Population Segmentation Using a Novel Socio-Demographic Dataset.

Appending market segmentation data to a national healthcare knowledge, attitude and behavior survey and medical claims by geocode can provide valuable insight for providers, payers and public health entities to better understand populations at a hyperlocal level and develop cohort-specific strategies for health improvement. A prolonged use case investigates population factors, including social determinants of health, in depression and develops cohort-level management strategies, utilizing market segmentation and survey data. Survey response scores for each segment were normalized against the average national score and appended to claims data to identify at-risk segment whose scores were compared with three socio-demographically comparable but not at-risk segments via Nonparametric Mann-Whitney U test to identify specific risk factors for intervention. The marketing segment, New Melting Point (NMP), was identified as at-risk. The median scores of three comparable segments differed from NMP in "Inability to Pay For Basic Needs" (121% vs 123%), "Lack of Transportation" (112% vs 153%), "Utilities Threatened" (103% vs 239%), "Delay Visiting MD" (67% vs 181%), "Delay/Not Fill Prescription" (117% vs 182%), "Depressed: All/Most Time" (127% vs 150%), and "Internet: Virtual Visit" (55% vs 130%) (all with p<0.001). The appended dataset illustrates NMP as having many stressors (e.g., difficult social situations, delaying seeking medical care). Strategies to improve depression management in NMP could employ virtual visits, or pharmacy incentives. Insights gleaned from appending market segmentation and healthcare utilization survey data can fill in knowledge gaps from claims-based data and provide practical and actionable insights for use by providers, payers and public health entities.

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review.

BACKGROUND: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. OBJECTIVE: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. METHODS: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. RESULTS: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. CONCLUSIONS: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.

Effectiveness of non-pharmaceutical interventions related to social distancing on respiratory viral infectious disease outcomes: A rapid evidence-based review and meta-analysis.

OBJECTIVES: Non-pharmaceutical interventions (e.g. quarantine and isolation) are used to mitigate and control viral infectious disease, but their effectiveness has not been well studied. For COVID-19, disease control efforts will rely on non-pharmaceutical interventions until pharmaceutical interventions become widely available, while non-pharmaceutical interventions will be of continued importance thereafter. METHODS: This rapid evidence-based review provides both qualitative and quantitative analyses of the effectiveness of social distancing non-pharmaceutical interventions on disease outcomes. Literature was retrieved from MEDLINE, Google Scholar, and pre-print databases (BioRxiv.org, MedRxiv.org, and Wellcome Open Research). RESULTS: Twenty-eight studies met inclusion criteria (n = 28). Early, sustained, and combined application of various non-pharmaceutical interventions could mitigate and control primary outbreaks and prevent more severe secondary or tertiary outbreaks. The strategic use of non-pharmaceutical interventions decreased incidence, transmission, and/or mortality across all interventions examined. The pooled attack rates for no non-pharmaceutical intervention, single non-pharmaceutical interventions, and multiple non-pharmaceutical interventions were 42% (95% confidence interval = 30% - 55%), 29% (95% confidence interval = 23% - 36%), and 22% (95% confidence interval = 16% - 29%), respectively. CONCLUSION: Implementation of multiple non-pharmaceutical interventions at key decision points for public health could effectively facilitate disease mitigation and suppression until pharmaceutical interventions become available. Dynamics around R 0 values, the susceptibility of certain high-risk patient groups to infection, and the probability of asymptomatic cases spreading disease should be considered.

The Perceived Impact and Usability of a Care Management and Coordination System in Delivering Services to Vulnerable Populations: Mixed Methods Study.

BACKGROUND: People with complex needs, such as those experiencing homelessness, require concurrent, seamless support from multiple social service agencies. Sonoma County, California has one of the nation's largest homeless populations among largely suburban communities. To support client-centered care, the county deployed a Care Management and Coordination System (CMCS). This system comprised the Watson Care Manager (WCM), a front-end system, and Connect 360, which is an integrated data hub that aggregates information from various systems into a single client record. OBJECTIVE: The aim of this study is to evaluate the perceived impact and usability of WCM in delivering services to the homeless population in Sonoma County. METHODS: A mixed methods study was conducted to identify ways in which WCM helps to coordinate care. Interviews, observations, and surveys were conducted, and transcripts and field notes were thematically analyzed and directed by a grounded theory approach. Responses to the Technology Acceptance Model survey were analyzed. RESULTS: A total of 16 participants were interviewed, including WCM users (n=8) and department leadership members (n=8). In total, 3 interdisciplinary team meetings were observed, and 8 WCM users were surveyed. WCM provided a central shared platform where client-related, up-to-date, comprehensive, and reliable information from participating agencies was consolidated. Factors that facilitated WCM use were users' enthusiasm regarding the tool functionalities, scalability, and agency collaboration. Constraining factors included the suboptimal awareness of care delivery goals and functionality of the system among the community, sensitivities about data sharing and legal requirements, and constrained funding from government and nongovernment organizations. Overall, users found WCM to be a useful tool that was easy to use and helped to enhance performance. CONCLUSIONS: WCM supports the delivery of care to individuals with complex needs. Integration of data and information in a CMCS can facilitate coordinated care. Future research should examine WCM and similar CMCSs in diverse populations and settings.

Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.

OBJECTIVE: Identify how novel datasets and digital health technology, including both analytics-based and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement. METHODS: A state-of-the-art literature review with systematic methods was performed on MEDLINE, Embase, and the Cochrane Library databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates. RESULTS: The search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy - advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation - enrich SDoH insights using multiple data sources, and use analytics-based and AI-based methods to aggregate data; and 3) use analytics-based and AI-based methods to assess and address SDoH - retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions. CONCLUSIONS: If multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health. This study identified examples of AI-based use cases in public health informatics, and this literature is very limited.

Population Health Measurement at Centers for Medicare & Medicaid Services: Bridging the Gap Between Public Health and Clinical Quality.

As Medicare and Medicaid increasingly shift to alternative payment models focused on population-based payments, there is an urgent need to develop measures of population health that can drive health improvement. In response, an assessment and design project established a framework for developing population health measures from a payer perspective, conducted environmental scans of existing measures and available data infrastructure, and conducted a gap analysis informing measure development and infrastructure needs. The work, summarized here, makes recommendations for creating a set of core measures, demonstrates some of the key challenges in applying a traditional quality measure development framework to population health, and complements recent efforts by the National Academy of Medicine and others with a focus on a payer perspective.

Impact of Medicare's Hospital-Acquired Condition policy on infections in safety net and non-safety net hospitals.

BACKGROUND: Policymakers may wish to align healthcare payment and quality of care while minimizing unintended consequences, particularly for safety net hospitals. OBJECTIVE: To determine whether the 2008 Centers for Medicare and Medicaid Services Hospital-Acquired Conditions policy had a differential impact on targeted healthcare-associated infection rates in safety net compared with non-safety net hospitals. DESIGN: Interrupted time-series design. SETTING AND PARTICIPANTS: Nonfederal acute care hospitals that reported central line-associated bloodstream infection and ventilator-associated pneumonia rates to the Centers for Disease Control and Prevention's National Health Safety Network from July 1, 2007, through December 31, 2013. RESULTS: We did not observe changes in the slope of targeted infection rates in the postpolicy period compared with the prepolicy period for either safety net (postpolicy vs prepolicy ratio, 0.96 [95% CI, 0.84-1.09]) or non-safety net (0.99 [0.90-1.10]) hospitals. Controlling for prepolicy secular trends, we did not detect differences in an immediate change at the time of the policy between safety net and non-safety net hospitals (P for 2-way interaction, .87). CONCLUSIONS: The Centers for Medicare and Medicaid Services Hospital-Acquired Conditions policy did not have an impact, either positive or negative, on already declining rates of central line-associated bloodstream infection in safety net or non-safety net hospitals. Continued evaluations of the broad impact of payment policies on safety net hospitals will remain important as the use of financial incentives and penalties continues to expand in the United States.

The New Hampshire public health network: creating local public health infrastructure through community-driven partnerships.

In 1997, under the auspices of the Turning Point program, New Hampshire's public health stakeholders convened a strategic planning process to transform the state public health system. What emerged was a fundamental vision that the public health system could only be improved by strengthening the capacity of local communities to address local health issues. A plan was developed to create regional public health structures, in areas with no local health departments, to deliver essential public health services at the local level. Seven years later, that plan has become the New Hampshire Public Health Network. The network now covers 67% of the New Hampshire population and includes 113 (48%) cities and towns. Pre- and postevaluations to assess local public health infrastructure at the inception of the program and following 2 years of funding and technical assistance showed significant improvement in local public health capacity and performance. This article describes the development of local public health structures in New Hampshire where none had previously existed.

Optimal resource allocation for curing Chlamydia trachomatis infection among asymptomatic women at clinics operating on a fixed budget.

GOAL: The goal was to determine the optimal strategy for screening coverage, test selection, and treatment for infection in asymptomatic women for a given family-planning-program budget. STUDY DESIGN: We developed a resource allocation model to determine the optimal strategy using data from 5078 visits by women universally screened for infection in a publicly funded family planning clinic system in Philadelphia. We maximized the number of infected women cured from the clinic perspective and maximized the cost-savings from the healthcare system perspective. The model incorporated the following age distributions: <20 years (27%), 20 to 24 years (30%), and >24 years (43%), with prevalences of 10.6%, 6.9%, and 2.3%, respectively. We modeled two screening test assays (DNA probe and ligase chain reaction [LCR] for cervical specimens) and two treatments (doxycycline and azithromycin). The model allowed for different test and treatment choices by age group. RESULTS: At the baseline annual budget of $6 per visit, the strategy that maximized both the number of infected women cured and cost savings would be to screen all women with DNA probe and to treat all women with positive tests with azithromycin. This strategy would result in 183 women cured at a cost-savings of $140,176. Sensitivity analysis showed that the total budget had a great impact on the optimal strategy, incorporating screening coverage, test selection, and treatment. CONCLUSIONS: Using resource allocation models enables clinic managers operating with a fixed budget to identify a strategy that maximizes the number of asymptomatic women cured and cost savings when the clinic age distribution and age-specific prevalences are known.