Emergency Care in India: A Retrospective Cross-sectional Analysis of Health Management and Information System and Global Burden of Disease

India; global burden of disease; health management information system; emergency care; Meetings: The study findings were presented at the Society of Academic Emergency Medicine (SAEM) in May 2022. Article type: Original Research Article Word count: 3,142 words No. of figures: 7 No. of supplementary tables: 2 Conflicts of Interest: Siddhesh Zadey is the co-founding director of the Association for Socially Applicable Research (ASAR). He represents ASAR at the Permanent Council of The G4 Alliance. Siddhesh Zadey is the Chair of the SOTA Care in South Asia Working Group, The G4 Alliance, and the Drafting Committee Member for Maharashtra State Mental Health Policy. Other authors declare no conflicts of interest. Funding: None Patient Consent: Not applicable 1 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. Data Sharing Statement: The dataset, data dictionaries, and data analysis for this paper are available upon request by contacting Siddhesh Zadey, BS-MS, MScGH, at [email protected]. Author contributions: SZ and JRNV conceived the study. SZ supervised the study. GU, PKK, TG, PN, and SZ performed the data extraction and compilation. TG, PN, GU, and PKK completed the data analysis. SZ, GU, and PKK drafted the manuscript, and all authors contributed substantially to its revision. SZ takes responsibility for the paper as a whole.

Background: To understand the utilization and burden of emergency medical conditions (EMCs), we assessed EM Department (EMD) data from the Health Management and Information System (HMIS) of India and EMC from the Global Burden of Diseases (GBD).

This was a retrospective cross-sectional analysis of HMIS and GBD data for 2019. We extracted EMD registrations, admissions, and deaths from HMIS and incidence, deaths, and DALYs from GBD for 31 EMCs at the national and state levels. We analyzed HMIS and GBD data for proportions and rates of registrations, deaths, and incident cases relative to population counts and hospital admission numbers.

In 2019, 119,103,358 patients (8,935.66 per 100,000 people) were registered at EMDs. The national EMD registration rate was 6,744.21 per 100,000 hospital admissions and the EMD death rate was 43,939.49 per 100,000 inpatient deaths. Only 12.14% of all HMIS registrations had cause-specific data. GBD estimated 2,047,175,737 EMC incident cases nationally, accounting for 27.22% of all-cause incidence, 51.71% of all-cause mortality, and 42.30% of all-cause DALYs. Trauma-related registrations were 9.27% in HMIS while injuries in GBD accounted for 7% of EMCs. Overall, HMIS EMD registrations were lower than GBD EMC incidence numbers, with regional variations.

The study reveals gaps in the EMD utilization as per HMIS data compared to the EMC burden estimates from GBD. Improved data integration and reporting can address regional disparities. 2 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint

The estimated global burden of emergency medical conditions (EMCs) in 2015 was 14,035 disability-adjusted life years (DALYs) per 100,000 people, with the most significant burden occurring in low- and middle-income countries (LMICs), where trauma is an important cause of morbidity and mortality. 1,2 Pathologies manifesting as EMCs were among the top 15 leading causes of death and DALYs worldwide. Studies conducted in 2015 suggested that globally, 90% of deaths and 84% of DALYs related to EMCs could have been prevented with timely emergency care. Thus, it highlights the crucial role that Emergency Medicine Departments (EMDs) can play in reducing this burden. Data scarcity, variability across sources, and concerns about data quality severely hamper the assessment of the burden, needs, and utilization of emergency care services in India and other LMICs.3,4 Given these challenges, there is an urgent need to enhance the capacity and utilization of services provided by EMDs. 5 Efforts to improve emergency care research in India, and other LMICs have revealed challenges such as inadequate data collection, ethical research standards, and insufficient research infrastructure.6 India ranks 144th out of 195 countries in terms of health system access and capacity to seek immediate care during medical emergencies, as assessed by the Global Burden of Disease (GBD).7,8 Research on policy decisions in emergency care in India has highlighted progress and challenges concerning training staff, standardizing practices, and promoting emergency care. Since recognizing emergency medicine as a specialty in 2009, formal training programs have expanded. 9 The National Health Policy (2017) emphasizes enhancing emergency care services and infrastructure. However, challenges in implementation remain due to inconsistent training standards and regulatory oversight .9 Despite these efforts, inadequate infrastructure in rural areas, overcrowding in emergency departments, and insufficient public awareness about emergency services still exist, indicating a need for continued policy development and research .10 Significant gaps exist in current research, underscoring the need for a comprehensive analysis of EMCs, consistent data collection and standardization practices, and studies of sub-national data. Addressing these gaps requires a more systematic and thorough approach to understanding the utilization and burden of emergency conditions, focusing on directing research investments accordingly. We conducted a retrospective cross-sectional analysis to analyze the Indian emergency systemic capacity in response to the burden of EMCs. Firstly, we evaluated EMD registrations, deaths, and cause-specific registrations measured by Health Management Information Systems (HMIS) for total admissions and population rates. Secondly, we evaluated the incidence, deaths, and DALYs of 31 EMCs recorded by the GBD framework. We then analyzed specific outcome metrics in HMIS and GBD, including EMD and EMC numbers, rates, and proportions for population and cause-specific metrics. Lastly, we explored data consistency and organization between HMIS and GBD data and analyzed the similarities and differences in the metrics derived from each system.

