Cerebrovascular mortality: trend and seasonality in Brazilian capitals, 2000–2019

Romero, Luis Sauchay; Jacobson, Ludmilla da Silva Viana; Hacon, Sandra de Souza

doi:10.11606/s1518-8787.2023057004813

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Sumário

Original Article • Rev. Saúde Pública 57 • 2023 • https://doi.org/10.11606/s1518-8787.2023057004813 copy

Cerebrovascular mortality: trend and seasonality in Brazilian capitals, 2000–2019

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

OBJECTIVE

To evaluate the trend and seasonality of cerebrovascular mortality rates in the adult population of Brazilian capitals from 2000 to 2019.

METHODS

This is an ecological and descriptive study of a time series of mortality due to cerebrovascular causes in adults (≥ 18 years) living in Brazilian capitals from 2000 to 2019, based on the Brazilian Mortality Information System. Descriptive statistical techniques were applied in the exploratory analysis of data and in the summary of specific, standardized rates and ratios by sociodemographic characteristics. The jointpoint regression model was used to estimate the trend of cerebrovascular mortality rates by gender, age groups, and geographic regions. The seasonal variability of rates by geographic regions was estimated using the generalized additive model by smoothing cubic splines.

RESULTS

People aged over 60 years comprised 77% of all cerebrovascular deaths. Women (52%), white individuals (47%), single people (59%), and those with low schooling (57%, elementary school) predominated in our sample. Recife (20/1,000 inhab.) and Vitória (16/1,000 inhab.) showed the highest crude mortality rates. Recife (49/10,000 inhab.) and Palmas (47/10,000 inhab.) prevailed after we applied standardized rates. Cerebrovascular mortality rates in Brazil show a favorable declining trend for adults of all genders. Seasonality influenced rate increase from July to August in almost all region capitals, except in the North, which rose in March, April, and May.

CONCLUSIONS

Deaths due to cerebrovascular causes prevailed in older single adults with low schooling. The trend showed a tendency to decline and winter, the greatest risk. Regional differences can support decision-makers in implementing public policies to reduce cerebrovascular mortality.

Mortality; Seasons; Cerebral Hemorrhage; Brain Ischemia

Capitals	Population 2010	CBVD mortality	Death ratio				MR of the period	Age-standardized MR

	(≥ 18 years old)	n (%)	By gender	By race	By schooling	By marital status	(× 10,000 inhabitants)	(× 10,000 inhabitants)
South
Porto Alegre	1,084,136	28,808 (4.86)	0.79	4.29	2.17	1.63	13.29	29.02
Florianópolis	327,561	4,121 (0.69)	0.9	10.79	2.48	1.22	6.29	17.8
Curitiba	1,320,385	19,415 (3.27)	0.92	8.66	2.5	1.22	7.35	20.49
Southeast
São Paulo	8,411,089	120,124 (20.25)	0.91	2.31	3.08	1.43	7.14	18.19
Rio de Janeiro	4,815,996	91,987 (15.51)	0.84	1.3	2.32	1.71	9.55	20.62
Belo Horizonte	1,818,852	31,474 (5.31)	0.86	0.78	3.7	1.52	8.65	21.06
Vitória	250,027	8,158 (1.38)	0.97	0.98	4.05	1.32	16.31	41.65
Northeast
Salvador	1,993,228	29,170 (4.92)	0.79	0.33	2.55	1.52	7.32	22.92
Aracaju	414,052	11,394 (1.92)	0.94	0.42	5.19	1.51	13.76	44.05
Maceió	648,527	17,832 (3.01)	0.98	0.5	5.84	1.36	13.75	44.79
Recife	1,140,476	45,149 (7.61)	0.92	0.55	3.6	1.48	19.79	49.32
João Pessoa	526,510	12,836 (2.16)	0.93	0.65	4.71	1.13	12.19	35.4
Natal	585,879	11,411 (1.92)	0.98	0.94	5.16	1.22	9.74	28.11
Fortaleza	1,762,994	30,754 (5.18)	0.94	0.5	4.6	1.12	8.72	26.63
Teresina	579,481	14,511 (2.45)	1	0.2	9.22	0.96	12.52	42.19
São Luis	717,173	14,416 (2.43)	0.96	0.44	4.08	1.19	10.05	36.36
North
Palmas	154,133	2,570 (0.43)	1.11	0.35	7.24	0.99	8.34	46.56
Belém	992,891	21,525 (3.63)	0.95	0.32	3.01	1.08	10.84	33.35
Macapá	247,698	2,643 (0.45)	1.22	0.35	5.18	1.68	5.34	24.53
Boa Vista	181,411	1,949 (0.33)	1.31	0.39	2.91	1.28	5.37	24.37
Manaus	1,185,261	13,041 (2.20)	1.04	0.43	3.19	1.38	5.5	23.12
Rio Branco	217,280	3,024 (0.51)	1.19	0.35	6.07	1.38	6.96	26.88
Porto Velho	289,577	4,150 (0.70)	1.19	0.54	6.12	0.76	7.17	30.32
Midwest
Cuiabá	395,410	6,688 (1.13)	1.12	0.46	3.25	1.31	8.46	27.28
Campo Grande	566,483	10,324 (1.74)	1.06	1.04	3.96	1.51	9.11	26.06
Goiânia	966,773	17,910 (3.02)	1.04	1.25	3.77	1.2	9.26	28.63
Brasília	1,830,065	17,789 (3.00)	0.95	1.02	2.97	1.58	4.86	17.63
Brazil
Brazil	33,423,348	593,173 (100)	0.91	1.05	3.17	1.41	8.87	24.23

