Elvin Hsing Geng and colleagues discuss mechanism mapping and its utility in conceptualizing and understanding how implementation strategies produce desired effects.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Ingrid Eshun-Wilson and colleagues summarize gaps in primary HIV implementation research methods and reporting, and propose areas for future methodological development.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
3.
The Doctor’s Oldest Tool Geng, Elvin H
New England journal of medicine/The New England journal of medicine,
01/2022, Letnik:
386, Številka:
1
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For a primary care physician, widespread Covid denialism triggers memories of a patient dying of AIDS who insisted that HIV was not the cause of the disease. Getting through to him had turned out to ...be a matter of human relations rather than scientific facts.
Three members of PLOS Medicine's editorial board who are leading researchers in implementation science define the characteristics of high-quality studies and invite their submission to the journal.
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Doctor as Street-Level Bureaucrat Geng, Elvin H
New England journal of medicine/The New England journal of medicine,
01/2021, Letnik:
384, Številka:
2
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Doctors are street-level bureaucrats, making decisions that can have particularly significant effects on vulnerable people. Often when we’re faced with a choice between defending the system and ...advocating for the patient, we instinctively do the former.
Optimizing services to facilitate engagement and retention in care of people living with HIV (PLWH) on antiretroviral therapies (ARTs) is critical to decrease HIV-related morbidity and mortality and ...HIV transmission. We systematically reviewed the literature for the effectiveness of implementation strategies to reestablish and subsequently retain clinical contact, improve viral load suppression, and reduce mortality among patients who had been lost to follow-up (LTFU) from HIV services.
We searched 7 databases (PubMed, Cochrane, ERIC, PsycINFO, EMBASE, Web of Science, and the WHO regional databases) and 3 conference abstract archives (CROI, IAC, and IAS) to find randomized trials and observational studies published through 13 April 2020. Eligible studies included those involving children and adults who were diagnosed with HIV, had initiated ART, and were subsequently lost to care and that reported at least one review outcome (return to care, retention, viral suppression, or mortality). Data were extracted by 2 reviewers, with discrepancies resolved by a third. We characterized reengagement strategies according to how, where, and by whom tracing was conducted. We explored effects, first, among all categorized as LTFU from the HIV program (reengagement program effect) and second among those found to be alive and out of care (reengagement contact outcome). We used random-effect models for meta-analysis and conducted subgroup analyses to explore heterogeneity. Searches yielded 4,244 titles, resulting in 37 included studies (6 randomized trials and 31 observational studies). In low- and middle-income countries (LMICs) (N = 16), tracing most frequently involved identification of LTFU from the electronic medical record (EMR) and paper records followed by a combination of telephone calls and field tracing (including home visits), by a team of outreach workers within 3 months of becoming LTFU (N = 7), with few incorporating additional strategies to support reengagement beyond contact (N = 2). In high-income countries (HICs) (N = 21 studies), LTFU were similarly identified through EMR systems, at times matched with other public health records (N = 4), followed by telephone calls and letters sent by mail or email and conducted by outreach specialist teams. Home visits were less common (N = 7) than in LMICs, and additional reengagement support was similarly infrequent (N = 5). Overall, reengagement programs were able to return 39% (95% CI: 31% to 47%) of all patients who were characterized as LTFU (n = 29). Reengagement contact resulted in 58% (95% CI: 51% to 65%) return among those found to be alive and out of care (N = 17). In 9 studies that had a control condition, the return was higher among those in the reengagement intervention group than the standard of care group (RR: 1.20 (95% CI: 1.08 to 1.32, P < 0.001). There were insufficient data to generate pooled estimates of retention, viral suppression, or mortality after the return.
While the types of interventions are markedly heterogeneity, reengagement interventions increase return to care. HIV programs should consider investing in systems to better characterize LTFU to identify those who are alive and out of care, and further research on the optimum time to initiate reengagement efforts after missed visits and how to best support sustained reengagement could improve efficiency and effectiveness.
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...in wicked problems, the elusive problem formulation precludes a shared understanding of success, or even progress, by stakeholders. ...in wicked problems, the absence of a shared agenda (and ...measures of progress) undermines aligned and effective action. ...the HIV response has used the cascade framework to define clear targets and extract commitments from elected leaders to meet those targets, thereby enabling measurable progress. The initiative has enrolled over 350 cities around the world—with the mayors of London, Paris, and other cosmopolitan cities all vocal advocates of 90–90–90. Because the cascade is reported, progress toward 90–90–90 can be widely assessed, creating some basis for accountability.
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Evidence, in multiple forms, is a foundation of implementation science. For public health and clinical practice, evidence includes the following: type 1 evidence on etiology and burden; type 2 ...evidence on effectiveness of interventions; and type 3: evidence on dissemination and implementation (D&I) within context. To support a vision for development and use of evidence in D&I science that is more comprehensive and equitable (particularly for type 3 evidence), this article aims to clarify concepts of evidence, summarize ongoing debates about evidence, and provide a set of recommendations and tools/resources for addressing the "how-to" in filling evidence gaps most critical to advancing implementation science.
Because current conceptualizations of evidence have been relatively narrow and insufficiently characterized in our opinion, we identify and discuss challenges and debates about the uses, usefulness, and gaps in evidence for implementation science. A set of questions is proposed to assist in determining when evidence is sufficient for dissemination and implementation. Intersecting gaps include the need to (1) reconsider how the evidence base is determined, (2) improve understanding of contextual effects on implementation, (3) sharpen the focus on health equity in how we approach and build the evidence-base, (4) conduct more policy implementation research and evaluation, and (5) learn from audience and stakeholder perspectives. We offer 15 recommendations to assist in filling these gaps and describe a set of tools for enhancing the evidence most needed in implementation science.
To address our recommendations, we see capacity as a necessary ingredient to shift the field's approach to evidence. Capacity includes the "push" for implementation science where researchers are trained to develop and evaluate evidence which should be useful and feasible for implementers and reflect community or stakeholder priorities. Equally important, there has been inadequate training and too little emphasis on the "pull" for implementation science (e.g., training implementers, practice-based research). We suggest that funders and reviewers of research should adopt and support a more robust definition of evidence. By critically examining the evolving nature of evidence, implementation science can better fulfill its vision of facilitating widespread and equitable adoption, delivery, and sustainment of scientific advances.
The COVID-19 vaccination campaign in the US has been immensely successful in vaccinating those who are receptive, further increases in vaccination rates however will require more innovative ...approaches to reach those who remain hesitant. Developing vaccination strategies that are modelled on what people want could further increase uptake. To inform COVID-19 vaccine distribution strategies that are aligned with public preferences we conducted a discrete choice experiment among the US public (N = 2,895) between March 15 to March 22, 2021. We applied sampling weights, evaluated mean preferences using mixed logit models, and identified latent class preference subgroups. On average, the public prioritized ease, preferring single to two dose vaccinations (mean preference: -0.29; 95%CI: -0.37 to -0.20), vaccinating once rather than annually (mean preference: -0.79; 95%CI: -0.89 to -0.70) and reducing waiting times at vaccination sites. Vaccine enforcement reduced overall vaccine acceptance (mean preference -0.20; 95%CI: -0.30 to -0.10), with a trend of increasing resistance to enforcement with increasing vaccine hesitancy. Latent class analysis identified four distinct preference phenotypes: the first prioritized inherent "vaccine features" (46.1%), the second were concerned about vaccine "service delivery" (8.8%), a third group desired "social proof" of vaccine safety and were susceptible to enforcement (13.2%), and the fourth group were "indifferent" to vaccine and service delivery features and resisted enforcement (31.9%). This study identifies several critical insights for the COVID-19 public health response. First, identifying preference segments is essential to ensure that vaccination services meet the needs of diverse population subgroups. Second, making vaccination easy and promoting autonomy by simplifying services and offering the public choices (where feasible) may increase uptake in those who remain deliberative. And, third vaccine mandates have the potential to increase vaccination rates in susceptible groups but may simultaneously promote control aversion and resistance in those who are most hesitant.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK