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.

Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study.

OBJECTIVE: To investigate determining factors of happiness during the COVID-19 pandemic. DESIGN: Observational study. SETTING: Large online surveys in Japan before and during the COVID-19 pandemic. PARTICIPANTS: A random sample of 25 482 individuals who are representatives of the Japanese population. MAIN OUTCOME MEASURE: Self-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness. RESULTS: Among the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6-8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic. CONCLUSION: Using machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.

Diagnosing Influenza Infection from Pharyngeal Images using Deep Learning: Machine Learning Approach.

BACKGROUND: The global burden of influenza is substantial. It is a major disease that causes annual epidemics and occasionally, pandemics. Given that influenza primarily infects the upper respiratory system, it may be possible to diagnose influenza infection by applying deep learning to pharyngeal images. OBJECTIVE: We aimed to develop a deep learning model to diagnose influenza infection using pharyngeal images and clinical information. METHODS: We recruited patients who visited clinics and hospitals because of influenza-like symptoms. In the training stage, we developed a diagnostic prediction artificial intelligence (AI) model based on deep learning to predict polymerase chain reaction (PCR)-confirmed influenza from pharyngeal images and clinical information. In the validation stage, we assessed the diagnostic performance of the AI model. In additional analysis, we compared the diagnostic performance of the AI model with that of three physicians and interpreted the AI model using importance heatmaps. RESULTS: We enrolled a total of 7,831 patients at 64 hospitals between Nov 1, 2019 and Jan 21, 2020 in the training stage and 659 patients (including 196 patients with PCR-confirmed influenza) at 11 hospitals between Jan 25, 2020 and Mar 13, 2020 in the validation stage. The area under the receiver operating characteristic curve for the AI model was 0.90 (95% confidence interval, 0.87-0.93), and its sensitivity and specificity were 76% (70%-82%) and 88% (85%-91%), respectively, outperforming three physicians. In the importance heatmaps, the AI model often focused on follicles on the posterior pharyngeal wall. CONCLUSIONS: We developed the first AI model that can accurately diagnose influenza from pharyngeal images, which has the potential to help physicians to make a timely diagnosis.

The effect of obesity on in-hospital mortality among patients with COVID-19 receiving corticosteroids.

BACKGROUND AND AIMS: Obesity has been reported to be one of the most frequent comorbidities in COVID-19 patients and associated with higher rates of in-hospital mortality compared to non-obese patients. Acute kidney injury (AKI) is also known to be a complication associated with obesity in critically-ill COVID-19 patients. We aimed to investigate whether obesity was associated with increased risk of in-hospital mortality and AKI among patients with COVID-19 treated with corticosteroids. METHODS: We utilized 9965 hospitalized COVID-19 patient data and divided patients who were treated with corticosteroids into 6 groups by body mass index (BMI) (less than 18.5, 18.5-25, 25-30, 30-35, 35-40, 40 kg/m2 or greater). The association between BMI and in-hospital mortality and between BMI and incidence rate of AKI during admission among COVID-19 patients receiving corticosteroids were retrospectively investigated. RESULTS: There were 4587 study participants receiving corticosteroids (mean age 66.5 ± 15.5 years, men 56.6%, mean BMI 29.0 ± 7.2 kg/m2). The smooth spline curve suggested a J-shape association between BMI and in-hospital mortality. Patients with BMI above 40 kg/m2 exhibited a higher in-hospital mortality and higher incidence rate of AKI during admission compared to patients with BMI between 25 and 30 kg/m2. The differences in in-hospital mortality and the rate of AKI were larger among patients with severe COVID-19. CONCLUSIONS: Class III obesity was associated with high in-hospital mortality and AKI in patients with COVID-19 treated by corticosteroids. Clinicians must stay vigilant on the impact of class III obesity and development of AKI to disease trajectory of COVID-19 patients.

Association Between Trust in Government and Practice of Preventive Measures During the COVID-19 Pandemic in Japan.

