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Epidemiological Characteristics of Emergency Medical Service Calls in a Large Chinese City: A Retrospective Analysis of 191,730 Prehospital Dispatches in 2024

This retrospective analysis of 191,730 EMS dispatches in a large Chinese city during 2024 reveals that emergency demand is heavily concentrated among older adults and driven by injuries and time-sensitive medical conditions with distinct diurnal and age-related patterns, highlighting the need for optimized staffing, targeted prevention strategies, and the diversion of low-acuity non-emergency transport to dedicated services to improve resource efficiency.

Original authors: Hao Zou, Guanguan Luo, Cheng Jiang, Jing Wan, Yong Zou, Jian Xia

Published 2026-07-11

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CC BY 4.0

Original authors: Hao Zou, Guanguan Luo, Cheng Jiang, Jing Wan, Yong Zou, Jian Xia

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the city of Wuhan as a giant, bustling stage where a massive cast of 191,730 people stepped into the spotlight of an ambulance call in 2024. This study is like a high-speed camera capturing every single one of those moments to see who is calling, why, and when.

The Cast of Characters: Who is on Stage?
The main actors in this drama are older. The average age of a patient is 62 years, and nearly half (46.4%) are 65 or older. Think of it as a retirement community that suddenly decided to host a massive, chaotic block party. While there are plenty of young people, they make up a tiny slice of the pie (only 2.9% are under 15). Also, the stage is slightly more crowded with men than women, with a ratio of about 1.22 men for every 1 woman.

The Plot: What's Happening?
The story has two main genres. The most common plot twist is injury (like a fall or a car crash), which accounts for about 25% of all calls. The second most common genre is "we don't know exactly what's wrong yet" (symptoms like feeling dizzy or sick), which makes up about 20.5%.

Here is where the age of the character changes the story:

Young and Middle-Aged Adults: Their stories are usually about injuries or alcohol. In fact, there were 9,524 calls specifically about alcohol intoxication. These calls happen mostly at night (between 8:00 PM and 1:00 AM) and involve young men (average age 34).
Older Adults: As the characters get older, the story shifts dramatically. Injuries and alcohol fade away, replaced by strokes, heart problems, and breathing issues. The older the person, the more likely they are to have a serious medical emergency.

The Schedule: When Does the Show Start?
The show isn't random; it follows a strict clock.

The Daily Rush: The busiest time is 10:00 AM, when 7.9% of all calls happen. The quietest time is 4:00 AM, when only 1.6% of calls come in.
The Monthly Rhythm: The month with the most calls is December (9.2%), while June is the quietest (7.9%).
The Weekly Rhythm: Mondays are the busiest day (15.0%), while weekends are slightly quieter.

The Race Against Time: How Fast Do They Get There?
When the alarm rings, the ambulance crew races to the scene. On average, they arrive in 10.3 minutes.

About 76.2% of the time, they get there within 15 minutes.
However, the race gets slightly slower during the morning rush hour (around 10:00 AM) because there are so many calls at once.
Interestingly, the system does not speed up the response time based on how serious the call is. Whether it's a life-or-death heart attack or a minor injury, the ambulance arrives in roughly the same amount of time (about 10 minutes).

The Ending: Who Goes to the Hospital?
Most people (82.8%) get taken to the hospital. But there is a big exception: cardiac arrest (when the heart stops). Only about 30.1% of these patients are transported to the hospital. Why? Because in many of these cases, the patient has already passed away before the ambulance arrives.

Also, the older the patient, the less likely they are to be transported. For kids (0–14), 87.5% go to the hospital. For the very elderly (85+), that number drops to 80.1%. This matches the fact that the oldest patients have the highest death rates before or during the trip.

The Hidden Cost: The "Non-Emergency" Detour
Here is a surprising twist: nearly 28% of all ambulance calls (53,444 calls) were not emergencies. These were trips to move patients between hospitals, take them home after discharge, or drive them long distances.

Even though these calls are only about 28% of the total, they gobble up 38.2% of the total time the ambulances are on the road.
Why? Because these trips are much longer. An emergency trip takes about 33 minutes, but a non-emergency trip takes about 49 minutes.
The Conflict: These long, non-urgent trips happen mostly during the day, right when the real emergencies (the morning rush at 10:00 AM) are peaking. It's like having a delivery truck clog up the highway right when the fire trucks need to get through.

The Takeaway
The study suggests that to fix this traffic jam, the city needs to be smarter about who gets an emergency ambulance. The data shows that:

Morning shifts need more staff to handle the 10:00 AM rush.
Young men need better safety strategies for injuries and alcohol-related incidents at night.
Older adults need specific care for heart and stroke issues.
Non-emergency trips (like moving patients between hospitals) should probably be handled by a different, dedicated service so they don't steal the emergency ambulances from people who really need them during the morning peak.

The authors measured all of this using real records from 2024, so these numbers are facts, not guesses. They found a clear pattern: the city's emergency needs are changing as the population ages, and the current system is getting crowded with non-urgent trips at the worst possible times.

Technical Summary: Epidemiological Characteristics of EMS Calls in Wuhan (2024)

Problem Statement
Emergency Medical Service (EMS) call records represent a continuous, population-level data source essential for prehospital resource planning. However, detailed epidemiological descriptions from individual Chinese cities remain limited. Existing large-scale analyses in China have predominantly focused on multi-year trends, specific disease groups, or the impact of the COVID-19 pandemic. There is a comparative scarcity of comprehensive, single-year descriptions that simultaneously examine the temporal distribution of calls, presenting complaints, field diagnoses, and—critically—their statistical association with patient age. Understanding the joint structure of when people call, why they call, and who is calling is necessary to inform operational decisions regarding shift design, vehicle deployment, and public health interventions.

Methodology
This study is a retrospective, cross-sectional analysis of 191,730 EMS dispatches recorded by the centralized 120 emergency call center in Wuhan, China, between January 1 and December 31, 2024.

Data Source: Routinely collected operational records containing unique dispatch identifiers, patient demographics (age, sex), dispatcher triage (presenting complaints), attending physician field diagnoses, vital signs, interventions, acuity grades, and a chain of operational timestamps.
Variable Derivation:
- Time: Call times were derived from 14-digit encoded dispatch identifiers (validated against recorded call times with a median absolute difference of 1 second).
- Categorization: Free-text presenting complaints were mapped to 17 clinically meaningful categories. Field diagnoses were grouped according to International Statistical Classification of Diseases and Related Health Problems (ICD-10) chapters.
- Intervals: Six prehospital time intervals were calculated (e.g., activation, travel-to-scene, response, on-scene, transport, total prehospital) based on sequential timestamps.
- Dispatch Types: Calls were classified into "emergency responses" and "non-emergency transport" (inter-hospital transfers, discharge transport, long-distance transport).
Statistical Analysis: Distributions were summarized descriptively. Associations between age groups and presenting complaints/diagnoses were tested using Pearson $\chi^2$ tests, with Cramér's $V$ used to quantify effect size. Analyses were performed in Python 3.

Key Results

Demographics: The median patient age was 62 years (IQR 39–76), with 46.4% of patients aged $\ge$ 65 years. The male-to-female ratio was 1.22.
Temporal Patterns: Call volume exhibited a pronounced diurnal pattern, peaking at 10:00 (7.9% of daily calls) and reaching a nadir at 04:00 (1.6%). Monthly variation was modest, with December being the busiest month (9.2%).
Clinical Profile:
- Presenting Complaints: Trauma/injury was the leading category, followed by altered consciousness/neurological symptoms, cardiac/chest symptoms, and stroke. Alcohol intoxication accounted for 5.0% of calls.
- Field Diagnoses: Injury (ICD-10 S00–T14) was the leading diagnostic group (24.9%), followed by symptoms/ill-defined conditions (R00–R99; 20.5%) and cerebrovascular disease (I60–I69; 8.5%).
Age-Disease Association: There was a strong, statistically significant association between age and both presenting complaint and diagnosis ( $p < 0.001$ ). Injury and alcohol intoxication clustered in young and middle-aged adults, while cerebrovascular disease, cardiac arrest, and respiratory illness increased steeply with age.
Mortality and Transport: Scene/transport mortality rose monotonically with age, from 0.46% (0–14 years) to 4.54% ( $\ge$ 85 years). Overall, 82.8% of dispatches resulted in hospital transport; however, only 30.1% of cardiac arrest cases were transported (the remainder pronounced dead on scene).
Response Times: The median response interval (call to scene) was 10.3 minutes. Response times were slower during high-volume daytime hours (10.7–10.8 minutes) compared to overnight hours (9.4–9.8 minutes). Notably, response times were uniform across acuity grades, including cardiac arrest.
Non-Emergency Transport: Non-emergency transport accounted for 27.9% of all dispatches but consumed an estimated 38.2% of annual ambulance-hours due to longer durations. These calls were concentrated in daytime hours, comprising nearly 50% of calls during the 10:00–11:00 peak, directly competing with acute emergency demand.

Significance and Claims
The authors claim that this study provides a baseline for understanding the joint structure of EMS demand in a large Chinese city. The findings support several operational conclusions:

Resource Allocation: The pronounced morning peak and age-dependent case-mix suggest a need for morning-weighted staffing and pre-positioning of specialized resources (e.g., stroke and cardiac readiness) for older populations.
Targeted Prevention: The clustering of injury and alcohol-related calls in young and middle-aged males, particularly at night, supports targeted road-safety and alcohol-prevention strategies.
System Efficiency: The uniformity of response times across acuity grades indicates a lack of differential prioritization for time-critical calls (e.g., cardiac arrest), suggesting an opportunity for dispatch protocol optimization.
Capacity Release: The disproportionate consumption of ambulance time by low-acuity non-emergency transport (particularly discharge and inter-hospital transfers) during peak hours identifies a specific target for reform. The authors propose diverting eligible non-emergency transport to dedicated medical-transport services to release emergency capacity.

Limitations
The authors acknowledge that the study is limited to a single city and year, potentially affecting generalizability. Diagnoses were based on operational triage and field impressions rather than a gold standard, and approximately 15% of records fell into "unspecified" categories. Additionally, the study reports call distributions rather than population incidence rates due to a lack of denominator data, and outcome assessment was limited to death before or during transport.

Technical Summary: Epidemiological Characteristics of EMS Calls in Wuhan (2024)

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