Association between physician characteristics and payments from industry in 2015-2017: observational study.

OBJECTIVE:To investigate the association between physician characteristics and the value of industry payments. DESIGN:Observational study. SETTING AND PARTICIPANTS:Using the 2015-2017 Open Payments reports of industry payments linked to the Physician Compare database, we examined the association between physician characteristics (physician sex, years in practice, medical school attended and specialty) and the industry payment value, adjusting for other physician characteristic and institution fixed effects (effectively comparing physicians practicing at the same institution). MAIN OUTCOME MEASURES:Our primary outcome was the value of total industry payments to physicians including (1) general payments (all forms of payments other than those classified for research purpose, eg, consulting fees, food, beverage), (2) research payments (payments for research endeavours under a written contract or protocol) and (3) ownership interests (eg, stock or stock options, bonds). We also investigated each category of payment separately. RESULTS:Of 544 264 physicians treating Medicare beneficiaries, a total of $5.8 billion in industry payments were made to 365 801 physicians during 2015-2017. The top 5% of physicians, by cumulative payments, accounted for 91% of industry payments. Within the same institution, male physicians, physicians with 21-30 years in practice and physicians who attended top 50 US medical schools (based on the research ranking) received higher industry payments. Across specialties, orthopaedic surgeons, neurosurgeons and endocrinologists received the highest payments. When we investigated individual types of payment, we found that orthopaedic surgeons received the highest general payments; haematologists/oncologists were the most likely to receive research payments and surgeons were the most likely to receive ownership interests compared with other types of physicians. CONCLUSIONS:Industry payments to physicians were highly concentrated among a small number of physicians. Male sex, longer length of time in clinical practice, graduated from a top-ranked US medical school and practicing certain specialties, were independently associated with higher industry payments

Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management

高血圧診療における次世代の個別化医療戦略を提唱 --機械学習により個人の治療効果を予測する時代へ--. 京都大学プレスリリース. 2023-04-05.[Background] In medicine, clinicians treat individuals under an implicit assumption that high-risk patients would benefit most from the treatment (‘high-risk approach’). However, treating individuals with the highest estimated benefit using a novel machine-learning method (‘high-benefit approach’) may improve population health outcomes. [Methods] This study included 10 672 participants who were randomized to systolic blood pressure (SBP) target of either 0) versus the high-risk approach (treating individuals with SBP ≥130 mmHg). Using transportability formula, we also estimated the effect of these approaches among 14 575 US adults from National Health and Nutrition Examination Surveys (NHANES) 1999–2018. [Results] We found that 78.9% of individuals with SBP ≥130 mmHg benefited from the intensive SBP control. The high-benefit approach outperformed the high-risk approach [average treatment effect (95% CI), +9.36 (8.33–10.44) vs +1.65 (0.36–2.84) percentage point; difference between these two approaches, +7.71 (6.79–8.67) percentage points, P-value <0.001]. The results were consistent when we transported the results to the NHANES data. [Conclusions] The machine-learning-based high-benefit approach outperformed the high-risk approach with a larger treatment effect. These findings indicate that the high-benefit approach has the potential to maximize the effectiveness of treatment rather than the conventional high-risk approach, which needs to be validated in future research

Trends in Cardiovascular Risk Factors by Income among Japanese Adults Aged 30-49 Years from 2017-2020: A nationwide longitudinal cohort study

Objective: Income is a major social determinant of cardiovascular health. However, individual-level evidence regarding the trends in cardiovascular risk factors by income level among young working-age adults is limited. We thus aimed to examine the trends in cardiovascular risk factors among men and women aged 30-49 years by their income levels. Methods: This nationwide longitudinal study included Japanese adults aged 30-49 years, who annually participated in the national health screening program from 2017 to 2020. Modified Poisson regression models were used to investigate trends in the prevalence of cardiovascular risk factors (obesity, hypertension, diabetes, and dyslipidemia) according to tertiles of individuals’ annual income, adjusting for potential confounders. Results: Among 58 814 adults, 50 024 (85%) were men; the mean (SD) age was 42.1 (5.4) years. Over the study period, the low-income group consistently showed a higher prevalence of obesity, hypertension, and diabetes than the high-income group. The difference in the prevalence of these diseases, particularly hypertension, across income groups increased from 2017 to 2020 among both men (low-income vs high-income: +5.73% [95% CI, 4.72-6.73] in 2017 and +8.26% [95% CI, 7.11-9.41] in 2020) and women (low-income vs high-income: +2.53% [95% CI, 0.99-4.06] in 2017 and +3.83% [95% CI, 1.93-5.73] in 2020). Conclusion: Among adults aged 30-49 years in Japan, a country with a universal healthcare coverage system, we found an increase in the gap of cardiovascular risk factors by income levels over the last 4 years. Careful monitoring of the increasing social disparities is needed to achieve cardiovascular health equity at this life stage

Bias amplification in the g-computation algorithm for time-varying treatments: a case study of industry payments and prescription of opioid products

BACKGROUND: It is often challenging to determine which variables need to be included in the g-computation algorithm under the time-varying setting. Conditioning on instrumental variables (IVs) is known to introduce greater bias when there is unmeasured confounding in the point-treatment settings, and this is also true for near-IVs which are weakly associated with the outcome not through the treatment. However, it is unknown whether adjusting for (near-)IVs amplifies bias in the g-computation algorithm estimators for time-varying treatments compared to the estimators ignoring such variables. We thus aimed to compare the magnitude of bias by adjusting for (near-)IVs across their different relationships with treatments in the time-varying settings. METHODS: After showing a case study of the association between the receipt of industry payments and physicians' opioid prescribing rate in the US, we demonstrated Monte Carlo simulation to investigate the extent to which the bias due to unmeasured confounders is amplified by adjusting for (near-)IV across several g-computation algorithms. RESULTS: In our simulation study, adjusting for a perfect IV of time-varying treatments in the g-computation algorithm increased bias due to unmeasured confounding, particularly when the IV had a strong relationship with the treatment. We also found the increase in bias even adjusting for near-IV when such variable had a very weak association with unmeasured confounders between the treatment and the outcome compared to its association with the time-varying treatments. Instead, this bias amplifying feature was not observed (i.e., bias due to unmeasured confounders decreased) by adjusting for near-IV when it had a stronger association with the unmeasured confounders (≥0.1 correlation coefficient in our multivariate normal setting). CONCLUSION: It would be recommended to avoid adjusting for perfect IV in the g-computation algorithm to obtain a less biased estimate of the time-varying treatment effect. On the other hand, it may be recommended to include near-IV in the algorithm unless their association with unmeasured confounders is very weak. These findings would help researchers to consider the magnitude of bias when adjusting for (near-)IVs and select variables in the g-computation algorithm for the time-varying setting when they are aware of the presence of unmeasured confounding

Changes in industry marketing payments to physicians during the covid-19 pandemic: quasi experimental, difference-in-difference study

[Objective] To determine changes in industry marketing payments to physicians due to the covid-19 pandemic. [Design] Quasi experimental, difference-in-difference study. [Data source] US nationwide database of licensed physicians, the National Plan and Provider Enumeration System, which was linked to a database of industry marketing payments made to physicians, Open Payments. [Population] All licensed US physicians from 2018 to 2020 and those who received payments from industry. [Main outcome measures] Changes in the value and the number of monthly industry payments physician received before (January-February 2020) and during the pandemic (April-December 2020) were assessed, adjusting for physicians’ characteristics (gender and specialty). As the control, data for the same months in 2019 were used. Industry payments by type of payments (eg, meals, travel, consulting fees, speaker compensation, honorariums), were also examined. [Results] Among 880 589 US physicians included in this study, 267 463 (30.4%) physicians received a total of 4 117 482 non-research payments with $626 million ($710 per physician; £610; €708) in 2020 (40-44% decrease from $1047m in 2018 and$1115m in 2019). Industry payments decreased significantly in the months of the covid-19 pandemic (adjusted change in the value of −48.4%; 95% confidence interval −50.6 to −46.2; P<0.001; and adjusted change in the number of −47.4%, 95% confidence interval −47.7 to −47.1; P<0.001), particularly for meals and travel fees. No evidence was seen of a decrease in the number of industry payments for consulting and honorariums. A similar pattern was observed across physicians’ gender and specialty. [Conclusions] Industry payments to physicians, particularly those involving physical interactions such as meals and travel, substantially decreased during the pandemic. How such changes affect prescription practices and the quality of clinical practice in the long term should be investigated

Association of Daily Step Patterns With Mortality in US Adults

1週間の歩行パターンと死亡リスクの関連を明らかに --週2回しっかり歩くことで健康は維持できるか？--. 京都大学プレスリリース. 2023-03-30.[Importance] Previous studies have shown that individuals who regularly walk, particularly 8000 daily steps or more, experience lower mortality. However, little is known about the health benefits of walking intensively only a few days a week. [Objective] To evaluate the dose-response association between the number of days an individual takes 8000 steps or more and mortality among US adults. [Design, Setting, and Participants] This cohort study evaluated a representative sample of participants aged 20 years or older in the National Health and Nutrition Examination Surveys 2005-2006 who wore an accelerometer for 1 week and their mortality data through December 31, 2019. Data were analyzed from April 1, 2022, to January 31, 2023. [Exposures] Participants were grouped by the number of days per week they took 8000 steps or more (0 days, 1-2 days, and 3-7 days). [Main Outcomes and Measures] Multivariable ordinary least squares regression models were used to estimate adjusted risk differences (aRDs) for all-cause and cardiovascular mortality during the 10-year follow-up, adjusting for potential confounders (eg, age, sex, race and ethnicity, insurance status, marital status, smoking, comorbidities, and average daily step counts). [Results] Among 3101 participants (mean [SD] age, 50.5 [18.4] years; 1583 [51.0%] women and 1518 [49.0%] men; 666 [21.5%] Black, 734 [23.7%] Hispanic, 1579 [50.9%] White, and 122 [3.9%] other race and ethnicity), 632 (20.4%) did not take 8000 steps or more any day of the week, 532 (17.2%) took 8000 steps or more 1 to 2 days per week, and 1937 (62.5%) took 8000 steps or more 3 to 7 days per week. Over the 10-year follow-up, all-cause and cardiovascular deaths occurred in 439 (14.2%) and 148 (5.3%) participants, respectively. Compared with participants who walked 8000 steps or more 0 days per week, all-cause mortality risk was lower among those who took 8000 steps or more 1 to 2 days per week (aRD, −14.9%; 95% CI −18.8% to −10.9%) and 3 to 7 days per week (aRD, −16.5%; 95% CI, −20.4% to −12.5%). The dose-response association for both all-cause and cardiovascular mortality risk was curvilinear; the protective association plateaued at 3 days per week. Different thresholds for the number of daily steps between 6000 and 10 000 yielded similar results. [Conclusions and Relevance] In this cohort study of US adults, the number of days per week taking 8000 steps or more was associated with a lower risk of all-cause and cardiovascular mortality in a curvilinear fashion. These findings suggest that individuals may receive substantial health benefits by walking just a couple days a week

Retirement and cardiovascular disease: a longitudinal study in 35 countries

引退すると心疾患リスクが2.2％ポイント減 --35か国約10万人の追跡調査--. 京都大学プレスリリース. 2023-05-29.BACKGROUND: Many countries have been increasing their state pension age (SPA); nonetheless, there is little consensus on whether retirement affects the risk of cardiovascular disease (CVD). This study examined the associations of retirement with CVD and risk factors. METHODS: We used harmonized longitudinal datasets from the Health and Retirement Study and its sister surveys in 35 countries. Data comprised 396 904 observations from 106 927 unique individuals aged 50-70 years, with a mean follow-up period of 6.7 years. Fixed-effects instrumental variable regressions were performed using the SPA as an instrument. RESULTS: We found a 2.2%-point decrease in the risk of heart disease [coefficient = -0.022 (95% confidence interval: -0.031 to -0.012)] and a 3.0%-point decrease in physical inactivity [-0.030 (-0.049 to -0.010)] among retirees, compared with workers. In both sexes, retirement was associated with a decreased heart disease risk, whereas decreased smoking was observed only among women. People with high educational levels showed associations between retirement and decreased risks of stroke, obesity and physical inactivity. People who retired from non-physical labour exhibited reduced risks of heart disease, obesity and physical inactivity, whereas those who retired from physical labour indicated an increased risk of obesity. CONCLUSIONS: Retirement was associated with a reduced risk of heart disease on average. Some associations of retirement with CVD and risk factors appeared heterogeneous by individual characteristics

Using Video Activity Reports to Support Remote Project-Based Learning

Distance learning has been expanding. Learner engagement is particularly important in project-based learning (PBL), but the interaction between teacher and learner and the understanding of learner status, including engagement, is not easy. This study aims to support teacher-learner communication based on learner engagement for remote PBL. In this paper, we propose the use of video activity reports by learners to estimate and understand learner engagement and to demonstrate its feasibility on the basis of the relationship between verbal and nonverbal information that can be obtained from video activity reports and learner engagement. Analysis of 232 video activity reports submitted by eight graduate students while working on remote research-based PBLs reveals that learner engagement decreases (1) when the report contained negative words, (2) when filled pauses were frequent or long, and (3) when silent pauses were infrequent or short. Furthermore, the feasibility of an AI-based support system is demonstrated through the design and implementation of a prototype

Association Between Aldosterone and Hypertension Among Patients With Overt and Subclinical Hypercortisolism

INTRODUCTION: Hypertension is one of the most common clinical features of patients with overt and subclinical hypercortisolism. Although previous studies have shown the coexistence of autonomous cortisol and aldosterone secretion, it is unclear whether aldosterone plays a role in hypertension among patients with hypercortisolism. Therefore, we examined the associations of plasma aldosterone concentrations (PACs) with hypertension among patients with overt and subclinical hypercortisolism. METHODS: This single-center retrospective cohort study included patients with adrenal tumor and serum cortisol levels after 1-mg dexamethasone suppression test >1.8 µg/dL (50 nmol/L). Using multivariable regression models adjusting for baseline characteristics, we investigated the association of PACs with systolic blood pressure and postoperative improvement of hypertension after the adrenalectomy. RESULTS: Among 89 patients enrolled in this study (median age, 51 years), 21 showed clinical signs of Cushing syndrome (overt hypercortisolism) and 68 did not show clinical presentations (subclinical hypercortisolism). We found that higher PACs were significantly associated with elevated systolic blood pressure among patients with subclinical hypercortisolism (adjusted difference [95% CI] = +0.59 [0.19-0.99], P = 0.008) but not among those with overt hypercortisolism. Among 33 patients with subclinical hypercortisolism and hypertension who underwent adrenalectomy, the postoperative improvement of hypertension was significantly associated with higher PACs at baseline (adjusted risk difference [95% CI] = +1.45% [0.35-2.55], P = 0.01). CONCLUSION: These findings indicate that aldosterone may contribute to hypertension among patients with subclinical hypercortisolism. Further multi-institutional and population-based studies are required to validate our findings and examine the clinical effectiveness of the intervention targeting aldosterone for such patients

Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine Learning Approach in the MESA Study

Background: Coronary artery calcium (CAC) has been widely recognized as an important predictor of cardiovascular disease (CVD). Given the finite resources, it is important to identify individuals who would receive the most benefit from detecting positive CAC by screening. However, the evidence is limited as to whether the burden of positive CAC on CVD differs by multi-dimensional individual characteristics. We sought to investigate the heterogeneity in the association between positive coronary artery calcium (CAC) and incident cardiovascular disease (CVD). Methods: This cohort study included adults aged ≥45 years free of cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis. After propensity score matching in a 1:1 ratio, we applied a machine-learning causal forest model to (i) evaluate the heterogeneity in the association between positive CAC and incident CVD and (ii) predict the increase in CVD risk at 10-year when CAC>0 (vs. CAC=0) at the individual level. We then compared the estimated increase in CVD risk when CAC>0 to the absolute 10-year atherosclerotic CVD (ASCVD) risk calculated by the 2013 ACC/AHA pooled cohort equations. Results: Across 3, 328 adults in our propensity score-matched analysis, our causal forest model showed the heterogeneity in the association between CAC>0 and incident CVD. We found a dose-response relationship of the estimated increase in CVD risk when CAC>0 with higher 10-year ASCVD risk. Almost all individuals (2293/2428 [94.4%]) with borderline or higher ASCVD risk showed ≥2.5% increase in CVD risk when CAC>0. Even among 900 adults with low ASCVD risk, 689 (69.2%) showed ≥2.5% increase in CVD risk when CAC>0; these individuals were more likely to be male, Hispanic, and have unfavorable CVD risk factors than others. Conclusions: The expected increases in CVD risk when CAC>0 were heterogeneous across individuals. Moreover, nearly 70% of people with low ASCVD risk showed a large increase in CVD risk when CAC>0, highlighting the need for CAC screening among such low-risk individuals. Future studies are needed to assess whether targeting individuals for CAC measurements based on not only the absolute ASCVD risk but also the expected increase in CVD risk when CAC>0 improves cardiovascular outcomes