Periodontal Disease and Risk of Dementia in Medicare Patients with Hepatitis C Virus

Background: Periodontal disease and hepatitis C virus (HCV) represent chronic infectious states that are common in elderly adults. Both conditions have independently been associated with an increased risk for dementia. Chronic infections are thought to lead to neurodegenerative changes in the central nervous system possibly by promoting a proinflammatory state. This is consistent with growing literature on the etiological role of infections in dementia. Few studies have previously evaluated the association of periodontal disease with dementia in HCV patients. Objective: To examine whether periodontal disease increases the risk of developing Alzheimer’s disease and related dementias (ADRD) among HCV patients in Medicare claims data. Methods: We used Medicare claims data for HCV patients to assess the incidence rate of ADRD with and without exposure to periodontal disease between 2014 and 2017. Cox multivariate regression was used to estimate the association between periodontal disease and development of ADRD, controlling for age, gender, race, ZIP-level income and education, and medical comorbidities. Results: Of 439,760 HCV patients, the incidence rate of ADRD was higher in patients with periodontal diseases compared to those without (10.84% versus 9.26%, p < 0.001), and those with periodontal disease developed ADRD earlier compared to those without periodontal disease (13.99 versus 21.60 months, p < 0.001). The hazard of developing ADRD was 1.35 times higher in those with periodontal disease (95% CI, 1.30 to 1.40, p < 0.001) after adjusting for all covariates, including age. Conclusion: Periodontal disease increased the risk of developing ADRD among HCV patients in a national Medicare claims dataset.

Trends in Acuity of Residents in Assisted Living

Assisted living serves as a substitute for nursing home residents with low care needs, especially in markets with a high proportion of dually eligible Medicare beneficiaries. This study examines trends in the acuity of residents in assisted living communities over time in comparison to nursing homes to characterize how substitution has affected the resident compositions of both settings. We also examine how trends in acuity are shaped by dual eligibility. Using Medicare claims data, we identify cross-sectional samples of beneficiaries in each setting from 2007-2017. The proportion of residents in assisted living with high care needs has increased 18% in assisted living communities compared to 8.7% in nursing homes. Acuity levels are higher among dually eligible assisted living residents compared to assisted living residents who are not dually eligible. Policy makers and administrators should examine whether assisted living is prepared to provide care for an increasingly acute population.

Pennsylvania’s Transition to Medicaid Managed Long-Term Services and Supports

Implemented through five health plans, Ohio’s MyCare demonstration began in 2014 and was designed to coordinate primary, acute care, behavioral health and long-term services in the major urban areas of the state. Individuals who are dually eligible for both Medicaid and Medicare and who reside in specified geographic regions must enroll into a managed MyCare plan. MyCare beneficiaries are assigned to two primary categories: community well and those needing long-term services and supports (LTSS). Individuals receiving the integrated MyCare intervention were expected to have lower acute care hospitalizations, lower long-term nursing home use, better longevity and lower overall health and long-term care costs. Using a propensity score matching design, the evaluation compared MyCare enrollees to comparison group members in non-MyCare counties of the state, using Medicaid and Medicare claims data. The 120,000 MyCare program participants represented about half of the dual eligible individuals in the state.

Risk of hospitalized and non-hospitalized gastrointestinal bleeding in ALLHAT trial participants receiving diuretic, ACE-inhibitor, or calcium-channel blocker

Objectives This post-trial data linkage analysis was to utilize the data of Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) participants linked with their Medicare data to examine the risk of hospitalized and non-hospitalized gastrointestinal (GI) bleeding associated with antihypertensives. Settings ALLHAT was a multicenter, randomized, double-blind, active-controlled trial conducted in a total of 42,418 participants aged ≥55 years with hypertension in 623 North American centers. Data for ALLHAT participants who were aged at ≥65 have been linked with their Medicare claims data. Participants A total of 16,676 patients (4,480 for lisinopril, 4,537 for amlodipine, and 7,659 for chlorthalidone) with complete Medicare claims data were available for the final analysis. Results The cumulative incidences through March 31, 2002 of hospitalized GI bleeding were 5.4%, 5.8% and 5.4% for amlodipine, lisinopril, and chlorthalidone arms, respectively, but were not statistically significant among the 3 arms after adjusting for confounders in Cox regression models. The cumulative incidences of non-hospitalized GI bleeding were also similar across the 3 arms (12.0%, 12.2% and 12.0% for amlodipine, lisinopril, and chlorthalidone, respectively). The increased risk of GI bleeding by age was statistically significant after adjusting for confounders (HR = 1.04 per year, 95% CI: 1.03–1.05). Smokers also had a significantly higher risk of having hospitalized GI bleeding (1.45, 1.19–1.76). Hispanics, those who used aspirin or atenolol in-trial, had diabetes, more education, and a history of stroke had a significantly lower risk of having GI bleeding than their counterparts. Other factors such as gender, history of CHD, prior antihypertensive use, use of estrogen in women, and obesity did not have significant effects on the risk of GI bleeding. Conclusion There were no statistically significant differences on the risk of hospitalized or non-hospitalized GI bleeding among the 3 ALLHAT trial arms (amlodipine, lisinopril, and chlorthalidone) during the entire in-trial follow-up.

