Impact of the Dry Law on road traffic mortality in Brazilian states: an interrupted time series analysis

Nunes, Hélio Rubens de Carvalho; Murta-Nascimento, Cristiane; Lima, Maria Cristina Pereira

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Revista Brasileira de Epidemiologia

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ORIGINAL ARTICLE • Rev. bras. epidemiol. 24 • 2021 • https://doi.org/10.1590/1980-549720210045 copy

Impact of the Dry Law on road traffic mortality in Brazilian states: an interrupted time series analysis

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Objective:

To assess the impact of 2008 Public Law number 11,705, also known as Dry Law (DL-08), on mortality from road traffic accidents (RTA), in each of the 27 Brazilian Federative Units (BFUs).

Methods:

Ecological study of interrupted time series with RTA data from 2002 to 2015, totalizing 168 months. Data were obtained from the Mortality Information System, the Brazilian Institute of Geography and Statistics, and from the National Traffic Department. Autoregressive integrated moving average (ARIMA) models were adjusted to assess the impact of DL-08 in each BFUs.

Results:

After the implementation of the DL-08, there was a significant decrease in mortality from RTA in the state of Santa Catarina (pre DL-08 = 2.60 ± 0.30 and post DL-08 = 2.32 ± 0.35; p < 0.001) and in the Federal District (pre DL-08 = 2.22 ± 0.40 and post DL-08 = 1.76 ± 0.35; p = 0.002), a significant increase in mortality in the states of Acre, Amazonas, Rondônia, Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, Pernambuco, Alagoas, Sergipe and Mato Grosso, and a stability in the other states. The sensitivity analysis conducted over a shorter time series with 24 months showed results similar to those obtained with the 168-month series for most of the 27 BFUs.

Conclusion:

The DL-08 had a heterogeneous impact on mortality from traffic accidents on BFUs.

Keywords:
Interrupted time series analysis; Accidents traffic; Time series studies. Ecological studies

FU	Pre DL-08	Post DL-08	Nt*	DL-08	p value^&
FU	(n = 78)	(n = 90)	Nt*	w₀ ^# (p^$)	p value^&
North
Acre	1.22 ± 0.45	1.50 ± 0.57	ARMA (3,2)	0.22 (0.005)	0.345
Amapá	1.56 ± 0.62	1.42 ± 0.62	ARMA (2,2)	-0.16 (0.068)	0.191
Amazonas	0.91 ± 0.24	1.00 ± 0.23	MA (1)	0.08 (0.030)	0.231
Pará	1.10 ± 0.20	1.51 ± 0.31	AR (1)	0.07 (0.204)	0.195
Rondônia	1.96 ± 0.47	2.75 ± 0.67	ARMA (1,1)	0.71 (< 0.001)	0.691
Roraima	2.25 ± 0.99	2.53 ± 0.99	IMA (1,1)	0.08 (0.798)	0.677
Tocantins	2.51 ± 0.62	3.00 ± 0.68	ARMA (3,3)	0.18 (0.315)	0.645
Northeast
Alagoas	1.61 ± 0.41	1.97 ± 0.43	ARMA (1,1)	0.31 (0.008)	0.771
Bahia	0.93 ± 0.19	1.37 ± 0.25	IMA (1,1)	0.01 (0.948)	0.216
Ceará	1.67 ± 0.23	2.08 ± 0.40	ARMA (1,1)	0.35 (< 0.001)	0.146
Maranhão	1.09 ± 0.25	1.85 ± 0.39	MA (1)	0.22 (0.004)	0.817
Paraíba	1.49 ± 0.32	1.95 ± 0.39	AR (1)	0.13 (< 0.001)	0.174
Pernambuco	1.41 ± 0.18	1.73 ± 0.23	ARMA (2,3)	0.30 (< 0.001)	0.252
Piauí	1.80 ± 0.44	2.93 ± 0.59	AR (1)	0.34 (0.007)	0.467
Rio Grande do Norte	1.20 ± 0.27	1.46 ± 0.28	MA (0,0,1)	0.25 (< 0.001)	0.994
Sergipe	1.77 ± 0.48	2.26 ± 0.47	ARMA (2,2)	0.30 (< 0.001)	0.163
Mid-West
Distrito Federal	2.22 ± 0.40	1.76 ± 0.35	MA (1)	-0.08 (0.002)	0.326
Goiás	2.21 ± 0.33	2.52 ± 0.35	ARMA (1,1)	0.20 (0.085)	0.074
Mato Grosso	2.59 ± 0.46	3.02 ± 0.48	MA (2)	0.41 (< 0.001)	0.760
Mato Grosso do Sul	2.44 ± 0.42	2.59 ± 0.47	ARMA (1,1)	0.04 (0.741)	0.910
Southeast
Espírito Santo	2.28 ± 0.38	2.34 ± 0.38	AR (2)	0.003 (0.972)	0.410
Minas Gerais	1.48 ± 0.22	1.75 ± 0.19	IMA (1,1)	0.01 (0.904)	0.503
Rio de Janeiro	1.55 ± 0.18	1.38 ± 0.19	ARIMA (2,1,1)	-0.10 (0.341)	0.256
São Paulo	1.46 ± 0.13	1.34 ± 0.15	ARMA (4,7)	-0.06 (0.627)	0.111
South
Paraná	2.43 ± 0.30	2.46 ± 0.33	IMA (0,1,2)	-0.01 (0.894)	0.119
Rio Grande do Sul	1.58 ± 0.18	1.52 ± 0.19	IMA (1,1)	-0.18 (0.060)	0.295
Santa Catarina	2.60 ± 0.30	2.32 ± 0.35	ARMA (2,2)	-0.08 (< 0.001)	0.921

