The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal ...Read More

ObjectiveWe assessed the impact of key population variables (age, gender, income and education) on perceptions of governmental effectiveness in communicating about COVID-19, helping meet needs for food and shelter, providing physical and mental healthcare services, and allocating dedicated resources to vulnerable populations.DesignCross-sectional study carried out in June 2020.Participants and setting13 426 individuals from 19 countries.ResultsMore than 60% of all respondents felt their government had communicated adequately during the pandemic. National variances ranged from 83.4% in China down to 37.2% in Brazil, but overall, males and those with a higher income were more likely to rate government communications highly. Almost half (48.8%) of the respondents felt their government had ensured adequate access to physical health services (ranging from 89.3% for Singapore to 27.2% for Poland), with higher ratings reported by younger and higher-income respondents. Ratings of mental health support were lower overall (32.9%, ranging from 74.8% in China to around 15% in Brazil and Sweden), but highest among younger respondents. Providing support for basic necessities of food and housing was rated highest overall in China (79%) and lowest in Ecuador (14.6%), with higher ratings reported by younger, higher-income and better-educated respondents across all countries. The same three demographic groups tended to rate their country’s support to vulnerable groups more highly than other respondents, with national scores ranging from around 75% (Singapore and China) to 19.5% (Sweden). Subgroup findings are mostly independent of intercountry variations with 15% of variation being due to intercountry differences.ConclusionsThe tendency of younger, better-paid and better-educated respondents to rate their country’s response to the pandemic more highly, suggests that government responses must be nuanced and pay greater attention to the needs of less-advantaged citizens as they continue to address this pandemic.

