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“I can’t do it”: A qualitative study exploring case and contact experiences with COVID-19 contact tracing

Abstract

Background

Low engagement in contact tracing for COVID-19 dramatically reduces its impact, but little is known about how experiences, environments and characteristics of cases and contacts influence engagement.

Methods

We recruited a convenience sample of COVID-19 cases and contacts from the New Haven Health Department’s contact tracing program for interviews about their contact tracing experiences. We analyzed transcripts thematically, organized themes using the Capability, Opportunity, Motivation, Behavior (COM-B) model, and identified candidate interventions using the linked Behavior Change Wheel Framework.

Results

We interviewed 21 cases and 12 contacts. Many felt physically or psychologically incapable of contact tracing participation due to symptoms or uncertainty about protocols. Environmental factors and social contacts also influenced engagement. Finally, physical symptoms, emotions and low trust in and expectations of public health authorities influenced motivation to participate.

Conclusion

To improve contact tracing uptake, programs should respond to clients’ physical and emotional needs; increase clarity of public communications; address structural and social factors that shape behaviors and opportunities; and establish and maintain trust. We identify multiple potential interventions that may help achieve these goals.

Peer Review reports

Background

Contact tracing, a non-pharmaceutical intervention used to limit transmission of a variety of infectious diseases [1, 2], was widely adopted in response to the COVID-19 pandemic [3, 4], with demonstrated reductions in case incidence [5, 6] and mortality [7] and several additional benefits including delivery of education and linkage to social support resources. However, traditional contact tracing is challenging to implement because it depends on a chain of four independent behaviors expected of cases and contacts: (1) testing, (2) answering phone calls, (3) participating in interviews with contact tracers, and (4) isolating or quarantining when indicated. Cases and contacts may engage in some, all, or none of these, yet the overall impact of contact tracing relies on their cumulative completion rate [8]. A recent evaluation of 14 U.S. COVID-19 contact tracing programs [9] found rates of interview completion among cases below 60%, a threshold defined early in the pandemic as a minimum for mitigating epidemic growth [10]. Additional studies outside the U.S. found rates of adherence to isolation and quarantine as low as 25% [11, 12].

Given the importance of contact tracing in the ongoing pandemic response, it is critical to identify and understand elements that influence participation. What is currently known about engagement in contact tracing is derived from studies of other infectious diseases, digital contact tracing, or single steps of contact tracing such as self-isolation [13,14,15,16]. Yet, many behavior change theories, models and frameworks exist to help classify elements influencing engagement in COVID-19 contact tracing and identify behavioral interventions to address them. The Capability, Opportunity, Motivation, Behavior (COM-B) model [17] is particularly suited for this objective given its linkage to the Behavior Change Wheel [17] implementation framework, and has been used to identify and select behavior-modifying interventions in other contexts [18, 19]. COM-B proposes three primary determinants (domains) of behavior: (1) Capability, (2) Opportunity, and (3) Motivation. These domains include sub-domains that further categorize these influences. Capability includes Physical Capability (physical strengths or abilities) and Psychological Capability (prerequisite knowledge, mental skills/stamina); Opportunity includes Physical Opportunity (physical environment and resources) and Social Opportunity (social factors, norms, relationships); and Motivation includes Reflective Motivation (intentional thought processes) and Automatic Motivation (impulses or emotions).

The goal of this study is to elicit, from qualitative interviews with COVID-19 cases and contacts, elements that influenced their engagement and apply an implementation mapping [20] approach using the COM-B model and Behavior Change Wheel framework to identify potential strategies to promote engagement in contact tracing.

