Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California.

The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often used to estimate the population size yet may produce implausible and unreliable estimates due to model misspecification and small cell sizes. A novel targeted minimum loss-based estimation (TMLE) model developed for capture-recapture makes several notable improvements to conventional modeling: "targeting" the parameter of interest, flexibly fitting the data to alternative functional forms, and limiting bias from small cell sizes. Using simulations and empirical data from the San Francisco, California, Department of Public Health's human immunodeficiency virus (HIV) surveillance registry, we evaluated the performance of the TMLE model and compared results with those of other common models. Based on 2,584 people observed on 3 lists reportable to the surveillance registry, the TMLE model estimated the number of San Francisco residents living with HIV as of December 31, 2019, to be 13,523 (95% confidence interval: 12,222, 14,824). This estimate, compared with a "ground truth" of 12,507, was the most accurate and precise of all models examined. The TMLE model is a significant advancement in capture-recapture studies, leveraging modern statistical methods to improve estimation of the sizes of hidden populations.

Reduced dengue incidence following city-wide wMel Wolbachia mosquito releases throughout three Colombian cities: Interrupted time series analysis and a prospective case-control study.

BACKGROUND: The introduction of Wolbachia (wMel strain) into Aedes aegypti mosquitoes reduces their capacity to transmit dengue and other arboviruses. Randomised and non-randomised studies in multiple countries have shown significant reductions in dengue incidence following field releases of wMel-infected Ae. aegypti. We report the public health outcomes from phased, large-scale releases of wMel-Ae. aegypti mosquitoes throughout three contiguous cities in the Aburrá Valley, Colombia. METHODOLOGY/PRINCIPAL FINDINGS: Following pilot releases in 2015-2016, staged city-wide wMel-Ae. aegypti deployments were undertaken in the cities of Bello, Medellín and Itagüí (3.3 million people) between October 2016 and April 2022. The impact of the Wolbachia intervention on dengue incidence was evaluated in two parallel studies. A quasi-experimental study using interrupted time series analysis showed notified dengue case incidence was reduced by 95% in Bello and Medellín and 97% in Itagüí, following establishment of wMel at ≥60% prevalence, compared to the pre-intervention period and after adjusting for seasonal trends. A concurrent clinic-based case-control study with a test-negative design was unable to attain the target sample size of 63 enrolled virologically-confirmed dengue (VCD) cases between May 2019 and December 2021, consistent with low dengue incidence throughout the Aburrá Valley following wMel deployments. Nevertheless, VCD incidence was 45% lower (OR 0.55 [95% CI 0.25, 1.17]) and combined VCD/presumptive dengue incidence was 47% lower (OR 0.53 [95% CI 0.30, 0.93]) among participants resident in wMel-treated versus untreated neighbourhoods. CONCLUSIONS/SIGNIFICANCE: Stable introduction of wMel into local Ae. aegypti populations was associated with a significant and sustained reduction in dengue incidence across three Colombian cities. These results from the largest contiguous Wolbachia releases to-date demonstrate the real-world effectiveness of the method across large urban populations and, alongside previously published results, support the reproducibility of this effectiveness across different ecological settings. TRIAL REGISTRATION: NCT03631719.

Pre-pregnancy gene expression signatures are associated with subsequent improvement/worsening of rheumatoid arthritis during pregnancy.

BACKGROUND: While many women with rheumatoid arthritis (RA) improve during pregnancy and others worsen, there are no biomarkers to predict this improvement or worsening. In our unique RA pregnancy cohort that includes a pre-pregnancy baseline, we have examined pre-pregnancy gene co-expression networks to identify differences between women with RA who subsequently improve during pregnancy and those who worsen. METHODS: Blood samples were collected before pregnancy (T0) from 19 women with RA and 13 healthy women enrolled in our prospective pregnancy cohort. RA improvement/worsening between T0 and 3rd trimester was assessed by changes in the Clinical Disease Activity Index (CDAI). Pre-pregnancy expression profiles were examined by RNA sequencing and differential gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules correlated with the improvement/worsening of RA during pregnancy and to assess their functional relevance. RESULTS: Of the 19 women with RA, 14 improved during pregnancy (RAimproved) while 5 worsened (RAworsened). At the T0 baseline, however, the mean CDAI was similar between the two groups. WGCNA identified one co-expression module related to B cell function that was significantly correlated with the worsening of RA during pregnancy and was significantly enriched in genes differentially expressed between the RAimproved and RAworsened groups. A neutrophil-related expression signature was also identified in the RAimproved group at the T0 baseline. CONCLUSION: The pre-pregnancy gene expression signatures identified represent potential biomarkers to predict the subsequent improvement/worsening of RA during pregnancy, which has important implications for the personalized treatment of RA during pregnancy.

