Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort‐based

Abstract

Background and Aim
Though emergency medical services (EMS) respond to all types of emergency calls, they do not always result in the patient being transported to the hospital. This study aimed to explore the determinants influencing emergency call‐response‐based conveyance decisions in a Middle Eastern ambulance service.

Methods
This retrospective quantitative analysis of 93,712 emergency calls to the Hamad Medical Corporation Ambulance Service (HMCAS) between January 1 and May 31, 2023, obtained from the HMCAS electronic system, was analyzed to determine pertinent variables. Sociodemographic, emergency dispatch‐related, clinical, and miscellaneous predictors were analyzed. Descriptive, bivariate, ridge logistic regression, and combination analyses were evaluated.

Results
23.95% (N = 21,194) and 76.05% (N = 67,285) resulted in patient nontransport and transportation, respectively. Sociodemographic analysis revealed that males predominantly activated EMS resources, and 60% of males (n = 12,687) were not transported, whilst 65% of females (n = 44,053) were transported. South Asians represented a significant proportion of the transported patients (36%, n = 24,007). “Home” emerged as the primary emergency location (56%, n = 37,725). Bivariate analysis revealed significant associations across several variables, though multicollinearity was identified as a challenge. Ridge regression analysis underscored the role of certain predictors, such as missing provisional diagnoses, in transportation decisions. The upset plot shows that hypertension and diabetes mellitus were the most common combinations in both groups.

Conclusions
This study highlights the nuanced complexities governing conveyance decisions. By unveiling patterns such as male predominance, which reflects Qatar's expatriate population, and specific temporal EMS activity peaks, this study accentuates the importance of holistic patient assessment that transcends medical histories.

Full text

Received: 7 November 2023
|
Revised: 5 March 2024
|
Accepted: 1 April 2024
DOI: 10.1002/hsr2.2056
ORIGINAL RESEARCH
Epidemiology of prehospital emergency calls according to
patient transport decision in a middle eastern emergency
care environment: Retrospective cohort‐based
Hassan Farhat
1,2,3
|Guillaume Alinier
1,4,5,6
|Kawther El Aifa
1
|
Ahmed Makhlouf
1,7
|Padarath Gangaram
1,8
|Ian Howland
1
|Andre Jones
1
|
Cyrine Abid
9
|Mohamed Chaker Khenissi
1
|Ian Howard
1
|
Moncef Khadhraoui
10
|Nicholas Castle
1
|Loua Al Shaikh
1
|James Laughton
1
|
Imed Gargouri
11
1
Ambulance Service, Hamad Medical
Corporation, Doha, Qatar
2
Faculty of Sciences, University of Sfax, Sfax,
Tunisia
3
Faculty of Medicine ‘Ibn El Jazzar’, University
of Sousse, Sousse, Tunisia
4
University of Hertfordshire, Hatfield, UK
5
Weill Cornell Medicine‐Qatar, Doha, Qatar
6
Northumbria University, Newcastle upon
Tyne, UK
7
College of Engineering, Qatar University,
Doha, Qatar
8
Faculty of Health Sciences, Durban
University of Technology, Durban,
South Africa
9
Laboratory of Screening Cellular and
Molecular Process, Centre of Biotechnology
of Sfax, University of Sfax, Sfax, Tunisia
10
Higher Institute of Biotechnology,
University of Sfax, Sfax, Tunisia
11
Faculty of Medicine, University of Sfax, Sfax,
Tunisia
Correspondence
Hassan Farhat, Ambulance Service, Hamad
Medical Corporation, Doha PO Box 3050,
Qatar.
Email: Hfarhat1@hamad.qa
Funding information
Qatar National Library
Abstract
Background and Aim: Though emergency medical services (EMS) respond to all
types of emergency calls, they do not always result in the patient being transported
to the hospital. This study aimed to explore the determinants influencing emergency
call‐response‐based conveyance decisions in a Middle Eastern ambulance service.
Methods: This retrospective quantitative analysis of 93,712 emergency calls to the
Hamad Medical Corporation Ambulance Service (HMCAS) between January 1 and
May 31, 2023, obtained from the HMCAS electronic system, was analyzed to
determine pertinent variables. Sociodemographic, emergency dispatch‐related,
clinical, and miscellaneous predictors were analyzed. Descriptive, bivariate, ridge
logistic regression, and combination analyses were evaluated.
Results: 23.95% (N= 21,194) and 76.05% (N= 67,285) resulted in patient nontran-
sport and transportation, respectively. Sociodemographic analysis revealed that
males predominantly activated EMS resources, and 60% of males (n= 12,687) were
not transported, whilst 65% of females (n= 44,053) were transported. South Asians
represented a significant proportion of the transported patients (36%, n= 24,007).
“Home”emerged as the primary emergency location (56%, n= 37,725). Bivariate
analysis revealed significant associations across several variables, though multi-
collinearity was identified as a challenge. Ridge regression analysis underscored the
role of certain predictors, such as missing provisional diagnoses, in transportation
decisions. The upset plot shows that hypertension and diabetes mellitus were the
most common combinations in both groups.
Conclusions: This study highlights the nuanced complexities governing conveyance
decisions. By unveiling patterns such as male predominance, which reflects Qatar's
Health Sci. Rep. 2024;7:e2056. wileyonlinelibrary.com/journal/hsr2
|
1of13
https://doi.org/10.1002/hsr2.2056
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2024 The Authors. Health Science Reports published by Wiley Periodicals LLC.

expatriate population, and specific temporal EMS activity peaks, this study
accentuates the importance of holistic patient assessment that transcends medical
histories.
KEYWORDS
cohort study, emergency medical service, Middle East, patient decisions, prehospital care
1|INTRODUCTION
Emergency Medical Services (EMS) constitute a fundamental
cornerstone of prehospital care to guarantee prompt medical
interventions for patients outside traditional healthcare settings.