Readers are invited to submit letters for publication in this department. Submit letters online at http://joem.edmgr.com. Choose "Submit New Manuscript." A signed copyright assignment and financial disclosure form must be submitted with the letter. Form available at www.joem.org under Author and Reviewer information. To the Editor: The most widely utilized Nucleic Acid Amplification Test (NAAT) to detect SARS-CoV-2 RNA is the reverse transcriptase-polymerase chain reaction (RT-PCR) test, manufactured by many companies targeting one or more genomic regions of the virus. Although there is a several log difference in the sensitivity of the different RT-PCR tests to pick up viral RNA, many have sufficient analytical sensitivity to detect a viral load during the preinfectious stage in infected individuals.1–6 However, none of the tests have sufficient clinical sensitivity to detect virus during the first several days after infection, nor are they 100% sensitive at the time of peak infectiveness.7,8 Much has been written about the issue of false negative RT-PCR tests in symptomatic, presymptomatic, and asymptomatic persons infected with the virus.7,8 Less has been published about the problem of false positive RT-PCR or other NAAT tests. In the United States, because of a shortage of tests and testing facilities during the early months of the pandemic tests were primarily used for diagnoses to identify a person with an active infection associated with signs or symptoms of COVID-19 or who had definite or suspected recent exposure to the virus.9 Later, the Federal Drug Administration (FDA) approved testing to be extended to screen for infection in individuals without known or suspected exposure to SARS-CoV-2 living in congregate settings, such as long-term care facilities or prisons.9 Finally, periodic screening programs have been developed for educational institutions, sport teams, and the workplace to detect asymptomatic, presymptomatic, and symptomatic infected individuals early and isolate them to reduce them infecting others. The overall accuracy of a RT-PCR test is based upon its sensitivity representing the ability to detect infected individuals and the specificity, which is the percentage of uninfected people who test negative. The FDA has published recommendations concerning the data and information that test manufacturers should supply in their application for Emergency Use Authorization (EUA).10 For analytical specificity they ask for in vitro cross-reactivity studies to demonstrate that the test does not react with related pathogens, high prevalence disease agents, and normal or pathogenic flora that are reasonably likely to be encountered in a clinical specimen.11 Many of the RT-PCR assays have a 100% sensitivity in this analysis as reported by the manufacturers.12 For clinical evaluation, the FDA recommends testing 30 positive clinical samples and 30 individual negative samples and comparing the results of the test under consideration to an existing EUA RT-PCR test of high sensitivity. Acceptable clinical performance is defined as a minimum 95% positive and negative percent agreement (PPA and NPA). For a screening indication, the PPA recommendation remains at more than or equal to 95% and the NPA is raised to more than or equal to 98% to reduce false positive test results.11 In actual use, the clinical sensitivity and specificity of many of these tests is lower in part because of issues surrounding sample collection, handling, and analysis.8,12,13 The performance of these tests when deployed depends not only on their clinical sensitivities and specificities, but also the prevalence of SARS-CoV-2 infections in the setting in which the test is being used. If we consider a test that conforms to the FDA's recommendations for performance in a diagnostic (95% sensitivity and specificity) or screening setting (95% sensitivity, 98% specificity), we can compare its ideal clinical performance when the prevalence of active infection may be 10% (a diagnostic setting) and a prevalence of 1%, as may be found in a screening program. In the diagnostic example, for every 10,000 individuals there will be 1000 infected and 9000 uninfected persons. Of the infected persons, 950 will be detected by the test (true positives) and 50 will be missed (false negatives). For the 9000 uninfected people, 8820 will correctly have negative tests (true negatives) and 180 will be positive (false positive). The positive predictive value (PPV) is the proportion of all positive tests that are true positives, in this case 950/(950 + 180) or 84%. Thus, most of the positive tests are true positives. Doing these same calculations for the screening scenario, 100 of the 10,000 individuals are infected and 9900 are not. The test will detect 95 of the infected persons and five will be falsely negative. For those who are not infected, 9702 will be correctly diagnosed and 198 will be false positives. The PPV is 95/95 + 198 or 32.4%. In this case, 2/3 of the positive results are false positives. For a prevalence of 0.1%, the PPV drops to 4.5%. Table 1 lists various factors that have been documented to contribute to false-positive RT-PCR results.12,14–19 Based upon our own experience in investigating groups of false-positive RT-PCR results and discussions with laboratory directors, the two most common problems are contamination and determining the cut-off for stating that a specimen is positive with a low viral load versus being called indeterminate or equivocal. The WHO, and an international consortium of experts have addressed these issues and have produced a checklist for laboratories to reduce possible causes of false-positive RT-PCR results and how to handle equivocal results.19,20 TABLE 1 - Causes of False Positive SARS-CoV-2 RT-PCR Results (Modified From Ref12,13) Contamination during Sampling (eg, an infected worker or surfaces; aerosolization of virus during collection)15 Extraction (eg, aerosolization in containment hood) PCR amplification Production of Lab Reagents (eg, manufacturers of the positive control may have contaminated other reagents produced in the same facility; contamination of other consumables)17–19 Contamination of the equipment by high viral titer specimens (eg, sample carryover)16 Cross-reaction with other viruses (eg, other coronaviruses) Sample mix-ups Software problems Data entry or transmission errors Miscommunicating results Variations in parameters around the LOD and definition of an indeterminate result14,16,20 Assuming that an indeterminate result is a positive Non-specific reactions15 LOD, limit of detection; RT-PCR, reverse transcriptase-polymerase chain reaction. The overdiagnosis of SARS-CoV-2 infection has multiple potential adverse effects (Table 2)12,21: the inconvenience, financial, and psychological issues affecting those misdiagnosed; the possible exposure of uninfected individuals to infected people in hospital or congregate living areas; misdiagnosed persons foregoing social distancing and the masks use because they think that they are immune from COVID-19; and temporary closure of a business because of the need to quarantine coworkers. In addition, the overdiagnosis can inflate the number of asymptomatic infections in public health statistics. TABLE 2 - Impacts of False Positive Results (Modified From Ref12,21) Unnecessary isolation of individuals and quarantining of close contacts with financial and psychological strains16,22 Unnecessary contact tracing and testing23 Wasteful consumption of personal protective equipment Delays in surgical or other procedures16,23 Prolong hospital stays16,23 with wasteful consumption of PPE Potentially harboring uninfected individuals with infected individuals in hospitals and congregate living areas with possible nosocomial infection16,22 Possible exposure to inappropriate medical treatment Individual given false sense of security about immunity so may not follow public health guidelines or receive vaccination Impede correct diagnosis of patients with symptoms Overdiagnosis may distort epidemiologic statistics by including false-positives to estimate prevalence, hospitalization, and death rates as well as modeling (eg, some individuals classified as asymptomatic carriers may actually had a false positive test) PPE, Personal Protective Equipment. Recognizing that a positive RT-PCR result may be a false positive may be difficult. If a RT-PCR-positive individual has signs or symptoms of COVID-19 or has had exposure to somebody who has been shown or suspected of harboring the virus, it is prudent to assume that the result is a true positive, as has been the recommendation of the WHO and the Centers for Disease Control.24,25 However, in an asymptomatic individual without known close contact with an infectious individual, especially in a low prevalence setting, the finding of a positive RT-PCR test result should raise the possibility that the result is a false positive. "Red flags" that should alert the laboratory personnel include finding an acute rise in the percentage of positive results in comparison to the days and weeks before for all of the samples run in the lab or from a particular collection site, noting that multiple positive samples were in close proximity on the plates in the PCR platform, or finding that the high volume of positive samples exhibit high cycle time (Ct) values that could be associated with a low viral load or issues affecting the cut-off for calling a sample positive, indeterminate, or negative.19,20 In these situations, the laboratory should re-extract the original sample and rerun it on the original PCR platform or a different platform with a similar sensitivity. If this cannot be done, a new sample should be obtained and tested.19,20 We have examined the issue of false positive results in a screening setting for a segment of the entertainment industry. The various unions that represent members involved in studio and TV productions have provided guidelines for testing and other safety measures in a publication, The Safe Way Forward.26 They have divided productions into several zones each with their own PCR testing requirements from testing three times a week to testing every 2 weeks. From September 27 through December 5, 2020, The Walt Disney Company performed 122,300 PCR tests at TV production sites, of which 323 were positive (0.26%). After removing the 84 positive tests found during the pre-employment screening, which leads to the individual entering isolation and not working on a production, the positivity rate during production was 0.19%. This rate is low in comparison to the average US rate during that same period (4.1% to 10.5%)27 because members are in a screening program and tested frequently. Also, the studios have instituted strict safety measures.26 In some, but not all instances, an unexpected positive result in an asymptomatic cast or crew member who had prior negative PCR tests, led to an evaluation of whether the test was a false positive by retesting the person 24 or more hours after the positive test on at least two occasions. If both retests were negative, we considered the first test to be a false positive. Of the 239 positive tests found after the pre-employment tests were removed, 54 (22.6%) were deemed to be false positives, giving a positive predictive value of 77.4%. An important caveat to these numbers is that there was a selection bias in who was investigated for the possibility of a false positive result. As noted, all the individuals were asymptomatic and had at least a negative pre-employment test, and many had multiple negative PCR tests before a positive appeared. Also, there were "outbreaks" of positive tests due to documented contamination of reagents or mistakes in programing of the PCR platform. Finding multiple asymptomatic individuals who may not have been in contact with each other led to retesting of the original nasal swabs and testing of freshly obtained new specimens. Since we did not systematically reevaluate every positive test, we may have underestimated the false positive rate. Our experience and the data reviewed above has led us to develop an algorithm for evaluating an unexpected positive result in an asymptomatic individual without known close contact with an actively infected person in a screening setting for the entertainment industry (Fig. 1). We feel that this algorithm should be applicable to any screening situation and conforms to the recommendation of the WHO, the United Kingdom, and the Norwegian Institute of Public Health,20,28,29 as well as multiple authors.12,15,21,30,31FIGURE 1: Management of a positive molecular test in a screening setting.In summary, we have provided additional evidence that false positive SARS-CoV-2 PCR test results do occur in the clinical setting and are especially a problem in a low prevalence screening situation where the prior probability of a positive test is low. Although it is acknowledged that resource limitations may constrain the amount of retesting performed, we posit that the human and economic costs of considering all positive results to be definitive evidence of infection warrant an evaluation for the possibility that the result is falsely positive in an asymptomatic individual without known exposure to an actively infected person.