	Impact of DL-08
	48-month series	168-month series
	w₀* (p value^#)	w₀ ^$ (p value^&)
${\hat{w}}_{0}$ in the same direction and significant p value in both time series
Santa Catarina	-0.09 (< 0.001)	-0.08 (< 0.001)
Distrito Federal	-0.07 (0.005)	-0.08 (0.002)
Rondônia	0.60 (0.001)	0.71 (< 0.001)
Pernambuco	0.19 (< 0.001)	0.30 (< 0.001)
Sergipe	0.28 (< 0.001)	0.30 (< 0.001)
Mato Grosso	0.51 (< 0.001)	0.41 (< 0.001)
Stable TA mortality after DL-08 in both time series
Amapá	0.03 (0.806)	-0.16 (0.068)
Pará	0.19 (0.202)	0.07 (0.204)
Roraima	-0.13 (0.400)	0.08 (0.798)
Bahia	0.002 (0.983)	0.01 (0.948)
Mato Grosso do Sul	0.01 (0.892)	0.04 (0.741)
Espírito Santo	-0.21 (0.222)	0.003 (0.972)
Minas Gerais	0.02 (0.757)	0.01 (0.904)
Rio de Janeiro	-0.14 (0.165)	-0.10 (0.341)
São Paulo	-0.06 (0.634)	-0.06 (0.627)
Rio Grande do Sul	-0.22 (0.053)	-0.18 (0.060)
${\hat{w}}_{0}$ in the same direction and significant p value only in the longer time series
Acre	0.15 (0.123)	0.22 (0.005)
Ceará	0.08 (0.480)	0.35 (< 0.001)
Maranhão	0.22 (0.051)	0.22 (0.004)
Paraíba	0.06 (0.138)	0.13 (< 0.001)
Rio Grande do Norte	0.10 (0.189)	0.25 (< 0.001)
${\hat{w}}_{0}$ in opposite directions and significant p value only in the longer time series
Amazonas	-0.09 (0.153)	0.08 (0.030)
Alagoas	-0.03 (0.780)	0.31 (0.008)
Piauí	-0.09 (0.631)	0.34 (0.007)
${\hat{w}}_{0}$ in the same direction and significant p value only in the shorter time series
Goiás	0.29 (<0.001)	0.20 (0.085)
Paraná	-0.26 (0.017)	-0.01 (0.894)
${\hat{w}}_{0}$ in opposite directions and significant p value only in the shorter time series
Tocantins	-0.63 (0.009)	0.18 (0.315)

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[1] Corresponding author: Hélio Rubens de Carvalho Nunes. Faculdade de Medicina de Botucatu, Universidade Estadual Paulista “Júlio de Mesquita Filho”. Av. Prof. Mário Rubens Guimarães Montenegro, s/n., Vila Paraíso, CEP: 18618-687, Botucatu, SP, Brazil. E-mail: hrcn@outlook.com.br