EMS ensures timely patient transfer to dedicated facilities for
comprehensive medical examination and treatment. The continuous
development of EMS has mirrored the dynamic necessities of a
populational requirement for assured, efficacious emergency care.
1
A
competitive prehospital care system is an important indicator of
effective patient outcomes.
2
To ensure service excellence, EMS have instituted dedicated
emergency helplines (e.g., 999, 911, and 190) in some Middle Eastern
and North African countries and indicated their unwavering
allegiance to public health and safety.
3
Various patients refuse
transportation to healthcare institutions after emergency response
and onsite medical care provision.
4
This behavioral conundrum
impacts EMS efficiency and judicious resource allocation, with
broader ramifications for patient health outcomes that necessitate
the identification of epidemiological decision‐making‐related factors.
Qatar, similar to its Middle Eastern counterparts, presents a rich
tapestry of demographics, with male‐dominated demographic con-
figurations predominantly populated by South Asians and Arabs,
including indigenous Qataris.
5
Qatar's leading prehospital emergency
medical care provider is the Hamad Medical Corporation Ambulance
Service (HMCAS) stands as the sole provider in the country, ensuring
emergency medical responses for the community through the 999
emergency call service.
6
The emergency response units (ERU) are
distributed across eight hubs and locations where paramedics
commence their shifts, replenish their response units, and respond
to all emergency calls to the HMCAS communication call centre in
the National Command Center (NCC).
3
On receiving a call, operators
identify a medical emergency and transfer the call to the HMCAS
emergency medical dispatchers (EMD) for processing and triage using
the computer‐aided ProQA™dispatch system.
7
The EMD then
dispatches the most appropriate ERU and provides emergency callers
prehospital safety and lifesaving instructions until the ERU arrives.
The ERU crew provides appropriate emergency medical assessment
and treatment if needed, according to their HMCAS Clinical Practice
Guidelines (CPG)‐defined scope of practice.
8
In Qatar, patients or
their legal guardians can refuse transportation to the hospital by
signing the electronic patient report form (ePCR). The HMCAS
operational ethos gravitates towards encouraging patient
conveyance to hospitals rather than primary healthcare centers and
does not involve clinician‐advised non‐conveyance, given the risk of
undertriaging due to language barriers or unusual critical clinical
presentations that require in‐hospital diagnostic intervention and
clinical care. The HMCAS ERU consist of Alpha, Bravo, Charlie, and
Delta units. Alpha and Bravo have two Ambulance Paramedics (AP)
competent in conducting emergency medical evaluations and
administering emergency treatment. Charlie's units consist of a
Critical Care Paramedic and Assistant equipped for more advanced
interventions. Delta units, led by a senior supervisor, manage multi‐
agency scenes.
The complex epidemiological framework guiding patient‐
conveyance decisions in the Middle East remains under‐explored. A
granular analysis of potential determinants will enable judicious
strategies and informed decision‐making. We posited that an
amalgamation of human sociodemographic, clinical, and potentially
systemic factors contributes to conveyance determination in Qatar.
This study aimed to outline the various determinants influencing
patients' conveyance decisions following prehospital emergency calls
in the Middle Eastern environment.
2|METHODS
2.1 |Study design and setting
This retrospective quantitative cohort (Transported vs. Not Trans-
ported) analysis of 93,712 emergency calls received between January
1 and May 2023 involved data from the HMCAS electronic record
system managed by the business intelligence (BI) division. This
study adhered to the Consolidated Standards of Reporting Trials
(CONSORT) guidelines for cohort studies and was approved by the
HMC Medical Research Center (Reference: MRC‐01‐22‐264). We
used R‐Studio™for data arrangement and analyses.
2.2 |Participants
The inclusion criterion was 999 emergency calls that resulted in at
least one ERU dispatch wherein the paramedic performed onsite
patient assessment, with either hospital conveyance or a patient
decision against it. The exclusion criteria were: (1) cases involving a
deceased patient and (2) calls originating from healthcare facilities, as
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patients can still receive timely medical attention, offsetting the need
for advanced care, unlike that in community‐based 999 emergency
callers.
2.3 |Variables
The HMCAS BI team provided an initial data set comprising 73
variables. After data wrangling, six variables underwent nomenclature
adjustments, 14 new variables were derived or transformed from
their preliminary configurations, and 47 were excluded. Two
supplementary variables, namely the longitude and latitude of the
999 calls, were incorporated to construct the emergency call map.
Twenty variables were retained for in‐depth analysis and classified as
outcome variables and sociodemographic, EMD‐related, clinical, and
miscellaneous predictors.
2.3.1 |Outcome variable
The variable “Handover”was designated as the outcome variable to
segment the cohort into “Transported”and “Not Transported”groups
as follows:
i. “Transported”group: Patients who were conveyed to hospitals
following a 999 call and an on‐scene assessment by the
HMCAS crew.
ii. “Not Transported”group: Encompassed three sub‐categories:
“Refused Transport ‐Treated At Scene,”“Refused Transport,”and
“Treated At Scene ‐Not Transported.”
Entries labeled “DOA (death on arrival) Not Transported”were
systematically omitted from the analysis.
2.3.2 |Sociodemographic predictors
The categorical variables were: (1) sex, (2) nationalities represented as
“Nationalities_CAT,”(3) age categorized as “Age_CAT,”(4) region, and
(5) weight categorized as “Weight_CAT.”Categorization of age and
weight is a common practice in the clinical field and helps provide a
more nuanced understanding of risk factors across different
subgroups.
9
2.3.3 |Emergency medical dispatch‐related
predictors
i. The Categorical Variables were: (1) Call Service Owner denoted
as “CFS_Owner,”(2) emergency caller's geographical coordinates
represented as “Location_LAT”and “Location_Long,”(3) type of
location denoted as “LocationType,”4) ProQA™Protocol Labeled
as “ProtocolName,”(5) dispatch type: defined as “DispatchType,”
(6) response priority levels for the scene (“PriorityToScene”) and
hospital (“PriorityToHospital”), and (7) ERU type denoted as
“Unit_Type.”
ii. The Continuous Variables were: (1) Unit identification time in
minutes from the 999 call until the nearest unit is identified,
referred to as “TimeToFindTheNearestUnit.”(2) Response
Duration was defined as the time the ERU took to reach the
scene, labeled “TimeToReachOnScene.”(3) Patient Interaction
Duration: Span from the paramedic's arrival to either the patient
handover to a healthcare facility or obtaining a refusal form
signature, referred to as “TimeWithPatientUntilAvailable.”(4)
Unit in‐Dispatch duration: Duration from the dispatch of the
ERU until it is available for the next call, denoted as
“TimeFromDispatchUntilAvailable.”
2.3.4 |Clinical predictors
The clinical predictors are all categorical and include (1) Provisional
diagnoses categorized as “ProvisonalDiagnoses_CAT,”(2) Receiving
facility denoted “TransportedTo,”(3) Receiving unit referred to as
“PatientTriagedArea.”The comorbidities were each under a separate
variable, including (1) Pregnancy “CurrentlyPregnant,”(2) Asthma, (3)
Cardio‐Artery‐Disease “CAD,”(4) Chronic Obstructive Pulmonary
Disease “COPD,”(5) Cardio‐vascular Accident “CVA,”(6) Seizure, (7)
Diabetes Mellitus “DM,”(8) Hypertension, (9) Surgeries, (10) Others,
(11) None, and (12) Unknown.
2.3.5 |Miscellaneous predictors
i. Categorical: (1) Week of the year “WeekNumber,”(2) Day of the
week “Week Day.”
ii. Continuous: (3) Hour of the day when an emergency call was
received, referred to as “Hour_Received.”
2.4 |Statistical methods
Descriptive statistical analyses were performed by calculating the
count and percentage of categorical variables and the median for
continuous variables. Shewhart Statistical Process Control (SPC)
charts were designed to observe the time‐series variations in
transported patients following 999 emergency calls. A function for
bivariate analysis was created in R (Supporting Information S1:
Appendix 1and Appendix 2). The null hypothesis (H
0
) was: “There is
no correlation between Handover and the studied categorical variables.”
The chi‐square test for categorical variables determined a significant
association between two categorical variables. For specific variables
that retained categories with low counts after exhaustive iterations
where the chi‐square test was unsuitable because of data sparsity,
the variables were refined. Fisher's exact test was used as needed.
Cramer's V coefficient was used to measure the strength of the
FARHAT ET AL.
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association of categorical predictors with transport groups (range: 0
[indicating no association] to 1 [indicating perfect association]).
10
Odds ratios (OR), which measure the exposure‐outcome association
and indicate the intergroup odds of an event happening, were
calculated. For continuous variables, mean intergroup differences
were determined using the Mann–Whitney Utest.
11
Ridge regres-
sion, used to handle multicollinearity, was used,
12
and the outcome
variable ‘Handover’was transformed into a categorical format to
facilitate binary logistic regression.
13
The ridge regression model
facilitated the extraction of coefficients indicating the influence
probabilities of each predictor on “Handover.”Comorbidity is crucial
in patient management and prognosis.
14,15
A comorbidity combina-
tion analysis was conducted by creating UpSet plots, a visualization
technique to depict more than three intersecting sets,
16,17
It enabled
a greater understanding of the interaction and confluence of
different comorbidities on patient‐conveyance decisions.
3|RESULTS
3.1 |Descriptive statistics
Among the 88,479 participants enrolled after data wrangling
(Figure 1), 67,285 (76.05%) and 21,194 (23.95%) were and were
not transported, respectively. Supporting Information S1: Appendix 3
shows unstable weekly variations in the number of transported
patients, which increased interpretation‐related challenges, whilst the
intraday variation showed increasing proportions of transported
FIGURE 1 Map for distribution of patients transported and not transported by the HMCAS. (The dimensions of the map are determined
automatically by the ‘ggmap’package according to the coordinates provided)
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TABLE 1 Descriptive statistics results.
Characteristic
Not transported Transported
N= 21,194
1
N= 67,285
1
1. SOCIODEMOGRAPHIC PREDICTORS
Gender
Female 8505 (40%) 23,209 (34%)
Male 12,687 (60%) 44,053 (65%)
Missing 2 (<0.1%) 23 (<0.1%)
Nationalities_CAT
Qatari 5094 (24%) 11,187 (17%)
GCC Other 893 (4.2%) 2513 (3.7%)
MENA 5,363 (25%) 14,463 (21%)
East Asia & Pacific 968 (4.6%) 3699 (5.5%)
South Asia 5052 (24%) 24,007 (36%)
Sub‐Saharan Africa 1899 (9.0%) 6848 (10%)
Europe and Central Asia 936 (4.4%) 1565 (2.3%)
North America 139 (0.7%) 244 (0.4%)
Latin America &
Caribbean
45 (0.2%) 90 (0.1%)
Other 495 (2.3%) 1257 (1.9%)
Missing 310 (1.5%) 1412 (2.1%)
Region
Urban 9667 (46%) 31,361 (47%)
Rural 3441 (16%) 12,705 (19%)
Missing 8086 (38.1%) 23,219 (34.1%)
Age categories
Age<14 2488 (12%) 7459 (11%)
14≤Age<29 6021 (28%) 17,669 (26%)
29≤Age<44 7314 (35%) 24,273 (36%)
44≤Age<59 2698 (13%) 9894 (15%)
59≤Age<75 1720 (8.1%) 5167 (7.7%)
75≤Age<90 845 (4.0%) 2300 (3.4%)