Data sources and extraction 3 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint The Ministry of Health and Family Welfare (MoHFW), Government of India, has implemented a Health Monitoring Information System (HMIS) to monitor and give essential inputs for policy formulation and relevant program interventions. 11 We extracted data on EM registrations, inpatient admissions through EMD, and deaths and all-cause inpatient admissions, outpatient admissions, and inpatient deaths for 2019-20. The publicly available HMIS provided EM case classification for trauma, burns, snake bites, acute cardiac events, cerebrovascular accidents, and obstetric complications. For rates, we used the state-wise mid-year projections for 2019 for rural, urban, and total populations based on the 2011 Census of India.12 The GBD study systematically assesses publicly available and contributed data on disease and injury incidence, prevalence, and mortality.13 We extracted data on 31 conditions that met the criteria defined by Chang and colleagues, who described EMCs as 'diseases which, if not diagnosed and treated within hours to days of onset, often led to serious physical or mental disability or death’ for the year 2019-20. 8 Since India did not have any yellow fever or Ebola cases in 2019, we removed these illnesses from the list of 31 conditions, leaving us with 29 EMC. We extracted absolute and rate (per 100,000 people) numbers for cause-specific and all-cause incidence, deaths, and DALYs. Outcomes and Analysis We used HMIS data to create national and state-level aggregates of registrations and deaths recorded at EMD. Population-level registration case rates and death rates (per 100,000 people) were calculated using mid-year population projections from the census. To assess the EMC disease burden in India, we used GBD's estimated incidence, death, and DALY rates at the state and national levels. Supplementary Table 1 enlists EMC outcomes calculated from GBD and the EMD outcomes from HMIS with their definitions and formulae. We analyzed HMIS and GBD data to assess both the quality of HMIS reporting and the burden of EMC. We first mapped overlapping conditions between GBD and HMIS (Supplementary Table 2). We then compared the HMIS EMD registration proportions with the GBD EMC incidence proportions to assess the utilization of EM services. Next, we assessed the mortality burden by comparing the death rates between HMIS EMD and GBD EMC. Finally, to evaluate the standard of case registrations at EMD, we compared HMIS EMD Registration Numbers to GBD EMC incidence numbers.

