Gender-specific factors associated with case complexity in middle-aged and older adults-Evidence from a large population-based study.

OBJECTIVES: To investigate gender-specific factors associated with case complexity in a population-based sample of middle-aged and older adults using a holistic approach to complexity. METHODS: Data were derived from the 8-year follow-up home visits of the ESTHER study-a German population-based study in middle-aged and older adults. Cross-sectional analyses were conducted for 2932 persons (aged 57-84). Complexity was assessed by the well-established INTERMED for the elderly interview, which uses a holistic approach to the definition of case complexity. The association between various bio-psycho-social variables and case complexity was analyzed using gender-specific logistic regression models, adjusted for sociodemographic factors (age, marital status, education). RESULTS: Prevalence of complexity was 8.3% with significantly higher prevalence in female (10.6%) compared to male (5.8%) participants (p < 0.001). Variables associated with increased odds for complexity in both, women and men were: being divorced (odds ratio [OR] women: 1.86, 95% CI 1.05-3.30; OR men: 3.19, 1.25-8.12), higher total somatic morbidity (women: 1.08, 1.04-1.12; men: 1.06, 1.02-1.11), higher depression severity (women: 1.34, 1.28-1.40; men: 1.35, 1.27-1.44), and higher loneliness scores (women: 1.19, 1.05-1.36; men: 1.23, 1.03-1.47). Women (but not men) with obesity (Body mass index [BMI] ≥30) had higher odds (1.79, 1.11-2.89) for being complex compared to those with a BMI <25. High oxidative stress measured by derivatives of reactive oxygen metabolites in serum was associated with 2.02 (1.09-3.74) higher odds for complexity only in men. CONCLUSIONS: This study provides epidemiological evidence on gender differences in prevalence and factors associated with case complexity in middle-aged and older adults. Moreover, this study adds to the holistic understanding of complexity by identifying novel variables linked to complexity among middle-aged and older individuals. These factors include loneliness for both genders, and high oxidative stress for men. These findings should be confirmed in future longitudinal studies.

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study.

BACKGROUND: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.

[General practice care for patients with chronic non-cancer pain: A rapid review as part of the RELIEF project]. / Hausärztliche Versorgung von Patient:innen mit chronischen nicht-tumorbedingten Schmerzen: ein Rapid Review im Rahmen des RELIEF-Projekts.

INTRODUCTION: Approximately one fifth of the German population suffers from chronic pain, which is often associated with limitations in coping with everyday life, social isolation and psychological comorbidities such as anxiety and depression. The importance of a treatment approach that considers biological, psychological, and social factors (bio-psycho-social model) as well as non-drug interventions is emphasized in current guidelines, but presents challenges for primary care practices. To support the implementation of evidence-based best practice recommendations, the RELIEF project (Resource-oriented case management to implement recommendations for patients with chronic pain and frequent use of analgesics in general practices) aims to develop a case management program for the primary care of patients with chronic non-tumor pain. METHODS: Prior to intervention development, a rapid review was conducted to identify best practice recommendations for the care of patients with chronic non-tumor pain, barriers and strategies to their implementation, and gaps in care in current guidelines and literature. Selective searches of guidelines, PubMed, the Cochrane Library, bibliographies of relevant publications, and the gray literature focused on assessment and monitoring, education, promotion of self-care, and rational pharmacotherapy. RESULTS: Numerous recommendations on assessment and monitoring were identified, but only a few studies examined their feasibility in primary care practices. Guidelines contained few specific recommendations on content and format of patient education on chronic pain. Recommendations for non-drug self-care measures were mainly related to physical activity, relaxation techniques, behavioral therapy techniques and external applications. Especially for the area of physical activity, numerous barriers but also strategies for a successful implementation could be identified. DISCUSSION: In a potential primary care model for patients with chronic non-tumor pain, pain assessment should aim to identify patients who need support in implementing medication and non-medication interventions in the primary care setting and/or could benefit from specialized care. To implement recommendations for pain education, primary care physicians need educational materials in a variety of formats and levels of detail that ideally could be processed by patients at home and then get addressed in practices using simple key questions. Non-drug measures should be an explicit part of the treatment plan. CONCLUSION: Many of the identified recommendations for the treatment of patients with chronic non-tumor pain can also be considered relevant for the primary care setting. Specific guidelines and concepts for primary care physicians that include setting-specific characteristics at the physician, patient, and system levels would be desirable for a successful implementation of these recommendations.