Region (Capitals)	Age	Female				Male

		Period	Year(s) of trend change	APC (95%CI)	AAPC (95%CI)	Period	Year(s) of trend change	APC (95%CI)	AAPC (95%CI)
South (Florianópolis; Porto Alegre; Curitiba)	18–29	2000–2019			–5.8 (-17.2 to 7.2)	2000–2019			-1.1 (-3.9 to 1.9)
		2000–2007	2007	-19.0* (-27.6 to -9.3)		2000–2019		-1.1 (-3.9 to 1.9)
		2007–2010	2010	26.4 (-45.7 to 194.5)
		2010–2019		-3.9 (-11.1 to 3.8)
	30–59	2000–2019			-5.8* (-6.4 to -5.1)	2000–2019			-4.8* (-6.1 to -3.4)
		2000–2019		-5.8* (-6.4 to -5.1)		2000–2004	2004	3.4 (-1.5 to 8.6)
						2004–2013	2013	-8.5* (-10.1 to -7.0)
						2013–2019		-4.2* (-6.7 to -1.7)
	≥ 60	2000–2019			-3.9* (-4.7 to -3.0)	2000–2019			-3.9* (-4.9 to -2.8)
		2000–2007	2007	-1.2 (-3.3 to 0.9)		2000–2004	2004	0.0 (-4.9 to 5.2)
		2007–2019		-5.4* (-6.3 to -4.5)		2004–2019		-4.9* (-5.5 to -4.2)
	≥ 18	2000–2019			-4.0* (-4.8 to -3.2)	2000–2019			-4.0* (-4.9 to -3.0)
		2000–2007	2007	-1.5 (-3.5 to 0.5)		2000–2004	2004	0.1 (-4.4 to 4.8)
		2007–2019		-5.4* (-6.8 to -4.6)		2004–2019		-5.0* (-5.6 to -4.4)
Southeast (São Paulo; Rio de Janeiro; Belo Horizonte; Vitória)	18–29	2000–2019			-1.8* (-3.0 to -0.5)	2000–2019			-2.5* (-4.0 to -1.0)
	18–29	2000–2019		-1.8* (-3.0 to -0.5)				-2.5* (-4.0 to -1.0)
	30–59	2000–2019			-4.4* (-4.7 to -4.1)	2000–2019			-4.0* (-4.4 to -3.6)
		2000–2019		-4.4* (-4.7 to -4.1)		2000–2007	2007	-5.2* (-6.1 to -4.2)
						2007–2019		-3.3* (-3.8 to -2.9)
	≥ 60	2000–2019			-3.3* (-3.6 to -3.0)	2000–2019			-3.4* (-3.8 to -3.0)
		2000–2019		-3.3* (-3.6 to -3.0)		2000–2009	2009	-2.7* (-3.4 to -2.0)
						2009–2017		-4.0* (-4.5 to -3.4)
	≥ 18	2000–2019			-1.8* (-2.0 to -1.5)	2000–2019			-1.8* (-2.0 to -1.7)
		2000–2019		-1.8* (-2.0 to -1.5)				-1.8* (-2.0 to -1.7)
Northeast (Salvador; João Pessoa; Maceió; Teresina; São Luis; Aracajú; Recife; Natal; Fortaleza)	18–29	2000–2019			-1.6* (-2.9 to -0.2)	2000–2019			-1.2* (-2.5 to -0.2)
		2000–2019		-1.6* (-2.9 to -0.2)				-1.2* (-2.5 a-0.2)
	30–59	2000–2019			-2.7* (-3.3 to -2.0)	2000–2019			-2.2* (-2.5 to -1.8)
Northeast (Salvador; João Pessoa; Maceió; Teresina; São Luis; Aracajú; Recife; Natal; Fortaleza)		2000–2005	2005	-0.5 (-2.8 to -1.9)		2000–2019		-2.2* (-2.5 to -1.8)
		2005–2019		-3.4* (-3.9 to -3.0)
	≥ 60	2000–2019			-2.4* (-2.8 to -2.0)	2000–2019			-2.2* (-2.6 to -1.8)
		2000–2006	2006	-0.1 (-1.4 to 1.2)		2000–2005	2005	0.4 (-1.0 to -1.8)
		2006–2019		-3.5* (-3.8 to -3.1)		2005–2019		-3.1* (-3.4 to -2.8)
	≥ 18	2000–2019			-0.5* (-0.9 to -0.0)	2000–2019			-0.5* (-0.7 to -0.3)
		2000–2006	2006	1.0 (-0.3 to 2.3)		2000–2019		-0.5* (-0.7a -0.3)
		2006–2019		-1.2* (-1.6 to -0.8)
North (Palmas; Boa Vista; Belém; Porto Velho; Manaus; Macapá; Rio Branco)	18–29	2000–2019			1.1 (-1.3 to 3.5)	2000–2019			-0.4 (-3.0 to 2.3)
		2000–2019		1.1 (-1.3 to 3.5)		2000–2019		-0.4 (-3.0 to 2.3)
	30–59	2000–2019			-2.2* (-2.7 to -1.6)	2000–2019			-2.3* (-2.8 to -1.9)
		2000–2019		-2.2* (-2.7 to -1.6)		2000–2019		-2.3* (-2.8 to -1.9)
	≥ 60	2000–2019			-1.9* (-2.4 to -1.4)	2000–2019			-1.7* (-2.4 to -1.1)
		2000–2019		-1.9* (-2.4 to -1.4)		2000–2015	2015	-1.0* (-1.4 to -0.6)
						2015–2019		-4.5* (-7.5 to -1.5)
	≥ 18	2000–2019			-0.1 (-0.5 to 0.3)	2000–2019			0.1 (-0.2 to 0.4)
				-0.1 (-0.5 to 0.3)				0.1 (-0.2 to 0.4)
Midwest (Campo Grande; Cuiabá; Goiânia; Brasília)	18–29	2000–2019			-1.4 (-3.8 to 1.2)	2000–2019			-1.2 (-3.4 to 1.0)
		2000–2019		-1.4 (-3.8 to 1.2)		2000–2019		-1.2 (-3.4a 1.0)
	30–59	2000–2019			-3.9* (-4.4 to -3.3)	2000–2019			-3.7* (-4.1 to -3.2)
		2000–2019		-3.9* (-4.4 to -3.3)		2000–2019		-3.7* (-4.1 to -3.2)
	≥ 60	2000–2019			-2.7* (-4.1 to -1.3)	2000–2019			-3.0* (-3.5 to -2.6)
		2000–2009	2009	-1.8* (-2.5 to -1.1)		2000–2019		-3.0* (-3.5 to -2.6)
		2009–2013	2013	-5.7* (-9.4 to -1.9)
		2013–2016	2016	1.0 (-6.6 to 9.3)
		2016–2019	2019	-5.0* (-8.6 to -1.2)
	≥ 18	2000–2019			-0.2* (-0.6 to -0.2)	2000–2019			-1.0* (-1.4 to -0.6)
		2000–2019		-0.2* (-0.6 to -0.2)		2000–2019		-1.0* (-1.4 to -0.6)

Macroregion	Explanatory variables	Coefficient	Effective degree of freedom	p-value	Model adjustment	GCV

					R² / DE
South	Intercept	0.7	-	< 0.001*	0.78 / 79.1%	0.0047
	f(month)	-	6.15	< 0.001*
	f(time)	-	5.90	< 0.001*
Southeast	Intercept	0.65	-	< 0.001*	0.83 / 84.3%	0.0013
	f(month)	-	7.60	< 0.001*
	f(time)	-	6.77	< 0.001*
Northeast	Intercept	0.88	-	< 0.001*	0.54 / 58.6%	0.0019
	f(month)	-	8.39	< 0.001*
	f(time)	-	8.69	< 0.001*
North	Intercept	0.58	-	< 0.001*	0.18 / 23.7%	0.0026
	f(month)	-	7.74	< 0.001*
	f(time)	-	6.69	< 0.005*
Midwest	Intercept	0.55	-	< 0.001*	0.42 / 46.3%	0.0021
	f(month)	-	8.23	< 0.001*
	f(time)	-	7.47	< 0.001*
Brazil	Intercept	0.70	-	< 0.001*	0.83 / 84.5%	0.0007
	f(month)	-	8.50	< 0.001*
	f(time)	-	5.53	< 0.001*

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[1] Correspondence: Luis Sauchay Romero Estrada de Curicica, 2,000, Apto 18 22780-192 Rio de Janeiro, RJ, Brasil E-mail: lusaurom@gmail.com