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.

Patient satisfaction with the quality of care received is associated with adherence to antidepressant medications.

BACKGROUND: There is a paucity of evidence on the association between satisfaction with quality of care and adherence to antidepressants. OBJECTIVES: To examine the association between patient satisfaction with healthcare and adherence to antidepressants. METHODS: A cohort study design was used to identify antidepressant users from the 2010-2016Medical Expenditure Panel Survey data, a national longitudinal complex survey study design on the cost and healthcare utilization of the noninstitutionalized population in the United States. The Consumer Assessment of Healthcare Providers and Systems were used to measure participants' satisfaction with access and quality of care, patient-provider communication and shared decision-making (SDM). Patients were considered satisfied if they ranked the quality of care at ≥9 (range: 0[worst]- 10[best]). Antidepressant adherence was measured based on medication refill and complete discontinuation. MEPS sampling survey-weighted multivariable-adjusted logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between satisfaction and adherence to antidepressants. We tested for the potential presence of reverse associations by restricting the analysis to new users of antidepressants. The roles of patient-provider communication and SDM on the satisfaction-adherence association were examined through structural equation models (SEM). RESULTS: Among 4,990 (weighted counts = 8,661,953) antidepressant users, 36% were adherent while 39% discontinued antidepressants therapy. Half of antidepressant users were satisfied with the healthcare received. Satisfied patients were 26% (OR = 1.26, 95%CI: 1.08, 1.47) more likely to adhere and 17% (OR = 0.83, 95%CI: 0.71, 0.96) less likely to discontinue, compared to unsatisfied antidepressant users. Patient satisfaction was also associated with higher odds (OR = 1.41, 95%CI: 1.06, 1.88) of adherence among a subgroup of new users of antidepressants. The SEM analysis revealed that satisfaction was a manifestation of patient-provider communication (ß = 2.03, P-value<0.001) and SDM (ß = 1.14, P-value<0.001). CONCLUSIONS: Patient satisfaction is a potential predictor of antidepressant adherence. If our findings are confirmed through intervention studies, improving patient-provider communication and SDM could likely drive both patient satisfaction and adherence to antidepressants.

The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness.

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.

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.

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis.

BACKGROUND: The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES: The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION: State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.

Optimizing Medication Distribution in Automated Dispensing Cabinets: Dashboard Implementation and Evaluation.

Purpose: To determine the impact of a business intelligence dashboard tool to optimize automated dispensing cabinets (ADCs). Methods: A pre-post implementation design was used to evaluate key performance indicators (KPI) before and after the implementation of a dashboard tool to optimize ADCs. Eleven ADCs were optimized in 2 phases according to dashboard recommendations: (1) removal of unused medications over 90 days, (2) adjusting periodic automatic replenishment (PAR) levels, and (3) addition of commonly dispensed medications. The KPI measures that were assessed included inventory cost, no. of stocked medications, stockout percentage, vend to refill ratio, and missing dose messages from nursing. An interrupted-time-series regression was used to quantify the impact of ADCs on the means of measured KPIs. Results: Differences in mean distribution of all KPIs, except missing dose, between the pre- and post-ADC periods during the Phase 1 period were statistically significant: inventory cost (54.2 vs 56), stockout percentage (1.55 vs 1.12), vend to refill ratio (6.83 vs 6.14), and missing dose messages (221 vs 229). Only the mean ADC utilization (57.3 vs 64) and missing dose (228 vs 179) were statistically different between the pre- and post-ADC periods in Phase 2. The interrupted-time-series analysis showed that Phase 1 optimization significantly reduced the cost of inventory (ß = -$1.238.00, P < .01), no. Stocked medications (ß = -8.2, P < .01), percent stockout (ß = -.49%, P < .01), vend-to-refill ratio (ß = -1.29%, P<.01) and ADC utilization (ß = -.2, P < .01). Conclusion: Automated dispensing cabinets optimization, through the use of a dashboard tool, had a positive impact on almost all measured KPIs.

Predictors of Influenza and Pneumococcal Vaccination Among Participants in the Women's Health Initiative.

OBJECTIVE: Older adults typically experience higher rates of severe disease and mortality than the general population after contracting an infectious disease. Vaccination is critical for preventing disease and severe downstream outcomes; however, vaccination rates among older adults are suboptimal. We assessed predictors associated with pneumococcal and seasonal influenza vaccination among older women. METHODS: We used data from the Women's Health Initiative, a nationwide cohort of women. We ascertained seasonal influenza and pneumococcal vaccination status through a questionnaire administered in 2013. We limited analyses to women aged ≥65 years at questionnaire administration. We used logistic regression to estimate associations between demographic, lifestyle, and health-related factors and vaccination and explored stratification by race. RESULTS: Of participants who responded to each question, 84.3% (n = 60 578) reported being vaccinated for influenza and 85.5% (n = 59 015) for pneumonia. The odds of reporting influenza vaccination were significantly lower among non-Hispanic Black participants than among non-Hispanic White participants (odds ratio [OR] = 0.53; 95% CI, 0.49-0.58), women with no health insurance versus private health insurance (OR = 0.61; 95% CI, 0.54-0.68), and women living in rural versus urban settings (OR = 0.84; 95% CI, 0.73-0.96). Current smoking, lower education levels, and having comorbid conditions were associated with lower likelihood of being vaccinated for influenza (than not); past pneumonia diagnosis and being currently married were associated with a higher likelihood. We observed similar associations for pneumococcal vaccination coverage. CONCLUSIONS: These findings reinforce the need to enact policy and implement programs to improve access to, education and awareness about, and provider recommendations for these critical disease-prevention tools. Results from our study should guide strategies for SARS-CoV-2 vaccination.

A comprehensive assessment of statin discontinuation among patients who concurrently initiate statins and CYP3A4-inhibitor drugs; a multistate transition model.

AIMS: The aim of this study was to describe the 1-year direct and indirect transition probabilities to premature discontinuation of statin therapy after concurrently initiating statins and CYP3A4-inhibitor drugs. METHODS: A retrospective new-user cohort study design was used to identify (N = 160 828) patients who concurrently initiated CYP3A4 inhibitors (diltiazem, ketoconazole, clarithromycin, others) and CYP3A4-metabolized statins (statin DDI exposed, n = 104 774) vs. other statins (unexposed to statin DDI, n = 56 054) from the MarketScan commercial claims database (2012-2017). The statin DDI exposed and unexposed groups were matched (2:1) through propensity score matching techniques. We applied a multistate transition model to compare the 1-year transition probabilities involving four distinct states (start, adverse drug events [ADEs], discontinuation of CYP3A4-inhibitor drugs, and discontinuation of statin therapy) between those exposed to statin DDIs vs. those unexposed. Statistically significant differences were assessed by comparing the 95% confidence intervals (CIs) of probabilities. RESULTS: After concurrently starting stains and CYP3A, patients exposed to statin DDIs, vs. unexposed, were significantly less likely to discontinue statin therapy (71.4% [95% CI: 71.1, 71.6] vs. 73.3% [95% CI: 72.9, 73.6]) but more likely to experience an ADE (3.4% [95% CI: 3.3, 3.5] vs. 3.2% [95% CI: 3.1, 3.3]) and discontinue with CYP3A4-inhibitor therapy (21.0% [95% CI: 20.8, 21.3] vs. 19.5% [95% CI: 19.2, 19.8]). ADEs did not change these associations because those exposed to statin DDIs, vs. unexposed, were still less likely to discontinue statin therapy but more likely to discontinue CYP3A4-inhibitor therapy after experiencing an ADE. CONCLUSION: We did not observe any meaningful clinical differences in the probability of premature statin discontinuation between statin users exposed to statin DDIs and those unexposed.

More than treatment refusal: a National Cancer Database analysis of adjuvant treatment refusal and racial survival disparities among women with endometrial cancer.

