Incidence trend of type 2 diabetes from 2012 to 2021 in Germany: an analysis of health claims data of 11 million statutorily insured people.

AIMS/HYPOTHESIS: The aim of the study is to describe the time trend of type 2 diabetes incidence in the largest state of Germany, Bavaria, from 2012 to 2021, and to compare the incidence rates during the pandemic period (2020-2021) to the pre-pandemic period (2012-2019). METHODS: This secondary data analysis uses health claims data provided by the Bavarian Association of Statutory Health Insurance Physicians (KVB), covering approximately 11 million insurees, accounting for 85% of the total population of Bavaria, Germany. Newly diagnosed type 2 diabetes cases in adults (≥20 years) coded as E11 (Diabetes mellitus, Type 2) or E14 (Unspecified diabetes mellitus) under ICD-10, German modification (ICD-10-GM) for the study period 2012 to 2021 were included. Annual and quarterly age-standardised incidence rates (ASIR) stratified by sex, age and region were calculated using the European standard population. Sex-specific crude incidence rates (CIR) were calculated using 10-year age groups. Regression analyses adjusted for time trends, seasonal effects, and pandemic effects were used to analyse the incidence trend and to assess the effect of the pandemic. RESULTS: Overall, 745,861 new cases of type 2 diabetes were diagnosed between 2012 and 2021: 50.4% (376,193 cases) in women. The male/female ratio remained stable over the observation period, while the median age at diagnosis decreased from 61 to 58 years in men and from 66 years to 61 years in women. ASIR were consistently higher for men compared with women, with the yearly difference remaining stable over time (2012: 18%; 2021: 20%). An overall decreasing trend in ASIR was observed during the study period, with a strong decrease from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021 for both sexes. For men, ASIR decreased from 1514 per 100,000 person-years in 2012 to 995 per 100,000 person-years in 2021 (4.6% average annual reduction), and for women from 1238 per 100,000 person-years in 2012 to 796 per 100,000 person-years in 2021 (4.8% average annual reduction). This downward trend was also observed for age groups above 50 years. Regression analyses showed no significant change in incidence rates during the pandemic period (2020 and 2021) compared with the pre-pandemic period. CONCLUSIONS/INTERPRETATION: For the first time, a 10-year incidence trend of type 2 diabetes is reported for Germany, showing a strong decline from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021. The incidence trend of type 2 diabetes appears not to have been affected by the first 2 years of the COVID-19 pandemic. Despite an overall increasing prevalence, the incidence is decreasing, potentially resulting from robust screening by family physicians, reducing the median age at diagnosis by 3 to 5 years. However, further investigation is needed to fully identify the reasons for the declining incidence trend. Continued incidence monitoring is necessary to identify the long-term trend and the potential effect of the pandemic on diagnoses of type 2 diabetes.

Invited commentary: A future of data-rich pharmacoepidemiology studies- transitioning to large-scale linked EHR+claims data.

Electronic health record (EHR) data are seen as an important source for Pharmacoepidemiology studies. In the US healthcare system, EHR systems often only identify fragments of patients' health information across the care continuum, including primary care, specialist care, hospitalizations, and pharmacy dispensing. This leads to unobservable information in longitudinal evaluations of medication effects causing unmeasured confounding, misclassification, and truncated follow-up times. A remedy is to link EHR data with longitudinal claims data which record all encounters during a defined enrollment period across all care settings. We evaluate EHR and claims data sources in three aspects relevant to etiologic studies of medical products: data continuity, data granularity, and data chronology. Reflecting on the strengths and limitations of EHR and insurance claims data, it becomes obvious that they complement each other. The combination of both will improve the validity of etiologic studies and expand the range of questions that can be answered. As the research community transitions towards a future state with access to large-scale combined EHR+claims data, we outline analytic templates to improve the validity and broaden the scope of pharmacoepidemiology studies in the current environment where EHR data are available only for a subset of patients with claims data.

High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.

