Comparative performance of comorbidity indices in predicting health care-related behaviors and outcomes among Medicaid enrollees with type 2 diabetes

Factors Associated With Receipt of Diabetes Self-Management Education and Support for Type 2 Diabetes: Potential for a Population Health Management Approach

BACKGROUND

Population health management approaches can help target diabetes resources like Diabetes Self-Management Education and Support (DSMES) to individuals at the highest risk of complications and poor outcomes. Little is known about patient characteristics associated with DSMES receipt since widespread uptake of telemedicine for diabetes care in 2020.

METHODS

In this retrospective cohort study, we used electronic medical record (EMR) data to assess patterns of DSMES delivery from May 2020 to May 2022 among adults who used telemedicine for type 2 diabetes (T2D) endocrinology care in a large integrated health system. Multilevel regression models were used to evaluate the association of key patient characteristics with DSMES receipt.

RESULTS

Of 3530 patients in the overall cohort, 401 patients (11%) received DSMES. In adjusted multivariable logistic regression, higher baseline HbA1c (odds ratios [OR] 3.10 [95% confidence interval 2.22-4.33] for HbA1c ≥9% vs <7%), insulin regimen complexity (OR 3.53 [2.59-4.80] for multiple daily injections vs no insulin), and number of noninsulin medications (OR 1.17 [1.05-1.30] per 1 additional medication) were significantly associated with receipt of DSMES, whereas rurality and area-level deprivation of patient residence were not.

CONCLUSIONS

Diabetes Self-Management Education and Support remains underutilized in this cohort of adults using telemedicine to access endocrinology care for T2D. Factors contributing to clinical complexity increased the odds of receiving DSMES. These results support a potential population health management approach using EMR data, which could target DSMES resources to those at higher risk of poor outcomes. This risk-stratified approach may be even more effective now that more people can access DSMES via telemedicine in addition to in-person care.

Comparison of Three Comorbidity Measures for Predicting In-Hospital Death through a Clinical Administrative Nacional Database

BACKGROUND

Various authors have validated scales to measure comorbidity. However, the prognosis capacity variation according to the comorbidity measurement index used needs to be determined in order to identify which is the best predictor.

AIMS

To quantify the differences between the Charlson (CCI), Elixhauser (ECI) and van Walraven (WCI) comorbidity indices as prognostic factors for in-hospital mortality and to identify the best comorbidity measure predictor.

METHODS

A retrospective observational study that included all hospitalizations of patients over 18 years of age, discharged between 2017 and 2021 in the hospital, using the Minimum Basic Data Set (MBDS). We calculated CCI, ECI, WCI according to ICD-10 coding algorithms. The correlation and concordance between the three indices were evaluated by Spearman's rho and Intraclass Correlation Coefficient (ICC), respectively. The logistic regression model for each index was built for predicting in-hospital mortality. Finally, we used the receiver operating characteristic (ROC) curve for comparing the performance of each index in predicting in-hospital mortality, and the Delong method was employed to test the statistical significance of differences.

RESULTS

We studied 79,425 admission episodes. The 54.29% were men. The median age was 72 years (interquartile range [IQR]: 56-80) and in-hospital mortality rate was 4.47%. The median of ECI was = 2 (IQR: 1-4), ICW was 4 (IQR: 0-12) and ICC was 1 (IQR: 0-3). The correlation was moderate: ECI vs. WCI rho = 0.645, p < 0.001; ECI vs. CCI rho = 0.721, p < 0.001; and CCI vs. WCI rho = 0.704, p < 0.001; and the concordance was fair to good: ECI vs. WCI Intraclass Correlation Coefficient type A (ICC_A) = 0.675 (CI 95% 0.665-0.684) p < 0.001; ECI vs. CCI ICC_A = 0.797 (CI 95% 0.780-0.812), p < 0.001; and CCI vs. WCI ICC_A = 0.731 (CI 95% 0.667-0.779), p < 0.001. The multivariate regression analysis demonstrated that comorbidity increased the risk of in-hospital mortality, with differences depending on the comorbidity measurement scale: odds ratio [OR] = 2.10 (95% confidence interval [95% CI] 2.00-2.20) p > |z| < 0 using ECI; OR = 2.31 (CI 95% 2.21-2.41) p > |z| < 0 for WCI; and OR = 2.53 (CI 95% 2.40-2.67) p > |z| < 0 employing CCI. The area under the curve [AUC] = 0.714 (CI 95% 0.706-0.721) using as a predictor of in-hospital mortality CCI, AUC = 0.729 (CI 95% 0.721-0.737) for ECI and AUC = 0.750 (CI 95% 0.743-0.758) using WCI, with statistical significance (p < 0.001).

CONCLUSION

Comorbidity plays an important role as a predictor of in-hospital mortality, with differences depending on the measurement scale used, the van Walraven comorbidity index being the best predictor of in-hospital mortality.

Real-world utilization of top-down and step-up therapy and initial costs in Crohn disease

BACKGROUND: Medication treatment strategies for Crohn disease (CD) include step-up (SU) therapy, beginning with oral anti-inflammatory agents, and top-down (TD) therapy, beginning with biologics or immunomodulators. The real-world utilization and short-term medical costs associated with these treatment strategies are not well described. OBJECTIVE: To examine the prevalence of TD therapy use over time and compare the first-year direct medical expenditures among patients initiating CD medication treatment with SU and TD therapy in a real-world setting. METHODS: We conducted a retrospective cohort study of Optum Clinformatics Data Mart examining adult patients with CD newly initiated on medication therapy from 2010 to 2018. Included patients had a CD-indicated medication dispensed within 60 days after their initial CD diagnosis, were continuously enrolled in the health plan throughout the study period, and did not have comorbidities treated with a biologic also indicated for CD. A generalized linear model was used to quantify the differences in adjusted mean first-year CD-specific, direct nonpharmacy medical costs between users of TD and SU therapy. RESULTS: We identified 3,157 patients newly initiating medication therapy for CD (2,392 [75.8%] patients treated with SU therapy and 765 [24.2%] treated with TD therapy). The use of TD therapy over the study period increased from 17% in 2011 to 31% in 2017. TD therapy was also associated with a 149.8% ($1,230) higher adjusted average per-patient first-year CD-direct nonpharmacy medical cost compared with SU therapy (adjusted ratio of cost for TD compared with SU [2.498, 95% CI = 2.12-2.95]). CONCLUSIONS: In patients newly initiating medication therapy for CD, TD therapy use increased between 2010 and 2017 and was associated with higher first-year nonpharmacy medical expenditure. These findings align with the strategy of initiating TD therapy in patients with a higher disease burden. Further research is needed to determine long-term overall health care costs and clinical outcomes associated with SU and TD strategies in a real-world setting. DISCLOSURES: Dr Caffrey received research funding from Gilead, Merck, Pfizer, and Shionogi and is a speaker for Merck. The views expressed are those of the author and do not necessarily reflect the position or policy of the US Department of Veterans Affairs. Material is based on work supported, in part, by the Office of Research and Development.

Necrotising Otitis Externa - is poor outcome predictable? The application of a diagnosis-based scoring system in patients with skull base osteomyelitis

Background

The increased incidence of necrotising otitis externa over the last decade has had a significant burden on healthcare providers. Several factors may affect outcome, and stratifying risk may allow personalised treatment.

Method

Retrospectively identified patients were prospectively surveyed over 12 months. The Cox proportional hazards model was used to identify predictors of poor prognosis.

Results

Twenty-six patients with necrotising otitis externa (average age of 80 years) were admitted from 2018 to 2019. At one year, 19 per cent mortality was observed. A high Charlson Comorbidity Index was associated with increased mortality (p = 0.03), prolonged in-patient stay (p = 0.047) and increased odds of adverse outcomes (odds ratio = 1.48, 95 per cent confidence interval = 0.26–2.67, p = 0.019). The Charlson Comorbidity Index in our prognostic model was validated using the receiver operating characteristic curve (area under the curve = 0.76). Charlson Comorbidity Index score of 5 or more independently predicted one-year morbidity and mortality (hazard ratio = 1.30, 95 per cent confidence interval = 0.94–1.79, p = 0.03).

Conclusion

Risk-stratifying patients may enable clinicians to holistically counsel patients and tailor their treatment to improve their prognosis and subsequently alleviate the burden of necrotising otitis externa.

Analyzing disparities in COVID-19 testing trends according to risk for COVID-19 severity across New York City

BACKGROUND

Given the interplay between race and comorbidities on COVID-19 morbidity and mortality, it is vital that testing be performed in areas of greatest need, where more severe cases are expected. The goal of this analysis is to evaluate COVID-19 testing data in NYC relative to risk factors for COVID-19 disease severity and demographic characteristics of NYC neighborhoods.

METHODS

COVID-19 testing and the racial/ethnic composition of NYC Zip Code Tabulation Areas (ZCTA) were obtained from the NYC Coronavirus data repository and the American Community Survey, respectively. The prevalence of neighborhood-level risk factors for COVID-19 severity according to the Centers for Disease Control and Prevention criteria for risk of severe illness and complications from COVID-19 were used to create a ZCTA-level risk index. Poisson regressions were performed to study the ratio of total tests relative to the total ZCTA population and the proportion of positive tests relative to the total tests performed over time.

RESULTS

From March 2nd-April 6th, the total tests/population (%) was positively associated with the proportion of white residents (IRR_adj: 1.0003, 95% CI: 1.0003-1.0004) and the COVID risk index (IRR_adj: 1.038, 95% CI: 1.029-1.046). The risk index (IRR_adj: 1.017, 95% CI: 0.939-1.101) was not associated with total tests performed from April 6th-May 12th, and inversely associated from May 12th-July 6th (IRR_adj: 0.862, 95% CI: 0.814-0.913). From March 2nd-April 6th the COVID risk index was not statistically associated (IRR_adj: 1.010, 95% CI: 0.987-1.034) with positive tests/total tests. From April 6th-May 12th, the COVID risk index was positively associated (IRR_adj: 1.031, 95% CI: 1.002-1.060), while from May 12th-July 6th, the risk index was inversely associated (IRR_adj: 1.135, 95% CI: 1.042-1.237) with positivity.

CONCLUSIONS

Testing in NYC has suffered from the lack of availability in high-risk populations, and was initially limited as a diagnostic tool for those with severe symptoms, which were mostly concentrated in areas where vulnerable residents live. Subsequent time periods of testing were not targeted in areas according to COVID-19 disease risk, as these areas still experience more positive tests.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity

OBJECTIVE

To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.

STUDY DESIGN AND SETTING

Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).

RESULTS

Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.

CONCLUSIONS

Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.

TRIAL REGISTRATION NUMBER

PROSPERO id: CRD42018088129.

The Update on Instruments Used for Evaluation of Comorbidities in Total Hip Arthroplasty

INTRODUCTION

It is a well-established fact that concomitant diseases can affect the outcome of total hip arthroplasty (THA). Therefore, careful preoperative assessment of a patient's comorbidity burden is a necessity, and it should be a part of routine screening as THA is associated with a significant number of complications. To measure the multimorbidity, dedicated clinical tools are used.

METHODS

The article is a systematic review of instruments used to evaluate comorbidities in THA studies. To create a list of available instruments for assessing patient's comorbidities, the search of medical databases (PubMed, Web of Science, Embase) for indices with proven impact on revision risk, adverse events, mortality, or patient's physical functioning was performed by two independent researchers.

RESULTS

The initial search led to identifying 564 articles from which 26 were included in this review. The measurement tools used were: The Charlson Comorbidity Index (18/26), Society of Anesthesiology classification (10/26), Elixhauser Comorbidity Method (6/26), and modified Frailty Index (5/26). The following outcomes were measured: quality of life and physical function (8/26), complications (10/26), mortality (8/26), length of stay (6/26), readmission (5/26), reoperation (2/26), satisfaction (2/26), blood transfusion (2/26), surgery delay or cancelation (1/26), cost of care (1/26), risk of falls (1/26), and use of painkillers (1/26). Further research resulted in a comprehensive list of eleven indices suitable for use in THA outcomes studies.

CONCLUSION

The comorbidity assessment tools used in THA studies present a high heterogeneity level, and there is no particular system that has been uniformly adopted. This review can serve as a help and an essential guide for researchers in the field.

Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database

The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/time-intensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process.

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

OBJECTIVES

To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription.

STUDY DESIGN AND SETTING

We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3 L index score of ≥5% after 6-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally and by using internal-external cross-validation (IECV).

