Alzheimer incidence and prevalence with and without asthma: A Medicare cohort study.

BACKGROUND: International data suggest that asthma, like other inflammatory diseases, might increase Alzheimer disease (AD) risk. OBJECTIVE: We sought to explore risk pathways and future mitigation strategies by comparing diagnostic claims-based AD incidence and prevalence among US patients with asthma with those without asthma. METHODS: This cohort study included a national Medicare 20% random sample (2013-2015). Adult patients with asthma with more than 12 months continuous Medicare were compared with subjects without asthma overall and as matched. Asthma was defined by 1 inpatient or 2 outpatient codes for asthma. The main outcomes were 2-year incident or prevalent AD defined by International Classification of Diseases, Ninth Revision code 331.0 or Tenth Revision code G30.0, G30.1, G30.8, or G30.9. RESULTS: Among 5,460,732 total beneficiaries, 678,730 patients were identified with baseline asthma and more often identified as Black or Hispanic, were Medicaid eligible, or resided in a highly disadvantaged neighborhood than those without asthma. Two-year incidence of AD was 1.4% with asthma versus 1.1% without asthma; prevalence was 7.8% versus 5.4% (both P ≤ .001). Per 100,000 patients over 2 years, 303 more incident AD diagnoses occurred in those with asthma, with 2,425 more prevalent cases (P < .001). Multivariable models showed that asthma had greater odds of 2-year AD incidence (adjusted odds ratio, 1.33 [95% CI, 1.29-1.36]; matched 1.2 [95% CI, 1.17-1.24]) and prevalence (adjusted odds ratio, 1.48 [95% CI, 1.47-1.50]; matched 1.25 [95% CI, 1.22-1.27]). CONCLUSIONS: Asthma was associated with 20% to 33% increased 2-year incidence and 25% to 48% increased prevalence of claims-based AD in this nationally representative US sample. Future research should investigate risk pathways of underlying comorbidities and social determinants as well as whether there are potential asthma treatments that may preserve brain health.

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.

Drivers of Geographic Patterns in Outpatient Antibiotic Prescribing in the United States.

In a retrospective, ecological analysis of US medical claims, visit rates explained more of the geographic variation in outpatient antibiotic prescribing rates than per-visit prescribing. Efforts to reduce antibiotic use may benefit from addressing the factors that drive higher rates of outpatient visits, in addition to continued focus on stewardship.

Spatiotemporal Trends in Group A Streptococcal Pharyngitis in the United States.

BACKGROUND: Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States, with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the United States is poorly characterized. METHODS: We used outpatient claims data from individuals with private medical insurance between 2010 and 2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. RESULTS: The South had the most visits per person (yearly average, 39.11 visits per 1000 people; 95% confidence interval, 36.21-42.01) and the West had the fewest (yearly average, 17.63 visits per 1000 people; 95% confidence interval, 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. CONCLUSIONS: The burden and timing of GAS pharyngitis varied across the continental United States, with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures.

Association between SARS-CoV-2 infection during pregnancy and gestational diabetes: a claims-based cohort study.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) may be associated with gestational diabetes mellitus (GDM); however, evidence is limited by sample sizes and lack of control groups. METHODS: To assess the GDM risk after COVID-19 in pregnancy, we constructed a retrospective cohort of pregnancies ending March 2020-October 2022 using medical claims. People with COVID-19 diagnosis claims from conception to 21 gestational weeks (n = 57,675) were matched 1:2 to those without COVID-19 during pregnancy (n =115,350) by age-range, pregnancy start month, and encounter year-month. GDM (claim ≥23 gestational weeks) relative risk and risk difference overall, by race and ethnicity, and variant period were estimated using log-binomial models. RESULTS: GDM risk was higher among those with COVID-19 during pregnancy compared to those without (adjusted risk ratio, aRR = 1.12, 95% CI: 1.08-1.15). GDM risk was significantly associated with COVID-19 in non-Hispanic (NH) White (aRR = 1.08, 95% CI: 1.04-1.14), NH Black (aRR=1.15, 95% CI: 1.07-1.24), and Hispanic (aRR = 1.17, 95% CI: 1.10-1.24) groups. GDM risk was significantly higher during pre-Delta (aRR = 1.17, 95% CI: 1.11-1.24) as compared to Omicron (aRR = 1.07, 95% CI: 1.02-1.13) periods, but neither differed from the Delta period (aRR = 1.10, 95% CI: 1.04-1.17). The adjusted risk difference was 0-2% for all models. CONCLUSIONS: COVID-19 during pregnancy was modestly associated with GDM in claims-based data, especially during earlier SARS-CoV-2 variant periods. As these associations are based on COVID-19 in claims data, studies employing systematic testing are warranted.

Using Insurance Claims Data to Estimate Blastomycosis Incidence, Vermont, USA, 2011-2020.

The epidemiology of blastomycosis in Vermont, USA, is poorly understood. Using insurance claims data, we estimated the mean annual blastomycosis incidence was 1.8 patients/100,000 persons during 2011-2020. Incidence and disease severity were highest in north-central counties. Our findings highlight a need for improved clinical awareness and expanded surveillance.

Treatment sequencing after failure to alectinib in patients with anaplastic lymphoma kinase-positive non-small-cell lung cancer.

Alectinib is the first-line therapy for anaplastic lymphoma kinase-positive non-small-cell lung cancer. Although some guidelines have recommended using other anaplastic lymphoma kinase inhibitors after alectinib failure, evidence for such regimens in patients who fail to respond to alectinib is limited. This study involved using administrative claims data from acute care hospitals in Japan. We extracted the data of 634 patients diagnosed with lung cancer between September 1, 2014, and January 31, 2023, who received alectinib treatment before treatment with another anaplastic lymphoma kinase inhibitor. We assessed distributions of patients according to their treatment sequencing and prognosis among three periods defined based on the initial marketing dates of lorlatinib and brigatinib. The type of anaplastic lymphoma kinase inhibitors after alectinib failure changed over time. In the most recent period, lorlatinib (58%) and brigatinib (40%) became predominant. Two-year overall survival improved over time (47%-84%), accompanied by an increased 2-year proportion of patients who continuously used anaplastic lymphoma kinase inhibitors after alectinib failure (13%-44%). The times to treatment discontinuation of the regimen between patients treated with lorlatinib and brigatinib were similar, with a hazard ratio of 1.02 (95% confidence interval, 0.64-1.64) in the period after marketing brigatinib. This study provides insights into the evolving treatment landscape for patients with anaplastic lymphoma kinase-positive non-small-cell lung cancer who experience failed alectinib treatment and highlights the need for further studies and data accumulation to determine the optimal treatment strategy.

Death ORACL: An Algorithm to Predict Death Using Insurance Claims Data.

The inability to identify dates of death in insurance claims data is the United States is a major limitation to retrospective claims-based research. While deaths result in disenrollment, disenrollment can also occur due to changes in insurance providers. We created an algorithm to differentiate between disenrollment from health plans due to death and disenrollment for other reasons. We identified 5,259,735 adults who disenrolled from private insurance between 2007 and 2018. Using death dates ascertained from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data, 7.6% of all disenrollments were classified as resulting from death. We used elastic net regression to build an algorithm using claims data in the year prior to disenrollment; candidate predictors included medical conditions, individual demographic characteristics, treatment utilization, and structural factors related to health insurance eligibility and coding. Using a predicted probability threshold of 0.9 (selected to reflect the corresponding known prevalence of mortality), internal validation found that the algorithm classified death at disenrollment with a positive predictive value of 0.815, sensitivity of 0.721 and specificity of 0.986 (AUC=0.97). Independent data sources were used for external validation and for an applied example. Code for implementation is publicly available.

Development and Validation of an Algorithm to Predict Stillbirth Gestational Age in Medicaid Billing Records.

INTRODUCTION: With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies. OBJECTIVE: We developed and validated a claims-based algorithm to predict GA at stillbirth. METHOD: We linked the stillbirths identified in MAX 1999-2013 to Florida Fetal Death Records (FDRs) to obtain clinical estimates of GA (N=825). We tested several algorithms including using a fixed median GA, median GA at the time of specific prenatal screening tests, and expanded versions considering additional predictors of stillbirth within including linear regression and random forest models. We estimated the proportion of pregnancies with differences of ± 1, 2, 3 and 4 weeks between the predicted and FDR GA and the model mean square error (MSE). We validated the selected algorithms in two external samples. RESULTS: The best performing algorithm was a random forest model (MSE of 12.67 weeks2) with 84% of GAs within ± 4 weeks. Assigning a fixed GA of 28 weeks resulted in an MSE of 60.21 weeks2 and proportions of GA within ± 4 weeks of 32%. We observed consistent results in the external samples. DISCUSSION: Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.

Identifying Pediatric Hypertension in Observational Data: Comparing Clinical and Claims Cohorts in Real World Data.

We conducted retrospective public health surveillance using data from 2006 to 2016 in seven integrated delivery systems from FDA's Sentinel System. We identified pediatric hypertensive patients by clinical and claims-based definitions and compared demographics, baseline profiles and follow-up time profiles. Among 3,757,803 pediatric patients aged 3 to 17 years, we identified 781,722 children and 551,246 teens with at least three blood pressure measures over 36-months. Of these, 70,315 children (9%) and 47,928 teens (8.7%) met the clinical definition for hypertension and 22,465 (2.8%) children and 60,952 (11%) of teens met the clinical definition for elevated, non-hypertensive blood pressure. Of the 3.7M patients, we identified 3,246 children and 7,293 teens with any claim for hypertension (claims definition). Evidence of hypertension claims among those meeting our clinical definition was poor; 2.2% and 7.3% of clinically hypertensive children and teens had corresponding claims for hypertension. Baseline profiles for claims-based hypertensive patients suggest greater severity of disease compared to clinical patients. Claims-based patients showed higher rates of all-cause mortality during follow-up. Pediatric hypertension in claims-based data sources is under-captured but may serve as a marker for greater disease severity. Investigators should understand coding practices when selecting real-world data sources for future pediatric hypertension work.

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.

Invited Commentary: Raising a High-Pressure Alarm About Pediatric Hypertension.

Hypertension is a common "silent killer" in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits ("clinical cohort") in comparison to diagnosis codes ("claims-based cohort"). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration's Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in EHR data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.

Influence of incomplete death information on cumulative risk estimates in US claims data.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology.

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.

Treatment Patterns, Healthcare Resource Utilization, and Costs of Patients With Chronic Lymphocytic Leukemia or Small Lymphocytic Lymphoma in the US.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia among US adults and has experienced a rapidly evolving treatment landscape; yet current data on treatment patterns in clinical practice and economic burden are limited. This study aimed to provide an up-to-date description of real-world characteristics, treatments, and costs of patients with CLL or small lymphocytic lymphoma (SLL). MATERIALS AND METHODS: Using retrospective data from the Optum Clinformatics DataMart database (January 2013 to December 2021), adults with diagnosis codes for CLL/SLL on two different dates were selected. An adapted algorithm identified lines of therapy (LOT). Treatment patterns were stratified by the index year pre- and post-2018. Healthcare resource utilization and costs were evaluated per patient-years. RESULTS: A total of 18 418 patients with CLL/SLL were identified, 5226 patients (28%) were treated with ≥1 LOT and 1728 (9%) with ≥2 LOT. Among patients diagnosed with CLL in 2014-2017 and ≥1 LOT (N = 2585), 42% used targeted therapy and 30% used chemoimmunotherapy in first line (1L). The corresponding proportions of patients diagnosed with CLL in 2018-2021 (N = 2641) were 54% and 16%, respectively. Total costs were numerically 3.5 times higher and 4.9 times higher compared with baseline costs among patients treated with 1L+ and 3L+, respectively. CONCLUSION: This study documented the real-world change in CLL treatment landscape and the substantial economic burden of patients with CLL/SLL. Specifically, targeted therapies were increasingly used as 1L treatments and they were part of more than half of 1L regimens in recent years (2018-2021).