Methods

Study setting

This study contributed to a multiple methods evaluation of the New Haven Health Department’s (NHHD) COVID-19 contact tracing program [21,22,23], which operated from March to June 2020. This program was staffed by NHHD employees, but primarily utilized university volunteers, including members of the study team (TS, CS). Volunteers signed confidentiality agreements with the NHHD allowing them to assist with contact tracing and access limited client data necessary for their assigned tasks. Multilingual volunteers were utilized to reach non-English speaking clients. New Haven, Connecticut is part of the New York Metropolitan Area and home to roughly 130,000 racially and ethnically diverse residents (30% White, 33% Black/African-American, 31% Hispanic/Latinx, and 5% Asian) [24]. Between April and June 2020, the contact tracing program reached nearly 1,300 COVID-19 cases and almost 1,100 contacts.

Study population eligibility and recruitment

We invited consecutive adult COVID-19 cases and their close contacts if they were documented as adult clients in the NHHD contact tracing registry and successfully reached for a tracing interview within the preceding 7–28 days. We sought to interview both cases and contacts because successful contact tracing depends on the attitudes, motivations, and actions of cases as well as their contacts. We set a target recruitment of 15 cases and 15 contacts based on estimates of the number of interviews needed to reach thematic saturation [25]. Participants received a $20 gift card upon completing an interview. Enrollment continued until the NHHD’s contact tracing program ended in June 2020.

Data collection procedures and analysis

We obtained basic demographic data (age, sex, language preference, and race/ethnicity in cases only) from the NHHD registry. Our semi-structured interview guide explored two topics: (1) experiences related to four key contact tracing behaviors (testing, answering phone calls, participating in contact tracing interviews, and adhering to isolation or quarantine) (Supplementary Fig. 1), and (2) recommendations to improve contact tracing procedures. The current analysis primarily focuses on the first topic.

The interview team included a male MD/PhD student (TS) and a female, Spanish-speaking MPH student (CA) who were trained in qualitative interviewing. They telephoned clients up to three times over a one-week period to invite them to participate in the study and left voice messages with callback numbers if clients did not answer. If invited clients did not call back within one week from the initial invitation, no additional contact was attempted. Interviews were conducted via telephone, audio-recorded, and subsequently transcribed verbatim (and translated to English, if applicable) using an automated service (Trint, London, United Kingdom). Transcripts were proofread against the recordings and corrected as needed. Participants did not review the data or study findings. TS and LG iteratively assessed the content of interviews on a weekly basis for saturation until no new themes emerged.

The coding team (TS and LG) adapted a codebook from a prior qualitative evaluation of contact tracing [23] and added new codes as needed. They independently coded all transcripts and met to resolve any coding discrepancies. They then entered the coded transcripts into ATLAS.ti (Version 8, Berlin, Germany) for analysis.

TS, JLD, and LG analyzed the coded data [26] to identify preliminary themes, and subsequently narrowed the analytic scope to the four client behaviors of interest. They classified themes and supporting quotes as barriers to or facilitators of participation in contact tracing, and organized them within all relevant COM-B domains [17].

Results

Study sample characteristics

Between May 25 and July 9, 2020, we telephoned 64 cases and 83 contacts of whom 35 cases and 38 contacts answered or called back, and 21 cases and 12 contacts agreed to participate. Three contacts had tested positive for COVID-19 by the time of the study interview. Participants’ median age was 41, 61% were female, and the largest racial/ethnic group in our sample was Hispanic/Latinx (48%) (Table 1), which is roughly representative of the NHHD’s client population during this timeframe [22].

Table 1 Participant characteristics

Themes, facilitators and barriers

We identified seven themes that cut across the four behaviors and three COM-B domains (Table 2). While the themes were broadly similar across case and contact groups, we note relevant differences below when applicable, summarize the individual themes within each COM-B domain, and present supporting quotes in Table 3.

Table 2 Facilitators and barriers mapped onto behaviors, themes, and COM-B domains (Capability, Opportunity, Motivation)
Table 3 Supporting quotes within each theme and COM-B domain

Capability domain

We identified two themes, Symptom Severity and Essential Knowledge, related to participants’ capacity to participate in contact tracing. Symptom Severity describes how COVID-19 symptoms influenced their Physical Capability. Essential Knowledge describes how knowledge about the purpose of and procedures for testing and tracing influenced their Psychological Capability.

