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The European Journal of Health... Aug 2022In this population-based cohort study, billing data from German statutory health insurance (BARMER, 10% of population) are used to develop a prioritisation model for...
In this population-based cohort study, billing data from German statutory health insurance (BARMER, 10% of population) are used to develop a prioritisation model for COVID-19 vaccinations based on cumulative underlying conditions. Using a morbidity-based classification system, prevalence and risks for COVID-19-related hospitalisations, ventilations and deaths are estimated. Trisomies, behavioural and developmental disorders (relative risk: 2.09), dementia and organic psychoorganic syndromes (POS) (2.23) and (metastasised) malignant neoplasms (1.99) were identified as the most important conditions for escalations of COVID-19 infection. Moreover, optimal vaccination priority schedules for participants are established on the basis of individual cumulative escalation risk and are compared to the prioritisation scheme chosen by the German Government. We estimate how many people would have already received a vaccination prior to escalation. Vaccination schedules based on individual cumulative risk are shown to be 85% faster than random schedules in preventing deaths, and as much as 57% faster than the German approach, which was based primarily on age and specific diseases. In terms of hospitalisation avoidance, the individual cumulative risk approach was 51% and 28% faster. On this basis, it is concluded that using individual cumulative risk-based vaccination schedules, healthcare systems can be relieved and escalations more optimally avoided.
Topics: COVID-19; COVID-19 Vaccines; Cohort Studies; Hospitalization; Humans; Risk Adjustment; Vaccination
PubMed: 34799804
DOI: 10.1007/s10198-021-01408-8 -
Journal of the American College of... Jan 2018The burden of atherosclerotic cardiovascular disease (ASCVD) in high-income countries is mostly borne by the elderly. With increasing life expectancy, clear guidance on... (Review)
Review
The burden of atherosclerotic cardiovascular disease (ASCVD) in high-income countries is mostly borne by the elderly. With increasing life expectancy, clear guidance on sensible use of statin therapy to prevent a first and potentially devastating ASCVD event is critically important to ensure a healthy aging population. Since 2013, 5 major North American and European guidelines on statin use in primary prevention of ASCVD have been released by the American College of Cardiology/American Heart Association, the UK National Institute for Health and Care Excellence, the Canadian Cardiovascular Society, U.S. Preventive Services Task Force, and the European Society of Cardiology/European Atherosclerosis Society. Guidance on using statin therapy in primary ASCVD prevention in the growing elderly population (>65 years of age) differs markedly. The authors discuss the discrepant recommendations, place them into the context of available evidence, and identify circumstances in which uncertainty may hamper the appropriate use of statins in the elderly.
Topics: Age Factors; Aged; Atherosclerosis; Cardiovascular Diseases; Global Health; Humans; Hydroxymethylglutaryl-CoA Reductase Inhibitors; Practice Guidelines as Topic; Primary Prevention; Risk Adjustment
PubMed: 29301631
DOI: 10.1016/j.jacc.2017.10.080 -
JAMA Health Forum Mar 2022Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the .
IMPORTANCE
Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the .
OBJECTIVE
To develop an -based classification framework for predicting diverse health care payment, quality, and performance outcomes.
DESIGN SETTING AND PARTICIPANTS
Physician teams mapped all diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using , mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage.
MAIN OUTCOMES AND MEASURES
Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000.
RESULTS
A total of 65 901 460 person-years were split into 90% estimation/10% validation samples (n = 6 604 259). In all, 3223 DXIs were created: 2435 main effects, 772 modifiers, and 16 scaled items. Stepwise regressions predicting annual health care spending (mean [SD], $5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs.
CONCLUSIONS
In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.
Topics: Delivery of Health Care; Health Expenditures; Humans; International Classification of Diseases; Patient Protection and Affordable Care Act; Risk Adjustment; United States
PubMed: 35977291
DOI: 10.1001/jamahealthforum.2022.0276 -
JAMA Health Forum Mar 2023Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment...
IMPORTANCE
Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity.
OBJECTIVE
To examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries.
DESIGN, SETTING, AND PARTICIPANTS
Using data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022.
EXPOSURES
Census block group-level ADI.
MAIN OUTCOMES AND MEASURES
Regression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries.
RESULTS
Among a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs.
CONCLUSIONS AND RELEVANCE
In this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.
Topics: Aged; Humans; Female; United States; Male; Medicare; Risk Adjustment; Health Equity; Cross-Sectional Studies; Health Expenditures
PubMed: 37000433
DOI: 10.1001/jamahealthforum.2023.0266 -
The American Journal of Managed Care Feb 2020The authors disagree with previous research concluding that the Home Health Care Consumer Assessment of Healthcare Providers and Services (CAHPS) publicly reported data...
The authors disagree with previous research concluding that the Home Health Care Consumer Assessment of Healthcare Providers and Services (CAHPS) publicly reported data are insufficiently adjusted for patient comorbidities.
Topics: Carcinoma, Hepatocellular; Centers for Medicare and Medicaid Services, U.S.; Health Care Surveys; Humans; Liver Neoplasms; Risk Adjustment; United States
PubMed: 32059090
DOI: 10.37765/ajmc.2020.42391 -
PloS One 2022This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and...
OBJECTIVES
This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and medication-based ones in patients with chronic obstructive pulmonary disease (COPD).
METHODS
This was a population-based retrospective cohort study. Data used in this study were sourced from the Taiwan National Health Insurance Research Database. The study population comprised all patients who were hospitalized due to COPD for the first time in the target year of 2012. Each qualified patient was individually followed for one year starting from the index date to assess two outcomes of interest, medical expenditures within one year after discharge and in-hospital mortality of patients. To assess how well the added comorbidity measures would improve the fitted model, we calculated the log-likelihood ratio statistic G2. Subsequently, we compared risk adjustment performance of the comorbidity indices by using the Harrell c-statistic measure derived from multiple logistic regression models.
RESULTS
Analytical results demonstrated that that comorbidity measures were significant predictors of medical expenditures and mortality of COPD patients. Specifically, in the category of diagnosis-based comorbidity indices the Elixhauser index was superior to other indices, while the RxRisk-V index was a stronger predictor in the framework of medication-based codes, for gauging both medical expenditures and in-hospital mortality by utilizing information from the index hospitalization only as well as the index and prior hospitalizations.
CONCLUSIONS
In conclusion, this work has ascertained that comorbidity indices are significant predictors of medical expenditures and mortality of COPD patients. Based on the study findings, we propose that when designing the payment schemes for patients with chronic diseases, the health authority should make adjustments in accordance with the burden of health care caused by comorbid conditions.
Topics: Comorbidity; Hospital Mortality; Humans; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Risk Adjustment
PubMed: 35802678
DOI: 10.1371/journal.pone.0270468 -
Zeitschrift Fur Evidenz, Fortbildung... Jun 2021The quality indicators of the Initiative Qualitätsmedizin e. V. (IQM) have been developed as triggers to examine treatment processes for opportunities for improvement....
INTRODUCTION
The quality indicators of the Initiative Qualitätsmedizin e. V. (IQM) have been developed as triggers to examine treatment processes for opportunities for improvement. Published quality results have partly been used for external quality comparisons in the media. Therefore, member hospitals of IQM demanded to investigate if methods of risk adjustment should be applied in the calculation of the quality indicators. After a hearing of experts had been held, a task force was founded to conduct test calculations on risk adjustment methods.
METHODS
Specific risk adjustment models for mortality in myocardial infarction, heart failure, stroke, pneumonia, and colectomy in colorectal cancer were developed in the database of national German DRG data of the year 2016. These models were used to calculate standardized mortality ratios (SMR) per indicator in a sample of 172 member hospitals of IQM based on the data of the year 2018. Median SMR per indicator were compared to median SMR based on a standardization by age and gender, which is the standard procedure in IQM. Correlations between the different SMR were calculated. Quality of care was judged by two different approaches: a) a descriptive discrepancy of |0.1| from the SMR value of 1, and b) a significant discrepancy from 1 using the 95% confidence limits. The effect of using the specific risk adjustment in relation to the standard procedure was investigated for both approaches (a and b).
RESULTS
The specific risk adjustment methods showed an area under the curve between 0.72 and 0.84. The median differences between the SMR based on standardization by age and gender and the SMR based on specific risk adjustment were small (between 0 and 0.4); Spearman's correlations were between 0.90 and 0.99. Changes in the judgement of quality of care in comparison to the national average occurred in 3.9% (mortality from pneumonia) to 20.6% of the hospitals (mortality from heart failure) in descriptive comparisons. When the judgement was based on confidence limits changes were observed in 1.6% (mortality after colectomy) to 17.4% of the hospitals (mortality from heart failure).
DISCUSSION
Implementing specific risk adjustment models had only minor effects on the distribution of risk-adjusted mortality compared to the standard procedure, but the judgement of quality of care could change for a fifth of the hospitals in individual indicators. Concerning methodological and practical reasons, the task force recommends further development of risk adjustment methods for selected indicators. This should be accompanied by studies on the validity of inpatient administrative data for quality management as well as by efforts to improve the usefulness of these data for such purposes.
Topics: Germany; Hospital Mortality; Humans; Inpatients; Quality Indicators, Health Care; Risk Adjustment
PubMed: 34023246
DOI: 10.1016/j.zefq.2021.04.003 -
Israel Journal of Health Policy Research Apr 2020In 2015, mental health services were added to the Israeli National Health Insurance package of services. As such, these services are financed by the budget which is...
BACKGROUND
In 2015, mental health services were added to the Israeli National Health Insurance package of services. As such, these services are financed by the budget which is allocated to the Health Plans according to a risk adjustment scheme. An inter-ministerial team suggested a formula by which the mental health budget should be allocated among the Health Plans. Our objective in this study was to develop alternative rates based on individual data, and to evaluate the ones suggested.
METHODS
The derivation of the new formula is based on our previous study of all psychiatric inpatients in Israel in the years 2012-2013 (n = 27,446), as well as outpatients in one psychiatric clinic in the same period (n = 6115). Based on Ministry of Health and clinic data we identified predictors of mental health services consumption. Age, gender, marital status and diagnosis were used as risk adjusters to calculate the capitation rates for outpatient care and inpatient care, respectively. All prices of services were obtained from the Ministry of Health tariffs. These rates were modified to include non-users using restricted models.
RESULTS
The mental health capitation scales are typically "humped" with regard to age. The rates for ambulatory care varied from a minimum 0.19 of the average consumption for males above the age of 85 to a maximum of 1.93 times the average for females between the ages of 45-54. For inpatient services the highest rate was 409.25 times the average for single, male patients with schizophrenia spectrum diagnoses, aged 45-54. The overall mental health scale ranges from 2.347 times the average for men aged 45-54, to 0.191 for women aged 85+. The modified scale for the entire post-reform package of benefits (including both mental health care and physical health care) is increasing with age to 4.094 times the average in men aged over 85. The scale is flatter than the pre-reform scale.
CONCLUSIONS
The risk adjustment rates calculated for outpatient care are substantially different from the ones suggested by the inter-ministerial team. The inpatient rates are new, and indicate that for patients with schizophrenia, a separate risk-sharing arrangement might be desirable. Adopting the rates developed in this analysis would decrease the budget shares of Clalit and Leumit with their relatively older populations, and increase Maccabi and Meuhedet's shares. Future research should develop a risk-adjustment scheme which covers directly both mental and physical care provided by the Israeli Health Plans, using their data.
Topics: Adult; Aged; Aged, 80 and over; Female; Hospitalization; Humans; Inpatients; Israel; Male; Mental Health; Middle Aged; Outpatients; Risk Adjustment; Risk Assessment
PubMed: 32290866
DOI: 10.1186/s13584-020-00373-6 -
The American Journal of Managed Care Feb 2016Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are... (Review)
Review
OBJECTIVES
Case-mix adjustment is generally considered indispensable for fair comparison of healthcare performance. Inaccurate results are also unfair to patients as they are ineffective for improving quality. However, little is known about what factors should be adjusted for. We reviewed case-mix factors included in adjustment models for key diabetes indicators, the rationale for their inclusion, and their impact on performance.