The article, COVID-19 Medical Vulnerability Indicators: Predictive Local Data Model for Equity in Public Health Decision-Making (2021), is an important contribution to identifying and prioritizing the needs of Los Angeles' public healthcare in responding to the COVID-19 pandemic crisis [...].

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal ...Read More

Context: Poor physical and mental health and substance use disorder can be causes and consequences of homelessness. Approximately 2.1 million persons per year in the United States experience homelessness. People experiencing homelessness have high rates of emergency department use, hospitalization, substance use treatment, social services use, arrest, and incarceration. Objectives: A standard approach to treating homeless persons with a disability is called Treatment First, requiring clients be “housing ready”—that is, in psychiatric treatment and substance-free—before and while receiving permanent housing. A more recent approach, Housing First, provides permanent housing and health, mental health, and other supportive services without requiring clients to be housing ready. To determine the relative effectiveness of these approaches, this systematic review compared the effects of both approaches on housing stability, health outcomes, and health care utilization among persons with disabilities experiencing homelessness. Design: A systematic search (database inception to February 2018) was conducted using 8 databases with terms such as “housing first,” “treatment first,” and “supportive housing.” Reference lists of included studies were also searched. Study design and threats to validity were assessed using Community Guide methods. Medians were calculated when appropriate. Eligibility Criteria: Studies were included if they assessed Housing First programs in high-income nations, had concurrent comparison populations, assessed outcomes of interest, and were written in English and published in peer-reviewed journals or government reports. Main Outcome Measures: Housing stability, physical and mental health outcomes, and health care utilization. Results: Twenty-six studies in the United States and Canada met inclusion criteria. Compared with Treatment First, Housing First programs decreased homelessness by 88% and improved housing stability by 41%. For clients living with HIV infection, Housing First programs reduced homelessness by 37%, viral load by 22%, depression by 13%, emergency departments use by 41%, hospitalization by 36%, and mortality by 37%. Conclusions: Housing First programs improved housing stability and reduced homelessness more effectively than Treatment First programs. In addition, Housing First programs showed health benefits and reduced health services use. Health care systems that serve homeless patients may promote their health and well-being by linking them with effective housing services.

AffiliationsBobby Milstein is with ReThink Health (an initiative of the Rippel Foundation) and the Massachusetts Institute of Technology Sloan School of Management, Cambridge, MA. Jonathan Fielding is with the University of California, Los Angeles Fielding School of Public Health and Geffen School of Medicine.

Landmark reports from reputable sources have concluded that the United States wastes hundreds of billions of dollars every year on medical care that does not improve health outcomes. While there is widespread agreement over how wasteful medical care spending is defined, there is no consensus on its magnitude or categories. A shared understanding of the magnitude and components of the issue may aid in systematically reducing wasteful spending and creating opportunities for these funds to improve public health.To this end, we performed a review and crosswalk analysis of the literature to retrieve comprehensive estimates of wasteful medical care spending. We abstracted each source's definitions, categories of waste, and associated dollar amounts. We synthesized and reclassified waste into 6 categories: clinical inefficiencies, missed prevention opportunities, overuse, administrative waste, excessive prices, and fraud and abuse.Aggregate estimates of waste varied from $600 billion to more than $1.9 trillion per year, or roughly $1800 to $5700 per person per year. Wider recognition by public health stakeholders of the human and economic costs of medical waste has the potential to catalyze health system transformation.

Objectives. To quantify changes in US health care spending required to reach parity with high-resource nations by 2030 or 2040 and identify historical precedents for these changes.Methods. We analyzed multiple sources of historical and projected spending from 1970 through 2040. Parity was defined as the Organisation for Economic Co-operation and Development (OECD) median or 90th percentile for per capita health care spending.Results. Sustained annual declines of 7.0% and 3.3% would be required to reach the median of other high-resource nations by 2030 and 2040, respectively (3.2% and 1.3% to reach the 90th percentile). Such declines do not have historical precedent among US states or OECD nations.Conclusions. Traditional approaches to reducing health care spending will not enable the United States to achieve parity with high-resource nations; strategies to eliminate waste and reduce the demand for health care are essential.Public Health Implications. Excess spending reduces the ability of the United States to meet critical public health needs and affects the country's economic competitiveness. Rising health care spending has been identified as a threat to the nation's health. Public health can add voices, leadership, and expertise for reversing this course.

Many actors in the response to COVID-19 are holding out for a vaccine to be developed. But in the meantime, tried and tested public-health measures for controlling outbreaks can be implemented. A scorecard can be used to assess governments' responses to the outbreak.

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal ...Read More

AffiliationsJonathan Fielding is with the Department of Health Policy & Management, Fielding School of Public Health, and the Department of Pediatrics, Geffen School of Medicine, University of California, Los Angeles.

Immunization represents one of the greatest public health achievements. Vaccines save lives, make communities more productive and strengthen health systems. They are critical to attaining the UN Sustainable Development Goals. Vaccination also represents value for investment in public health. It is undisputedly one of the most cost-effective ways of avoiding disease, each year preventing 2-3 million deaths globally. We the concerned scientists, public health professionals, physicians, and child health advocates issue this Salzburg Statement along with the International Working Group on Vaccination and Public Health Solutions, proclaiming our unwavering commitment to universal childhood vaccination, and our pledge to support the development, testing, implementation, and evaluation of new, effective, and fact-based communication programs. Our goal is to explain vaccinations to parents or caregivers, answer their questions, address their concerns, and maintain public confidence in the personal, family and community protection that childhood vaccines provide. Every effort will also be made to communicate the dangers associated with these childhood illnesses to parents and communities since this information seems to have been lost in the present-day narrative. While vaccine misinformation has led to serious declines in community vaccination rates that require immediate attention, in other communities, particularly in low-income countries, issues such as lack of access. and unstable supply of vaccines need to be addressed.

BACKGROUND:Current methods for assessing strength of evidence prioritize the contributions of randomized controlled trials (RCTs). The objective of this study was to characterize strength of evidence (SOE) tools in recent use, identify their application to lifestyle interventions for improved longevity, vitality, or successful aging, and to assess implications of the findings. METHODS:The search strategy was created in PubMed and modified as needed for four additional databases: Embase, AnthropologyPlus, PsycINFO, and Ageline, supplemented by manual searching. Systematic reviews and meta-analyses of intervention trials or observational studies relevant to lifestyle intervention were included if they used a specified SOE tool. Data was collected for each SOE tool. Conditions necessary for assigning the highest SOE grading and treatment of prospective cohort studies within each SOE rating framework were summarized. The expert panel convened to discuss the implications of findings for assessing evidence in the domain of lifestyle medicine. RESULTS AND CONCLUSIONS:A total of 15 unique tools were identified. Ten were tools developed and used by governmental agencies or other equivalent professional bodies and were applicable in a variety of settings. Of these 10, four require consistent results from RCTs of high quality to award the highest rating of evidence. Most SOE tools include prospective cohort studies only to note their secondary contribution to overall SOE as compared to RCTs. We developed a new construct, Hierarchies of Evidence Applied to Lifestyle Medicine (HEALM), to illustrate the feasibility of a tool based on the specific contributions of diverse research methods to understanding lifetime effects of health behaviors. Assessment of evidence relevant to lifestyle medicine requires a potential adaptation of SOE approaches when outcomes and/or exposures obviate exclusive or preferential reliance on RCTs. This systematic review was registered with the International Prospective Register of Systematic Reviews, PROSPERO [CRD42018082148].

Expanded in-school instructional time (EISIT) may reduce racial/ethnic educational achievement gaps, leading to improved employment, and decreased social and health risks. When targeted to low-income and racial/ethnic minority populations, EISIT may thus promote health equity. Community Guide systematic review methods were used to search for qualified studies (through February 2015, 11 included studies) and summarize evidence of the effectiveness of EISIT on educational outcomes. Compared with schools with no time change, schools with expanded days improved students' test scores by a median of 0.05 standard deviation units (range, 0.0-0.25). Two studies found that schools with expanded day and year improved students' standardized test scores (0.04 and 0.15 standard deviation units). Remaining studies were inconclusive. Given the small effect sizes and a lack of information about the use of added time, there is insufficient evidence to determine the effectiveness of EISIT on academic achievement and thus health equity.

Students may lose knowledge and skills achieved in the school year during the summer break, with losses greatest for students from low-income families. Community Guide systematic review methods were used to summarize evaluations (published 1965-2015) of the effectiveness of year-round school calendars (YRSCs) on academic achievement, a determinant of long-term health. In single-track YRSCs, all students participate in the same school calendar; summer breaks are replaced by short "intersessions" distributed evenly throughout the year. In multi-track YRSCs, cohorts of students follow separate calendar tracks, with breaks at different times throughout the year. An earlier systematic review reported modest gains with single-track calendars and no gains with multi-track calendars. Three studies reported positive and negative effects for single-track programs and potential harm with multi-track programs when low-income students were assigned poorly resourced tracks. Lack of clarity about the role of intersessions as simple school breaks or as additional schooling opportunities in YRSCs leaves the evidence on single-track programs insufficient. Evidence on multi-track YRSCs is also insufficient.

Objective: To improve the understanding of local health departments' (LHDs') capacity for and perceived barriers to using quantitative/economic modeling information to inform policy and program decisions. Design: We developed, tested, and deployed a novel survey to examine this topic. Setting: The study's sample frame included the 200 largest LHDs in terms of size of population served plus all other accredited LHDs (n = 67). The survey was e-mailed to 267 LHDs; respondents completed the survey online using SurveyMonkey. Participants: Survey instructions requested that the survey be completed from the perspective of the entire health department by LHD's top executive or designate. A total of 63 unique LHDs responded (response rate: 39%). Main Outcome Measure(s): Capacity for quantitative and economic modeling was measured in 5 categories (routinely use information from models we create ourselves; routinely use information from models created by others; sometimes use information from models we create ourselves; sometimes use information from models created by others; never use information from modeling). Experience with modeling was measured in 4 categories (very, somewhat, not so, not at all). Results: Few (9.5%) respondents reported routinely using information from models, and most who did used information from models created by others. By contrast, respondents reported high levels of interest in using models and in gaining training in their use and the communication of model results. The most commonly reported barriers to modeling were funding and technical skills. Nearly all types of training topics listed were of interest. Conclusions: Across a sample of large and/or accredited LHDs, we found modest levels of use of modeling coupled with strong interest in capacity for modeling and therefore highlight an opportunity for LHD growth and support. Both funding constraints and a lack of knowledge of how to develop and/or use modeling are barriers to desired progress around modeling. Educational or funding opportunities to promote capacity for and use of quantitative and economic modeling may catalyze use of modeling by public health practitioners.

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal ...Read More

Vaccination is one of public health's greatest achievements, responsible for saving billions of lives. Yet, 20% of children worldwide are not fully protected, leading to 1·5 million child deaths annually from vaccine-preventable diseases. Millions more people have severe disabling illnesses, cancers, and disabilities stemming from underimmunisation. Reasons for falling vaccination rates globally include low public trust in vaccines, constraints on affordability or access, and insufficient governmental vaccine investments. Consequently, an emerging crisis in vaccine hesitancy ranges from hyperlocal to national and worldwide. Outbreaks often originate in small, insular communities with low immunisation rates. Local outbreaks can spread rapidly, however, transcending borders. Following an assessment of underlying determinants of low vaccination rates, we offer an action based on scientific evidence, ethics, and human rights that spans multiple governments, organisations, disciplines, and sectors.