban dwellings, with the remaining 80 percent residing in rural communities [12]. The Government of Meghalaya was the first in India to promise UHC to its population regardless of their socio-economic status via the scale-up and expansion of its local health insurance scheme–the Megha Health Insurance Scheme (MHIS) [13]. The MHIS was first launched in 2012 and provides access to secondary and tertiary care to all enrolled citizens of the state through a network of empanelled public and private hospitals. It was subsumed within the auspices of the AB-PMJAY in 2018, where service coverage is provided according to a list of included packages of care defined by the National Health Authority (NHA) and continues to be implemented by the MHIS office, who operate as the administering State Health Authority (SHA). The scheme offers financial protection in case of hospitalization due to medical emergencies and covers a wide range of health conditions and services free at the point of delivery. Most care-packages include secondary and tertiary care, and pharmaceuticals are provided for, if administered directly to patients in empanelled hospitals.

As the Government of Meghalaya looks to refine and further expand the MHIS under their ambitious goal to provide the entire state population with UHC [13], it is important to first assess the operational performance of the scheme to date in order to create a robust evidence base from which informed decisions for shaping the future of the MHIS can be made. The objectives of this analysis were threefold: to understand the success of the MHIS in reaching target beneficiaries for enrolment; to identify the patterns of care provision and key trends in service utilisation and spending; and to generate evidence-based recommendations for consideration by the government to enhance the operational performance of the MHIS.

Methods

A retrospective descriptive analysis was undertaken to identify patterns of enrolment, service provision and health spending in Meghalaya over the past six years (2013–2018).

Data

De-identified medical insurance enrolment and claims data were made available to the research team at the Indian Institute of Public Health Shillong (IIPHS). Data comprised three iterations of the MHIS scheme, referred to herein as MHIS I (May 2013—July 2015), MHIS II (August 2015 –December 2016), and MHIS III (January 2017 –September 2018). All patient identifiable information was removed by the MHIS administrators prior to data transfer. A total of 43 Microsoft excel files were transferred by the MHIS team. The enrolment files included one line of data for each unique enrolee with information on date of enrolment, age, gender, and region of residence. The claims files, which could be linked to the enrolment files by a unique registration number, had one line of data for each procedure claimed for at each visit. Each line of claims data also included information on where the care was provided, which type of facility provided the care, the amount claimed, the length of stay associated with the claim, and the package of care under which the procedure was claimed for.

Data analysis

Enrolment and claims data files were transferred into STATA (STATA v 14.2) for data cleaning and analysis. These files were manually checked against their respective data dictionaries for values that were missing or incomplete. A high level of heterogeneity in values entered under ‘package claimed’ was identified, necessitating the research team to examine each line in the claims data files manually in order to identify aberrant package information and match it to the appropriate package text, where possible. Common reasons for heterogeneity included typing and/or spelling mistakes, and additional or missing spaces between words. The MHIS team was consulted to resolve queries and data discrepancies. Where it was unclear what package was claimed for and this could not be resolved, these lines were removed from the analyses.

Enrolment data was analysed by distribution across locations (districts), socio-economic status (households below and above poverty line), and by demographic variables (age and sex). Claims data were analysed by volume of individual claims and total financial provision for aggregate individual claims under each package. Descriptive statistics were generated for both enrolment and claims data, disaggregated across the three time periods of interest (MHIS I (May 2013—July 2015), MHIS II (August 2015 –December 2016), and MHIS III (January 2017 –September 2018), and by geographic region, gender, and age. As the entire population of Meghalaya is eligible for coverage under the MHIS, except for those employed by the Government and their families, who are covered under the Employees State Insurance Scheme (ESIS), total eligible population was calculated by subtracting the number of households who identify as being covered by the ESIS from the state population. The number of MHIS enrollees was then divided by the total eligible population in order to generate information on proportion of eligible population currently enrolled in the scheme.

Results

Enrolment data

Enrolment data is presented in Table 1.

Table 1

Enrolment across all 3 phases of MHIS, 2013–2018.

Enrolment	MHIS I	MHIS II	MHIS III
Age groups	N (%)	N (%)	N (%)
1–5 years	97289 (13.4)	157683 (10.2)	200661 (12.9)
6–18 years	226729 (31.1)	287985 (18.6)	407208 (26.2)
19–45 years	295753 (40.6)	832761 (53.8)	712601 (45.8)
46–60 years	77707 (10.7)	189832 (12.3)	167103 (10.7)
>60 years	30550 (4.2)	80356 (5.2)	69435 (4.5)
Sex
Males	325336 (44.7)	825975 (53.3)	742195 (47.7)
Females	401724 (55.2)	718494 (46.4)	810214 (52.0)
Others	968 (0.1)	4148 (0.3)	4599 (0.3)
Districts
East Khasi Hills	187883 (25.8)	336800 (21.7)	361167 (23.2)
West Khasi Hills	62635 (8.6)	130847 (8.4)	146449 (9.4)
South West Khasi Hills	31205 (4.3)	56589 (3.7)	58198 (3.7)
Ri Bhoi	77928 (10.7)	104736 (6.8)	141774 (9.1)
East Jaintia Hills	45982 (6.3)	94804 (6.1)	86709 (5.6)
West Jaintia Hills	92450 (12.7)	199027 (12.9)	199426 (12.8)
West Garo Hills	59121 (8.1)	238392 (15.4)	201128 (12.9)
South West Garo Hills	72545 (10.0)	143339 (9.3)	129226 (8.3)
North Garo Hills	48194 (6.6)	103415 (6.7)	93869 (6.0)
East Garo Hills	28090 (3.9)	60747 (3.9)	71912 (4.6)
South Garo Hills	21995 (3.0)	79921 (5.2)	67150 (4.3)
Total	7,28,028	15,48,617	15,57,008

Enrolment statistics indicated that a significant increase in enrolments was observed for MHIS II in comparison to MHIS I, with the number of enrollees almost doubling in the scheme’s second iteration (MHIS I = 7,28,028 enrollees; MHIS II = 15,48,617). However, this increase was not observed for MHIS III, where the number of people enrolled in the scheme increased by less than 10,000 additional enrollees (15,57,008 enrolled). The number of current enrollees in MHIS as a proportion of the total number of eligible enrollees in the state obtained from census data indicate that 54.77% of the eligible population were enrolled in MHIS III.

Claims data

Care provider

A breakdown of total claims made and total financial provision for claims by type of care provider is provided in Table 2.

Table 2

Distribution of claims by type of service providers.