RESULTS

In 3,582 patients with complete data, of whom 1,046 (29.2%) showed deterioration in HRQoL, and 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being, and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13). HRQoL/functionality had the strongest prognostic value.

CONCLUSION

The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high risk of dHRQoL.

REGISTRATION

PROSPERO ID: CRD42018088129.

Derivation and validation of a novel comorbidity-based delirium risk index to predict postoperative delirium using national administrative healthcare database

OBJECTIVE

To derive and validate a comorbidity-based delirium risk index (DRI) to predict postoperative delirium.

DATA SOURCE/STUDY SETTING

Data of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20-25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.

STUDY DESIGN

Thirty-six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson-Deyo Index for goodness-of-fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness-of-fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.

DATA COLLECTION

Hip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD-9 codes. Postoperative delirium was defined by using ICD-9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.

PRINCIPAL FINDINGS

The derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson-Deyo Comorbidity Index with better goodness-of-fit and predictive performance.

CONCLUSIONS

Delirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.

PREDICTORS FOR ADVERSE OUTCOMES IN DIABETIC KETOACIDOSIS IN A MULTIHOSPITAL HEALTH SYSTEM

Objective: To determine predictors of prolonged length of stay (LOS), 30-day readmission, and 30-day mortality in a multihospital health system. Methods: We performed a retrospective review of 531 adults admitted with diabetic ketoacidosis (DKA) to a multihospital health system between November 2015 and December 2016. Demographic and clinical data were collected. Linear regression was used to calculate odds ratios (ORs) for predictors and their association with prolonged LOS (3.2 days), 30-day readmission, and 30-day mortality. Results: Significant predictors for prolonged LOS included: intensive care unit (ICU) admission (OR, 2.12; 95% confidence interval [CI], 1.38 to 3.27), disease duration (nonlinear) (OR, 1.28; 95% CI, 1.10 to 1.49), non-white race (OR, 1.73; 95% CI, 1.15 to 2.60), age at admission (OR, 1.03; 95% CI, 1.01 to 1.04), and Elixhauser index (EI) (OR, 1.21; 95% CI, 1.13 to 1.29). Shorter time to consult after admission (median [Q1, Q3] of 11.3 [3.9, 20.7] vs. 14.8 [7.4, 37.3] hours, P<.001) was associated with a shorter LOS. Significant 30-day readmission predictors included: Medicare insurance (OR, 2.35; 95% CI, 1.13 to 4.86) and EI (OR, 1.31; 95% CI, 1.21 to 1.41). Endocrine consultation was associated with reduced 30-day readmission (OR, 0.51; 95% CI, 0.28 to 0.92). A predictive model for mortality was not generated because of low event rates. Conclusion: EI, non-white race, disease duration, age, Medicare, and ICU admission were associated with adverse outcomes. Endocrinology consultation was associated with lower 30-day readmission, and earlier consultation resulted in a shorter LOS. Abbreviations: CI = confidence interval; DKA = diabetic ketoacidosis; EI = Elixhauser index; HbA1c = hemoglobin A1c; ICD = International Classification of Diseases; ICU = intensive care unit; LOS = length of stay; OR = odds ratio; Q = quartile.

Comparative Performance of Diagnosis-based and Prescription-based Comorbidity Scores to Predict Health-related Quality of Life

OBJECTIVES

To compare the performance of the health-related quality of life-comorbidity index (HRQoL-CI) with the diagnosis-based Charlson, Elixhauser, and combined comorbidity scores and the prescription-based chronic disease score (CDS) in predicting HRQoL in Agency of Healthcare Research and Quality priority conditions (asthma, breast cancer, diabetes, and heart failure).

METHODS

The Medical Expenditure Panel Survey (2005 and 2007-2011) data was used for this retrospective study. Four disease-specific cohorts were developed that included adult patients (age 18 y and above) with the particular disease condition. The outcome HRQoL [physical component score (PCS) and mental component score (MCS)] was measured using the Short Form Health Survey, Version 2 (SF-12v2). Multiple linear regression analyses were conducted with the PCS and MCS as dependent variables. Comorbidity scores were compared using adjusted R.

