| Literature DB >> 33004064 |
Tyler A Jacobson1, Lauren E Smith1, Lisa R Hirschhorn2, Mark D Huffman3,4.
Abstract
With the threat of coronavirus disease 2019 (Covid-19) enduring in the United States, effectively and equitably implementing testing, tracing, and self-isolation as key prevention and detection strategies remain critical to safely re-opening communities. As testing and tracing capacities increase, frameworks are needed to inform design and delivery to ensure their effective implementation and equitable distribution, and to strengthen community engagement in slowing and eventually stopping Covid-19 transmission. In this commentary, we highlight opportunities for integrating implementation research into planned and employed strategies in the United States to accelerate reach and effectiveness of interventions to more safely relax social distancing policies and open economies, schools, and other institutions. Implementation strategies, such as adapting evidence-based interventions based on contextual factors, promoting community engagement, and providing data audit and feedback on implementation outcomes, can support the translation of policies on testing, tracing, social distancing, and public mask use into reality. These data can demonstrate how interventions are put into practice and where adaptation in policy or practice is needed to respond to the needs of specific communities and socially vulnerable populations. Incorporating implementation research into Covid-19 policy design and translation into practice is urgently needed to mitigate the worsening health inequities in the pandemic toll and response. Applying rigorous implementation research frameworks and evaluation systems to the implementation of evidence-based interventions which are adapted to contextual factors can promote effective and equitable pandemic response and accelerate learning both among local stakeholders as well as between states to further inform their varied experiences and responses to the pandemic.Entities:
Keywords: Contact tracing; Covid-19; Health inequities; Implementation outcomes; Implementation science; Social distancing policies; Testing
Mesh:
Year: 2020 PMID: 33004064 PMCID: PMC7527776 DOI: 10.1186/s12939-020-01293-2
Source DB: PubMed Journal: Int J Equity Health ISSN: 1475-9276
Using an implementation outcomes framework described by Proctor et al. (2011) [3] to evaluate testing strategies in response to the Covid-19 pandemic
| Implementation outcomes | Definitions | Evaluation of implementation strategies |
|---|---|---|
| Perception that the intervention is agreeable, palatable, or satisfactory | • Community attitudes towards testing | |
| • Documented barriers and facilitators to testing from qualitative surveys and focus groups | ||
| Considered from the perspective of individuals receiving testing | • Input and feedback from community leaders and organizations (not involved in operations) about testing strategies | |
| The intention to employ or adhere to an intervention | • Number of testing sites and daily testing rate by site | |
| Considered from the perspective of entities providing testing | • Testing strategies stratified by geographic location and entity | |
| Perceived fit, relevance, or compatibility of intervention for the given setting and problem | • Community and other stakeholders’ attitudes towards local testing strategies | |
| • Document changes in testing strategies compared to original protocols | ||
| The extent to which an intervention can be successfully used or carried out in a particular setting | • Input and feedback from entities operating testing | |
| • Availability of tests given demand (supply chain) | ||
| The degree to which an intervention was implemented as it was originally intended | • Number of improperly collected, transported, or handled samples | |
| • Whether test results are returned promptly and confidentially | ||
| Cost impact of an implementation effort | • Overall and per-test costs across geographic areas and/or by organizations providing testing | |
| Integration of intervention within a setting or its subsystems | • Number of tests, by test type, across key subgroupsa and representativeness of testing† | |
| • Proportions of communities tested and percent-positive rates in a given time period | ||
| How well an intervention is maintained over time within an organization or setting | • Maintenance of testing capacity and performance for population health over time and across key subgroupsab |
aKey subgroups: age, sex, race/ethnicity, language, geographic unit
bExplore reasons for variability across key subgroups