Mortality from chronic respiratory disease in Brazil: time trend and forecasts

Oliveira, Marcio Sacramento de; Montovani, Elisa Hypólito; Santana, Maria de Fátima Ebole de; Ponce de Leon, Antonio Carlos Monteiro; Marques, Márcio Candeias

doi:10.11606/s1518-8787.2022056003672

Brasil

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Original Article • Rev. Saúde Pública 56 • 2022 • https://doi.org/10.11606/s1518-8787.2022056003672 linkcopy

Mortality from chronic respiratory disease in Brazil: time trend and forecasts

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

OBJECTIVE To analyze the time trend of monthly mortality rates from chronic respiratory diseases in Brazil from 1996 to 2017, with forecasts for 2022, besides analyzing the possibility of achieving the goal of the Plano de Ações Estratégicas para o Enfrentamento das Doenças Crônicas Não Transmissíveis no Brasil (Strategic Action Plan to Tackle Chronic Noncommunicable Diseases in Brazil) from 2011 to 2022.

METHODS This is an ecological study that uses data from Sistema de Informações sobre Mortalidade (SIM – Mortality Information System), Sistema de Informações Demográficas e Socioeconômicas (Demographic and Socioeconomic Information System) and Pesquisa Nacional por Amostra de Domicílios Contínua (PNAD Contínua – Continuous National Household Sample Survey). We established the age range between 30 and 69 years old and the evolution of the rates over time was made by autoregressive integrated moving average models in R statistical tool.

RESULTS Premature mortality rates from chronic respiratory diseases are decreasing in Brazil as a whole, mostly in state capitals. There is also a trend to reach the Ministry of Health’s goal in most of the country. For capitals that tend not to reach the goal, there is an association between mortality and social indicators, healthcare network and frequency of smoking.

CONCLUSION This study intends to improve planning of the public health system for the control of chronic respiratory diseases.

Respiratory Tract Diseases, mortality; Chronic Disease Indicators; Health Programs and Plans, trends; Ecological Studies

Location	Min.	Q1	Median	Mean	Q3	Max.	SD
Brasil	1.32	1.88	2.19	2.33	2.72	4.59	0.59
Belém	0.60	2.02	2.72	3.05	3.82	8.38	1.44
Boa Vista	0.00	0.00	1.34	1.87	2.71	15.04	2.33
Macapá	0.26	0.99	1.50	1.59	2.02	5.32	0.78
Manaus	0.31	1.23	1.83	1.92	2.46	4.82	0.88
Palmas	0.00	0.00	0.93	1.74	2.85	20.09	2.60
Porto Velho	0.00	1.43	2.66	3.18	4.23	12.54	2.40
Rio Branco	0.00	1.67	3.22	3.40	4.87	12.71	2.28
Aracaju	0.00	1.08	1.92	2.33	3.42	7.83	1.56
Fortaleza	0.44	1.46	2.10	2.33	3.04	5.66	1.06
João Pessoa	0.00	1.32	1.92	2.14	2.80	7.29	1.20
Maceió	0.35	1.61	2.45	2.78	3.64	8.61	1.50
Natal	0.00	0.79	1.21	1.33	1.71	4.25	0.80
Recife	1.21	2.34	2.88	2.99	3.57	6.22	0.90
Salvador	0.72	2.24	3.41	3.54	4.63	8.94	1.56
São Luís	0.00	1.19	1.77	1.97	2.50	5.72	1.08
Teresina	0.00	1.07	1.70	1.88	2.50	7.08	1.14
Brasília	0.26	0.99	1.50	1.59	2.02	5.32	0.78
Campo Grande	0.00	1.33	2.02	2.10	2.74	6.12	1.05
Cuiabá	0.00	1.44	2.38	2.59	3.39	9.43	1.55
Goiânia	0.58	1.79	2.46	2.61	3.19	6.63	1.14
Belo Horizonte	0.52	1.18	1.68	1.92	2.46	6.25	0.97
Rio de Janeiro	1.20	1.83	2.24	2.42	2.88	5.15	0.78
São Paulo	1.13	1.70	2.12	2.25	2.73	5.07	0.70
Vitória	0.00	0.56	1.14	1.29	1.90	3.94	0.97
Curitiba	0.35	1.26	1.93	2.23	2.93	7.88	1.31
Florianópolis	0.00	0.93	1.62	1.92	2.59	7.09	1.32
Porto Alegre	0.93	2.08	2.73	3.06	3.88	9.90	1.43