HMIS Descriptive Analysis From April 2019 to March 2020, 119,103,358 patients (8,935.66 per 100,000 people) were registered at EMDs per the Indian HMIS. 114,180,292 (95.86%) of these registrations were recorded at public facilities. HMIS EMD Registration Rate The national HMIS EMD registration rate was 6744.21 registrations per 100,000 hospital admissions, with Manipur having the highest rate at 30124.86 and Rajasthan the lowest at 4 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint 1210.75. Manipur, Arunachal Pradesh, and Mizoram in the Northeast had higher registration rates than North and West Indian states such as Rajasthan and Gujarat, which had lower rates. Meghalaya and Nagaland had higher registration rates than their neighboring states. Kerala, Karnataka, Tamil Nadu, Telangana, and Andhra Pradesh had moderate to high registration rates, with Kerala leading the way. Goa stood out for its high registration rate despite its small size. (Figure 1A). HMIS EMD Death Rate The national HMIS EMD death rate was 43939.49 deaths per 100,000 inpatient deaths, with Haryana having the highest rate at 295977.61 and West Bengal having the lowest at 6913.65. Mizoram, Arunachal Pradesh, and Nagaland in the Northeast had lower death rates than Haryana and Punjab in North India and Kerala and Karnataka in the South. Himachal Pradesh and Uttarakhand had higher death rates than their neighbors. Goa, like before, stood out for its relatively high death rate despite its small size. (Figure 1B). HMIS Number of Registration Cause-Wise and Cause-Specific Registration Proportion Among the HMIS EMD registrations, nationally only 12.14% had category-specific data. Trauma accounted for 9,789,272 (8.22%) EMD registrations, followed by obstetric complications (2,221,216 or 1.86%), acute cardiac events (968,374 or 0.81%), cardiovascular accidents (670,878 or 0.56%), snake bites (518,674 or 0.43%), and burns (287,088 or 0.24%) (Figure 2A). Among trauma-related deaths in the registered causes, Arunachal Pradesh led with 90%, followed by Sikkim (89%), Puducherry (89%), and Mizoram (87%). Lakshadweep had the highest percentage of obstetric cases at 46%, followed by Chandigarh (53%), Andaman and Nicobar Islands (33%), and Maharashtra (25%). West Bengal had the highest number of burn cases registered at 13%, followed by Uttar Pradesh (11.6%) and Karnataka (8.7%). For snake bites, Maharashtra (15.47%), West Bengal (11.18%), and Tamil Nadu (8%) had the highest numbers, while Sikkim and Lakshadweep had the least. West Bengal (16.1%), Delhi (11.88%), and Karnataka (11.75%) had the highest rates of acute cardiac events in the country (Figure 2B). HMIS EM Population-Level Registration Rate The national HMIS EM Population-Level Registration Rate was 8539.51 registrations per 100,000 population, with Delhi having the highest rate of 48749.42 and Bihar having the lowest at 2058.91. North India had a wide range of EMD Population-Level Registration Rates, with Delhi having the highest relative to the rest. Goa was an exception in West India, with a higher rate than Gujarat and Maharashtra, which had lower rates. Kerala in the south had a significantly higher rate than Telangana, which had a lower rate when compared to its neighbors. The East and Northeast regions showed the most variation, with Arunachal Pradesh having the highest rate and Bihar having the lowest (Figure 3A). HMIS EM Population-Level Death Rate 5 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint The national HMIS EM population-level death rate was 68.93 deaths per 100,000 population, with Delhi having the highest rate (68.93) and Uttarakhand having the lowest (7.41). Delhi had previously had the highest rate compared to the rest of North India. Goa and Gujarat in the west had higher rates, while Maharashtra had lower rates. Karnataka had the highest rate in the south, while its immediate neighbor Kerala had much lower rates. The East and Northeast regions had the most significant variation, with Sikkim having the highest rate and West Bengal having the lowest. (Figure 3B). GBD Descriptive Analysis The GBD data reported 2,047,175,737 EMC incident cases in 2019, with 4,846,724 deaths and 197,518,102 DALYs. At the national level, EMCs accounted for 27.22% of all-cause disease incidence, 51.71% of all-cause mortality, and 42.30% of all-cause DALYs. GBD EMC Incidence Rates At the national level, diarrheal diseases had the highest incidence rates among EMC at 120,789.43 cases per 100,000, while rabies had the lowest incidence rate at 0.40 per 100,000. (Figure 4) Regionally, Odisha reported the highest EMC incidence rate at 173,414.91 cases per 100,000, and Nagaland reported the lowest incidence rate at 104,407.17 cases per 100,000 (Figure 5A). Fourteen states, especially those in central and some of North India, had EMC incident rates higher than the national value of 147,203.96 cases per 100,000. Sixteen states, predominantly in southern and some parts of North East India, had lower EMC incident rates compared to the national level. GBD EMC DALY Rates Nationally, injuries had the highest DALY rate for EMCs at 3,851.56 per 100,000, while Maternal Sepsis and Other Maternal Infections had the lowest rate at 9.60 DALYs per 100,000 (Figure 4). Regionally, Uttar Pradesh had the highest EMC DALY rate at 17,378.34 per 100,000, while Sikkim reported the lowest rate at 8,208.50 per 100,000 (Figure 5B). Nineteen states across the country reported DALY values lower than the national value of 14,202.71 per 100,000. Overall, it was observed that states with higher EMC incident rates showed higher DALYs and those with lower incident rates showed lower DALYs. GBD EMC Death Rates Nationally, ischemic heart disease had the highest EMC death rate at 109.23 deaths per 100,000, while Acute Glomerulonephritis had the lowest rate at 0.01 deaths per 100,000 (Figure 4). Regionally, Punjab reported the highest EMC death rate at 416.76 deaths per 100,000, and Mizoram recorded the lowest rate at 181.22 per 100,000 (Figure 5C). In total, 17 states reported EMC death rates higher than the national value of 348.51 per 100,000. Southern and North Eastern India, which had lower EMC incident rates, showed higher death rates. However, Telangana in South India had lower incident and death rates. Manipur and Arunachal Pradesh in North East India had high EMC incident rates and lower death rates. Central and certain parts of North India showed higher EMC incident and death rates compared to their respective national values. 6 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint GBD EMC Incidence Proportions The national GBD incidence proportion of EMCs was 27.22%. Odisha recorded the highest incidence proportion at 30.41%. Ten states had an incidence proportion higher than the national value. The incidence proportion for the state of Arunachal Pradesh (24.48%) was the lowest in the country (Figure 5D). GBD EMC Death Proportions The national GBD death proportion of EMCs was 51.71%. Punjab (57.54%) and Tamil Nadu (55.96%) had the highest death proportion due to EMCs. Thirteen states surpassed the national death proportion. Sikkim (42.22%) reported the lowest death proportions in the country (Figure 5E). Mapping of GBD EMC with HMIS EMD Registrations In HMIS, trauma-related registrations comprised 9.27% of total EMD registrations, whereas injuries in GBD made up 7% of the total incidents. Obstetric registrations accounted for 1.8% of HMIS and 0.85% of GBD. Emergency acute cardiac events represented 0.81% of HMIS EMD and 0.23% of GBD. Similarly, cardiovascular accidents constituted 0.56% of HMIS and 0.09% of GBD. Overall, cause-specific registrations in HMIS EMD, totaling 12.14%, overlapped with 8.1% of EMCs in GBD. Comparison of GBD EMC Incidence Proportion with HMIS EMD Registration Proportion Madhya Pradesh, Kerala, Sikkim, and Tripura had the highest difference between HMIS EMD Registration Proportion and GBD EMCs Incidence Proportion. Assam, Gujarat, Nagaland, and Punjab had the least. Madhya Pradesh and Meghalaya reported higher HMIS EMD Registration Proportions compared to GBD EMC incidence Proportions (Figure 6A). Comparison of GBD EMC Death Proportion with HMIS EMD Death Proportion Odisha, Madhya Pradesh, Uttar Pradesh, and Assam had the highest difference between HMIS EMD Death Proportion and GBD Death Proportion. Mizoram, Meghalaya, and Sikkim had the least. Haryana, Tamil Nadu, Uttarakhand, Meghalaya, and Rajasthan had higher HMIS EMD Death Proportions than GBD EMC death Proportions (Figure 6B). Comparison of HMIS EMD Registration with GBD EMC Incidence Overall, HMIS EMD registration numbers were lower than GBD EMC incidence numbers nationally. West Bengal, Tamil Nadu, and Kerala stood out with relatively higher registration numbers compared to their GBD incidence rates. On the other hand, Uttar Pradesh, Bihar, and Maharashtra had fewer registrations despite a higher EMC burden (Figure 7).

