Dataset Information

Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada.

ABSTRACT:

Importance

Nursing home residents have been disproportionately affected by coronavirus disease 2019 (COVID-19). Prevention recommendations emphasize frequent testing of health care personnel and residents, but additional strategies are needed.

Objective

To develop a reproducible index of nursing home crowding and determine whether crowding was associated with COVID-19 cases and mortality in the first months of the COVID-19 epidemic.

Design, setting, and participants

This population-based retrospective cohort study included more than 78 000 residents across more than 600 nursing homes in Ontario, Canada, and was conducted from March 29 to May 20, 2020.

Exposures

The nursing home crowding index equaled the mean number of residents per bedroom and bathroom.

Main outcomes and measures

The cumulative incidence of COVID-19 cases confirmed by a validated nucleic acid amplification assay and mortality per 100 residents; the introduction of COVID-19 into a home (≥1 resident case) was a negative tracer.

Results

Of 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78 607 residents (women, 54 160 [68.9%]; age ≥85 years, 42 919 [54.6%]). A total of 5218 residents (6.6%) developed COVID-19 infection, and 1452 (1.8%) died of COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes; 4496 infections (86%) occurred in 63 homes (10%). The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 homes (50%) had a high crowding index (≥2). Incidence in high crowding index homes was 9.7% vs 4.5% in low crowding index homes (P < .001), while COVID-19 mortality was 2.7% vs 1.3%, respectively (P < .001). The likelihood of COVID-19 introduction did not differ (high = 31.3% vs low = 30.2%; P = .79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with an increased incidence of infection (relative risk [RR] = 1.73, 95% CI, 1.10-2.72) and mortality (RR, 1.69; 95% CI, 0.99-2.87). A propensity score analysis yielded similar conclusions for infection (RR, 2.09; 95% CI, 1.30-3.38) and mortality (RR, 1.83; 95% CI, 1.09-3.08). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 998 COVID-19 cases (19.1%) and 263 deaths (18.1%).

Conclusions and relevance

In this cohort of Canadian nursing homes, crowding was common and crowded homes were more likely to experience larger and deadlier COVID-19 outbreaks.

SUBMITTER: Brown KA

PROVIDER: S-EPMC7653540 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundStudies conducted during the COVID-19 pandemic have shown that crowding in nursing homes is associated with high incidence of SARS-CoV-2 infections, but this effect has not been shown for other respiratory pathogens. We aimed to measure the association between crowding in nursing homes and outbreak-associated respiratory infection incidence and related mortality before the COVID-19 pandemic.MethodsWe conducted a retrospective cohort study of nursing homes in Ontario, Canada. We identified, characterised, and selected nursing homes through the Ontario Ministry of Long-Term Care datasets. Nursing homes that were not funded by the Ontario Ministry of Long-Term Care and homes that closed before January, 2020 were excluded. Outcomes consisting of respiratory infection outbreaks were obtained from the Integrated Public Health Information System of Ontario. The crowding index equalled the mean number of residents per bedroom and bathroom. The primary outcomes were the incidence of outbreak-associated infections and mortality per 100 nursing home residents per year. We examined the incidence of infections and deaths as a function of the crowding index by use of negative binomial regression with adjustment for three home characteristics (ie, ownership, number of beds, and region) and nine mean resident characteristics (ie, age, female sex, dementia, diabetes, chronic heart failure, renal failure, cancer, chronic obstructive pulmonary disease, and activities of daily living score).FindingsBetween Sept 1, 2014, and Aug 31, 2019, 5107 respiratory infection outbreaks in 588 nursing homes were recorded, of which 4921 (96·4%), involving 64 829 cases of respiratory infection and 1969 deaths, were included in this analysis. Nursing homes with a high crowding index had higher incidences of respiratory infection (26·4% vs 13·8%; adjusted rate ratio per one resident per room increase in crowding 1·89 [95% CI 1·64-2·17]) and mortality (0·8% vs 0·4%; 2·34 [1·88-2·92]) than did homes with a low crowding index.InterpretationRespiratory infection and mortality rates were higher in nursing homes with high crowding index than in homes with low crowding index, and the association was consistent across various respiratory pathogens. Decreasing crowding is an important safety target beyond the COVID-19 pandemic to help to promote resident wellbeing and decrease the transmission of prevalent respiratory pathogens.FundingNone.

