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

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.</p

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

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

Association of Cardiovascular Disease and Long-Term Exposure to Fine Particulate Matter (PM2.5) in the Southeastern United States

There is a well-documented association between ambient fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) morbidity and mortality. Exposure to PM2.5 can cause premature death and harmful and chronic health effects such as heart attack, diabetes, and stroke. The Environmental Protection Agency sets annual PM2.5 standards to reduce these negative health effects. Currently above an annual average level of 12.0 µg/m is considered unhealthy. Methods. We examined the association of long-term exposure to PM2.5 and CVD in a cohort of 44,610 individuals who resided in 12 states recruited into the Southern Community Cohort Study (SCCS). The SCCS was designed to recruit Black and White participants who received care from Federally Qualified Health Centers; hence, they represent vulnerable individuals from low-income families across this vast region. This study tests whether SCCS participants who lived in locations exposed to elevated ambient levels of PM2.5 concentrations were more likely to report a history of CVD at enrollment (2002–2009). Remotely sensed satellite data integrated with ground monitoring data provide an assessment of the average annual PM2.5 in urban and rural locations where the SCCS participants resided. We used multilevel logistic regression to estimate the associations between self-reported CVD and exposure to elevated ambient levels of PM2.5. Results. We found a 13.4 percent increase in the odds of reported CVD with exposure to unhealthy levels of PM2.5 exposure at enrollment. The SCCS participants with medical histories of hypertension, hypercholesterolemia, and smoking had, overall, 385 percent higher odds of reported CVD than those without these clinical risk factors. Additionally, Black participants were more likely to live in locations with higher ambient PM2.5 concentrations and report high levels of clinical risk factors, thus, they may be at a greater future risk of CVD. Conclusions: In the SCCS participants, we found a strong relation between exposures to high ambient levels of PM2.5 and self-reported CVD at enrollment

Association of diabetes and exposure to fine particulate matter (PM2.5) in the Southeastern United States

Fine particulate matter (PM2.5) exposure can cause premature death and harmful chronic disease such as diabetes. The U.S. Environmental Protection Agency (EPA) sets annual PM2.5 standards to reduce these negative health effects. Currently, annual average exposure over 12 µg/m3 is considered unhealthy. This study tests whether individuals living in locations exposed to elevated ambient levels of PM2.5 concentrations were more likely to self-report diabetes. We examined the association of long-term exposure to PM2.5 and diabetes at enrollment (2002–2009) in a cohort of 44,610 individuals residing in 12 states, recruited into the Southern Community Cohort Study (SCCS). Annual average PM2.5 was estimated using remotely sensed satellite data integrated with ground monitoring data at participants’ residence in urban and rural locations. We used multilevel mixed-effects logistic regression models to estimate the associations between self-reported diabetes and historical exposure to elevated ambient levels of PM2.5. We found a 10.1% increase in odds of reported diabetes with exposure to unhealthful levels of PM2.5 exposure (>12 µg/m3 at enrollment) compared to respondents living in areas with lower annual PM2.5 concentrations. Participants with medical histories of hypertension, hypercholesterolemia, and smoking had an overall 384% higher odds of reported diabetes than those without these clinical risk factors. Black participants were more likely to live in locations with higher ambient PM2.5 concentrations, report high levels of clinical risk factors, and had a 29.1% increase in odds of reported diabetes than Whites. In SCCS participants, exposures to high ambient levels of PM2.5 were associated with self-reported diabetes at enrollment. Reduction in PM2.5 standards for the U.S. are recommended

Risk of heart failure among postmenopausal women: a secondary analysis of the randomized trial of vitamin D plus calcium of the women\u27s health initiative

