Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To explore differences in costs and lengths of stay for cancer patients admitted to National Cancer Institute‐designated Comprehensive Cancer Centers, nondesignated academic medical centers, and community hospitals in New York State.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>We use patient‐level data from the New York State Statewide Planning and Research Cooperative System Hospital Inpatient Discharges dataset for the years 2017–2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We employ ordinary least squares and Poisson regressions to compare hospital costs and length of stay for cancer patients, controlling for hospital type, patient demographics, and patient health. Our key outcomes are differences in costs and lengths of stay.</jats:p></jats:sec><jats:sec><jats:title>Data Collection</jats:title><jats:p>We use data on patient demographics, total treatment costs, and lengths of stay for patients discharged from New York hospitals with cancer‐related diagnoses between 2017 and 2019.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We determine that inpatient costs were 27% higher (95% CI 0.252, 0.285), but length of stay was 12% shorter (95% CI −0.131, −0.100), in Comprehensive Cancer Centers relative to community hospitals.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The results imply that, in New York State, Comprehensive Cancer Centers are a magnet for more complex oncology cases and administer more expensive treatments. That expertise, however, seems to be responsible for more efficient care delivery and thorough discharge planning, allowing for shorter average lengths of stay.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To develop a natural language processing (NLP) algorithm that identifies social determinants of health (SDoH), including housing, transportation, food, and medication insecurities, social isolation, abuse, neglect, or exploitation, and financial difficulties for patients with Alzheimer's disease and related dementias (ADRD) from unstructured electronic health records (EHRs).</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We leveraged 1000 medical notes randomly selected from 7401 emergency department and inpatient social worker notes generated between 2015 and 2019 for 231 unique patients diagnosed with ADRD at Michigan Medicine.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We developed a rule‐based NLP algorithm for the identification of seven domains of SDoH noted above. We also compared the rule‐based algorithm with deep learning and regularized logistic regression approaches. These models were compared using accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). All notes were split into 700 notes for training NLP algorithms, and 300 notes for validation.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Social worker notes used in this study were extracted from the Michigan Medicine EHR database.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Of the 700 notes for training, F1 and AUC for the rule‐based algorithm were at least 0.94 and 0.95, respectively, for all SDoH categories. Of the 300 notes for validation, F1 and AUC were at least 0.80 and 0.97, respectively, for all SDoH except housing and medication insecurities. The deep learning and regularized logistic regression algorithms had unsatisfactory performance.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The rule‐based algorithm can accurately extract SDoH information in all seven domains of SDoH except housing and medication insecurities. Findings from the algorithm can be used by clinicians and social workers to proactively address social needs of patients with ADRD and other vulnerable patient populations.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To describe rates and predictors of perinatal intimate partner violence (IPV) and rates and predictors of not being screened for abuse among rural and urban IPV victims who gave birth.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>This analysis utilized 2016–2020 Pregnancy Risk Assessment Monitoring System (PRAMS) data from 45 states and three jurisdictions.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>This is a retrospective, cross‐sectional study using multistate survey data.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>This analysis included 201,413 survey respondents who gave birth in 2016–2020 (<jats:italic>n</jats:italic> = 42,193 rural and 159,220 urban respondents). We used survey‐weighted multivariable logistic regression models, stratified by rural/urban residence, to estimate adjusted predicted probabilities and 95% confidence intervals (CIs) for two outcomes: (1) self‐reported experiences of IPV (physical violence by a current or former intimate partner) and (2) not receiving abuse screening at health care visits before, during, or after pregnancy.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Rural residents had a higher prevalence of perinatal IPV (4.6%) than urban residents (3.2%). Rural respondents who were Medicaid beneficiaries, 18–35 years old, non‐Hispanic white, Hispanic (English‐speaking), or American Indian/Alaska Native had significantly higher predicted probabilities of experiencing perinatal IPV compared with their urban counterparts.</jats:p><jats:p>Among respondents who experienced perinatal IPV, predicted probabilities of not receiving abuse screening were 21.3% for rural and 16.5% for urban residents. Predicted probabilities of not being screened for abuse were elevated for rural IPV victims who were Medicaid beneficiaries, 18–24 years old, or unmarried, compared to urban IPV victims with those same characteristics.