Urgent Challenges for Local Public Health Informatics

This editorial highlights the urgent challenges for local public health informatics and provides solutions to face these challenges

Arterial roads and area socioeconomic status are predictors of fast food restaurant density in King County, WA

<p>Abstract</p> <p>Background</p> <p>Fast food restaurants reportedly target specific populations by locating in lower-income and in minority neighborhoods. Physical proximity to fast food restaurants has been associated with higher obesity rates.</p> <p>Objective</p> <p>To examine possible associations, at the census tract level, between area demographics, arterial road density, and fast food restaurant density in King County, WA, USA.</p> <p>Methods</p> <p>Data on median household incomes, property values, and race/ethnicity were obtained from King County and from US Census data. Fast food restaurant addresses were obtained from Public Health-Seattle & King County and were geocoded. Fast food density was expressed per tract unit area and per capita. Arterial road density was a measure of vehicular and pedestrian access. Multivariate logistic regression models containing both socioeconomic status and road density were used in data analyses.</p> <p>Results</p> <p>Over one half (53.1%) of King County census tracts had at least one fast food restaurant. Mean network distance from dwelling units to a fast food restaurant countywide was 1.40 km, and 1.07 km for census tracts containing at least one fast food restaurant. Fast food restaurant density was significantly associated in regression models with low median household income (p < 0.001) and high arterial road density (p < 0.001) but not with percent of residents who were nonwhite.</p> <p>Conclusion</p> <p>No significant association was observed between census tract minority status and fast food density in King County. Although restaurant density was linked to low household incomes, that effect was attenuated by arterial road density. Fast food restaurants in King County are more likely to be located in lower income neighborhoods and higher traffic areas.</p

Participation in One Health Networks and Involvement in the COVID-19 Pandemic Response: A Global Study

The COVID-19 pandemic exemplifies a One Health issue at the intersection of human, animal, and environmental health that requires collaboration across sectors to manage it successfully. The global One Health community includes professionals working in many different fields including human medicine, veterinary medicine, public health, ecosystem health, and, increasingly, social sciences. The aims of this cross-sectional study were to describe the involvement of the global One Health community in COVID-19 pandemic response activities. One Health networks (OHNs) have formed globally to serve professionals with common interests in collaborative approaches. We assessed the potential association between being part of an OHN and involvement in COVID-19 response activities. Data were collected in July-August 2020 using an online questionnaire that addressed work characteristics, perceived connection to OHNs, involvement in COVID-19 pandemic response activities, and barriers and facilitators to the involvement. The sample included 1,050 respondents from 94 countries across a range of organizations and work sectors including, but not restricted to, those typically associated with a One Health approach. Sixty-four percent of survey respondents indicated involvement in pandemic response activities. Being part of an OHN was positively associated with being involved in the COVID-19 response (odds ratio: 1.8, 95% confidence interval: 1.3–2.4). Lack of opportunities was a commonly reported barrier to involvement globally, with lack of funding the largest barrier in the WHO African region. This insight into diverse workforce involvement in the pandemic helps fill a gap in the global health workforce and public health education literature. An expanded understanding of the perceived roles and value of OHNs can inform targeted interventions to improve public health education and workforce capacity to prepare for and respond to public health emergencies

Disease Surveillance and Achieving Synergy In Public Health Quality Improvement

Efforts to describe and improve quality in public health are closely tied to disease surveillance systems and capabilities. This roundtable will engage participants around how a new national framework for public health quality relates to their work in surveillance. The audience will be invited to provide examples, from their experience, of feasible and practical variables to measure the priority public health aims; data sources; and gaps that are impeding progress in quality improvement. This roundtable will inform a new initiative to develop measures that resonate with the roles of public health at the local, state, federal and global levels

Knowledge Management Tools for the ISDS Community of Practice

This roundtable will seek feedback from end-users on the components needed to improve access to the shared knowledge of the ISDS Community of Practice (CoP). Participants will be organized into small groups to brainstorm and document content that could be included in an ISDS knowledge management repository. The small groups will then present summaries to all participants at the end of the session. The larger group will discuss prioritization for the knowledge management system and next steps for community engagement in this endeavor after the conference

