The interplay between infection risk factors of SARS-CoV-2 and mortality: a cross-sectional study from a cohort of long-term care nursing home residents.

BACKGROUND: Covid-19 pandemic has particularly affected older people living in Long-term Care settings in terms of infection and mortality. METHODS: We carried out a cross-sectional analysis within a cohort of Long-term care nursing home residents between March first and June thirty, 2020, who were ≥ 65 years old and on whom at least one PCR test was performed. Socio-demographic, comorbidities, and clinical data were recorded. Facility size and community incidence of SARS-CoV-2 were also considered. The outcomes of interest were infection (PCR positive) and death. RESULTS: A total of 8021 residents were included from 168 facilities. Mean age was 86.4 years (SD = 7.4). Women represented 74.1%. SARS-CoV-2 infection was detected in 27.7% of participants, and the overall case fatality rate was 11.3% (24.9% among those with a positive PCR test). Epidemiological factors related to risk of infection were larger facility size (pooled aOR 1.73; P < .001), higher community incidence (pooled aOR 1.67, P = .04), leading to a higher risk than the clinical factor of low level of functional dependence (aOR 1.22, P = .03). Epidemiological risk factors associated with mortality were male gender (aOR 1.75; P < .001), age (pooled aOR 1.16; P < .001), and higher community incidence (pooled aOR 1.19, P = < 0.001) whereas clinical factors were low level of functional dependence (aOR 2.42, P < .001), Complex Chronic Condition (aOR 1.29, P < .001) and dementia (aOR 1.33, P <0.001). There was evidence of clustering for facility and health area when considering the risk of infection and mortality (P < .001). CONCLUSIONS: Our results suggest a complex interplay between structural and individual factors regarding Covid-19 infection and its impact on mortality in nursing-home residents.

COVIDApp as an Innovative Strategy for the Management and Follow-Up of COVID-19 Cases in Long-Term Care Facilities in Catalonia: Implementation Study.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has caused an unprecedented worldwide public health crisis that requires new management approaches. COVIDApp is a mobile app that was adapted for the management of institutionalized individuals in long-term care facilities. OBJECTIVE: The aim of this paper is to report the implementation of this innovative tool for the management of long-term care facility residents as a high-risk population, specifically for early identification and self-isolation of suspected cases, remote monitoring of mild cases, and real-time monitoring of the progression of the infection. METHODS: COVIDApp was implemented in 196 care centers in collaboration with 64 primary care teams. The following parameters of COVID-19 were reported daily: signs/symptoms; diagnosis by reverse transcriptase-polymerase chain reaction; absence of symptoms for ≥14 days; total deaths; and number of health care workers isolated with suspected COVID-19. The number of at-risk centers was also described. RESULTS: Data were recorded from 10,347 institutionalized individuals and up to 4000 health care workers between April 1 and 30, 2020. A rapid increase in suspected cases was seen until day 6 but decreased during the last two weeks (from 1084 to 282 cases). The number of confirmed cases increased from 419 (day 6) to 1293 (day 22) and remained stable during the last week. Of the 10,347 institutionalized individuals, 5,090 (49,2%) remained asymptomatic for ≥14 days. A total of 854/10,347 deaths (8.3%) were reported; 383 of these deaths (44.8%) were suspected/confirmed cases. The number of isolated health care workers remained high over the 30 days, while the number of suspected cases decreased during the last 2 weeks. The number of high-risk long-term care facilities decreased from 19/196 (9.5%) to 3/196 (1.5%). CONCLUSIONS: COVIDApp can help clinicians rapidly detect and remotely monitor suspected and confirmed cases of COVID-19 among institutionalized individuals, thus limiting the risk of spreading the virus. The platform shows the progression of infection in real time and can aid in designing new monitoring strategies.