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

This retrospective cross-sectional analysis of 2019 data reveals significant gaps between India's Health Management and Information System emergency department utilization figures and the Global Burden of Disease estimates for emergency medical conditions, highlighting the need for improved data integration and reporting to address regional disparities.

The Gist

The Big Picture: A Tale of Two Counters

Imagine India as a massive, bustling city. In this city, there are two different people trying to count how many people are getting sick and need emergency help (like heart attacks, car crashes, snake bites, or severe infections).

1. The Hospital Clerk (HMIS): This person sits at the front desk of government hospitals. They only count people who actually walk through the door, fill out a form, and get registered.
2. The Weather Forecaster (GBD): This person stands on a hill looking at the whole city. They use satellites, surveys, and complex math to estimate how many people should be getting sick, even if they never make it to a hospital.

This study is like a detective comparing the Hospital Clerk's notebook with the Weather Forecaster's map to see why the numbers don't match.

The Investigation: What Did They Find?

1. The "Missing Millions" Gap
The Weather Forecaster (GBD) estimated that in 2019, there were over 2 billion emergency medical cases across India. That's a huge number!
However, the Hospital Clerk (HMIS) only recorded about 119 million patients.

• The Analogy: Imagine a concert where the ticket seller says 100,000 people bought tickets. But the security guard at the gate only counted 5,000 people walking in.
• The Reality: The study found that the hospital records are missing a massive amount of data. This doesn't necessarily mean 2 billion people got sick and died; it means the "counting system" in the hospitals is broken, incomplete, or that many people are getting sick but never reaching a hospital.

2. The "Blind Spot" in the Notebook
When the researchers looked at the Hospital Clerk's notebook, they found a shocking problem: 88% of the entries had no details.

• The Analogy: It's like a teacher taking attendance but only writing down "Present" for 88% of the students, without writing their names or why they are there.
• The Reality: Only about 12% of the patients had their specific condition (like "snake bite" or "heart attack") written down. The rest were just generic numbers. This makes it impossible to know exactly what kind of help is needed most.

3. The Regional Rollercoaster
The study looked at different states (like different neighborhoods in our city) and found wild differences.

• The "Busy" Neighborhoods: States like Kerala and Delhi had very high numbers of people walking into emergency rooms. This suggests they have better roads, ambulances, and hospitals that people can actually reach.
• The "Quiet" Neighborhoods: States like Bihar or Rajasthan had very low numbers.
• The Twist: Sometimes, a state with low hospital visits had high death rates. This is like a neighborhood where people are dying at home because they can't get to the hospital in time.

4. The "Three Delays" Problem
Why are the numbers so low in the hospital records compared to the real need? The paper suggests three main reasons (The "Three Delays"):

1. The Decision Delay: People wait too long to decide to go to the doctor.
2. The Travel Delay: They decide to go, but there are no ambulances or the roads are too bad to get there.
3. The Treatment Delay: They arrive at the hospital, but the staff is untrained or the equipment is missing, so they can't get help fast enough.

Why Does This Matter? (The "So What?")

The "Broken Thermometer" Metaphor
Right now, India's emergency care system is like a doctor trying to treat a patient with a broken thermometer. If you don't know the patient's temperature (the real data), you can't prescribe the right medicine (the right resources).

Because the data is missing or messy:

• The government doesn't know where to send ambulances.
• They don't know which states need more doctors for snake bites or heart attacks.
• They can't tell if a new policy is actually saving lives.

The Solution: Fixing the System

The authors suggest a few simple but powerful fixes:

1. Standardize the Notebook: Every hospital, whether public or private, needs to use the same format for writing down patient details. No more blank entries!
2. Train the Staff: The people at the front desk need training to understand why accurate data matters.
3. Include the Private Sector: Right now, the "Hospital Clerk" mostly counts government hospitals. Private hospitals are huge in India, but their data is often missing. We need to count them too.
4. Bridge the Gap: We need to build better roads and ambulance services so that the "Weather Forecaster's" estimate of sick people matches the "Hospital Clerk's" actual count of people arriving for help.

The Bottom Line

This study is a wake-up call. It tells us that while India has a system to record emergency care, that system is currently leaking information. To save lives, India needs to fix its data collection first. You can't fix a leaky roof if you don't know where the holes are, and you can't fix a healthcare system if you don't know who is getting sick and where.

By cleaning up the data, India can ensure that emergency care reaches the people who need it most, turning the "missing millions" into saved lives.

Technical Summary

1. Problem Statement

Emergency Medical Conditions (EMCs) represent a critical public health challenge in India, where 90% of EMC-related deaths could be prevented with timely care. However, India ranks 144th globally in access to emergency care. The core problem addressed in this study is the fragmentation and inconsistency of data regarding emergency care utilization and disease burden.

• Data Gaps: There is no standardized, population-level database for EMCs in India. Existing data is siloed between the Health Management Information System (HMIS), which tracks facility-level admissions, and the Global Burden of Disease (GBD) study, which provides modeled population-level estimates.
• Discrepancies: It is unclear how well HMIS data (actual hospital registrations) aligns with GBD estimates (projected disease incidence), leading to potential undercounting of cases, misallocation of resources, and an inability to accurately assess the "unmet need" for emergency care.
• Systemic Issues: Emergency services are fragmented across states, with variations in protocols, resource availability (ambulances, blood banks), and data reporting quality, particularly regarding private sector participation.

