A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake.

Population health management is an emerging technique to link and analyse patient data across several organisations in order to identify population needs and plan care. It is increasingly used in England and has become more important as health policy has sought to drive greater integration across health and care organisations. This protocol describes a mixed-methods process evaluation of an innovative population health management system in North Central London, England, serving a population of 1.5 million. It focuses on how staff have used a specific tool within North Central London's population health management system designed to reduce inequities in COVID-19 vaccination. The COVID-19 vaccination Dashboard was first deployed from December 2020 and enables staff in North London to view variations in the uptake of COVID-19 vaccinations by population characteristics in near real-time. The evaluation will combine interviews with clinical and non-clinical staff with staff usage analytics, including the volume and frequency of staff Dashboard views, to describe the tool's reach and identify possible mechanisms of impact. While seeking to provide timely insights to optimise the design of population health management tools in North Central London, it also seeks to provide longer term transferable learning on methods to evaluate population health management systems.

Schizophrenia population health management: perspectives of and lessons learned from population health decision makers.

BACKGROUND: Despite therapeutic advances for patients with schizophrenia, improving patient outcomes and reducing the cost of care continue to challenge formulary decision makers. OBJECTIVES: To (1) understand the perspectives of formulary decision makers on challenges to optimal schizophrenia population management and (2) identify best practices and recommendations for mitigating these challenges. METHODS: This mixed-methods study, conducted in a double-blind manner, comprised in-depth telephone interviews with formulary decision makers from February through May 2020, and a web-based follow-on survey that was sent to all participants in October 2020. US-based formulary decision makers were recruited if they were directly involved in schizophrenia drug formulary or coverage decision making for national or regional payers, health systems, or behavioral health centers. Formulary decision makers' perceptions of challenges, policies, and programs related to schizophrenia population health management were assessed generally and in the context of the COVID-19 pandemic. RESULTS: 19 formulary decision makers participated in the interviews and 18 (95%) completed the survey. Participants reported a spectrum of patient- and payer-driven challenges in schizophrenia population health management, including medication nonadherence, high pharmacy and medical costs, and frequent hospitalizations and emergency department visits. Participants noted that COVID-19 had worsened all identified challenges, although patient unemployment (mean score of 2.00 on a scale of 1 [made much worse] to 5 [made much better]) and reduced access to psychiatric care (mean score, 2.12) were most negatively affected. The most common strategies implemented in order to improve schizophrenia population health management included case management (89%), telemedicine (83%), care coordination programs (72%), strategies to mitigate barriers to accessing medication (61%), and providing nonmedical services to address social determinants of health (56%). Participants noted that, ideally, all treatments for schizophrenia would be available on their formularies without utilization management policies in place in order to increase accessibility to medication, but cost to the health plans made that difficult. Whereas 61% of respondents believed that long-acting injectable antipsychotics (LAIs) were currently underused in their organizations, only 28% represented organizations with open access policies for LAIs. Participants believed that among patients with schizophrenia, LAIs were most beneficial for those with a history of poor or uncertain adherence to oral medications (mean score of 4.50 on a scale of 1 [not at all beneficial] to 5 [extremely beneficial]) and those with recurring emergency department visits and inpatient stays (mean score, 3.94). Study participants reported slightly increased use of LAIs (mean score of 3.17 on a scale of 1 [negatively impacted] to 5 [positively impacted]) among their patients with schizophrenia in response to the COVID-19 pandemic; 29% of participants reported easing access restrictions for LAIs. CONCLUSIONS: Participants described persisting challenges and various approaches intended to improve schizophrenia population health management. They also recommended strategies to optimize future health management for this population, including expanding programs to address social determinants of health and mitigating barriers to accessing treatment. DISCLOSURES: This study was funded by Janssen Scientific Affairs, LLC. Roach, Graf, Pednekar, and Chou are employees of PRECISIONheor, which received financial support from Janssen Scientific Affairs, LLC, to conduct this study. Chou owns equity in Precision Medicine Group, the parent company of PRECISIONheor. Lin and Benson are employees of Janssen Scientific Affairs, LLC. Doshi has served as a consultant, advisory board member, or both, for Acadia, Allergan, Boehringer Ingelheim, Janssen, Merck, Otsuka, and Sage Therapeutics and has received research funding from AbbVie, Biogen, Humana, Janssen, Novartis, Merck, Pfizer, PhRMA, Regeneron, Sanofi, and Valeant.

Evolution of Specimen Self-Collection in the COVID-19 Era: Implications for Population Health Management of Infectious Disease.

Laboratory testing is an important component in the diagnosis of respiratory tract infections such as with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, specimen collection not only risks exposure of health care workers and other patients to infection, but also necessitates use of personal protective equipment that may be in short supply during periods of heightened disease activity. Self-collection of nasal or oropharyngeal swabs offers an alternative to address these drawbacks. Although studies in the past decade have demonstrated the utility of this approach for respiratory infections, it has not been widely adopted in routine clinical practice. The rapid spread of coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has focused attention on the need for safe, convenient, timely, and scalable methods for collecting upper respiratory specimens for testing. The goals of this article are to highlight the literature regarding self-collected nasal or oropharyngeal specimens for respiratory pathogen testing; discuss the role of self-collection in helping prevent the spread of the COVID-19 disease from infected patients and facilitating a shift toward "virtual" medicine or telemedicine; and describe the current and future state of self-collection for infectious agents, and the impacts these approaches can have on population health management and disease diagnosis and prevention.

The Essential Role of Technology in the Public Health Battle Against COVID-19.

Technology has played an important role in responding to the novel coronavirus (SARS-CoV-2) and subsequent COVID-19 pandemic. The virus's blend of lethality and transmissibility have challenged officials and exposed critical limitations of the traditional public health apparatus. However, throughout this pandemic, technology has answered the call for a new form of public health that illustrates opportunities for enhanced agility, scale, and responsiveness. The authors share the Microsoft perspective and illustrate how technology has helped transform the public health landscape with new and refined capabilities - the efficacy and impact of which will be determined by history. Technologies like chatbot and virtualized patient care offer a mechanism to triage and distribute care at scale. Artificial intelligence and high-performance computing have accelerated research into understanding the virus and developing targeted therapeutics to treat infection and prevent transmission. New mobile contact tracing protocols that preserve patient privacy and civil liberties were developed in response to public concerns, creating new opportunities for privacy-sensitive technologies that aid efforts to prevent and control outbreaks. While much progress is still needed, the COVID-19 pandemic has highlighted technology's importance to public health security and pandemic preparedness. Future multi-stakeholder collaborations, including those with technology organizations, are needed to facilitate progress in overcoming the current pandemic, setting the stage for improved pandemic preparedness in the future. As lessons are assessed from the current pandemic, public officials should consider technology's role and continue to seek opportunities to supplement and improve on traditional approaches.

Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study.

OBJECTIVES: To use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm. DESIGN: Individuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis. SETTING: A major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK. PARTICIPANTS: 1 013 940 individuals from 78 contributing general practices. RESULTS: Compared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions. CONCLUSIONS: PHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Business Closures, Stay-at-Home Restrictions, and COVID-19 Testing Outcomes in New York City.

INTRODUCTION: In response to the coronavirus disease 2019 (COVID-19) pandemic, New York City closed all nonessential businesses and restricted the out-of-home activities of residents as of March 22, 2020. This order affected different neighborhoods differently, as stores and workplaces are not randomly distributed across the city, and different populations may have responded differently to the out-of-home restrictions. This study examines how the business closures and activity restrictions affected COVID-19 testing results. An evaluation of whether such actions slowed the spread of the pandemic is a crucial step in designing effective public health policies. METHODS: Daily data on the fraction of COVID-19 tests yielding a positive result at the zip code level were analyzed in relation to the number of visits to local businesses (based on smartphone location) and the number of smartphones that stayed fixed at their home location. The regression model also included vectors of fixed effects for the day of the week, the calendar date, and the zip code of residence. RESULTS: A large number of visits to local businesses increased the positivity rate of COVID-19 tests, while a large number of smartphones that stayed at home decreased it. A doubling in the relative number of visits increases the positivity rate by about 12.4 percentage points (95% CI, 5.3 to 19.6). A doubling in the relative number of stay-at-home devices lowered it by 2.0 percentage points (95% CI, -2.9 to -1.2). The business closures and out-of-home activity restrictions decreased the positivity rate, accounting for approximately 25% of the decline observed in April and May 2020. CONCLUSION: Policy measures decreased the likelihood of positive results in COVID-19 tests. These specific policy tools may be successfully used when comparable health crises arise in the future.

COVID-19 Mitigation Steps Provide a Blueprint for Malaria Control and Elimination.

Most countries around the world have responded promptly to the novel coronavirus disease (COVID-19) challenge by adopting considered and scientifically guided strategies for its containment. However, the situation is more complex for nations where malaria is endemic, as they now have the additional burden of COVID-19. In such nations, the healthcare systems are either in the preparatory or containment phase of the current pandemic. This enforced, sudden, and sharp public health refocus is likely to result in the disruption of annual malaria control activities such as distribution of insecticide-impregnated bed nets, indoor residual spraying of insecticide, maintenance of malaria surveillance, and mass provision of antimalarial drugs. Nonetheless, we feel that the best facets of COVID-19 public health management can become new guiding principles in malaria-endemic countries to improve malaria control and hasten malaria elimination. Redirection against malaria of the best public health initiatives used in COVID-19 containment could fast-track the global goal of a malaria-free world. Such public health advancement could be one positive outcome from the scourge of COVID-19.