Project description:BackgroundIn order to protect health workers from SARS-CoV-2, there is need to characterise the different types of patient facing health workers. Our first aim was to determine both the infection status and seroprevalence of SARS-CoV-2 in health workers. Our second aim was to evaluate the occupational and demographic predictors of seropositivity to inform the country's infection prevention and control (IPC) strategy.Methods and principal findingsWe invited 713 staff members at 24 out of 35 health facilities in the City of Bulawayo in Zimbabwe. Compliance to testing was defined as the willingness to uptake COVID-19 testing by answering a questionnaire and providing samples for both antibody testing and PCR testing. SARS-COV-2 antibodies were detected using a rapid diagnostic test kit and SAR-COV-2 infection was determined by real-time (RT)-PCR. Of the 713 participants, 635(89%) consented to answering the questionnaire and providing blood sample for antibody testing while 560 (78.5%) agreed to provide nasopharyngeal swabs for the PCR SARS-CoV-2 testing. Of the 635 people (aged 18-73) providing a blood sample 39.1% reported a history of past COVID-19 symptoms while 14.2% reported having current symptoms of COVID-19. The most-prevalent co-morbidity among this group was hypertension (22.0%) followed by asthma (7.0%) and diabetes (6.0%). The SARS-CoV-2 sero-prevalence was 8.9%. Of the 560 participants tested for SARS-CoV-2 infection, 2 participants (0.36%) were positive for SAR-CoV-2 infection by PCR testing. None of the SARS-CoV-2 antibody positive people were positive for SAR-CoV-2 infection by PCR testing.Conclusion and interpretationIn addition to clinical staff, several patient-facing health workers were characterised within Zimbabwe's health system and the seroprevalence data indicated that previous exposure to SAR-CoV-2 had occurred across the full spectrum of patient-facing staff with nurses and nurse aides having the highest seroprevalence. Our results highlight the need for including the various health workers in IPC strategies in health centres to ensure effective biosecurity and biosafety.

Project description:OBJECTIVE:The 340B program allows safety-net hospitals to acquire discounted outpatient drugs and charge payers full price. We examined whether 340B participation increases safety-net engagement. DATA SOURCES:340B participation data, Medicare hospital cost reports, American Hospital Association Survey, and Schedule 990 nonprofit hospital tax returns. STUDY DESIGN:Quasi-experimental difference-in-differences design comparing 340B hospitals (the "treatment" group) before and after participating to changes over time to three alternative "control" groups: all other nonprofit and public hospitals, hospitals that are not participating during our study, and hospitals that were not-yet-participating but started after 2015. Outcome measures include a range of safety-net care measures that are alternatives to the standard uncompensated care: charity care, community benefit spending, charity care policies, and low-profit service-line provision. DATA EXTRACTION:We extracted data on all nonprofit and public hospitals from 2011 to 2015. We linked 340B participation data to Medicare hospital cost reports and American Hospital Association data using Medicare hospital identifiers. 990 Data was linked on name and address. PRINCIPAL FINDINGS:New 340B participation was not associated with a change in uncompensated care, but was associated with a 28.9 percent increase in charity care spending (SE = 8.8), or about $880,000 per hospital. However, total community benefit spending (including charity care) did not change. 340B was associated with an increase in the probability of offering discounted care (4.3 percentage points, SE = 1.6) from 84 to 88 percent and an increase in the income eligibility limit for discounted care (18.9 percentage points, SE = 5.6) from 294 to 313 percent. Participation was not associated with the probability of offering low-profit medical care services. CONCLUSIONS:Alternative measures show that newly participating hospitals may increase charity care, potentially through offering more patients discounted care. However, increases appear to be fully offset by reductions in other community benefit programs.

Project description:ImportanceNo consensus exists on how to define safety-net hospitals (SNHs) for research or policy decision-making. Identifying which types of hospitals are classified as SNHs under different definitions is key to assessing policies that affect SNH funding.ObjectiveTo examine characteristics of SNHs as classified under 3 common definitions.Design, setting, and participantsThis cross-sectional analysis includes noncritical-access hospitals in the Healthcare Cost and Utilization Project State Inpatient Databases from 47 US states for fiscal year 2015, linked to the Centers for Medicare & Medicaid Services Hospital Cost Reports and to the American Hospital Association Annual Survey. Data were analyzed from March 1 through September 30, 2018.ExposuresHospital characteristics including organizational characteristics, scope of services provided, and financial attributes.Main outcomes and measuresDefinitions of SNH based on Medicaid and Medicare Supplemental Security Income inpatient days historically used to determine Medicare Disproportionate Share Hospital (DSH) payments; Medicaid and uninsured caseload; and uncompensated care costs. For each measure, SNHs were defined as those within the top quartile for each state.ResultsThe 2066 hospitals in this study were distributed across the Northeast (340 [16.5%]), Midwest (587 [28.4%]), South (790 [38.2%]), and West (349 [16.9%]). Concordance between definitions was low; 269 hospitals (13.0%) or fewer were identified as SNHs under any 2 definitions. Uncompensated care captured smaller (200 of 523 [38.2%]) and more rural (65 of 523 [12.4%]) SNHs, whereas DSH index and Medicaid and uncompensated caseload identified SNHs that were larger (264 of 518 [51.0%] and 158 of 487 [32.4%], respectively) and teaching facilities (337 of 518 [65.1%] and 229 of 487 [47.0%], respectively) that provided more essential services than non-SNHs. Uncompensated care also distinguished remarkable financial differences between SNHs and non-SNHs. Under the uncompensated care definition, median (interquartile range [IQR]) bad debt ($27.1 [$15.5-$44.3] vs $12.8 [$6.7-$21.6] per $1000 of operating expenses; P < .001) and charity care ($19.9 [$9.3-$34.1] vs $9.1 [$4.0-$18.7] per $1000 of operating expenses) were twice as high and median (IQR) unreimbursed costs ($32.6 [$12.4-$55.4] vs $23.6 [$9.0-$42.7] per $1000 of operating expenses; P < .001) were 38% higher for SNHs than for non-SNHs. Safety-net hospitals defined by uncompensated care burden had lower median (IQR) total (4.7% [0%-9.9%] vs 5.8% [1.2%-11.2%]; P = .003) and operating (0.3% [-8.0% to 7.2%] vs 2.3% [-3.9% to 8.9%]; P < .001) margins than their non-SNH counterparts, whereas differences between SNH and non-SNH profit margins generally were not statistically significant under the other 2 definitions.Conclusions and relevanceDifferent SNH definitions identify hospitals with different characteristics and financial conditions. The new DSH formula, which accounts for uncompensated care, may lead to redistributed payments across hospitals. Our results may inform which types of hospitals will experience funding changes as DSH payment policies evolve.

Dataset Information

Patient Characteristics and Subsequent Health Care Use by Location of SARS-CoV-2 Testing Initiation in a Safety-Net Health System.

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