Mimicking superinfection exclusion disrupts alphavirus infection and transmission in the yellow fever mosquito Aedes aegypti.

Multiple viruses, including pathogenic viruses, bacteriophages, and even plant viruses, cause a phenomenon termed superinfection exclusion whereby a currently infected cell is resistant to secondary infection by the same or a closely related virus. In alphaviruses, this process is thought to be mediated, at least in part, by the viral protease (nsP2) which is responsible for processing the nonstructural polyproteins (P123 and P1234) into individual proteins (nsP1-nsP4), forming the viral replication complex. Taking a synthetic biology approach, we mimicked this naturally occurring phenomenon by generating a superinfection exclusion-like state in Aedes aegypti mosquitoes, rendering them refractory to alphavirus infection. By artificially expressing Sindbis virus (SINV) and chikungunya virus (CHIKV) nsP2 in mosquito cells and transgenic mosquitoes, we demonstrated a reduction in both SINV and CHIKV viral replication rates in cells following viral infection as well as reduced infection prevalence, viral titers, and transmission potential in mosquitoes.

Impact of Vietnam-era G.I. Bill eligibility on later-life blood pressure distribution: evidence from the Vietnam draft lottery natural experiment.

We quantified the impact of Vietnam-era G.I Bill eligibility, which subsidized college education for eligible Veterans, on the later-life blood pressure distribution by exploiting the Vietnam draft lottery natural experiment. We restricted Health and Retirement Study data (2006-2018) to men born between 1947-1953 (N=1,970). We estimated intention-to-treat effects at the mean and 1st-99th quantiles of blood pressure using linear and quantile regressions. Our outcomes were systolic blood pressure (SBP), diastolic blood pressure (DBP), hypertension, and self-reported stroke. We proxied G.I. Bill eligibility using lottery-defined draft eligibility. We also conducted analyses stratified by childhood socioeconomic status (cSES) defined based on a previously validated measure. Draft eligibility reduced mean blood pressure outcomes (e.g., effect on SBP: -1.33 [95% confidence interval (CI) -2.85, 0.19]). Draft eligibility also had larger protective effects at higher quantiles of the SBP and DBP distributions relative to lower quantiles (effects on SBP at the 10th and 90th quantiles: -0.33mmHg [95% CI -2.35,1.68]; -3.00mmHg [95% CI -5.68,-0.32]). Draft eligibility had protective effects on blood pressure among low and medium cSES men but opposite effects among high cSES men. G.I. Bill eligibility reshaped the blood pressure distribution to one of lower morbidity risk, particularly among low and medium cSES men.

Medicaid Expansion and Racial-Ethnic and Sex Disparities in Cardiovascular Diseases Over 6 Years: A Generalized Synthetic Control Approach.

BACKGROUND: Studies have suggested Medicaid expansion enacted in 2014 has resulted in a reduction in overall cardiovascular disease (CVD) mortality in the United States. However, it is unknown whether Medicaid expansion has a similar effect across race-ethnicity and sex. We investigated the effect of Medicaid expansion on CVD mortality across race-ethnicity and sex. METHODS: Data come from the behavioral risk factor surveillance system and the US Centers for Disease Control's Wide-ranging Online Data for Epidemiologic Research, spanning the period 2000-2019. We used the generalized synthetic control method, a quasi-experimental approach, to estimate effects. RESULTS: Medicaid expansion was associated with -5.36 (mean difference [MD], 95% confidence interval [CI] = -22.63, 11.91) CVD deaths per 100,000 persons per year among Blacks; -4.28 (MD, 95% CI = -30.08, 21.52) among Hispanics; -3.18 (MD, 95% CI = -8.30, 1.94) among Whites; -5.96 (MD, 95% CI = -15.42, 3.50) among men; and -3.34 (MD, 95% CI = -8.05, 1.37) among women. The difference in mean difference (DMD) between the effect of Medicaid expansion in Blacks compared with Whites was -2.18; (DMD, 95% CI = -20.20, 15.83); between that in Hispanics compared with Whites: -1.10; (DMD, 95% CI = -27.40, 25.20) and between that in women compared with men: 2.62; (DMD, 95% CI = -7.95, 13.19). CONCLUSIONS: Medicaid expansion was associated with a reduction in CVD mortality overall and in White, Black, Hispanic, male, and female subpopulations. Also, our study did not find any difference or disparity in the effect of Medicaid on CVD across race-ethnicity and sex-gender subpopulations, likely owing to imprecise estimates.

Population Health Implications of Medicaid Prerelease and Transition Services for Incarcerated Populations.

Policy Points A large population of incarcerated people may be eligible for prerelease and transition services under the new Medicaid Reentry Section 1115 Demonstration Opportunity. We estimated the largest relative population increases in Medicaid coverage from the opportunity may be expected in smaller and more rural states. We found that mental illness, hepatitis C, and chronic kidney disease prevalence rates were sufficiently high among incarcerated populations to likely skew overall Medicaid population prevalence of these diseases when prerelease and transition services are expanded, implying the need for planning of additional data exchange and service delivery infrastructure by state Medicaid plans. CONTEXT: As states expand prerelease and transition services for incarcerated individuals under the Medicaid Reentry Section 1115 Demonstration Opportunity, we sought to systematically inform Medicaid state and plan administrators regarding the population size and burden of disease data available on incarcerated populations in both jails and prisons in the United States. METHODS: We analyzed data on eligibility criteria for new Medicaid prerelease and transition services based on incarceration length and health conditions across states. We estimated the potentially eligible populations in prisons and jails, considering various incarceration lengths and health status requirements. We also compared disease prevalence in the incarcerated population with that of the existing civilian Medicaid population. FINDINGS: We found that rural and smaller states would experience a disproportionately large proportion of their Medicaid populations to be eligible for prerelease and transition services if new Medicaid eligibility rules were broadly applied. Self-reported psychological distress was notably higher among incarcerated individuals compared with those currently on Medicaid. The prevalence rates of previously diagnosed chronic hepatitis C and kidney disease were also much higher in the incarcerated population than the existing civilian Medicaid population. CONCLUSIONS: We estimated large volumes of potentially Medicaid-eligible entrants as coverage policy changes take effect over the coming years, particularly impacting smaller and more rural states. Our findings reveal very high disease prevalence rates among the incarcerated population subject to new Medicaid coverage, including specific chronic, infectious, and behavioral health conditions that state Medicaid programs, health plans, and providers may benefit from advanced planning to address.

Financing Thresholds for Sustainability of Community Health Worker Programs for Patients Receiving Medicaid Across the United States.

States have turned to novel Medicaid financing to pay for community health worker (CHW) programs, often through fee-for-service or capitated payments. We sought to estimate Medicaid payment rates to ensure CHW program sustainability. A microsimulation model was constructed to estimate CHW salaries, equipment, transportation, space, and benefits costs across the U.S. Fee-for-service rates per 30-min CHW visit (code 98960) and capitated rates were calculated for financial sustainability. The mean CHW hourly wage was $23.51, varying from $15.90 in Puerto Rico to $31.61 in Rhode Island. Overhead per work hour averaged $43.65 nationwide, and was highest for transportation among other overhead categories (65.1% of overhead). The minimum fee-for-service rate for a 30-min visit was $53.24 (95% CI $24.80, $91.11), varying from $40.44 in South Dakota to $70.89 in Washington D.C. The minimum capitated rate was $140.18 per member per month (95% CI $105.94, $260.90), varying from $113.55 in South Dakota to $176.58 in Washington D.C. Rates varied minimally by metro status but more by panel size. Higher Medicaid fee-for-service and capitated rates than currently used may be needed to support financial viability of CHW programs. A revised payment estimation approach may help state officials, health systems and plans discussing CHW program sustainability.

Evaluation of the sugar-sweetened beverage tax in Oakland, United States, 2015-2019: A quasi-experimental and cost-effectiveness study.