Project description:ImportanceCOVID-19 has had devastating effects on the health and well-being of older adult residents and health care professionals in nursing homes. Uncertainty about the associated consequences of these adverse effects on the use of medications common to this care setting remains.ObjectiveTo examine the association between the COVID-19 pandemic and prescription medication changes among nursing home residents.Design, setting, and participantsThis population-based cohort study with an interrupted time-series analysis used linked health administrative data bases for residents of all nursing homes (N = 630) in Ontario, Canada. During the observation period, residents were divided into consecutive weekly cohorts. The first observation week was March 5 to 11, 2017; the last observation week was September 20 to 26, 2020.ExposuresOnset of the COVID-19 pandemic on March 1, 2020.Main outcomes and measuresWeekly proportion of residents dispensed antipsychotics, benzodiazepines, antidepressants, anticonvulsants, opioids, antibiotics, angiotensin receptor blockers (ARBs), and angiotensin-converting enzyme (ACE) inhibitors. Autoregressive integrated moving average models with step and ramp intervention functions tested for level and slope changes in weekly medication use after the onset of the pandemic and were fit on prepandemic data for projected trends.ResultsAcross study years, the annual cohort size ranged from 75 850 to 76 549 residents (mean [SD] age, 83.4 [10.8] years; mean proportion of women, 68.9%). A significant increased slope change in the weekly proportion of residents who were dispensed antipsychotics (parameter estimate [β] = 0.051; standard error [SE] = 0.010; P < .001), benzodiazepines (β = 0.026; SE = 0.003; P < .001), antidepressants (β = 0.046; SE = 0.013; P < .001), trazodone hydrochloride (β = 0.033; SE = 0.010; P < .001), anticonvulsants (β = 0.014; SE = 0.006; P = .03), and opioids (β = 0.038; SE = 0.007; P < .001) was observed. The absolute difference in observed vs estimated use in the last week of the pandemic period ranged from 0.48% (for anticonvulsants) to 1.52% (for antipsychotics). No significant level or slope changes were found for antibiotics, ARBs, or ACE inhibitors.Conclusions and relevanceIn this population-based cohort study, statistically significant increases in the use of antipsychotics, benzodiazepines, antidepressants, anticonvulsants, and opioids followed the onset of the COVID-19 pandemic, although absolute differences were small. There were no significant changes for antibiotics, ARBs, or ACE inhibitors. Studies are needed to monitor whether changes in pharmacotherapy persist, regress, or accelerate during the course of the pandemic and how these changes affect resident-level outcomes.

Project description:During the first COVID-19 pandemic wave (1st CoPW), nursing homes (NHs) experienced a high rate of COVID-19 infection and death. Residents who survived the COVID-19 infection may have become frailer. This study aimed to determine the predictive value of having a COVID-19 infection during the 1st CoPW for 2-year mortality in NH residents. This was a retrospective study conducted in three NHs. Residents who had survived the 1st CoPW (March to May 2020) were included. The diagnosis of COVID-19 was based on the results of a positive reverse transcriptase-polymerase chain reaction test. The collected data also included age, sex, length of residence in the NH, disability status, legal guardianship status, nutritional status, need for texture-modified food, hospitalization or Emergency Department visits during lockdown and SARS-COV2 vaccination status during the follow-up. Non-adjusted and adjusted Cox models were used to analyse factors associated with 2-year post-1st CoPW mortality. Among the 315 CoPW1 survivors (72% female, mean age 88 years, 48% with severe disability), 35% presented with COVID-19. Having a history of COVID-19 was not associated with 2-year mortality: hazard ratio (HR) [95% confidence interval] = 0.96 [0.81-1.13], p = 0.62. The factors independently associated with 2-year mortality were older age (for each additional year, HR = 1.05 [1.03-1.08], p < 0.01), severe disability vs. moderate or no disability (HR = 1.35 [1.12-1.63], p < 0.01) and severe malnutrition vs. no malnutrition (HR = 1.29 [1.04-1.60], p = 0.02). Considering that vaccination campaign started during the follow-up, mortality was associated with severe malnutrition before and severe disability after the start of the campaign. Vaccination was independently associated with better survival (HR 0.71 [0.55-0.93], p = 0.02). Having survived a COVID-19 infection during the 1st CoPW did not affect subsequent 2-year survival in older adults living in NHs. Severe malnutrition and disability remained strong predictor of mortality in this population, whereas vaccination was associated to better survival.

Project description:ObjectivesThe coronavirus disease 2019 (COVID-19) pandemic has severely affected nursing home residents. Given the continued high incidence of COVID-19, and the likelihood that new variants and other infectious agents may cause future outbreaks, we sought to understand the relationship of nursing home quality ratings and measures of COVID-19 outbreak severity and persistence.DesignWe analyzed nursing home facility-level data on COVID-19 cases and deaths, county-level COVID-19 rates, and nursing home data from the Centers for Medicare & Medicaid Services (CMS), including ratings from the CMS Nursing Home Five-Star Quality Rating System. We used regression analysis to examine the association between star ratings and cumulative COVID-19 incidence and mortality as well as persistent high resident incidence.SettingAll nursing homes in the CMS COVID-19 Nursing Home Dataset reporting data that passed quality assurance checks for at least 20 weeks and that were included in the January 2021 Nursing Home Care Compare update.ParticipantsResidents of the included nursing homes.MeasurementsCumulative resident COVID-19 incidence and mortality through January 10, 2021; number of weeks with weekly resident incidence of COVID-19 in the top decile nationally.ResultsAs of January 10, 2021, nearly all nursing homes (93.6%) had reported at least one case of COVID-19 among their residents, more than three-quarters (76.9%) had reported at least one resident death, and most (83.5%) had experienced at least 1 week in the top decile of weekly incidence. In analyses adjusted for facility and county-level characteristics, we found generally consistent relationships between higher nursing home quality ratings and lower COVID-19 incidence and mortality, as well as with fewer high-incidence weeks.ConclusionNursing home quality ratings are associated with COVID-19 incidence, mortality, and persistence. Nursing homes receiving five-star ratings, for overall quality as well as for each domain, had lower COVID-19 rates among their residents.