Symptom severity theme

Several participants described how symptoms, such as shortness of breath, made it difficult or infeasible to answer phone calls or speak to contact tracers. One case was hospitalized at the time of the contact tracing call, and his daughter spoke on his behalf. Other participants noted that moderate or severe symptoms also made isolation especially difficult.

Essential knowledge theme

Limited awareness of COVID-19 symptoms, testing locations, or contact tracing procedures acted as a barrier to contact tracing engagement for several participants. For example, not knowing how personal data would be used or protected caused some participants, particularly cases, to be wary of fully engaging with the interview, although some contact tracers successfully addressed these concerns. Other participants were confused by quarantine and isolation instructions.

Opportunity domain

We identified two themes, Structural Context and Interpersonal Ties, related to participants’ possibility of participating in contact tracing. Structural Context describes how structural factors (fixed economic, social, and policy factors) influenced Physical and Social Opportunity to participate in contact tracing. Interpersonal Ties describes ways in which social roles and connections with family, friends, or colleagues further influenced their Social Opportunity.

Structural context theme

Participants identified multiple structural factors including lacking transportation or receiving tracing calls at inconvenient times that hindered or delayed participant engagement. Although the NHHD had a referral system to address food, housing and other client needs, study participants frequently cited concerns about loss of income, housing instability, and food insecurity as barriers to isolation/quarantine. Isolation and quarantine were even more difficult in homes with inadequate space to allow household members to effectively separate from one another. Several participants received food from clinics and volunteer organizations, and some had access to paid leave from their workplace. One systems-level facilitator identified by several participants was policy-mandated testing, requiring testing in order to enter health care clinics or travel internationally.

Social Opportunity for engagement in contact tracing was influenced by access to medical providers and language services. Those lacking health insurance or established relationships with care providers experienced difficulties accessing care during isolation/quarantine. For participants whose preferred language was not English, language barriers made answering calls and participating in contact tracing interviews infeasible or challenging, although some noted that multilingual outreach workers or translation services enabled successful interaction with the program.

Interpersonal ties theme

Participants often described how relationships with family or friends could encourage testing and tracing behaviors and reassure participants about the contact tracing experience. Peers frequently encouraged engagement in testing or tracing, with some cases even alerting their contacts to expect tracing calls. As previously noted, family members often helped by answering phone calls for symptomatic cases and caring for those in isolation or quarantine. Interpersonal ties also hindered contact tracing efforts. Some cases did not provide tracers with information about their contacts (names and phone numbers), thereby preventing the health department from reaching them. Caregiving responsibilities at home (e.g., for children) posed additional barriers to adhering to isolation/quarantine guidelines.

Motivation domain

We identified some aspects of Symptom Severity and three additional themes, Anticipated Outcomes, Trust in Authority, and Emotional Responses, that related to participants’ motivation to participate in contact tracing. Symptom Severity describes ways in which symptoms, or lack thereof, influenced their Reflective Motivation. Anticipated Outcomes describes ways in which their beliefs in the consequences of participation, whether positive, negative, or neutral, also influenced Reflective Motivation. Trust in Authority is the last theme associated with Reflective Motivation, and it describes the influence of participants’ trust in providers and health systems. Emotional Responses describes ways in which participants’ emotions influenced their Automatic Motivation.

Symptom severity theme

Participants frequently described how symptoms prompted testing or isolation. By contrast, one contact without any symptoms described the quarantine experience as feeling “so abstract” because the lack of symptoms made it “hard to keep telling myself this is real.”

Anticipated outcomes themes

Participants varied in their expectations about the consequences of participation with contact tracing. Several tested or answered phone calls to ensure that they received adequate social or medical support, even when asymptomatic. One participant tested out of curiosity, while others assumed their status was positive based on known exposures and chose not to test. Some participants reported participating in testing and tracing mainly to prevent transmission to others. By contrast, skepticism about the benefits of testing, or fear of unwanted consequences (e.g., mandatory isolation) reduced engagement.