BACKGROUND: Research suggests that preventive measures are critical to reducing the spread of coronavirus disease 2019 (COVID-19), but evidence regarding the association between trust in government and the practice of preventive measures is limited. OBJECTIVE: To examine whether the practice of preventive measures against COVID-19 differs by one's level of trust in government. DESIGN: A cross-sectional analysis using the Japan COVID-19 and Society Internet Survey (JACSIS) conducted in August and September 2020. PARTICIPANTS: A nationally representative sample of Japanese individuals aged 15 through 79 years. MAIN MEASURES: The primary outcome was the composite score for COVID-19 preventive measures, defined as the percentage of preventive measures an individual reported to be practicing (out of nine measures: social distancing, wearing masks, avoiding closed spaces, avoiding crowded spaces, avoiding close contact settings, hand washing, avoiding touching one's face, respiratory hygiene, and surface disinfection). The secondary outcomes were (1) support for stay-at-home requests, (2) use of a contact-tracing app, and (3) receipt of the influenza vaccine in the previous season. KEY RESULTS: Our analysis included a total of 25,482 individuals. After adjusting for potential confounders, we found that individuals with high trust in government were likely to practice preventive measures more frequently compared to those with low trust (adjusted composite scores, 83.8% for high- vs. 79.5% for low-trust individuals; adjusted difference, +4.3 percentage points [pp]; 95% CI, +2.4 to +6.2pp; P<0.001). We also found that high trust in government was associated with higher likelihoods of support for stay-at-home requests, use of a contact-tracing app, and receipt of the influenza vaccine in the previous season. CONCLUSIONS: High trust in government was associated with a higher intensity of practicing COVID-19 preventive measures among Japanese individuals at the national level. Our findings may provide useful information to develop and design effective public health interventions.

Family caregiving and changes in mental health status in Japan during the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had a widespread significant impact on mental health. However, the effect of providing care to family members (informal caregiving) on changes in mental health status during the pandemic remains unclear. METHODS: Using cross-sectional data from a large internet survey conducted between August and September 2020, we investigated the association of informal caregiving status with the incidence of mental health deterioration (increased loneliness, self-reported deterioration in mental health, and new suicidal ideation) in Japan during the COVID-19 pandemic. RESULTS: Among 25,482 participants (mean age 48.8 [standard deviation 17.3]; 50.3% women), 2,500 (9.8%) were providing informal care during the pandemic. After adjusting for potential confounders, informal caregivers were more likely than non-caregivers to experience increased loneliness (adjusted odds ratio [aOR] 2.16; 95% confidence interval [CI] 1.70-2.76), self-reported deterioration in mental health (aOR 1.54; 95% CI 1.14-2.08), and new incidence of suicidal ideation (aOR 3.65; 95% CI 1.92-6.92). The degree of mental health deterioration depended on the intensity of care. For example, the incidence rates of new suicidal ideation were 15.0%, 5.2%, and 3.6% for individuals who provided high-intensity caregiving, those who provided low-intensity caregiving, and non-caregivers, respectively (p-for-trend<0.001). The stratified analysis by gender showed that informal caregiving was associated with self-reported deterioration in mental health status among women (aOR 2.19; 95% CI 1.49-3.21) but not men (aOR 1.08; 95% CI 0.75-1.56). CONCLUSION: Informal caregivers were more likely to experience mental health deterioration than non-caregivers during the COVID-19 pandemic.

Health and Public Health Implications of COVID-19 in Asian Countries

ABSTRACT This paper discusses the impacts of the COVID-19 pandemic on population health in Asian countries. Compared to European countries, Asian countries, in general, experienced much fewer COVID-19 cases and deaths. The underlying factors in this difference would include the earlier and more stringent nonpharmaceutical interventions, differences in the age structure, the geographical characteristics in Asia, and the experience of past outbreaks of emerging diseases, though further studies are needed to investigate the exact mechanisms. Still, regarding the aim of developing resilient health?care systems, there remain several lessons from the COVID-19 pandemic experience in Asian countries that could inform more effective management of future pandemics.

Physician visits and medication prescriptions for major chronic diseases during the COVID-19 pandemic in Japan: retrospective cohort study.