Summary The primary aim of this study was to evaluate the national EMD systemic capacity in addressing the burden of EMCs. Our analysis shows a substantial disparity: only 119,103,358 HMIS EMD registrations were recorded against 2,047,175,737 GBD EMC incident cases. Only 12.14% of HMIS EMD registrations overlapped with 8.1% of GBD EMCs, indicating a 7 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint significant inconsistency in the reported cases. Across states, HMIS registration numbers, incidence proportion, and death proportion lagged significantly behind GBD numbers. However, GBD data remained relatively consistent across states with minor fluctuations. Disparities in data reporting across states suggest consistency in data collection and reporting practices. Variations in access to emergency care varied across states, potentially reflecting regional disparities in healthcare resources and infrastructure. Data reporting in HMIS may encounter challenges stemming from both on-ground access limitations and inaccuracies in reporting. Relevance of findings Very few studies look at the emergency capacity and burden of EMCs in India on a regional basis. The Jai Prakash Narayan Apex Trauma Center (JPNATC), jointly with the NITI Aayog (Government of India), showed that noncommunicable diseases (NCDs) account for nearly 61.8% of all EMCs in India, out of which cardiovascular diseases account for the majority of the incidences (28.1%) in 2016. Injuries account for 10.7%, while communicable, maternal, perinatal, and nutritional conditions account for 27.5% of the EMC burden. 14 The JPNATC study emphasizes the presence of pre-hospital care in India, which is provided by approximately 17,000 ambulances spread across all states. However, these ambulances are understaffed in terms of resources and personnel. Furthermore, the study emphasizes the scarcity of trained personnel available at both the pre-hospital and hospital care levels despite the presence of NMC-accredited EM residency programs in both government and private settings.14 Our study adds to these findings by highlighting how the EMC burden and EMD care in India can be better evaluated to bring about necessary changes in the quality of care provided through capacity building and resource strengthening. Policy Implications The findings of the study highlight critical policy implications for strengthening emergency department services and improving the overall response to EMCs at both the national and state levels. The discrepancies in registration, incidence, and mortality rates highlight the need for a focused strategy to improve the efficiency and effectiveness of emergency care systems. To begin with, a reliable data registration and capacity evaluation process must be established at each level of the healthcare system, namely hospital, district, and state. 15 The emphasis should be on the lowest level, notably the emergency department. Regular checks and audits should be carried out to verify that EMCs are accurately reported. To improve registration data quality, clearly defined nomenclature and standardized conditions must be implemented. This includes improved infrastructure and training to ensure that emergency protocols and treatment methodologies are consistent and aligned with proposed national guidelines. 16 Additionally, efforts should be directed toward developing a national framework for emergency medical services, outlining standardized protocols for the identification, registration, and treatment of EMCs. This framework should serve as a guide for states to align their emergency care systems with national standards, ensuring consistency and comparability of data across the country.2 8 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint States with discrepancies between HMIS EMD registrations and GBD EMC data, such as Manipur, Goa, Arunachal Pradesh, Rajasthan, Chhattisgarh, and Gujarat, should receive specific interventions. Tailored training programs and capacity-building initiatives should be implemented to close the identified gaps and improve data reporting accuracy. Furthermore, states with high EMD death rates, such as Haryana, Punjab, and Sikkim, require targeted interventions to improve emergency care quality and reduce mortality rates. On the other hand, states such as Meghalaya, Mizoram, and West Bengal, which had disparities in death rates between HMIS and GBD data, require concentrated efforts to improve data accuracy and reporting at the emergency department level. Strengths and limitations In this novel nationwide assessment of emergency capacity and burden, we conduct a comprehensive analysis of HMIS and GBD data, investigating their relationships with overlapping health indicators. The findings have direct policy implications, guiding strategic focus on regions that need to strengthen emergency departments and improve reporting mechanisms. This study also addresses the shortcomings in HMIS data, as well as the need for standardization and improved reporting of emergency department conditions to streamline emergency care resource allocation. The study has several limitations. First, the data used in this study is from 2019-2020. Only nine out of 31 GBD conditions were mapped to seven available HMIS conditions, limiting the study's ability to capture and represent the true numbers accurately. This further limits the comparability and reliability of findings due to underrepresented or missing data, skewing the overall assessment. The study also has

