Supplemental Nutrition Assistance Program and Adherence to Antihypertensive Medications.

Importance: Nonadherence to antihypertensive medications is associated with uncontrolled blood pressure, higher mortality rates, and increased health care costs, and food insecurity is one of the modifiable medication nonadherence risk factors. The Supplemental Nutrition Assistance Program (SNAP), a social intervention program for addressing food insecurity, may help improve adherence to antihypertensive medications. Objective: To evaluate whether receipt of SNAP benefits can modify the consequences of food insecurity on nonadherence to antihypertensive medications. Design, Setting, and Participants: A retrospective cohort study design was used to assemble a cohort of antihypertensive medication users from the linked Medical Expenditure Panel Survey (MEPS)-National Health Interview Survey (NHIS) dataset for 2016 to 2017. The MEPS is a national longitudinal survey on verified self-reported prescribed medication use and health care access measures, and the NHIS is an annual cross-sectional survey of US households that collects comprehensive health information, health behavior, and sociodemographic data, including receipt of SNAP benefits. Receipt of SNAP benefits in the past 12 months and food insecurity status in the past 30 days were assessed through standard questionnaires during the study period. Data analysis was performed from March to December 2021. Exposure: Status of SNAP benefit receipt. Main Outcomes and Measures: The main outcome, nonadherence to antihypertensive medication refill adherence (MRA), was defined using the MEPS data as the total days' supply divided by 365 days for each antihypertensive medication class. Patients were considered nonadherent if their overall MRA was less than 80%. Food insecurity status in the 30 days prior to the survey was modeled as the effect modifier. Inverse probability of treatment (IPT) weighting was used to control for measured confounding effects of baseline covariates. A probit model was used, weighted by the product of the computed IPT weights and MEPS weights, to estimate the population average treatment effects (PATEs) of SNAP benefit receipt on nonadherence. A stratified analysis approach was used to assess for potential effect modification by food insecurity status. Results: This analysis involved 6692 antihypertensive medication users, of whom 1203 (12.8%) reported receiving SNAP benefits and 1338 (14.8%) were considered as food insecure. The mean (SD) age was 63.0 (13.3) years; 3632 (51.3%) of the participants were women and 3060 (45.7%) were men. Although SNAP was not associated with nonadherence to antihypertensive medications in the overall population, it was associated with a 13.6-percentage point reduction in nonadherence (PATE, -13.6 [95% CI, -25.0 to -2.3]) among the food-insecure subgroup but not among their food-secure counterparts. Conclusions and Relevance: This analysis of a national observational dataset suggests that patients with hypertension who receive SNAP benefits may be less likely to become nonadherent to antihypertensive medication, especially if they are experiencing food insecurity. Further examination of the role of SNAP as a potential intervention for preventing nonadherence to antihypertensive medications through prospectively designed interventional studies or natural experiment study designs is needed.

Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults.

INTRODUCTION: Polypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations). METHODS: A cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE. RESULTS: We investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes. CONCLUSIONS: We demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.

Prevalence and Factors Associated with Subjective Memory Complaint in a Semi-Rural Community in Chile.

BACKGROUND: Subjective memory complaints (SMC) are commonly studied in older adults and have been identified as potentially prodromal to dementia and Alzheimer's disease. Studies among younger adults from South America are lacking. OBJECTIVE: To estimate the prevalence of SMC and the factors associated with it among Maule Cohort (MAUCO) participants. METHODS: We performed a cross-sectional analysis to estimate the prevalence of SMC and investigated its associated factors from MAUCO baseline data (N = 6,687). Within groups defined by age (38-59, 60-74) and global cognition (Mini-Mental State Examination: ≥26, 25-22, ≤21), multinomial logistic regression models evaluated risk factors for SMC (Yes, Sometimes, No). RESULTS: Overall, SMC prevalence was 16.4%; 15.9% (95% CI 14.9-16.9%) among younger and 17.6% (15.8-19.4%) among older participants. Female sex, comorbidities, and bad/fair self-reported health status (SRHS) were generally associated with higher odds of SMC. CONCLUSION: Overall prevalence of SMC was 16%. Different factors were associated with the odds of SMC depending on age and global cognitive status. Future SMC studies should include sex-specific assessments, evaluate SRHS as a moderator of SMC reporting, and the influence of the SARS-CoV-2 pandemic on SMC reporting.

Polypharmacy in a semirural community in Chile: Results from Maule Cohort.

PURPOSE: Although polypharmacy in younger populations is a growing public health concern, most studies addressing polypharmacy focus on elderly populations. Thus, polypharmacy is not yet well understood in younger populations. METHODS: Baseline data from the Maule Cohort (MAUCO) (adults aged 38-74 years) were used to study the prevalence of polypharmacy and associated participant characteristics using logistic and zero-inflated negative binomial regressions. Factors studied include age, sex, self-rated health, education, smoking, obesity, diabetes, hypertension, and other chronic conditions. RESULTS: Polypharmacy was reported by 10% of participants overall, with higher prevalence among older (≥60 years) vs middle aged (<60 years) participants (overall: 20.9% vs 6.0%, P < .0001; for those reporting any medication use: 30.2% vs 15.9%, P < .0001). Middle-aged adults reported different patterns of medication use by polypharmacy status, while older adults reported similar medication use patterns regardless of polypharmacy. Diabetes, hypertension, dyslipidemia, cardiovascular diseases, hypothyroidism, and osteomuscular diseases were significantly associated with polypharmacy. Analyses also revealed that there are MAUCO participants who are potentially being undertreated for conditions like depression. CONCLUSIONS: Research into medication use among younger and middle-aged adults and development of possible tools to deprescribe medications in this population are warranted. However, it is important that patients who need treatment receive it, and so both potential overtreatment and undertreatment need further study in this population.