Background Multimorbidity among older adults presents growing clinical and economic challenges, particularly in medically underserved states such as Arkansas. However, it remains unclear whether chronic conditions documented in routine healthcare encounters exhibit structured patterns of diagnosis co-documentation or primarily reflect incidental co-documentation. Understanding these patterns may inform future studies of multimorbidity and healthcare utilization. Objective To characterize statewide, sex-stratified patterns of claim-level diagnosis co-documentation among Arkansas adults aged ≥ 65 years using administrative claims data. Methods We analyzed 2024 IQVIA administrative claims for Arkansas residents aged ≥ 65 years (1408,707 claims from 35,253 unique patients). Analyses were conducted at the claim level; therefore, individuals could contribute multiple claims, and results reflect utilization-weighted diagnosis co-documentation rather than patient-level disease prevalence or multimorbidity. Twenty-five chronic condition categories were defined using ICD-10-CM codes. Pairwise diagnosis co-documentation was quantified using Lewontin's standardized disequilibrium coefficient (D′), which compares observed joint documentation with the expectation under statistical independence. Sex-stratified D′ matrices, disease connectedness metrics, and hierarchical clustering were used to characterize patterns of diagnosis co-documentation. Results Hypertension was the most frequently documented chronic condition among claims for both females (22.2%) and males (23.7%) and exhibited the highest positive connectedness in both sex-stratified analyses. Among females, the strongest diagnosis co-documentation patterns involved hypertension with dyslipidemia, coronary artery disease (CAD), obesity, thyroid disorders, and heart failure (D′ = 0.46–0.69). Among males, the strongest diagnosis co-documentation patterns involved hypertension with dyslipidemia (D′ = 0.73), obesity (D′ = 0.64), CAD (D′ = 0.57), and thyroid disorders (D′ = 0.56). Females demonstrated relatively greater diagnosis co-documentation among musculoskeletal, endocrine, and mental health conditions, whereas males showed more concentrated diagnosis co-documentation among cardiometabolic and organ dysfunction conditions. Negative D′ values identified disease pairs that were infrequently co-documented within the same claims (e.g., autoimmune disease–chronic gastrointestinal disease among males, D′ = −1.00), reflecting absent or infrequent claim-level joint documentation. Conclusions In this claim-level analysis of Arkansas adults aged ≥ 65 years represented in the IQVIA administrative claims database, diagnosis co-documentation exhibited structured, sex-specific patterns, with hypertension demonstrating the highest positive connectedness among the 25 chronic conditions. These utilization-weighted findings provide a descriptive basis for hypothesis generation and support future longitudinal, patient-level studies to evaluate temporal disease relationships and the potential role of cardiovascular–renal–metabolic (CVRM) risk management.
Purpose Chronic pain (CP) is highly prevalent in the U.S. and imposes substantial health and economic burdens. Understanding which biopsychosocial and treatment-related factors drive pain progression and improvement is essential for developing targeted prevention strategies. Methods Using the 2019–2020 National Health Interview Survey Longitudinal Cohort data, we applied Random Forest machine learning algorithms with permutation-based feature importance to predict four one-year pain transitions: nonchronic-to-chronic pain progression, CP-to-high-impact chronic pain (HICP) progression, complete CP improvement (CP to no pain), and partial CP improvement (CP to nonchronic pain). Analyses were stratified by age, sex, race/ethnicity, education, and urban-rural residence. Results Comorbidity count was a universal predictor across all four outcomes and nearly every subgroup. Opioid use ranked first for HICP progression and predicted higher odds of progression. Socioeconomic factors, particularly employment status, ranked highest among older, male, non-Hispanic White, and less-educated adults, whereas social relations ranked highest among racial and ethnic minorities. Predictors shifted from health conditions for CP progression toward pain treatment use and social and economic factors for HICP progression. Conclusions Predictors of pain progression and improvement differ by transition stage and across populations, highlighting the need for equity-informed pain management strategies tailored to population-specific risk profiles.
Purpose To assess how sex differences in hypertension management have evolved over a 25-year period in Switzerland, focusing on screening, diagnosis, treatment, and control, and to determine whether age modifies these differences. Methods We analyzed self-reported data from six consecutive Swiss Health Surveys (1997–2022) from samples representative of the Swiss population. Multivariable logistic regression models were adjusted for sociodemographic and lifestyle covariates. Age-stratified analyses were conducted using age > 50 years as a proxy for menopausal status. Results Among 94,785 participants (55.3% female), females consistently had a higher likelihood than males of reporting blood pressure screening (e.g., adjusted odds ratio (aOR) 1.62, 95% CI 1.50–1.75 in 2022), with no narrowing of the trend over time. In contrast, from 2012 onward, females had a lower likelihood of reporting a diagnosis of hypertension (e.g., aOR 0.73, 95% CI 0.68–0.79 in 2022) or antihypertensive medication use among those diagnosed (e.g., aOR 0.84, 95% CI 0.74–0.95 in 2022), with gaps widening over time. No sex differences in self-reported blood pressure control were found among those reporting treatment. Age-stratified analyses showed consistent sex differences across both age groups, though the magnitude varied. Conclusion Despite comparable treatment outcomes, our findings show a paradoxical sex difference in earlier stages of hypertension management, higher screening yet lower treatment in females.
Food insecurity has increased with inequities likely patterned by intersecting social identities/positions like education and race/ethnicity. Anxiety or depression symptoms may restrict material resources and increase food insecurity. Using intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA), we examined whether inequities in persistent food insecurity based on age, gender, race/ethnicity, and education were modified by anxiety or depression symptoms. In a national cohort of adults (n=4,933) followed over 2021-2023, 23% experienced food insecurity, with a higher prevalence among those with symptoms (49%). In the null model, 20% of the variance in food insecurity lies in the intersectional social strata. Age, gender, race/ethnicity, and education explained 99% of the between-stratum variance among adults with and 94% among adults without symptoms. Predicted probabilities of food insecurity were highest among adults younger than 55 years, non-college-educated, identified as Black and Hispanic. Symptoms of anxiety and depression were associated with 14–38 percentage point increase in predicted food insecurity. One stratum comprising White women aged 40-54 years without a college education or symptoms experienced food insecurity that deviated from the expected average effects. Findings suggest addressing food insecurity inequities tied to social positioning using universal proportionate interventions that integrate mental health support.
Background Confidence intervals are increasingly recommended to be interpreted as compatibility intervals rather than dichotomised according to statistical significance. However, no uniform framework exists for interpreting interval estimates across superiority and non-inferiority trials, particularly when different combinations of superiority and non-inferiority margins are specified. Methods We developed a unified framework for interpreting interval estimates in superiority and non-inferiority trials based on the position of a user-provided interval estimate relative to prespecified superiority and non-inferiority margins. The framework was implemented in an updated version of the freely available online Conclusion Generator (https://apps.biostat.au.dk/erikparner/Conclusion_app/) and in standalone R functions available through GitHub (https://github.com/erikparner/conclusion_generator). Both implementations automatically generate standardised concise and elaborated conclusion text for superiority, non-inferiority, and combined trial designs. Results The framework classifies all possible interval estimates into predefined interpretation scenarios, providing consistent interpretations of treatment effects while avoiding dichotomous terminology based on statistical significance. The updated Conclusion Generator supports trials specifying superiority margins, non-inferiority margins, or both, and extends its application by adding support for mean differences and refined standardised wording. Conclusions This unified framework provides a consistent approach to interpreting interval estimates across superiority and non-inferiority trials and is freely available as a web application and standalone R functions.
