Accessibility of HIV Services in Philadelphia: Location-Allocation Analysis.

INTRODUCTION: As the first step in the HIV care continuum, timely diagnosis is central to reducing transmission of the virus and ending the HIV epidemic. Studies have shown that distance from a testing site is essential for ease of access to services and educational material. This study shows how location-allocation analysis can be used to improve allocation of HIV testing services utilizing existing publicly available data from 2015 to 2019 on HIV prevalence, testing site location, and factors related to HIV in Philadelphia, Pennsylvania. METHODS: The ArcGIS Location-Allocation analytic tool was used to calculate locations for HIV testing sites using a method that minimizes the distance between demand-point locations and service facilities. ZIP code level demand was initially specified on the basis of the percentage of late HIV diagnoses and in a sensitivity analysis on the basis of a composite of multiple factors. Travel time and distance from demand to facilities determined the facility location allocation. This analysis was conducted from 2021 to 2022. RESULTS: Compared with the 37 facilities located in 20 (43%) Philadelphia ZIP codes, the model proposed reallocating testing facilities to 37 (79%) ZIP codes using percent late diagnoses to define demand. On average, this would reduce distance to the facilities by 65% and travel time to the facilities by 56%. Results using the sensitivity analysis were similar. CONCLUSIONS: A wider distribution of HIV testing services across the city of Philadelphia may reduce distance and travel time to facilities, improve accessibility of testing, and in turn increase the percentage of people with knowledge of their status.

Partisan Polarization of Childhood Vaccination Policies, 1995‒2020.

Objectives. To examine trends in partisan polarization of childhood vaccine bills and the impact of polarization on bill passage in the United States. Methods. We performed content analysis on 1497 US state bills (1995-2020) and obtained voting returns for 228 legislative votes (2011‒2020). We performed descriptive and statistical analyses using 2 measures of polarization. Results. Vote polarization rose more rapidly for immunization than abortion or veterans' affairs bills. Bills in 2019-2020 were more than 7 times more likely to be polarized than in 1995-1996 (odds ratio [OR] = 7.04; 95% confidence interval [CI] = 3.54, 13.99). Bills related to public health emergencies were more polarized (OR = 1.76; 95% CI = 1.13, 2.75). Sponsor polarization was associated with 34% lower odds of passage (OR = 0.66; 95% CI = 0.42, 1.03). Conclusions. State lawmakers were more divided on vaccine policy, but partisan bills were less likely to pass. Bill characteristics associated with lower polarization could signal opportunities for future bipartisanship. Public Health Implications. Increasing partisan polarization could alter state-level vaccine policies in ways that jeopardize childhood immunization rates or weaken responsiveness during public health emergencies. Authorities should look for areas of bipartisan agreement on how to maintain vaccination rates. (Am J Public Health. 2022;112(10):1471-1479. https://doi.org/10.2105/AJPH.2022.306964).

Towards Eliminating Nonmedical Vaccination Exemptions Among School-Age Children.

The increase in childhood vaccine hesitancy and corresponding use of nonmedical exemptions to abstain from vaccination has deleteriously impacted the public's health. This has many in the field calling for widespread elimination of nonmedical school-entry exemptions, as has been done in six states to date: West Virginia, Mississippi, California, New York, Maine, and Connecticut. By eliminating nonmedical exemptions, vaccination rates can be improved, with the corresponding decline in vaccine-preventable disease incidence. Yet the path towards widespread adoption of these policies presents legislative and judicial implications which evolve with the changing political landscape. In this this article, we discuss legislative actions concerning the expansion of exemptions, whether the widespread elimination of nonmedical exemptions would be effective from a practical and legal end, and how the COVID-19 pandemic has influenced such legislation, with specific focus on Delaware.

Disparities of COVID-19 and HIV Occurrence Based on Neighborhood Infection Incidence in Philadelphia, Pennsylvania.

Objectives. To evaluate the occurrence of HIV and COVID-19 infections in Philadelphia, Pennsylvania, through July 2020 and identify ecological correlates driving racial disparities in infection incidence. Methods. For each zip code tabulation area, we created citywide comparison Z-score measures of COVID-19 cases, new cases of HIV, and the difference between the scores. Choropleth maps were used to identify areas that were similar or dissimilar in terms of disease patterning, and weighted linear regression models helped identify independent ecological predictors of these patterns. Results. Relative to COVID-19, HIV represented a greater burden in Center City Philadelphia, whereas COVID-19 was more apparent in Northeast Philadelphia. Areas with a greater proportion of Black or African American residents were overrepresented in terms of both diseases. Conclusions. Although race is a shared nominal upstream factor that conveys increased risk for both infections, an understanding of separate structural, demographic, and economic risk factors that drive the overrepresentation of COVID-19 cases in racial/ethnic communities across Philadelphia is critical. Public Health Implications. Difference-based measures are useful in identifying areas that are underrepresented or overrepresented with respect to disease occurrence and may be able to elucidate effective or ineffective mitigation strategies. (Am J Public Health. 2022;112(3):408-416. https://doi.org/10.2105/AJPH.2021.306538).

Effect of Adjustment for Case Misclassification and Infection Date Uncertainty on Estimates of COVID-19 Effective Reproduction Number.

BACKGROUND: Surveillance data captured during the COVID-19 pandemic may not be optimal to inform a public health response, because it is biased by imperfect test accuracy, differential access to testing, and uncertainty in date of infection. METHODS: We downloaded COVID-19 time-series surveillance data from the Colorado Department of Public Health & Environment by report and illness onset dates for 9 March 2020 to 30 September 2020. We used existing Bayesian methods to first adjust for misclassification in testing and surveillance, followed by deconvolution of date of infection. We propagated forward uncertainty from each step corresponding to 10,000 posterior time-series of doubly adjusted epidemic curves. The effective reproduction number (Rt), a parameter of principal interest in tracking the pandemic, gauged the impact of the adjustment on inference. RESULTS: Observed period prevalence was 1.3%; median of the posterior of true (adjusted) prevalence was 1.7% (95% credible interval [CrI]: 1.4%, 1.8%). Sensitivity of surveillance declined over the course of the epidemic from a median of 88.8% (95% CrI: 86.3%, 89.8%) to a median of 60.8% (95% CrI: 60.1%, 62.6%). The mean (minimum, maximum) values of Rt were higher and more variable by report date, 1.12 (0.77, 4.13), compared to those following adjustment, 1.05 (0.89, 1.73). The epidemic curve by report date tended to overestimate Rt early on and be more susceptible to fluctuations in data. CONCLUSION: Adjusting for epidemic curves based on surveillance data is necessary if estimates of missed cases and the effective reproduction number play a role in management of the COVID-19 pandemic.

Misinformation

In their article, the authors acknowledged that misinformation (disseminated via social media) is damaging and sows distrust in public health: this has been well established.2 Misinformation and its more nefarious relative, disinformation, are indeed a problem for public health scientists whose interest is promoting health. AstraZeneca's release of their coronavirus disease 2019 vaccine clinical trial protocol is a proactive example (an "inoculant" in the framework's terminology) of transparency to strengthen public confidence.5 An open and transparent science is crucial in the era of the "reproducibility crisis. From "infodemics" to health promotion: a novel framework for the role of social media in public health.

A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.

Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate due to undercounting and misdiagnosing. Using a Bayesian approach, we sought to reduce bias in the estimates of prevalence of COVID-19 in Philadelphia, PA at the ZIP code level. After evaluating various modeling approaches in a simulation study, we estimated true prevalence by ZIP code with and without conditioning on an area deprivation index (ADI). As of June 10, 2020, in Philadelphia, the observed citywide period prevalence was 1.5%. After accounting for bias in the surveillance data, the median posterior citywide true prevalence was 2.3% when accounting for ADI and 2.1% when not. Overall the median posterior surveillance sensitivity and specificity from the models were similar, about 60% and more than 99%, respectively. Surveillance of COVID-19 in Philadelphia tends to understate discrepancies in burden for the more affected areas, potentially misinforming mitigation priorities.

It can be dangerous to take epidemic curves of COVID-19 at face value.

During an epidemic with a new virus, we depend on modelling to plan the response: but how good are the data? The aim of our work was to better understand the impact of misclassification errors in identification of true cases of COVID-19 on epidemic curves. Data originated from Alberta, Canada (available on 28 May 2020). There is presently no information of sensitivity (Sn) and specificity (Sp) of laboratory tests used in Canada for the causal agent for COVID-19. Therefore, we examined best attainable performance in other jurisdictions and similar viruses. This suggested perfect Sp and Sn 60-95%. We used these values to re-calculate epidemic curves to visualize the potential bias due to imperfect testing. If the sensitivity improved, the observed and adjusted epidemic curves likely fall within 95% confidence intervals of the observed counts. However, bias in shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. These issues are minor early in the epidemic, but hundreds of undiagnosed cases are likely later on. It is therefore hazardous to judge progress of the epidemic based on observed epidemic curves unless quality of testing is better understood.

Application of state law in the public health emergency response to COVID-19: an example from Delaware in the United States.

The unprecedented COVID-19 pandemic of 2019-2020 generated an equally unprecedented response from government institutions to control contagion. These legal responses included shelter in place orders, closure of non-essential businesses, limiting public gatherings, and mandatory mask wearing, among others. The State of Delaware in the United States experienced an outbreak later than most states but a particularly intense one that required a rapid and effective public health response. We describe the ways that Delaware responded through the interplay of public health, law, and government action, contrasting the state to others. We discuss how evolution of this state's public heath legal response to the pandemic can inform future disease outbreak policies.

Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA.

BACKGROUND: Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA. METHODS: We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test. RESULTS: Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate. CONCLUSION: The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.