Racism is an ethical issue for healthcare artificial intelligence.

There is growing attention and evidence that healthcare AI is vulnerable to racial bias. Despite the renewed attention to racism in the United States, racism is often disconnected from the literature on ethical AI. Addressing racism as an ethical issue will facilitate the development of trustworthy and responsible healthcare AI.

From Theory to Practice: Mitigating the Harm of Online Harassment in Public Health.

This article uses the theoretical framework of the networked public to understand the dynamics of online harassment of public health professionals. Coauthors draw on their experiences with health communication on social media, in a local public health department, and in news media to illustrate the utility of this framework. Their stories also highlight the need to build a more proactive approach to online harassment in public health.

Association between wealth, insurance coverage, urban residence, median age and COVID-19 deaths across states in Nigeria.

This study measures associations between COVID-19 deaths and sociodemographic factors (wealth, insurance coverage, urban residence, age, state population) for states in Nigeria across two waves of the COVID-19 pandemic: February 27th 2020 to October 24th 2020 and October 25th 2020 to July 25th 2021. Data sources include 2018 Nigeria Demographic and Health Survey and Nigeria Centre for Disease Control (NCDC) COVID-19 daily reports. It uses negative binomial models to model deaths, and stratifies results by respondent gender.

Use of machine learning methods to understand discussions of female genital mutilation/cutting on social media.

Female genital mutilation/cutting (FGM/C) describes several procedures that involve injury to the vulva or vagina for nontherapeutic reasons. Though at least 200 million women and girls living in 30 countries have undergone FGM/C, there is a paucity of studies focused on public perception of FGM/C. We used machine learning methods to characterize discussion of FGM/C on Twitter in English from 2015 to 2020.

COVID-19 Mortality by Race and Ethnicity in US Metropolitan and Nonmetropolitan Areas, March 2020 to February 2022.

Importance: Prior research has established that Hispanic and non-Hispanic Black residents in the US experienced substantially higher COVID-19 mortality rates in 2020 than non-Hispanic White residents owing to structural racism. In 2021, these disparities decreased.

Objective: To assess to what extent national decreases in racial and ethnic disparities in COVID-19 mortality between the initial pandemic wave and subsequent Omicron wave reflect reductions in mortality vs other factors, such as the pandemic's changing geography.

Towards better Data Science to address racial bias and health equity.

Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and communication approaches to be antiracist and focused on serving minoritized groups that have long-standing worse health indicators than majority groups.

Association of Neighborhood Racial and Ethnic Composition and Historical Redlining With Built Environment Indicators Derived From Street View Images in the US.

Importance: Racist policies (such as redlining) create inequities in the built environment, producing racially and ethnically segregated communities, poor housing conditions, unwalkable neighborhoods, and general disadvantage. Studies on built environment disparities are usually limited to measures and data that are available from existing sources or can be manually collected.

Objective: To use built environment indicators generated from online street-level images to investigate the association among neighborhood racial and ethnic composition, the built environment, and health outcomes across urban areas in the US.

Geographic and Temporal Patterns in Covid-19 Mortality by Race and Ethnicity in the United States from March 2020 to February 2022.

Prior research has established that American Indian, Alaska Native, Black, Hispanic, and Pacific Islander populations in the United States have experienced substantially higher mortality rates from Covid-19 compared to non-Hispanic white residents during the first year of the pandemic. What remains less clear is how mortality rates have changed for each of these racial/ethnic groups during 2021, given the increasing prevalence of vaccination. In particular, it is unknown how these changes in mortality have varied geographically.

Diet during the COVID-19 pandemic: An analysis of Twitter data.

In this study, we measured the association between county characteristics and changes in healthy-food, fast-food, and alcohol tweets during the COVID-19 pandemic in the United States. Our analytic dataset consisted of 1,282,316 geotagged tweets that referenced food consumption posted before (63.2%) and during (36.

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.

Addressing the socioeconomic divide in computational modeling for infectious diseases.

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics, yet these concepts are often at the margins of the computational modeling community. Building on recent research studies in the area of digital and computational epidemiology, we provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

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.

COVID-19 and beyond: Use of digital technology for pandemic response in Africa.

The use of technology has been ubiquitous in efforts to combat the ongoing COVID-19 pandemic. In this perspective, we review technologies and new approaches developed at the start of the pandemic; efforts earmarked by a flexible approach to problem solving, local tech entrepreneurship, and swift adoption of technology. We performed a systematic review of the use of technology during the initial wave of the COVID-19 pandemic in most African countries.

Privacy-first health research with federated learning.

Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion.

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study.

Background: The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years.

Forecasting influenza-like illness trends in Cameroon using Google Search Data.

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.

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.

Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States.

Physical distancing has been the primary strategy to control COVID-19 in the United States. We used mobility data from a large, anonymized sample of smartphone users to assess the relationship between neighbourhood income and physical distancing during the pandemic. We found a strong gradient between neighbourhood income and physical distancing.

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models.