Hospital Type	MHIS-I	MHIS-II	MHIS-III
Number of Claims and Share of Public & Private Hospitals (in %)
Public	19937 (48%)	45129 (58%)	75997 (52%)
Private	21868 (52%)	33194 (42%)	69215 (47%)
Other states*	83 (0.1%)	157 (0%)	1280 (1%)
Total	41888 (100%)	78480 (100%)	146492 (100%)
Amount Claimed (in INR Million) and Share of Public & Private Hospitals (in %)
Public	68.87 (41%)	259.50 (50%)	367.05 (39%)
Private	95.76 (58%)	250.45 (48%)	538.59 (57%)
Other states*	1.62 (1%)	7.32 (1%)	42.67 (4%)
Total	166.25 (100%)	517.28 (100%)	948.31 (100%)

* Other states indicates that care was provided outside of the state of Meghalaya

Analysis of the most recent claims data under MHIS III (claims made between 2017–2018) revealed that more than half of the total amount claimed during this period (57%, INR 538,592,642) accrued to the 18 private hospitals empanelled under the scheme. The large network of public healthcare providers (159 in total) accounted for 39% of claims made (INR 367,048,292) during this period. In the earlier phases of MHIS I and II, the total amount of claims accrued to the private sector as a proportion of total amount claimed were 58% (MHIS II; INR 95,757,996) and 48% (MHIS I; INR 250,450,988) respectively. Financial provision for claims made outside of state increased from a 1% share of total spending to a 4% share of total spending between MHIS I and MHIS III.

Volume of claims

The top 15 packages as indicated by volume of claims in MHIS III are presented in Fig 1.

Fig 1

Highest volume packages by number of claims for MHIS III.

Key: ICU–intensive care unit; ANC–antenatal care.

The highest package by volume was General Ward Unspecified (GWU) 42%, followed by cat/dog bite (11%), maternal care packages (normal delivery, antenatal care, vaginal delivery with episiotomy and peritoneal repair, caesarean delivery; 21.0% collectively), cataract care (1%), Intensive Care Unit (ICU) care (1%), and renal dialysis (0.9%). Data for MHIS II is presented in Fig 2 below.

Fig 2

Highest volume packages by number of claims for MHIS II.

Key: ICU–intensive care unit; ANC–antenatal care.

In MHIS II, GWU represented over half of all claims by volume (59%), followed by maternal care (normal deliveries and peritoneum repair, caesarean delivery including lower segment C-section; 13.6%) and malaria (3%). Cat/dog bites, cataract care and ICU care contributed to 1.6%, 1.0% and 1.3% respectively in MHIS II. In MHIS I, GWU was responsible for 64.8% of the total number of claims, followed by maternal care packages (normal delivery, normal delivery with episiotomy and peritoneal repair, caesarean delivery including lower segment C-section; 14.7%) and ‘General ward -ICU’ (4%).

Amount claimed

Fig 3 provides a summary of the packages that accrued the highest financial dispensation across MHIS III.

Fig 3

Financial provision (INR, in millions) for packages claimed under MHIS III.

Key: ICU–intensive care unit; ANC–antenatal care.

The package code ‘General Ward Unspecified’ (GWU) accounted for almost half of all spending (48%, INR 239,246,000), followed by normal deliveries (9%, INR 44,834,175) and C-section deliveries (7%, INR 35,098,065). Lower respiratory tract infection (4.1%, INR 20,648,420), intraocular lens replacement for cataract (4.5%, INR 22573237), and viral fever (4.4%, INR 21961387) are other notable areas of high spending

Fig 4 provides a summary of the packages that accrued to highest financial dispensation in MHIS II.

Fig 4

Financial provision (INR, in millions) for packages claimed under MHIS III.

Key: ICU–intensive care unit; ANC–antenatal care.

In MHIS II, the top three packages by volume of spending were GWU (52%, INR 195,632,000), Malaria (8%, INR 30,344,100) and normal deliveries (7%, INR 27,480,000). In MHIS I, these were GWU (47%, INR 75,023,931), ‘General ward-ICU’ (6%, INR 10,053,250), followed by lower segment C-section (6%, INR 9,289,500), normal delivery with episiotomy (6%, INR 8,932,750) and normal deliveries (5%, INR 8,426,875).

Further analyses

A more in-depth analysis was undertaken for the two highest-volume claims packages, ‘General Ward Unspecified (GWU)’ and ‘Dog/Cat bite’, in order to better understand observed trends in relation to provision of care and financial spending under these packages.

General Ward Unspecified (GWU)

The raw number of claims made for GWU doubled from MHIS I (26,892) to MHIS III (57,337), however, the number of these claims as a proportion of the total number of claims reduced from 65% to 42%. Females of 19–45 years were the highest claimants under this category in MHIS III. Analysis of manual entries of reason for claim revealed that acute gastroenteritis contributed to the highest proportion of claims under the GWU category (21%) followed by acute respiratory tract infection (13%). I. Other conditions categorized under GWU included recurrent vomiting with dehydration, typhoid and viral fever, urinary tract infection, reproductive and child health, dysentery, accelerated hypertension, auditory processing disorders, scrub typhus and cancer (site unspecified). The median length of stay in a hospital under the GWU category was 4 days across all three phases of the MHIS. Claims made for GWU were also more frequent in public as compared to private facilities (55% and 45% respectively). The average amount claimed (INR) under the GWU package in public hospitals was INR 4408 and INR 4193 in private hospitals. Average amount claimed under this category was similar to that in MHIS II (INR 4408 and INR 4193 respectively in public and private hospitals). The average amount claimed under this category in MHIS II and III is substantially higher than that of the average amount claimed under this package in the MHIS I (INR 2916 and INR 2647 respectively in public and private hospitals).

Dog/Cat bite

Claims made under the ‘dog/cat bite’ category, indicating the administration of an anti-rabies injection, contributed to the second highest volume of claims (11%) in MHIS III as compared to 1.6% of all claims in MHIS II. This package was not included in MHIS-I. A high financial provision for claims made under dog/cat bite was made in MHIS II (1.2%, INR 4386375), and this increased substantially in MHIS III (2.3%, INR 11643535). The majority of these claims were made in public sector facilities in both phases of MHIS (96% in MHIS III and 98% in MHIS II). When Cat/Dog bite data for MHIS-III were disaggregated by district, analysis showed that East Khasi Hills (20%) and West Garo Hills (12.5%) were the top contributing districts to these high claims in the state, however this aligns with general volume of claims disaggregated by district for all claims against population density

Discussion

In this paper, we present a novel and comprehensive descriptive analysis of insurance claims and enrolment data from the Megha Health Insurance Scheme (MHIS) in the North East of India over a period of six years from its inception in 2013 through to 2018. This is the first analysis of the operational performance of the scheme against its UHC-oriented objectives to provide equitable access to essential healthcare for the state’s population. Important insights into the provision of and spending on publicly-funded healthcare in the state of Meghalaya are presented in this paper.