BACKGROUND: While a 2021 federal commission recommended that the United States government levy a sugar-sweetened beverage (SSB) tax to improve diabetes prevention and control efforts, evidence is limited regarding the longer-term impacts of SSB taxes on SSB purchases, health outcomes, costs, and cost-effectiveness. This study estimates the impact and cost-effectiveness of an SSB tax levied in Oakland, California. METHODS AND FINDINGS: An SSB tax ($0.01/oz) was implemented on July 1, 2017, in Oakland. The main sample of sales data included 11,627 beverage products, 316 stores, and 172,985,767 product-store-month observations. The main analysis, a longitudinal quasi-experimental difference-in-differences approach, compared changes in beverage purchases at stores in Oakland versus Richmond, California (a nontaxed comparator in the same market area) before and 30 months after tax implementation (through December 31, 2019). Additional estimates used synthetic control methods with comparator stores in Los Angeles, California. Estimates were inputted into a closed-cohort microsimulation model to estimate quality-adjusted life years (QALYs) and societal costs (in Oakland) from 6 SSB-associated disease outcomes. In the main analysis, SSB purchases declined by 26.8% (95% CI -39.0 to -14.7, p < 0.001) in Oakland after tax implementation, compared with Richmond. There were no detectable changes in purchases of untaxed beverages or sweet snacks or purchases in border areas surrounding cities. In the synthetic control analysis, declines in SSB purchases were similar to the main analysis (-22.4%, 95% CI -41.7% to -3.0%, p = 0.04). The estimated changes in SSB purchases, when translated into declines in consumption, would be expected to accrue QALYs (94 per 10,000 residents) and significant societal cost savings (>$100,000 per 10,000 residents) over 10 years, with greater gains over a lifetime horizon. Study limitations include a lack of SSB consumption data and use of sales data primarily from chain stores. CONCLUSIONS: An SSB tax levied in Oakland was associated with a substantial decline in volume of SSBs purchased, an association that was sustained more than 2 years after tax implementation. Our study suggests that SSB taxes are effective policy instruments for improving health and generating significant cost savings for society.

The Acquisition and Retention of Lumpy Skin Disease Virus by Blood-Feeding Insects Is Influenced by the Source of Virus, the Insect Body Part, and the Time since Feeding.

Lumpy skin disease virus (LSDV) is a poxvirus that causes severe systemic disease in cattle and is spread by mechanical arthropod-borne transmission. This study quantified the acquisition and retention of LSDV by four species of Diptera (Stomoxys calcitrans, Aedes aegypti, Culex quinquefasciatus, and Culicoides nubeculosus) from cutaneous lesions, normal skin, and blood from a clinically affected animal. The acquisition and retention of LSDV by Ae. aegypti from an artificial membrane feeding system was also examined. Mathematical models of the data were generated to identify the parameters which influence insect acquisition and retention of LSDV. For all four insect species, the probability of acquiring LSDV was substantially greater when feeding on a lesion compared with feeding on normal skin or blood from a clinically affected animal. After feeding on a skin lesion LSDV was retained on the proboscis for a similar length of time (around 9 days) for all four species and for a shorter time in the rest of the body, ranging from 2.2 to 6.4 days. Acquisition and retention of LSDV by Ae. aegypti after feeding on an artificial membrane feeding system that contained a high titer of LSDV was comparable to feeding on a skin lesion on a clinically affected animal, supporting the use of this laboratory model as a replacement for some animal studies. This work reveals that the cutaneous lesions of LSD provide the high-titer source required for acquisition of the virus by insects, thereby enabling the mechanical vector-borne transmission. IMPORTANCE Lumpy skin disease virus (LSDV) is a high consequence pathogen of cattle that is rapidly expanding its geographical boundaries into new regions such as Europe and Asia. This expansion is promoted by the mechanical transmission of the virus via hematogenous arthropods. This study quantifies the acquisition and retention of LSDV by four species of blood-feeding insects and reveals that the cutaneous lesions of LSD provide the high titer virus source necessary for virus acquisition by the insects. An artificial membrane feeding system containing a high titer of LSDV was shown to be comparable to a skin lesion on a clinically affected animal when used as a virus source. This promotes the use of these laboratory-based systems as replacements for some animal studies. Overall, this work advances our understanding of the mechanical vector-borne transmission of LSDV and provides evidence to support the design of more effective disease control programmes.

Eliminating Food Insecurity in the USA: a Target Trial Emulation Using Observational Data to Estimate Effects on Health-Related Quality of Life.