Age≥90 108 (0.5%) 353 (0.5%)
Missing 0 (0%) 170 (0.3%)
Weight_CAT
Weight<45 2273 (11%) 6833 (10%)
45≤Weight<70 8609 (41%) 24,513 (36%)
70≤Weight<95 9159 (43%) 31,244 (46%)
95≤Weight<120 995 (4.7%) 4018 (6.0%)
Weight≥120 154 (0.7%) 629 (0.9%)
Missing 4 (<0.1%) 48 (<0.1%)
TABLE 1 (Continued)
Characteristic
Not transported Transported
N= 21,194
1
N= 67,285
1
2. EMERGENCY MEDICAL DISPATCH‐RELATED PREDICTORS
CFS owner
EMS 10,096 (48%) 34,499 (51%)
No call taking/missing 8084 (38%) 23,193 (34%)
Other 3014 (14%) 9,593 (14%)
Dispatch type
Zulu (Z) 2611 (12%) 11,309 (17%)
Yankee (Y) 6285 (30%) 24,338 (36%)
Xray (X) 4170 (20%) 8337 (12%)
Tango (T) 17 (<0.1%) 23 (<0.1%)
Uncompleted ProQa 26 (0.1%) 85 (0.1%)
No call taking/Missing 8084 (38%) 23,193 (34%)
Not in use 1 (<0.1%) 0 (0%)
Location type
Airport 3266 (15%) 2433 (3.6%)
Beach/sea/ocean 85 (0.4%) 296 (0.4%)
Farm 36 (0.2%) 232 (0.3%)
Home 10,259 (48%) 37,725 (56%)
Industrial area 118 (0.6%) 1589 (2.4%)
Other 946 (4.5%) 2175 (3.2%)
Public area 737 (3.5%) 2,119 (3.1%)
Recreation (sport) 52 (0.2%) 252 (0.4%)
School 353 (1.7%) 1222 (1.8%)
Street (road) 4206 (20%) 13,762 (20%)
Work 523 (2.5%) 3941 (5.9%)
Missing 613 (2.9%) 1539 (2.3%)
Priority to scene
P1 17,411 (82%) 56,595 (84%)
P2 3459 (16%) 10,382 (15%)
Missing 324 (1.5%) 308 (0.5%)
Priority to hospital
P1 0 (0%) 3367 (5.0%)
P2 0 (0%) 62,418 (93%)
P3 0 (0%) 436 (0.6%)
Missing 0 (0%) 1064 (1.6%)
ERU Type
Alpha 8453 (40%) 29,612 (44%)
(Continues)
FARHAT ET AL.
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patients from 9 a.m. to 12 p.m. that significantly decreased in the
evening and early morning.
Table 1presents descriptive data on the study population. The
sociodemographic predictors showed a predominantly male repre-
sentation in both the transported (65%; 44,053) and non‐transported
(60%; 12,687) groups. Within nationalities, South Asians constituted
the largest portion of the transported group (36%; 24,007), whereas
the non‐transported group exhibited roughly equivalent proportions
of Qataris, Middle East, North Africa (MENA), and South Asians.
Approximately half of the individuals in both categories resided in
urban areas (Table 1and Figure 1). The age demographic most
represented across both groups was 29–44 years. A significant
proportion of both groups weighed 70–95 kg.
The median response times were 6.2 and 5.9 min (within the
international benchmark) (Figure 2), and the time from ERU dispatch
until assigned was 63.3 and 43.1 min for transported and non‐
transported patients, respectively. The Yankee (Y) and “No call
taking/Missing”dispatch type was predominant among transported
and non‐transported patients, respectively, which includes walk‐in
patients who visit nearby HMCAS ERU standby points instead of
calling 999 because of location proximity. As expected, the primary
emergency location was “Homes”for both categories because 999
emergency calls are community‐generated. In both groups, the
majority had a “P1”priority to the scene where ambulances moved
with lights and sirens.
3
Alpha was the predominant ERU category. For
the ProQA™call‐taking protocols, RTA (P29) and sick persons (P26)
were the predominant protocols used in both groups (Supporting
Information S1: Appendix 4). For clinical variables, patients were
predominantly transported to governmental healthcare facilities
without prenotification requirements. Most patients were triaged at
an adult assessment Emergency Department (ED), as they did not
require critical care. Despite several comorbidities, a significant
percentage of patients in both groups presented without known
TABLE 1 (Continued)
Characteristic
Not transported Transported
N= 21,194
1
N= 67,285
1
Bravo 1425 (6.7%) 641 (1.0%)
Charlie 996 (4.7%) 4724 (7.0%)
Delta 1157 (5.5%) 3730 (5.5%)
Hazmat 285 (1.3%) 1488 (2.2%)
Life Flight (LF) 72 (0.3%) 543 (0.8%)
Specialized Emergency
Management
322 (1.5%) 1202 (1.8%)
Other 397 (1.9%) 2152 (3.2%)
Missing 8087 (38%) 23,193 (34%)
3. CLINICAL PREDICTORS
Transported to
Airport clinics 0 (0%) 1642 (2.4%)
Governmental with no
prenotification
0 (0%) 58,064 (86%)
Governmental with
prenotification
0 (0%) 331 (0.5%)
Pediatric Emergency
Care (pec)
0 (0%) 5763 (8.6%)
Private 0 (0%) 457 (0.7%)
Other 0 (0%) 144 (0.2%)
Patient triaged area
Adult assessment ED 0 (0%) 36,325 (54%)
Low acuity ED 0 (0%) 16,713 (25%)
Bypass criteria ED 0 (0%) 3703 (5.5%)
Obstetrics/gynecology
ED
0 (0%) 2426 (3.6%)
Pediatric ED 0 (0%) 5589 (8.3%)
Dialysis 0 (0%) 2 (<0.1%)
Other 0 (0%) 1037 (1.5%)
Comorbidities
Asthma 777 (3.7%) 2368 (3.5%)
CAD 750 (3.5%) 2996 (4.5%)
COPD 64 (0.3%) 261 (0.4%)
Cva 140 (0.7%) 911 (1.4%)
Seizure 170 (0.8%) 1096 (1.6%)
DM 2678 (13%) 9093 (14%)
Hypertension 2768 (13%) 9953 (15%)
None 13,892 (66%) 40,072 (60%)
Others 2285 (11%) 9533 (14%)
Surgeries 360 (1.7%) 1797 (2.7%)
Unknown 810 (3.8%) 4489 (6.7%)
Currently pregnant 306 (1.4%) 2884 (4.3%)
TABLE 1 (Continued)
Characteristic
Not transported Transported
N= 21,194
1
N= 67,285
1
4. MISCELLANEOUS PREDICTORS
Weekday
Sunday 1644 (7.8%) 5686 (8.5%)
Monday 2135 (10%) 7960 (12%)
Tuesday 1748 (8.2%) 6787 (10%)
Wednesday 2207 (10%) 7088 (11%)
Thursday 1798 (8.5%) 5248 (7.8%)
Friday 1725 (8.1%) 5487 (8.2%)
Saturday 1853 (8.7%) 5836 (8.7%)
Missing 8084 (38%) 23,193 (34%)
1
n(%); Median (IQR)
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comorbidities, with the predominant category of a provisional
diagnosis of low‐acuity trauma and medical care (Supporting
Information S1: Appendix 4). Considering miscellaneous predictors,
despite considerable missing data, Monday was the peak day for
prehospital 999 emergencies in the transported group, whereas the
non‐transported group saw a weekend surge (Table 1).