Location	Equation	Sigma2	MAPE (%)	Trend
Brasil	∆ẑ_t = 0,5785∆z_t–1 – 0,9253u_t–1 – 0,4879u_t–12 –0,2251u_t–13	0.02	2.94	Decreasing
Belém	∆ẑ_t = –0,4177∆z_t–1 – 0,3695u_t–1 – 0,4865u_t–2 – 0,8696∆z_t–12 + 0,0281u_t–12 – 0,7212u_t–13 – 0,0964u_t–14 – 0,2105u_t–15	1.06	29.16	Increasing
Boa Vista	∆ẑ_t = –0,8365∆z_t–1 – 0,1131u_t–1 – 0,8126u_t–2 + 0,2129∆z_t–12 – 0,1860u_t–12 – 0,0713u_t–2	2.34	39.13	Decreasing
Macapá	∆ẑ_t = –0,1152∆z_t–1 –0,9375u_t–1 –0,610∆z_t–12 + 0,6822u_t–12 + 0,1924u_t–13 –0,0045x_t	1.79	41.33	Decreasing
Manaus	∆ẑ_t = –0,0755∆z_t–1–0,9305u_t–1 + 0,8866∆z_t–12 –0,8506u_t–12 + 0,1254u_t–13 –0,0042x_t	0.58	44.37	Decreasing
Palmas	∆ẑ_t = –0,6530∆z_t–1 – 0,2855u_t–1 – 0,6947u_t–2 + 0,1092u_t–3 – 0,1290u_t–4 – 0,4751∆z_t–12	3.69	29.42	Increasing
Porto Velho	∆ẑ_t = –0,8657u_t–1–0,1249u_t–12	2.86	69.79	NA
Rio Branco	∆ẑ_t = –0,7347∆z_t–1 – 0,5022∆z_t–2 – 0,3746∆z_t–3 – 0,2346∆z_t–4	5.23	83.79	NA
Aracaju	∆ẑ_t = –0,0973∆z_t–1 + 0,0956∆z_t–2 – 0,2061∆z_t–3 – 0,8573u_t–1	1.52	40.99	Decreasing
Fortaleza	∆ẑ_t = 0,2635∆z_t–1 – 0,9302u_t–1 – 0,0085x_t	0.48	28.87	Decreasing
João Pessoa	∆ẑ_t = –0,7849∆z_t–1 – 0,0480u_t–1 – 0,7135u_t–2 – 0,0083x_t	0.92	49.62	Decreasing
Maceió	∆ẑ_t = –0,8756∆z_t–1 + 0,0225∆z_t–12 – 0,0163u_t–12 – 0,0104x_t	1.16	34.27	Decreasing
Natal	∆ẑ_t = –0,8459u_t–1	0.42	91.66	NA
Recife	∆ẑ_t = –0,8636u_t–1 – 0,7568∆z_t–12 – 0,0654∆z_t–13 + 0,7071u_t–12	0.76	22.81	Increasing
Salvador	∆ẑ_t = 0,2305∆z_t–1 – 0,9429u_t–1 – 0,0161x_t	0.60	28.55	Decreasing
São Luís	∆ẑ_t = 0,1169∆z_t–1 – 0,6843∆z_t–2 – 0,9558u_t–1 + 0,5828u_t–2 – 0,4866u_t–3 – 0,0225u_t–4 – 0,0824u_t–5	0.63	35.33	Increasing
Teresina	∆ẑ_t = 0,1373∆z_t–1 – 0,9492u_t–1 + 0,1342∆z_t–12 – 0,0171∆z_t–13	0.70	104.75	NA
Brasília	∆ẑ_t = –0,1638∆z_t–1 – 0,8374∆z_t–2 – 0,0663∆z_t–3 – 0,4886∆z_t–4 – 0,6258u_t–1 + 0,9044u_t–2 – 0,9111u_t–3 + 0,4809u_t–4 – 0,7722u_t–5 + 0,0348∆z_t–12 + 0,1801∆z_t–13 – 0,0076x_t	0.27	41.60	Decreasing
Campo Grande	∆ẑ_t = –0,7989∆z_t–1 + 0,1317∆z_t–2 + 0,1151∆z_t–3–0,1016u_t–1–0,8287u_t–2 + 0,0206∆z_t–12 + 0,0883∆z_t–13	0.78	33.33	Increasing
Cuiabá	∆ẑ_t = –0,9197u_t–1	1.40	162.21	NA
Goiânia	∆ẑ_t = 0,0376∆z_t–1 + 0,1849∆z_t–2 – 0,8867u_t–1 – 0,6050∆z_t–12 – 0,2614∆z_t–13	0.86	33.58	Decreasing
Belo Horizonte	∆ẑ_t = –0,6022∆z_t–1 – 0,8742∆z_t–2 – 0,2019u_t–1 + 0,3535u_t–2 – 0,7581u_t–3 – 0,2362u_t–4 + 0,2448∆z_t–12 – 0,010x_t	0.28	25.10	Decreasing
Rio de Janeiro	∆ẑ_t = 0,3222∆z_t–1 + 0,1314∆z_t–2 – 0,9127u_t–1 + 0,1836∆z_t–12 – 0,0110x_t	0.15	13.81	Decreasing
São Paulo	∆ẑ_t = 0,3580∆z_t–1 – 0,9188u_t–1 + 0,2552u_t–12 + 0,2628u_t–13 – 0,0071x_t	0.12	12.93	Decreasing
Vitória	∆ẑ_t = –0,7866∆z_t–1 – 0,1029∆z_t–2 – 0,1812u_t–1 – 0,7338u_t–2 + 0,4307∆z_t–12 – 0,4943u_t–12	0.91	47.10	Increasing
Curitiba	∆ẑ_t = 0,02542∆z_t–1 – 0,9633u_t–1 – 0,7916∆z_t–12 + 0,139u_t–12 – 0,5454u_t–13	0.52	35.63	Increasing
Florianópolis	∆ẑ_t = –0,9353u_t–1 – 0,8766u_t–12	1.10	37.01	Decreasing
Porto Alegre	∆ẑ_t = 0,0204∆z_t–1 – 0,7605∆z_t–2 – 0,6562u_t–1 + 0,6890u_t–2 – 0,7344u_t–3 – 0,1944u_t–4 + 0,3525∆z_t–12 + 0,2395∆z_t–13	0.80	34.00	Decreasing

