ABSTRACT
OBJECTIVETo determine if occupation is a risk factor for probable reinfection, hospitalization, and death from COVID-19 in Peruvian healthcare workers infected with SARS-CoV-2. MATERIAL AND METHODSRetrospective cohort study. Healthcare workers who presented SARS-CoV-2 infection between March 1, 2020 and August 9, 2021 were included. Occupational cohorts were reconstructed from the following sources of information: the National Epidemiological Surveillance System, molecular tests (NETLAB), results of serology and antigen tests (SICOVID-19), National Registry of Health Personnel (INFORHUS) and National Information System of Deaths (SINADEF). The incidence of probable reinfection, hospitalization, and death from COVID-19 was obtained in the cohorts of health auxiliaries and technicians, nursing staff, obstetricians, physicians, and other healthcare workers. We evaluated whether occupation was a risk factor for probable reinfection, hospitalization, and death from COVID-19 using a log-binomial generalized linear model, obtaining the adjusted relative risk (RR AJ). RESULTS90,672 healthcare workers were included. 8.1% required hospitalization, 1.7% died from COVID-19, and 2.0% had probable reinfection. A similar incidence of probable reinfection was found in the 5 cohorts (1.9%-2.2%). Physicians had a higher incidence of hospitalization (13.2%) and death (2.6%); however, they were also those who presented greater susceptibility linked to non-occupational variables such as age and comorbidities. The multivariate analysis found that physicians (RR=1.691; CI 95: 1.556-1.837) had a higher risk of hospitalization and that the occupation of health technician and assistant was the only one that constituted a risk factor for mortality from COVID-19 (RR =1.240; 95% CI: 1.052-1.463). CONCLUSIONSPeruvian health technicians and auxiliaries have a higher risk of death from COVID-19 linked to their occupation, while doctors have higher mortality due to non-occupational factors. Physicians had a higher risk of hospitalization independent of the presence of comorbidities and age; likewise, all occupations had a similar risk of probable reinfection.
ABSTRACT
OBJECTIVETo determine the epidemiological characteristics of deaths from COVID-19 in Peru from March 28 to May 21, 2020, 85 days after the report of the first confirmed case. MATERIAL AND METHODSCase series type study. Deaths from various sources were investigated, including the COVID-19 Epidemiological Surveillance and the National System of Deaths. Deaths that met the definition of a confirmed case and had a positive (reactive) result of RT-PCR or rapid test were considered for the analysis. From these sources, epidemiological variables were obtained and a time analysis was performed, defining as the pre-hospital time the time from the onset of symptoms to the date of hospitalization and hospital time from the date of hospitalization to the date of death. RESULTS3,244 confirmed deaths were included in the study. Deaths were more frequent in males (71.8%), elders (68.3%), residents of the department of Lima (41.8%), and others from the coast (37.7%). In 81.3% of cases, the death occurred in a public hospital, 16.0% died at home, shelter, penitentiary institution, public highway, or in transit to a hospital, and 31.1% had some comorbidity. Statistical difference was observed in pre-hospital time according to age group (p <0.001) and gender (p = 0.037), being significantly higher in adults, elders, and women. There was a statistically significant difference in hospital time according to geographic area, showing a significantly shorter time in the departments of the coast (p <0.001) and Andean region (p = 0.014) compared to Lima. The cases that were seen in private clinics (p = 0.001) survived longer than those seen in public hospitals. CONCLUSIONDeaths from COVID-19 occur mainly in male, elders, on the coast, with considerable deaths at home, in shelters, penitentiaries, public roads, or in transit to a hospital. Pre-hospital time is affected by age group and gender; while, hospital time is also influenced by the region of origin and the health care provider.