504 - Periodontal Disease and Risk of Dementia in Medicare Patients with Hepatitis C Virus

Objective:To examine whether periodontal disease increases the risk of developing Alzheimer’s disease and related dementias (ADRD) among hepatitis C patients in Medicare claims data.Background:Periodontal disease and hepatitis C virus (HCV) represent chronic infectious statesthat are common in elderly adults. Both conditions have independently been associated with an increased risk for dementia. Chronic infections are thought to lead to neurodegenerative changes in the central nervous system possibly by promoting a proinflammatory state. This is consistent with growing literature on the etiological role of infections in dementia. No studies have evaluated the association of periodontal disease with dementia in HCV patients.Methods:We used Medicare claims data for HCV patients to assess the incidence rate of ADRDwith and without exposure to periodontal disease between 2014 and 2017. Diagnosis of periodontal disease, HCV, and ADRD were based on ICD-9 and ICD-10 codes. A Cox multivariate regression model was used to estimate the association between periodontal disease and development of ADRD, controlling for age, gender, race, ZIP-level income and education, and medical comorbidities.Results:Of the 440,578 patients in the dataset, the incidence rate of ADRD in the periodontal disease group was higher compared to those without periodontal disease (10.77% vs. 9.27%, p<0.001, and those with periodontal disease developed ADRD earlier compared to those withoutperiodontal disease (1.15 vs. 1.78 years, p<0.001). The hazard of developing ADRD was 1.23 times higher in those with periodontal disease (95% CI, 1.19 to 1.27, p< 0.001) after adjusting for all covariates, including age.Conclusion:Periodontal disease increased the risk of developing ADRD in HCV patients in anational Medicare claims dataset.

Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications

Background With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. Objective In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. Methods We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis–specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). Results A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. Conclusion We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

Using a simple prescription gap to determine warfarin discontinuation can lead to substantial misclassification

Background: Warfarin remains widely used and a key comparator in studies of other direct oral anticoagulants. As longer-than-needed warfarin prescriptions are often provided to allow for dosing adjustments according to International Normalized Ratios (INRs), the common practice of using a short allowable gap between dispensings to define warfarin discontinuation may lead to substantial misclassification of warfarin exposure. We aimed to quantify such misclassification and determine the optimal algorithm to define warfarin discontinuation. Methods: We linked Medicare claims data from 2007 to 2014 with a multi-center electronic health records system. The study cohort comprised patients ≥65 years with atrial fibrillation and venous thromboembolism initiating warfarin. We compared results when defining warfarin discontinuation by (1) different gaps (3, 7, 14, 30, 60 days) between dispensings; and (2) having a gap ≤60 days or bridging larger gaps if there was INR ordering at least every 42 days (60_INR). Discontinuation was considered misclassified if there was an INR 2 within 7 days after the discontinuation date. Results: Among 3,229 patients, a shorter gap resulted in a shorter mean follow-up time (82, 95, 117, 159, 196, and 259 days for gaps of 3, 7, 14, 30, 60, and 60_INR, respectively; p <0.001). Incorporating INR (60_INR) can reduce misclassification of warfarin discontinuation from 68% to 4% (p <0.001). The on-treatment risk estimation of clinical endpoints varied significantly by discontinuation definitions. Conclusions: Using a short gap between warfarin dispensings to define discontinuation may lead to substantial misclassification, which can be improved by incorporating intervening INR codes.

Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia

We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency’s monitors with Medicare claims data to illustrate the use of secondary data to document associations. Additionally, we describe results from a previous study that uses an IV for pollution and finds that PM2.5’s effects on dementia are larger than non-causal associations.