BACKGROUND: Disparities in adjuvant treatment between Black and White women with endometrial cancer exist and contribute to worse outcomes among Black women. However, factors leading to disparate treatment receipt are understudied. OBJECTIVE: We examined whether patient refusal of adjuvant treatment (chemotherapy or radiation) differed between Black and White women and whether treatment refusal mediated racial disparities in survival among women with endometrial cancer. STUDY DESIGN: We used the National Cancer Database, a hospital-based cancer registry, to identify non-Hispanic Black and non-Hispanic White women diagnosed with endometrial cancer from 2004 to 2016 who either received or refused recommended radiation or chemotherapy. We used logistic regression to estimate multivariable-adjusted odds ratios and 95% confidence intervals for associations between race and treatment refusal. We also examined predictors of treatment refusal in race-specific models. Accelerated failure time models were used to estimate absolute differences in overall survival by race. We used causal mediation analysis to estimate the proportion of racial differences in overall survival attributable to racial differences in adjuvant treatment refusal. We considered the overall study population and strata defined by histology, and adjusted for sociodemographic, tumor, and facility characteristics. RESULTS: Our analysis included 75,447 endometrial cancer patients recommended to receive radiation and 60,187 endometrial cancer patients recommended to receive chemotherapy, among which 6.4% and 11.4% refused treatment, respectively. Among Black women recommended for radiation or chemotherapy, 6.4% and 9.6% refused, respectively. Among White women recommended for radiation or chemotherapy, 6.4% and 11.8% refused, respectively. After adjusting for sociodemographic variables, facility characteristics, and tumor characteristics, Black women were more likely to refuse chemotherapy than White women (adjusted odds ratio, 1.26; 95% confidence interval, 1.15-1.37), but no difference in radiation refusal was observed (adjusted odds ratio, 1.00; 95% confidence interval, 0.91-1.11). Some predictors of radiation refusal varied by race, namely income, education, histology, stage, and chemotherapy receipt (P interactions<.05), whereas predictors of chemotherapy refusal were generally similar between Black and White women. Among women recommended for radiation, Black women survived an average of 4.3 years shorter than White women, which did not seem attributable to differences in radiation refusal. Among women recommended for chemotherapy, Black women survived an average of 3.2 years shorter than White women of which 1.9 months (4.9%) could potentially be attributed to differences in chemotherapy refusal. CONCLUSION: We observed differences in chemotherapy refusal by race, and those differences may be responsible for up to about 2 months of the overall 3.2-year survival disparity between White and Black women. Radiation refusal did not explain any of the 4.3-year disparity among women recommended for radiation. Treatment refusal accounts for, at most, a small fraction of the total racial disparity in endometrial cancer survival. Although a better understanding of the reasons for patient treatment refusal and subsequent intervention may help improve outcomes for some women, other causes of disparate outcomes, particularly those reflecting the social determinants of health, must be investigated.

Geographic Region, Racial/Ethnic Disparities, and Late-Life Depression: Results From a Large US Cohort of Older Adults.

OBJECTIVES: To determine associations between geographic region and late-life depression (LLD) severity, item-level symptom burden, and treatment; to evaluate whether racial/ethnic disparities in LLD, previously observed in the overall sample, vary by region. METHODS: We included 25,502 VITAL (Vitamin D and Omega-3 Trial) participants and administered the Patient Health Questionnaire-8 for depressive symptoms; participants also reported medication and/or counseling care for depression. Multivariable regression analyses were performed. RESULTS: Despite overall lower LLD severity and item-level symptom burden in the Midwest versus Northeast, higher LLD severity and item-level burden were observed among minorities, especially Black and Hispanic adults, compared to non-Hispanic whites in this region. Racial/ethnic disparities in item-level symptoms (e.g., anhedonia, sadness, psychomotor changes) varied by region. There were no significant differences in depression care by region; furthermore, regional variation was not observed in racial disparities in care: e.g., among those with clinician/physician-diagnosed depression, Blacks versus non-Hispanic whites had greater than 50% lower odds of treatment in all regions. CONCLUSION: LLD varied by geographic region. Furthermore, magnitudes of racial/ethnic disparities in LLD severity and item-level symptom burden, but not depression care, differed by region.

Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis.

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)-the ratio of the prevalence among BAAs to that among nHWs-as an index of BAA-nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains-health behaviors, clinical care, social and economic and physical environment-as contributors to BAA-nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (ß = -0.21, p < 0.001) and social and economic (ß = -0.11, p < 0.01) domains were significantly inversely associated with the observed BAA-nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA-nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke.

A multistate transition model for statin-induced myopathy and statin discontinuation.

The overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.

Association Between Patient-Clinician Relationships and Adherence to Antihypertensive Medications Among Black Adults: An Observational Study Design.

