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Toxoplasmosis is a zoonotic infection contracted through Toxoplasma gondii–contaminated food, soil, or water. Seroprevalence in Germany is high, but estimates of disease incidence are scarce. We investigated incidences for various toxoplasmosis manifestations using anonymized healthcare claims data from Germany for 2011–2016. Patients with a toxoplasmosis diagnosis during the annual observational period were considered incident. The estimated incidence was adjusted to the general population age/sex distribution. We estimated an annual average of 8,047 toxoplasmosis patients in Germany. The average incidence of non–pregnancy-associated toxoplasmosis patients was 9.6/100,000 population. The incidence was highest in 2011, at 10.6 (95% CI 9.4–12.6)/100,000 population, and lowest in 2016, at 8.0 (95% CI 7.0–9.4)/100,000 population. The average incidence of toxoplasmosis during pregnancy was 40.3/100,000 pregnancies. We demonstrate a substantial toxoplasmosis disease burden in Germany. Public health and food safety authorities should implement toxoplasmosis-specific prevention programs.

Summary Aims With self‐reporting of erectile dysfunction (ED) in population‐based surveys, men with ED may not represent men who are bothered sufficiently to seek an ED diagnosis and treatment. We used real‐world observational data to assess: 1) the prevalence of ED diagnosis or treatment by age subgroups; and 2) the relationship of age with ED diagnosis or treatment after controlling for ED‐related comorbidities in the USA. Methods This cross‐sectional study used de‐identified claims data (MarketScan® databases; primary analysis). Sensitivity analysis was conducted using electronic health records (Humedica® database). Inclusion criteria were men aged ≥18 years with a 360‐day continuous enrollment before the index date. We assessed the prevalence of ED diagnosis or phosphodiesterase type 5 inhibitor (PDE5I) prescription by age and the risk for ED diagnosis or treatment by age after controlling for comorbidities (hypertension, other cardiovascular disease, diabetes mellitus, depression and benign prostatic hyperplasia). Results Of 19,833,939 men meeting inclusion criteria in the primary analysis, only 1 108 842 (5.6%) had an ED diagnosis or PDE5I prescription (mean [SD] age: 55.2 [11.2] years). Prevalence of ED diagnosis or treatment increased from age 18–29 years (0.4%) to 60–69 years (11.5%), then decreased in the seventh (11.0%), eighth (4.6%), and ninth (0.9%) decades. Men with ED diagnosis or treatment had a higher prevalence of any comorbidity (63.1% vs 29.3% for men without ED) and of each comorbid condition. In multivariate analyses, age was an independent risk factor for ED diagnosis or treatment. Sensitivity analysis provided consistent results. Conclusions In a real‐world setting in the USA, the prevalence of ED diagnosis or PDE5I treatment is generally low, increases with age, decreases in very old men, and is associated with increased prevalence of comorbidities. Age is an independent risk factor for ED diagnosis or treatment after controlling for comorbidities.

Health claims databases offer opportunities for studies on large populations of patients with kidney disease and health outcomes in a non-experimental setting. Among others, their unique features enable studies on healthcare costs or on longitudinal, epidemiological data with nationwide coverage. However, health claims databases also have several limitations. Because clinical data and information on renal function are often lacking, the identification of patients with kidney disease depends on the actual presence of diagnosis codes only. Investigating the validity of these data is therefore crucial to assess whether outcomes derived from health claims data are truly meaningful. Also, one should take into account the coverage and content of a health claims database, especially when making international comparisons. In this article, an overview is provided of international health claims databases and their main publications in the area of nephrology. The structure and contents of the Dutch health claims database will be described, as well as an initiative to use the outcomes for research and the development of the Dutch Kidney Atlas. Finally, we will discuss to what extent one might be able to identify patients with kidney disease using health claims databases, as well as their strengths and limitations.