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets.

BACKGROUND: Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. METHODS: In this density-sampled case-control study of Californians, January 2005-September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. For each month, we sampled controls from the state population in a 4:1 ratio with cases. Exposures were monthly densities of county-level pawn and nonpawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs ("bars/pubs"). We used case-control-weighted G-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. RESULTS: There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RD pawn dealers : 0.06, 95% CI: 0.05, 0.08; RD off-premises outlets : 0.01, 95% CI: 0.01, 0.03), but nonpawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. CONCLUSIONS: Our results are consistent with the hypothesis that limiting pawn firearm dealers and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

Pregnancy-associated systemic gene expression compared to a pre-pregnancy baseline, among healthy women with term pregnancies.

Background: Pregnancy is known to induce extensive biological changes in the healthy mother. Little is known, however, about what these changes are at the molecular level. We have examined systemic expression changes in protein-coding genes and long non-coding (lnc) RNAs during and after pregnancy, compared to before pregnancy, among healthy women with term pregnancies. Methods: Blood samples were collected from 14 healthy women enrolled in our prospective pregnancy cohort at 7 time-points (before, during and after pregnancy). Total RNA from frozen whole blood was used for RNA sequencing. Following raw read alignment and assembly, gene-level counts were obtained for protein-coding genes and long non-coding RNAs. At each time-point, cell type proportions were estimated using deconvolution. To examine associations between pregnancy status and gene expression over time, Generalized Estimating Equation (GEE) models were fitted, adjusting for age at conception, and with and without adjusting for changes in cell type proportions. Fold-changes in expression at each trimester were examined relative to the pre-pregnancy baseline. Results: Numerous immune-related genes demonstrated pregnancy-associated expression, in a time-dependent manner. The genes that demonstrated the largest changes in expression included several that were neutrophil-related (over-expressed) and numerous immunoglobulin genes (under-expressed). Estimated cell proportions revealed a marked increase in neutrophils, and less so of activated CD4 memory T cells, during pregnancy, while most other cell type proportions decreased or remained unchanged. Adjusting for cell type proportions in our model revealed that although most of the expression changes were due to changes in cell type proportions in the bloodstream, transcriptional regulation was also involved, especially in down-regulating expression of type I interferon inducible genes. Conclusion: Compared to a pre-pregnancy baseline, there were extensive systemic changes in cell type proportions, gene expression and biological pathways associated with different stages of pregnancy and postpartum among healthy women. Some were due to changes in cell type proportions and some due to gene regulation. In addition to providing insight into term pregnancy among healthy women, these findings also provide a "normal" reference for abnormal pregnancies and for autoimmune diseases that improve or worsen during pregnancy, to assess deviations from normal.

Estimating distribution of length of stay in a multi-state model conditional on the pathway, with an application to patients hospitalised with Covid-19.

Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length of stay in a given state are well established. The focus of this paper is on the distribution of the time spent in different states conditional on the complete pathway taken through the states, which we call 'conditional length of stay'. This work is motivated by questions about length of stay in hospital wards and intensive care units among patients hospitalised due to Covid-19. Conditional length of stay estimates are useful as a way of summarising individuals' transitions through the multi-state model, and also as inputs to mathematical models used in planning hospital capacity requirements. We describe non-parametric methods for estimating conditional length of stay distributions in a multi-state model in the presence of censoring, including conditional expected length of stay (CELOS). Methods are described for an illness-death model and then for the more complex motivating example. The methods are assessed using a simulation study and shown to give unbiased estimates of CELOS, whereas naive estimates of CELOS based on empirical averages are biased in the presence of censoring. The methods are applied to estimate conditional length of stay distributions for individuals hospitalised due to Covid-19 in the UK, using data on 42,980 individuals hospitalised from March to July 2020 from the COVID19 Clinical Information Network.

The role of firearm and alcohol availability in firearm suicide: A population-based weighted case-control study.

Firearm availability has been linked to firearm self-harm, but the joint relationship with alcohol availability, while supported by theory, has not been examined. This study sought to quantify the separate and joint relations of community firearm and alcohol availability with individual-level risk of (fatal and nonfatal) firearm self-harm. We conducted a case-control study of California residents, 2005-2015, using statewide mortality, hospital, firearm transfer, and alcohol license data. We estimated monthly marginal risk differences per 100,000 in the overall population and in white men aged 50+ under various hypothetical changes to firearm and alcohol availability and assessed additive interactions using case-control-weighted g-computation. In the overall population, non-pawn shop firearm dealer density was associated with firearm self-harm (RD: 0.02, 95% CI: 0.003, 0.04) but pawn shop firearm dealer and alcohol outlet densities were not. Secondary analyses revealed a relationship between firearm sales density and firearm self-harm (RD: 0.07, 95% CI: 0.04, 0.10). There were no additive interactions between measures of firearm and alcohol availability. Among older white men, generally the same exposures were related to self-harm as in the overall population, but point estimates were substantially larger. Findings suggest community-level approaches to reducing firearm sales may help mitigate suicide risk.

Association of Wildfire Air Pollution With Clinic Visits for Psoriasis.

This cross-sectional study examines whether clinic visits and online search interest for psoriasis were associated with wildfire air pollution after a delayed lag period.

Impact of randomised wmel Wolbachia deployments on notified dengue cases and insecticide fogging for dengue control in Yogyakarta City.

BACKGROUND: Releases of Wolbachia (wMel)-infected Aedes aegypti mosquitoes significantly reduced the incidence of virologically confirmed dengue in a previous cluster randomised trial in Yogyakarta City, Indonesia. Following the trial, wMel releases were extended to the untreated control areas, to achieve city-wide coverage of Wolbachia. OBJECTIVE: In this predefined analysis, we evaluated the impact of the wMel deployments in Yogyakarta on dengue hemorrhagic fever (DHF) case notifications and on the frequency of perifocal insecticide spraying by public health teams. METHODS: Monthly counts of DHF cases notified to the Yogyakarta District Health Office between January 2006 and May 2022 were modelled as a function of time-varying local wMel treatment status (fully- and partially-treated vs untreated, and by quintile of wMel prevalence). The frequency of insecticide fogging in wMel-treated and untreated areas was analysed using negative binomial regression. RESULTS: Notified DHF incidence was 83% lower in fully treated vs untreated periods (IRR 0.17 [95% CI 0.14, 0.20]), and 78% lower in areas with 80-100% wMel prevalence compared to areas with 0-20% wMel (IRR 0.23 [0.17, 0.30]). A similar intervention effect was observed at 60-80% wMel prevalence as at 80-100% prevalence (76% vs 78% efficacy, respectively). Pre-intervention, insecticide fogging occurred at similar frequencies in areas later randomised to wMel-treated and untreated arms of the trial. After wMel deployment, fogging occurred significantly less frequently in treated areas (IRR 0.17 [0.10, 0.30]). CONCLUSIONS: Deployments of wMel-infected Aedes aegypti mosquitoes resulted in an 83% reduction in the application of perifocal insecticide spraying, consistent with lower dengue case notifications in wMel-treated areas. These results show that the Wolbachia intervention effect demonstrated previously in a cluster randomised trial was also measurable from routine surveillance data.

Evaluating tools for capture-recapture model selection to estimate the size of hidden populations: it works in practice, but does it work in theory?

PURPOSE: Capture-recapture methods estimate the size of hidden populations by leveraging the proportion of overlap of the population on independent lists. Log-linear modeling relaxes the assumption of list independence, but best model selection criteria remain uncertain. Incorrect model selection can deliver incorrect and even implausible size estimates. METHODS: We used simulations to model when capture-recapture methods deliver biased or unbiased estimates and compare model selection criteria. Simulations included five scenarios for list dependence among three incomplete lists of population of interest. We compared metrics of log-linear model selection, accuracy, and precision. We also compared log-linear model performance to the decomposable graph approach (a Bayesian model average), the sparse multiple systems estimation (SparseMSE) approach that accounts for zero or low cell counts, and the Sample Coverage approach. RESULTS: Log-linear models selected by Akaike's information criterion (AIC) calculated accurate population size estimates in all scenarios except for those with sparse or zero cell counts. In these scenarios, the decomposable graph and the Sample Coverage models produced more accurate size estimates. CONCLUSIONS: Conventional capture-recapture model selection fails with sparse cell counts. This naïve approach to model selection should be replaced with the implementation of multiple different models in order triangulate the truth in real-world applications.

Association of Exposure to Wildfire Air Pollution With Exacerbations of Atopic Dermatitis and Itch Among Older Adults.

This cross-sectional study evaluates the association of exposure to wildfire air pollution with exacerbations of atopic dermatitis and itch among adults aged 65 years or older.