3.2 |Bivariate and multivariate analyses
Table 2presents the association level of both cohort groups with the
remaining variables. Most variables showed significant associations
between both groups and continuous and categorical variables
(p< 0.01). The strength of these associations could be inferred from
the Cramer's V values; for instance, “ProtocolName”has a moderate
association strength of 0.15, while “LocationType”has a stronger
association at 0.22. It is worth noting, however, that some conditions
like “Asthma”and “COPD”were not significantly associated with both
“Handover”groups. Variables with an OR greater than one had a
greater likelihood of the “Handover”event not being in the Not
Transported group. An undetermined OR indicates a potentially
strong but nonquantifiable association warranting large‐sample inves-
tigation. For the Mann‐Whitney‐U tests, significant differences in
distributions were observed for “Hour_Received,”“TimeToReachOn-
Scene,”“TimeWithPatientUntilAvailable,”and “TimeFromDispatchUn-
tilAvailable”(p< 0.01). However, “TimeToFindTheNearestUnit”did not
show a statistically significant difference. The significant association in
the bivariate analysis with most variables indicate that many examined
variables were significantly associated or showed intergroup differ-
ences, suggesting potential multicollinearity, which could distort the
estimated regression coefficients.
3.3 |Ridge logistic regression analysis
To address potential issues related to multicollinearity, ridge
regression was applied to our data set (Table 3and Figure 3) after
encoding the categorical variables and preventing the regression
model from being overly influenced by correlated predictors to
ensure more reliable findings. Figure 5included the Lambda plots,
FIGURE 2 Mirror plots of dispatch and response durations distribution.
FARHAT ET AL.
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histogram for predicted probabilities, and the receiver operating
characteristic area under the curve (ROC AUC) plot. The Lambda
plots enable minimizing the model's generalization error, resulting in
more robust results. The histogram for predicted probabilities
provides insights into the model's calibration, specifically how well
the predicted probabilities align with the observed outcomes.
Notably, a significant number of predicted probabilities cluster
around a value of 1, coherent with the findings presented in the
ROC AUC and Table 3and indicative of a high likelihood for certain
cases to be transported. The ROC AUC is a graphical representation
TABLE 2 Bivariate analysis.
1) Summary of chi‐square tests for categorical variables
Variable Chi‐Square statistic Degrees of freedom p‐Value Cramer's V Odds ratio
CFS_Owner 103.82 3 <0.01 ——
ProtocolName 1915.60 33 <0.01 0.15 —
DispatchType 1081.50 6 <0.01 0.04 —
PriorityToScene 274.40 2 <0.01 0.06 —
PriorityToHospital 88,479 4 <0.01 1 —
Week Day 180.78 7 <0.01 0.04 —
Region 131.18 3 <0.01 0.04 —
LocationType 4,422.80 11 <0.01 0.22 —
Hour_Received 455.86 23 <0.01 0.07 —
Gender 225.43 2 <0.01 0.05 —
Nationalities_CAT 1672.55 10 <0.01 0.14
Age_CAT 161.36 7 <0.01 0.05 —
Weight_CAT 179.03 5 <0.01 0.05 —
Unit_Type 2,775.06 8 <0.01 0.17 —
WeekNumber 163.14 14 <0.01 0.04 —
Asthma 0.97 1 0.32 0.00 0.96
CAD 32.98 1 <0.01 0.02 1.27
COPD 3.02 1 0.08 0.00 1.29
CVA 65.43 1 <0.01 0.03 2.06
Seizure 77.53 1 <0.01 0.03 2.04
DM 10.71 1 <0.01 0.01 1.08
Hypertension 39.135 1 <0.01 0.02 1.15
None 242.91 1 <0.01 0.05 0.77
Others 159.45 1 <0.01 0.04 1.36
Surgeries 63.63 1 <0.01 0.03 1.59
Unknown 231.97 1 <0.01 0.05 1.78
CurrentlyPregnant 724.17 2 <0.01 ‐‐
2) Summary of Mann–Whitney‐Utests for continuous variables
Variable Statistic p‐Value CI_lower CI_upper
Hour_Received 730,904,066 <0.01 4.12 × 10
−08
4.66 × 10
−07
TimeToFindTheNearestUnit 713,927,809 0.77 −6.15 × 10
−08
1.22 × 10
−08
TimeToReachOnScene 678,944,256 <0.01 −0.35 −0.25
TimeWithPatientUntilAvailable 527,265,766 <0.01 −13.44 −12.48
TimeFromDispatchUntilAvailable 544,683,551 <0.01 −1.57 −1.47
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that illustrates the diagnostic ability of our logistic ridge regression
model at varying classification thresholds. It ranges between zero and
one. The closer to 1, the better. The coefficients in Table 3indicated a
high likelihood for certain cases to be transported. Positive
coefficients indicated that the chances of a ‘Transported’outcome
increased as certain predictors increased. For example, patients with
missing provisional diagnoses are more likely to be transported.
