Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 22
Filter
1.
BMJ Open ; 12(1): e053820, 2022 01 11.
Article in English | MEDLINE | ID: covidwho-1632977

ABSTRACT

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.


Subject(s)
COVID-19 , Cross-Sectional Studies , Health Policy , Humans , Research Design , SARS-CoV-2
2.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569347

ABSTRACT

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , Health Status Indicators , Adult , Aged , COVID-19/diagnosis , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Vaccines , Cross-Sectional Studies , Epidemiologic Methods , Female , Humans , Male , Middle Aged , Patient Acceptance of Health Care/statistics & numerical data , Social Media/statistics & numerical data , United States/epidemiology , Young Adult
3.
Curr HIV/AIDS Rep ; 2021 Nov 26.
Article in English | MEDLINE | ID: covidwho-1536352

ABSTRACT

PURPOSE OF REVIEW: To introduce readers to policy modeling, a multidisciplinary field of quantitative analysis, primarily used to help guide decision-making. This review focuses on the choices facing educational administrators, from K-12 to universities in the USA, as they confronted the COVID-19 pandemic. We survey three key model-based approaches to mitigation of SARS-CoV-2 spread in schools and on university campuses. RECENT FINDINGS: Frequent testing, coupled with strict attention to behavioral interventions to prevent further transmission can avoid large outbreaks on college campuses. K-12 administrators can greatly reduce the risks of severe outbreaks of COVID-19 in schools through various mitigation measures including classroom infection control, scheduling and cohorting strategies, staff and teacher vaccination, and asymptomatic screening. Safer re-opening of college and university campuses as well as in-person instruction for K-12 students is possible, under many though not all epidemic scenarios if rigorous disease control and screening programs are in place.

4.
Ann Intern Med ; 174(8): 1090-1100, 2021 08.
Article in English | MEDLINE | ID: covidwho-1497804

ABSTRACT

BACKGROUND: The COVID-19 pandemic has induced historic educational disruptions. In April 2021, about 40% of U.S. public school students were not offered full-time in-person education. OBJECTIVE: To assess the risk for SARS-CoV-2 transmission in schools. DESIGN: An agent-based network model was developed to simulate transmission in elementary and high school communities, including home, school, and interhousehold interactions. SETTING: School structure was parametrized to reflect average U.S. classrooms, with elementary schools of 638 students and high schools of 1451 students. Daily local incidence was varied from 1 to 100 cases per 100 000 persons. PARTICIPANTS: Students, faculty, staff, and adult household members. INTERVENTION: Isolation of symptomatic individuals, quarantine of an infected individual's contacts, reduced class sizes, alternative schedules, staff vaccination, and weekly asymptomatic screening. MEASUREMENTS: Transmission was projected among students, staff, and families after a single infection in school and over an 8-week quarter, contingent on local incidence. RESULTS: School transmission varies according to student age and local incidence and is substantially reduced with mitigation measures. Nevertheless, when transmission occurs, it may be difficult to detect without regular testing because of the subclinical nature of most children's infections. Teacher vaccination can reduce transmission to staff, and asymptomatic screening improves understanding of local circumstances and reduces transmission. LIMITATION: Uncertainty exists about the susceptibility and infectiousness of children, and precision is low regarding the effectiveness of specific countermeasures, particularly with new variants. CONCLUSION: With controlled community transmission and moderate mitigation, elementary schools can open safety, but high schools require more intensive mitigation. Asymptomatic screening can facilitate reopening at higher local incidence while minimizing transmission risk. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention through the Council of State and Territorial Epidemiologists, National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, and Facebook.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Risk Assessment , Schools , Age Factors , COVID-19 Vaccines/administration & dosage , Disease Susceptibility , Humans , Mass Screening , Pandemics , Physical Distancing , Quarantine , SARS-CoV-2 , United States/epidemiology
5.
Am J Epidemiol ; 190(11): 2474-2486, 2021 11 02.
Article in English | MEDLINE | ID: covidwho-1493669

ABSTRACT

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.


