Severe Acute Respiratory Syndrome in Pernambuco: comparison of patterns before and during the COVID-19 pandemic

Silva, Amanda Priscila de Santana Cabral; Maia, Lívia Teixeira de Souza; Souza, Wayner Vieira de

doi:10.1590/1413-812320202510.2.29452020

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ARTICLE • Ciênc. saúde coletiva 25 (suppl 2) • Oct 2020 • https://doi.org/10.1590/1413-812320202510.2.29452020 copy

Severe Acute Respiratory Syndrome in Pernambuco: comparison of patterns before and during the COVID-19 pandemic

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The aim of this study was to analyze the Severe Acute Respiratory Syndrome (SARS) pattern in Pernambuco before and during a COVID-19 pandemic. Ecological study conducted from January to June, 2015 to 2019 and from January 1 to June 15, 2020. The detection rates by municipality and by Regional Health of residence were calculated. The spatial area of SARS was estimated through the risk ratio. Before the pandemic, there were 5,617 cases of SARS, 187 cases/month and 23.8 cases/100 thousand inhabitants, while during the pandemic there were 15,100 cases, 2,516 cases/month and 320.3 cases/100 thousand inhabitants, which represents a 13-fold increase in detection. The following expanded (p < 0,001): the occurrence in elderly people, the collection of samples and the identification of SARS etiological agent with predominance of SARS by COVID-19. Most municipalities experienced a 20-fold higher detection than expected, suggesting a process of virus spread to the hinterlands. The excess risk associate with lower IDHM, the condition of the municipality being the headquarters of the Regional Health and the presence of a highway in the municipality. The change in the pattern of occurrence of SRAG, combined with Spatial analysis may contribute to action planning at different levels of management.

Key words
COVID-19; Pandemics; Epidemiological monitoring; Spatial analysis

Analyzed variables	Pre-pandemic^* * January 1 to June 30 of 2015 to 2019,		Pandemic^ January 1 to June 15, 2020.		X²; P-value
Analyzed variables	n	%	n	%	X²; P-value
Age group
< 10	4,417	78.6	1,146	7.6	10,670; p<0.001
10_39	489	8.7	2,357	15.6
40_59	277	4.9	4,297	28.5
≥ 60	434	7.7	7,300	48.3
Gender
Male	2,989	53.2	8,011	53.1	0.014; p=0.906
Female	2,627	46.8	7,067	46.9
Sample collected
Yes	3,364	60.9	11,649	96.5	70.6; p<0.001
No	2,160	39.1	417	3.5
Final classification
SARS due to Influenza and other respiratory viruses	628	11.5	196	2.5	5,960; p<0.001
SARS due to another etiological agent	18	0.3	28	0.4
SARS due to COVID	0	0.0	5,235	66.0
SARS unspecified	4,807	88.2	2,470	31.2

	Variables	β	Wald	Valor de p	OR	IC95%
Initial Model	Municipal Human Development Index	Wald	P-value	OR	95%CI	0,789 - 0,943
	Proportion of vulnerable to poverty	-0.148	10.604	0.001	0.863	0.789 - 0.943
	Proportion of extremely poor	0.019	0.048	0.692	1.019	0.927 - 1.120
	Presence of federal highway	-0.013	0.081	0.776	0.987	0.904 - 1.078
	No	Benchmark category			1.0
	Yes	0.550	2.912	0.088	1.733	0.922 - 3.259
	Regional Health Office Headquarters
	No	Benchmark category			1.0
	Yes	1.780	5.658	0.017	5.929	1.368 - 25.700
	Final Model	β	Wald	Valor de p	OR	IC95%
Final Model	Municipal Human Development Index	-0.150	12.420	0.001	0.861	0.792 - 0.936
	Presence of federal highway
	No	Benchmark category			1.0
	Yes	0.547	2.908	0.088	1.728	0.922 - 3.240
	Regional Health Office Headquarters
	No	Benchmark category			1.0
	Yes	1.760	5.627	0.018	5.810	1.358 - 24.864

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E-mail: cienciasaudecoletiva@fiocruz.br

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[1] Collaborations

APSC Silva and WV Souza contributed substantially to the design of the manuscript, analysis and interpretation of data, critical review of the content. LTS Maia contributed significantly to the interpretation of data and critical review of the content; all authors approved the final version of the manuscript.