We recently developed a machine-learning subgrouping algorithm, iterative causal forest (iCF), to identify subgroups with heterogeneous treatment effects (HTEs) using predefined covariates. However, such predefined covariates may miss or poorly define important features leading to inaccurate subgrouping. To address such limitations, we developed a new semi-automatic subgrouping algorithm, hdiCF, which adapts methodology from high-dimensional propensity score for feature recognition in claims data. The hdiCF algorithm has 3 steps: 1) high-dimensional feature identification by International Classification of Diseases, Current Procedural Terminology, and Anatomical Therapeutic Chemical codes (in/outpatient diagnoses, procedures, prescriptions) and creation of ordinal variables by frequency of occurrence; 2) propensity score trimming and high-dimensional feature preparation; 3) iCF implementation to identify subgroups. We applied hdiCF in a 20% random sample of fee-for-service Medicare beneficiaries who initiated sodium-glucose cotransporter-2 inhibitors (SGLT2i) or glucagon-like peptide-1 receptor agonists to identify subgroups with HTEs for incidence of hospitalized heart failure. HdiCF findings were consistent with studies suggesting SGLT2i to be more beneficial for patients with pre-existing heart failure or chronic kidney disease. HdiCF is not dependent on prior hypotheses about HTEs and identifies subgroups with markers for potential HTEs in real-world evidence studies where active-comparator, new-user study designs limit the potential for unmeasured confounding.

Long-term multimorbidity trajectories in older adults: The role of cancer, demographics, and health behaviors.

BACKGROUND: Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. METHODS: Via Medicare claims linked to Cancer Prevention Study II data, group-based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. RESULTS: In 82,754 participants (mean age, 71.6 years [SD, 5.1 years]; 56.9% female), cancer survivors (n = 11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR], 1.36; 95% CI, 1.29-1.45), and (2) high scores that increased over time (start high and increase; OR, 1.51; 95% CI, 1.38-1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR, 0.73; 95% CI, 0.64-0.84, remain high; OR, 0.42; 95% CI, 0.33-0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR, 2.83; 95% CI, 2.32-3.45 vs. normal weight), although being physically active offset some obesity-related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR, 6.86; 95% CI, 4.41-10.66 vs. never smokers). CONCLUSIONS: Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend.

Opioid and Non-Opioid Pharmacotherapy Use for Pain Management Among Privately Insured Pediatric Patients With Cancer in the United States.

BACKGROUND: This study examined the trends and patterns of opioid and non-opioid pharmacotherapy use among a large national sample of privately insured pediatric patients with cancer in the United States. MATERIALS AND METHODS: We identified pediatric (aged < 21) patients diagnosed with central nervous system (CNS), lymphoma, gonadal, leukemia, or bone cancer from MarketScan data 2005-2019. We examined the proportion of patients who filled a prescription for the following 5 types of pharmacotherapy: opioid, anticonvulsant, non-steroidal anti-inflammatory drug (NSAID), antidepressant, and muscle relaxant during active cancer treatment. We assessed the trends and patterns in pharmacotherapy using multivariable logistic regressions. RESULTS: Among 4174 patients included, 2979 (71%) had an opioid prescription; 746 (18%), 384 (9%), 202 (5%), and 169 (4%) had anticonvulsant, NSAID, antidepressant and muscle relaxant prescriptions, respectively. Multivariable logistic regression showed a nonlinear trend in the use of opioids among pediatric patients with cancer over time such that use slightly increased until 2012 (OR of 1.40 [95% CI, 1.12-1.73] for 2012 vs. 2006) but then decreased thereafter (OR of 0.51 [0.37-0.68] for 2018 vs. 2012). The use of anticonvulsants, NSAIDs, and muscle relaxants increased significantly linearly over time (all P < .005). CONCLUSION: There has been a downward trend in the use of opioids in recent years among pediatric patients with cancer and an upward trend in the use of non-opioid pharmacotherapy for pain management potentially as an alternative to opioids.

