RESUMO
BACKGROUND: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. METHODS: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. RESULTS: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. CONCLUSIONS: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.
Assuntos
COVID-19 , Programas de Rastreamento , Humanos , Haiti/epidemiologia , COVID-19/epidemiologia , COVID-19/mortalidade , Masculino , Feminino , Adulto , Pessoa de Meia-Idade , Incidência , Programas de Rastreamento/estatística & dados numéricos , Adulto Jovem , SARS-CoV-2 , Adolescente , Idoso , Fatores Socioeconômicos , Teste para COVID-19/estatística & dados numéricosRESUMO
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay. Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (râ¯=â¯0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, râ¯=â¯-0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (pâ¯<â¯0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, râ¯=â¯0.65, pâ¯<â¯0.01). With one exception -a country where isolation was not pursued-, all countries showed a negative correlation between TP and TPMI (râ¯=â¯0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.