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>IPV is more common among rural birthing people, and rural IPV victims are at higher risk of not being screened for abuse compared with their urban peers. IPV prevention and support interventions are needed in rural communities and should focus on universal abuse screening during health care visits and targeted support for those at greatest risk of perinatal IPV.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate whether primary care providers' participation in the Comprehensive Primary Care Plus Initiative (CPC+) was associated with changes in their delivery of high‐value services.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>Medicare Physician &amp; Other Practitioners public use files from 2013 to 2019, 2017 to 2019 Medicare Part B claims for a 5% random sample of Medicare Fee‐for‐Service (FFS) beneficiaries, the Area Health Resources File, the National Plan &amp; Provider Enumeration System files, and public use datasets from the Centers for Medicare &amp; Medicaid Services Physician Compare.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We used a difference‐in‐difference approach with a propensity score‐matched comparison group to estimate the association of CPC+ participation with the delivery of annual wellness visits (AWVs), advance care planning (ACP), flu shots, counseling to prevent tobacco use, and depression screening. These services are prominent examples of high‐value services, providing benefits to patients at a reasonable cost. We examined both the likelihood of delivering these services within a year and the count of services delivered per 1000 Medicare FFS beneficiaries per year.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Secondary data are linked at the provider level.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We find that CPC+ participation was associated with increases in the likelihood of delivering AWVs (13.03 percentage points by CPC+'s third year, <jats:italic>p</jats:italic> &lt; 0.001) and the number of AWVs per 1000 Medicare FFS beneficiaries (44 more AWVs by CPC+'s third year, <jats:italic>p</jats:italic> &lt; 0.001). We also find that CPC+ participation was associated with more flu shots per 1000 beneficiaries (52 more shots by CPC+'s third year, <jats:italic>p</jats:italic> &lt; 0.001) but not with the likelihood of delivering flu shots. We did not find consistent evidence for the association between CPC+ participation and ACP services, counseling to prevent tobacco use, or depression screening.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>CPC+ participation was associated with increases in the delivery of AWVs and flu shots, but not other high‐value services.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To compare the Encounter Data System (EDS) and Medicare Provider Analysis and Review (MedPAR) completeness and medical coding of Medicare Advantage hospitalizations.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>FY 2016–FY 2019 data limited to hospitals paid under Medicare's Inpatient Prospective Payment System.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Secondary data analysis.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Completeness of EDS and MedPAR data was estimated using the total number of unique hospitalizations in both data sources as denominator. Deriving this denominator involved matching cases in the EDS and MedPAR by MA enrollee, discharge date, and hospital. The higher the match rate, the more informative the comparison of EDS and MedPAR medical coding of the same hospitalization. EDS and MedPAR codes were assessed for similarity on six measures of Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment and identical diagnosis and procedure codes.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>EDS hospitalizations' completeness increased steadily each year from 90% to 93%, driven by the 23 largest Medicare Advantage Organizations, which account for 83% of total cases. MedPAR completeness was relatively stable (89%) and benefited from 91% completeness among the largest hospitals, which are often teaching hospitals and account for 63% of MedPAR cases. By 2019, 97% of medical cases were assigned the same MS‐DRG, indicating the high consistency of the severity level coding, since 98% were assigned the same base MS‐DRGs, which include all severity levels for the same condition. Without chart reviews, medical cases with identical diagnosis codes increased from 87% to 92%.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The EDS has a completeness advantage over MedPAR for studies of non‐teaching disproportionate share (DSH) hospitals and individual hospitals generally. MedPAR is only slightly less complete for hospitalizations of teaching DSH hospitals and large hospitals in general. A highly consistent EDS and MedPAR medical coding of matched cases is an important finding since the matched cases are 88% of EDS and 90% of MedPAR cases.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To determine whether county‐level or patient‐level social risk factors are associated with patient‐reported outcomes after total knee replacement when added to the comprehensive joint replacement risk‐adjustment model.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Patient and outcomes data from the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement cohort were merged with the Social Vulnerability Index from the Centers for Disease Control and Prevention.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>This prospective longitudinal cohort measured the change in patient‐reported pain and physical function from baseline to 12 months after surgery. The cohort included a nationally diverse sample of adult patients who received elective unilateral knee replacement between 2012 and 2015.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Using a national network of over 230 surgeons in 28 states, the cohort study enrolled patients from diverse settings and collected one‐year outcomes after the surgery. Patients &lt;65 years of age or who did not report outcomes were excluded.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>After adjusting for clinical and demographic factors, we found patient‐reported race, education, and income were associated with patient‐reported pain or functional scores. Pain improvement was negatively associated with Black race (CI = −8.71, −3.02) and positively associated with higher annual incomes (≥$45,00) (CI = 0.07, 2.33). Functional improvement was also negatively associated with Black race (CI = −5.81, −0.35). Patients with higher educational attainment (CI = −2.35, −0.06) reported significantly less functional improvement while patients in households with three adults reported greater improvement (CI = 0.11, 4.57). We did not observe any associations between county‐level social vulnerability and change in pain or function.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>We found patient‐level social factors were associated with patient‐reported outcomes after total knee replacement, but county‐level social vulnerability was not. Our findings suggest patient‐level social factors warrant further investigation to promote health equity in patient‐reported outcomes after total knee replacement.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>The study aims to analyze the relationship between care integration and care quality, and to examine if the relationship varies by patient risk.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>The key independent variables used validated measures derived from a provider survey of functional (i.e., administrative and clinical systems) and social (i.e., patient integration, professional cooperation, professional coordination) integration. Survey responses represented data from a stratified sample of 59 practice sites from 17 health systems. Dependent variables included three quality measures constructed from patient‐level Medicare data: colorectal cancer screening among patients at risk, patient‐level 30‐day readmission, and a practice‐level Healthcare Effectiveness Data and Information Set (HEDIS) composite measure of publicly reported, individual measures of ambulatory clinical quality performance.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Method</jats:title><jats:p>We obtained quality‐ and beneficiary‐level covariate data for the 41,966 Medicare beneficiaries served by the 59 practices in our survey sample.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We estimated hierarchical linear models to examine the association between care integration and care quality and the moderating effect of patients' clinical risk score. We graphically visualized the moderating effects at ±1 standard deviation of our z‐standardized independent and moderating variables and performed simple slope tests.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Our analyses uncovered a strong positive relationship between social integration, specifically patient integration, and the quality of care a patient receives (e.g., a 1‐point increase in a practice's patient integration was associated with 0.31‐point higher HEDIS composite score, <jats:italic>p</jats:italic> &lt; 0.01). Further, we documented positive and significant associations between aspects of social and functional integration on quality of care based on patient risk.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The findings suggest social integration matters for improving the quality of care and that the relationship of integration to quality is not uniform for all patients. Policymakers and practitioners considering structural integrations of health systems should direct attention beyond structure to consider the potential for social integration to impact outcomes and how that might be achieved.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To develop weights to estimate state population‐based hospitalization rates for all residents of a state using only data from in‐state hospitals which exclude residents treated in other states.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases (SID), 2018–2019, 47 states+DC.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We identified characteristics for patients hospitalized in each state differentiating movers (discharges for patients hospitalized outside state of residence) from stayers (discharges for patients hospitalized in state of residence) and created weights based on 2018 data informed by these characteristics. We calculated standard errors using a sampling framework and compared weight‐based estimates against complete observed values for 2019.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>SID are based on administrative billing records collected by hospitals, shared with statewide data organizations, and provided to HCUP.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Of 34,186,766 discharged patients in 2018, 4.2% were movers. A higher share of movers (vs. stayers) lived in state border and rural counties; a lower share had discharges billed to Medicaid or were hospitalized for maternal/neonatal services. The difference between 2019 observed and estimated total discharges for all included states and DC was 9402 (mean absolute percentage error = 0.2%). We overestimated discharges with an expected payer of Medicaid, from the lowest income communities, and for maternal/neonatal care. We underestimated discharges with an expected payer of private insurance, from the highest income communities, and with injury diagnoses and surgical services. Estimates for most subsets were not within a 95% confidence interval, likely due to factors impossible to account for (e.g., hospital closures/openings, shifting consumer preferences).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The weights offer a practical solution for researchers with access to only a single state's data to account for movers when calculating population‐based hospitalization rates.</jats:p></jats:sec>