Longitudinal trajectories of a claims-based frailty measure during adjuvant chemotherapy in women with stage I-III breast cancer.

BACKGROUND: Frailty is a dynamic syndrome characterized by reduced physiological reserve to maintain homeostasis. Prospective studies have reported frailty worsening in women with breast cancer during chemotherapy, with improvements following treatment. We evaluated whether the Faurot frailty index, a validated claims-based frailty measure, could identify changes in frailty during chemotherapy treatment and identified predictors of trajectory patterns. METHODS: We included women (65+ years) with stage I-III breast cancer undergoing adjuvant chemotherapy in the SEER-Medicare database (2003-2019). We estimated the Faurot frailty index (range: 0-1; higher scores indicate greater frailty) at chemotherapy initiation, 4 months postinitiation, and 10 months postinitiation. Changes in frailty were compared to a matched noncancer comparator cohort. We identified patterns of frailty trajectories during the year following chemotherapy initiation using K-means clustering. RESULTS: Twenty-one thousand five hundred and ninety-nine women initiated adjuvant chemotherapy. Mean claims-based frailty increased from 0.037 at initiation to 0.055 4 months postchemotherapy initiation and fell to 0.049 10 months postinitiation. Noncancer comparators experienced a small increase in claims-based frailty over time (0.055-0.062). We identified 6 trajectory patterns: a robust group (78%), 2 resilient groups (16%), and 3 nonresilient groups (6%). Black women and women with claims for home hospital beds, wheelchairs, and Parkinson's disease were more likely to experience nonresilient trajectories. CONCLUSIONS: We observed changes in a claims-based frailty index during chemotherapy that are consistent with prior studies using clinical measures of frailty and identified predictors of nonresilient frailty trajectories. Our study demonstrates the feasibility of using claims-based frailty indices to assess changes in frailty during cancer treatment.

Real-world dosing of regorafenib and outcomes among patients with metastatic colorectal cancer: a retrospective analysis using US claims data.

BACKGROUND: The randomized, dose-optimization, open-label ReDOS study in US patients with metastatic colorectal cancer (CRC) showed that, compared with a standard dosing approach, initiating regorafenib at 80 mg/day and escalating to 160 mg/day depending on tolerability increased the proportion of patients reaching their third treatment cycle and reduced the incidence of adverse events without compromising efficacy. Subsequently, the ReDOS dose-escalation strategy was included as an alternative regorafenib dosing option in the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines. A retrospective analysis was conducted using a US claims database to assess whether inclusion of this dose-escalation strategy in NCCN Guidelines has influenced the use of flexible dosing in routine US clinical practice, and to describe clinical outcomes pre- and post-inclusion in NCCN Guidelines. METHODS: Patients with CRC in the Optum's de-identified Clinformatics® Data Mart database initiating regorafenib for the first time between January 2016 and June 2020 were stratified based on whether they initiated regorafenib pre- or post-inclusion of ReDOS in NCCN Guidelines, and in two groups: flexible dosing (< 160 mg/day; < 84 tablets in the first treatment cycle) and standard dosing (160 mg/day; ≥ 84 tablets in the first treatment cycle). The primary endpoints were the proportion of patients who initiated their third treatment cycle and the mean number of treatment cycles per group. RESULTS: 703 patients initiated regorafenib during the study period, of whom 310 (44%) initiated before and 393 (56%) initiated after inclusion of ReDOS in NCCN Guidelines. After inclusion in the guidelines, the proportion of patients who received flexible dosing increased from 21% (n = 66/310) to 45% (n = 178/393), the proportion who received standard dosing decreased from 79% (n = 244/310) to 55% (n = 215/393), the proportion who initiated their third treatment cycle increased from 36% (n = 113/310) to 46% (n = 179/393), and the mean (standard deviation) number of treatment cycles increased from 2.6 (2.9) to 3.2 (3.1). CONCLUSIONS: Following inclusion of ReDOS in NCCN Guidelines, real-world data suggest that US clinicians have markedly increased use of flexible dosing in clinical practice, potentially maximizing clinical benefits and safety outcomes for patients with metastatic CRC receiving regorafenib.