Trust in authority theme

Trust in the health system and guidelines motivated many to participate in contact tracing, while fears about misuse of data or mishandling of medical care acted as barriers. Signs of disorganization in outreach efforts, such as duplicate calls, also diminished program credibility and led to client frustration and mistrust. Several strategies (e.g., tracer being able to confirm a case’s date of birth, using Caller IDs) counteracted these tendencies and may have increased motivation to engage in contact tracing. While trust in health systems was often low, participants mentioned their pre-established relationships with known medical providers as reasons for engaging in contact tracing.

Emotional responses theme

Many participants described feeling shocked or anxious upon receiving a positive test result or exposure notification, and others anticipated being stigmatized by others in the community. These emotions could distract participants during the contact tracing call, but tracers who communicated clearly and empathically helped some remain calm. Others found the calls frustrating, particularly when they were numerous, duplicative, or timely. One participant “didn’t continue the call” because she received several calls from separate tracers due to an error in which she appeared in the database as multiple unique entries. Other emotions that affected participants during isolation/quarantine were loneliness and boredom. Coping strategies such as communicating electronically with family and friends and staying physically active mitigated such feelings and made isolation/quarantine more tolerable.

Discussion

Contact tracing’s ability to reduce transmission of COVID-19 is limited due to many factors including short incubation periods and high transmission rates, yet contact tracing remains valuable by providing opportunities to establish and maintain trust in the health system through delivery of social support and education. This is one of the first studies to qualitatively examine the experiences of COVID-19 cases and contacts with contact tracing, and its findings may help to understand and address elements influencing participation in testing, answering phone calls, interviews, and isolation/quarantine. Low rates of contact tracing acceptance across jurisdictions in conjunction with our findings highlight the fact that many cases and contacts simply cannot or will not participate fully in this intervention, as the title of this article suggests. Below, we situate our findings within the contact tracing literature, and apply the Behavior Change Wheel to suggest solutions in the form of intervention functions targeting facilitators/barriers we identified in this study, to help increase client engagement with COVID-19 contact tracing (Table 4).

Table 4 Potential interventions and intervention functions within each COM-B Domain and Theme

Our findings suggest that symptom severity and baseline knowledge influenced participants’ Capability to engage in contact tracing. Other studies similarly highlight the importance of community awareness in increasing adherence to health guidance both in the setting of COVID and elsewhere [13, 27]. However, physical symptoms may uniquely influence contact tracing for COVID-19, given the reliance on timeliness of tracing and propensity for symptoms to prevent engagement. This contrasts with contact tracing for sexually transmitted infections or TB in which incubation periods and end-goals of tracing differ, permitting more time to conduct contact tracing. Several potential intervention activities can address these elements. Alternative modes of data collection, such as digital communications or web-app surveys implemented in several U.S. states [28], could increase access to cases with moderate-to-severe symptoms. However, the use of such technology may be limited due to access to devices or technology literacy, and future research should further elucidate the potential impact of these methods. To increase engagement, programs could also improve health literacy by expanding community-wide education about COVID-19 and when and where to seek testing. When getting tested, individuals could be informed to expect and answer contact tracing calls should they test positive and be assured about data privacy and confidentiality concerns. Clear and standardized instructions on the duration and rules for isolation and quarantine might also improve adherence and reduce confusion, although continually evolving guidelines make this goal challenging. Given the difficulty that clients reported understanding and retaining this information, especially when receiving potentially upsetting news about a COVID-19 diagnosis or exposure, printed or electronic informational booklets could be provided at the time of testing or client interview.