STUDY DESIGN
Systematic review.
METHODS
This systematic review included studies published up to June 2013 addressing case-mix factors for 6 key diabetes indicators: 2 outcomes and 2 process indicators for glycated hemoglobin (A1C), low-density lipoprotein cholesterol, and blood pressure. Factors were categorized as demographic, diabetes-related, comorbidity, generic health, geographic, or care-seeking, and were evaluated on the rationale for inclusion in the adjustment models, as well as their impact on indicator scores and ranking.
RESULTS
Thirteen studies were included, mainly addressing A1C value and measurement. Twenty-three different case-mix factors, mostly demographic and diabetes-related, were identified, and varied from 1 to 14 per adjustment model. Six studies provided selection motives for the inclusion of case-mix factors. Marital status and body mass index showed a significant impact on A1C value. For the other factors, either no or conflicting associations were reported, or too few studies (n ≤ 2) investigated this association.
CONCLUSIONS
Scientific knowledge about the relative importance of case-mix factors for diabetes indicators is emerging, especially for demographic and diabetes-related factors and indicators on A1C, but is still limited. Because arbitrary adjustment potentially results in inaccurate quality information, meaningful stratification that demonstrates inequity in care might be a better guide, as it can be a driver for quality improvement.
Topics: Age Factors; Body Mass Index; Cholesterol, LDL; Comorbidity; Diabetes Mellitus; Diagnosis-Related Groups; Glycated Hemoglobin; Health Status; Humans; Patient Acceptance of Health Care; Risk Adjustment; Sex Factors; Socioeconomic Factors
PubMed: 26881319
DOI: No ID Found -
Infection Control and Hospital... Oct 2016OBJECTIVE To identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk...
OBJECTIVE To identify comorbid conditions associated with surgical site infection (SSI) among patients undergoing renal transplantation and improve existing risk adjustment methodology used by the Centers for Disease Control and Prevention National Healthcare Safety Network (NHSN). PATIENTS Patients (≥18 years) who underwent renal transplantation at University of Maryland Medical Center January 1, 2010-December 31, 2011. METHODS Trained infection preventionists reviewed medical records to identify surgical site infections that developed within 30 days after transplantation, using NHSN criteria. Patient demographic characteristics and risk factors for surgical site infections were identified through a central data repository. International Statistical Classification of Disease, Ninth Revision, Clinical Modification codes were used to analyze individual component comorbid conditions and calculate the Charlson and Elixhauser comorbidity indices. These indices were compared with the current NHSN risk adjustment methodology. RESULTS A total of 441 patients were included in the final cohort. In bivariate analysis, the Charlson components of cerebrovascular disease, peripheral vascular disease, and rheumatologic disorders and Elixhauser components of obesity, rheumatoid arthritis, and weight loss were significantly associated with the outcome. A model utilizing the variables from the NHSN methodology had a c-statistic of 0.56 (95% CI, 0.48-0.63), whereas a model that also included comorbidities from the Charlson and Elixhauser indices had a c-statistic of 0.65 (95% CI, 0.58-0.73). The model with all 3 risk adjustment scores performed best and was statistically different from the NHSN model alone, demonstrated by improvement in the c statistic (0.65 vs 0.56). CONCLUSION Risk adjustment models should incorporate electronically available comorbid conditions. Infect Control Hosp Epidemiol 2016;1-6.
Topics: Academic Medical Centers; Adult; Aged; Baltimore; Body Mass Index; Centers for Disease Control and Prevention, U.S.; Comorbidity; Female; Humans; Infection Control Practitioners; International Classification of Diseases; Kidney Transplantation; Logistic Models; Male; Medical Records; Middle Aged; Retrospective Studies; Risk Adjustment; Risk Factors; Surgical Wound Infection; United States
PubMed: 27418295
DOI: 10.1017/ice.2016.140