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ImportanceThe COVID-19 pandemic has claimed nearly 6 million lives globally as of February 2022. While pandemic control efforts, including contact tracing, have traditionally been the purview of state and local health departments, the COVID-19 pandemic outpaced health department capacity, necessitating actions by private health systems to investigate and control outbreaks, mitigate transmission, and support patients and communities.ObjectiveTo investigate the process of designing and implementing a volunteer-staffed contact tracing program at a large academic health system from April 2020 to May 2021, including program structure, lessons learned through implementation, results of case investigation and contact tracing efforts, and reflections on how constrained resources may be best allocated in the current pandemic or future public health emergencies.Design, Setting, and ParticipantsThis case series study was conducted among patients at the University of Pennsylvania Health System and in partnership with the Philadelphia Department of Public Health. Patients who tested positive for COVID-19 were contacted to counsel them regarding safe isolation practices, identify and support quarantine of their close contacts, and provide resources, such as food and medicine, needed during isolation or quarantine.ResultsOf 5470 individuals who tested positive for COVID-19 and received calls from a volunteer, 2982 individuals (54.5%; median [range] age, 42 [18-97] years; 1628 [59.4%] women among 2741 cases with sex data) were interviewed; among 2683 cases with race data, there were 110 Asian individuals (3.9%), 1476 Black individuals (52.7%), and 817 White individuals (29.2%), and among 2667 cases with ethnicity data, there were 366 Hispanic individuals (13.1%) and 2301 individuals who were not Hispanic (82.6%). Most individuals lived in a household with 2 to 5 people (2125 of 2904 individuals with household data [71.6%]). Of 3222 unique contacts, 1780 close contacts (55.2%; median [range] age, 40 [18-97] years; 866 [55.3%] women among 1565 contacts with sex data) were interviewed; among 1523 contacts with race data, there were 69 Asian individuals (4.2%), 705 Black individuals (43.2%), and 573 White individuals (35.1%), and among 1514 contacts with ethnicity data, there were 202 Hispanic individuals (12.8%) and 1312 individuals (83.4%) who were not Hispanic. Most contacts lived in a household with 2 to 5 people (1123 of 1418 individuals with household data [79.2%]). Of 3324 cases and contacts who completed a questionnaire on unmet social needs, 907 (27.3%) experienced material hardships that would make it difficult for them to isolate or quarantine safely. Such hardship was significantly less common among White compared with Black participants (odds ratio, 0.20; 95% CI, 0.16-0.25).Conclusions and RelevanceThese findings demonstrate the feasibility and challenges of implementing a case investigation and contact tracing program at an academic health system. In addition to successfully engaging most assigned COVID-19 cases and close contacts, contact tracers shared health information and material resources to support isolation and quarantine, thus filling local public health system gaps and supporting local pandemic control.

ImportanceDigital health information has many potential health applications, but privacy is a growing concern among consumers and policy makers. Consent alone is increasingly seen as inadequate to safeguard privacy.ObjectiveTo determine whether different privacy protections are associated with consumers’ willingness to share their digital health information for research, marketing, or clinical uses.Design, Setting, and ParticipantsThis 2020 national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample with oversampling of Black and Hispanic individuals. Willingness to share digital information across 192 different scenarios reflecting the product of 4 possible privacy protections, 3 uses of information, 2 users of information, and 2 sources of digital information was evaluated. Each participant was randomly assigned 9 scenarios. The survey was administrated between July 10 and July 31, 2020, in Spanish and English. Analysis for this study was conducted between May 2021 and July 2022.Main Outcomes and MeasuresParticipants rated each conjoint profile on a 5-point Likert scale measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results are reported as adjusted mean differences.ResultsOf the 6284 potential participants, 3539 (56%) responded to the conjoint scenarios. A total of 1858 participants (53%) were female, 758 (21%) identified as Black, 833 (24%) identified as Hispanic, 1149 (33%) had an annual income less than $50 000, and 1274 (36%) were 60 years or older. Participants were more willing to share health information with the presence of each individual privacy protection, including consent (difference, 0.32; 95% CI, 0.29-0.35; P P P P Conclusions and RelevanceIn this survey study of a nationally representative sample of US adults, consumers’ willingness to share personal digital health information for health purposes was associated with the presence of specific privacy protections beyond consent alone. Additional protections, including data transparency, oversight, and data deletion may strengthen consumer confidence in sharing their personal digital health information.