Our data also suggest that environment and social ties strongly influenced Opportunity to engage in contact tracing. Consistent with previous literature [13, 14, 29], participants noted how access to medical care and support resources and social vulnerability influenced contact tracing behaviors. The data suggest that providing transportation to testing sites, offering home-testing, hiring multilingual contact tracers, offering paid work leave, supporting caregiving or urgent errand needs, and delivering care packages of food, masks, and cleaning supplies could all help promote contact tracing behaviors. Previous studies drawing on focus groups with COVID-19 contact tracers or with the general population support these strategies [23, 27]. However, cost may limit feasibility in some settings, and home-testing would also depend on clients reporting their results. Our qualitative analyses suggested that having cases notify contacts about what to expect from a contact tracing call and helping contacts identify peer resources to support isolation and quarantine can all be valuable. Communication between cases and contacts is often encouraged or relied upon in contact tracing for other communicable diseases [30] and may partially explain the previously observed correlations between success rates of contact outreach within case-contact clusters [22]. In contrast, social norms in some communities may reinforce a lack of adherence to COVID-19 health guidance [11, 29].

Last, our analysis suggested that symptom severity, anticipated outcomes, trust in health systems, and emotions could influence client Motivation to participate in contact tracing. Recent studies also emphasize that anticipated benefits of participation [14, 15, 29, 31] and trust in authority are important predictors of adherence to public health interventions [32, 33]. Transparent communications and strong patient-provider relationships can help build and maintain such trust [34,35,36] while misinformation [37] and privacy concerns [34] can undermine it. We also found that initiating tracing through known healthcare professionals or using Caller ID and addressing privacy concerns reinforced credibility and built trust, while redundant or uncoordinated efforts did the opposite. Potential interventions to increase trust in contact tracing indirectly supported by our analysis include hiring community members as contact tracers [38] and using peer- and provider-driven messaging campaigns to educate on the safety and purpose of contact tracing. We also observed that emotions affected motivation, including fear and anticipated stigma, as described with TB contact tracing [13]. Our analysis also suggests that equipping tracers with good communication skills is important. Training tracers to address shock or anxiety may help clients remain engaged when receiving test results or exposure notifications, and knowing how to elicit and address client needs is critical to success. Other interventions, stemming indirectly from the study findings, include connecting clients to mental health hotlines or online communication forums. Helping them remain active at home may decrease loneliness and boredom associated with isolation and quarantine. Furthermore, improving the coordination of outreach efforts and allowing clients choices in the method and frequency of outreach may enhance engagement.

Using COM-B in this study enhanced the utility of our findings by connecting the identified barriers and facilitators and possible interventions to the Behavior Change Wheel [17]. Some of the potential mechanisms for influencing change are shown for the interventions proposed in Table 4, including (1) Enablement (i.e., increasing means of engagement or reducing barriers), (2) Education (i.e., increasing knowledge), (3) Persuasion, (i.e., using communication to create positive or negative feelings), (4) Modeling, (i.e., providing an example of desired behavior), (5) Training, (i.e., imparting new skills), and (6) Incentivization, (i.e., establishing an expectation of reward). Figure 1 displays the suggested interventions mapped according to their function and the contact tracing behaviors they might affect, based upon our analysis. Of note, interventions affecting multiple behaviors or affecting earlier steps in the contact tracing process may be more beneficial than interventions affecting only single or downstream behaviors. Future implementation and evaluation of these interventions should consider feasibility and acceptability of each function based on local context and resources.

Fig. 1
figure 1

Suggested interventions mapped according to sequential behaviors they might affect (top) and Behavior Change Wheel function (left)

Limitations and strengths

There are several limitations to this study. First, poor recall of experiences over time may have introduced some inaccuracies in the data, though we sought to minimize this by interviewing clients soon after their original contact tracing call. Second, social desirability or response biases may have influenced participants to present themselves in positive terms, although interviewers were trained to be non-judgmental towards and supportive of participants to minimize this possibility. Third, although we achieved data saturation, we recruited participants from a single contact tracing program, and no data were available from clients who were not reached by or declined to participate with the program. This limits the transferability of our findings to different settings or populations with lower baseline health engagement (i.e., those who never answered contact tracer calls). Fourth, we acknowledge that the relationships between themes are not fully elucidated by our analysis, reflecting a potential limitation of COM-B and the Behavior Change Wheel to synthesize the insights gained from this study.