Subject(s)
COVID-19 , Health Policy , Bias , Humans , Interrupted Time Series Analysis , SARS-CoV-2
6.
MDM Policy Pract ; 6(2): 23814683211049249, 2021.
Article in English | MEDLINE | ID: covidwho-1477249

ABSTRACT

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

10.
Health Aff (Millwood) ; 40(9): 1514, 2021 09.
Article in English | MEDLINE | ID: covidwho-1398947
11.
Clin Infect Dis ; 73(Suppl 2): S138-S145, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1373634

ABSTRACT

BACKGROUND: Although much of the public health effort to combat coronavirus disease 2019 (COVID-19) has focused on disease control strategies in public settings, transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within households remains an important problem. The nature and determinants of household transmission are poorly understood. METHODS: To address this gap, we gathered and analyzed data from 22 published and prepublished studies from 10 countries (20 291 household contacts) that were available through 2 September 2020. Our goal was to combine estimates of the SARS-CoV-2 household secondary attack rate (SAR) and to explore variation in estimates of the household SAR. RESULTS: The overall pooled random-effects estimate of the household SAR was 17.1% (95% confidence interval [CI], 13.7-21.2%). In study-level, random-effects meta-regressions stratified by testing frequency (1 test, 2 tests, >2 tests), SAR estimates were 9.2% (95% CI, 6.7-12.3%), 17.5% (95% CI, 13.9-21.8%), and 21.3% (95% CI, 13.8-31.3%), respectively. Household SARs tended to be higher among older adult contacts and among contacts of symptomatic cases. CONCLUSIONS: These findings suggest that SARs reported using a single follow-up test may be underestimated, and that testing household contacts of COVID-19 cases on multiple occasions may increase the yield for identifying secondary cases.


Subject(s)
COVID-19 , SARS-CoV-2 , Aged , Family Characteristics , Humans , Incidence , Motivation
12.
Lancet Public Health ; 6(10): e760-e770, 2021 10.
Article in English | MEDLINE | ID: covidwho-1345513

ABSTRACT

BACKGROUND: Residents of prisons have experienced disproportionate COVID-19-related health harms. To control outbreaks, many prisons in the USA restricted in-person activities, which are now resuming even as viral variants proliferate. This study aims to use mathematical modelling to assess the risks and harms of COVID-19 outbreaks in prisons under a range of policies, including resumption of activities. METHODS: We obtained daily resident-level data for all California state prisons from Jan 1, 2020, to May 15, 2021, describing prison layouts, housing status, sociodemographic and health characteristics, participation in activities, and COVID-19 testing, infection, and vaccination status. We developed a transmission-dynamic stochastic microsimulation parameterised by the California data and published literature. After an initial infection is introduced to a prison, the model evaluates the effect of various policy scenarios on infections and hospitalisations over 200 days. Scenarios vary by vaccine coverage, baseline immunity (0%, 25%, or 50%), resumption of activities, and use of non-pharmaceutical interventions (NPIs) that reduce transmission by 75%. We simulated five prison types that differ by residential layout and demographics, and estimated outcomes with and without repeated infection introductions over the 200 days. FINDINGS: If a viral variant is introduced into a prison that has resumed pre-2020 contact levels, has moderate vaccine coverage (ranging from 36% to 76% among residents, dependent on age, with 40% coverage for staff), and has no baseline immunity, 23-74% of residents are expected to be infected over 200 days. High vaccination coverage (90%) coupled with NPIs reduces cumulative infections to 2-54%. Even in prisons with low room occupancies (ie, no more than two occupants) and low levels of cumulative infections (ie, <10%), hospitalisation risks are substantial when these prisons house medically vulnerable populations. Risks of large outbreaks (>20% of residents infected) are substantially higher if infections are repeatedly introduced. INTERPRETATION: Balancing benefits of resuming activities against risks of outbreaks presents challenging trade-offs. After achieving high vaccine coverage, prisons with mostly one-to-two-person cells that have higher baseline immunity from previous outbreaks can resume in-person activities with low risk of a widespread new outbreak, provided they maintain widespread NPIs, continue testing, and take measures to protect the medically vulnerable. FUNDING: Horowitz Family Foundation, National Institute on Drug Abuse, Centers for Disease Control and Prevention, National Science Foundation, Open Society Foundation, Advanced Micro Devices.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Disease Outbreaks , Prisons , SARS-CoV-2/isolation & purification , Adolescent , Adult , Aged , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Vaccines/administration & dosage , California/epidemiology , Female , Humans , Male , Middle Aged , Models, Theoretical , Organizational Policy , Prisons/organization & administration , Risk Assessment , Vaccination/statistics & numerical data , Young Adult
13.
J Gen Intern Med ; 36(10): 3096-3102, 2021 10.
Article in English | MEDLINE | ID: covidwho-1320128