Data Quality: A Systematic Review of the Biosurveillance Literature

OBJECTIVE: To highlight how data quality has been discussed in the biosurveillance literature in order to identify current gaps in knowledge and areas for future research. INTRODUCTION: Data quality monitoring is necessary for accurate disease surveillance. However it can be challenging, especially when “real-time” data are required. Data quality has been broadly defined as the degree to which data are suitable for use by data consumers [1]. When compromised at any point in a health information system, data of low quality can impair the detection of data anomalies, delay the response to emerging health threats [2], and result in inefficient use of staff and financial resources. While the impacts of poor data quality on biosurveillance are largely unknown, and vary depending on field and business processes, the information management literature includes estimates for increased costs amounting to 8–12% of organizational revenue and, in general, poorer decisions that take longer to make [3]. METHODS: -How has data quality been defined and/or discussed? -What measurements of data quality have been utilized? -What methods for monitoring data quality have been utilized? -What methods have been used to mitigate data quality issues? -What steps have been taken to improve data quality? The search included PubMed, ISDS and AMIA Conference Proceedings, and reference lists. PubMed was searched using the terms “data quality,” “biosurveillance,” “information visualization,” “quality control,” “health data,” and “missing data.” The titles and abstracts of all search results were assessed for relevance and relevant articles were reviewed using the structured matrix. RESULTS: The completeness of data capture is the most commonly measured dimension of data quality discussed in the literature (other variables include timeliness and accuracy). The methods for detecting data quality issues fall into two broad categories: (1) methods for regular monitoring to identify data quality issues and (2) methods that are utilized for ad hoc assessments of data quality. Methods for regular monitoring of data quality are more likely to be automated and focused on visualization, compared with the methods described as part of special evaluations or studies, which tend to include more manual validation. Improving data quality involves the identification and correction of data errors that already exist in the system using either manual or automated data cleansing techniques [4]. Several methods of improving data quality were discussed in the public health surveillance literature, including development of an address verification algorithm that identifies an alternative, valid address [5], and manual correction of the contents of databases [6]. Communication with the data entry personnel or data providers, either on a regular basis (e.g., annual report) or when systematic data entry errors are identified, was mentioned in the literature as the most common step to prevent data quality issues. CONCLUSIONS: In reviewing the biosurveillance literature in the context of the data quality field, the largest gap appears to be that the data quality methods discussed in literature are often ad hoc and not consistently implemented. Developing a data quality program to identify the causes of lower quality health data, address data quality problems, and prevent issues would allow public health departments to more efficiently and effectively conduct biosurveillance and to apply results to improving public health practice

Data Quality: A Systematic Review of the Biosurveillance Literature

A literature review of data quality issues highlights how the quality of health data has been discussed in the biosurveillance literature and frames it in relation to the broader data quality (DQ) field. Results of the literature review include: completeness as the most commonly measured dimension of DQ; methods for regular DQ monitoring and occasional evaluation; various methods of improving data quality; and communication with the data entry personnel as the most common preventative step. The results suggest that developing a DQ program could facilitate understanding the sources of poor DQ; recognizing DQ problems; and improving DQ for improved efficiency and effectiveness of biosurveillance systems

Disease Surveillance and Achieving Synergy In Public Health Quality Improvement

OBJECTIVE: To examine disease surveillance in the context of a new national framework for public health quality and to solicit input from practitioners, researchers, and other stakeholders to identify potential metrics, pivotal research questions, and actions for achieving synergy between surveillance practice and public health quality. INTRODUCTION: National efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. Measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. They also reveal opportunities for innovations and improvements. Similar quality improvement efforts in the health care system are beginning to bear fruit. There has been a need, however, for a framework for assessing public health quality that provides a standard, yet is flexible and relevant to agencies at all levels. The U.S. Health and Human Services (HHS) Office of the Assistant Secretary for Health, working with stakeholders, recently developed and released a Consensus Statement on Quality in the Public Health System that introduces a novel evaluation framework. They identified nine aims that are fundamental to public health quality improvement efforts and six cross-cutting priority areas for improvement, including population health metrics and information technology; workforce development; and evidence-based practices (1). Applying the HHS framework to surveillance expands measures for surveillance quality beyond typical variables (e.g., data quality and analytic capabilities) to desired characteristics of a quality public health system. The question becomes: How can disease surveillance help public health services to be more population centered, equitable, proactive, health-promoting, risk-reducing, vigilant, transparent, effective, and efficient—the desired features of a quality public health system? Any agency with a public health mission, or even a partial public health mission (e.g., tax-exempt hospitals), can use these measures to develop strategies that improve both the quality of the surveillance enterprise and public health systems, overall. At this time, input from stakeholders is needed to identify valid and feasible ways to measure how surveillance systems and practices advance public health quality. What exists now and where are the gaps? METHODS: Improving public health by applying quality measures to disease surveillance will require innovation and collaboration among stakeholders. This roundtable will begin a community dialogue to spark this process. The first goal will be to achieve a common focus by defining the nine quality aims identified in the HHS Consensus Statement. Attendees will draw from their experience to discuss how surveillance practice advances the public health aims and improves public health. We will also identify key research questions needed to provide evidence to inform decision-making. RESULTS: —How is surveillance used to identify and address health disparities and, thereby, make public health more equitable? What are the data sources? Are there targets? How can research and evaluation help to enhance this surveillance capability and direct action? —How do we identify and address factors that inhibit quality improvement in surveillance? What are the gaps in knowledge, skills, systems, and resources? —Where can standardization play a positive role in the evaluation of quality in public health surveillance? —How can we leverage resources by aligning national, state, and local goals? —What are the key research questions and the quality improvement projects that can be implemented using recognized models for improvement? —How can syndromic surveillance, specifically, advance the priority aims? The roundtable will conclude with a list of next steps to develop metrics that resonate with the business practices of public health at all levels