Location	Standardized rate, 2011	Standardized rate, 2022 (National Plan)	Standardized rate, 2022 (expected)	Expected variation % (2011–2022)	National Plan’s Goal
Brazil	23.65	18.94	17.45	-26.22	Tends to reach
Belém	23.11	18.51	14.28	-38.19	Tends to reach
Boa Vista	10.04	8.04	13.63	+35.80	Tends not to reach
Macapá	14.85	11.89	11.03	-25.69	Tends to reach
Manaus	20.51	16.42	10.69	-47.86	Tends to reach
Palmas	18.69	14.97	10.28	-44.98	Tends to reach
Porto Velho	30.51	24.43	NA	NA	NA
Rio Branco	36.76	29.43	NA	NA	NA
Aracaju	18.40	14.74	7.94	-56.87	Tends to reach
Fortaleza	19.23	15.40	7.28	-62.14	Tends to reach
João Pessoa	21.74	17.41	6.22	-71.39	Tends to reach
Maceió	28.71	22.99	4.78	-83.35	Tends to reach
Natal	13.35	10.69	NA	NA	NA
Recife	35.64	28.54	32.43	-9.01	Tends not to reach
Salvador	33.30	26.66	4.86	-84.41	Tends to reach
São Luís	16.28	13.04	17.01	+4.50	Tends not to reach
Teresina	20.37	16.31	NA	NA	NA
Brasília	13.94	11.16	3.11	-77.70	Tends to reach
Campo Grande	19.92	15.95	18.11	-9.13	Tends not to reach
Cuiabá	21.84	17.49	NA	NA	NA
Goiânia	22.96	18.38	8.30	-63.84	Tends to reach
Belo Horizonte	15.72	12.59	2.72	-82.67	Tends to reach
Rio de Janeiro	24.58	19.68	8.94	-63.61	Tends to reach
São Paulo	22.47	17.99	11.55	-48.62	Tends to reach
Vitória	15.11	12.10	12.41	-17.86	Tends not to reach
Curitiba	16.57	13.27	7.79	-52.97	Tends to reach
Florianópolis	17.56	14.06	6.28	-64.20	Tends to reach
Porto Alegre	30.89	24.73	23.25	-24.73	Tends to reach

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[TFN1] Q1: first quartile; Q3: third quartile; SD: standard deviation.