Integrating water quality data with a Bayesian network model to improve spatial and temporal phosphorus attribution: Application to the Maumee River Basin

Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrient sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies of nutrient source attribution have focused on large watersheds or counties at annual time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a generalizable Bayesian network model for phosphorus source attribution at the subwatershed scale (12-digit Hydrologic Unit Code). Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow information simulated by the SWAT model, and in-stream water quality measurements using Approximate Bayesian Computation to derive a posterior that attributes phosphorus contributions to subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach currently adopted by environmental agencies. Fertilizer contributes more soluble reactive phosphorus than manure, while manure contributes most of the unreactive phosphorus. While developed for the specific context of Maumee River Basin, our lightweight and generalizable model framework could be adapted to other regions and pollutants and could help inform targeted environmental regulation and enforcement.

Limitations of Reporting Requirements under California's Livestock Antimicrobial Restriction Law

BACKGROUND

Antimicrobial use in livestock production is considered a key contributor to growing antimicrobial resistance in bacteria. In 2015, California became the first state to enact restrictions on routine antimicrobial use in livestock production via Senate Bill 27 (SB27). SB27 further required the California Department of Food and Agriculture (CDFA) to collect and disseminate data on antimicrobial use in livestock production.

OBJECTIVE

The goal of this report is to assess whether CDFA's data release allows us to evaluate how antimicrobial use changed after the implementation of SB27.

METHODS

We combine the CDFA data with feed drug concentration ranges from the Code of Federal Regulation to evaluate the spread of plausible antimicrobial use trends. We also estimate antimicrobial consumption rates using data from the National Agricultural Statistical Service (NASS) and compare these to changes in medicated feed production reported by the CDFA.

DISCUSSION

We show that CDFA's reported data are insufficient to reliably estimate whether antimicrobial usage has increased or decreased, most notably because no information is provided about the mass of antimicrobials approved for use or medicated feed drug concentrations. After incorporating additional external data on feed drug concentrations, one can at best provide uninformative bounds on the effect of SB27. We find some evidence that antimicrobial use has decreased by incorporating data on national sales of antimicrobials for food-producing animals, but the weakness of this inference underlines the need for improved data collection and dissemination, especially as other states seek to implement similar policies. We provide recommendations on how to improve reporting and data collection under SB27. https://doi.org/10.1289/EHP13702.

Implications of predicting race variables from medical images

AI-predicted race variables pose risks and opportunities for studying health disparities.

Integrating social services with disease investigation: A randomized trial of COVID-19 high-touch contact tracing

COVID-19 exposed and exacerbated health disparities, and a core challenge has been how to adapt pandemic response and public health in light of these disproportionate health burdens. Responding to this challenge, the County of Santa Clara Public Health Department designed a model of "high-touch" contact tracing that integrated social services with disease investigation, providing continued support and resource linkage for clients from structurally vulnerable communities. We report results from a cluster randomized trial of 5,430 cases from February to May 2021 to assess the ability of high-touch contact tracing to aid with isolation and quarantine. Using individual-level data on resource referral and uptake outcomes, we find that the intervention, randomized assignment to the high-touch program, increased the referral rate to social services by 8.4% (95% confidence interval, 0.8%-15.9%) and the uptake rate by 4.9% (-0.2%-10.0%), with the most pronounced increases in referrals and uptake of food assistance. These findings demonstrate that social services can be effectively combined with contact tracing to better promote health equity, demonstrating a novel path for the future of public health.

Automated vs. manual case investigation and contact tracing for pandemic surveillance: Evidence from a stepped wedge cluster randomized trial

BACKGROUND

Case investigation and contact tracing (CICT) is an important tool for communicable disease control, both to proactively interrupt chains of transmission and to collect information for situational awareness. We run the first randomized trial of COVID-19 CICT to investigate the utility of manual (i.e., call-based) vs. automated (i.e., survey-based) CICT for pandemic surveillance.

METHODS

Between December 15, 2021 and February 5, 2022, a stepped wedge cluster randomized trial was run in which Santa Clara County ZIP Codes progressively transitioned from manual to automated CICT. Eleven individual-level data fields on demographics and disease dynamics were observed for non-response. The data contains 106,522 positive cases across 29 ZIP Codes.

FINDINGS

Automated CICT reduced overall collected information by 29 percentage points (SE = 0.08, p < 0.01), as well as the response rate for individual fields. However, we find no evidence of differences in information loss by race or ethnicity.

INTERPRETATIONS

Automated CICT can serve as a useful alternative to manual CICT, with no substantial evidence of skewing data along racial or ethnic lines, but manual CICT improves completeness of key data for monitoring epidemiologic patterns.

FUNDING

This research was supported in part by the Stanford Office of Community Engagement and the Stanford Institute for Human-Centered Artificial Intelligence.

Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing

An academic team at Stanford University worked with the County of Santa Clara Public Health Department to develop a machine-learning system for language matching in Covid-19 contact tracing through a collaborative design process. Although developed for a specific public health activity, the setting is representative of a wide range of health care delivery contexts, and the experience lays out how health care organizations can develop accountable algorithms that improve care, mitigate risk, and enable evaluation by stakeholders. Key elements of the design process involved: (1) a partnership and stakeholder consultation to develop a common understanding of and iteration around algorithmic design; (2) the use of a model understandable to all stakeholders, which exhibited only modest performance degradation relative to more complex models; and (3) a randomized controlled trial and qualitative survey of how the algorithm impacted real-world contact tracing, providing an evaluation that goes beyond narrow technical measures of algorithmic performance.

Science Translation During the COVID-19 Pandemic: An Academic-Public Health Partnership to Assess Capacity Limits in California

On the basis of an extensive academic-public health partnership around COVID-19 response, we illustrate the challenge of science-policy translation by examining one of the most common nonpharmaceutical interventions: capacity limits. We study the implementation of a 20% capacity limit in retail facilities in the California Bay Area. Through a difference-in-differences analysis, we show that the intervention caused no material reduction in visits, using the same large-scale mobile device data on human movements (mobility data) originally used in the academic literature to support such limits. We show that the lack of effectiveness stems from a mismatch between the academic metric of capacity relative to peak visits and the policy metric of capacity relative to building code. The disconnect in metrics is amplified by mobility data losing predictive power after the early months of the pandemic, weakening the policy relevance of mobility-based interventions. Nonetheless, the data suggest that a better-grounded rationale for capacity limits is to reduce risk specifically during peak hours. To enhance the connection between science, policy, and public health in future times of crisis, we spell out 3 strategies: living models, coproduction, and shared metrics. (Am J Public Health. 2022;112(2):308-315. https://doi.org/10.2105/AJPH.2021.306576).

Evaluation of Allocation Schemes of COVID-19 Testing Resources in a Community-Based Door-to-Door Testing Program

Question

What are effective mechanisms to identify and reach vulnerable populations and equalize access to COVID-19 testing resources in the presence of substantial demographic disparities?

Findings

In this cohort study of 756 participants, a door-to-door program with community-based health workers was associated with a substantial increase in the proportion of Latinx and elderly individuals undergoing testing, relative to neighborhood testing sites. The protocol associated with the greatest increase in testing at-risk individuals was uncertainty sampling, followed by local knowledge, and then targeting households in areas with a high number of index cases.

Meaning

These findings suggest that community-based testing programs that allocate resources using uncertainty sampling might effectively reduce COVID-19 testing disparities.

Importance

Overcoming social barriers to COVID-19 testing is an important issue, especially given the demographic disparities in case incidence rates and testing. Delivering culturally appropriate testing resources using data-driven approaches in partnership with community-based health workers is promising, but little data are available on the design and effect of such interventions.

Objectives

To assess and evaluate a door-to-door COVID-19 testing initiative that allocates visits by community health workers by selecting households in areas with a high number of index cases, by using uncertainty sampling for areas where the positivity rate may be highest, and by relying on local knowledge of the health workers.