Key Points Question What is the role of patient preference in racial disparities in opioid prescribing for patients with acute pain, and does providing clinicians with additional data about their patients mitigate disparities? Findings In this secondary analysis of 1012 patients with acute pain who were recruited for a multicenter randomized clinical trial, Black patients were less likely than White patients to receive a prescription for opioids, regardless of their treatment preference. These disparities were not mitigated by providing clinicians with additional data about their patients’ preferences and risks. Meaning This study’s findings suggest that differences in patient preference do not explain racial disparities in opioid prescribing; further research is needed to assess the factors associated with these disparities., Importance Although racial disparities in acute pain control are well established, the role of patient analgesic preference and the factors associated with these disparities remain unclear. Objective To characterize racial disparities in opioid prescribing for acute pain after accounting for patient preference and to test the hypothesis that racial disparities may be mitigated by giving clinicians additional information about their patients’ treatment preferences and risk of opioid misuse. Design, Setting, and Participants This study is a secondary analysis of data collected from Life STORRIED (Life Stories for Opioid Risk Reduction in the ED), a multicenter randomized clinical trial conducted between June 2017 and August 2019 in the emergency departments (EDs) of 4 academic medical centers. Participants included 1302 patients aged 18 to 70 years who presented to the ED with ureter colic or musculoskeletal back and/or neck pain. Interventions The treatment arm was randomized to receive a patient-facing intervention (not examined in this secondary analysis) and a clinician-facing intervention that consisted of a form containing information about each patient’s analgesic treatment preference and risk of opioid misuse. Main Outcomes and Measures Concordance between patient preference for opioid-containing treatment (assessed before ED discharge) and receipt of an opioid prescription at ED discharge. Results Among 1302 participants in the Life STORRIED clinical trial, 1012 patients had complete demographic and treatment preference data available and were included in this secondary analysis. Of those, 563 patients (55.6%) self-identified as female, with a mean (SD) age of 40.8 (14.1) years. A total of 455 patients (45.0%) identified as White, 384 patients (37.9%) identified as Black, and 173 patients (17.1%) identified as other races. After controlling for demographic characteristics and clinical features, Black patients had lower odds than White patients of receiving a prescription for opioid medication at ED discharge (odds ratio [OR], 0.42; 95% CI, 0.27-0.65). When patients who did and did not prefer opioids were considered separately, Black patients continued to have lower odds of being discharged with a prescription for opioids compared with White patients (among those who preferred opioids: OR, 0.43 [95% CI, 0.24-0.77]; among those who did not prefer opioids: OR, 0.45 [95% CI, 0.23-0.89]). These disparities were not eliminated in the treatment arm, in which clinicians were given additional data about their patients’ treatment preferences and risk of opioid misuse. Conclusions and Relevance In this secondary analysis of data from a randomized clinical trial, Black patients received different acute pain management than White patients after patient preference was accounted for. These disparities remained after clinicians were given additional patient-level data, suggesting that a lack of patient information may not be associated with opioid prescribing disparities. Trial Registration ClinicalTrials.gov Identifier: NCT03134092, This secondary analysis of data from a randomized clinical trial characterizes racial disparities in opioid prescribing and examines whether a clinician intervention describing patient treatment preference and risk of opioid misuse was able to mitigate disparities among patients with acute pain.

ImportanceOpioid overdose rates continue to increase, and extant literature suggests that many individuals who use heroin were first introduced to opioids through a medical prescription.ObjectiveTo explore patient experiences related to decisions regarding analgesia after an emergency department visit within the context of a randomized clinical trial aimed to test the efficacy of risk communication interventions on treatment preference, risk recall, and use of opioids.Design, Setting, and ParticipantsThis qualitative study of 36 patients making decisions regarding analgesia included qualitative interviews with participants in 2 risk intervention groups. Interviews were audio recorded, transcribed, and edited to remove identifying information to protect the confidentiality of participants. Interviews were conducted from June 4, 2019, to August 6, 2019. We conducted thematic analysis from August to December 2019 using a mixed inductive and deductive approach. Participants received $20 in compensation. The study was conducted in 4 geographically diverse emergency departments in the United States. Participants were adults presenting to the emergency department with either musculoskeletal back or neck pain or kidney stone–related pain. Eligibility criteria included being aged 18 to 70 years, capable of providing informed consent, English speaking or having English comprehension, eligible for emergency department discharge within 24 hours of enrollment, and able to access email or a smartphone.InterventionsParticipants enrolled from the main randomized clinical trial received 1 of 2 risk interventions: a probabilistic opioid risk tool or a narrative-enhanced probabilistic risk tool (ie, participants viewed eight 1- to 3-minute short videos of patients discussing their experiences with pain treatment and positive and negative experiences with opioid use).Main Outcomes and MeasuresFactors reported by participants to have influenced their decision-making regarding acute pain and treatment.ResultsThirty-six participants were interviewed, 18 in the group who received the probabilistic risk tool alone and 18 in the group who received the additional narrative-enhanced probabilistic risk tool intervention. The median age was 38 years (range, 21-67 years), 22 individuals were female (61%), 14 were Black or African American (39%), and 14 were White (39%). Five themes emerged from the analysis in the following domains: the factors associated with the risk interventions; clinician paternalism; analgesia attributes and previous experiences; individual self-identity, attitudes, and values; and perceptions of clinician bias.Conclusions and RelevanceMost participants commented on the powerful lessons they learned from the risk interventions. More research is needed to understand how patients incorporate risk information into their decision-making process.

This qualitative study assesses the association between digital technology and emerging challenges for health policy and privacy., Key Points Question What challenges for health privacy are associated with digital technology? Findings In this qualitative study, 5 key challenges for health privacy were associated with digital technology: invisibility (people unaware of how they are tracked), inaccuracy (flawed data), immortality (data never expire), marketability (data are frequently bought and sold), and identifiability (individuals can be readily reidentified). Meaning The findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections., Importance Digital technology is part of everyday life. Digital interactions generate large amounts of data that can reveal information about the health of individual consumers (the digital health footprint). Objective Τo describe health privacy challenges associated with digital technology. Design, Setting, and Participants For this qualitative study, In-depth, semistructured, qualitative interviews were conducted with 26 key experts from diverse fields in the US between January 1 and July 31, 2018. Open-ended questions and hypothetical scenarios were used to identify sources of digital information that contribute to consumers’ health-relevant digital footprints and challenges for health privacy. Participants also completed a survey instrument on which they rated the health relatedness of digital data sources. Main Outcomes and Measures Health policy challenges associated with digital technology based on qualitative responses to expert interviews. Results Although experts’ ratings of digital data sources suggested a possible distinction between health and nonhealth data, qualitative interviews uniformly indicated that all data can be health data, particularly when aggregated across sources and time. Five key characteristics of the digital health footprint were associated with health privacy policy challenges: invisibility (people are unaware of how their data are tracked), inaccuracy (data in the digital health footprint can be inaccurate), immortality (data have no expiration date and are aggregated over time), marketability (data have immense commercial value and are frequently bought and sold), and identifiability (individuals can be readily reidentified and anonymity is nearly impossible to achieve). There are virtually no regulatory structures in the US to protect health privacy in the context of the digital health footprint. Conclusions and Relevance The findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections.

This cross-sectional survey study assesses use of consumer digital information for COVID-19 control that US adults consider to be acceptable and the factors associated with higher or lower approval of use of this information., Key Points Question What uses of consumer digital information for COVID-19 control are most acceptable to US adults, and what factors are associated with higher or lower approval of use of this information? Findings In this cross-sectional survey study of 6284 US adults, approval was generally low for use of consumer digital data for activities such as case identification, digital contact tracing, policy setting, and enforcing quarantines. Political ideology and race/ethnicity were associated with approval for scenarios in which digital data were used, whereas local COVID-19 incidence and family experience with COVID were not. Meaning The findings suggest that in current and future pandemics, public health departments should use multiple strategies to gain public trust and accelerate adoption of tools such as digital contact tracing applications., Importance Curbing COVID-19 transmission is currently the greatest global public health challenge. Consumer digital tools used to collect data, such as the Apple-Google digital contact tracing program, offer opportunities to reduce COVID-19 transmission but introduce privacy concerns. Objective To assess uses of consumer digital information for COVID-19 control that US adults find acceptable and the factors associated with higher or lower approval of use of this information. Design, Setting, and Participants This cross-sectional survey study obtained data from a nationally representative sample of 6284 US adults recruited by email from the web-based Ipsos KnowledgePanel in July 2020. Respondents evaluated scenarios reflecting uses of digital data for COVID-19 control (case identification, digital contact tracing, policy setting, and enforcement of quarantines). Main Outcomes and Measures Levels of support for use of personal digital data in 9 scenarios to mitigate the spread of COVID-19 infection, rated on a Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Multivariable linear regression models were fitted for each scenario and included factors hypothesized to be associated with views about digital data use for COVID-19 mitigation measures. Black and Hispanic survey respondents were oversampled; thus, poststratification weights were used so that results are representative of the general US population. Results Of 6284 individuals invited to participate in the study, 3547 responded, for a completion rate of 56%. A total of 1762 participants (52%) were female, 715 (21%) identified as Black, 790 (23%) identified as Hispanic, and 1224 (36%) were 60 years or older; mean (SD) age was 51.7 (16.6) years. Approval of scenarios was low, ranging from 28% to 43% (52%-67% when neutral responses were included). Differences were found based on digital data source (smartphone vs social media: coefficient, 0.29 [95% CI, 0.23-0.35]; P