We estimate that approximately 55% of the total eligible population are currently enrolled in MHIS. This is in line with the estimated 56% population-level insurance coverage published by the National Sample Survey Office of India [14]. Enrolment data analyses demonstrated an increasing pattern of enrolment over time, with the sharpest increase in number of enrolled beneficiaries between MHIS I and MHIS II. Enrolment has been highest in the age-group 19–45 years across all phases, matching demographic distribution of population by age. We observed a small decline in the enrolment of males within the 19–60 years age bracket in the transition of MHIS II to MHIS III. Upon further examination, we hypothesize two primary drivers for this. Firstly, the period upon which MHIS II was open was relatively short (1yr 4 months) as compared to MHIS I (2yrs 2 months) and III (1yr 9 months), narrowing the window within which enrolments could be processed. Secondly, enrolment kiosks for MHIS are open during usual working hours, possibly limiting those who work during office hours from easily enrolling in the scheme. Given the higher proportion of 19–60-year-old males in formal employment, as compared to women of the same age, it is perhaps unsurprising that we would observe a decline in growth of enrolment in this demographic. We also observed a decrease in enrolment growth between the different phases of the scheme in some districts as compared to others. Combined, our findings indicate that the Government of Meghalaya is on track to providing Universal Health Coverage (UHC) to the state’s population with over half of all eligible beneficiaries enrolled. However, in order to reach the remaining 45% of eligible beneficiaries, enrolment campaigns will need to target those demographics for which a decline in enrolment growth has been observed. Targeted activities could include lengthening enrolment kiosk opening hours to enhance the window of opportunity for enrolment for those employed to work during core business hours, and the implementation of mobile enrolment clinics to ensure expansion of enrolment in more rural and remote districts.

Analysis of claims data by provider showed that more than half of all claims accrued to the private sector facilities across all phases of the scheme. This is highly disproportionate to facility availability, where public facilities empanelled under the scheme outnumber empanelled private facilities across the state at a rate of almost 10 to 1. However, this is a common observation across India where members of the public frequently cite perception of superior quality in the private sector as a reason for favouring private facilities over those provided by the public sector [15-17]. Almost 90% of the state’s private facilities are located in the capital city Shillong. While private facilities are more likely to provide specialized care services, the highly inflated rate of service provision in private facilities in comparison to public hospitals outstrips expected demand for specialized services as indicated by state-level burden of disease [2, 18]. With 80% of the state population residing in rural areas, travel to Shillong for private facility care is impeding the realization of UHC, which requires equitable access to high quality care. Efforts by the Government of Meghalaya to mitigate this disparity in care delivery should be bi-directional to both assess and address the potential requirements of public facilities towards strengthening capacity to deliver more specialist and high-quality care, and to work with and educate the local community regarding the accessibility and quality of care available at public facilities.

Analysis of claims data by both amount spent and by volume indicate that the package General Ward Unspecified (GWU) contributed to the highest volume of claims and the most substantial financial provision for any package of care across all three phases of the MHIS. GWU claims were made more frequently in public as compared to private facilities, and the amount claimed under this category in MHIS II and III doubled compared to that spent on the same package under MHIS I. Similar overuse of this package has been reported in Odisha previously. Where reimbursement rates are pre-determined per package by the MHIS, the GWU is unique in that there are no such pre-defined parameters for reimbursable amount. Meticulous analysis of hand-entered reasons for claims under this package revealed that the most common entries for this claim were for pre-existing packages, including typhoid, vomiting and diarrhoeal disease, and fever. When comparing amount claimed under GWU for these manual entries against the set reimbursement rate for the correct package of care, we noted that oftentimes the pre-set package rate would have been higher had the correct package been allocated. This indicates that GWU is most likely being used in administrative error due to an inability or unwillingness to locate the correct package of care. The use of the GWU package as a coverall for conditions ranging from everything from fever to cancer could impede the state’s ability to match spending to clinical need and apply adequately rigorous governance mechanisms to both clinical care provision and hospital claims management under the MHIS. Further qualitative analysis investigating healthcare provider and administrator use of the GWU package could be utilised to inform future policy targeted at enhancing the correct use of package codes and decreasing the overuse of the GWU. Information architecture could also be updated to incorporate enhanced digital accountability into the processing of certain ‘red flag’ packages such as the GWU, which necessitate additional data to be input before claims are processed [19].

The highly unexpected finding of claims for dog and cat bite across MHIS II and III, accounting for close to 20% of all claims, second only to GWU in MHIS III, is a cause for significant concern. This package is unique in that for a complete course of anti-rabies injections for dog and cat bite, a potentially-infected individual is required to attend the facility on five separate occasions, one for each anti-rabies injection. The care provider is permitted to submit a separate claim for the individual upon each presentation for injection [20]. State-level rabies surveillance statistics reveal low levels of animal infection and very low transmission to humans in Meghalaya [21]. However, given that anti-rabies injections are intended to be administered as post-exposure prophylaxis, the frequency of animal bite is a more important indicator of need. Local media reports suggest that cases of dog bites are indeed increasing at an alarming rate in the state, presenting a major challenge to both individuals and the health systems that provide their care [22, 23] Given the extremely high rate at which anti-rabies injections are being administered to the Meghalayan population under the MHIS, and the substantial financial provision being made for this, further investigation should be undertaken as a matter of urgency to better understand the situation and identify potential cost-effective policy solutions to reduce the societal burden of animal bites in the community.

There are a number of limitations to this analysis that should be recognised. Firstly, we provide here only top-level descriptive statistics of patterns of enrolment, volume of claims, and amount spent on claims over time. We have not triangulated this data with additional sources such as census data on household spending, government reports of spending on health, nor clinical burden of disease. As such, we are limited to comment only on the data provided by the MHIS at this point in time. Secondly, the way in which MHIS claims data is manually entered into an electronic system, means that human errors are frequent, as is common for similar administrative data [24]. This significantly impeded our ability to collate and analyze data, and indeed numerous claims were excluded on the basis of unclear or incomplete entries. Thirdly, we have not undertaken a detailed fiscal space analysis necessary to provide a comprehensive overview of how the spending under MHIS relates to overall spending by the state of Meghalaya on health. Such an analysis would be beneficial to provide insights into how, where, and how much budgetary allocations are being made on health within the state across the various levels of care provision.

