Initiating Second-Line Antidiabetic Medication Among Older Adults With Type 2 Diabetes on Metformin

BACKGROUND: Antidiabetic medications (ADM), especially sulfonylureas (SFU) and basal insulin (BI), are associated with increased risk of hypoglycemia, which is especially concerning among older adults in poor health. The objective of this study was to investigate prescribing patterns of ADM in older adults according to their health status.METHODS: This case control study analyzed administrative claims between 2013-2017 from a large national payer. The study population was derived from a nationwide database of 84,720 U.S. adults aged ≥65, who were enrolled in Medicare Advantage health insurance plans. Participants had type 2 diabetes on metformin monotherapy, and started a second-line ADM during the study period. The exposure was a binary variable for health status, with poor health defined by end-stage medical conditions, dementia, or residence in a long-term nursing facility. The outcome was a variable identifying which second-line ADM class was started, categorized as SFU, BI, or other (i.e. all other ADM classes combined).RESULTS: Over half of participants (54%) received SFU as initial second-line ADM, 14% received BI, and 32% received another ADM. In multivariable models, the odds of filling SFU or BI was higher for participants in poor health than those in good or intermediate health [OR 1.13 (95% CI 1.05-1.21) and OR 2.34 (95% CI 2.14-2.55), respectively]. SFU and BI were also more commonly filled by older adults with poor glycemic control.CONCLUSIONS: Despite clinical consensus to use caution prescribing SFU and BI among older adults in poor health, these medications remain frequently used in this particularly vulnerable population.

Reporting of abstracts in studies that used routinely collected data for exploring drug treatment effects: a cross-sectional survey

Background In recent years, studies that used routinely collected data (RCD), such as electronic medical records and administrative claims, for exploring drug treatment effects, including effectiveness and safety, have been increasingly published. Abstracts of such studies represent a highly attended source for busy clinicians or policy-makers, and are important for indexing by literature database. If less clearly presented, they may mislead decisions or indexing. We thus conducted a cross-sectional survey to systematically examine how the abstracts of such studies were reported. Methods We searched PubMed to identify all observational studies published in 2018 that used RCD for assessing drug treatment effects. Teams of methods-trained collected data from eligible studies using pilot-tested, standardized forms that were developed and expanded from “The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology” (RECORD-PE) statement. We used descriptive analyses to examine how authors reported data source, study design, data analysis, and interpretation of findings. Results A total of 222 studies were included, of which 118 (53.2%) reported type of database used, 17 (7.7%) clearly reported database linkage, and 140 (63.1%) reported coverage of data source. Only 44 (19.8%) studies stated a predefined hypothesis, 127 (57.2%) reported study design, 140 (63.1%) reported statistical models used, 142 (77.6%) reported adjusted estimates, 33 (14.9%) mentioned sensitivity analyses, and 39 (17.6%) made a strong claim about treatment effect. Studies published in top 5 general medicine journals were more likely to report the name of data source (94.7% vs. 67.0%) and study design (100% vs. 53.2%) than those in other journals. Conclusions The under-reporting of key methodological features in abstracts of RCD studies was common, which would substantially compromise the indexing of this type of literature and prevent the effective use of study findings. Substantial efforts to improve the reporting of abstracts in these studies are highly warranted.

Validation of an algorithm to identify incident interstitial lung disease in patients with rheumatoid arthritis

Background/purpose Interstitial lung disease (ILD) is an important problem for patients with rheumatoid arthritis (RA). However, current approaches to ILD case finding in real-world data have been evaluated only in limited settings and identify only prevalent ILD and not new-onset disease. Our objective was to develop, refine, and validate a claims-based algorithm to identify both prevalent and incident ILD in RA patients compared to the gold standard of medical record review. Methods We used administrative claims data 2006–2015 from Medicare to derive a cohort of RA patients. We then identified suspected ILD using variations of ILD algorithms to classify both prevalent and incident ILD based on features of the data that included hospitalization vs. outpatient setting, physician specialty, pulmonary-related diagnosis codes, and exclusions for potentially mimicking pulmonary conditions. Positive predictive values (PPV) of several ILD algorithm variants for both prevalent and incident ILD were evaluated. Results We identified 234 linkable RA patients with sufficient data to evaluate for ILD. Overall, 108 (46.2%) of suspected cases were confirmed as ILD. Most cases (64%) were diagnosed in the outpatient setting. The best performing algorithm for prevalent ILD had a PPV of 77% (95% CI 67–84%) and for incident ILD was 96% (95% CI 85–100%). Conclusion Case finding in administrative data for both prevalent and incident interstitial lung disease in RA patients is feasible and has reasonable accuracy to support population-based research and real-world evidence generation.

Gender Related Differences in Gastrointestinal Bleeding With Oral Anticoagulation in Atrial Fibrillation

Background: DOACs are characterized by a higher incidence of gastrointestinal bleeding and this may be different among males and females. Female patients were underrepresented in the DOAC pivotal trials. We aimed to assess real-world differences in gastrointestinal bleeding with oral anticoagulants (DOACs and VKAs) among males and females with atrial fibrillation. Methods: We performed a population-based retrospective analysis on linked administrative claims. Atrial fibrillation patients of 65 years and above were considered. Bleeding risk factors were assessed through HASBED and previous history of gastrointestinal disease. A time-to-event analysis compared gastrointestinal bleeding between males and females. Results: The overall cohort consisted of 15338 (55% female) DOAC and 44542 (50% female) VKA users. Most of the patients showed HASBED ≥2. Incidence rate of GI bleeding was higher in females as compared to males among DOAC users (0.90% vs 0.59%), and significant gender difference in GI bleeding was found, after adjustment, in the Cox regression analysis (HR 1.48, 95%CI 1.02-2.16). In the VKA group, no significant difference among genders was found in the time-to-event analysis. Conclusions: Our data suggest that female patients treated with DOACs have a higher risk of GI bleeding versus male patients; this difference is not observed in VKA patients.

Prevalence of comorbidities and concomitant medication use in acromegaly: analysis of real-world data from the United States

Purpose Patients receiving treatment for acromegaly often experience significant associated comorbidities for which they are prescribed additional medications. We aimed to determine the real-world prevalence of comorbidities and concomitant medications in patients with acromegaly, and to investigate the association between frequency of comorbidities and number of concomitantly prescribed medications. Methods Administrative claims data were obtained from the IBM® MarketScan® database for a cohort of patients with acromegaly, identified by relevant diagnosis codes and acromegaly treatments, and a matched control cohort of patients without acromegaly from January 2010 through April 2020. Comorbidities were identified based on relevant claims and assessed for both cohorts. Results Overall, 1175 patients with acromegaly and 5875 matched patients without acromegaly were included. Patients with acromegaly had significantly more comorbidities and were prescribed concomitant medications more so than patients without acromegaly. In the acromegaly and control cohorts, respectively, 67.6% and 48.4% of patients had cardiovascular disorders, the most prevalent comorbidities, and 89.0% and 68.3% were prescribed > 3 concomitant medications (p < 0.0001). Hypopituitarism and hypothalamic disorders, sleep apnea, malignant neoplasms and cancer, and arthritis and musculoskeletal disorders were also highly prevalent in the acromegaly cohort. A moderate, positive correlation (Spearman correlation coefficient 0.60) was found between number of comorbidities and number of concomitant medications in the acromegaly cohort. Conclusion Compared with patients without acromegaly, patients with acromegaly have significantly more comorbidities and are prescribed significantly more concomitant medications. Physicians should consider the number and type of ongoing medications for individual patients before prescribing additional acromegaly treatments.

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein, the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.