ABSTRACT

BACKGROUND: Correctional institutions nationwide are seeking to mitigate COVID-19-related risks. OBJECTIVE: To quantify changes to California's prison population since the pandemic began and identify risk factors for COVID-19 infection. DESIGN: For California state prisons (March 1-October 10, 2020), we described residents' demographic characteristics, health status, COVID-19 risk scores, room occupancy, and labor participation. We used Cox proportional hazard models to estimate the association between rates of COVID-19 infection and room occupancy and out-of-room labor, respectively. PARTICIPANTS: Residents of California state prisons. MAIN MEASURES: Changes in the incarcerated population's size, composition, housing, and activities. For the risk factor analysis, the exposure variables were room type (cells vs. dormitories) and labor participation (any room occupant participating in the prior 2 weeks) and the outcome variable was incident COVID-19 case rates. KEY RESULTS: The incarcerated population decreased 19.1% (119,401 to 96,623) during the study period. On October 10, 2020, 11.5% of residents were aged ≥60, 18.3% had high COVID-19 risk scores, 31.0% participated in out-of-room labor, and 14.8% lived in rooms with ≥10 occupants. Nearly 40% of residents with high COVID-19 risk scores lived in dormitories. In 9 prisons with major outbreaks (6,928 rooms; 21,750 residents), dormitory residents had higher infection rates than cell residents (adjusted hazard ratio [AHR], 2.51 95% CI, 2.25-2.80) and residents of rooms with labor participation had higher rates than residents of other rooms (AHR, 1.56; 95% CI, 1.39-1.74). CONCLUSION: Despite reductions in room occupancy and mixing, California prisons still house many medically vulnerable residents in risky settings. Reducing risks further requires a combination of strategies, including rehousing, decarceration, and vaccination.


Subject(s)
COVID-19 , Prisoners , California/epidemiology , Humans , Prisons , Risk Factors , SARS-CoV-2
14.
Am J Epidemiol ; 190(11): 2474-2486, 2021 11 02.
Article in English | MEDLINE | ID: covidwho-1284854

ABSTRACT

Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.


Subject(s)
COVID-19 , Health Policy , Bias , Humans , Interrupted Time Series Analysis , SARS-CoV-2
15.
Health Aff (Millwood) ; 40(6): 870-878, 2021 06.
Article in English | MEDLINE | ID: covidwho-1225818

ABSTRACT

With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5 percent White, 39.1 percent Latino, 5.3 percent Black, and 14.4 percent Asian) experienced 59,258 COVID-19 deaths through April 14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October 2020, combining data from 15.4 million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6 percent versus 2.9 percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.


Subject(s)
COVID-19 , California , Health Status Disparities , Humans , Minority Groups , SARS-CoV-2 , United States
19.
Med Decis Making ; 41(4): 379-385, 2021 05.
Article in English | MEDLINE | ID: covidwho-1153777

ABSTRACT

Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.


Subject(s)
COVID-19 , Communicable Diseases , Models, Theoretical , Pandemics , Policy Making , Policy , Public Health , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control , Communicable Diseases/transmission , Decision Making , Forecasting , Humans , SARS-CoV-2 , Uncertainty
SELECTION OF CITATIONS
SEARCH DETAIL
...