Cases diagnosed with “Cardiac Arrest”were more likely to be
transported as expected, while a “Hypoglycemia”diagnosis tended to
decrease transportation likelihood.
3.4 |Comorbidities combination analysis
UpSet plots, as exemplified in Figures 4and 5,serveacriticalrole
in the visualization and analysis of complex datasets, particularly
when assessing the presence of intersecting data sets. In our
study, the UpSet plot was employed to discern the patterns of
comorbidities among the patients, specifically the co‐occurrence
of DM and hypertension. This type of visual representation is
particularly useful for combination analysis as it allows for a clear
and concise depiction of how often different conditions appear
together within a data set. UpSet plots provide an intuitive means
of displaying intersections across categorized groups, such as the
coexistence of DM and hypertension among patients. In this case,
the plot explained that most patients did not have a significant
medical history combining these two conditions regardless of their
group categorization. Additionally, Figure 4offers a comparative
insight, highlighting that the comorbidity of DM and hypertension
was more frequently observed within the ‘Transported’group
versus the “Not Transported”group, suggesting potential implica-
tions for patient transport decisions. UpSet plots helped facilitate a
better understanding of the underlying patterns in patient medical
histories and their possible impacts on treatment and transport
outcomes.
4|DISCUSSION
Deciding patient transportation ensures the effectiveness of the 999‐
emergency response and mitigates morbidity and mortality risks. This
cohort study explored various dimensions of EMS utilization and
identified determinants of conveyance decisions, revealing both
congruence and departure from the prevailing literature.
One salient finding was the conspicuous male predominance
across transported and non‐transported cohorts, which resonates
with empirical evidence from recent studies of non‐conveyance
decisions in Gulf Cooperation Council (GCC) countries, including
during the COVID‐19 pandemic, and highlighted a similar male‐
centric inclination for EMS activation.
18
We identified a marked representation of South Asian demo-
graphics, especially within transported patient groups. Juxtaposed
against recent literature, as most South Asian populations include
low‐income workers compared to patients from other ethnicities, the
prehospital healthcare service in Qatar is equally accessible to both
citizens and expatriate populations.
6,19
Broader ethnicity‐in‐
healthcare discussions inevitably entangle complex strands of
socioeconomic status and health behaviors
20
that are potentially
insightful.
A temporal pattern in our data set indicated a 9 a.m. to 12 p.m.
surge in patient‐conveyance activities, potentially indicating
workplace‐associated stressors as important triggers, as previously
reported.
21
Such discernments can strategically guide EMS resource
allocation and optimize spatiotemporal response protocols.
TABLE 3 Ridge regression coefficients analysis results.
Variable Coefficients
PriorityToHospitalMissing 3.08
ProvisonalDiagnoses_CATMissing 2.11
TransportedToNot Applicable −1.78
PriorityToHospitalP2 1.70
PatientTriagedAreaNot Applicable −1.65
GenderMissing 1.55
TransportedToOther 1.48
TransportedToGouvernemental no Prenotif 1.16
PriorityToHospitalP3 1.11
Weight_CATMissing 0.97
ProvisonalDiagnoses_CATDOA 0.95
PriorityToHospitalP1 0.92
TransportedToPrivate 0.74
TransportedToPEC 0.70
ProvisonalDiagnoses_CATCardiac Arrest 0.64
ProtocolName9 0.54
ProtocolName31 −0.54
TransportedToGouvernmental with Prenotif 0.54
PatientTriagedAreaDialysis 0.48
PatientTriagedAreaLow_Acuity_ED 0.48
PatientTriagedAreaPaed_ED 0.47
ProvisonalDiagnoses_CATCOPD 0.47
ProvisonalDiagnoses_CATCardiovas_Other 0.42
ProvisonalDiagnoses_CATHemothorax 0.41
LocationTypeBeach/Sea/Ocean 0.40
PatientTriagedAreaByPass_Crit_ED 0.39
ProvisonalDiagnoses_CATCroup/Epiglottitis 0.38
ProtocolName15 0.37
ProvisonalDiagnoses_CATHypoglycemia −0.37
ProvisonalDiagnoses_CATPneumothorax 0.37
FARHAT ET AL.
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For locational tendencies, our data unequivocally positions
“Homes”at the epicenter of emergencies. Though granular household
risk factors as potential emergency catalysts have been investigated,
other issues, such as compromised indoor air quality, deficient
lighting, and structural integrity of domiciles as risk multipliers, have
been highlighted in empirical studies.
22
Identifying contributory
variables necessitates a comprehensive home‐safety exploration
model involving community education, audits, and interagency
collaboration for safer dwellings. Moreover, the free prehospital
healthcare in Qatar potentially incentivises the use of emergency
care at home and refusal of transportation, possibly to avoid
congested Emergency Departments (ED). Conversely, some, antici-
pating bypassing extended ED waiting times,
23
may consent to
transportation. Public awareness campaigns could guide the public
towards alternative healthcare options, such as health centers while
clarifying optimal care pathways.
The unpredictable nature of medical emergencies, as evident
from our cohort's considerable representation of patients without
known comorbidities, disrupts conventional clinical expectations.
Despite the considerable literature on health conditions that amplify
the risk of emergencies,
24
our observations prompt a broader
investigation considering, for instance, the elements of medical crises
identified by other researchers,
25
including latent environmental
factors, genetic propensities, and undiagnosed medical conditions.
During patient assessment, HMCAS clinicians should evaluate the big
picture, not just medical history, but also their environmental,
genetic, and psychosocial domains. Within this framework, the
HMCAS advocates the use of the IMIST‐AMBO (Identification,
Medical complaint/Mechanism, Injuries/Information related to the
complaint, Symptoms, Treatment, Allergies, Medication, Background
history, and other information) during patient handovers within
healthcare facilities. IMIST‐AMBO ensures that essential details
pertaining to patient complaints are communicated consistently, thus
mitigating the risk of oversight and constitute a particularly beneficial
approach compared to other handover tools (e.g., Situation, Back-
ground, Assessment, Recommendation [SBAR]), which might inad-
vertently bypass certain contextual and comorbidity‐related details.