OBJECTIVES: There have been concerns that patients with chronic conditions may be avoiding in-person physician visits due to fear of COVID-19, leading to lower quality of care. We aimed to investigate changes in physician visits and medication prescriptions for chronic diseases before and during the COVID-19 pandemic at the population level. DESIGN: Retrospective cohort study. SETTING: Nationwide claims data in Japan, 2018-2020. PARTICIPANTS: Working-age population (aged 18-74 years) who visited physicians and received any prescriptions for major chronic diseases (hypertension, diabetes and dyslipidaemia) before the pandemic. OUTCOME MEASURES: The outcomes were the monthly number of physician visits, the monthly proportion of physician visits and the monthly proportion of days covered by prescribed medication (PDC) during the pandemic (April-May 2020, as the first state of emergency over COVID-19 was declared on 7 April, and withdrawn nationally on 25 May). RESULTS: Among 10 346 patients who visited physicians for chronic diseases before the pandemic, we found a temporary decline in physician visits (mean number of visits was 1.9 in March vs 1.7 in April; p<0.001) and an increase in the proportion of patients who did not visit any physicians during the pandemic (15% in March vs 24% in April; p<0.001). Physician visits returned to the baseline in May (the mean number of visits: 1.8, and the proportion of patients who did not visit any physicians: 9%). We observed no clinically meaningful difference in PDC between before and during the pandemic (eg, 87% in March vs 87% in April; p=0.45). A temporary decline in physician visits was more salient in seven prefectures with a larger number of COVID-19 cases than in other areas. CONCLUSIONS: Although the number of physician visits declined right after the COVID-19 outbreak, it returned to the baseline one month later; patients were not skipping medications during the pandemic.

Age and Social Disparities in the Use of Telemedicine During the COVID-19 Pandemic in Japan: Cross-sectional Study.

BACKGROUND: The use of telemedicine outpatient visits has increased dramatically during the COVID-19 pandemic in many countries. Although disparities in access to telemedicine by age and socioeconomic status (SES) have been well-documented, evidence is limited as to how these disparities changed during the COVID-19 pandemic. Moreover, the equity of patient access to telemedicine has been scarcely reported in Japan, despite the huge potential for telemedicine expansion. OBJECTIVE: We aimed to investigate changes due to age and SES disparities in telemedicine use during the COVID-19 pandemic in Japan. METHODS: Using data from a large internet survey conducted between August 25 and September 30, 2020, in Japan, we examined the associations of participant age and SES (educational attainment, urbanicity of residence, and income level) with their telemedicine use in the following two time periods during the pandemic: April 2020 and August-September 2020. RESULTS: Of the 24,526 participants aged 18 to 79 years (50.8% [n=12,446] women), the proportion of individuals who reported using telemedicine increased from 2.0% (n=497) in April 2020 to 4.7% (n=1159) in August-September 2020. After adjusting for potential confounders, younger individuals were more likely to use telemedicine than older individuals in April 2020. Although this pattern persisted in August-September 2020, we also observed a substantial increase in telemedicine use among individuals aged 70 to 79 years (adjusted rates, 0.2% in April 2020 vs 3.8% in August-September 2020; P<.001 after multiple comparisons). We found disparities in telemedicine use by SES in August-September 2020 that did not exist in April 2020. In August-September 2020, individuals with a university degree were more likely to use telemedicine than those with a high school diploma or less (adjusted rates, 6.6% vs 3.5%; P<.001). Individuals living in urban areas exhibited higher rates of telemedicine use than those living in rural areas only in August-September 2020 (adjusted rates, 5.2% vs 3.8%; P<.001). Disparities in telemedicine use by income level were not observed in either time period. CONCLUSIONS: In general, younger individuals increased their use of telemedicine compared to older individuals during the pandemic, although individuals in their 70s also increased their use of telemedicine. Disparities in telemedicine use by educational attainment and urbanicity of residence widened during the COVID-19 pandemic.

Factors associated with serious psychological distress during the COVID-19 pandemic in Japan: a nationwide cross-sectional internet-based study.