Future research on the nature and pattern of care provision under the MHIS is warranted for a more comprehensive impact evaluation into who the scheme is reaching, how care is being provided, and where taxpayer money is being spent under the scheme. Analyses of enrolment data may be triangulated against census information to more comprehensively assess enrolment of eligible beneficiaries against geography, gender, caste, religious or tribal status, and socioeconomic status. Service uptake and utilisation behaviour should be assessed against clinical need in order to investigate both the face validity of claims data in providing insights into the health of the population, and the quality of the scheme in providing care according to population need. Finally, a comprehensive assessment should be undertaken to assess the impact of care provision on health outcomes and financial risk protection, where similar analyses have indicated sub-par performance of other government-sponsored health insurance schemes across India [25-29].

Health insurance enrolment and claims data can provide an invaluable resource in which to assess operational performance of a scheme against its objectives. Here, we have tracked enrolment of eligible beneficiaries under the MHIS and trends in the care that was provided to them over a six year period. With over 55% of the eligible population currently enrolled in the MHIS, the government of Meghalaya is making headway in its ambitions to provide Universal Health Coverage to the state’s population. Analysis of claims data indicates a number of areas for further data gathering and service improvement, including disproportionate use of private facilities to deliver public health services, an overuse of the general ward unspecified package of services, and an extremely high rate of anti-rabies post exposure prophylaxis provision in the state. The government of Meghalaya can use enrolment and claims data to facilitate real-time tracking of the operational performance of the scheme and use this information to make evidence-based decisions for future policy to enhance provision of healthcare under the MHIS.

3 Feb 2022

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1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: No

Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Parameters for defining success of other schemes could be mentioned, (in terms of population coverage, service coverage and reduction in out of pocket expenditures is mentioned). Otherwise, making such a statement may is not useful in the article.

2. Most schemes were designed and implemented by states and even after RSBY was launched in 2008, States implemnted their own schemes in paralell. So the statement on demonstrating ability to absorb funds needs clarity.

3. RSBY only had a coverage of upto Rs.30,000 per family of 5 per annum, when all other schemes had a coverage of Rs.1.5 – 2 lakhs. Mentioning RSBY as comprehensive is incorrect. Authors may want to define comprehensives of RSBY in specific terms.

4. 'RSBY was not replaced' by PMJAY – authors may want to use more appropriate language such as RSBY was ‘succeeded by” by PMJAY.

5.Design of PMJAY was a consultative process where states were represented. line 90-91 is an incorrect statement.

PMJAY is implemented by the National Health Authority with guidelines being drawn through a consultative process with states having the flexibility to modify to local context.

6.Authors have only conducted a descriptive analysis of the claims. This should be clearly stated.

7. The analysis only pertains to population coverage (enrollment), claims analysis according to defined packages and prublic or private hospital where service was provided. Quality of care was not explored thus title should be revised to not be mis-leading.

8. The results could be substantiated by answering 'why' question either from program documents or literature on the scheme or experiences from other schemes.

9. To understand the scheme better - the details of coverage, especially service coverage should be mentioned either in the inrtoduction or as a specific section.

10. A comparision of results to NSSO Health and Morbidity data 2017-18 for Meghalaya State could be useful to analyse the scheme status - interms of insurance coverage, cost of coverage or out of pcokect expneditures and services /packages usually covered.

Reviewer #2: The paper seeks to analyse care provision, utilisation, and quality of the MHIS through analysis of the enrolment and claims data of the MHIS I to III, from 2013 to 2018 and to track patterns of enrolment of beneficiaries in the scheme over this period of time, trends in care utilisation behaviours, and patterns of care provision, The objectives of this analysis were threefold: to understand the success of the MHIS in reaching target beneficiaries for enrolment; to understand the patterns of care provision and key trends in both Government health spending and health service utilisation; and to examine how claims data could inform our understanding of the quality of care provided under the MHIS. This review has kept this as the objective to be attained by the paper.

The authors had access to enrolment and claims data should use them to answer the questions they had set for themselves and additional questions that arise from the data they have presented. Listed below are some instance where such questions remain unanswered.

1. For utilisation, the percentage of eligible population who are enrolled and the percentage of enrolled population who used the scheme ( weighted by the propensity to consume health care in the state) are good indicators. The percentage of eligible persons who were enrolled is provided for the total population. But the data presented has aspects that need to be explained, to meet the objectives of the paper. MHIS II to III shows a decline is some population groups e.g. Males, ages 19- 60 and above) and in some districts (East Jantia and Garo Hills). In order to ‘track the patterns of enrolment in the scheme’ it is important to answer whether this had implications to the reach of the scheme. [ The additional enrolment mentioned in the paper is accounted for by some demographic groups and districts ]. Authors could look at the enrolment as a percentage of eligible population in these categories and see if the dip is real or a function of demographic shift. Since they had access to claims data they could look for an explanation in the utilisation pattern of these groups in the earlier years to see if this impacted the willingness to enrol in later years. If the claims data will also demonstrate whether the fall off is linked to lack of geographic proximity, which will reduce access costs which are not reimbursed. This would have clarified a phenomenon found in their table and could have equity implications which are not explored.

2. The paper notes that larger share of claims (57%, INR 538,592,642) accrued to the 18 private hospitals empanelled under MHIS-III. The large network of public healthcare providers (159 in total) accounted for 39% (INR 367,048,292) during this period.

MHIS claims are for hospital based secondary and tertiary level treatment. So unless the private and government hospitals are of the same capacity, their utilisation may not be comparable. The reason for higher amount of claims for a few private hospitals could be that they have specialised treatment facilities that most government hospitals do not have. One way of verification would be the number of claims for private and government sector, another average amount per claim in government and private hospitals, or share of government and private hospitals in each package, as some packages are high cost (e.g. Laparoscopic Cholecystectomy). If private hospitals specialised in them they would get greater share of claim amount treating lesser number of cases. This could also be the result of a deliberate policy: government takes care of primary and secondary treatment and prefers to purchase tertiary care from private sector. Government hospitals appear to treat a greater percentage of patients with common ailments (55% to 45%) which is covered under General Ward Unspecified.

Since the data on enrolled hospitals are available it may be good to categorise the hospitals as tertiary, secondary and primary and look at the intra category differences between private and government.

3. Paper mentions that claims reimbursement is based on package rates, by which the amount reimbursed for a procedure is pre-determined. But average rates widely for the same procedures across the three schemes (e.g., Caesarean section.) Authors need to explain the discrepancy between the narrative and the table.

The paper notes (L 228)that the average claim amount for GWU in MHIS II and III is double that of the MHIS I but does not explain whether this is due to increase in package rate/per day of admission as the average length of stay is the same.

4. In discussion section the authors states that (L 250) the gender distribution of enrolment has

been equal and static over all phases of MHIS, indicating gender parity by enrolment since

the scheme’s inception. This is not borne by table 1. Male enrolment moved from 44.7 to 53.3 to 47.7. While gender distribution of health services may be skewed against females what is to be examined in this paper, is whether enrolment and utilisation follows the same pattern in other published studies on government funded health insurance schemes. Both the references cited deal with consumption of health care in general not access to government funded health insurance schemes.

5. Paper states that (L. 254) Analysis of claims data by provider showed that more than half of the claims were accrued to the private sector hospitals in all phases of the scheme. I could not find the data to support this in the paper.

6. The analysis of the reasons for overuse of GWU is not convincing. It is commonly seen that this happens due to ignorance, carelessness or unwillingness to take the effort to search for the correct package to book. It could also be that it being a catch all package it may reflect the sum of all conditions and procedures for which the scheme has not prescribed a package.

For procedures for which package rates exist, it needs to be verified the hospital would earn more money by GWU than the package. If the specified condition has a lower package rate, then booking under GWU will be deliberate and fraudulent. Authors also need to state if the practice was the same across private and government hospitals.

7. The policy recommendation to halt preventive vaccination for dog/cat bite is not justified by arguments. The rate of rabies infection does not determine the number of anti-rabies injections; the number of animal bites do. Easy availability of preventive vaccination reduces the number of infections. The paper reports that most of these injections were taken in government facilities, where the inventory would have to be run down to match the number of injections administered. With universal coverage of MHIS there is no incentive to fudge the figures unless the drug claimed but not used is being shipped outside the state. Before recommending a policy measure the issue needs to be examined in depth.

While this is the first analysis of the data and more detailed investigations may follow later, the paper has observations, which raise alternate explanations and should be resolved with the data referred to in the paper itself. Authors would do well to provide conclusions, to the questions they have posed, after examining alternate possibilities, based on the data they have quoted in the paper.

The paper has many unhappy usage of language which need to be corrected before publication.

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Reviewer #1: No

Reviewer #2: No

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4 May 2022

Reviewer #1:

1. Parameters for defining success of other schemes could be mentioned, (in terms of population coverage, service coverage and reduction in out of pocket expenditures is mentioned). Otherwise, making such a statement may is not useful in the article.

We thank the reviewer for this comment. We can confirm that we have now added lines detailing parameters for success of other schemes according to the three dimensions of Universal Health Coverage as defined by the WHO (i.e. population coverage, service coverage, financial coverage). Where we have referenced other schemes in both the introduction and discussion, we have now made to sure to clearly define the UHC dimensions of relevance to the scheme being discussed and how this relates to the operational performance of the MHIS against its UHC objectives.

2. Most schemes were designed and implemented by states and even after RSBY was launched in 2008, States implemented their own schemes in parallel. So the statement on demonstrating ability to absorb funds needs clarity.

Thank you for this comment. We have amended this paragraph to provide greater clarity. We refer to progressive health reforms, highlighting successes of social insurance schemes Aarogysri and BSBY, and then move on to highlight that not all reforms were successful across the country due to an inability to absorb Government funding. We have provided two references for this, both of which report on substantial underspend in ‘high priority states’ in healthcare, due to poor infrastructure and inadequate human resource capacity.

3. RSBY only had a coverage of upto Rs.30,000 per family of 5 per annum, when all other schemes had a coverage of Rs.1.5 – 2 lakhs. Mentioning RSBY as comprehensive is incorrect. Authors may want to define comprehensives of RSBY in specific terms.

We thank the reviewer for this comment. We agree with this comment and have now removed the word ‘comprehensive’ to rectify this oversight.

4. 'RSBY was not replaced' by PMJAY – authors may want to use more appropriate language such as RSBY was ‘succeeded by” by PMJAY.

We thank the reviewer for this comment. We confirm that we have replaced the word ‘replaced’ with the words ‘succeeded by’ as requested.

5.Design of PMJAY was a consultative process where states were represented. line 90-91 is an incorrect statement.

We thank the reviewer for this perceptive comment. We had intended to reflect the centrally-administered nature of the scheme via the NHA, however we recognise that in fact the way the sentence was previously worded may have been misleading. We have amended the paragraph to reflect that PMJAY was designed according to a consultative process between Central and state health leaders, is implemented by the National Health Authority and nodal State health authorities, with states having the flexibility to modify to local context.The following text has been added/amended to address this comment:

This scheme was succeeded in 2018 with the much-anticipated Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which expanded both the range of services provided and the pool of potential beneficiaries, and is intended to provide free access to healthcare for some 500 million people in the country.(11) The scheme was designed through a consultative process between central and state Governments, and implemented by the National Health Authority and nodal State Health Authorities, though states have the flexibility to modify terms of beneficiary eligibility and the package of services available to suit local needs.

6.Authors have only conducted a descriptive analysis of the claims. This should be clearly stated.

Thank you for this comment. We have now amended the methods section of the paper in a number of ways to address this comment and strengthen the section. We have included the following line in the beginning of the methods section: A retrospective descriptive analysis was undertaken to identify patterns of enrollment, service provision and health spending in Meghalaya over the past six years (2013 - 2018). We have then divided the methods into 2 sections: Data, and analysis. In the analyses section, we have included 2 additional lines on how data was analyzed and descriptive statistics generated.

The following text has been added to the methods:

A retrospective descriptive analysis was undertaken to identify patterns of enrolment, service provision and health spending in Meghalaya over the past six years (2013 - 2018).

…..

Descriptive statistics were generated for both enrolment and claims data, disaggregated across the three time periods of interest (MHIS I (May 2013 - July 2015), MHIS II (August 2015 – December 2016), and MHIS III (January 2017 – September 2018)), and by geographic region, gender, and age. As the entire population of Meghalaya is eligible for coverage under the MHIS, except for those employed by the Government and their families, who are covered under the Employees State Insurance Scheme (ESIS), total eligible population was calculated by extracting the number of households who identify as being covered by the ESIS from the State population. The number of MHIS enrollees was then divided by the total eligible population in order to generate information on proportion of eligible population currently enrolled in the scheme.

7. The analysis only pertains to population coverage (enrollment), claims analysis according to defined packages and public or private hospital where service was provided. Quality of care was not explored thus title should be revised to not be mis-leading.

Thank you for this comment. We have removed the word quality from the title and present our paper now with the following title: An analysis of public health insurance enrolment and claims data from Meghalaya: Insights into the provision of care in North East India

8. The results could be substantiated by answering 'why' question either from program documents or literature on the scheme or experiences from other schemes.

Thank you for this comment. We have now attempted to better answer the ‘why’ questions raised in interpreting our results in the discussion of the manuscript. We have done this in two primary ways: 1) citing additional literature pertaining to the operational performance of government-sponsored health insurance schemes within and outside of India, and 2) by conducting additional analyses to better understand key results. We have now added a number of new figures to the manuscript to better illustrate key findings and added sections to the discussion to provide a more thorough analysis of the implications of our findings. We hope this satisfies the reviewers request, as the changes that we have made in this respect have been substantial.

9. To understand the scheme better - the details of coverage, especially service coverage should be mentioned either in the introduction or as a specific section.

Thank you for this comment. We have added more information to the introduction to provide further detail regarding the service coverage of the MHIS. The following text has been added:

The MHIS was first launched in 2012 and provides access to secondary and tertiary care to all enrolled citizens of the State through a network of empanelled public and private hospitals. It was subsumed within the auspices of the AB-PMJAY in 2018, where service coverage is provided according to a list of included packages of care defined by the National Health Authority (NHA) and continues to be implemented by the MHIS office, who operate as the administering State Health Authority (SHA). The scheme offers financial protection in case of hospitalization due to medical emergencies and covers a wide range of health conditions and services free at the point of delivery. Only secondary and tertiary care is covered under the scheme, and pharmaceuticals are provided for, if administered directly to patients in hospital.

10. A comparison of results to NSSO Health and Morbidity data 2017-18 for Meghalaya State could be useful to analyse the scheme status - interms of insurance coverage, cost of coverage or out of pocket expenditures and services /packages usually covered.

We thank you for this useful suggestion. We have now added a line to the discussion to add support to the credibility of our findings for health insurance enrolment by stating that our estimates obtained from MHIS enrolment data analysis broadly match population coverage estimates provided by the NSSO. Please note that while we recognize that reduction in out of pocket expenditure is an essential component of UHC, we have not explored this in our manuscript. We have analysed spending data only from Government-sponsored claims made to the MHIS, under which secondary care is provided free at the point of delivery. We have recommended in our discussion that a thorough investigation into whether being enrolled in the MHIS has afforded the Meghalaya community financial risk protection. The following text has been added:

We estimate that approximately 55% of the total eligible population are currently enrolled in MHIS. This is in line with the estimated 56% population-level insurance coverage published by the National Sample Survey Office of India.

Reviewer #2: The paper seeks to analyse care provision, utilisation, and quality of the MHIS through analysis of the enrolment and claims data of the MHIS I to III, from 2013 to 2018 and to track patterns of enrolment of beneficiaries in the scheme over this period of time, trends in care utilisation behaviours, and patterns of care provision, The objectives of this analysis were threefold: to understand the success of the MHIS in reaching target beneficiaries for enrolment; to understand the patterns of care provision and key trends in both Government health spending and health service utilisation; and to examine how claims data could inform our understanding of the quality of care provided under the MHIS. This review has kept this as the objective to be attained by the paper.

The authors had access to enrolment and claims data should use them to answer the questions they had set for themselves and additional questions that arise from the data they have presented. Listed below are some instance where such questions remain unanswered.

We thank the reviewer for this thorough and considered review. We would like the reviewer to know that we have significantly amended the manuscript on the basis of the comments received and believe that it is a much stronger manuscript because of these changes. We are grateful to the reviewer for taking the time to provide such substantial feedback.

1. For utilisation, the percentage of eligible population who are enrolled and the percentage of enrolled population who used the scheme (weighted by the propensity to consume health care in the state) are good indicators. The percentage of eligible persons who were enrolled is provided for the total population. But the data presented has aspects that need to be explained, to meet the objectives of the paper. MHIS II to III shows a decline is some population groups e.g. Males, ages 19- 60 and above) and in some districts (East Jantia and Garo Hills). In order to ‘track the patterns of enrolment in the scheme’ it is important to answer whether this had implications to the reach of the scheme. [ The additional enrolment mentioned in the paper is accounted for by some demographic groups and districts ]. Authors could look at the enrolment as a percentage of eligible population in these categories and see if the dip is real or a function of demographic shift. Since they had access to claims data they could look for an explanation in the utilisation pattern of these groups in the earlier years to see if this impacted the willingness to enrol in later years. If the claims data will also demonstrate whether the fall off is linked to lack of geographic proximity, which will reduce access costs which are not reimbursed. This would have clarified a phenomenon found in their table and could have equity implications which are not explored.

We thank the reviewer for this detailed comment. We have made a number of changes to the manuscript the address this comment. Firstly, we have incorporated more detailed information into the methods regarding how eligibility was calculated. The following paragraph has been added to the methods:

As the entire population of Meghalaya is eligible for coverage under the MHIS, except for those employed by the Government and their families, who are covered under the Employees State Insurance Scheme (ESIS), total eligible population was calculated by subtracting the number of households who identify as being covered by the ESIS from the State population. The number of MHIS enrollees was then divided by the total eligible population in order to generate information on proportion of eligible population currently enrolled in the scheme.

We undertook further analysis in order to understand the trends in regards to decline in enrolment growth for males 19-60 years, and those in certain districts, as highlighted by the reviewer. We believe that this is not representative of a demographic shift, but more likely the product of two interacting phenomena: 1) the MHIS II window was far smaller than that of MHIS I and III, therefore restricting the time in which the scheme was open for enrolment and 2) the enrolment kiosks were only open during working hours, prohibiting those of working age, who are predominantly males of 19-60years, from enrolling. We have added the following text to the discussion to highlight this.

We observed a small decline in the enrolment of males within the 19-60 years age bracket in the transition of MHIS II to MHIS III. Upon further examination, we hypothesize two primary drivers for this. Firstly, the period upon which MHIS II was open was relatively short (1yr 4 months) as compared to MHIS I (2yrs 2 months) and III (1yr 8 months), narrowing the window within which enrolments could be processed. Secondly, enrolment kiosks for MHIS are open during usual working hours, prohibiting those in formal employment who work during office hours from easily enrolling in the scheme. Given the higher proportion of 19–60-year-old males in formal employment, as compared to women of the same age, it is perhaps unsurprising that we would observe a decline and stagnation in growth of enrolment in this demographic. We also observed a decrease in enrolment growth between the different phases of the scheme in some districts as compared to others. Combined, our findings indicate that the Government of Meghalaya is on track to providing Universal Health Coverage (UHC) to the state’s population with over half of all eligible beneficiaries enrolled. However, in order to reach the remaining 45% of eligible beneficiaries, enrolment campaigns will need to target those demographics for which a decline in enrolment growth has been observed. Targeted activities could include lengthening enrolment kiosk opening hours and mobile enrolment clinics to more rural and remote districts.