2. Methodology

The study employed a retrospective cross-sectional analysis using 2019 data (pre-pandemic baseline) from two primary sources:

• Data Sources:
  • HMIS (India): Extracted data on Emergency Medicine Department (EMD) registrations, inpatient admissions, and deaths from public and private facilities. Data was aggregated at national and state levels.
  • GBD 2019: Extracted incidence and mortality data for 31 defined EMCs (reduced to 29 for India after excluding yellow fever and Ebola).
• Definitions & Framework:
  • Used the Chang et al. framework to define EMCs as conditions requiring diagnosis and treatment within hours to days to prevent disability or death.
  • Mapped overlapping conditions between GBD and HMIS categories (e.g., trauma, obstetric complications, cardiac events).
• Analytical Approach:
  • Metrics Calculated: Registration rates, death rates, and proportions relative to population (per 100,000) and hospital admissions.
  • Comparative Analysis: Compared HMIS observed data against GBD modeled estimates to identify gaps in utilization and reporting.
  • Geospatial Analysis: Analyzed variations across India's 28 states and 8 Union Territories.
• Ethical Considerations: No ethics approval was required as the study utilized publicly available aggregate-level data.

3. Key Contributions

• First Comparative Assessment: This is one of the first studies to systematically compare HMIS facility data with GBD population estimates for emergency care in India.
• Quantification of the "Unmet Need": By juxtaposing GBD incidence (theoretical burden) with HMIS registrations (actual utilization), the study quantifies the gap between the disease burden and the healthcare system's response.
• Identification of Data Quality Issues: The study highlights that only 12.14% of HMIS registrations had cause-specific data, revealing a severe lack of granularity in current reporting systems.
• Policy Roadmap: It provides state-specific benchmarks, identifying regions with high underutilization (e.g., Bihar, Rajasthan) versus high reporting efficiency (e.g., Kerala, Delhi), offering a targeted approach for resource allocation.

4. Key Results

A. HMIS Data (Observed Utilization)

• Volume: 119.1 million patients were registered at EMDs in 2019 (8,935 per 100,000 population).
• Cause-Specific Data: Only 12.14% of registrations had specific cause coding.
  • Trauma: 8.22% of total registrations.
  • Obstetric Complications: 1.86%.
  • Acute Cardiac Events: 0.81%.
• Regional Disparities:
  • Registration Rates: Highest in Manipur (30,124 per 100k admissions) and lowest in Rajasthan (1,210). Delhi had the highest population-level rate (48,749 per 100k).
  • Death Rates: Highest in Haryana (74,746 per 100k hospital deaths) and lowest in West Bengal.

B. GBD Data (Modeled Burden)

• Incidence: 2.04 billion EMC incident cases estimated nationally. EMCs accounted for 27.22% of all-cause incidence and 51.71% of all-cause mortality.
• Top Conditions: Diarrheal diseases had the highest incidence rate; Ischemic heart disease had the highest death rate.
• Regional Burden: Odisha had the highest incidence proportion (30.41%), while Mizoram had the lowest (23.19%).

C. The Gap (HMIS vs. GBD)

• Massive Discrepancy: HMIS registrations (119 million) are significantly lower than GBD estimated incidence (2 billion), suggesting massive underreporting or underutilization.
• Cause-Specific Mismatches:
  • Trauma: 8.22% in HMIS vs. 7% in GBD.
  • Obstetric: 1.8% in HMIS vs. 0.85% in GBD.
  • Cardiac: 0.81% in HMIS vs. 0.23% in GBD.
• State-Level Variations: States like Kerala, Sikkim, and Andhra Pradesh showed the largest differences between HMIS registration proportions and GBD incidence proportions.

5. Significance and Implications

Policy Implications:

• Data Infrastructure: The study underscores the urgent need for a dedicated, standardized emergency medicine database in India that includes both public and private sectors.
• Standardization: Implementation of a unified nomenclature (similar to the UK's Emergency Care Data Set) is required to improve data quality and comparability.
• Targeted Interventions:
  • High Gap States: Manipur, Goa, and Rajasthan require capacity building to improve data reporting accuracy.
  • High Mortality States: Haryana, Punjab, and Sikkim need focused clinical interventions to reduce EMD death rates.
• Addressing the "Three Delays": The gap between GBD and HMIS suggests barriers in seeking care, reaching facilities, and receiving treatment, necessitating improvements in pre-hospital care (ambulances) and public awareness.

Clinical and Research Significance:

• Clinical Practice: The findings highlight the need for local benchmarks to guide clinical practice and resource distribution.
• Future Research: The study calls for localized data collection to better capture the "unmet clinical care need" and suggests that future studies must address the limitations of current aggregate data, particularly regarding private sector inclusion and Union Territories.

Conclusion:
The study concludes that while India faces a massive burden of emergency conditions (over 50% of all deaths), the health system's recorded utilization is disproportionately low. Bridging the gap between GBD estimates and HMIS data through improved reporting, infrastructure investment, and standardized protocols is critical for equitable and timely emergency care delivery in India.