This study is strengthened by its use of qualitative data collected at the onset of the COVID-19 pandemic. This time period was critical in shaping the course of the pandemic and learning from the experiences and decisions within this timeframe is important for future improvement. While many quantitative contact tracing studies have been published, few provide the reasons why clients fail to engage. Eliciting participant experiences in their own words adds strength to findings reported elsewhere and also yielded new insights into the complexities of increasing contact tracing uptake, such as the role of social network communication in shaping contact tracing behaviors. Another strength is the inclusion of both cases and contacts, as well as both English and Spanish speakers. A final strength is the use of the COM-B model to frame the analysis and findings. This approach allowed us to link our identified themes with relevant COM-B domains and potential intervention activities, many of which are transferable to other similar settings.

Conclusion

This study is among the first to explore, from the perspective of COVID-19 cases and contacts, how their environments, experiences, and perceptions may shape contact tracing behaviors. Within the COM-B framework, Capability was shaped by symptom severity and COVID-19-relevant knowledge, Opportunity was shaped by structural, environmental, and social factors, and Motivation was shaped by symptoms, anticipated consequences of engagement, trust, and emotional responses. Tracing strategies may benefit from accounting for and addressing the many environmental- and client-level elements identified herein, and clients’ symptoms and peer interactions may be more influential in the setting of COVID-19 contact tracing compared to other disease contexts.

Data Availability

Data may be made available upon reasonable request to the New Haven Health Department.

References

  1. Fox GJ, Barry SE, Britton WJ, Marks GB. Contact investigation for tuberculosis: a systematic review and meta-analysis. Eur Respir J. 2013;41(1):140–56.

    Article  PubMed  Google Scholar 

  2. Ooi PL, Lim S, Chew SK. Use of quarantine in the control of SARS in Singapore. Am J Infect Control. 2005;33(5):252–7.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Jian SW, Cheng HY, Huang XT, Liu DP. Contact tracing with digital assistance in Taiwan’s COVID-19 outbreak response. Int J Infect Dis. 2020;101:348–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lee SW, Yuh WT, Yang JM, Cho YS, Yoo IK, Koh HY, et al. Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey. JMIR Med Inform. 2020;8(8):e20992.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fetzer T, Graeber T. Does Contact Tracing Work? Quasi-Experimental Evidence from an Excel Error in England. Working Paper No. 521. 2020.

  6. Girum T, Lentiro K, Geremew M, Migora B, Shewamare S. Global strategies and effectiveness for COVID-19 prevention through contact tracing, screening, quarantine, and isolation: a systematic review. Trop Med Health. 2020;48(1):91.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Yalaman A, Basbug G, Elgin C, Galvani AP. Cross-country evidence on the association between contact tracing and COVID-19 case fatality rates. Sci Rep. 2021;11(1):2145.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Davis EL, Lucas TCD, Borlase A, Pollington TM, Abbott S, Ayabina D, et al. Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence. Nat Commun. 2021;12(1):5412.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lash RR, Moonan PK, Byers BL, Bonacci RA, Bonner KE, Donahue M, et al. COVID-19 Case Investigation and Contact Tracing in the US, 2020. JAMA Netw Open. 2021;4(6):e2115850-e.

    Article  Google Scholar 

  10. Prioritizing COVID-19 Contact Tracing Mathematical Modeling Methods and Findings 2020 [March 29, 2021]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/prioritization/mathematicalmodeling.html.

  11. Smith LE, Amlot R, Lambert H, Oliver I, Robin C, Yardley L, et al. Factors associated with adherence to self-isolation and lockdown measures in the UK: a cross-sectional survey. Public Health. 2020;187:41–52.

    Article  CAS  PubMed  Google Scholar 

  12. Steens A, Freiesleben de Blasio B, Veneti L, Gimma A, Edmunds WJ, Van Zandvoort K, et al. Poor self-reported adherence to COVID-19-related quarantine/isolation requests, Norway, April to July 2020. Euro Surveill. 2020;25(37).

  13. Ayakaka I, Ackerman S, Ggita JM, Kajubi P, Dowdy D, Haberer JE, et al. Identifying barriers to and facilitators of tuberculosis contact investigation in Kampala, Uganda: a behavioral approach. Implement Sci. 2017;12(1):33.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Megnin-Viggars O, Carter P, Melendez-Torres GJ, Weston D, Rubin GJ. Facilitators and barriers to engagement with contact tracing during infectious disease outbreaks: A rapid review of the evidence. PLoS ONE. 2020;15(10):e0241473.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Heffner J, Vives ML, FeldmanHall O. Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic. Pers Individ Dif. 2021;170:110420.

    Article  PubMed  Google Scholar 

  16. Shelby T, Caruthers T, Kanner OY, Schneider R, Lipnickas D, Grau LE, et al. Pilot Evaluations of Two Bluetooth Contact Tracing Approaches on a University Campus: Mixed Methods Study. JMIR Form Res. 2021;5(10):e31086.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42-.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Arden MA, Hutchings M, Whelan P, Drabble SJ, Beever D, Bradley JM, et al. Development of an intervention to increase adherence to nebuliser treatment in adults with cystic fibrosis: CFHealthHub. Pilot Feasibility Stud. 2021;7(1):1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bonner C, Jansen J, McKinn S, Irwig L, Doust J, Glasziou P, et al. General practitioners’ use of different cardiovascular risk assessment strategies: a qualitative study. Med J Aust. 2013;199(7):485–9.

    Article  PubMed  Google Scholar 

  20. Fernandez ME, Ten Hoor GA, van Lieshout S, Rodriguez SA, Beidas RS, Parcel G, et al. Implementation Mapping: Using Intervention Mapping to Develop Implementation Strategies. Front Public Health. 2019;7:158.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Niccolai L, Shelby T, Weeks B, Schenck C, Goodwin J, Hennein R, et al. Community Trace: Rapid Establishment of a Volunteer Contact Tracing Program for COVID-19. Am J Public Health. 2020:e1-e4.

  22. Shelby T, Schenck C, Weeks B, Goodwin J, Hennein R, Zhou X, et al. Lessons Learned From COVID-19 Contact Tracing During a Public Health Emergency: A Prospective Implementation Study. Frontiers in Public Health. 2021;9(1196).

  23. Shelby T, Hennein R, Schenck C, Clark K, Meyer AJ, Goodwin J, et al. Implementation of a volunteer contact tracing program for COVID-19 in the United States: A qualitative focus group study. PLoS ONE. 2021;16(5):e0251033.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. QuickFacts. New Haven City, Connecticut. Unites States Census Bureau.

  25. Guest G, Bunce A, Johnson L. How Many Interviews Are Enough?:An Experiment with Data Saturation and Variability. Field Methods. 2006;18(1):59–82.

    Article  Google Scholar 

  26. Braun V, Clarke V. Thematic analysis. APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological. APA handbooks in psychology®. Washington, DC, US: American Psychological Association; 2012. p. 57–71.

  27. Benham JL, Lang R, Kovacs Burns K, MacKean G, Leveille T, McCormack B, et al. Attitudes, current behaviours and barriers to public health measures that reduce COVID-19 transmission: A qualitative study to inform public health messaging. PLoS ONE. 2021;16(2):e0246941.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Oklahoma Contact Tracing now Includes Text Messages [7/19/2021]. Available from: https://oklahoma.gov/covid19/newsroom/2020/july/oklahoma-contact-tracing-now-includes-text-messages-07242020.html.

  29. Coroiu A, Moran C, Campbell T, Geller AC. Barriers and facilitators of adherence to social distancing recommendations during COVID-19 among a large international sample of adults. PLoS ONE. 2020;15(10):e0239795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Centers for Disease C, Prevention. Recommendations for partner services programs for HIV infection, syphilis, gonorrhea, and chlamydial infection. MMWR Recomm Rep. 2008;57(RR-9):1–83. quiz CE1-4.