Sick leave before, during, and after short-term outpatient psychotherapy: a cohort study on sick leave days and health care costs between behavioral and psychodynamic psychotherapies on anonymized claims data.

BACKGROUND: Sick leave due to mental disorders poses a relevant societal and economic burden. Research on sick leave over a patient journey of individuals who received one of two treatment approaches - either behavioral (BT) or psychodynamic (PDT) psychotherapy - is scarce. METHODS: We conducted a cohort study on anonymized German claims data for propensity-score matched patients who received short-term outpatient BT or PDT. We analyzed sick leave days and direct health care costs one year before, during, and one year after psychotherapy. RESULTS: We analyzed data of patients who received BT and PDT, with N = 14 530 patients per group after matching. Patients showed sick leave days per person year of 33.66 and 35.05 days before, 35.99 and 39.74 days during, and 20.03 and 20.95 days after BT and PDT, respectively. Sick leave rates were overall higher in patients who received PDT. Both patient groups showed reductions of roughly 14 sick leave days per year, or 40%, from before to after therapy without a difference between BT and PDT (difference-in-difference [DiD] = -0.48, 95%-confidence interval [CI] -1.61 to 0.68). Same applies to direct health care costs which reduced in both groups by roughly 1800 EUR (DiD = 0, 95%-CI -158 to 157). CONCLUSIONS: Results suggest similar reductions in sick leave days and direct health care costs from before to after BT and PDT. As sick leave is discussed to serve as an indicator of overall health and functioning in mental disorders, both treatments may have a similar positive impact on mental health.

Poststroke seizures and epilepsy increase the risk of dementia among stroke survivors: A population-based study.

OBJECTIVE: With global aging, the occurrence of stroke and associated outcomes like dementia are on the rise. Seizures and epilepsy are common poststroke complications and have a strong connection to subsequent dementia. This study examines the relationship between poststroke seizures (PSS) or poststroke epilepsy (PSE) and dementia using a national health care database. METHODS: We conducted a retrospective study using data from the Taiwan National Health Insurance Research Database from 2009 to 2020. We identified acute stroke patients from 2010 to 2015, excluding those with pre-existing neurological conditions. Based on age, sex, stroke severity level, and the year of index stroke, patients with PSS or PSE were matched to those without. The main outcome was incident dementia. RESULTS: This study included 62 968 patients with an average age of 63 years, with males accounting for 62.9%. Of them, 60.3% had ischemic strokes, and 39.7% had hemorrhagic strokes. After an average follow-up period of 5.2 years, dementia developed in 15.9% of patients who had PSS or PSE, as opposed to 8.4% of those without these conditions. A time-dependent Fine and Gray competing risk analysis showed that PSS and PSE were significantly associated with dementia across all stroke types. Subgroup analyses revealed significantly increased risk of dementia across all age groups (<50, 50-64, and ≥65 years), sexes, and various stroke severity levels. The link between PSS or PSE and dementia was particularly pronounced in men, with a less distinct correlation in women. SIGNIFICANCE: The risk of incident dementia was higher in patients with PSS or PSE. The potential for therapeutic interventions for seizures and epilepsy to reduce poststroke dementia underscores the importance of seizure screening and treatment in stroke survivors.

Myelofibrosis and anemia: A German claims data analysis to describe epidemiology and current treatment.

OBJECTIVES: There is limited data on the incidence, prevalence, and treatments for myelofibrosis (MF) in Germany. This retrospective study examined claims data from 3.3 million insured individuals, spanning from 2010 to 2021. METHODS: Four sensitivity scenarios were explored to identify cases of MF. Point prevalence and cumulative incidence of MF were determined as of December 31, 2021, and within 2021, respectively. A cross-sectional analysis used the main scenario definition of MF to identify cases and evaluate the period prevalence of patients receiving treatment for symptoms and/or splenomegaly, including first-line (1L) Janus kinase inhibitor (JAKi), second-line, or further (2L+) MF-related treatment therapies during 2021. The prevalence of anemia treatment was also reported. RESULTS: The estimated standardized point prevalence of MF on December 31, 2021, was 9.9-12.4 cases per 100 000 persons, and cumulative incidence in 2021 was 1.2-1.8 cases per 100 000 persons. Standardized period prevalence in 2021 for MF patients receiving 1L JAKi and/or 2L+ MF-related treatment was 4.0 cases per 100 000. Among these patients, 47.1%-53.7% required treatment for anemia, resulting in a period prevalence of 1.9-2.2 cases per 100 000 individuals. CONCLUSION: The data reveal gaps in MF treatments and the need to improve patient quality of life.

A flexible approach to measure care coordination based on patient-sharing networks.

BACKGROUND: Effective care coordination may increase clinical efficiency, but its measurement remains difficult. The established metric "care density" (CD) measures care coordination based on patient-sharing among physicians, but it may be too rigid to generalize across disorders and countries. Therefore, we propose an extension called fragmented care density (FCD), which allows varying weights for connections between different types of providers. We compare both metrics in their ability to predict hospitalizations due to schizophrenia. METHODS: We conducted a longitudinal cohort study based on German claims data from 2014 through 2017 to predict quarterly hospital admissions. 21,016 patients with schizophrenia from the federal state Baden-Württemberg were included. CD and FCD were calculated based on patient-sharing networks. The weights of FCD were optimized to predict hospital admissions during the first year of a 24-month follow-up. Subsequently, we employed likelihood ratio tests to assess whether adding either CD or FCD improved a baseline model with control variables for the second follow-up year. RESULTS: The inclusion of FCD significantly improved the baseline model, Χ2(1) = 53.30, p < 0.001. We found that patients with lower percentiles in FCD had an up to 21% lower hospitalization risk than those with median or higher values, whereas CD did not affect the risk. CONCLUSIONS: FCD is an adaptive metric that can weight provider relationships based on their relevance for predicting any outcome. We used it to better understand which medical specialties need to be involved to reduce hospitalization risk for patients with schizophrenia. As FCD can be modified for different health conditions and systems, it is broadly applicable and might help to identify barriers and promoting factors for effective collaboration.

Duration of effectiveness of the COVID-19 vaccine in Japan: a retrospective cohort study using large-scale population-based registry data.

BACKGROUND: Most evidence of the waning of vaccine effectiveness is limited to a relatively short period after vaccination. METHODS: Data obtained from a linked database of healthcare administrative claims and vaccination records maintained by the municipality of a city in the Kanto region of Japan were used in this study. The study period extended from April 1, 2020, to December 31, 2022. The duration of the effectiveness of the COVID-19 vaccine was analyzed using a time-dependent piecewise Cox proportional hazard model using the age, sex and history of cancer, diabetes, chronic obstructive pulmonary disease, asthma, chronic kidney disease, and cardiovascular disease as covariates. RESULTS: Among the 174,757 eligible individuals, 14,416 (8.3%) were diagnosed with COVID-19 and 936 (0.54%) were hospitalized for COVID-19. Multivariate analysis based on the time-dependent Cox regression model with reference of non-vaccine group revealed a lower incidence of COVID-19 in the one-dose group (hazard ratio, 0.76 [95% confidence interval, 0.63-0.91]), two-dose (0.89 [0.85-0.93]), three-dose (0.80 [0.76-0.85]), four-dose (0.93 [0.88-1.00]), and five-dose (0.72 [0.62-0.84]) groups. A lower incidence of COVID-19-related hospitalization was observed in the one-dose group (0.42 [0.21-0.81]), two-dose (0.44 [0.35-0.56]), three-dose (0.38 [0.30-0.47]), four-dose (0.20 [0.14-0.28]), and five-dose (0.11 [0.014-0.86]) groups. Multivariable analyses based on the time-dependent piecewise Cox proportional hazard model with reference of non-vaccine group revealed significant preventive effects of the vaccine for 4 months for the incidence of COVID-19 and ≥ 6 months for hospitalization. CONCLUSIONS: Vaccine effectiveness showed gradual attenuation with time after vaccination; however, protective effects against the incidence of COVID-19 and hospitalization were maintained for 4 months and ≥ 6 months, respectively. These results may aid in formulating routine vaccination plans after the COVID-19 pandemic.

A panel vector autoregression analysis for the dynamics of medical and long-term care expenditures.

Although medical and long-term care expenditures for older adults are closely related, providing rigorous statistical analysis for their dynamic relationship is challenging. In this research, we propose a novel approach using the panel vector autoregression model to reveal the realized patterns of the interdependence. As an empirical application, we analyze monthly panel data on individuals in a city of Japan, where social insurance covers many formal services for long-term care. Our estimation results indicate the existence of intertemporal transition from expensive acute medical care to reasonable at-home medical care, then to at-home long-term care. Under this context, the enhancement of formal long-term care sector in Japan might have played an important role in the suppression of the total care cost in spite for its rapid aging over the past 2 decades. Additionally, we find that daycare plays multiple roles in Japanese long-term care, such as respite and rehabilitation, but there is no considerable transition from outpatient rehabilitation to daycare in the long-term care sector.

Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model.

OBJECTIVE: Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes. METHODS: Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5-18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5-11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model. RESULTS: The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications. CONCLUSIONS: Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy.

Evaluating the timing of triple therapy initiation for the treatment of asthma in Japan: prompt versus delayed.

OBJECTIVE: In Japan, the optimal initiation timing and efficacy of single-inhaler triple therapy (SITT) in asthma management remain unexplored. This study investigated SITT initiation timing following an asthma exacerbation, and examined patient demographics and clinical characteristics. METHODS: Observational, retrospective cohort study in patients with asthma aged ≥15 years who initiated SITT following their earliest observed asthma exacerbation (February-November 2021), using data from Japanese health insurance claims databases (JMDC and Medical Data Vision [MDV]). The study period ended May 2022 for JMDC and September 2022 for MDV. Descriptive analyses were performed independently by database. Variables evaluated included timing of SITT initiation post exacerbation (prompt, delayed and late, ≤30, 31-180 and >180 days post index, respectively), patient demographics, clinical characteristics, and pre-index treatment. RESULTS: Of patients in the JMDC and MDV databases, most initiated SITT promptly after an asthma exacerbation, 60.8% (n = 951/1565) and 44.4% (n = 241/543), respectively. Delayed initiation occurred in 22.6% (n = 354/1565) and 26.3% (n = 143/543) of patients, and late initiation occurred in 16.6% (n = 260/1565) and 29.3% (n = 159/543), respectively. Most patients were indexed on a moderate asthma-related exacerbation, 97.1% (n = 1519/1565) and 68.7% (n = 373/543), respectively. CONCLUSION: Most patients with asthma initiated SITT promptly following a moderate exacerbation, with delayed and late initiation more common among patients with complex clinical profiles. The findings underscore the necessity for future research to examine the interaction between patient characteristics, clinical outcomes, and the timing of SITT initiation to optimize treatment strategies, as clinical practice may vary by exacerbation severity.

Quantified trends in provider reimbursement and utilization volume in colorectal cancer screenings: analysis of Medicare claims from 2000 to 2019.