Identifying patient health conditions in observational studies is essential for accurately measuring healthcare practices and planning effective health policy interventions. This analysis evaluates the validity of the Rx-Risk Index, a tool that uses medication dispensing data to identify patient comorbidities and measure overall health. We examined an updated version of the Rx-Risk Index, reflecting changes in treatment practices, to assess its validity as a tool for identifying specific health conditions and as a measure of overall health to aid in risk adjustment in observational studies. We conducted a validation study using two Australian linked health datasets, the Person-Level Integrated Data Asset (PLIDA) and the National Health Data Hub (NHDH), from 2010 to 2018, focusing on individuals aged 65 years or older. The sensitivity, specificity, PPV/NPV, Cohen's kappa, and F1 scores were used to assess agreement between Rx-Risk Index conditions and two reference standards: patient self-reported conditions and hospital diagnosis. The Rx-Risk Index's predictive validity for one-year mortality was also evaluated using logistic regression, with model fit assessed by AIC and c-statistic. Data were analysed from 3,959 individuals in PLIDA and 157,709 individuals in NHDH. The Rx-Risk Index showed high sensitivity (≥75%) for diabetes, chronic airways disease, hyperlipidemia, and epilepsy against both self-reported conditions and hospital diagnoses. However, hyperlipidemia and hypertension showed lower specificity (<70%). High PPVs (≥78%) were observed for diabetes and renal failure. The agreement between the Rx-Risk Index and self-reported conditions was stronger (Cohen's kappa: 0.41-0.81 for 7 conditions) than between Rx-Risk Index and ICD10-AM diagnoses (kappa: 0.73 for one condition). The Rx-Risk Index was a strong predictor of one-year mortality, with c-statistic of 0.820 (95% CI: 0.817-0.825). Selected Rx-Risk Index conditions are reasonable proxies for identifying specific conditions, particularly those requiring pharmacological management. The Rx-Risk Index was a strong predictor of one-year mortality, suggesting it is a valid measure of overall health. This study demonstrates the Rx-Risk Index's potential to enhance disease classification and risk adjustment in observational studies, supporting informed decision-making in health policy planning.

Inflammatory bowel disease (IBD) diagnoses are increasing in Japan. Some patients have symptoms that are difficult to control, and further research on IBD is needed. Claims databases, which have a large sample size, can be useful for IBD research. However, it is unclear whether the International Classification of Diseases, Tenth Revision (ICD-10) codes alone can correctly identify IBD. We aimed to develop algorithms to identify IBD in claims databases. We used claims data from the Department of Gastroenterology, Tohoku University Hospital from 1 January 2016 to 31 December 2020. We developed 11 algorithms by combining the ICD-10 code, prescription drug, and workup information. We had access to the database which contains all the information for Crohn’s disease and ulcerative colitis patients who visited our department, and we used it as the gold standard. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value for each algorithm. We enrolled 19,384 patients, and among them, 1012 IBD patients were identified in the gold standard database. Among 11 algorithms, Algorithm 4 (ICD-10 code and ≥1 prescription drugs) showed a strong performance (PPV, 94.8%; sensitivity, 75.6%). The combination of an ICD-10 code and prescription drugs may be useful for identifying IBD among claims data.

Background Unmeasured confounders pose challenges when observational data are analysed in comparative effectiveness studies. Integrating high-dimensional administrative claims data may help adjust for unmeasured confounders. We determined whether distributed representations can compress high-dimensional administrative claims data to adjust for unmeasured confounders. Method Using the Japanese Diagnosis Procedure Combination (DPC) database from 1291 hospitals (between April 2018 and March 2020), we applied the word2vec algorithm to create distributed representations for all medical codes. We focused on patients with heart failure (HF) and simulated four risk-adjustment models: 1, no adjustment; 2, adjusting for previously reported confounders; 3, adjusting for the sum of distributed representation weights of administrative claims data on the day of hospitalisation (novel method); and 4, a combination of models 2 and 3. We re-evaluated a previous study on the effect of early rehabilitation in patients with HF and compared these risk-adjustment methods (models 1–4). Results Distributed representations were generated from the data of 15 998 963 in-patients, and 319 581 HF patients were identified. In the simulation study, Model 3 reduced the impact of unmeasured confounders and achieved better covariate balances than Model 1. Model 4 showed no increase in bias compared with the true model (Model 2) and was used as a reference model in the real-world application. When applied to a previous study, models 3 and 4 showed similar results. Conclusion Distributed representation can compress detailed administrative claims data and adjust for unmeasured confounders in comparative effectiveness studies.