On the use of the reproduction number for SARS-CoV-2: Estimation, misinterpretations and relationships with other ecological measures.

The basic reproduction number, R 0, and its real-time analogue, Rt , are summary measures that reflect the ability of an infectious disease to spread through a population. Estimation methods for Rt  have a long history, have been widely developed and are now enhanced by application to the COVID-19 pandemic. While retrospective analyses of Rt have provided insight into epidemic dynamics and the effects of control strategies in prior outbreaks, misconceptions around the interpretation of Rt have arisen with broader recognition and near real-time monitoring of this parameter alongside reported case data during the COVID-19 pandemic. Here, we discuss some widespread misunderstandings regarding the use of Rt  as a barometer for population risk and its related use as an 'on/off' switch for policy decisions regarding relaxation of non-pharmaceutical interventions. Computation of Rt  from downstream data (e.g. hospitalizations) when infection counts are unreliable exacerbates lags between when transmission happens and when events are recorded. We also discuss analyses that have shown various relationships between Rt  and measures of mobility, vaccination coverage and a test-trace-isolation intervention in different settings.

Disruption of spatiotemporal clustering in dengue cases by wMel Wolbachia in Yogyakarta, Indonesia.

Dengue exhibits focal clustering in households and neighborhoods, driven by local mosquito population dynamics, human population immunity, and fine scale human and mosquito movement. We tested the hypothesis that spatiotemporal clustering of homotypic dengue cases is disrupted by introduction of the arbovirus-blocking bacterium Wolbachia (wMel-strain) into the Aedes aegypti mosquito population. We analysed 318 serotyped and geolocated dengue cases (and 5921 test-negative controls) from a randomized controlled trial in Yogyakarta, Indonesia of wMel deployments. We find evidence of spatial clustering up to 300 m among the 265 dengue cases (3083 controls) in the untreated trial arm. Participant pairs enrolled within 30 days and 50 m had a 4.7-fold increase (compared to 95% CI on permutation-based null distribution: 0.1, 1.2) in the odds of being homotypic (i.e. potentially transmission-related) as compared to pairs occurring at any distance. In contrast, we find no evidence of spatiotemporal clustering among the 53 dengue cases (2838 controls) resident in the wMel-treated arm. Introgression of wMel Wolbachia into Aedes aegypti mosquito populations interrupts focal dengue virus transmission leading to reduced case incidence; the true intervention effect may be greater than the 77% efficacy measured in the primary analysis of the Yogyakarta trial.

Interval censored recursive forests.

We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN and Supplementary Materials for this article are available online.

Firearm Violence Following the Implementation of California's Gun Violence Restraining Order Law.

Importance: California's gun violence restraining order (GVRO) law, implemented beginning in 2016, allows for people at high risk of harming themselves or others with a firearm to be temporarily disarmed and prevented from purchasing firearms for 3 weeks to 1 year; many states have recently enacted similar laws. The research to date is on older and more limited risk-warrant laws. Objective: To determine whether implementation of the California GVRO law was associated with decreased rates of firearm assault or firearm self-harm in a large metropolitan county between 2016 and 2019. Design, Setting, and Participants: This serial cross-sectional study including data from 28 counties used the synthetic control method to evaluate differences in firearm violence between San Diego County and its synthetic control following implementation of the California law from 2016 to 2019. San Diego County was used as the treated unit because it issued substantially more GVROs than any other county in California during the study period. A total of 27 California counties that issued no or very few gun violence restraining orders from 2016 to 2019 and that had stable rates of firearm violence between 2005 and 2015 were included in the control pool. Data were analyzed from February 2021 to July 2021. Exposures: Implementation of the statewide GVRO law in 2016 in San Diego County. Main Outcomes and Measures: Annual rates of fatal and nonfatal firearm assault injuries and firearm self-harm injuries per 100 000 people. Results: In the study period, there were 355 GVROs in San Diego county, and a median (IQR) total of 8 (3-20) GVROs per donor county. The mean difference between the observed rate in San Diego County and the estimated rate in the synthetic San Diego County, 2016-2019, was -0.74 firearm assaults per 100 000 (-13% difference) and 0.13 firearm self-harm injuries per 100 000 (3% difference). Results from in-space placebo tests suggested that these differences cannot be distinguished from variation due to chance (pseudo-P values from a 1-sided test: P for assault = .35, P for self-harm = .67). Conclusions and Relevance: To our knowledge, this study was the first to analyze the association between GVRO implementation and firearm violence in California and the first to evaluate the association between risk-based firearm removal laws and firearm assault in any state. GVROs were not associated with reduced population-level rates of firearm violence in San Diego County, but this may change as the number of orders increases over time; the association between GVROs and firearm violence at the individual level cannot be inferred from our findings and should be the subject of future studies.

