What matters for health? Public views from eight countries.

INTRODUCTION: Despite growing scholarship on the social determinants of health (SDoH), wider action remains in its early stages. Broad public understanding of SDoH can help catalyse such action. This paper aimed to document public perception of what matters for health from countries with broad geographic, cultural, linguistic, population composition, language and income level variation. METHODS: We conducted an online survey in Brazil, China, Germany, Egypt, India, Indonesia, Nigeria and the USA to assess rankings of what respondents thought matters for health and what they perceived decision makers think matters for health. We analysed the percentages of each determinant rated as the most important for good health using two metrics: the top selection and a composite of the top three selections. We used two-tailed χ2 test for significance testing between groups. RESULTS: Of 8753 respondents, 56.2% (95% CI 55.1% to 57.2%) ranked healthcare as the most important determinant of good health using the composite metric. This ranking was consistent across countries except in China where it appeared second. While genetics was cited as the most important determinant by 22.3% (95% CI 21.5% to 23.2%) of the overall sample with comparable rates in most countries, the percentage increased to 33.3% (95% CI 30.5% to 36.3%) in Germany and 35.9% (95% CI 33.0% to 38.8%) in the USA. Politics was the determinant with the greatest absolute difference (18.5%, 95% CI 17.3% to 19.6%) between what respondents considered matters for health versus what they perceived decision makers think matters for health. CONCLUSION: The majority of people consider healthcare the most important determinant of health, well above other social determinants. This highlights the need for more investment in communication efforts around the importance of SDoH.

In medicine, how do we machine learn anything real?

Machine learning has traditionally operated in a space where data and labels are assumed to be anchored in objective truths. Unfortunately, much evidence suggests that the "embodied" data acquired from and about human bodies does not create systems that function as desired. The complexity of health care data can be linked to a long history of discrimination, and research in this space forbids naive applications. To improve health care, machine learning models must strive to recognize, reduce, or remove such biases from the start. We aim to enumerate many examples to demonstrate the depth and breadth of biases that exist and that have been present throughout the history of medicine. We hope that outrage over algorithms automating biases will lead to changes in the underlying practices that generated such data, leading to reduced health disparities.

COVID-19 Misinformation Spread in Eight Countries: Exponential Growth Modeling Study.

BACKGROUND: The epidemic of misinformation about COVID-19 transmission, prevention, and treatment has been going on since the start of the pandemic. However, data on the exposure and impact of misinformation is not readily available. OBJECTIVE: We aim to characterize and compare the start, peak, and doubling time of COVID-19 misinformation topics across 8 countries using an exponential growth model usually employed to study infectious disease epidemics. METHODS: COVID-19 misinformation topics were selected from the World Health Organization Mythbusters website. Data representing exposure was obtained from the Google Trends application programming interface for 8 English-speaking countries. Exponential growth models were used in modeling trends for each country. RESULTS: Searches for "coronavirus AND 5G" started at different times but peaked in the same week for 6 countries. Searches for 5G also had the shortest doubling time across all misinformation topics, with the shortest time in Nigeria and South Africa (approximately 4-5 days). Searches for "coronavirus AND ginger" started at the same time (the week of January 19, 2020) for several countries, but peaks were incongruent, and searches did not always grow exponentially after the initial week. Searches for "coronavirus AND sun" had different start times across countries but peaked at the same time for multiple countries. CONCLUSIONS: Patterns in the start, peak, and doubling time for "coronavirus AND 5G" were different from the other misinformation topics and were mostly consistent across countries assessed, which might be attributable to a lack of public understanding of 5G technology. Understanding the spread of misinformation, similarities and differences across different contexts can help in the development of appropriate interventions for limiting its impact similar to how we address infectious disease epidemics. Furthermore, the rapid proliferation of misinformation that discourages adherence to public health interventions could be predictive of future increases in disease cases.

Mapping disparities in homicide trends across Brazil: 2000-2014.

BACKGROUND: Homicides are a major problem in Brazil. Drugs and arms trafficking, and land conflicts are three of the many factors driving homicide rates in Brazil. Understanding long-term spatiotemporal trends and social structural factors associated with homicides in Brazil would be useful for designing policies aimed at reducing homicide rates. METHODS: We obtained data from 2000 to 2014 from the Brazil Ministry of Health (MOH) Mortality Information System and sociodemographic data from the Brazil Institute of Geography and Statistics (IBGE). First, we quantified the rate of change in homicides at the municipality and state levels. Second, we used principal component regression and k-medoids clustering to examine differences in temporal trends across municipalities. Lastly, we used Bayesian hierarchical space-time models to describe spatio-temporal patterns and to assess the contribution of structural factors. RESULTS: There were significant variations in homicide rates across states and municipalities. We noted the largest decrease in homicide rates in the western and southeastern states of Sao Paulo, Rio de Janeiro and Espirito Santo, which coincided with an increase in homicide rates in the northeastern states of Ceará, Alagoas, Paraiba, Rio Grande Norte, Sergipe and Bahia during the fifteen-year period. The decrease in homicides in municipalities with populations of at least 250,000 coincided with an increase in municipalities with 25,000 people or less. Structural factors that predicted municipality-level homicide rates included crude domestic product, urbanization, border with neighboring countries and proportion of population aged fifteen to twenty-nine. CONCLUSIONS: Our findings support both a dissemination hypothesis and an interiorization hypothesis. These findings should be considered when designing interventions to curb homicide rates.

Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity.

Importance: More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as genetics, diet, physical activity, and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts. Objective: To propose an approach for consistent measurement of the features of the built environment (ie, both natural and modified elements of the physical environment) and its association with obesity prevalence to allow for comparison across studies. Design: The cross-sectional study was conducted from February 14 through October 31, 2017. A convolutional neural network, a deep learning approach, was applied to approximately 150 000 high-resolution satellite images from Google Static Maps API (application programing interface) to extract features of the built environment in Los Angeles, California; Memphis, Tennessee; San Antonio, Texas; and Seattle (representing Seattle, Tacoma, and Bellevue), Washington. Data on adult obesity prevalence were obtained from the Centers for Disease Control and Prevention's 500 Cities project. Regression models were used to quantify the association between the features and obesity prevalence across census tracts. Main Outcomes and Measures: Model-estimated obesity prevalence (obesity defined as body mass index ≥30, calculated as weight in kilograms divided by height in meters squared) based on built environment information. Results: The study included 1695 census tracts in 6 cities. The age-adjusted obesity prevalence was 18.8% (95% CI, 18.6%-18.9%) for Bellevue, 22.4% (95% CI, 22.3%-22.5%) for Seattle, 30.8% (95% CI, 30.6%-31.0%) for Tacoma, 26.7% (95% CI, 26.7%-26.8%) for Los Angeles, 36.3% (95% CI, 36.2%-36.5%) for Memphis, and 32.9% (95% CI, 32.8%-32.9%) for San Antonio. Features of the built environment explained 64.8% (root mean square error [RMSE], 4.3) of the variation in obesity prevalence across all census tracts. Individually, the variation explained was 55.8% (RMSE, 3.2) for Seattle (213 census tracts), 56.1% (RMSE, 4.2) for Los Angeles (993 census tracts), 73.3% (RMSE, 4.5) for Memphis (178 census tracts), and 61.5% (RMSE, 3.5) for San Antonio (311 census tracts). Conclusions and Relevance: This study illustrates that convolutional neural networks can be used to automate the extraction of features of the built environment from satellite images for studying health indicators. Understanding the association between specific features of the built environment and obesity prevalence can lead to structural changes that could encourage physical activity and decreases in obesity prevalence.

A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports.

BACKGROUND: Underreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging. OBJECTIVE: The objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance. METHODS: Individuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health. RESULTS: More than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting. CONCLUSIONS: Crowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks.