Declines in Spending Despite Positive Returns on Investment: Understanding Public Health's Wrong Pocket Problem

McCullough, J. Mac

doi:10.3389/fpubh.2019.00159

Cited by 16 publications

(18 citation statements)

References 10 publications

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“…While previous studies have estimated the ROI of public health at 88 units of benefit per each cost unit, the fact that up to one fifth of the national GDP can be lost within a few weeks when asymptomatic cases are not detected –as shown in figures 1 E and F − suggest that the benefits induced by public health may be higher than previously estimated. 9-11 It is postulated that, to materialize and expand a positive relationship between public health and the economy, current paradigms may need to be re-assessed. While reductionist approaches may be invalid (such as the assumption that continuous data can be dichotomized without inducing errors 12 ), interdisciplinary approaches may support local and global efforts meant to rapidly and effectively identify and isolate asymptomatic cases and, consequently, prevent the dissemination of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Addressing a complicated problem: can COVID-19 asymptomatic cases be detected – and epidemics stopped− when testing is limited and the location of such cases unknown?

Rivas

Hoogesteyn

Hittner

et al. 2020

Preprint

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Can the COVID-19 pandemic be stopped when the principal disseminators −asymptomatic cases− are not easily observable? This question was addressed exploring the cumulative epidemiologic data reported by 51 countries, up to October 2, 2020. In particular, the validity of test positivity and its inverse (the ratio of tests performed per case detected) to indicate whether asymptomatic cases are being detected and isolated –even when only a minor percentage of the population is tested− was evaluated. By linking test positivity data to the number of COVID-19 related deaths reported per million inhabitants, the research question was answered: countries that expressed a high percentage of test positivity (>5%) reported, on average, 15 times more deaths than countries that exhibited <1% test positivity. It is suggested that such a large difference in outcomes is due to the exponential growth that epidemics may experience when silent (asymptomatic) cases are not detected and, consequently, the disease disseminates. Because temporal and geo-referenced data on test positivity may facilitate cost-effective, site-specific testing policies, it is postulated that the risk of uncontrolled epidemics may be ameliorated when test positivity is investigated.

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Section: Discussionmentioning

confidence: 99%

Addressing a complicated problem: can COVID-19 asymptomatic cases be detected – and epidemics stopped− when testing is limited and the location of such cases unknown?

Rivas

Hoogesteyn

Hittner

et al. 2020

Preprint

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“…This approach has also been described as the 'wrong pocket problem', i.e., the area where the investment is made does not only benefit from it: a larger area is benefited as well [29]. One example of this geographically grounded, cost-benefit oriented approach has recently been applied in an emergency vaccination against rabies conducted in Tanzania, where a limited intervention in a small ring that surrounded a large city also benefitted the large city [11].…”

Section: Discussionmentioning

confidence: 99%

“…When geographical-epidemiological relationships are considered, it is possible to identify policies that benefit not only the area of direct intervention but also the surrounding area –as commonly practiced in flood protection-related policies [28]. This approach has also been described as the ‘wrong pocket problem’, i.e., the area where the investment is made does not only benefit from it: a larger area is benefited as well [29]. One example of this geographically grounded, cost-benefit oriented approach has recently been applied in an emergency vaccination against rabies conducted in Tanzania, where a limited intervention in a small ring that surrounded a large city also benefitted the large city [11].…”

Section: Discussionmentioning

confidence: 99%

Toward a COVID-19 testing policy: where and how to test when the purpose is to isolate silent spreaders

Rivas

Hoogesteijn

Hittner

et al. 2020

Preprint

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SummaryBackgroundTo stop pandemics, such as COVID-19, infected individuals should be detected, treated if needed, and –to prevent contacts with susceptible individuals-isolated. Because most infected individuals may be asymptomatic, when testing misses such cases, epidemics may growth exponentially, inducing a high number of deaths. In contrast, a relatively low number of COVID-19 related deaths may occur when both symptomatic and asymptomatic cases are tested.MethodsTo evaluate these hypotheses, a method composed of three elements was evaluated, which included: (i) county- and country-level geo-referenced data, (ii) cost-benefit related considerations, and (iii) temporal data on mortality or test positivity (TP). TP is the percentage of infections found among tested individuals. Temporal TP data were compared to the tests/case ratio (T/C ratio) as well as the number of tests performed/million inhabitants (tests/mi) and COVID-19 related deaths/million inhabitants (deaths/mi).FindingsTwo temporal TP profiles were distinguished, which, early, displayed low (∼ 1 %) and/or decreasing TP percentages or the opposite pattern, respectively. Countries that exhibited >10 TP % expressed at least ten times more COVID-19 related deaths/mi than low TP countries. An intermediate pattern was identified when the T/C ratio was explored. Geo-referenced, TP-based analysis discovered municipalities where selective testing would be more cost-effective than alternatives.InterpretationsWhen TP is low and/or the T/C ratio is high, testing detects asymptomatic cases and the number of COVID-19 related deaths/mi is low. Geo-referenced TP data can support cost-effective, site-specific policies. TP promotes the prompt cessation of epidemics and fosters science-based testing policies.FundingNoneResearch in contextEvidence before this studyTo map this field, bibliographic searches were conducted in the Web of Science, which included the following results: (i) COVID-19 (95,133 hits), (ii) SARS COV-2 (33,680 hits), (iii) testing policy and COVID-19 (939 hits), (iv) testing policy and SARS COV-2 (340 hits), (v) testing policy and COVID-19 and asymptomatic (80 hits), (vi) testing policy and SARS COV-2 and asymptomatic (54 hits); (vii) test positivity and COVID-19 and validation (7 hits), and (viii) test positivity and SARS CoV-2 and validation (5 hits). Therefore, before this study, testing policy in relation to asymptomatic cases as well as test positivity represented a very low proportion (between ∼1 thousandth to ∼ 1 ten thousandth) of all publications. While many articles distinguished between diagnostic and screening tests, no paper was found in which testing policy is mentioned as part of a process ultimately designed to isolate all infected individuals. The few articles that mentioned test positivity only investigated symptomatic cases. These quanti/qualitative assessments led the authors to infer that neither testing policy nor test positivity had been adequately validated and/or investigated.Added value of this studyWe provide the first validation of test positivity as an estimate of disease prevalence under rapidly changing conditions: in pandemics, disease prevalence may vary markedly within short periods of time. We also address a double limitation of control campaigns against COVID-19, namely: it is unknown who and where to test. Asymptomatic cases are not likely to seek medical assistance: while they feel well, they silently spread this pandemic. Because they represent approximately half of all infected individuals, they are a large, moving, and invisible target. Where to find them is also unknown because (i) randomized testing is likely to fail and (ii) testing is very limited. Usually, the locations where infected people reside are not randomly distributed but geographically clustered, and, up to now less than four persons per thousand inhabitants are tested on a given day. However, by combining geo-referenced test positivity data with cost-benefit considerations, we generate approaches not only likely to induce high benefits without increasing costs but also free of assumptions: we measure bio-geography as it is.Implications of all the available evidenceThe fact that asymptomatic cases were not tested in many countries may explain the exponential growth and much higher number of deaths observed in those countries. Ineffective testing (and, therefore, ineffective isolation) can also result from the absence of geo-referenced data analysis. Because the geographical location where people reside, work, study, or shop is not a random event, the analysis of small greographical areas is essential. Only when actual geographical relationships are observed, optimal (cost-benefit oriented) testing policies can be devised.

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“…Despite the potential advantages of community-based CHTs, primary care based teams are more common because of the ease in identifying potential cost savings to justify funding. (5) Care provided in these kinds of patient-centered, accountable care settings has been found to be associated with lower total expenditures. (6) One example of a successful statewide community-based CHT model is Vermont, which has sponsored a statewide network of CHTs for almost two decades through the Blueprint for Health initiative (Blueprint).…”

Section: Introductionmentioning

confidence: 99%

Community Health Teams: A Qualitative Study about the Factors influencing the Decision-Making Process for Resource Allocation

Natkin

Broek-Altenburg

Benson

et al. 2022

Preprint

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Background: The purpose of this study was to explore the contextual factors influencing how individual Community Health Teams make decisions about what services to offer and how to allocate their resources. Methods: We conducted thirteen semi-structured interviews with Community Health Team (CHTs) leaders between January to March, 2021. We analyzed interviewees descriptions of their service offerings, resources allocation, and decision-making process to identify themes. Results: Four major themes emerged from the interview data as contextual factors influencing community health team program managers’ decision-making process: commitment to offering high quality care coordination, Blueprint’s stable and flexible structure, use of data in priority setting, and leveraging community partnerships and local resources. Conclusions: Community based CHTs with flexible funding allowed programs to tailor service offerings in response to community needs. It is important for teams to have access to community level data. Teams are cultivating and leveraging community partners to increase their care coordination capacity, which is focus of their work. CHTs are a model for leveraging community partnerships to increase service capacity and pubic engagement in health services for other states to replicate.

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Declines in Spending Despite Positive Returns on Investment: Understanding Public Health's Wrong Pocket Problem

Cited by 16 publications

References 10 publications

Addressing a complicated problem: can COVID-19 asymptomatic cases be detected – and epidemics stopped− when testing is limited and the location of such cases unknown?

Addressing a complicated problem: can COVID-19 asymptomatic cases be detected – and epidemics stopped− when testing is limited and the location of such cases unknown?

Toward a COVID-19 testing policy: where and how to test when the purpose is to isolate silent spreaders

Community Health Teams: A Qualitative Study about the Factors influencing the Decision-Making Process for Resource Allocation

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