Study Protocol. Evaluating the life-course health impact of a city-wide system approach to improve air quality in Bradford, UK: A quasi-experimental study with implementation and process evaluation.

BACKGROUND: Air quality is a major public health threat linked to poor birth outcomes, respiratory and cardiovascular disease, and premature mortality. Deprived groups and children are disproportionately affected. Bradford will implement a Clean Air Zone (CAZ) as part of the Bradford Clean Air Plan (B-CAP) in 2022 to reduce pollution, providing a natural experiment. The aim of the current study is to evaluate the impact of the B-CAP on health outcomes and air quality, inequalities and explore value for money. An embedded process and implementation evaluation will also explore barriers and facilitators to implementation, impact on attitudes and behaviours, and any adverse consequences. METHODS: The study is split into 4 work packages (WP). WP1A: 20 interviews with decision makers, 20 interviews with key stakeholders; 10 public focus groups and documentary analysis of key reports will assess implementation barriers, acceptability and adverse or unanticipated consequences at 1 year post-implementation (defined as point at which charging CAZ goes 'live'). WP1B: A population survey (n = 2000) will assess travel behaviour and attitudes at baseline and change at 1 year post-implementation). WP2: Routine air quality measurements will be supplemented with data from mobile pollution sensors in 12 schools collected by N = 240 pupil citizen scientists (4 within, 4 bordering and 4 distal to CAZ boundary). Pupils will carry sensors over four monitoring periods over a 12 month period (two pre, and two post-implementation). We will explore whether reductions in pollution vary by CAZ proximity. WP3A: We will conduct a quasi-experimental interrupted time series analysis using a longitudinal routine health dataset of > 530,000 Bradford residents comparing trends (3 years prior vs 3 years post) in respiratory health (assessed via emergency/GP attendances. WP3B: We will use the richly-characterised Born in Bradford cohort (13,500 children) to explore health inequalities in respiratory health using detailed socio-economic data. WP4: will entail a multi-sectoral health economic evaluation to determine value for money of the B-CAP. DISCUSSION: This will be first comprehensive quasi-experimental evaluation of a city-wide policy intervention to improve air quality. The findings will be of value for other areas implementing this type of approach. TRIAL REGISTRATION: ISRCTN67530835 https://doi.org/10.1186/ISRCTN67530835.

Pharmacotherapy used for alcohol and cocaine use disorders in a CAPS-AD of Minas Gerais

Abstract Substance use disorder is one of the major social and public health problems in the world. The present study analyzed the pharmacoepidemiological profile of patients treated at the Psychosocial Treatment Center for Alcohol and Substance Use Disorders (CAPS-AD) for treatment of alcohol use disorders (AUD), cocaine use disorders (CUD) and concomitant alcohol and cocaine use disorders (A-CUD) in the city of Betim-MG. The study used quantitative and descriptive data and was based on the evaluation of medical records of patients attended from January to December 2016. After analyzing 295 medical records, the majority of study participants were male (83.7 %) with an average age of 46.26 for AUD, 28.88 for CUD and 34.29 for A-CUD. The most prescribed drugs for AUD were diazepam (54.1 %), thiamine (37 %), complex B vitamins (29.5 %), and disulfiram (2.7 %); for CUD, diazepam (26.9 %) and haloperidol (23.1 %). It should be noticed that although contraindicated by the guidelines, chlorpromazine (42.3 %, 25.3 %, 20.3 %) was prescribed for CUD, AUD, and A-CUD respectively. Knowing the pharmacoepidemiological profile of CAPS-AD patients is extremely important for making decisions regarding which medicines to make available to the population.

Emerging dynamics from high-resolution spatial numerical epidemics.

Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.

A Nurse-Run Public Health Clinic for Individuals Utilizing a Community Outreach Center.

This article describes a nurse-run clinic for low-income individuals or individuals experiencing homelessness. We conducted a quality improvement project using data from 111 individuals over 194 visits. Thirty-seven percent of patients had difficulty managing their health and 22% reported being in an active health crisis. The visits resulted in patients leaving with their visit goal being met (86%), and 96% of nonemergent cases being diverted from the emergency room. The nurse-run clinic provided access to care and served as a safety net for a vulnerable population, providing support for this clinic model with this population.

The people, process, and technology for population management in community health.