Health claims data may be an efficient and easily accessible source to study chronic kidney disease (CKD) prevalence in a nationwide population. Our aim was to study Dutch claims data for their ability to identify CKD patients in different subgroups. From a laboratory database, we selected 24 895 adults with at least one creatinine measurement in 2014 ordered at an outpatient clinic. Of these, 15 805 had ≥2 creatinine measurements at least 3 months apart and could be assessed for the chronicity criterion. We estimated the validity of a claim-based diagnosis of CKD and advanced CKD. The estimated glomerular filtration rate (eGFR)-based definitions for CKD (eGFR < 60 mL/min/1.73 m ) and advanced CKD (eGFR < 30 mL/min/1.73 m ) satisfying and not satisfying the chronicity criterion served as reference group. Analyses were stratified by age and sex. In general, sensitivity of claims data was highest in the population with the chronicity criterion as reference group. Sensitivity was higher in advanced CKD patients than in CKD patients {51% [95% confidence interval (CI) 47-56%] versus 27% [95% CI 25-28%]}. Furthermore, sensitivity was higher in young versus elderly patients. In patients with advanced CKD, sensitivity was 72% (95% CI 62-83%) for patients aged 20-59 years and 43% (95% CI 38-49%) in patients ≥75 years. The specificity of CKD and advanced CKD was ≥99%. Positive predictive values ranged from 72% to 99% and negative predictive values ranged from 40% to 100%. When using health claims data for the estimation of CKD prevalence, it is important to take into account the characteristics of the population at hand. The younger the subjects and the more advanced the stage of CKD the higher the sensitivity of such data. Understanding which patients are selected using health claims data is crucial for a correct interpretation of study results.

Among older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data. This study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included. A total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models. NHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally.

Real-world data have become increasingly important in medical science and healthcare. A new, effective, and practically feasible statistical design is needed to unlock the potential of real-world data that decision-makers and practitioners can use to meet people’s healthcare needs. In the first half of the study, we validated our proposed new method by simulation, and in the second half, we conducted a clinical study on actual real-world data. We proposed the “Exact Matching Algorithm Using Administrative Health Claims Database Equivalence Factors (AHCDEFs)” using a target trial emulation framework. The simulation trials were conducted 500 times independently, considering the misclassification and chance errors of all variables and competing events of outcome. Two conventional methods, multivariate and propensity score analyses, were compared. Next, we estimated the effect of specific health guidance provided in Japan on the prevention of diabetes onset and medical expenditures. Our proposed novel method for real-world data returns improved estimates and fewer type I errors (the probability of erroneously determining that there is a difference when, in fact, there is no difference) than conventional methods. We quantitatively demonstrated the effectiveness of specific health guidance in Japan in preventing the onset of diabetes and reducing medical expenditures during five years. We proposed a new method for analyzing real-world data and an exact-matching algorithm using AHCDEFs. The larger the number of patients available for analysis, the more the AHCDEFs that can be matched, thereby removing the influence of confounding factors. This method will generate significant evidence when applied to real-world data.

The aim of this paper is to understand and to model claims arrival and reporting delay in general insurance. We calibrate two real individual claims data sets to the statistical model of Jewell and Norberg. One data set considers property insurance and the other one casualty insurance. For our analysis we slightly relax the model assumptions of Jewell allowing for non-stationarity so that the model is able to cope with trends and with seasonal patterns. The performance of our individual claims data prediction is compared to the prediction based on aggregate data using the Poisson chain-ladder method.