Projecting the impact of triple CFTR modulator therapy on intravenous antibiotic requirements in cystic fibrosis using patient registry data combined with treatment effects from randomised trials.

BACKGROUND: Cystic fibrosis (CF) is a life-threatening genetic disease, affecting around 10 500 people in the UK. Precision medicines have been developed to treat specific CF-gene mutations. The newest, elexacaftor/tezacaftor/ivacaftor (ELEX/TEZ/IVA), has been found to be highly effective in randomised controlled trials (RCTs) and became available to a large proportion of UK CF patients in 2020. Understanding the potential health economic impacts of ELEX/TEZ/IVA is vital to planning service provision. METHODS: We combined observational UK CF Registry data with RCT results to project the impact of ELEX/TEZ/IVA on total days of intravenous (IV) antibiotic treatment at a population level. Registry data from 2015 to 2017 were used to develop prediction models for IV days over a 1-year period using several predictors, and to estimate 1-year population total IV days based on standards of care pre-ELEX/TEZ/IVA. We considered two approaches to imposing the impact of ELEX/TEZ/IVA on projected outcomes using effect estimates from RCTs: approach 1 based on effect estimates on FEV1% and approach 2 based on effect estimates on exacerbation rate. RESULTS: ELEX/TEZ/IVA is expected to result in significant reductions in population-level requirements for IV antibiotics of 16.1% (~17 800 days) using approach 1 and 43.6% (~39 500 days) using approach 2. The two approaches require different assumptions. Increased understanding of the mechanisms through which ELEX/TEZ/IVA acts on these outcomes would enable further refinements to our projections. CONCLUSIONS: This work contributes to increased understanding of the changing healthcare needs of people with CF and illustrates how Registry data can be used in combination with RCT evidence to estimate population-level treatment impacts.

Prevention of Coronavirus Disease 2019 (COVID-19) by mRNA-Based Vaccines Within the General Population of California.

BACKGROUND: Estimates of coronavirus disease 2019 (COVID-19) vaccine effectiveness under real-world conditions, and understanding of barriers to uptake, are necessary to inform vaccine rollout. METHODS: We enrolled cases (testing positive) and controls (testing negative) from among the population whose SARS-CoV-2 molecular diagnostic test results from 24 February to 29 April 2021 were reported to the California Department of Public Health. Participants were matched on age, sex, and geographic region. We assessed participants' self-reported history of mRNA-based COVID-19 vaccine receipt (BNT162b2 and mRNA-1273). Participants were considered fully vaccinated 2 weeks after second dose receipt. Among unvaccinated participants, we assessed willingness to receive vaccination. We measured vaccine effectiveness (VE) via the matched odds ratio of prior vaccination, comparing cases with controls. RESULTS: We enrolled 1023 eligible participants aged ≥18 years. Among 525 cases, 71 (13.5%) received BNT162b2 or mRNA-1273; 20 (3.8%) were fully vaccinated with either product. Among 498 controls, 185 (37.1%) received BNT162b2 or mRNA-1273; 86 (16.3%) were fully vaccinated with either product. Two weeks after second dose receipt, VE was 87.0% (95% confidence interval: 68.6-94.6%) and 86.2% (68.4-93.9%) for BNT162b2 and mRNA-1273, respectively. Fully vaccinated participants receiving either product experienced 91.3% (79.3-96.3%) and 68.3% (27.9-85.7%) VE against symptomatic and asymptomatic infection, respectively. Among unvaccinated participants, 42.4% (159/375) residing in rural regions and 23.8% (67/281) residing in urban regions reported hesitancy to receive COVID-19 vaccination. CONCLUSIONS: Authorized mRNA-based vaccines are effective at reducing documented SARS-CoV-2 infections within the general population of California. Vaccine hesitancy presents a barrier to reaching coverage levels needed for herd immunity.

Risk Factors Associated With SARS-CoV-2 Infection Among Farmworkers in Monterey County, California.