    Google Scholar 

  31. Miyajima T, Murakami F. Self-Interested Framed and Prosocially Framed Messaging Can Equally Promote COVID-19 Prevention Intention: A Replication and Extension of Jordan et al.‘s Study (2020) in the Japanese Context. Front Psychol. 2021;12:605059.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pak A, McBryde E, Adegboye OA. Does High Public Trust Amplify Compliance with Stringent COVID-19 Government Health Guidelines? A Multi-country Analysis Using Data from 102,627 Individuals. Risk Manag Healthc Policy. 2021;14:293–302.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Figueiras MJ, Ghorayeb J, Coutinho MVC, Marôco J, Thomas J. Levels of Trust in Information Sources as a Predictor of Protective Health Behaviors During COVID-19 Pandemic: A UAE Cross-Sectional Study. Frontiers in Psychology. 2021;12(2780).

  34. Doty AMB, Powell RE, Carr BG, Nelson DB, Rising KL. Identification of Approaches to Improve Patient Trust in Health Systems: A Group Concept Mapping Study. J Healthc Manag. 2018;63(5):e116-e29.

    Google Scholar 

  35. Purvis RS, Hallgren E, Moore RA, Willis DE, Hall S, Gurel-Headley M, et al. Trusted Sources of COVID-19 Vaccine Information among Hesitant Adopters in the United States. Vaccines (Basel). 2021;9(12).

  36. Davis TC, Beyl R, Bhuiyan MAN, Davis AB, Vanchiere JA, Wolf MS, et al. COVID-19 Concerns, Vaccine Acceptance and Trusted Sources of Information among Patients Cared for in a Safety-Net Health System. Vaccines (Basel). 2022;10(6).

  37. Naeem SB, Bhatti R, Khan A. An exploration of how fake news is taking over social media and putting public health at risk. Health Info Libr J. 2021;38(2):143–9.

    Article  PubMed  Google Scholar 

  38. Kelly Johnson KD, Rachel Hennein T, Shelby, Xin Zhou, Amanda J, Gupta A, Ludomirsky J-M, Weiss, Marcella Nunez-Smith, Kristen Soto, J. Lucian Davis. Centering Health Equity Within COVID-19 Contact Tracing: Connecticut’s Community Outreach Specialist Program. Journal of Public Health Management and Practice. In press, 2022.

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Acknowledgements

We thank the New Haven Health Department for their partnership, insight, and guidance throughout this endeavor, without which this project would not have been possible. We also thank the study participants for sharing their experiences and perspectives in order to help their community and improve the COVID-19 response effort.

Funding

This study was funded by the Yale School of Public Health Dean’s Award. TS and RH were funded during the study period by a T32 training grant (T32GM007205).

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Authors and Affiliations

Authors

Contributions

TS, MB, LN, JLD, and LEG contributed to study conceptualization and design. MB and LEG supervised the study. TS, CA, BW and LEG contributed to data collection. TS, CA, AJG, RH, and CS contributed to data curation. TS, JLD, and LEG contributed to data analysis and initial drafting of the manuscript. All authors reviewed and edited the manuscript.

Ethics declarations

Study ethics

The study protocol was approved by the Yale Human Subjects Committee and all methods were carried out in accordance with the appropriate guidelines and regulations. We collected verbal consent at the start of each phone interview, prior to audio-recording.

Consent for publication

All authors consented to the publication of this manuscript.

Competing interests

The authors disclose that LN is a member of the Scientific Advisory Board for Moderna and Janssen Pharmaceuticals. The remaining authors have no disclosures.

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Shelby, T., Arechiga, C., Gupta, A.J. et al. “I can’t do it”: A qualitative study exploring case and contact experiences with COVID-19 contact tracing. BMC Public Health 22, 1963 (2022). https://doi.org/10.1186/s12889-022-14265-8

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  • DOI: https://doi.org/10.1186/s12889-022-14265-8