BACKGROUND: Food insecurity is associated with many aspects of poor health. However, trials of food insecurity interventions typically focus on outcomes of interest to funders, such as healthcare use, cost, or clinical performance metrics, rather than quality of life outcomes that may be prioritized by individuals who experience food insecurity. OBJECTIVE: To emulate a trial of a food insecurity elimination intervention, and quantify its estimated effects on health utility, health-related quality of life, and mental health. DESIGN: Target trial emulation using longitudinal, nationally representative data, from the USA, 2016-2017. PARTICIPANTS: A total of 2013 adults in the Medical Expenditure Panel Survey screened positive for food insecurity, representing 32 million individuals. MAIN MEASURES: Food insecurity was assessed using the Adult Food Security Survey Module. The primary outcome was the SF-6D (Short-Form Six Dimension) measure of health utility. Secondary outcomes were mental component score (MCS) and physical component score (PCS) of the Veterans RAND 12-Item Health Survey (a measure of health-related quality of life), Kessler 6 (K6) psychological distress, and Patient Health Questionnaire 2-item (PHQ2) depressive symptoms. KEY RESULTS: We estimated that food insecurity elimination would improve health utility by 80 QALYs per 100,000 person-years, or 0.008 QALYs per person per year (95% CI 0.002 to 0.014, p = 0.005), relative to the status quo. We also estimated that food insecurity elimination would improve mental health (difference in MCS [95% CI]: 0.55 [0.14 to 0.96]), physical health (difference in PCS: 0.44 [0.06 to 0.82]), psychological distress (difference in K6: -0.30 [-0.51 to -0.09]), and depressive symptoms (difference in PHQ-2: -0.13 [-0.20 to -0.07]). CONCLUSIONS: Food insecurity elimination may improve important, but understudied, aspects of health. Evaluations of food insecurity interventions should holistically investigate their potential to improve many different aspects of health.

The Underuse of Medicare's Prevention and Coordination Codes in Primary Care : A Cross-Sectional and Modeling Study.

BACKGROUND: Efforts to better support primary care include the addition of primary care-focused billing codes to the Medicare Physician Fee Schedule (MPFS). OBJECTIVE: To examine potential and actual use by primary care physicians (PCPs) of the prevention and coordination codes that have been added to the MPFS. DESIGN: Cross-sectional and modeling study. SETTING: Nationally representative claims and survey data. PARTICIPANTS: Medicare patients. MEASUREMENTS: Frequency of use and estimated Medicare revenue involving 34 billing codes representing prevention and coordination services for which PCPs could but do not necessarily bill. RESULTS: Eligibility among Medicare patients for each service ranged from 8.8% to 100%. Among eligible patients, the median use of billing codes was 2.3%, even though PCPs provided code-appropriate services to more patients, for example, to 5.0% to 60.6% of patients eligible for prevention services. If a PCP provided and billed all prevention and coordination services to half of all eligible patients, the PCP could add to the practice's annual revenue $124 435 (interquartile range [IQR], $30 654 to $226 813) for prevention services and $86 082 (IQR, $18 011 to $154 152) for coordination services. LIMITATION: Service provision based on survey questions may not reflect all billing requirements; revenues do not incorporate the compliance, billing, and opportunity costs that may be incurred when using these codes. CONCLUSION: Primary care physicians forego considerable amounts of revenue because they infrequently use billing codes for prevention and coordination services despite having eligible patients and providing code-appropriate services to some of those patients. Therefore, creating additional billing codes for distinct activities in the MPFS may not be an effective strategy for supporting primary care. PRIMARY FUNDING SOURCE: National Institute on Aging.

How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach : A Microsimulation Study.

BACKGROUND: The physician gender wage gap may be due, in part, to productivity-based compensation models that undervalue female practice patterns. OBJECTIVE: To determine how primary care physician (PCP) compensation by gender differs when applying existing productivity-based and alternative compensation models. DESIGN: Microsimulation. SETTING: 2016 to 2019 national clinical registry of 1222 primary care practices. PARTICIPANTS: Male and female PCPs matched on specialty, years since medical school graduation, practice site, and sessions worked. MEASUREMENTS: Net annual, full-time-equivalent compensation for male versus female PCPs, under productivity-based fee-for-service, panel size-based capitation without or with risk adjustment, and hybrid payment models. Microsimulation inputs included patient and visit characteristics and overhead expenses. RESULTS: Among 1435 matched male (n = 881) and female (n = 554) PCPs, female PCP panels included patients who were, on average, younger, had lower diagnosis-based risk scores, were more often female, and were more often uninsured or insured by Medicaid rather than by Medicare. Under productivity-based payment, female PCPs earned a median of $58 829 (interquartile range [IQR], $39 553 to $120 353; 21%) less than male PCPs. This gap was similar under capitation ($58 723 [IQR, $42 141 to $140 192]). It was larger under capitation risk-adjusted for age alone ($74 695 [IQR, $42 884 to $152 423]), for diagnosis-based scores alone ($114 792 [IQR, $49 080 to $215 326] and $89 974 [IQR, $26 175 to $173 760]), and for age-, sex-, and diagnosis-based scores ($83 438 [IQR, $28 927 to $129 414] and $66 195 [IQR, $11 899 to $96 566]). The gap was smaller and nonsignificant under capitation risk-adjusted for age and sex ($36 631 [IQR, $12 743 to $73 898]). LIMITATION: Panel attribution based on office visits. CONCLUSION: The gender wage gap varied by compensation model, with capitation risk-adjusted for patient age and sex resulting in a smaller gap. Future models might better align with primary care effort and outcomes. PRIMARY FUNDING SOURCE: None.

Quantifying and Modeling the Acquisition and Retention of Lumpy Skin Disease Virus by Hematophagus Insects Reveals Clinically but Not Subclinically Affected Cattle Are Promoters of Viral Transmission and Key Targets for Control of Disease Outbreaks.

Lumpy skin disease virus (LSDV) is a vector-transmitted poxvirus that causes disease in cattle. Vector species involved in LSDV transmission and their ability to acquire and transmit the virus are poorly characterized. Using a highly representative bovine experimental model of lumpy skin disease, we fed four model vector species (Aedes aegypti, Culex quinquefasciatus, Stomoxys calcitrans, and Culicoides nubeculosus) on LSDV-inoculated cattle in order to examine their acquisition and retention of LSDV. Subclinical disease was a more common outcome than clinical disease in the inoculated cattle. Importantly, the probability of vectors acquiring LSDV from a subclinical animal (0.006) was very low compared with that from a clinical animal (0.23), meaning an insect feeding on a subclinical animal was 97% less likely to acquire LSDV than one feeding on a clinical animal. All four potential vector species studied acquired LSDV from the host at a similar rate, but Aedes aegypti and Stomoxys calcitrans retained the virus for a longer time, up to 8 days. There was no evidence of virus replication in the vector, consistent with mechanical rather than biological transmission. The parameters obtained in this study were combined with data from studies of LSDV transmission and vector life history parameters to determine the basic reproduction number of LSDV in cattle mediated by each of the model species. This reproduction number was highest for Stomoxys calcitrans (19.1), followed by C. nubeculosus (7.1) and Ae. aegypti (2.4), indicating that these three species are potentially efficient transmitters of LSDV; this information can be used to inform LSD control programs.IMPORTANCE Lumpy skin disease virus (LSDV) causes a severe systemic disease characterized by cutaneous nodules in cattle. LSDV is a rapidly emerging pathogen, having spread since 2012 into Europe and Russia and across Asia. The vector-borne nature of LSDV transmission is believed to have promoted this rapid geographic spread of the virus; however, a lack of quantitative evidence about LSDV transmission has hampered effective control of the disease during the current epidemic. Our research shows subclinical cattle play little part in virus transmission relative to clinical cattle and reveals a low probability of virus acquisition by insects at the preclinical stage. We have also calculated the reproductive number of different insect species, therefore identifying efficient transmitters of LSDV. This information is of utmost importance, as it will help to define epidemiological control measures during LSDV epidemics and of particular consequence in resource-poor regions where LSD vaccination may be less than adequate.

BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS.

For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds on the conditional average treatment effect (CATE) when unobserved confounders have a bounded effect on the odds ratio of treatment selection. Our approach is scalable and allows flexible use of model classes in estimation, including nonparametric and black-box machine learning methods. Based on these bounds for the CATE, we propose a sensitivity analysis for the average treatment effect (ATE). Our semiparametric estimator extends/bounds the augmented inverse propensity weighted (AIPW) estimator for the ATE under bounded unobserved confounding. By constructing a Neyman orthogonal score, our estimator of the bound for the ATE is a regular root-n estimator so long as the nuisance parameters are estimated at the opn-1/4 rate. We complement our methodology with optimality results showing that our proposed bounds are tight in certain cases. We demonstrate our method on simulated and real data examples, and show accurate coverage of our confidence intervals in practical finite sample regimes with rich covariate information.

Supplemental Nutrition Assistance Program Participation and Health Care Use in Older Adults : A Cohort Study.

BACKGROUND: Older adults dually eligible for Medicare and Medicaid have particularly high food insecurity prevalence and health care use. OBJECTIVE: To determine whether participation in the Supplemental Nutrition Assistance Program (SNAP), which reduces food insecurity, is associated with lower health care use and cost for older adults dually eligible for Medicare and Medicaid. DESIGN: An incident user retrospective cohort study design was used. The association between participation in SNAP and health care use and cost using outcome regression was assessed and supplemented by entropy balancing, matching, and instrumental variable analyses. SETTING: North Carolina, September 2016 through July 2020. PARTICIPANTS: Older adults (aged ≥65 years) dually enrolled in Medicare and Medicaid but not initially enrolled in SNAP. MEASUREMENTS: Inpatient admissions (primary outcome), emergency department visits, long-term care admissions, and Medicaid expenditures. RESULTS: Of 115 868 persons included, 5093 (4.4%) enrolled in SNAP. Mean follow-up was approximately 22 months. In outcome regression analyses, SNAP enrollment was associated with fewer inpatient hospitalizations (-24.6 [95% CI, -40.6 to -8.7]), emergency department visits (-192.7 [CI, -231.1 to -154.4]), and long-term care admissions (-65.2 [CI, -77.5 to -52.9]) per 1000 person-years as well as fewer dollars in Medicaid payments per person per year (-$2360 [CI, -$2649 to -$2071]). Results were similar in entropy balancing, matching, and instrumental variable analyses. LIMITATION: Single state, no Medicare claims data available, and possible residual confounding. CONCLUSION: Participation in SNAP was associated with fewer inpatient admissions and lower health care costs for older adults dually eligible for Medicare and Medicaid. PRIMARY FUNDING SOURCE: National Institutes of Health.

Estimated Effect on Life Expectancy of Alleviating Primary Care Shortages in the United States.

BACKGROUND: Prior studies have reported that greater numbers of primary care physicians (PCPs) per population are associated with reduced population mortality, but the effect of increasing PCP density in areas of low density is poorly understood. OBJECTIVE: To estimate how alleviating PCP shortages might change life expectancy and mortality. DESIGN: Generalized additive models, mixed-effects models, and generalized estimating equations. SETTING: 3104 U.S. counties from 2010 to 2017. PARTICIPANTS: Children and adults. MEASUREMENTS: Age-adjusted life expectancy; all-cause mortality; and mortality due to cardiovascular disease, cancer, infectious disease, respiratory disease, and substance use or injury. RESULTS: Persons living in counties with less than 1 physician per 3500 persons in 2017 had a mean life expectancy that was 310.9 days shorter than for persons living in counties above that threshold. In the low-density counties (n = 1218), increasing the density of PCPs above the 1:3500 threshold would be expected to increase mean life expectancy by 22.4 days (median, 19.4 days [95% CI, 0.9 to 45.6 days]), and all such counties would require 17 651 more physicians, or about 14.5 more physicians per shortage county. If counties with less than 1 physician per 1500 persons (n = 2636) were to reach the 1:1500 threshold, life expectancy would be expected to increase by 56.3 days (median, 55.6 days [CI, 4.2 to 105.6 days]), and all such counties would require 95 754 more physicians, or about 36.3 more physicians per shortage county. LIMITATION: Some projections are based on extrapolations of the actual data. CONCLUSION: In counties with fewer PCPs per population, increases in PCP density would be expected to substantially improve life expectancy. PRIMARY FUNDING SOURCE: None.

Frequency of Routine Testing for Coronavirus Disease 2019 (COVID-19) in High-risk Healthcare Environments to Reduce Outbreaks.

Routine asymptomatic testing strategies for COVID-19 have been proposed to prevent outbreaks in high-risk healthcare environments. We used simulation modeling to evaluate the optimal frequency of viral testing. We found that routine testing substantially reduces risk of outbreaks, but may need to be as frequent as twice weekly.

Correction: Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials.

[This corrects the article DOI: 10.1371/journal.pmed.1002410.].

Global, Regional, and Country-Specific Lifetime Risks of Stroke, 1990 and 2016.

BACKGROUND: The lifetime risk of stroke has been calculated in a limited number of selected populations. We sought to estimate the lifetime risk of stroke at the regional, country, and global level using data from a comprehensive study of the prevalence of major diseases. METHODS: We used the Global Burden of Disease (GBD) Study 2016 estimates of stroke incidence and the competing risks of death from any cause other than stroke to calculate the cumulative lifetime risks of first stroke, ischemic stroke, or hemorrhagic stroke among adults 25 years of age or older. Estimates of the lifetime risks in the years 1990 and 2016 were compared. Countries were categorized into quintiles of the sociodemographic index (SDI) used in the GBD Study, and the risks were compared across quintiles. Comparisons were made with the use of point estimates and uncertainty intervals representing the 2.5th and 97.5th percentiles around the estimate. RESULTS: The estimated global lifetime risk of stroke from the age of 25 years onward was 24.9% (95% uncertainty interval, 23.5 to 26.2); the risk among men was 24.7% (95% uncertainty interval, 23.3 to 26.0), and the risk among women was 25.1% (95% uncertainty interval, 23.7 to 26.5). The risk of ischemic stroke was 18.3%, and the risk of hemorrhagic stroke was 8.2%. In high-SDI, high-middle-SDI, and low-SDI countries, the estimated lifetime risk of stroke was 23.5%, 31.1% (highest risk), and 13.2% (lowest risk), respectively; the 95% uncertainty intervals did not overlap between these categories. The highest estimated lifetime risks of stroke according to GBD region were in East Asia (38.8%), Central Europe (31.7%), and Eastern Europe (31.6%), and the lowest risk was in eastern sub-Saharan Africa (11.8%). The mean global lifetime risk of stroke increased from 22.8% in 1990 to 24.9% in 2016, a relative increase of 8.9% (95% uncertainty interval, 6.2 to 11.5); the competing risk of death from any cause other than stroke was considered in this calculation. CONCLUSIONS: In 2016, the global lifetime risk of stroke from the age of 25 years onward was approximately 25% among both men and women. There was geographic variation in the lifetime risk of stroke, with the highest risks in East Asia, Central Europe, and Eastern Europe. (Funded by the Bill and Melinda Gates Foundation.).

Improving hospital readmission prediction using individualized utility analysis.

OBJECTIVE: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was to determine whether ML models for allocating readmission-mitigating interventions have different usefulness based on their overall utility and discriminative ability. MATERIALS AND METHODS: We conducted a retrospective utility analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart, including 513,495 commercially-insured inpatients (mean [SD] age 69 [19] years; 294,895 [57%] Female) over the period January 2016 through January 2017 from all 50 states with mean 90 day cost of $11,552. Utility analysis estimates the cost, in dollars, of allocating interventions for lowering readmission risk based on the reduction in the 90-day cost. RESULTS: Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0.76 (95% CI 0.76, 0.77); allocating interventions based on a model trained to predict cost as a proxy achieved a higher utility of $175.94 per patient, and an AUC of 0.62 (95% CI 0.61, 0.62). A hybrid model combining both intervention strategies is comparable with the best models on either metric. Estimated utility varies by intervention cost and efficacy, with each model performing the best under different intervention settings. CONCLUSION: We demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study.

BACKGROUND: School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness. METHODS: We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors. RESULTS: At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures. CONCLUSIONS: School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.

PURPOSE OF REVIEW: Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020. RECENT FINDINGS: Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.