Background We assessed the associations between patient-clinician relationships (communication and involvement in shared decision-making [SDM]) and adherence to antihypertensive medications. Methods and Results The 2010 to 2017 Medical Expenditure Panel Survey (MEPS) data were analyzed. A retrospective cohort study design was used to create a cohort of prevalent and new users of antihypertensive medications. We defined constructs of patient-clinician communication and involvement in SDM from patient responses to the standard questionnaires about satisfaction and access to care during the first year of surveys. Verified self-reported medication refill information collected during the second year of surveys was used to calculate medication refill adherence; adherence was defined as medication refill adherence ≥80%. Survey-weighted multivariable-adjusted logistic regression models were used to measure the odds ratio (OR) and 95% CI for the association between both patient-clinician constructs and adherence. Our analysis involved 2571 Black adult patients with hypertension (mean age of 58 years; SD, 14 years) who were either persistent (n=1788) or new users (n=783) of antihypertensive medications. Forty-five percent (n=1145) and 43% (n=1016) of the sample reported having high levels of communication and involvement in SDM, respectively. High, versus low, patient-clinician communication (OR, 1.38; 95% CI, 1.14-1.67) and involvement in SDM (OR, 1.32; 95% CI, 1.08-1.61) were both associated with adherence to antihypertensives after adjusting for multiple covariates. These associations persisted among a subgroup of new users of antihypertensive medications. Conclusions Patient-clinician communication and involvement in SDM are important predictors of optimal adherence to antihypertensive medication and should be targeted for improving adherence among Black adults with hypertension.

Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data.

Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug event (ADEs) signals. Existing control selection approaches for case-control design included the dynamic/super control selection approach. The dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrollment history. Thus, using large-scale longitudinal health data, the dynamic/super control selection approach requires extraordinarily high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. The random control selection approach does not depend on ADE/enrollment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for the random control selection approach were between 0.586 and 0.600 compared to between 0.545 and 0.562 for dynamic/super control selection approaches. The random control selection approach was ~ 1000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, a case-control design-based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. The random control selection approach also significantly reduces computational time to prepare the case-control data sets.

Prevalence and quality of antihypertensive therapy among hypertension patients enrolled in the Ghana National Health Insurance Scheme.

BACKGROUND: Hypertension is a leading cause of morbidity in Ghana. However, there is insufficient data on the prevalence and quality of antihypertensive therapy. OBJECTIVES: To describe the prevalence of use and quality of antihypertensive therapy. METHODS: A cross-sectional study design was used to analyze the 2015 Ghana National Health Insurance Scheme (NHIS) electronic claims data. Hypertension diagnosis was defined using ICD-10 codes. The primary outcomes assessed were the prevalence of use and quality of antihypertensive therapy. Quality of antihypertensive therapy was defined as the use of antihypertensive agents recommended for treating hypertension patients with comorbid heart failure, myocardial Infarction/Coronary Artery Disease, diabetes, chronic kidney disease or stroke. We used multivariable logistic regression models to identify predictors of antihypertensive use and quality of therapy. RESULTS: Antihypertensive medication use was very high (86%) among the 161 873 hypertension patients covered under the Ghana NHIS. Only a third (32%) of hypertension patients received guideline-concordant therapy. Angiotensin receptor blockers were consumed at the highest dosages of 120 (Interquartile Range [IQR]: 60, 180) daily defined doses over a year. Males (odds ratio [OR] = 0.60; 95% Confidence Interval [CI]:0.58, 0.61) and those with comorbid stroke (OR = 0.91, 95% CI:0.84, 0.99), diabetes (OR = 0.72; 95% CI:0.69, 0.74) and stroke (OR = 0.74, 95%CI:0.68, 0.80) were less likely to use antihypertensives, all other predictors were associated with higher use. CONCLUSION: Antihypertensive medication use was very high among hypertension patients covered under the Ghana NHIS. However, there was indication of suboptimal quality of the antihypertensive therapy provided.

Dietary Patterns of Insulinemia, Inflammation and Glycemia, and Pancreatic Cancer Risk: Findings from the Women's Health Initiative.

BACKGROUND: Pancreatic cancer risk is increasing in countries with high consumption of Western dietary patterns and rising obesity rates. We examined the hypothesis that specific dietary patterns reflecting hyperinsulinemia (empirical dietary index for hyperinsulinemia; EDIH), systemic inflammation (empirical dietary inflammatory pattern; EDIP), and postprandial glycemia [glycemic index (GI); glycemic load (GL)] are associated with pancreatic cancer risk, including the potential modifying role of type 2 diabetes (T2D) and body mass index (BMI). METHODS: We calculated dietary scores from baseline (1993-1998) food frequency questionnaires among 129,241 women, 50-79 years-old in the Women's Health Initiative. We used multivariable-adjusted Cox regression to estimate HRs and 95% confidence intervals (95% CI) for pancreatic cancer risk. RESULTS: During a median 19.9 years of follow-up, 850 pancreatic cancer cases were diagnosed. We observed no association between dietary scores and pancreatic cancer risk overall. However, risk was elevated among participants with longstanding T2D (present >3 years before pancreatic cancer diagnosis) for EDIH. For each 1 SD increment in dietary score, the HRs (95% CIs) were: EDIH, 1.33 (1.06-1.66); EDIP, 1.26 (0.98-1.63); GI, 1.26 (0.96-1.67); and GL, 1.23 (0.96-1.57); although interactions were not significant (all P interaction >0.05). Separately, we observed inverse associations between GI [0.86 (0.76-0.96), P interaction = 0.0068] and GL [0.83 (0.73-0.93), P interaction = 0.0075], with pancreatic cancer risk among normal-weight women. CONCLUSIONS: We observed no overall association between the dietary patterns evaluated and pancreatic cancer risk, although women with T2D appeared to have greater cancer risk. IMPACT: The elevated risk for hyperinsulinemic diets among women with longstanding T2D and the inverse association among normal-weight women warrant further examination.

Insulinemic and Inflammatory Dietary Patterns Show Enhanced Predictive Potential for Type 2 Diabetes Risk in Postmenopausal Women.

OBJECTIVE: The empirical dietary index for hyperinsulinemia (EDIH) and empirical dietary inflammatory pattern (EDIP) scores assess the insulinemic and inflammatory potentials of habitual dietary patterns, irrespective of the macronutrient content, and are based on plasma insulin response or inflammatory biomarkers, respectively. The glycemic index (GI) and glycemic load (GL) assess postprandial glycemic potential based on dietary carbohydrate content. We tested the hypothesis that dietary patterns promoting hyperinsulinemia, chronic inflammation, or hyperglycemia may influence type 2 diabetes risk. RESEARCH DESIGN AND METHODS: We calculated dietary scores from baseline (1993-1998) food frequency questionnaires among 73,495 postmenopausal women in the Women's Health Initiative, followed through March 2019. We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% CIs for type 2 diabetes risk. We also estimated multivariable-adjusted absolute risk of type 2 diabetes. RESULTS: During a median 13.3 years of follow-up, 11,009 incident cases of type 2 diabetes were diagnosed. Participants consuming the most hyperinsulinemic or proinflammatory dietary patterns experienced greater risk of type 2 diabetes; HRs (95% CI) comparing highest to lowest dietary index quintiles were EDIH 1.49 (1.32-1.68; P trend < 0.0001) and EDIP 1.45 (1.29-1.63; P trend < 0.0001). The absolute excess incidence for the same comparison was 220 (EDIH) and 271 (EDIP) cases per 100,000 person-years. GI and GL were not associated with type 2 diabetes risk: GI 0.99 (0.88-1.12; P trend = 0.46) and GL 1.01 (0.89-1.16; P trend = 0.30). CONCLUSIONS: Our findings in this diverse cohort of postmenopausal women suggest that lowering the insulinemic and inflammatory potentials of the diet may be more effective in preventing type 2 diabetes than focusing on glycemic foods.

Structural and Social Determinants of Health Factors Associated with County-Level Variation in Non-Adherence to Antihypertensive Medication Treatment.

BACKGROUND: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. METHODS: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries' claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. RESULTS: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (ß = 0.31, P-value < 0.001) and weak social supports (ß = 0.27, P-value < 0.001), but inversely with healthy built environment (ß = -0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. CONCLUSION: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

Challenges facing drug utilization research in the Latin American region.

PURPOSE: The International Society of Pharmacoepidemiology (ISPE) in collaboration with the Latin America Drug Utilization Research Group (LatAm DURG), the Medicines Utilization Research in Africa (MURIA) group, and the Uppsala Monitoring Center, is leading an initiative to understand challenges to drug utilization research (DUR) in the Latin American (LatAm) and African regions with the goal of communicating results and proposing solutions to these challenges in four scientific publications. The purpose of this first manuscript is to identify the main challenges associated with DUR in the LatAm region. METHODS: Drug utilization (DU) researchers in the LatAm region voluntarily participated in multiple discussions, contributed with local data and reviewed successive drafts and the final manuscript. Additionally, we carried out a literature review to identify the most relevant publications related to DU studies from the LatAm region. RESULTS: Multiple challenges were identified in the LatAm region for DUR including socioeconomic inequality, access to medical care, complexity of the healthcare system, limited investment in research and development, limited institutional and organization resources, language barriers, limited health education and literacy. Further, there is limited use of local DUR data by decision makers particularly in the identification of emerging health needs coming from social and demographic transitions. CONCLUSIONS: The LatAm region faces challenges to DUR which are inherent in the healthcare and political systems, and potential solutions should target changes to the system.