Does health service funding go where the need is? A prototype spatial access analysis for new urban contracts data.

BACKGROUND: Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government. METHODS: In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study. RESULTS: We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago's high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout the city). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department. CONCLUSIONS: The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results.

Validation of a Machine Learning Model to Predict Childhood Lead Poisoning.

Importance: Childhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention. Objective: To validate a machine learning (random forest) prediction model of elevated blood lead levels (EBLLs) by comparison with a parsimonious logistic regression. Design, Setting, and Participants: This prognostic study for temporal validation of multivariable prediction models used data from the Women, Infants, and Children (WIC) program of the Chicago Department of Public Health. Participants included a development cohort of children born from January 1, 2007, to December 31, 2012, and a validation WIC cohort born from January 1 to December 31, 2013. Blood lead levels were measured until December 31, 2018. Data were analyzed from January 1 to October 31, 2019. Exposures: Blood lead level test results; lead investigation findings; housing characteristics, permits, and violations; and demographic variables. Main Outcomes and Measures: Incident EBLL (≥6 µg/dL). Models were assessed using the area under the receiver operating characteristic curve (AUC) and confusion matrix metrics (positive predictive value, sensitivity, and specificity) at various thresholds. Results: Among 6812 children in the WIC validation cohort, 3451 (50.7%) were female, 3057 (44.9%) were Hispanic, 2804 (41.2%) were non-Hispanic Black, 458 (6.7%) were non-Hispanic White, and 442 (6.5%) were Asian (mean [SD] age, 5.5 [0.3] years). The median year of housing construction was 1919 (interquartile range, 1903-1948). Random forest AUC was 0.69 compared with 0.64 for logistic regression (difference, 0.05; 95% CI, 0.02-0.08). When predicting the 5% of children at highest risk to have EBLLs, random forest and logistic regression models had positive predictive values of 15.5% and 7.8%, respectively (difference, 7.7%; 95% CI, 3.7%-11.3%), sensitivity of 16.2% and 8.1%, respectively (difference, 8.1%; 95% CI, 3.9%-11.7%), and specificity of 95.5% and 95.1% (difference, 0.4%; 95% CI, 0.0%-0.7%). Conclusions and Relevance: The machine learning model outperformed regression in predicting childhood lead poisoning, especially in identifying children at highest risk. Such a model could be used to target the allocation of lead poisoning prevention resources to these children.

Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health.

Foodborne illness is a serious and preventable public health problem affecting 1 in 6 Americans with cost estimates over $50 billion annually. Local health departments license and inspect restaurants to ensure food safety and respond to reports of suspected foodborne illness. The City of St. Louis Department of Health adopted the HealthMap Foodborne Dashboard (Dashboard), a tool that monitors Twitter for tweets about food poisoning in a geographic area and allows the health department to respond. We evaluated the implementation by interviewing employees of the City of St. Louis Department of Health involved in food safety. We interviewed epidemiologists, environmental health specialists, health services specialists, food inspectors, and public information officers. Participants viewed engaging innovation participants and executing the innovation as challenges while they felt the Dashboard had relative advantage over existing reporting methods and was not complex once in place. This study is the first to examine practitioner perceptions of the implementation of a new technology in a local health department. Similar implementation projects should focus more on process by developing clear and comprehensive plans to educate and involve stakeholders prior to implementation.

Availability of Foodborne Illness Reporting Mechanisms for the Public on Local Health Department Web Sites.

CONTEXT: Foodborne illness is a serious and preventable public health problem, with high health and economic tolls in the United States. Local governments play an important role in food safety, with local health departments (LHDs) responsible for licensing and inspecting restaurants. Foodborne illness complaints from the public result in identification of more serious and critical food safety violations than regularly scheduled inspections; however, few people report foodborne illness. Availability of existing methods for the public to report foodborne illness to LHDs across the United States was examined. OBJECTIVE: In 2016, data were collected and analyzed from a nationally representative stratified sample of 816 LHDs. Each LHD Web site was examined to determine whether the Web site included a way for constituents to report a suspected foodborne illness. RESULTS: Just 27.6% of LHD Web sites included a way for constituents to report a suspected foodborne illness. LHDs with reporting mechanisms were serving significantly larger populations and had significantly more staff members, higher revenues, and higher expenditures. Health departments with reporting mechanisms were also significantly more likely to conduct environmental health surveillance activities, to regulate, inspect, and/or license food service establishments, and to be involved in food safety policy. CONCLUSIONS: Consumer reports of suspected foodborne illness help identify serious and critical food safety violations in food establishments; however, foodborne illness is vastly underreported by the US public. While more evidence is needed on how current systems are working, increasing the visibility and availability of Web-based reporting mechanisms through the following strategies is recommended: (1) test and modify search functions on LHD Web sites to ensure consumers find reporting mechanisms; (2) add a downloadable form as an option for reporting; (3) coordinate with state health departments to ensure clear instructions are available for reporting at both state and local levels; and (4) consider linking directly to state health department reporting mechanisms.

Machine-learned epidemiology: real-time detection of foodborne illness at scale.

Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.

Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project.

CONTEXT: Foodborne illness affects 1 in 4 US residents each year. Few of those sickened seek medical care or report the illness to public health authorities, complicating prevention efforts. Citizens who report illness identify food establishments with more serious and critical violations than found by regular inspections. New media sources, including online restaurant reviews and social media postings, have the potential to improve reporting. OBJECTIVE: We implemented a Web-based Dashboard (HealthMap Foodborne Dashboard) to identify and respond to tweets about food poisoning from St Louis City residents. DESIGN AND SETTING: This report examines the performance of the Dashboard in its first 7 months after implementation in the City of St Louis Department of Health. MAIN OUTCOME MEASURES: We examined the number of relevant tweets captured and replied to, the number of foodborne illness reports received as a result of the new process, and the results of restaurant inspections following each report. RESULTS: In its first 7 months (October 2015-May 2016), the Dashboard captured 193 relevant tweets. Our replies to relevant tweets resulted in more filed reports than several previously existing foodborne illness reporting mechanisms in St Louis during the same time frame. The proportion of restaurants with food safety violations was not statistically different (P = .60) in restaurants inspected after reports from the Dashboard compared with those inspected following reports through other mechanisms. CONCLUSION: The Dashboard differs from other citizen engagement mechanisms in its use of current data, allowing direct interaction with constituents on issues when relevant to the constituent to provide time-sensitive education and mobilizing information. In doing so, the Dashboard technology has potential for improving foodborne illness reporting and can be implemented in other areas to improve response to public health issues such as suicidality, spread of Zika virus infection, and hospital quality.

A bright future: innovation transforming public health in Chicago.

Big cities continue to be centers for innovative solutions and services. Governments are quickly identifying opportunities to take advantage of this energy and revolutionize the means by which they deliver services to the public. The governmental public health sector is rapidly evolving in this respect, and Chicago is an emerging example of some of the changes to come. Governments are gradually adopting innovative informatics and big data tools and strategies, led by pioneering jurisdictions that are piecing together the standards, policy frameworks, and leadership structures fundamental to effective analytics use. They give an enticing glimpse of the technology's potential and a sense of the challenges that stand in the way. This is a rapidly evolving environment, and cities can work with partners to capitalize on the innovative energies of civic tech communities, health care systems, and emerging markets to introduce new methods to solve old problems.

Tweeting for and against public health policy: response to the Chicago Department of Public Health's electronic cigarette Twitter campaign.

BACKGROUND: In January 2014, the Chicago City Council scheduled a vote on local regulation of electronic cigarettes as tobacco products. One week prior to the vote, the Chicago Department of Public Health (CDPH) released a series of messages about electronic cigarettes (e-cigarettes) through its Twitter account. Shortly after the messages, or tweets, were released, the department's Twitter account became the target of a "Twitter bomb" by Twitter users sending more than 600 tweets in one week against the proposed regulation. OBJECTIVE: The purpose of our study was to examine the messages and tweet patterns in the social media response to the CDPH e-cigarette campaign. METHODS: We collected all tweets mentioning the CDPH in the week between the e-cigarette campaign and the vote on the new local e-cigarette policy. We conducted a content analysis of the tweets, used descriptive statistics to examine characteristics of involved Twitter users, and used network visualization and descriptive statistics to identify Twitter users prominent in the conversation. RESULTS: Of the 683 tweets mentioning CDPH during the week, 609 (89.2%) were anti-policy. More than half of anti-policy tweets were about use of electronic cigarettes for cessation as a healthier alternative to combustible cigarettes (358/609, 58.8%). Just over one-third of anti-policy tweets asserted that the health department was lying or disseminating propaganda (224/609, 36.8%). Approximately 14% (96/683, 14.1%) of the tweets used an account or included elements consistent with "astroturfing"-a strategy employed to promote a false sense of consensus around an idea. Few Twitter users were from the Chicago area; Twitter users from Chicago were significantly more likely than expected to tweet in support of the policy. CONCLUSIONS: Our findings may assist public health organizations to anticipate, recognize, and respond to coordinated social media campaigns.

Health department use of social media to identify foodborne illness - Chicago, Illinois, 2013-2014.

An estimated 55 million to 105 million persons in the United States experience acute gastroenteritis caused by foodborne illness each year, resulting in costs of $2-$4 billion annually. Many persons do not seek treatment, resulting in underreporting of the actual number of cases and cost of the illnesses. To prevent foodborne illness, local health departments nationwide license and inspect restaurants and track and respond to foodborne illness complaints. New technology might allow health departments to engage with the public to improve foodborne illness surveillance. For example, the New York City Department of Health and Mental Hygiene examined restaurant reviews from an online review website to identify foodborne illness complaints. On March 23, 2013, the Chicago Department of Public Health (CDPH) and its civic partners launched FoodBorne Chicago, a website (https://www.foodbornechicago.org) aimed at improving food safety in Chicago by identifying and responding to complaints on Twitter about possible foodborne illnesses. In 10 months, project staff members responded to 270 Twitter messages (tweets) and provided links to the FoodBorne Chicago complaint form. A total of 193 complaints of possible foodborne illness were submitted through FoodBorne Chicago, and 133 restaurants in the city were inspected. Inspection reports indicated 21 (15.8%) restaurants failed inspection, and 33 (24.8%) passed with conditions indicating critical or serious violations. Eight tweets and 19 complaint forms to FoodBorne Chicago described seeking medical treatment. Collaboration between public health professionals and the public via social media might improve foodborne illness surveillance and response. CDPH is working to disseminate FoodBorne Chicago via freely available open source software.