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.

Characteristics, healthcare utilization, and outcomes of patients with HER2-low breast cancer.

PURPOSE: Treatment for HER2-low [defined as ImmunoHistoChemistry (IHC) 1 + or 2 + and negative/normal in Situ Hybridization (ISH)] breast cancer patients is rapidly evolving, yet we lack critical information about the HER2-low population. METHODS: We conducted a retrospective cohort study of women aged 18 years or older diagnosed with breast cancer between 2010 and 2016 in North Carolina. Analyses were conducted for the overall cohort and a stage IV sub-cohort. We examined demographic and clinical characteristics, and characterized prevalence of HER2-low disease and healthcare utilization. We estimated adjusted rate ratios for the association between HER2 classifications and utilization outcomes, and hazard ratios for 3-year all cause mortality (stage IV only). RESULTS: The overall and stage IV cohorts included 12,965 and 635 patients, respectively. HER2-low patients represented more than half of both cohorts (59% overall, 53% stage IV). HER2-low patients were more likely than IHC 0 patients to have hormone receptor (HR)-positive disease. In the stage IV cohort, HER2-low patients were more likely to be Black (26% vs. 16% IHC 0, p = 0.0159). In both cohorts, rates of hospitalizations were slightly higher among HER2-low patients. There were no survival differences between HER2-low and IHC 0 among stage IV patients. CONCLUSION: New treatment options for HER2-low breast cancer may have potential for significant impact at the population level particularly for patients with stage IV disease. In light of racial differences between HER2-low and IHC 0 patients observed in our cohort, research- and practice-based efforts to ensure equitable adoption of new treatment guidelines for patients with HER2-low metastatic breast cancer will be essential.

Treatment patterns and clinical outcomes in patients with metastatic triple-negative breast cancer: a large-scale data analysis using the Japanese claims database.

PURPOSE: This study evaluated treatment patterns and clinical outcomes among patients with metastatic triple-negative breast cancer (mTNBC) in real-world clinical settings in Japan. METHODS: The treatment patterns, time to next treatment or death (TTNTD), time to treatment discontinuation, adverse events of interest, and medical costs of treating patients with mTNBC in first-, second-, and third-line settings were investigated using data of patients meeting the inclusion criteria between January 2017 and March 2022 in a Japanese medical claims database. The treatment regimens for mTNBC were defined according to the Japanese Breast Cancer Society Clinical Practice Guidelines. RESULTS: In this study, 2236 patients with mTNBC (median age 66.0 years; 99.8% female) were included in the first-line cohort. Of these, 46.6% and 20.8% were included in the second- and third-line cohorts, respectively. The two most frequently used treatments were capecitabine (19.1%) and S-1 (tegafur-gimeracil-oteracil) (14.5%) in the first-line cohort, eribulin (18.3%) and bevacizumab/paclitaxel (14.4%) in the second-line cohort, and eribulin (19.4%) and bevacizumab/paclitaxel (17.5%) in the third-line cohort. The TTNTD shortened as the line of therapy progressed (median 8.0, 6.5, and 5.2 months for the first-, second-, and third-line treatments, respectively). Nausea/vomiting and neutropenia/leukopenia occurred in 62.8% and 18.3% of all patients, respectively. The medical total costs per day were 6.7, 10.2, and 12.9 thousand yen during the first-/second-/third-line treatments, respectively. CONCLUSION: This study provides insight into current treatment patterns for mTNBC in Japan. The cost-benefit balance worsens with later-line treatment and a high unmet need for mTNBC drug treatment remains.