Dataset Information

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status.

ABSTRACT: Importance: Socioeconomic factors are associated with worse outcomes after hospitalization, but neither the Centers for Medicare & Medicaid Services (CMS) nor the Veterans Affairs (VA) health care system adjust for socioeconomic factors in profiling hospital mortality.

Objective: To evaluate changes in Veterans Affairs medical centers' (VAMCs') risk-standardized mortality rates among veterans hospitalized for heart failure and pneumonia after adjusting for socioeconomic factors.

Design, setting, and participants: In this cross-sectional study, retrospective data were used to assess 131 VAMCs' risk-standardized 30-day mortality rates with or without adjustment for socioeconomic covariates. The study population included 42?892 veterans hospitalized with heart failure and 39?062 veterans hospitalized with pneumonia from January 1, 2012, to December 31, 2014. Data were analyzed from March 1, 2019, to April 1, 2020.

Main outcomes and measures: The primary outcome was 30-day mortality after admission. Socioeconomic covariates included neighborhood disadvantage, race/ethnicity, homelessness, rurality, nursing home residence, reason for Medicare eligibility, Medicaid and Medicare dual eligibility, and VA priority.

Results: The study population included 42?892 veterans hospitalized with heart failure (98.2% male; mean [SD] age, 71.9 [11.4] years) and 39?062 veterans hospitalized with pneumonia (96.8% male; mean [SD] age, 71.0 [12.4] years). The addition of socioeconomic factors to the CMS models modestly increased the C statistic from 0.77 (95% CI, 0.77-0.78) to 0.78 (95% CI, 0.78-0.78) for 30-day mortality after heart failure and from 0.73 (95% CI, 0.72-0.73) to 0.74 (95% CI, 0.73-0.74) for 30-day mortality after pneumonia. Mortality rates were highly correlated (Spearman correlations of ?0.98) in models that included or did not include socioeconomic factors. With the use of the CMS model for heart failure, VAMCs in the lowest quintile had a mean (SD) mortality rate of 6.0% (0.4%), those in the middle 3 quintiles had a mean (SD) mortality rate of 7.2% (0.4%), and those in the highest quintile had a mean (SD) mortality rate of 8.8% (0.6%). After the inclusion of socioeconomic covariates, the adjusted mean (SD) mortality was 6.1% (0.4%) for hospitals in the lowest quintile, 7.2% (0.4%) for those in the middle 3 quintiles, and 8.6% (0.5%) for those in the highest quintile. The mean absolute change in rank after socioeconomic adjustment was 3.0 ranking positions (interquartile range, 1.0-4.0) among hospitals in the highest quintile of mortality after heart failure and 4.4 ranking positions (interquartile range, 1.0-6.0) among VAMCs in the lowest quintile. Similar findings were observed for mortality rankings in pneumonia and after inclusion of clinical covariates.

Conclusions and relevance: This study suggests that adjustments for socioeconomic factors did not meaningfully change VAMCs' risk-adjusted 30-day mortality rates for veterans hospitalized for heart failure and pneumonia. The implications of such adjustments should be examined for other quality measures and health systems.

SUBMITTER: Trivedi AN

PROVIDER: S-EPMC7716194 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status.

Trivedi Amal N AN Jiang Lan L Silva Gabriella G Wu Wen-Chih WC Mor Vincent V Fine Michael J MJ Kressin Nancy R NR Gutman Roee R

JAMA network open 20201201 12

<h4>Importance</h4>Socioeconomic factors are associated with worse outcomes after hospitalization, but neither the Centers for Medicare & Medicaid Services (CMS) nor the Veterans Affairs (VA) health care system adjust for socioeconomic factors in profiling hospital mortality.<h4>Objective</h4>To evaluate changes in Veterans Affairs medical centers' (VAMCs') risk-standardized mortality rates among veterans hospitalized for heart failure and pneumonia after adjusting for socioeconomic factors.<h4> ...[more]

