Disaggregation of Race and Ethnicity Group Data: Research-to-Practice Issues in Clinical Environments.

This Viewpoint discusses the importance of accurately categorizing and collecting race and ethnicity data, matching self-identity with race and ethnicity labels, in an effort to quantify the extent of health disparities.

The Model Minority Myth, Data Aggregation, and the Role of Medical Schools in Combating Anti-Asian Sentiment.

The COVID-19 pandemic has resulted in an alarming increase in hate incidents directed toward Asian Americans and Pacific Islanders (AAPIs), including verbal harassment and physical assault, spurring the nationwide #StopAsianHate movement. This rise in anti-Asian sentiment is occurring at a critical time of racial reckoning across the United States, galvanized by the Black Lives Matter movement, and of medical student calls for the implementation of antiracist medical curricula. AAPIs are stereotyped by the model minority myth, which posits that AAPIs are educated, hardworking, and therefore able to achieve high levels of success. This myth acts as a racial wedge between minorities and perpetuates harm that is pervasive throughout the field of medicine. Critically, the frequent aggregation of all AAPI subgroups as one monolithic community obfuscates socioeconomic and cultural differences across the AAPI diaspora while reinforcing the model minority myth. Here, the authors illustrate how the model minority myth and data aggregation have negatively affected the recruitment and advancement of diverse AAPI medical students, physicians, and faculty. Additionally, the authors discuss how data aggregation obscures health disparities across the AAPI diaspora and how the model minority myth influences the illness experiences of AAPI patients. Importantly, the authors outline specific actionable policies and reforms that medical schools can implement to combat anti-Asian sentiment and support the AAPI community.

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation.

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

Wearable Activity Trackers for Monitoring Adherence to Home Confinement During the COVID-19 Pandemic Worldwide: Data Aggregation and Analysis.

BACKGROUND: In the context of home confinement during the coronavirus disease (COVID-19) pandemic, objective, real-time data are needed to assess populations' adherence to home confinement to adapt policies and control measures accordingly. OBJECTIVE: The aim of this study was to determine whether wearable activity trackers could provide information regarding users' adherence to home confinement policies because of their capacity for seamless and continuous monitoring of individuals' natural activity patterns regardless of their location. METHODS: We analyzed big data from individuals using activity trackers (Withings) that count the wearer's average daily number of steps in a number of representative nations that adopted different modalities of restriction of citizens' activities. RESULTS: Data on the number of steps per day from over 740,000 individuals around the world were analyzed. We demonstrate the physical activity patterns in several representative countries with total, partial, or no home confinement. The decrease in steps per day in regions with strict total home confinement ranged from 25% to 54%. Partial lockdown (characterized by social distancing measures such as school closures, bar and restaurant closures, and cancellation of public meetings but without strict home confinement) does not appear to have a significant impact on people's activity compared to the pre-pandemic period. The absolute level of physical activity under total home confinement in European countries is around twofold that in China. In some countries, such as France and Spain, physical activity started to gradually decrease even before official commitment to lockdown as a result of initial less stringent restriction orders or self-quarantine. However, physical activity began to increase again in the last 2 weeks, suggesting a decrease in compliance with confinement orders. CONCLUSIONS: Aggregate analysis of activity tracker data with the potential for daily updates can provide information regarding adherence to home confinement policies.