Population health expands the focus of health care from individual, in-person care to the proactive management of cohorts that can occur asynchronously from a clinical encounter. In its most successful form, the approach segments populations by defined characteristics and promotes outreach and engagement to deliver targeted interventions, even among those who have missed recent or routine care. The triple aim, supported by the Institutes for Health Care Improvement, emphasizes improving the health of populations, cost of care, and patient and care team experience and has influenced new approaches in primary care. In primary care settings such as community health centers, the goal of improving outcomes leverages technology to expand focus from point-of-care interventions to population-level approaches to deliver high-quality preventive services and chronic disease management that benefit entire families and communities. Developments in informatics have introduced technology tools for population management and underscored the need to align technology with effective processes and stakeholder engagement for success. Informed by a review of the literature and observations across multiple implementations of population health strategies in community health, in this conceptual paper, we describe the steps (process), domains of team expertise (people), and health information technology components (technology) that contribute to the success of a population health strategy. We also explore future opportunities to expand the reach and impact of population health through patient engagement, analytics, interventions to address social determinants of health, responses to emerging public health priorities, and prioritization-of-use cases by assessing community-specific needs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Modeling Social Distancing Strategies to Prevent SARS-CoV-2 Spread in Israel: A Cost-Effectiveness Analysis.

OBJECTIVES: While highly effective in preventing SARS-CoV-2 spread, national lockdowns come with an enormous economic price. Few countries have adopted an alternative "testing, tracing, and isolation" approach to selectively isolate people at high exposure risk, thereby minimizing the economic impact. To assist policy makers, we performed a cost-effectiveness analysis of these 2 strategies. METHODS: A modified Susceptible, Exposed, Infectious, Recovered, and Deceased (SEIRD) model was employed to assess the situation in Israel, a small country with ∼9 million people. The incremental cost-effectiveness ratio (ICER) of these strategies as well as the expected number of infected individuals and deaths were calculated. RESULTS: A nationwide lockdown is expected to save, on average, 274 (median 124, interquartile range: 71-221) lives compared to the "testing, tracing, and isolation" approach. However, the ICER will be, on average, $45 104 156 (median $49.6 million, interquartile range: 22.7-220.1) to prevent 1 case of death. CONCLUSION: A national lockdown has a moderate advantage in saving lives with tremendous costs and possible overwhelming economic effects. These findings should assist decision makers dealing with additional waves of this pandemic.

Societal Effects Are a Major Factor for the Uptake of the Coronavirus Disease 2019 (COVID-19) Digital Contact Tracing App in The Netherlands.

OBJECTIVES: Our study investigates the extent to which uptake of a COVID-19 digital contact-tracing (DCT) app among the Dutch population is affected by its configurations, its societal effects, and government policies toward such an app. METHODS: We performed a discrete choice experiment among Dutch adults including 7 attributes, that is, who gets a notification, waiting time for testing, possibility for shops to refuse customers who have not installed the app, stopping condition for contact tracing, number of people unjustifiably quarantined, number of deaths prevented, and number of households with financial problems prevented. The data were analyzed by means of panel mixed logit models. RESULTS: The prevention of deaths and financial problems of households had a very strong influence on the uptake of the app. Predicted app uptake rates ranged from 24% to 78% for the worst and best possible app for these societal effects. We found a strong positive relationship between people's trust in government and people's propensity to install the DCT app. CONCLUSIONS: The uptake levels we find are much more volatile than the uptake levels predicted in comparable studies that did not include societal effects in their discrete choice experiments. Our finding that the societal effects are a major factor in the uptake of the DCT app results in a chicken-or-the-egg causality dilemma. That is, the societal effects of the app are severely influenced by the uptake of the app, but the uptake of the app is severely influenced by its societal effects.

The ethics of refusing to care for patients during the coronavirus pandemic: A Chinese perspective.

As a result of the coronavirus (COVID-19) pandemic, health professionals are faced with situations they have not previously encountered and are being forced to make difficult ethical decisions. As the first group to experience challenges of caring for patients with coronavirus, Chinese nurses endure heartbreak and face stressful moral dilemmas. In this opinion piece, we examine three related critical questions: Whether society has the right to require health professionals to risk their lives caring for patients; whether health professionals have the right to refuse to care for patients during the coronavirus pandemic; and what obligations there are to protect health professionals? Value of care, community expectations, legal obligations, professional and codes of practice may compel health professionals to put themselves at risks in emergency situations. The bioethical principles of autonomy, justice, beneficence and non-maleficence, as well as public health ethics, guide nurses to justify their decisions as to whether they are entitled to refuse to treat COVID-19 patients during the pandemic. We hope that the open discussion would support the international society in addressing similar ethical challenges in their respective situations during this public health crisis.

An Interactive Text Message Survey as a Novel Assessment for Bedtime Routines in Public Health Research: Observational Study.

BACKGROUND: Traditional research approaches, especially questionnaires and paper-based assessments, limit in-depth understanding of the fluid dynamic processes associated with child well-being and development. This includes bedtime routine activities such as toothbrushing and reading a book before bed. The increase in innovative digital technologies alongside greater use and familiarity among the public creates unique opportunities to use these technical developments in research. OBJECTIVE: This study aimed to (1) examine the best way of assessing bedtime routines in families and develop an automated, interactive, text message survey assessment delivered directly to participants' mobile phones and (2) test the assessment within a predominately deprived sociodemographic sample to explore retention, uptake, feedback, and effectiveness. METHODS: A public and patient involvement project showed clear preference for interactive text surveys regarding bedtime routines. The developed interactive text survey included questions on bedtime routine activities and was delivered for seven consecutive nights to participating parents' mobile phones. A total of 200 parents participated. Apart from the completion of the text survey, feedback was provided by participants, and data on response, completion, and retention rates were captured. RESULTS: There was a high retention rate (185/200, 92.5%), and the response rate was high (160/185, 86.5%). In total, 114 participants provided anonymized feedback. Only a small percentage (5/114, 4.4%) of participants reported problems associated with completing the assessment. The majority (99/114, 86.8%) of participants enjoyed their participation in the study, with an average satisfaction score of 4.6 out of 5. CONCLUSIONS: This study demonstrated the potential of deploying SMS text message-based surveys to capture and quantify real-time information on recurrent dynamic processes in public health research. Changes and adaptations based on recommendations are crucial next steps in further exploring the diagnostic and potential intervention properties of text survey and text messaging approaches.

Effects of the Out-of-pocket Payment Exemption in the Public Health Center on Medical Utilization of the Korean Elderly.

OBJECTIVES: The distribution of hospitals in Korea is unbalanced in terms of accessibility. Many local public health centers (PHCs) exempt out-of-pocket payments (OOPs) based on local government laws to increase coverage. However, this varies across administrative regions, as many make this exemption for the elderly, while others do not. This study aimed to evaluate the effects of the OOP exemption at local PHCs among elderly individuals. METHODS: This study used online data on Korean national law to gather information on individual local governments' regulations regarding OOP exemptions. Individual-level data were gathered from the 2018 Community Health Survey and regional-level data from public online sources. RESULTS: The study analyzed 132 regions and 44 918 elderly people. A statistical analysis of rate differences and 2-level multiple logistic regression were carried out. The rate difference according to whether elderly individuals resided in areas with the OOP exemption was 1.97%p (95% confidence interval [CI], 1.07 to 2.88) for PHC utilization, 1.37%p (95% CI, 0.67 to 2.08) for hypertension treatment, and 2.19%p (95% CI, 0.63 to 3.74) for diabetes treatment. The regression analysis showed that OOP exemption had an effect on hypertension treatment, with a fixed-effect odds ratio of 1.25 (95% CI, 1.05 to 1.48). CONCLUSIONS: The OOP exemption at PHCs can affect medical utilization in Korea, especially for hypertension treatment. The OOP exemption should be expanded to improve healthcare utilization in Korea.

Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends.

BACKGROUND: Sexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs. OBJECTIVE: We aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model's predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data. METHODS: Deidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with "STD symptoms," and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age. RESULTS: For gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: r=0.65; gonorrhea: r=0.72) but correlated highly (chlamydia: r=0.90; gonorrhea: r=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (r=0.79), 2013 (r=0.77), 2016 (0.80), and 2017 (r=0.84), with 2011, 2014, and 2015 showing moderate correlations (r=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years. CONCLUSIONS: Integrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response.

Finding the Edges of Problems: Social Media as an Exploratory Research Tool.

Social media use in public health and other health related research applications has seen a rapid increase in recent years. However, there has been very limited utilization of this growing digital sector in agricultural injury research. Social media offers immense potential in gathering informal data, both text and images, converting them into knowledge, which can open up avenues for research, policy, and practice. There are a number of ways social media data can be utilized in agricultural injury research. This paper touches on the adoption of these data sources in health research and discusses the use of social media as an exploratory research tool that can peer into and identify the edges of potential health and safety problems.

An Overview of Social Media Use in the Field of Public Health Nutrition: Benefits, Scope, Limitations, and a Latin American Experience.

Social media platforms are low-cost tools that can be used to address issues in public health nutrition, especially in countries where health-related institutions experience economic limitations. We aimed to emphasize the benefits of using social media to promote health that have been documented to date. To show social media's positive impact on population health literacy, we briefly describe an inexpensive systematic communication strategy implemented in our research center through 2 social media platforms, the lessons learned, and the strategy's short-term results. Because social media use in public health is a new field of study, this perspective also focuses on the current limitations and gaps in evidence that need to be addressed to translate the best practices into policy recommendations. In conclusion, the perspective highlights the role that health actors and governments should take to maximize the benefits of social media use.

Mitigating the Twin Threats of Climate-Driven Atlantic Hurricanes and COVID-19 Transmission.

The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations - proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.

Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study.