Importance: Essential workers in agriculture and food production have been severely affected by the ongoing COVID-19 pandemic. Objective: To identify risk factors associated with SARS-CoV-2 infection among farmworkers in California. Design, Setting, and Participants: This cross-sectional study invited farmworkers in California's Salinas Valley (Monterey County) receiving transcription-mediated amplification (TMA) tests for SARS-CoV-2 infection at federally qualified community clinics and community sites to participate. Individuals were eligible if they were not pregnant, were 18 years or older, had conducted farmwork since the pandemic started, and were proficient in English or Spanish. Survey data were collected and SARS-CoV-2 tests were conducted among participants from July 16 to November 30, 2020. Exposures: Sociodemographic, household, community, and workplace characteristics. Main Outcomes and Measures: TMA- and immunoglobulin G (IgG)-positive SARS-CoV-2 infection. Results: A total of 1107 farmworkers (581 [52.5%] women; mean [SD] age, 39.7 [12.6] years) were included in these analyses. Most participants were born in Mexico (922 [83.3%]), were married or living with a partner (697 [63.0%]), and worked in the fields (825 [74.5%]). Overall, 118 of 911 (13.0%) had a positive result on their TMA test for SARS-CoV-2 infection, whereas 201 of 1058 (19.0%) had antibody evidence of infection. In multivariable analyses accounting for recruitment venue and enrollment period, the incidence of TMA-positive SARS-CoV-2 infection was higher among those with lower than primary school-level education (adjusted relative risk [aRR], 1.32; 95% CI, 0.99-1.76; non-statistically significant finding), who spoke an Indigenous language at home (aRR, 1.30; 95% CI, 0.97-1.73; non-statistically significant finding), who worked in the fields (aRR, 1.60; 95% CI, 1.03-2.50), and who were exposed to a known or suspected COVID-19 case at home (aRR, 2.98; 95% CI, 2.06-4.32) or in the workplace (aRR, 1.59; 95% CI, 1.18-2.14). Positive results on IgG tests for SARS-CoV-2 infection were more common among those who lived in crowded housing (aRR, 1.23; 95% CI, 0.98-1.53; non-statistically significant finding), with children aged 5 years or younger (aRR, 1.40; 95% CI, 1.11-1.76), with unrelated roommates (aRR, 1.40; 95% CI, 1.19-1.64), and with an individual with known or suspected COVID-19 (aRR, 1.59; 95% CI, 1.13-2.24). The risk of IgG positivity was also higher among those with body mass index of 30 or greater (aRR, 1.65; 95% CI, 1.01-2.70) or diabetes (aRR, 1.31; 95% CI, 0.98-1.75; non-statistically significant finding). Conclusions and Relevance: In this cross-sectional study of farmworkers in California, both residential and workplace exposures were associated with SARS-CoV-2 infection. Urgent distribution of COVID-19 vaccines and intervention on modifiable risk factors are warranted given this population's increased risk of infection and the essential nature of their work.

US law enforcement policy predictors of race-specific police fatalities during 2015-16.

Mounting evidence suggests that law enforcement organizational factors contribute to higher incidence and racial disparities in police killings. To determine whether agency policies contribute to race-specific civilian fatalities, this exploratory study compared fatality rates among agencies with and without selected policies expected to reduce killings. A cross-section of 1085 fatalities in the 2015-2016 The Counted public-use database were matched to 481 agencies in the 2013 Law Enforcement Management and Administrative Statistics (LEMAS) database. Negative binomial regression estimated incidence rate ratios (IRR) adjusted for agency type, number of officers, percent female personnel, median income, percent with a bachelor's degree, violent crime rate, and population size, with inference using robust standard errors. Agencies with greater proportions of full-time personnel (range 43-100%) had lower rates of all (IRR = 0.85; 95% confidence interval [CI] = 0.77-0.93) and non-White civilian killings (IRR = 0.85; CI = 0.73-0.99). Mission statements predicted lower rates of all (IRR = 0.70; CI = 0.58-0.84) and White killings (IRR = 0.60; CI = 0.40-0.90). Community evaluation and more types of personnel incentives predicted lower rates of White (IRR = 0.82; CI = 0.68-0.99) and non-White killings (IRR = 0.94; CI = 0.89-1.00), respectively. Increasing video use predicted higher rates of White killings (IRR = 1.13; CI = 1.01-1.28). No policies were significantly associated with Black civilian killings. Law enforcement policies that help reduce police killings may vary across racial groups with the least benefit for Black civilians. Impact evaluations and meta-analyses of initiatives aimed to mitigate fatalities should be explored, particularly policies to address anti-Black bias. A national registry tracking all police killings and agency policies is urgently needed to inform law enforcement policies aimed to mitigate civilian fatalities.