Evaluation of the Completeness of ALS Case Ascertainment in the U.S. National ALS Registry: Application of the Capture-Recapture Method

Introduction: The Centers for Disease Control and Prevention (CDC) National Amyotrophic Lateral Sclerosis (ALS) Registry is the first national registry for a chronic neurologic disease in the U.S. and uses a combination of case-finding methods including administrative healthcare data and patient self-registration. Methods: We applied capture-recapture methodology to estimate the completeness of the Registry for ascertaining patients with ALS for the first full year and the fourth years of the Registry (2011, 2014). The Registry uses the combination of two national administrative claims databases (Medicare and Veterans Affairs) with a self-register option at the registry portal. We conducted descriptive analyses of the demographic and clinical characteristics of the ALS cases identified by each of the sources and estimated the completeness of case ascertainment for each of the three ALS Registry sources individually, pairwise, and in all combinations. Results: Case-finding completeness was 54% in 2011 and improved to 56% in 2014. A smaller proportion of ALS patients under age 65 were ascertained than those 65 or older and ascertainment was also lower for non-White than White patients. The uncorrected ALS prevalence was 4.3/100,000 in 2011 (in 2014 5.0/100,000), but after correction for under-ascertainment, annual prevalence in 2011 was 7.9/100,000 (95% CI 7.6-8.2) (in 2014 was 8.9/100,000 (95% CI 8.7-9.2)). Discussion/Conclusion: Our findings indicate that administrative healthcare databases are a very efficient method for identifying the majority of ALS prevalent cases in the National ALS Registry and that the inclusion of a web registry portal for patients to self-register is important to ensure a more representative population for estimating ALS prevalence. Nonetheless, more than 40% of ALS cases were not ascertained by the Registry, with individuals younger than age 65 and people of color underrepresented. Recommendations are provided for additional methods that can be considered to improve the completeness of case ascertainment.

Age of First Oral Health Examination and Dental Treatment Needs of Medicaid-Enrolled Children

Introduction: Early childhood caries (ECC), despite being preventable, remains the most prevalent disease of childhood, particularly in children between the ages of 2 and 5 y. The association between the type of health care provider completing initial oral health examinations and subsequent dental caries in children under 6 y of age is unclear. Objective: The objective of the current study is to longitudinally assess the association between age at first oral health examination and provider type at first oral health examination on dental treatment for children under 6 y of age. Methods: Deidentified administrative claims data were used from the IBM Marketscan Multi-State Medicaid Database (n = 2.41 million Medicaid-enrolled children younger than 6 y in 13 states from 2012 to 2017). A Kaplan–Meier survival analysis was used to examine the association between age at first oral health examination and provider type with first treatment of dental caries at follow-up. Results: The adjusted hazard ratio (HR) of dental caries for children whose first oral health examination at 4 y of age is 5.425 times higher than for children whose first oral health examination was before 1 y of age (95% confidence interval [CI], 5.371–5.479). The adjusted HR of dental caries for children seen by pediatric dentists (HR = 1.215; 95% CI, 1.207–1.223) and physicians (HR = 2.618; 95% CI, 2.601–2.635) was higher than those seen by a general dentist. Conclusions: Findings from this study highlight the importance of children having their first oral health examination no later than 12 mo of age in accordance with existing guidelines and referrals from physicians to prevent the need for invasive treatment. Knowledge of Transfer Statement: Results of this study emphasize the need for a child’s first oral health examination to be completed no later than 12 mo of age to prevent dental caries. Reinforcement and referrals by physicians based on this recommendation facilitate early establishment of a dental home in young children.

Competition and physician-induced demand in a healthcare market with regulated price: evidence from Ghana

Using panel data of administrative claims spanning 36 months (2017–2019) and an instrumental variable method, this study examines whether physician-induced demand for hypertension disease care exists in Ghana’s healthcare system where price is regulated, and there is no co-payment. We find that an increase in competition—measured as a high doctor-to-population ratio at the district level—leads to an increase in the number of physician visits, suggesting physician-induced demand exists, and that effects are greater for large hospitals and public health providers. This result is further supported by alternative measures and specifications showing that physicians’ revenue from medication and gross revenue increase as the physician density increases. These pattern suggest that physicians in high density areas, faced with a decrease in number of patients per physician, make up for the decline in income by inducing more patient visits.