A spatial analysis of urban transit accidents assisted by Emergency Mobile Care Services: an analysis of space and time

Mendonça, Marcela Franklin Salvador de; Silva, Amanda Priscila de Santana Cabral; Castro, Claudia Cristina Lima de

doi:10.1590/1980-5497201700040014

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Revista Brasileira de Epidemiologia

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ORIGINAL ARTICLE • Rev. bras. epidemiol. 20 (04) • Oct-Dec 2017 • https://doi.org/10.1590/1980-5497201700040014 copy

A spatial analysis of urban transit accidents assisted by Emergency Mobile Care Services: an analysis of space and time

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Introduction:

Urban transit accident are a global public health problem. The objective of this study was to describe the profile of the victims and the occurrences of urban transit accidents attended to by emergency mobile care services (Serviço de Atendimento Móvel de Urgência- SAMU) in Recife, and their distribution based on spatial analysis.

Methodology:

An ecological study, developed through secondary data from emergency mobile care services in Recife, referring to the total number of occurrences of urban transit accidents attended to from January 1 to June 30, 2015. The spatial analysis was performed using the Moran index.

Results:

Basic support units performed most of the emergency services (89.2%). Among the victims, there was a predominance of males (76.8%) and an age group of 20 - 29 years old (31.5%). Collisions were responsible for 59.9% of the transit accidents, and motorcycles for 61.6% of the accidents among all means of transportation. Friday was the day that showed the highest risk for treatment, and there was a concentration of events between 6:00 am - 8:59am and 6:00pm - 8:59pm. The MoranMap identified critical areas where calls came from traffic accidents during the period analyzed.

Discussion:

The records of the mobile service from the spatial analysis are an important source of information for health surveillance.

Conclusion:

The spatial analysis of urban transit accidents identified regions with a positive spatial correlation, providing subsidies to the logistical planning of emergency mobile care services. This study is groundbreaking in that it offers such information about the region.

Keywords:
Traffic accidents; Emergency medical services; Spatial analysis; Health services research; Uses of epidemiology; Descriptive epidemiology

Variable	n (1,225)	%
Type of ambulance
Basic support unit	1,093	89.2
Advanced support unit	79	6.4
Not informed	53	4.3
Destination units
UPA Imbiribeira	169	13.8
UPA Caxangá	151	12.3
Restoration hospital	131	10.7
UPA Torrões	124	10.1
Other public units	266	21.7
Private units	157	12.8
Military hospitals	11	0.9
Other situations^a	67	5.5
Not informed	149	12.2
Gender
Male	941	76.8
Female	259	21.1
Not informed	25	2
Age range (in years)
0 - 9	7	0.6
10 - 19	119	9.7
20 - 29	386	31.5
30 - 39	296	24.2
40 - 49	180	14.7
50 - 59	89	7.3
60 or more	47	3.8
Not informed	101	8.2
Nature of the accident
Collision	734	59.9
Vehicle tipped or overturned	17	1.4
Crashed into fixed objects	17	1.4
Victim run over	131	10.7
Victim fell in/from the vehicle	170	13.9
Not informed	156	12.7
Means of transportation of the victim
Car	92	7.5
Motorcycle	755	61.6
On foot	83	6.8
Bicycle	43	3.5
Others	5	0.4
Not informed	247	20.2

Variable	n (1.225)	%	IR^a	RR^b	95%CI^c	p-value
Day of the week*
Sunday	175	14.3	1.10	1.14	0.92 - 1.41	0.24
Monday	183	14.9	1.15	1.19	0.96 - 1.47	0.11
Tuesday	154	12.6	0.96	1.00
Wednesday	159	13.0	0.99	1.03	0.83 - 1.29	0.77
Thursday	183	14.9	1.15	1.19	0.96 - 1.47	0.11
Friday	198	16.2	1.24	1.29	1.04 - 1.59	0.01
Saturday	173	14.1	1.08	1.12	0.90 - 1.40	0.29
Time**
12:00am- 2:59am	40	3.3	0.25	1.00
3:00am- 5:59am	41	3.3	0.26	1.02	0.66 - 1.58	1.00
6:00am- 8:59am	207	16.9	1.30	5.17	3.69 - 7.26	< 0.00
9:00am- 11:59am	160	13.1	1.00	4.00	2.83 - 5.66	< 0.00
12:00pm-2:59pm	179	14.6	1.12	4.47	3.18 - 6.30	< 0.00
3:00pm-5:59pm	186	15.2	1.16	4.65	3.30 - 6.54	< 0.00
6:00pm-8:59pm	224	18.3	1.40	5.60	4.00 - 7.84	< 0.00
9:00pm-11:59pm	142	11.6	0.89	3.55	2.50 - 5.04	< 0.00
Not informed	46	3.8

Neighborhoods	IR⁽ⁱ⁾	RR⁽ⁱⁱ⁾	95%CI⁽ⁱⁱⁱ⁾
Areias^b	7.99	1.04	0.69 - 1.57
Barro^a,b,c	4.20	0.55	0.32 - 0.93
Boa Viagem^a,b,c	4.20	0.55	0.42 - 0.72
Boa Vista^a,b	30.66	4.00	2.99 - 5.35
Bomba do Hemeterio^a,b	16.93	2.21	1.33 - 3.67
Campo Grande^a,b	23.18	3.02	2.00 - 4.57
Cidade Universitaria^a	151.93	19.82	11.52 - 34.08
Derby^a,b	124.63	16.26	11.13 - 23.76
Engenho do Meio^a,b	14.98	1.95	1.19 - 3.20
Espinheiro^a,b	12.82	1.67	0.99 - 2.83
Estancia^a	13.45	1.75	1.02 - 3.03
Ibura^a,b,c	14.35	1.87	1.49 - 2.36
Imbiribeira^b,c	8.47	1.11	0.81 - 1.51
Ipsep^b,c	8.40	1.10	0.70 - 1.70
Iputinga^a,b	4.21	0.55	0.36 - 0.83
Jardim Sao Paulo^a,b	8.16	1.06	0.71 - 1.58
Jordao^b,c	8.28	1.08	0.68 - 1.72
Recife^a	177.63	23.17	12.87 - 41.75
Santo Amaro^b	21.21	2.77	2.14 - 3.57
Santo Antonio^a	335.44	43.76	23.74 - 80.72
Município	7.67	1.00	-

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[1] Corresponding author: Marcela Franklin Salvador de Mendonça. Rua da Macaiba, 46, III Etapa, Rio Doce, Olinda, PE, Brasil. E-mail: marcelafs.mendonca@gmail.com