AIM: Colorectal cancer (CRC) screening rates in the United States remain persistently below guideline targets, partly due to suboptimal patient utilization and provider reimbursement. To guide long-term national utilization estimates and set reasonable screening adherence targets, this study aimed to quantify trends in utilization of and reimbursement for CRC screenings using Medicare claims. METHOD: Inflation-adjusted reimbursements and utilization volume associated with each CRC screening code were abstracted from Medicare claims between 2000 and 2019. Screenings, screenings/100 000 enrolees and reimbursement/screening were analysed with linear regression and compared with the equality of slopes tests. Average reimbursement per screening was compared using analysis of variance with Dunnett's T3 multiple comparisons test. RESULTS: The growth rate of multitarget stool DNA tests (mt-sDNA)/100 000 was the highest at 170.4 screenings/year (R2 = 0.99, p ≤ 0.001), while that of faecal occult blood tests/100 000 was the lowest at -446.4 screenings/year (R2 = 0.90, p ≤ 0.001) (p ≤ 0.001). Provider reimbursements averaged $546.95 (95% CI $520.12-$573.78) per mt-sDNA screening, significantly higher than reimbursements for all invasive screenings. Only FOBTs significantly increased in reimbursement per screening at $0.62/year (R2 = 0.91, p ≤ 0.001). CONCLUSION: We derived forecastable trend numbers for utilization and provider reimbursement. Faecal immunochemical tests/100 000 and mt-sDNA screenings/100 000 increased most rapidly during the entire study period. The number of nearly all invasive screenings/100 000 decreased rapidly; the number of colonoscopies/100 000 increased slightly, probably due to superior diagnostic strength. These trends indicate the that replacement of other invasive modalities with accessible noninvasive screenings will account for much of future screening behaviour and thus reductions in CRC incidence and mortality, especially given providers' reimbursement incentive to screen average-risk patients with stool-based tests.

Inventory of real-world data sources in Japan: Annual survey conducted by the Japanese Society for Pharmacoepidemiology Task Force.

PURPOSE: The Database Task Force of the Japan Society for Pharmacoepidemiology began its annual surveys of databases available for clinico and pharmacoepidemiological studies in 2010. In this report, we summarize the characteristics of the databases available in Japan based on the results of our 2021 survey to illustrate the recent developments in the infrastructure for database research in Japan. METHODS: We included 20 major databases from the academia, government, or industry that were accessible to third parties. We used a web-based questionnaire to ask the database providers about their characteristics, such as their organization, data source(s), numbers of individuals enrolled, age distribution, code(s) used, and average follow-up periods. RESULTS: We received responses from all 20 databases approached: eight hospital-based databases, six insurer-based databases, four pharmacy-based databases, and two in the "other" category. Among them, 17 contained information from medical claims, pharmacy claims, and/or Diagnosis Procedure Combination data. Most insurer databases contained health check-up data that could be attached to the claims component. Some hospital-based databases had data from electronic medical records. Most insurer-based databases collected data from the insurers of working-age employees and therefore had limited coverage of older people. Most databases coded their medication data using the Japanese reimbursement codes, and many provided Anatomical Therapeutic Chemical Classification codes. CONCLUSIONS: The number of databases available for clinico and pharmacoepidemiological research and the proportion of the population they cover are increasing in Japan. The differences in their characteristics mean that the appropriate database must be selected for a particular study purpose.

Evaluation of Medication Adherence Among Prevalent Users in Hypertension, Dyslipidemia, and Diabetes Using Health Insurance Claims: A Population-Based Cohort Study in Japan.

PURPOSE: Hypertension (HT), dyslipidemia (DL), and diabetes mellitus (DM) are major risk factors for cardiovascular diseases. Despite the wide availability of medications to reduce this risk, poor adherence to medications remains an issue. The aim of this study is to evaluate medication adherence of prevalent users in these disease medications (HT, DL, DM) using claims data. Factors associated with non-adherence were also examined. METHODS: Of 7538 participants of the Tsuruoka Metabolomics Cohort Study, 3693 (HT: 2702, DL: 2112, DM: 661) were identified as prevalent users of these disease medications. Information on lifestyle was collected through a questionnaire. Adherence was assessed by a proportion of days covered (PDC) and participants with PDC ≥0.8 were defined as adherent. Predictors of non-adherence were determined by performing multivariable logistic regression. RESULTS: Medication adherence differed by treatment status. Among those without comorbidities, those with HT-only showed the highest adherence (90.2%), followed by those with DM-only (81.2%) and those with DL-only (80.8%). Factors associated with non-adherence in each medication group were skipping breakfast and poor understanding of medications among those with HT medications, females, having comorbidities, having a history of heart disease, and drinking habit among those with DL medications, and good sleep quality and skipping breakfast among those with DM medications. CONCLUSION: While participants showed high medication adherence, differences were observed across medication groups. The identified predictors of non-adherence could help target those in need of adherence support.