26
Additionally, the HMCAS has institutionalized exemplary EMS
standards through the CPGs
8
that underline the significance of
persistent professional upskilling amid inherent uncertainties in the
clinical practice.
27
In our examination of the comorbidities, the emphasis on DM
and hypertension aligns seamlessly with current research trajec-
tories
21
between these morbidities and heightened vulnerabilities
that accentuate the urgency for more specialized care protocols and
documentation systems for post‐event symptoms.
28
FIGURE 3 Plots of the results of the ridge regression analysis.
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Bivariate and logistic ridge regression analyses enhanced our
understanding of the determinants of the ‘Transported’versus “Not
Transported”outcomes and improved model stability and predictive
validity. This multitiered approach provides a nuanced understanding
of factors influencing the likelihood of transportation. It yields a
model that is particularly effective in predicting which cases are most
likely to require transportation, thus offering actionable insights for
clinical decision‐making.
The findings explicated by the combination analysis using the UpSet
plot carry profound implications for the stratification and management of
patient care, particularly in the emergency medical context. It is evident
from Figures 4and 5that comorbidities such as DM and hypertension are
commonly present in conjunction withmostpatientswithcomorbidities
studied. This observation might suggest recalibrating the clinical assump-
tions regarding comorbidities within the population. Figures 4and 5
revealed a marked propensity for the dual presence of these conditions in
the ‘Transported’group, compared to the ‘Not Transported’cohort. This
differential pattern highlights the need for heightened clinical vigilance
and resource allocation for transporting patients more likely to present
with complex medical backgrounds. Such findings advocate for ‘tailored’
patient assessment protocols, ensuring these comorbidities are consid-
ered in therapeutic decision‐making. The utility of UpSet plots enables
healthcare decision‐makers with a nuanced understanding of patient
comorbidities, guiding more informed and efficacious intervention
strategies.
In summary, this study's empirical findings regarding EMS and
patient conveyance decisions emphasize the layered complexities
that affect conveyance decisions. Nuanced demographic insights into
clinical ambivalence demonstrate the intricate prehospital work
environment, necessitating sustained academic engagement and
introspection for optimized service delivery.
5|LIMITATION
Complemented by the ridge regression model, the descriptive data
emphasized the crucial role of missing informationinclinical examination
and model formulation. Despite HMCAS' use of a digital system for
recording clinical and nonclinical details, a significant percentage of data
was missing, which, if found, could enhance the validity of our conclusions
and provide insights that more closely mirror real‐world scenarios.
Furthermore, despite careful consideration, strong interrelations between
some variables increased the risk of multicollinearity and potential
confounders, adding complexity to data interpretation.
Additionally, it is important to acknowledge the inherent
limitations associated with the retrospective design of our study.
FIGURE 4 Comorbidities upset plots for combination analysis for patients who have been transported.
FARHAT ET AL.
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Such a design is prone to risks of errors and biases that are typically
less prevalent in prospective studies, such us recall and selection
biases, which may influence the generalizability of our findings.
6|CONCLUSION
This prehospital study highlights the intricate variables influencing
patient‐conveyance decisions, spanning sociocultural factors
and clinical ambiguities. Our findings corroborate and challenge
the existing literature. Notably, patterns such as male predomi-
nance and activity spikes during specific hours necessitate
advanced analytical techniques for insightful interpretations. The
unpredictable nature of the prehospital setting warrants enhanced
training and comprehensive patient assessment approaches that
consider factors beyond medical history. Moreover, our methodo-
logical challenges emphasize the importance of refining the
analytical tools. Our study underscores the dynamic nature of
prehospital care and stresses the need for continuous academic
engagement. As the prehospital landscape evolves, this study
emphasizes the importance of innovation and introspection in
successful and effective emergency care delivery.
AUTHOR CONTRIBUTIONS
Hassan Farhat: Conceptualization; investigation; writing—original
draft; visualization; formal analysis; data curation; software; methodol-
ogy; writing—review & editing; funding acquisition. Guillaume Alinier:
Writing—review & editing; supervision; validation. Kawther El Aifa:
Writing—review & editing; validation. Ahmed Makhlouf: Validation.
Padarath Gangaram: Conceptualization; project administration. Ian
Howland:Projectadministration.Andre Jones:Writing—review &
editing. Cyrine Abid:Writing—review & editing. Mohamed Chaker
Khenissi: Project administration; resources. Ian Howard:Project
administration. Moncef Khadhraoui: Supervision; project administration.
Nicholas Castle: Project administration. Loua Al Shaikh:Project
administration. James Laughton:Supervision;Writing—review & editing;
resources. Imed Gargouri: Project administration; supervision.
ACKNOWLEDGMENTS
We wish to acknowledge the executive and management teams of
the HMCAS Business IntelligenceTeam. The publication of this article
was funded by the Qatar National Library.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
FIGURE 5 Comorbidities UpSet plots for combination analysis for patients who have not been transported.
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DATA AVAILABILITY STATEMENT
Anonymised data that support the findings of this study are available
from the corresponding author for review upon request. The data is
available from the first author and can be provided upon a reasonable
request pending the approval of the ethical board of the medical
research centre of Hamad Medical Corporation. The data that
support the findings of this study are available on request from the
corresponding author. The data are not publicly available due to
privacy or ethical restrictions.
ETHICS STATEMENT
The study was conducted in accordance with the guidelines of the
Declaration of Helsinki and approved by the Hamad Medical
Corporation Medical Research Center (reference MRC‐01‐22‐264).
TRANSPARENCY STATEMENT
The lead author Hassan FARHAT affirms that this manuscript is an
honest, accurate, and transparent account of the study being
reported; that no important aspects of the study have been omitted;
and that any discrepancies from the study as planned (and, if relevant,
registered) have been explained.