RESULTS

Of 140,046 adult participants, the study cohort included 7436 asthma (5.3%), 1054 breast cancer (0.8%), 13,829 diabetes (9.9%), and 937 heart failure (0.7%) patients. Among individual scores, HRQoL-CI was best at predicting PCS and MCS. Adding prescription-based comorbidity scores to HRQoL-CI in the same model improved prediction of PCS and MCS. HRQoL-CI+CDS performed the best in predicting PCS (adjusted R): asthma (43.7%), breast cancer (31.7%), diabetes (32.7%), and heart failure (20.0%). HRQoL-CI+CDS and Elixhauser+CDS had superior and comparable performance in predicting MCS (adjusted R): asthma (HRQoL-CI+CDS=20.1%; Elixhauser+CDS=19.6%), breast cancer (HRQoL-CI+CDS=12.9%; Elixhauser+CDS=14.1%), diabetes (HRQoL-CI+CDS=17.7%; Elixhauser+CDS=17.7%), and heart failure (HRQoL-CI+CDS=18.1%; Elixhauser+CDS=17.7%).

CONCLUSIONS

HRQoL-CI performed best in predicting HRQoL. Combining prescription-based scores to diagnosis-based scores improved the prediction of HRQoL.

Modification of claims-based measures improves identification of comorbidities in non-elderly women undergoing mastectomy for breast cancer: a retrospective cohort study

Background

Accurate identification of underlying health conditions is important to fully adjust for confounders in studies using insurer claims data. Our objective was to evaluate the ability of four modifications to a standard claims-based measure to estimate the prevalence of select comorbid conditions compared with national prevalence estimates.

Methods

In a cohort of 11,973 privately insured women aged 18–64 years with mastectomy from 1/04–12/11 in the HealthCore Integrated Research Database, we identified diabetes, hypertension, deficiency anemia, smoking, and obesity from inpatient and outpatient claims for the year prior to surgery using four different algorithms. The standard comorbidity measure was compared to revised algorithms which included outpatient medications for diabetes, hypertension and smoking; an expanded timeframe encompassing the mastectomy admission; and an adjusted time interval and number of required outpatient claims. A χ2 test of proportions was used to compare prevalence estimates for 5 conditions in the mastectomy population to national health survey datasets (Behavioral Risk Factor Surveillance System and the National Health and Nutrition Examination Survey). Medical record review was conducted for a sample of women to validate the identification of smoking and obesity.

Results

Compared to the standard claims algorithm, use of the modified algorithms increased prevalence from 4.79 to 6.79 % for diabetes, 14.75 to 24.87 % for hypertension, 4.23 to 6.65 % for deficiency anemia, 1.78 to 12.87 % for smoking, and 1.14 to 6.31 % for obesity. The revised estimates were more similar, but not statistically equivalent, to nationally reported prevalence estimates. Medical record review revealed low sensitivity (17.86 %) to capture obesity in the claims, moderate negative predictive value (NPV, 71.78 %) and high specificity (99.15 %) and positive predictive value (PPV, 90.91 %); the claims algorithm for current smoking had relatively low sensitivity (62.50 %) and PPV (50.00 %), but high specificity (92.19 %) and NPV (95.16 %).

Conclusions

Modifications to a standard comorbidity measure resulted in prevalence estimates that were closer to expected estimates for non-elderly women than the standard measure. Adjustment of the standard claims algorithm to identify underlying comorbid conditions should be considered depending on the specific conditions and the patient population studied.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1636-7) contains supplementary material, which is available to authorized users.

Refined comorbidity index based on dimensionality of comorbidity for use in studies of health-related quality of life

PURPOSE

To refine two subscales of the health-related quality of life comorbidity index (HRQoL-CI) into a single index measure.

METHODS

The 2010 and 2012 Medical Expenditure Panel Surveys were utilized as development and validation datasets, respectively. The least absolute shrinkage and selection operator was applied to select important comorbidity candidates associated with HRQoL. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess dimensionality in comorbidity. Statistical weights were derived based on standardized factor loadings from CFA and regression coefficients from the model predicting HRQoL. Prediction errors and model R(2) values were compared between HRQoL-CI and Charlson CI (CCI).

RESULTS

Eighteen comorbid conditions were identified. CFA models indicated that the second-order multidimensional comorbidity structure had a better fit to the data than did the first-order unidimensional structure. The predictive performance of the refined scale under a multidimensional structure utilizing statistical weights outperformed the original scale and CCI in terms of average prediction error and R(2) in the prediction models (R(2) values from refined scale model are 0.25, 0.30, and 0.28 versus those from CCI of 0.10, 0.09, and 0.06 for general health, SF-6D, and EQ-5D, respectively).

CONCLUSION

The dimensionality of comorbidity and the weight scheme significantly improved the performance of the refined HRQoL-CI. The refined single HRQoL-CI measure appears to be an appropriate and valid instrument specific for risk adjustment in studies of HRQoL. Future research that validates the refined scales for different cultures, age groups, and healthcare settings is warranted.

Descriptive epidemiology of epilepsy in the U.S. population: A different approach

OBJECTIVE

Determine prevalence and incidence of epilepsy within two health insurance claims databases representing large sectors of the U.S.

METHODS

A retrospective observational analysis using Commercial Claims and Medicare (CC&M) Supplemental and Medicaid insurance claims data between January 1, 2007 and December 31, 2011. Over 20 million continuously enrolled lives of all ages were included. Our definition of a prevalent case of epilepsy was based on International Classification of Diseases, Ninth Revision, Clinical Modification-coded diagnoses of epilepsy or seizures and evidence of prescribed antiepileptic drugs. Incident cases were identified among prevalent cases continuously enrolled for ≥ 2 years before the year of incidence determination with no epilepsy, seizure diagnoses, or antiepileptic drug prescriptions recorded.

RESULTS

During 2010 and 2011, overall age-adjusted prevalence estimate, combining weighted estimates from all datasets, was 8.5 cases of epilepsy/1,000 population. With evaluation of CC&M and Medicaid data separately, age-adjusted prevalence estimates were 5.0 and 34.3/1,000 population, respectively, for the same period. The overall age-adjusted incidence estimate for 2011, combining weighted estimates from all datasets, was 79.1/100,000 population. Age-adjusted incidence estimates from CC&M and Medicaid data were 64.5 and 182.7/100,000 enrollees, respectively. Incidence data should be interpreted with caution due to possible misclassification of some prevalent cases.

SIGNIFICANCE

The large number of patients identified as having epilepsy is statistically robust and provides a credible estimate of the prevalence of epilepsy. Our study draws from multiple U.S. population sectors, making it reasonably representative of the U.S.-insured population.

Differences in healthcare expenditures for inflammatory bowel disease by insurance status, income, and clinical care setting

Background. Socioeconomic factors and insurance status have not been correlated with differential use of healthcare services in inflammatory bowel disease (IBD). Aim. To describe IBD-related expenditures based on insurance and household income with the use of inpatient, outpatient, emergency, and office-based services, and prescribed medications in the United States (US). Methods. We evaluated the Medical Expenditure Panel Survey from 1996 to 2011 of individuals with Crohn's disease (CD) or ulcerative colitis (UC). Nationally weighted means, proportions, and multivariate regression models examined the relationships between income and insurance status with expenditures. Results. Annual per capita mean expenditures for CD, UC, and all IBD were $10,364 (N = 238), $7,827 (N = 95), and $9,528, respectively, significantly higher than non-IBD ($4,314, N = 276, 372, p < 0.05). Publicly insured patients incurred the highest costs ($18,067) over privately insured ($8,014, p < 0.05) or uninsured patients ($5,129, p < 0.05). Among all IBD patients, inpatient care composed the highest proportion of costs ($3,392, p < 0.05). Inpatient costs were disproportionately higher for publicly insured patients. Public insurance had higher odds of total costs than private (OR 2.13, CI [1.08-4.19]) or no insurance (OR 4.94, CI [1.26-19.47]), with increased odds for inpatient and emergency care. Private insurance had higher costs associated with outpatient care, office-based care, and prescribed medicines. Low-income patients had lower costs associated with outpatient (OR 0.38, CI [0.15-0.95]) and office-based care (OR 0.21, CI [0.07-0.62]). Conclusions. In the US, high inpatient utilization among publicly insured patients is a previously unrecognized driver of high IBD costs. Bridging this health services gap between SES strata for acute care services may curtail direct IBD-related costs.