PMID: 33270121

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Project description:BackgroundDeaths from pneumonia were decreasing globally prior to the COVID-19 pandemic, but it is unclear whether this was due to changes in patient populations, illness severity, diagnosis, hospitalization thresholds, or treatment. Using clinical data from the electronic health record among a national cohort of patients initially diagnosed with pneumonia, we examined temporal trends in severity of illness, hospitalization, and short- and long-term deaths.DesignRetrospective cohort PARTICIPANTS: All patients >18 years presenting to emergency departments (EDs) at 118 VA Medical Centers between 1/1/2006 and 12/31/2016 with an initial clinical diagnosis of pneumonia and confirmed by chest imaging report.ExposuresYear of encounter.Main measuresHospitalization and 30-day and 90-day mortality. Illness severity was defined as the probability of each outcome predicted by machine learning predictive models using age, sex, comorbidities, vital signs, and laboratory data from encounters during years 2006-2007, and similar models trained on encounters from years 2015 to 2016. We estimated the changes in hospitalizations and 30-day and 90-day mortality between the first and the last 2 years of the study period accounted for by illness severity using time covariate decompositions with model estimates.ResultsAmong 196,899 encounters across the study period, hospitalization decreased from 71 to 63%, 30-day mortality 10 to 7%, 90-day mortality 16 to 12%, and 1-year mortality 29 to 24%. Comorbidity risk increased, but illness severity decreased. Decreases in illness severity accounted for 21-31% of the decrease in hospitalizations, and 45-47%, 32-24%, and 17-19% of the decrease in 30-day, 90-day, and 1-year mortality. Findings were similar among underrepresented patients and those with only hospital discharge diagnosis codes.ConclusionsOutcomes for community-onset pneumonia have improved across the VA healthcare system after accounting for illness severity, despite an increase in cases and comorbidity burden.

Project description:PurposeProgressive Tinnitus Management (PTM) is an evidence-based interdisciplinary stepped-care approach to improving quality of life for patients with tinnitus. PTM was endorsed by Department of Veterans Affairs (VA) Audiology leadership in 2009. Factors affecting implementation of PTM are unknown. We conducted a study to: 1) estimate levels of PTM program implementation in VA Audiology and Mental Health clinics across the country; and 2) identify barriers and facilitators to PTM implementation based on the experiences of VA audiologists and mental health providers.MethodWe conducted an anonymous, web-based survey targeting Audiology and Mental Health leaders at 144 major VA facilities. Quantitative analyses summarized respondents' facility characteristics and levels of program implementation (full PTM, partial PTM, or no PTM). Qualitative analyses identified themes in factors influencing the implementation of PTM across VA sites.ResultsSurveys from 87 audiologists and 66 mental health clinicians revealed that few facilities offered full PTM; the majority offered partial or no PTM. Inductive analysis of the open-ended survey responses identified seven factors influencing implementation of PTM: 1) available resources, 2) service collaboration, 3) prioritization, 4) Veterans' preferences and needs, 5) clinician training, 6) awareness of (evidence-based) options, and 7) perceptions of scope of practice.ConclusionResults suggest wide variation in services provided, a need for greater engagement of mental health providers in tinnitus care, and an interest among both audiologists and mental health providers in receiving tinnitus-related training. Future research should address barriers to PTM implementation, including methods to: 1) improve understanding among mental health providers of their potential role in tinnitus management; 2) enhance coordination of tinnitus-related care between health care disciplines; and 3) collect empirical data on Veterans' need for and interest in PTM, including delivery by telehealth modalities.

Project description:ImportanceLittle contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.ObjectiveTo assess and compare mortality and readmission rates among men in VA and non-VA hospitals.Design, setting, and participantsCross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).ExposuresHospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.Main outcomes and measuresFor each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.ResultsWe studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; -0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; -0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, -0.22; 95% CI, -0.40 to -0.04) and HF (-0.63; 95% CI, -0.95 to -0.31), and mortality rates for pneumonia were not significantly different (-0.03; 95% CI, -0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).Conclusions and relevanceAmong older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small.