Design, Setting, and Participants

This cohort study was performed from December 18, 2020, to February 18, 2021. Community health workers visited households in neighborhoods in East San Jose, California, based on index cases or uncertainty sampling while retaining discretion to use local knowledge to administer tests. The health workers, also known as promotores de salud (hereinafter referred to as promotores) spent a mean of 4 days a week conducting door-to-door COVID-19 testing during the 2-month study period. All residents of East San Jose were eligible for COVID-19 testing. The promotores were selected from the META cooperative (Mujeres Empresarias Tomando Acción [Entrepreneurial Women Taking Action]).

Interventions

The promotores observed self-collection of anterior nasal swab samples for SARS-CoV-2 reverse transcriptase–polymerase chain reaction tests.

Main Outcomes and Measures

A determination of whether door-to-door COVID-19 testing was associated with an increase in the overall number of tests conducted, the demographic distribution of the door-to-door tests vs local testing sites, and the difference in positivity rates among the 3 door-to-door allocation strategies.

Results

A total of 785 residents underwent door-to-door testing, and 756 were included in the analysis. Among the 756 individuals undergoing testing (61.1% female; 28.2% aged 45-64 years), door-to-door COVID-19 testing reached different populations than standard public health surveillance, with 87.6% (95% CI, 85.0%-89.8%) being Latinx individuals. The closest available testing site only reached 49.0% (95% CI, 48.3%-49.8%) Latinx individuals. Uncertainty sampling provided the most effective allocation, with a 10.8% (95% CI, 6.8%-16.0%) positivity rate, followed by 6.4% (95% CI, 4.1%-9.4%) for local knowledge, and 2.6% (95% CI, 0.7%-6.6%) for index area selection. The intervention was also associated with increased overall testing capacity by 60% to 90%, depending on the testing protocol.

Conclusions and Relevance

In this cohort study of 785 participants, uncertainty sampling, which has not been used conventionally in public health, showed promising results for allocating testing resources. Community-based door-to-door interventions and leveraging of community knowledge were associated with reduced demographic disparities in testing.

How US law will evaluate artificial intelligence for covid-19

Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks

Menu labeling, calories, and nutrient density: Evidence from chain restaurants

The Food and Drug Administration's menu labeling rule requires chain restaurants to prominently display calories, while leaving other nutritional information (e.g., fat, sodium, sugar) to the request of consumers. We use rich micronutrient data from 257 large chain brands and 24,076 menu items to examine whether calories are correlated with widely used "nutrient profile" scores that measure healthfulness based on nutrient density. We show that calories are indeed statistically significant predictors of nutrient density. However, as a substantive matter, the correlation is highly attenuated (partial R2 < 0.01). Our findings (a) suggest that the promise of calorie labeling to improve nutrient intake quality at restaurants is limited and (b) clarify the basis for transparency of nutrient composition beyond calories to promote healthy menu choices.

Feasible Policy Evaluation by Design: A Randomized Synthetic Stepped-Wedge Trial of Mandated Disclosure in King County

Evidence-based policy is limited by the perception that randomized controlled trials (RCTs) are expensive and infeasible. We argue that carefully tailored research design can overcome these challenges and enable more widespread randomized evaluations of policy implementation. We demonstrate how a stepped-wedge (randomized rollout) design that adapts synthetic control methods overcame substantial practical, administrative, political, and statistical constraints to evaluating King County's new food safety rating system. The core RCT component of the evaluation came at little financial cost to the government, allowed the entire county to be treated, and resulted in no functional implementation delay. The case of restaurant sanitation grading has played a critical role in the scholarship on information disclosure, and our study provides the first evidence from a randomized trial of the causal effects of grading on health outcomes. We find that the grading system had no appreciable effects on foodborne illness, hospitalization, or food handling practices but that the system may have marginally increased public engagement by encouraging higher reporting.