ORCID
Hassan Farhat http://orcid.org/0000-0001-5448-9401
Guillaume Alinier http://orcid.org/0000-0003-4255-4450
Padarath Gangaram http://orcid.org/0000-0001-5282-5045
Cyrine Abid http://orcid.org/0000-0003-2358-6510
James Laughton http://orcid.org/0000-0002-9486-5157
REFERENCES
1. Roudsari BS, Nathens AB, Arreola‐Risa C, et al. Emergency Medical
Service (EMS) systems in developed and developing countries. Injury.
2007;38:1001‐1013.
2. Abdulnassir FH, Alinier A. Access to emergency healthcare.
Healthcare Access. Vol 171. IntechOpen; 2022.
3. Alinier G, Wilson P, Reimann T, Morris B. Influential factors on urban
and rural response times for emergency ambulances in Qatar.
Mediterr J Emerg Med. 2018;8:13.
4. Forsgärde E‐S, Elmqvist C, Fridlund B, Svensson A, Andersson R,
Rööst M. Patients' aged ≥65 years dispositions during ambulance
assignments, including factors associated with non‐conveyance to
hospital: a longitudinal and comparative study. BMJ Open. 2020;
10:e038885.
5. Abdulla SAA. SMART population screening and management in
Qatar. ClinMed. 2019;6(1):099. doi:10.23937/2377-3634/1410099
6. Demir S, Tunçbilek Z, Alinier G. Prehospital emergency health
services in Qatar. J Paramed Pract. 2022;14(11):456.
7. Clwason JJ, Dernocoeur KB, Murray C. Principles of Emergency
Medical Dispatch. 5th ed. The International Academy of EMD; 2014.
8. Howard I, Howland I, Castle N, Al Shaikh L, Owen R. Retrospective
identification of medication related adverse events in the emergency
medical services through the analysis of a patient safety register. Sci
Rep. 2022;12:2622.
9. Zhang Y. New Advances in Machine Learning. InTech Open; 2010.
10. Mitchell R, Bue O, Nou G, et al. Validation of the interagency
integrated triage tool in a resource‐limited, urban emergency
department in Papua New Guinea: a pilot study. Lancet Regional
Health ‐Western Pacific. 2021;13:100194.
11. Barber JA. Open access follow up for inflammatory bowel disease.
BMJ. 2000;320:1730.
12. Cintami A, Nugraha J. Ridge trace and HKB methods in ridge regression
to solve multicollinearity. AIP Conf Proc. 2023;2720(1):020024.
13. Meijer RJ, Goeman JJ. Efficient approximate k‐fold and leave‐one‐
out cross‐validation for ridge regression. Biometrical J. 2013;55:
141‐155.
14. Eggert LE, He Z, Collins W, et al. Asthma phenotypes, associated
comorbidities, and long‐term symptoms in COVID‐19. Allergy.
2022;77:173‐185.
15. Martos‐Benítez FD, Soler‐Morejón CD, García‐del Barco D. Chronic
comorbidities and clinical outcomes in patients with and without
COVID‐19: a large population‐based study using national administrative
healthcare open data of Mexico. Intern Emerg Med. 2021;16:1507‐1517.
16. Batra N. The Epidemiologist R Handbook. Applied Epi Incorporated; 2022.
17. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the
visualization of intersecting sets and their properties. Bioinformatics.
2017;33:2938‐2940.
18. Alrazeeni DM, Sheikh SA, Mobrad A, et al. Epidemiology of non‐
transported emergency medical services calls in Saudi Arabia. Saudi
Med J. 2016;37:575‐578.
19. Lederman J, Lindström V, Elmqvist C, Löfvenmark C, Djärv T. Non‐
conveyance in the ambulance service: a population‐based cohort
study in Stockholm, Sweden. BMJ Open. 2020;10:e036659.
20. Andrew E, Nehme Z, Cameron P, Smith K. Drivers of increasing
emergency ambulance demand. Prehosp Emerg Care. 2020;24:385‐393.
21. Al‐Thani MH, Al‐Mutawa KA, Alyafei SA, et al. Characterizing
epidemiology of prediabetes, diabetes, and hypertension in Qataris:
a cross‐sectional study. PLoS One. 2021;16:e0259152.
22. He M, Zhong Y, Chen Y, Zhong N, Lai K. Association of short‐term
exposure to air pollution with emergency visits for respiratory
diseases in children. iScience. 2022;25:104879.
23. Jazairi AFHA, Alinier G, Jazairi AFHA, Alinier G. Access to
Emergency Healthcare. Healthcare Access. IntechOpen; 2021.
doi:10.5772/intechopen.98574
24. Amalberti R, Auroy Y, Berwick D, Barach P. Five system barriers to
achieving ultrasafe health care. Ann Intern Med. 2005;142:756‐764.
25. Oosterwold J, Sagel D, Berben S. Factors influencing the decision to
convey or not to convey elderly people to the emergency
department after emergency ambulance attendance: a systematic
mixed studies review. BMJ Open. 2018;8(8):e021732.
26. Shah Y, Alinier G, Pillay Y. Clinical handover between paramedics and
emergency department staff: SBAR and IMIST‐AMBO acronyms.
Paramed Practice. 2016;6(2):4889. doi:10.12968/ippr.2016.6.2.37
27. Gangaram P, Menacho AM, Alinier G. Crisis resource management in
relation to empowering people to speak up in emergency medical
service clinical practice settings. J Paramed Pract. 2017;9:60‐65.
28. Long BJ, Serrano LA, Cabanas JG, Bellolio MF. Opportunities for
emergency medical services (EMS) care of syncope. Prehosp Disaster
Med. 2016;31:349‐352.
SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Farhat H, Alinier G, El Aifa K, et al.
Epidemiology of prehospital emergency calls according to
patient transport decision in a middle eastern emergency care
environment: retrospective cohort‐based. Health Sci Rep.
2024;7:e2056. doi:10.1002/hsr2.2056
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