Dataset Information

JUE Insight: Is Hospital Quality Predictive of Pandemic Deaths? Evidence from US Counties.

ABSTRACT: In the large literature on the spatial-level correlates of COVID-19, the association between quality of hospital care and outcomes has received little attention to date. To examine whether county-level mortality is correlated with measures of hospital performance, we assess daily cumulative deaths and pre-crisis measures of hospital quality, accounting for state fixed-effects and potential confounders. As a measure of quality, we use the pre-pandemic adjusted five-year penalty rates for excess 30-day readmissions following pneumonia admissions for the hospitals accessible to county residents based on ambulance travel patterns. Our adjustment corrects for socio-economic status and down-weighs observations based on small samples. We find that a one-standard-deviation increase in the quality of local hospitals is associated with a 2% lower death rate (relative to the mean of 20 deaths per 10,000 people) one and a half years after the first recorded death.

SUBMITTER: Kunz JS

PROVIDER: S-EPMC9221951 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo measure the effect of social distancing on reducing daily deaths, infections and hospital resources needed for coronavirus disease 2019 (COVID-19) patients during the first wave of the pandemic in Nordic countries.MethodsThe observations of social distancing, daily deaths, infections along with the needed hospital resources for COVID-19 patient hospitalizations including the numbers of all hospital beds, beds needed in ICUs and infection wards, nursing staffs needed in ICUs and infection wards were collected from the Institute for Health Metrics and Evaluation (IHME) by the University of Washington. The observations of social distancing were based on the reduction in human contact relative to background levels for each location quantified by cell phone mobility data collected from IHME. The weighted data per 100,000 population gathered in a 40-day period of the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden. Statistical technique of panel data analysis is used to measure the associations between social distancing and COVID-19 indicators in long-run.ResultsResults of dynamic long-run models confirm that a 1% rise in social distancing by reducing human contacts may decline daily deaths, daily infections, all hospital beds needed, beds/nurses needed in ICUs and beds/nurses needed in infection wards due COVID-19 pandemic by 1.13%, 15.26%, 1.10%, 1.17% and 1.89%, respectively. Moreover, results of error correction models verify that if the equilibriums between these series are disrupted by a sudden change in social distancing, the lengths of restoring back to equilibrium are 67, 62, 40, 22 and 49 days for daily deaths, daily infections, all hospital beds needed, nurses/beds needed in ICUs and nurses/beds needed in infection wards, respectively.ConclusionProper social distancing was a successful policy for tackling COVID-19 with falling mortality and infection rates as well as the needed hospital resources for patient hospitalizations in Nordic countries. The results alert governments of the need for continuously implementing social distancing policies while using vaccines to prevent national lockdowns and reduce the burden of patient hospitalizations.

Dataset Information

JUE Insight: Is Hospital Quality Predictive of Pandemic Deaths? Evidence from US Counties.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets