Public Metrics Research Articles

Территориальное общественное самоуправление в развитии системы управления социально-экономическими процессами в регионе

The validity of the topic directly correlates with the changes in federal, regional, and municipal agendas which have covered the issues of residents-driven decision making at the municipal level (territorial public self-government, participatory investments, development of the municipal public councils (chambers), establishment of open-access platforms like “Upravliaem Vmeste”, “Aktivnyi Grazhdanin”, residents-driven public control over the authorities and budget spending) over the recent decade. This particularly raises scientific interest and focuses on issues in terms of a scientific discourse. Trends in the municipal economy and residents-driven territorial transformations lead to a detailed analysis of the social economic context of the territorial public self-government development and its impact on the region. Therefore, this defines territorial public self-government as a subject of research in the particular regions and municipal units to quantify the current trends and territorial public self-government provisions as an economic constituency of a municipal unit. Methodologically, the study is based on statistical (evaluation of dynamic series of indicators) and correlational (diagnostics of development interconnections) analysis. This paper theoretically justifies territorial public self-government as a phenomenon with a social economic context and proves the statement that territorial public self-government is being developed as an economic constituency of a municipal unit. To do this, the authors conducted a correlational analysis of territorial public self-government development quantitative metrics and some indicators of social economic development of Perm Krai municipal units (a model region). The study covers 2017–2020 data, which is considered to be reliable under the correlational analysis methodology. The authors managed to reveal a consistent positive correlation between the territorial public self-government indicators, and some indicators of economic and social development over several years. The paper describes correlational connections over the territories. The study identified groups of municipal units with consistent negative or positive correlation coefficients. This leads to the following conclusions: territorial public self-government can be looked upon as an economic constituency, a part of a municipal economy in terms of the connections between the management of a municipal unit and implementation of some aspects of social economic policy; particular cases with territorial features should be analyzed to focus on a consistent positive correlation revealed in the analyzed municipal units.

LCIA Formatter.

SummaryLife Cycle Assessment (LCA) is an established and standardized methodology to comprehensively assess environmental and public health metrics across industries and products (International Organization for Standardization, 2006). The United States Environmental Protection Agency (USEPA) is developing an open source LCA tool ecosystem (Ingwersen, 2019). The ecosystem includes tools to automate the creation of life cycle inventory (LCI) datasets, which account for flows to and from nature for steps across the life cycle of products or services, and tools for life cycle impact assessment (LCIA) to support classification and characterization of the cumulative LCI to potential impacts. Impacts are expressed via indicators, either midpoint or endpoint, corresponding to different points on the environmental cause-effect chain model (Frischknecht & Jolliet, 2016). This paper describes a USEPA LCA ecosystem tool ‘LCIA formatter’ that extracts LCIA information from original source methods and converts the data for interoperability with the rest of the USEPA LCA ecosystem tools.

Do Charter Schools Outspend Public Schools Online? Evidence from Texas

ABSTRACT As open enrollment public charter school districts have continued to grow in Texas, researchers and policymakers have continued to question how these public charter school districts spend public taxpayer dollars. Although extensive research has addressed how public charter school districts spend money to recruit teachers, complete capital projects, and hire school leaders, no studies have examined how public charter school districts compete with traditional public school districts online to drive traffic and interest in their schools. Using Texas Education Agency and SEMrush data (web metrics, such as organic keywords, organic traffic, and traffic cost) from the 2018–2019 school year, this study compares public charter school district and traditional public school district web metrics to learn more about how charter school districts continue to open and drive enrollment. Results suggest that, when compared to traditional public school districts in Texas, public charter school districts in Texas are less web popular by keyword search (p < .00) and traffic (p < .00), yet these districts spend more on driving Internet traffic to their websites (p < .00) than traditional public school districts. Implications for research, policy, and competition between open enrollment public charter school districts and traditional public school districts are addressed.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19.

