Report 46: Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.

The SARS-CoV-2 Gamma variant spread rapidly across Brazil, causing substantial infection and death waves. We use individual-level patient records following hospitalisation with suspected or confirmed COVID-19 to document the extensive shocks in hospital fatality rates that followed Gamma's spread across 14 state capitals, and in which more than half of hospitalised patients died over sustained time periods. We show that extensive fluctuations in COVID-19 in-hospital fatality rates also existed prior to Gamma's detection, and were largely transient after Gamma's detection, subsiding with hospital demand. Using a Bayesian fatality rate model, we find that the geographic and temporal fluctuations in Brazil's COVID-19 in-hospital fatality rates are primarily associated with geographic inequities and shortages in healthcare capacity. We project that approximately half of Brazil's COVID-19 deaths in hospitals could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization, and pandemic preparedness are critical to minimize population wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. NOTE: The following manuscript has appeared as 'Report 46 - Factors driving extensive spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals' at https://spiral.imperial.ac.uk:8443/handle/10044/1/91875 . ONE SENTENCE SUMMARY: COVID-19 in-hospital fatality rates fluctuate dramatically in Brazil, and these fluctuations are primarily associated with geographic inequities and shortages in healthcare capacity.

Religious Involvement and Adaptation in Female Family Caregivers.

OBJECTIVES: To examine the relationship between religious involvement (RI) and adaptation of women caring for family members with severe physical or neurological disability. DESIGN: Two-site cross-sectional study. SETTING: Community. PARTICIPANTS: A convenience sample of 251 caregivers was recruited. RI and caregiver adaptation (assessed by perceived stress, caregiver burden, and depressive symptoms) were measured using standard scales, along with caregiver characteristics, social support, and health behaviors. Bivariate and multivariate analyses were conducted to identify relationships and mediating and moderating factors. RESULTS: Religious involvement (RI) was associated with better caregiver adaptation independent of age, race, education, caregiver health, care recipient's health, social support, and health behaviors (B = -0.09, standard error = 0.04, t = -2.08, P = .04). This association was strongest in caregivers aged 58-75 and spouses and for perceived stress in blacks. CONCLUSION: Religious involvement (RI) in female caregivers is associated with better caregiver adaptation, especially for those who are older, spouses of the care recipients, and blacks. These results are relevant to the development of future interventions that provide support to family caregivers.

Lessons learned at Parkland: how one hospital reworked its front end.

In redesigning its front-end workflow to include demographic verification and evaluation of patient financial need, Parkland Memorial Hospital, Dallas, learned the following lessons: Automation is necessary. Integrated technology provides significant benefits. The automated system should evaluate patient financial need. Front-end technology should ensure nondiscriminatory compliance with hospital policy. Community public relations should be maintained.

Battling bad debt.

Automated revenue cycle tools are giving hospitals the power to identify bad debt and charity cases as they access a facility and to dictate the appropriate course of action and workflow. To be successful, these tools must be regulatorily compliant and easily upgradeable, and should be accompanied by: Extensive staff training. An adjustment in workflow to match patient payment probability. Support of revenue cycle staff and physicians.