Hospitalization, death, and probable reinfection in Peruvian healthcare workers infected with SARS-CoV-2: a national retrospective cohort study

BACKGROUND

Peru has some of the worst outcomes worldwide as a result of the SARS-CoV-2 pandemic; it is presumed that this has also affected healthcare workers. This study aimed to establish whether occupation and other non-occupational variables were risk factors for possible reinfection, hospitalization, and mortality from COVID-19 in cohorts of Peruvian healthcare workers infected with SARS-CoV-2.

METHODS

Retrospective cohort study. Healthcare workers who presented SARS-CoV-2 infection between March 1, 2020, and August 6, 2021, were included. Occupational cohorts were reconstructed from the following sources of information: 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 technicians and health assistants, nursing staff, midwives, dentists, doctors, and other healthcare workers. We evaluated whether the occupation and other non-occupational variables were risk factors for probable reinfection, hospitalization, and death from COVID-19 using log-binomial and probit binomial models, obtaining the adjusted relative risk (RR_AJ).

RESULTS

90,398 healthcare workers were included in the study. Most cases were seen in technicians and health assistants (38.6%), and nursing staff (25.6%). 8.1% required hospitalization, 1.7% died from COVID-19, and 1.8% had probable reinfection. A similar incidence of probable reinfection was found in the six cohorts (1.7-1.9%). Doctors 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 (age and comorbidities). The multivariate analysis found that doctors (RR_AJ = 1.720; CI 95: 1.569-1.886) had a higher risk of hospitalization and that the occupation of technician and health assistant was the only one that constituted a risk factor for mortality from COVID-19 (RR_AJ = 1.256; 95% CI: 1.043-1.512).

CONCLUSIONS

Peruvian technicians and health assistants would have a higher risk of death from COVID-19 than other healthcare workers, while doctors have a higher incidence of death probably linked to the high frequency of non-occupational risk factors. Doctors present a higher risk of hospitalization independent of comorbidities and age; likewise, all occupations show a similar risk of probable reinfection.

Epidemiological Characteristics of Deaths from COVID-19 in Peru during the Initial Pandemic Response

BACKGROUND AND AIM

Peru is the country with the highest mortality rate from COVID-19 globally, so the analysis of the characteristics of deaths is of national and international interest. The aim was to determine the epidemiological characteristics of deaths from COVID-19 in Peru from 28 March to 21 May 2020.

METHODS

Deaths from various sources were investigated, including the COVID-19 Epidemiological Surveillance and the National System of Deaths (SINADEF). In all, 3851 deaths that met the definition of a confirmed case and had a positive result of RT-PCR or rapid test IgM/IgG, were considered for the analysis. We obtained the epidemiological variables and carried out an analysis of time defined as the pre-hospital time from the onset of symptoms to hospitalization, and hospital time from the date of hospitalization to death.

RESULTS

Deaths were more frequent in males (72.0%), seniors (68.8%) and residents of the region of Lima (42.7%). In 17.8% of cases, the death occurred out-of-hospital, and 31.4% had some comorbidity. The median of pre-hospital time was 7 days (IQR: 4.0-9.0) and for the hospital time was 5 days (IQR: 3.0-9.0). The multivariable analysis with Poisson regression with robust variance found that the age group, comorbidity diagnosis and the region of origin significantly influenced pre-hospital time; while sex, comorbidity diagnosis, healthcare provider and the region of origin significantly influenced hospital time.

CONCLUSION

Deaths occurred mainly in males, seniors and on the coast, with considerable out-of-hospital deaths. Pre-hospital time was affected by age group, the diagnosis of comorbidities and the region of origin; while, hospital time was influenced by gender, the diagnosis of comorbidities, healthcare provider and the region of origin.

Epidemiological and transmissibility analysis of influenza A(H1N1)v in a southern hemisphere setting: Peru

We present a preliminary analysis of 1,771 confirmed cases of influenza A(H1N1)v reported in Peru by 17 July including the frequency of the clinical characteristics, the spatial and age distribution of the cases and the estimate of the transmission potential. Age-specific frequency of cases was highest among school age children and young adults, with the lowest frequency of cases among seniors, a pattern that is consistent with reports from other countries. Estimates of the reproduction number lie in the range of 1.2 to 1.7, which is broadly consistent with previous estimates for this pandemic in other regions. Validation of these estimates will be possible as additional data become available.

Epidemiological and transmissibility analysis of influenza A(H1N1)v in a southern hemisphere setting: Peru

We present a preliminary analysis of 1,771 confirmed cases of influenza A(H1N1)v reported in Peru by 17 July including the frequency of the clinical characteristics, the spatial and age distribution of the cases and the estimate of the transmission potential. Age-specific frequency of cases was highest among school age children and young adults, with the lowest frequency of cases among seniors, a pattern that is consistent with reports from other countries. Estimates of the reproduction number lie in the range of 1.2 to 1.7, which is broadly consistent with previous estimates for this pandemic in other regions. Validation of these estimates will be possible as additional data become available.