Project description:Massachusetts' health care reform substantially decreased the percentage of uninsured residents. However, less is known about how reform affected access to care, especially according to insurance type.To assess access to care in Massachusetts after implementation of health care reform, based on insurance status and type.We surveyed a convenience sample of 431 patients presenting to the Emergency Department of Massachusetts' second largest safety net hospital between July 25, 2009 and March 20, 2010.Demographic and clinical characteristics, insurance coverage, measures of access to care and cost-related barriers to care.Patients with Commonwealth Care and Medicaid, the two forms of insurance most often newly-acquired under the reform, reported similar or higher utilization of and access to outpatient visits and rates of having a usual source of care, compared with the privately insured. Compared with the privately insured, a significantly higher proportion of patients with Medicaid or Commonwealth Care Type 1 (minimal cost sharing) reported delaying or not getting dental care (42.2 % vs. 27.1 %) or medication (30.0 % vs. 7.0 %) due to cost; those with Medicaid also experienced cost-related barriers to seeing a specialist (14.6 % vs. 3.5 %) or getting recommended tests (15.6 % vs. 5.9 %). Those with Commonwealth Care Types 2 and 3 (greater cost sharing) reported significantly more cost-related barriers to obtaining care than the privately insured (45.0 % vs. 16.0 %), to seeing a primary care doctor (25.0 % vs. 6.0 %) or dental provider (58.3 % vs. 27.1 %), and to obtaining medication (20.8 % vs. 7.0 %). No differences in cost-related barriers to preventive care were found between the privately and publicly insured.Access to care improved less than access to insurance following Massachusetts' health care reform. Many newly insured residents obtained Medicaid or state subsidized private insurance; cost-related barriers to access were worse for these patients than for the privately insured.

Project description:BackgroundPrior studies reported survival benefits from early initiation of adjuvant chemotherapy for stage III colon cancer, but this evidence was derived from studies that may be sensitive to time-related biases. Therefore, we aimed to estimate the effect of initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks on overall and disease-free survival among stage III colon cancer patients using a study design that helps address time-related biases.MethodsWe used institutional registry data from JPS Oncology and Infusion Center, a Comprehensive Community Cancer Program. Eligible patients were adults aged < 80 years, diagnosed with first primary stage III colon cancer between 2011 and 2017, and received surgical resection with curative intent. We emulated a target trial with sequential eligibility. We subsequently pooled the trials and estimated risk ratios (RRs) along with 95% confidence limits (CL) for all-cause mortality and recurrence or death at 5-years between initiators and non-initiators of adjuvant chemotherapy ≤8 or ≤ 12 weeks using pseudo-observations and a marginal structural model with stabilized inverse probability of treatment weights.ResultsOur study population comprised 222 (for assessing initiation ≤8 weeks) and 310 (for assessing initiation ≤12 weeks) observations, of whom the majority were racial/ethnic minorities (64-65%), or uninsured with or without enrollment in our hospital-based medical assistance program (68-71%). Initiation of adjuvant chemotherapy ≤8 weeks of surgical resection did not improve overall survival (RR for all-cause mortality = 1.04, 95% CL: 0.57, 1.92) or disease-free survival (RR for recurrence or death = 1.07, 95% CL: 0.61, 1.88). The results were similar for initiation of adjuvant chemotherapy ≤12 weeks of surgical resection.ConclusionsOur results suggest that the overall and disease-free survival benefits of initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection may be overestimated in prior studies, which may be attributable to time-related biases. Nevertheless, our estimates were imprecise and differences in population characteristics are an alternate explanation. Additional studies that address time-related biases are needed to clarify our findings.

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.

Project description:ImportanceIn the US, federal value-based incentive programs are more likely to penalize safety-net institutions than non-safety-net institutions. Whether these programs differentially change the rates of targeted health care-associated infections in safety-net vs non-safety-net hospitals is unknown.ObjectiveTo assess the association of Hospital-Acquired Condition Reduction Program (HACRP) and Hospital Value-Based Purchasing (HVBP) implementation with changes in rates of targeted health care-associated infections and disparities in rates among safety-net and non-safety-net hospitals.Design, setting, and participantsThis interrupted time series included all US acute care hospitals enrolled in the Preventing Avoidable Infectious Complications by Adjusting Payment study that participated in mandatory reporting to the National Healthcare Safety Network from January 1, 2013, through June 30, 2018. Hospital characteristics were obtained from the 2015 American Hospital Association annual survey. Penalty statuses for 2015 to 2018 were obtained from Hospital Compare. Data were analyzed between July 9, 2018, and October 1, 2019.ExposuresHACRP and HVBP implementation in fiscal year 2015 or 2016.Main outcomes and measuresThe primary outcomes were rates of 4 health care-associated infections: central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), surgical site infection (SSI) after colon surgical procedures, and SSI after abdominal hysterectomy procedures. Regression models were fit using generalized estimating equations to assess the association of HACRP and HVBP implementation with health care-associated infection rates and disparities in infection rates.ResultsOf the 618 acute care hospitals included in this study, 473 (76.5%) were non-safety net and 145 (23.5%) were considered safety net. In these hospitals, HACRP and HVBP implementation was not associated with improvements in level or trend for any health care-associated infection examined (eg, CAUTI in safety-net hospitals: incidence rate ratio [IRR] for level change, 0.98 [95% CI, 0.79-1.23; P = .89]; IRR for change in slope, 1.00 [95% CI, 0.97-1.03; P = .80]). Before program implementation, infection rates were statistically significantly higher for safety-net than for non-safety-net hospitals for CLABSI (IRR, 1.23; 95% CI, 1.07-1.42; P = .004), CAUTI (IRR, 1.38; 95% CI, 1.16-1.64; P < .001), and SSI after colon surgical procedure (odds ratio [OR], 1.26; 95% CI, 1.06-1.50; P = .009). The disparity persisted over time when comparing the last year of the study with the first year (CLABSI: ratio of ratios [ROR], 0.93 [95% CI, 0.77-1.13; P = .48]; CAUTI: ROR, 0.90 [95% CI, 0.73-1.10; P = .31]; SSI after colon surgical procedures: ROR, 0.96 [95% CI, 0.78-1.20; P = .75]). Rates of SSI after abdominal hysterectomy procedure were similar in safety-net and non-safety-net hospitals before implementation (OR, 1.13; 95% CI, 0.91-1.40; P = .27) but higher after implementation (OR, 1.43; 95% CI, 1.11-1.83; P = .006), although this change was not significant (ROR, 1.20; 95% CI, 0.91-1.59; P = .20).Conclusions and relevanceThis study found that HACRP and HVBP implementation was not associated with any improvements in targeted health care-associated infections among safety-net or non-safety-net hospitals or with changes in disparities in infection rates. Given the persistent health care-associated infection rate disparities, these programs appear to function as a disproportionate penalty system for safety-net hospitals that offer no measurable benefits for patients.

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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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