2. The paper notes that larger share of claims (57%, INR 538,592,642) accrued to the 18 private hospitals empanelled under MHIS-III. The large network of public healthcare providers (159 in total) accounted for 39% (INR 367,048,292) during this period.MHIS claims are for hospital based secondary and tertiary level treatment. So unless the private and government hospitals are of the same capacity, their utilisation may not be comparable. The reason for higher amount of claims for a few private hospitals could be that they have specialised treatment facilities that most government hospitals do not have. One way of verification would be the number of claims for private and government sector, another average amount per claim in government and private hospitals, or share of government and private hospitals in each package, as some packages are high cost (e.g. Laparoscopic Cholecystectomy). If private hospitals specialised in them they would get greater share of claim amount treating lesser number of cases. This could also be the result of a deliberate policy: government takes care of primary and secondary treatment and prefers to purchase tertiary care from private sector. Government hospitals appear to treat a greater percentage of patients with common ailments (55% to 45%) which is covered under General Ward Unspecified. Since the data on enrolled hospitals are available it may be good to categorise the hospitals as tertiary, secondary and primary and look at the intra category differences between private and government.

We thank the reviewer for this comment. We recognize that it is true that the private hospitals empaneled under the MHIS are largely specialty and super-specialty hospitals, while most public facilities lack such expertise and infrastructure. However, we believe that the primary driver in private care seeking and delivery goes far beyond specialty clinical need as outlined in the Meghalayan burden of disease estimates. We believe that there are important implications for health system strengthening and universal health coverage from the disproportionate use of private hospitals for government-funded care in the State, of which 90% are located in the capital despite 80% of the population living In rural communities. We have added new references and the following text to the manuscript to address this point:

Almost 90% of the state’s private facilities are located in the capital city Shillong. While private facilities are more likely to provide specialized care services, the highly inflated rate of service provision in private facilities in comparison to public hospitals outstrips expected demand for specialized services as indicated by state-level burden of disease.(2,19) With 80% of the state population residing in rural areas, travel to Shillong for private facility-delivered care is impeding the realization of UHC, which requires equitable access to high quality care. Efforts by the Government of Meghalaya to mitigate this disparity in care delivery should be bi-directional to both assess and address the potential requirements of public facilities towards strengthening capacity to deliver more specialist and high-quality care, and to work with and educate the local community regarding the accessibility and quality of care available at public facilities.

3. Paper mentions that claims reimbursement is based on package rates, by which the amount reimbursed for a procedure is pre-determined. But average rates widely for the same procedures across the three schemes (e.g., Caesarean section.) Authors need to explain the discrepancy between the narrative and the table.

The paper notes (L 228) that the average claim amount for GWU in MHIS II and III is double that of the MHIS I but does not explain whether this is due to increase in package rate/per day of admission as the average length of stay is the same.

Response:

We thank the reviewer for this comment, which we believe reflects inadequate explanation of the ‘General Ward Unspecified (GWU)’ on our behalf, rather than data discrepancy or inaccuracy. There are pre-determined package rates for all procedures carried out under MHIS, and this is true for all packages except the GWU. This is why we present total estimates of volume of care and financial dispensation for all packages in the newly added figures 1a,b, and 2a,b, and provide further detail on ‘average’ amount claimed per claim in general ward unspecified because this package is unique. We have added the following text to make this point more clearly:

Where reimbursement rates are pre-determined per package by the MHIS, the GWU is unique in that there are no such clear parameters for reimbursable amount. It appears that this lack of clear boundaries for reimbursement under this package is contributing to its overuse. Meticulous analysis of hand-entered reasons for claims under this package revealed that the most common entries for this claim were for pre-existing packages that already exist within the MHIS, including typhoid, vomiting and diarrhoeal disease, and fever. When comparing amount claimed under GWU for these manual entries against the set reimbursement rate for the correct package of care, we noted that oftentimes the pre-set package rate would have been higher had the correct package been allocated. This indicates that GWU is most likely being used in administrative error due to an inability or unwillingness to locate the correct package of care.

4. In discussion section the authors states that (L 250) the gender distribution of enrolment has been equal and static over all phases of MHIS, indicating gender parity by enrolment since the scheme’s inception. This is not borne by table 1. Male enrolment moved from 44.7 to 53.3 to 47.7. While gender distribution of health services may be skewed against females what is to be examined in this paper, is whether enrolment and utilisation follows the same pattern in other published studies on government funded health insurance schemes. Both the references cited deal with consumption of health care in general not access to government funded health insurance schemes.

We thank the reviewer for this comment. We considered and discussed this comment in great detail as a team and decided to remove the line on gender parity in enrolment and utilization, as we felt that we did not currently have all the information necessary to substantiate this point. The primary reason for this is that we feel that further analyses is required to better understand care utilisation through a gender lens by examining claims for common conditions that affect both men and women, such as cardiovascular disease and diabetes, against population-level estimates of disease burden, to assess whether both males and females are equitably accessing care. At present, care utilisation is skewed in the direction of females under the MHIS due to the high proportion of claims being made for maternal care packages. There are well-recognised reasons for this, such as the incentivization of ASHA workers to accompany women to institutional care for their delivery. We did not have time to undertake this additional analyses at present, and hope that it may form the basis of a program of work on health system strengthening through a gender lens by a PhD student at IIPH in Meghalaya using data from MHIS I through to the more recent MHIS IV.

5. Paper states that (L. 254) Analysis of claims data by provider showed that more than half of the claims were accrued to the private sector hospitals in all phases of the scheme. I could not find the data to support this in the paper.

Thank you for this comment. We apologise for this oversight and have added a table and accompanying text to the manuscript:

Hospital Type MHIS-I MHIS-II MHIS-III

Number of Claims and Share of Public & Private Hospitals (in %)

Public 19937 (48%) 45129 (58%) 75997 (52%)

Private 21868 (52%) 33194 (42%) 69215 (47%)

Other States* 83 (0%) 157 (0%) 1280 (1%)

Total 41888 (100%) 78480 (100%) 146492 (100%)

Amount Claimed (in INR Million) and Share of Public & Private Hospitals (in %)

Public 68.87 (41%) 259.50 (50%) 367.05 (39%)

Private 95.76 (58%) 250.45 (48%) 538.59 (57%)

Other States* 1.62 (1%) 7.32 (1%) 42.67 (4%)

Total 166.25 (100%) 517.28 (100%) 948.31 (100%)

Analysis of the most recent claims data under MHIS III (claims made between 2017-2018) revealed that more than half of the total amount claimed during this period (57%, INR 538,592,642) accrued to the 18 private hospitals empaneled under the scheme. The large network of public healthcare providers (159 in total) accounted for 39% of claims made (INR 367,048,292) during this period. In the earlier phases of MHIS-I and II, the total amount of claims accrued to the private sector as a proportion of total amount claimed were 58% (MHIS II; INR 95,757,996) and 48% (MHIS I; INR 250,450,988) respectively. Financial provision for claims made by empanelled specialty hospitals located outside of state increased from a 1% share of total spending to a 4% share of total spending between MHIS I and MHIS III.

6. The analysis of the reasons for overuse of GWU is not convincing. It is commonly seen that this happens due to ignorance, carelessness or unwillingness to take the effort to search for the correct package to book. It could also be that it being a catch all package it may reflect the sum of all conditions and procedures for which the scheme has not prescribed a package. For procedures for which package rates exist, it needs to be verified the hospital would earn more money by GWU than the package. If the specified condition has a lower package rate, then booking under GWU will be deliberate and fraudulent. Authors also need to state if the practice was the same across private and government hospitals.

We thank the reviewer for this very useful comment. We also refer you to our related response to comment 3 above. We have taken this comment into great consideration, undertaken additional data-checking and analysis, and amended the manuscript accordingly. Please see the additional text that has been added to the discussion section of the manuscript below:

Where reimbursement rates are pre-determined per package by the MHIS, the GWU is unique in that there are no such clear parameters for reimbursable amount. It appears that this lack of clear boundaries for reimbursement under this package is contributing to its overuse. Meticulous analysis of hand-entered reasons for claims under this package revealed that the most common entries for this claim were for pre-existing packages that already exist within the MHIS, including typhoid, vomiting and diarrhoeal disease, and fever. When comparing amount claimed under GWU for these manual entries against the set reimbursement rate for the correct package of care, we noted that oftentimes the pre-set package rate would have been higher had the correct package been allocated. This indicates that GWU is most likely being used in administrative error due to an inability or unwillingness to locate the correct package of care.

In regards to whether this trend was observed in both public and private facilities, we include the following text in the results section of the paper:

Claims made for GWU were more frequent in public as compared to private facilities (55% and 45% respectively).

7. The policy recommendation to halt preventive vaccination for dog/cat bite is not justified by arguments. The rate of rabies infection does not determine the number of anti-rabies injections; the number of animal bites do. Easy availability of preventive vaccination reduces the number of infections. The paper reports that most of these injections were taken in government facilities, where the inventory would have to be run down to match the number of injections administered. With universal coverage of MHIS there is no incentive to fudge the figures unless the drug claimed but not used is being shipped outside the state. Before recommending a policy measure the issue needs to be examined in depth.

We thank the reviewer for this insightful comment and have reflected on this point. First, we would like to clarify that it was not our intention to recommend that the government should de-fund anti-rabies injections, rather, we had intended to raise the possibility of merging the distribution of anti-rabies injections under a single program to reduce inefficiency, where at present anti-rabies injections are funded by the government in both the MHIS and the State-wide anti-rabies program. We fully appreciate that distribution of anti-rabies vaccination is a product of animal bite and not of infection, and have sought to clarify this in the manuscript. We have also added more text to reflect on the potential causes of this high volume of anti-rabies vaccinations, and potential policy measures.

We have significantly amended our discussion of this topic and now include the following replacement text in the discussion of dog/cat bite claims in the paper:

State-level rabies surveillance statistics reveal low levels of animal infection and very low transmission to humans in Meghalaya.(21) However, given that anti-rabies injections are intended to be administered as post-exposure prophylaxis, the frequency of animal bite is a more important indicator of need. Local media reports suggest that cases of dog bites are indeed increasing at an alarming rate in the state, presenting a major challenge to both individuals and the health systems that provide their care.(22,23). Given the extremely high rate at which anti-rabies injections are being administered to the Meghalayan population under the MHIS, and the substantial financial provision being made for this, further investigation should be undertaken as a matter of urgency to better understand the situation and identify potential cost-effective policy solutions to reduce the societal burden of animal bites on the Meghalayan community.

While this is the first analysis of the data and more detailed investigations may follow later, the paper has observations, which raise alternate explanations and should be resolved with the data referred to in the paper itself. Authors would do well to provide conclusions, to the questions they have posed, after examining alternate possibilities, based on the data they have quoted in the paper. The paper has many unhappy usage of language which need to be corrected before publication.

Once again, we are sincerely grateful to the reviewer for their thorough and useful comments. We have made substantial edits to the paper in its entirety and feel that it is much stronger as a result. We have gone to extra lengths to seek alternate explanations of some of our key findings, and have indeed changed our conclusions in relation to the high use of the dog/cat bite package of care. Regarding the comment on “unhappy” use of language, we have made renewed efforts to address this issue and hope the paper reads better now.

Submitted filename: PlosOne_reviewers comments.docx

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10 May 2022

An analysis of Government-sponsored health insurance enrolment and claims data from Meghalaya: Insights into the provision of health care in North East India

PONE-D-22-01529R1

Dear Dr.Eliza K Dutta ,

At the oustet, i would  like to congratulate the authors for conducting comprehensive analysis and submitting exhaustiv response to issues raised by the both reviewers.

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

The mansucript submitted and reviewed highlights, important procedural analytical insights in the implementation, utilisation , drivers of utilisations of MHIS in the region studied. The manuscript indicates low penetartion of MHIS as only 55% of the eligible popuation is enrolled yet. The foremost component of UHC, in achieving the left out objective is maximum coverage/enrollment of those who are eligible. State authorities and policy makers need to step up, prioritize and plan further penetration of the scheme to most underprivilged and inaccessibile one.There is need to substatiate the process along with rectification of the operational issues to realize the goal of UHC in the state.

Reviewers' comments:

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes: Rajeev Sadanandan

25 May 2022

PONE-D-22-01529R1

An analysis of Government-sponsored health insurance enrolment and claims data from Meghalaya: Insights into the provision of health care in North East India

Dear Dr. Dutta:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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