The role of meteorological and air quality factors in moderating the transmission of SARS‐CoV‐2 and severity of COVID‐19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID‐19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID‐19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID‐19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Using Network Analysis and Machine Learning to Identify Virus Spread Trends in COVID-19

The outbreak of Coronavirus Disease 2019 (COVID-19) has infected and killed millions of people globally, resulting in a pandemic with enormous global impact. This disease affects the respiratory system, and the viral agent that causes it, SARS-CoV-2, spreads through droplets of saliva, as well as through coughing and sneezing. As an extremely transmissible viral infection, COVID-19 is causing significant damage to the economies of both developed and lower- and middle-income countries because of its direct impact on the health of citizens and the containment measures taken to curtail the virus. Methods to reduce or control the spread of the virus and protect the global population are needed to avoid further deaths, long-term health issues, and prolonged economic impact. The most effective approach to reduce viral spread and avoid a substantial collapse of the health system, in the absence of vaccines, is nonpharmaceutical interventions (NPI) such as enforcing social containment restrictions, monitoring overall population mobility, implementing widespread viral testing, and increasing hygiene measures. Our approach consists of combining network analytics with machine learning models by using a combination of anonymized health and telecommunications data to better understand the correlation between population movements and virus spread. This approach, called location network analysis (LNA), allows for accurate prediction of possible new outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health metrics and can be used either to intensify social containment policies to avoid further spread or to ease them to reopen the economy. LNA can also help to retrospectively evaluate the effectiveness of policy responses to COVID-19.

How hospitals can improve their public quality metrics: a decision-theoretic model.

The public reporting of hospitals' quality of care is providing additional motivation for hospitals to deliver high-quality patient care. Hospital Compare, a consumer-oriented website by the Centers for Medicare and Medicaid Services (CMS), provides patients with detailed quality of care data on most US hospitals. Given that many quality metrics are the aggregate result of physicians' individual clinical decisions, the question arises if and how hospitals could influence their physicians so that their decisions positively contribute to hospitals' quality goals. In this paper, we develop a decision-theoretic model to explore how three different hospital interventions-incentivization, training, and nudging-may affect physicians' decisions. We focus our analysis on Outpatient Measure 14 (OP-14), which is an imaging quality metric that reports the percentage of outpatients with a brain computed tomography (CT) scan, who also received a same-day sinus CT scan. In most cases, same-day brain and sinus CT scans are considered unnecessary, and high utilizing hospitals aim to reduce their OP-14 metric. Our model captures the physicians' imaging decision process accounting for medical and behavioral factors, in particular the uncertainty in clinical assessment and a physician's diagnostic ability. Our analysis shows how hospital interventions of incentivization, training, and nudging affect physician decisions and consequently OP-14. This decision-theoretic model provides a foundation to develop insights for policy makers on the multi-level effects of their policy decisions.

Estimates of drug treated diabetes incidence and prevalence using Australian administrative pharmaceutical data.

IntroductionThe incidence and prevalence of diabetes within a population are important public health metrics. Pharmaceutical administrative data may offer a resource that can contribute to quantifying these measures using the recorded signals derived from the drugs used to treat people with diabetes.ObjectiveTo estimate the longitudinal incidence and prevalence of drug treated (DT) diabetes in Australia utilising an Australian Pharmaceutical Benefits Scheme (PBS) dataset and compare estimates with community survey data for all diabetes reported in the Australian National Health Survey (NHS).MethodsPersons with DT diabetes were identified within the PBS dataset using assigned Anatomic Therapeutic Chemical codes for ‘Drugs used in diabetes’. Prevalent persons with DT diabetes were determined by a single annual treatment, and incident cases from the earliest treatment with diabetes medications. Counts were aggregated by age group and utilised Australian national census data as a denominator to calculate diabetes disease frequencies for the period 2004–14. Comparison of PBS prevalence data was made with NHS surveys over equivalent years.ResultsThe age adjusted incidence of DT diabetes was 3.4/1000 in 2006 and increased to 3.8/1000 in 2011 and 5.1/1000 in 2014. Age adjusted prevalence of DT diabetes in Australia also rose from 26.7/1000 in 2006 to 32.1/1000 in 2011 and 42.1/1000 in 2014. DT diabetes prevalence estimates correlated with NHS estimates of self-reported diabetes prevalence across age groups and in 2014 was r = 0.987. However, PBS estimates of DT diabetes prevalence generally underestimated NHS values of self-reported diabetes in older age groups with mean percentage differences of –22% to –3%. In contrast, PBS data captured more younger persons with diabetes in comparison to NHS data. These differences were then used to adjust DT diabetes incidence rates to provide age specific estimates that could potentially reflect diabetes incidence estimates acquired by community survey.ConclusionsPBS data representing dispensed medications prescribed to persons with diabetes offers a perspective for the assessment of diabetes incidence and prevalence. PBS derived DT diabetes prevalence estimates correlate well with community survey estimates of self-reported diabetes, but underestimate NHS data in older age groups. Calibrated DT incidence estimates may potentially reflect community survey derived diabetes incidence estimates and may offer a method for longitudinal monitoring.

The Evolution of Gambling-Related Harm Measurement: Lessons from the Last Decade.

Jurisdictions around the world have a self-declared mandate to reduce gambling-related harm. However, historically, this concept has suffered from poor conceptualisation and operationalisation. However, recent years have seen swift advances in measuring gambling harm, based on the principle of it being a quantifiable decrement to the health and wellbeing of the gambler and those connected to them. This review takes stock of the background and recent developments in harm assessment and summarises recent research that has validated and applied the Short Gambling Harms Screen and related instruments. We recommend that future work builds upon the considerable psychometric evidence accumulated for the feasibility of direct elicitation of harmful consequences. We also advocate for grounding harms measures with respect to scalar changes to public health utility metrics. Such an approach will avoid misleading pseudo-clinical categorisations, provide accurate population-level summaries of where the burden of harm is carried, and serve to integrate gambling research with the broader field of public health.

Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders because it provides brain biomarkers. However, only highly trained doctors can interpret EEG signals due to its complexity. Machine learning has been successfully trained with EEG signals for classifying mental disorders, but a time consuming and disorder-dependant feature engineering (FE) and subsampling process is required over raw EEG data. Deep Learning (DL) is positioned as a prominent research field to process EEG data because (i) it features automated FE by taking advantage of raw EEG signals improving results and (ii) it can be trained over the vast amount of data generated by EEG. In this work, a systematic mapping study has been performed with 46 carefully selected primary studies. Our goals were (i) to provide a clear view of which are the most prominent study topics in diagnosis and prognosis of mental disorders by using EEG with DL, and (ii) to give some recommendations for future works. Some results are: epilepsy was the predominant mental disorder present in around half of the studies, convolutional neural networks also appear in approximate 50% of the works. The main conclusions are (i) processing EEG with DL to detect mental disorders is a promising research field and (ii) to objectively compare performance between studies: public datasets, intra-subject validation, and standard metrics should be used. Additionally, we suggest to pay more attention to ease the reproducibility, and to use (when possible) an available framework to explain the results of the created DL models.

Estimated seroprevalence of SARS-CoV-2 antibodies among adults in Orange County, California

Clinic-based estimates of SARS-CoV-2 may considerably underestimate the total number of infections. Access to testing in the US has been heterogeneous and symptoms vary widely in infected persons. Public health surveillance efforts and metrics are therefore hampered by underreporting. We set out to provide a minimally biased estimate of SARS-CoV-2 seroprevalence among adults for a large and diverse county (Orange County, CA, population 3.2 million). We implemented a surveillance study that minimizes response bias by recruiting adults to answer a survey without knowledge of later being offered SARS-CoV-2 test. Several methodologies were used to retrieve a population-representative sample. Participants (n = 2979) visited one of 11 drive-thru test sites from July 10th to August 16th, 2020 (or received an in-home visit) to provide a finger pin-prick sample. We applied a robust SARS-CoV-2 Antigen Microarray technology, which has superior measurement validity relative to FDA-approved tests. Participants include a broad age, gender, racial/ethnic, and income representation. Adjusted seroprevalence of SARS-CoV-2 infection was 11.5% (95% CI: 10.5–12.4%). Formal bias analyses produced similar results. Prevalence was elevated among Hispanics (vs. other non-Hispanic: prevalence ratio [PR] = 1.47, 95% CI 1.22–1.78) and household income < $50,000 (vs. > $100,000: PR = 1.42, 95% CI: 1.14 to 1.79). Results from a diverse population using a highly specific and sensitive microarray indicate a SARS-CoV-2 seroprevalence of ~ 12 percent. This population-based seroprevalence is seven-fold greater than that using official County statistics. In this region, SARS-CoV-2 also disproportionately affects Hispanic and low-income adults.

Effectiveness of VR immersive applications for public speaking enhancement

Augmented and Virtual Reality (VR) technology has recently proved to be useful in many learning and training scenarios. VR applications designed to practice communication skills (also known as Public Speaking Training or PST) are currently among the newest and still-unexplored solutions whose effectiveness is still to be tested. The current paper evaluates the Quality of Experience for a public speaking VR system where speakers can experience a talk session in front of a wide audience that actively reacts to their statements. A double stimulus experiment (with a real and virtual audience) was carried out in order to measure the visual quality, the immersiveness and the effectiveness of the approach. Objective evaluations, users’ feedback and public speaking metrics showed that the VR set-up enhanced speakers’ gesture and speech control when compared to performing in front of a real audience.

SARS-CoV-2 Surveillance in the Middle East and North Africa: Longitudinal Trend Analysis.

BackgroundThe COVID-19 pandemic has disrupted the lives of millions and forced countries to devise public health policies to reduce the pace of transmission. In the Middle East and North Africa (MENA), falling oil prices, disparities in wealth and public health infrastructure, and large refugee populations have significantly increased the disease burden of COVID-19. In light of these exacerbating factors, public health surveillance is particularly necessary to help leaders understand and implement effective disease control policies to reduce SARS-CoV-2 persistence and transmission.ObjectiveThe goal of this study is to provide advanced surveillance metrics, in combination with traditional surveillance, for COVID-19 transmission that account for weekly shifts in the pandemic speed, acceleration, jerk, and persistence to better understand a country’s risk for explosive growth and to better inform those who are managing the pandemic. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed.MethodsUsing a longitudinal trend analysis study design, we extracted 30 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in MENA as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsThe regression Wald statistic was significant (χ25=859.5, P<.001). The Sargan test was not significant, failing to reject the validity of overidentifying restrictions (χ2294=16, P=.99). Countries with the highest cumulative caseload of the novel coronavirus include Iran, Iraq, Saudi Arabia, and Israel with 530,380, 426,634, 342,202, and 303,109 cases, respectively. Many of the smaller countries in MENA have higher infection rates than those countries with the highest caseloads. Oman has 33.3 new infections per 100,000 population while Bahrain has 12.1, Libya has 14, and Lebanon has 14.6 per 100,000 people. In order of largest to smallest number of cumulative deaths since January 2020, Iran, Iraq, Egypt, and Saudi Arabia have 30,375, 10,254, 6120, and 5185, respectively. Israel, Bahrain, Lebanon, and Oman had the highest rates of COVID-19 persistence, which is the number of new infections statistically related to new infections in the prior week. Bahrain had positive speed, acceleration, and jerk, signaling the potential for explosive growth.ConclusionsStatic and dynamic public health surveillance metrics provide a more complete picture of pandemic progression across countries in MENA. Static measures capture data at a given point in time such as infection rates and death rates. By including speed, acceleration, jerk, and 7-day persistence, public health officials may design policies with an eye to the future. Iran, Iraq, Saudi Arabia, and Israel all demonstrated the highest rate of infections, acceleration, jerk, and 7-day persistence, prompting public health leaders to increase prevention efforts.

The emerging role of measuring and quantifying infectious disease preparedness: A comparative analysis of four public health preparedness metrics

The emerging role of measuring and quantifying infectious disease preparedness: A comparative analysis of four public health preparedness metrics

Deep learning techniques for recommender systems based on collaborative filtering

AbstractIn the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

Macromarketing Metrics of Consumer Well-Being: An Update

This commentary updates much of the research related to the various constructs and metrics of consumer well-being. Specifically, I review the research to date concerning several conceptualizations and metrics of consumer well-being from both public and academic sectors. Public sector metrics include Cost of Living, Total Consumption Expenditure, Consumer Complaint, and Quality. Selected contributions to consumer well-being metrics from the academic sector include Shopping Satisfaction, Shopping Well-Being, Possession Satisfaction, Acquisition/Possession Satisfaction, Consumption Life Cycle, Community, Need Satisfaction, Perceived Value in Life, and Bottom-up Spillover. I then provide some thoughts about how these metrics can be improved and ideas that may spur future research. In doing so, I address issues related to construct validity to public sector metrics by demonstrating association with human development measures. Metrics from the academic sector can also be improved by testing association with newly established constructs of well-being (e.g., eudaimonia, social well-being) and introducing moderating effects of individual differences (e.g., consumer involvement, consumer lifestyle) as well as product-related differences (e.g., experiential versus material consumption; marketplace activities related to luxury versus nonluxury goods and services; and products beneficial to society versus “sin” products).

Quantitative analysis of SARS-CoV-2 RNA from wastewater solids in communities with low COVID-19 incidence and prevalence

In the absence of an effective vaccine to prevent COVID-19 it is important to be able to track community infections to inform public health interventions aimed at reducing the spread and therefore reduce pressures on health-care, improve health outcomes and reduce economic uncertainty. Wastewater surveillance has rapidly emerged as a potential tool to effectively monitor community infections through measuring trends of RNA signal in wastewater systems. In this study SARS-CoV-2 viral RNA N1 and N2 gene regions are quantified in solids collected from influent post grit solids (PGS) and primary clarified sludge (PCS) in two water resource recovery facilities (WRRF) serving Canada's national capital region, i.e., the City of Ottawa, ON (pop. ≈ 1.1M) and the City of Gatineau, QC (pop. ≈ 280K). PCS samples show signal inhibition using RT-ddPCR compared to RT-qPCR, with PGS samples showing similar quantifiable concentrations of RNA using both assays. RT-qPCR shows higher frequency of detection of N1 and N2 gene regions in PCS (92.7, 90.6%, n = 6) as compared to PGS samples (79.2, 82.3%, n = 5). Sampling of PCS may therefore be an effective approach for SARS-CoV-2 viral quantification, especially during periods of declining and low COVID-19 incidence in the community. The pepper mild mottle virus (PMMoV) is determined to have a less variable RNA signal in PCS over a three month period for two WRRFs, regardless of environmental conditions, compared to Bacteroides 16S rRNA or human 18S rRNA, making PMMoV a potentially useful biomarker for normalization of SARS-CoV-2 signal. PMMoV-normalized PCS RNA signal from WRRFs of two cities correlated with the regional public health epidemiological metrics, identifying PCS normalized to a fecal indicator (PMMoV) as a potentially effective tool for monitoring trends during decreasing and low-incidence of infection of SARS-Cov-2 in communities.

Historical Document Image Binarization: A Review

This review provides a comprehensive view of the field of historical document image binarization with a focus on the contributions made in the last decade. After the introduction of a standard benchmark dataset with the 2009 Document Image Binarization Contest, research in the field accelerated. Besides the standard methods for image thresholding, preprocessing, and post-processing, we review the literature on methods such as statistical models, pixel classification with learning algorithms, and parameter tuning. In addition to reviewing binarization algorithms, we discuss the available public datasets and evaluation metrics, including those that require pixel-level ground truth and those that do not. We conclude with recommendations for future work.

Public Performance Metrics: Driving Physician Motivation and Performance.

IntroductionAs providers transition from “fee-for-service” to “pay-for-performance” models, focus has shifted to improving performance. This trend extends to the emergency department (ED) where visits continue to increase across the United States. Our objective was to determine whether displaying public performance metrics of physician triage data could drive intangible motivators and improve triage performance in the ED.MethodsThis is a single institution, time-series performance study on a physician-in-triage system. Individual physician baseline metrics—number of patients triaged and dispositioned per shift—were obtained and prominently displayed with identifiable labels during each quarterly physician group meeting. Physicians were informed that metrics would be collected and displayed quarterly and that there would be no bonuses, punishments, or required training; physicians were essentially free to do as they wished. It was made explicit that the goal was to increase the number triaged, and while the number dispositioned would also be displayed, it would not be a focus, thereby acting as this study’s control. At the end of one year, we analyzed metrics.ResultsThe group’s average number of patients triaged per shift were as follows: Q1–29.2; Q2–31.9; Q3–34.4; Q4–36.5 (Q1 vs Q4, p < 0.00001). The average numbers of patients dispositioned per shift were Q1–16.4; Q2–17.8; Q3–16.9; Q4–15.3 (Q1 vs Q4, p = 0.14). The top 25% of Q1 performers increased their average numbers triaged from Q1–36.5 to Q4–40.3 (ie, a statistically insignificant increase of 3.8 patients per shift [p = 0.07]). The bottom 25% of Q1 performers, on the other hand, increased their averages from Q1–22.4 to Q4–34.5 (ie, a statistically significant increase of 12.2 patients per shift [p = 0.0013]).ConclusionPublic performance metrics can drive intangible motivators (eg, purpose, mastery, and peer pressure), which can be an effective, low-cost strategy to improve individual performance, achieve institutional goals, and thrive in the pay-for-performance era.

Pathologists’ Participation and Performance in the Medicare Physician Quality Reporting System

Abstract Objectives Public reporting of quality metrics has become increasingly important for pathologists to demonstrate excellence of care and to secure value-based payments from both public and private payers. This study evaluated the participation and performance of pathologists in the Medicare Physician Quality Reporting System (PQRS). Methods This study conducted a retrospective review of the Physician Compare data files from 2016. Specifically, pathologists enrolled in the Physician Compare Initiative were identified and merged with quality data using uniquely assigned National Provider Identifiers. Descriptive statistics and multiple logistic regression were performed using Stata (version 12.1) statistical software (StataCorp, College Station, TX). Results Among the 11,637 pathologists identified in the Physician Compare Initiative, 8,408 pathologists (73.3%) participated in the PQRS. Regression analysis indicated that female gender, foreign medical graduate (FMG), and a larger number of employees in the affiliated organization were associated with a lower likelihood of participating in the PQRS. To protect patient privacy, the Centers for Medicare and Medicaid Services (CMS) only released the quality metrics of 353 pathologists, which were computed based on 20 or more Medicare beneficiaries. The three most commonly reported quality metrics included PQRS#249 Barrett’s Esophagus, PQRS#395 Lung Cancer Reporting (Biopsy/Cytology Specimens), and PQRS#397 Melanoma Reporting. The mean performance rates for the three most common metrics were 98.8% (SD 8.3%), 96.8% (SD 11.9%), and 97.4% (SD 6.9%), respectively. Conclusion A majority of pathologists participated in the Medicare PQRS program in 2016. The performance rates of public available quality metrics remained at high levels with significant variations in some metrics. Future research should examine the quality performance of pathologists as they transition from the PQRS to the Merit-based Incentive Payment System (MIPS) under the Quality Payment Program.

A Quantitative Public Health Risk Assessment Tool for Planning for At-Risk Populations.

The Drexel University Center for Public Health Readiness and Communication developed a quantitative public health risk assessment tool that has been used in Pennsylvania and across the United States. The tool assesses hazard impacts on health care and public health metrics and incorporates the planning needs of at-risk populations. The flexible platform using Microsoft Excel allows planners to customize hazard assessment and use jurisdiction-specific data. Findings from the risk assessment can guide planning and evaluate preparedness progress over time.