Use of clinical classifications software to address ICD coding transition in large healthcare databases analyzed via high-dimensional propensity scores.

PURPOSE: The EUPAS26595 study characterized the rate of acute renal failure (ARF) in patients exposed to levetiracetam versus other antiepileptic drugs using healthcare claims data and a high-dimensional propensity score (hd-PS) for confounding adjustment. The data contained several coding systems by design and an update in International Classification of Diseases (ICD) coding dictionary. Such coding heterogeneity can affect the performance of hd-PS, and manually coding harmonization is not feasible. Our objective was to explore the impact of code aggregation via Clinical Classifications Software (CCS) on the analysis of a large claims-based database using hd-PS. METHODS: Patients with epilepsy, who were new-users of an antiepileptic drug, were identified from the IBM® MarketScan® Research Databases. We used CCS categories to harmonize coding and compared the results with other alternatives. Incidence rate ratios (IRRs) were computed using modified Poisson regression model with a robust variance estimator. RESULTS: For January 2008-October 2015 (before ICD update), 34 833 eligible patients initiated levetiracetam and 52 649 initiated a comparator drug; IRR (95% CI) for ARF for the hd-PS analysis was 1.34 (0.72-2.50) without CCS categories and 1.30 (0.71-2.39) with CCS categories. For January 2008-December 2017 (including ICD coding change), 45 672 eligible patients initiated levetiracetam and 64 664 initiated a comparator drug; IRR (95% CI) for the hd-PS analysis was 1.34 (0.78-2.29) without CCS categories and 1.37 (0.80-2.34) with CCS categories. CONCLUSIONS: Using single-level CCS categories to overcome differences in coding provides consistent results and can be used in studies that use large claims data and hd-PS for adjustment.

Validation of alopecia coding in US claims data among women of childbearing age.

BACKGROUND: Accurately identifying alopecia in claims data is important to study this rare medication side effect. OBJECTIVES: To develop and validate a claims-based algorithm to identify alopecia in women of childbearing age. METHODS: We linked electronic health records from a large healthcare system in Massachusetts (Mass General Brigham) with Medicaid claims data from 2016 through 2018 to identify all women aged 18 to 50 years with an ICD-10 code for alopecia, including alopecia areata, androgenic alopecia, non-scarring alopecia, or cicatricial alopecia, from a visit to the MGB system. Using eight predefined algorithms to identify alopecia in Medicaid claims data, we randomly selected 300 women for whom we reviewed their charts to validate the alopecia diagnosis. Positive predictive values (PPVs) were computed for the primary algorithm and seven algorithm variations, stratified by race. RESULTS: Out of 300 patients with at least 1 ICD-10 code for alopecia in the Medicaid claims, 286 had chart-confirmed alopecia (PPV = 95.3%). The algorithm requiring two diagnosis codes plus one prescription claim for alopecia treatment identified 55 patients (PPV = 100%). The algorithm requiring 1 diagnosis code for alopecia plus 1 procedure claim for intralesional triamcinolone injection identified 35 patients (PPV = 100%). Across all 8 algorithms tested, the PPV varied between 95.3% and 100%. The PPV for alopecia ranged from 94% to 100% in White and 96%-100% in 48 non-White women. The exact date of alopecia onset was difficult to determine in charts. CONCLUSION: At least one recorded ICD-10 code for alopecia in claims data identified alopecia in women of childbearing age with high accuracy.