BACKGROUND: Vitamin D supplementation may be an inexpensive intervention to reduce heart failure (HF) incidence. However, there are insufficient data to support this hypothesis. This study evaluates whether vitamin D plus calcium (CaD) supplementation is associated with lower rates of HF in postmenopausal women and whether the effects differ between those at high versus low risk for HF. METHODS AND RESULTS: Analyses were restricted to 35 983 (of original 36 282) women aged 50 to 79 years old in the Women\u27s Health Initiative randomized trial of CaD supplementation who were randomized 1:1 in a double-blinded fashion to receive 1000 mg/d of calcium plus 400 IU/d of vitamin D3 or placebo. Overall, 744 adjudicated incident HF cases (intervention, 363; control, 381) occurred during a median follow-up of 7.1 (interquartile range, 1.6) years. CaD supplementation, compared with placebo, was not associated with reduced HF risk in the overall population, hazard ratio, 0.95; P=0.46. However, CaD supplementation had differential effects (P interaction=0.005) in subgroups stratified by baseline risk status of HF defined by the presence (high risk=17 449) or absence (low risk=18 534) of pre-existing HF precursors including coronary heart diseases, diabetes mellitus, or hypertension: 37% (hazard ratio, 0.63 [95% confidence interval, 0.46-0.87]) lower risk of HF in the low-risk versus hazard ratio, 1.06; P=0.51, in the high-risk subgroups. CONCLUSIONS: CaD supplementation did not significantly reduce HF incidence in the overall cohort, however, it was beneficial among postmenopausal women without major HF precursors while of little value in high-risk subgroups. Additional studies are warranted to confirm these findings and investigate the underlying mechanism. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00000611

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

BackgroundPancreatic 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).MethodsWe 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.ResultsDuring 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 &gt;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 &gt;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.ConclusionsWe observed no overall association between the dietary patterns evaluated and pancreatic cancer risk, although women with T2D appeared to have greater cancer risk.ImpactThe 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

ObjectiveThe 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 methodsWe 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.ResultsDuring 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 &lt; 0.0001) and EDIP 1.45 (1.29-1.63; P trend &lt; 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).ConclusionsOur 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

A genome-wide association study meta-analysis of clinical fracture in 10,012 African American women.

BackgroundOsteoporosis is a major public health problem associated with excess disability and mortality. It is estimated that 50-70% of the variation in osteoporotic fracture risk is attributable to genetic factors. The purpose of this hypothesis-generating study was to identify possible genetic determinants of fracture among African American (AA) women in a GWAS meta-analysis.MethodsData on clinical fractures (all fractures except fingers, toes, face, skull or sternum) were analyzed among AA female participants in the Women's Health Initiative (WHI) (N&nbsp;=&nbsp;8155), Cardiovascular Health Study (CHS) (N&nbsp;=&nbsp;504), BioVU (N&nbsp;=&nbsp;704), Health ABC (N&nbsp;=&nbsp;651), and the Johnston County Osteoarthritis Project (JoCoOA) (N&nbsp;=&nbsp;291). Affymetrix (WHI) and Illumina (Health ABC, JoCoOA, BioVU, CHS) GWAS panels were used for genotyping, and a 1:1 ratio of YRI:CEU HapMap haplotypes was used as an imputation reference panel. We used Cox proportional hazard models or logistic regression to evaluate the association of ~&nbsp;2.5 million SNPs with fracture risk, adjusting for ancestry, age, and geographic region where applicable. We conducted a fixed-effects, inverse variance-weighted meta-analysis. Genome-wide significance was set at P&nbsp;&lt;&nbsp;5&nbsp;×&nbsp;10-&nbsp;8.ResultsOne SNP, rs12775980 in an intron of SVIL on chromosome 10p11.2, reached genome-wide significance (P&nbsp;=&nbsp;4.0&nbsp;×&nbsp;10-&nbsp;8). Although this SNP has a low minor allele frequency (0.03), there was no evidence for heterogeneity of effects across the studies (I2&nbsp;=&nbsp;0). This locus was not reported in any previous osteoporosis-related GWA studies. We also interrogated previously reported GWA-significant loci associated with fracture or bone mineral density in our data. One locus (SMOC1) generalized, but overall there was not substantial evidence of generalization. Possible reasons for the lack of generalization are discussed.ConclusionThis GWAS meta-analysis of fractures in African American women identified a potentially novel locus in the supervillin gene, which encodes a platelet-associated factor and was previously associated with platelet thrombus formation in African Americans. If validated in other populations of African descent, these findings suggest potential new mechanisms involved in fracture that may be particularly important among African Americans