BACKGROUND: Many public health departments use record linkage between surveillance data and external data sources to inform public health interventions. However, little guidance is available to inform these activities, and many health departments rely on deterministic algorithms that may miss many true matches. In the context of public health action, these missed matches lead to missed opportunities to deliver interventions and may exacerbate existing health inequities. OBJECTIVE: This study aimed to compare the performance of record linkage algorithms commonly used in public health practice. METHODS: We compared five deterministic (exact, Stenger, Ocampo 1, Ocampo 2, and Bosh) and two probabilistic record linkage algorithms (fastLink and beta record linkage [BRL]) using simulations and a real-world scenario. We simulated pairs of datasets with varying numbers of errors per record and the number of matching records between the two datasets (ie, overlap). We matched the datasets using each algorithm and calculated their recall (ie, sensitivity, the proportion of true matches identified by the algorithm) and precision (ie, positive predictive value, the proportion of matches identified by the algorithm that were true matches). We estimated the average computation time by performing a match with each algorithm 20 times while varying the size of the datasets being matched. In a real-world scenario, HIV and sexually transmitted disease surveillance data from King County, Washington, were matched to identify people living with HIV who had a syphilis diagnosis in 2017. We calculated the recall and precision of each algorithm compared with a composite standard based on the agreement in matching decisions across all the algorithms and manual review. RESULTS: In simulations, BRL and fastLink maintained a high recall at nearly all data quality levels, while being comparable with deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. All the deterministic algorithms had a shorter average computation time than the probabilistic algorithms. BRL had the slowest overall computation time (14 min when both datasets contained 2000 records). In the real-world scenario, BRL had the lowest trade-off between recall (309/309, 100.0%) and precision (309/312, 99.0%). CONCLUSIONS: Probabilistic record linkage algorithms maximize the number of true matches identified, reducing gaps in the coverage of interventions and maximizing the reach of public health action.

Validation of an Automated and Adjustable Blood Pressure System for Use with a Public Health Station.

OBJECTIVE: A new automated and adjustable blood pressure (BP) system has been developed to improve the accuracy of BP measurements on public-use health stations. This self-fitting BP system includes a mechanical cuff that wraps down to the user's arm prior to bladder inflation. The purpose of this study was to validate the adaptable BP system (ABPS) using the current standards from the Association for the Advancement of Medical Instrumentation (AAMI). METHODS: The AAMI/ISO 81060:2013 standards for clinical validation of non-invasive automated arterial BP measurement devices were followed precisely using the same arm sequential method. For each participant, BP was measured over multiple trials by trained observers alternating a reference sphygmomanometer with the ABPS. All study requirements were met with 85 qualifying participants, each with 3 valid paired determinations. RESULTS: The mean difference between ABPS BP and reference BP using all 255 paired determinations was -2.4 ± 7.7 mmHg for systolic and 1.7 ± 5.7 mmHg for diastolic. The standard deviation of the averaged paired determinations per participant was 6.3 mmHg for systolic and 5.2 mmHg for diastolic. Arm circumference measurements had a mean error of -2.1 ± 2.4 cm (R2 = 0.87). A new prediction model for arm circumference was validated using a holdout dataset (R2 = 0.94). CONCLUSION: The results of the study confirm that the ABPS met all benchmarks established by the AAMI. The device accurately measures BP across a wide range of arm circumferences (24-44 cm) and is suitable for use by individuals to self-monitor BP.

Needling Policy Makers and Sharpening the Debate: Do Syringe Exchange Programs Improve Health at the Population Level?

CONTEXT: In the midst of the current opioid epidemic, states have selected differing legislative routes implementing pathways to ensure access to clean needles and syringes. OBJECTIVE: To determine whether states that implemented laws supporting syringe exchange programs (SEPs) had reductions in transmission rates of hepatitis B, hepatitis C, and HIV infection compared with states without such laws. DESIGN AND SETTING: Utilizing a longitudinal panel design, we determined the legal status of SEPs in each state for years 1983-2016. Disease transmission rates for this period were estimated via a simple Poisson regression, with transmitted cases as the dependent variable, law categories as the predictor variables, and the log of state population as the exposure. The mean number of incident cases per state-year was also calculated. PARTICIPANTS: US states were utilized as the unit of analysis. RESULTS: Hepatitis B and hepatitis C mean transmission rate per 100 000 population declined in states with local ordinances/decriminalized statutes and legalized SEPs (hepatitis B: 71% and 81%, respectively, differences P < .001; hepatitis C: 8% and 38%, respectively, differences P < .001). Reductions in mean incident cases per state-year mirrored these findings. HIV infection among injection drug users yielded inconsistent results. CONCLUSIONS: Hepatitis B and hepatitis C transmission were reduced at the population level in states with SEP laws in a pattern reflecting the degree of legal intervention. HIV infection, based upon a smaller data set, showed a mixed impact. POLICY IMPLICATIONS: The results show promise that SEPs have population-level effects on disease transmission. States lacking SEPs should reconsider current policies.