Project description:ImportanceConcerns have been raised about the adequacy of health care access among patients cared for within the United States Department of Veterans Affairs (VA) health care system.ObjectivesTo determine wait times for new patients receiving care at VA medical centers and compare wait times in the VA medical centers with wait times in the private sector (PS).Design, setting, and participantsA retrospective, repeated cross-sectional study was conducted of new appointment wait times for primary care, dermatology, cardiology, or orthopedics at VA medical centers in 15 major metropolitan areas in 2014 and 2017. Comparison data from the PS came from a published survey that used a secret shopper survey approach. Secondary analyses evaluated the change in overall and unique patients seen in the entire VA system and patient satisfaction survey measures of care access between 2014 and 2017.Main outcomes and measuresThe outcome of interest was patient wait time. Wait times in the VA were determined directly from patient scheduling. Wait times in the PS were as reported in Merritt Hawkins surveys using the secret shopper method.ResultsCompared with the PS, overall mean VA wait times for new appointments in 2014 were similar (mean [SD] wait time, 18.7 [7.9] days PS vs 22.5 [7.3] days VA; P = .20). Department of Veterans Affairs wait times in 2014 were similar to those in the PS across specialties and regions. In 2017, overall wait times for new appointments in the VA were shorter than in the PS (mean [SD], 17.7 [5.9] vs 29.8 [16.6] days; P < .001). This was true in primary care (mean [SD], 20.0 [10.4] vs 40.7 [35.0] days; P = .005), dermatology (mean [SD], 15.6 [12.2] vs 32.6 [16.5] days; P < .001), and cardiology (mean [SD], 15.3 [12.6] vs 22.8 [10.1] days; P = .04). Wait times for orthopedics remained longer in the VA than the PS (mean [SD], 20.9 [13.3] vs 12.4 [5.5] days; P = .01), although wait time improved significantly between 2014 and 2017 in the VA for orthopedics while wait times in the PS did not change (change in mean wait times, increased 1.5 days vs decreased 5.4 days; P = .02). Secondary analysis demonstrated an increase in the number of unique patients seen and appointment encounters in the VA between 2014 and 2017 (4 996 564 to 5 118 446, and 16 476 461 to 17 331 538, respectively), and patient satisfaction measures of access also improved (satisfaction scores increased by 1.4%, 3.0%, and 4.0% for specialty care, routine primary care, and urgent primary care, P < .05).Conclusions and relevanceAlthough wait times in the VA and PS appeared to be similar in 2014, there have been interval improvements in VA wait times since then, while wait times in the PS appear to be static. These findings suggest that access to care within the VA has improved over time.

Project description:BackgroundInfections caused by carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing (CP) CRE are difficult to treat, resulting in high mortality in healthcare settings every year. The Veterans Health Administration (VHA) disseminated guidelines in 2015 and an updated directive in 2017 for control of CRE focused on laboratory testing, prevention, and management. The Consolidated Framework for Implementation Research (CFIR) framework was used to analyze qualitative interview data to identify contextual factors and best practices influencing implementation of the 2015 guidelines/2017 directive in VA Medical Centers (VAMCs). The overall goals were to determine CFIR constructs to target to improve CRE guideline/directive implementation and understand how CFIR, as a multi-level conceptual model, can be used to inform guideline implementation.MethodsSemi-structured interviews were conducted at 29 VAMCs with staff involved in implementing CRE guidelines at their facility. Survey and VHA administrative data were used to identify geographically representative large and small VAMCs with varying levels of CRE incidence. Interviews addressed perceptions of guideline dissemination, laboratory testing, staff attitudes and training, patient education, and technology support. Participant responses were coded using a consensus-based mixed deductive-inductive approach guided by CFIR. A quantitative analysis comparing qualitative CFIR constructs and emergent codes to sites actively screening for CRE (vs. non-screening) and any (vs. no) CRE-positive cultures was conducted using Fisher's exact test.ResultsForty-three semi-structured interviews were conducted between October 2017 and August 2018 with laboratory staff (47%), Multi-Drug-Resistant Organism Program Coordinators (MPCs, 35%), infection preventionists (12%), and physicians (6%). Participants requested more standardized tools to promote effective communication (e.g., electronic screening). Participants also indicated that CRE-specific educational materials were needed for staff, patient, and family members. Quantitative analysis identified CRE screening or presence of CRE as being significantly associated with the following qualitative CFIR constructs: leadership engagement, relative priority, available resources, team communication, and access to knowledge and information.ConclusionsEffective CRE identification, prevention, and treatment require ongoing collaboration between clinical, microbiology, infection prevention, antimicrobial stewardship, and infectious diseases specialists. Our results emphasize the importance of leadership's role in promoting positive facility culture, including access to resources, improving communication, and facilitating successful implementation of the CRE guidelines.

Dataset Information

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status.

Publications

Evaluation of Changes in Veterans Affairs Medical Centers' Mortality Rates After Risk Adjustment for Socioeconomic Status.

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