due to discrepancies in the registration of certain conditions in both the GBD database and the HMIS, indicating a lack of data completeness and coverage. Only 12% of registrations include data for defined conditions, which limits analysis and may underestimate disease burden. The absence of data from India's Union Territories in the GBD database is a significant limitation, preventing a comprehensive understanding of health trends and burdens on a national level.

EMCs form a significant 27.22% of all-cause disease incidence, 51.71% of all-cause mortality, and 42.30% of all-cause DALYs at the national level in India. However, only 119,103,358 HMIS EMD registrations were recorded, and they accounted for 298,370 deaths. Furthermore, a mere 12.14% of HMIS EMD registrations overlapped with 8.1% of GBD EMCs, indicating a gap in HMIS reporting. Establishing robust data registration processes, incentivizing accurate reporting, and strategically allocating resources are critical steps toward increasing emergency care efficiency and effectiveness across the country. Disparities in EMD registration and mortality rates between states necessitate policy interventions. However, due to data reporting discrepancies, these findings should be interpreted with caution. Future research should concentrate on standardizing cause-specific case definitions and addressing bias caused by under-reporting in HMIS.

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1 WHO. World Health Organization The World Health Report 1999: Making a difference. Health Millions. Health Millions 1999; 25: 3–5. 2 NITI Aayog. Emergency and Injury Care at District Hospitals in India: A Report of Current Status on Country Level Assessment. Govt. of India, 2020. 3 Pines JM, Hilton JA, Weber EJ, et al. International perspectives on emergency department crowding. Acad Emerg Med 2011; 18: 1358–70. 4 Patiño AM, Chen J, DeV os EL, et al. Emergency medicine around the world: analysis of the 2019 american college of emergency physicians international ambassador country reports. Journal of the American College of Emergency Physicians Open 2022; 3: e12681. 5 Saving Lives: Emergency Medical Services in India. https://isbinsight.isb.edu/saving-lives-emergency-medical-services-in-india/ (accessed June 8, 2024). 6 Reynolds TA, Sawe H, Rubiano AM, Shin SD, Wallis L, Mock CN. Strengthening health systems to provide emergency care. In: Jamison DT, Gelband H, Horton S, et al., eds. Disease control priorities: improving health and reducing poverty, 3rd edn. Washington (DC): The International Bank for Reconstruction and Development / The World Bank, 2017. DOI:10.1596/978-1-4648-0527-1_ch13. 7 Department of Emergency Medicine, JPNATC, AIIMS, NITI Aayog, Government of India. Emergency and Injury Care at District Hospitals in India: A Report of Current Status on Country Level Assessment. Department of Emergency Medicine, JPNATC, AIIMS, New Delhi, 2020. 8 Chang CY , Abujaber S, Reynolds TA, Camargo CA, Obermeyer Z. Burden of emergency conditions and emergency care usage: new estimates from 40 countries. Emerg Med J 2016; 33: 794–800. 9 Ministry of Health and Family Welfare. National Health Policy. Government of India, 2017 https://www.nhp.gov.in/nhpfiles/national_health_policy_2017.pdf (accessed Jan 16, 2022). 10 Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health 2018; 6: e1196–252. 11 Government of India. HMIS-Health Management Information System. https://hmis.nhp.gov.in/ (accessed Feb 19, 2021). 12 CENSUS OF INDIA 2011: POPULATION PROJECTIONS FOR INDIA AND STATES 2011–2036 . 2019; published online Nov. 13 GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1204–22. 10 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint 14 NITI Aayog D of EM. Emergency and Injury Care at Secondary and Tertiary Level Centres in India: A Report of Current Status on Country Level Assessment . AIIMS, 2021. 15 Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data 2019; 6: 54. 16 Hughes RG. Tools and Strategies for Quality Improvement and Patient Safety. In: Hughes RG, ed. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US), 2008. 11 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint FIGURES Figure 1: HMIS Emergency Medicine Department Rates for 35 States and Union Territories (2019-20) including A) Registration Rates and B) Death Rates. 12 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 2: HMIS Emergency Medicine Department Cause-Specific Registration Numbers (2019-20) at the A) National and B) State levels. 13 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 3: HMIS Emergency Medicine Department Population Level Rates for 35 States and Union Territories (2019-20) Including A) Registration Rates and B) Death Rates. 14 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 4: GBD Emergency Medical Conditions national rates (2019-20) Including Incidence, Death, and DALYs Rates. 15 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 5: GBD Emergency Medical Conditions for 35 States and Union Territories (2019-20) Including - A) Incidence Rates, B) DALY Rates, C) Death Rates, D) Incidence Proportion, and E) Death Proportion. 16 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 6: HMIS Emergency Medicine Department vs GBD Emergency Medical Conditions Proportions for 35 States and Union Territories(2019-20), Including - A) Incidence Proportions and B) Death Proportions 17 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint Figure 7: HMIS Emergency Department Registration Numbers vs. GBD Emergency Medical Conditions Incidence Numbers for 35 states and union territories (2019-20). 18 . CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted August 20, 2024. ; https://doi.org/10.1101/2024.08.16.24312130doi: medRxiv preprint