OBJECTIVE: To investigate the factors associated with serious psychological distress (SPD) during the COVID-19 pandemic in Japan. DESIGN: Nationwide cross-sectional study using survey data. SETTING: Internet survey using sampling weights for national estimates conducted between 25 August and 30 September 2020 in Japan. EXPOSURES: Demographics (age, gender), socioeconomic status (income level, employment type, educational attainment, marital status, family composition and caregiving burden); the experience of domestic violence (DV), the state of emergency and fear of and stigma related to COVID-19. MAIN OUTCOME MEASURES: Prevalence of SPD, defined as Kessler 6 Scale score ≥13. RESULTS: Among 25 482 respondents, 10.0% met the criteria of SPD. Overall, women (adjusted OR (aOR) 1.59; 95% CI 1.17 to 2.16; p=0.003), ages 15-29 (aOR 2.35 compared with ages 45-59 years; 95% CI 1.64 to 3.38; p<0.001), low-income level (aOR 1.70 compared with intermediate income; 95% CI 1.16 to 2.49; p=0.007), providing caregiving to family members (aOR 5.48; 95% CI 3.51 to 8.56; p<0.001), experiencing DV (aOR 5.72; 95% CI 3.81 to 8.59; p<0.001) and fear of COVID-19 (aOR 1.96; 95% CI 1.55 to 2.48; p<0.001) were associated with SPD. Among women aged 15-29 years, who have a higher risk of suicide during the COVID-19 pandemic in Japan, caregiving, DV, fear of COVID-19 and COVID-19-related stigma were associated with SPD. CONCLUSIONS: Economic situation, caregiving burden, DV and fear of COVID-19 were independently associated with SPD during the COVID-19 pandemic. Among young women, similar factors, except economic situation, were associated with SPD. Targeted interventions based on age and gender may be more effective in mitigating the negative impact of the COVID-19 pandemic on the population's mental health.

Association between participation in the government subsidy programme for domestic travel and symptoms indicative of COVID-19 infection in Japan: cross-sectional study.

OBJECTIVE: To investigate the association between participation in government subsidies for domestic travel (subsidise up to 50% of all travel expenses) introduced nationally in Japan on 22 July 2020 and the incidence of symptoms indicative of COVID-19 infections. DESIGN: Cross-sectional analysis of nationally representative survey data. SETTING: Internet survey conducted between 25 August and 30 September 2020 in Japan. Sampling weights were used to calculate national estimates. PARTICIPANTS: 25 482 survey respondents (50.3% (12 809) women; mean (SD) age, 48.8 (17.4) years). MAIN OUTCOME MEASURES: Incidence rate of five symptoms indicative of the COVID-19 infection (high fever, sore throat, cough, headache, and smell and taste disorder) within the past month of the survey, after adjustment for characteristics of individuals and prefecture fixed effects (effectively comparing individuals living in the same prefecture). RESULTS: At the time of the survey, 3289 (12.9%) participated in the subsidy programme. After adjusting for potential confounders, we found that participants in the subsidy programme exhibited higher incidence of high fever (adjusted rate, 4.7% for participants vs 3.7% for non-participants; adjusted OR (aOR) 1.83; 95% CI 1.34 to 2.48; p<0.001), sore throat (19.8% vs 11.3%; aOR 2.09; 95% CI 1.37 to 3.19; p=0.002), cough (19.0% vs 11.3%; aOR 1.96; 95% CI 1.26 to 3.01; p=0.008), headache (29.2% vs 25.5%; aOR 1.24; 95% CI 1.08 to 1.44; p=0.006) and smell and taste disorder (2.6% vs 1.8%; aOR 1.98; 95% CI 1.15 to 3.40; p=0.01) compared with non-participants. These findings remained qualitatively unaffected by additional adjustment for the use of 17 preventative measures (eg, social distancing, wearing masks and handwashing) and fear against the COVID-19 infection. CONCLUSIONS: The participation of the government subsidy programme for domestic travel was associated with a higher probability of exhibiting symptoms indicative of the COVID-19 infection.

Network interventions for managing the COVID-19 pandemic and sustaining economy.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).