Housing instability patterns among low-income, urban Black young adults in California and associations with mental health outcomes: baseline data from a randomized waitlist-controlled trial.

BACKGROUND: Deep-rooted racial residential segregation and housing discrimination have given rise to housing disparities among low-income Black young adults in the US. Most studies have focused on single dimensions of housing instability, and thus provide a partial view of how Black young adults experience multiple, and perhaps overlapping, experiences of housing instability including homelessness, frequent moves, unaffordability, or evictions. We aimed to illuminate the multiple forms of housing instability that Black young adults contend with and examine relationships between housing instability and mental health outcomes. METHODS: Using baseline data from the Black Economic Equity Movement (BEEM) guaranteed income trial with 300 urban low-income Black young adults (aged 18-24), we conducted a three-stage latent class analysis using nine housing instability indicators. We identified distinct patterns by using fit indices and theory to determine the optimal number of latent classes. We then used multinomial logistic regression to identify subpopulations disproportionately represented within unstable housing patterns. Finally, we estimated associations between housing experience patterns and mental health outcomes: depression, anxiety, and hope. RESULTS: We found high prevalence of housing instability with 27.3% of participants reporting experiences of homelessness in the prior year and 39.0% of participants reporting multiple measures of housing instability. We found the 4-class solution to be the best fitting model for the data based on fit indices and theory. Latent classes were characterized as four housing experience patterns: 1) more stably housed, 2) unaffordable and overcrowded housing, 3) mainly unhoused, and 4) multiple dimensions of housing instability. Those experiencing unaffordable and overcrowded housing and being mainly unhoused were more than four times as likely to have symptoms of depression (Unaffordable: aOR = 4.57, 95% CI: 1.64, 12.72; Unhoused: aOR = 4.67, 95% CI:1.18, 18.48) and more than twice as likely to report anxiety (Unaffordable: aOR = 2.28, 95% CI: 1.03, 5.04; Unhoused: aOR = 3.36, 95% CI: 1.12, 10.05) compared to the more stably housed pattern. We found that hope scores were similarly high across patterns. CONCLUSIONS: High prevalence of housing instability and mental health challenges among low-income Black young adults demands tailored interventions to reduce instability, given widening racial disparities and implications for future well-being into adulthood.

A Mixed Methods Evaluation of Pharmacists' Readiness to Provide Long-Acting Injectable HIV Pre-exposure Prophylaxis in California.

BACKGROUND: Pre-exposure prophylaxis (PrEP) uptake remains low among people who could benefit, some of whom may prefer alternatives to oral PrEP, such as long-acting injectable pre-exposure prophylaxis (LAI-PrEP). We evaluated the potential for LAI-PrEP provision in pharmacies through a mixed methods study of pharmacists in California, where Senate Bill 159 enables pharmacists to independently provide oral PrEP. METHODS: In 2022-2023, we conducted an online cross-sectional survey of California pharmacists and pharmacy students (n = 919) and in-depth interviews with pharmacists (n = 30), both of which included modules assessing attitudes about PrEP provision. Using log-binomial regression, we estimated prevalence ratios (PRs) comparing survey participants' willingness to provide LAI-PrEP by pharmacy- and individual-level characteristics. Qualitative interview data were analyzed using Rapid Qualitative Analysis to identify factors that may affect pharmacists' provision of LAI-PrEP. RESULTS: Half of the survey participants (53%) indicated that they would be willing to administer LAI-PrEP using gluteal injection in their pharmacy. Willingness was higher among participants who worked in pharmacies that provided vaccinations or other injections (56% vs. 46%; PR: 1.2; 95% confidence interval: 1.0-1.4) and/or oral PrEP under Senate Bill 159 (65% vs. 51%; PR: 1.3; 95% confidence interval: 1.1-1.5) than among participants whose pharmacies did not. Interviewed participants reported barriers to LAI-PrEP provision, including the need for increased training and staffing, a private room for gluteal injections, better medication access, and payment for services. CONCLUSION: Pharmacies offer a promising setting for increased LAI-PrEP access. However, pharmacists may require additional training, resources, and policy changes to make implementation feasible.

"I don't need my kid to be high": prioritizing harm reduction when using cannabis during pregnancy.

BACKGROUND: Cannabis is the most common illicit substance used in pregnancy. As use continues to increase, understanding peoples' behaviors surrounding cannabis use during pregnancy is needed to improve maternal and child health outcomes. The aim of this study was to better understand pregnant individuals' perceptions and knowledge of cannabis use and use patterns as well as the social and environmental factors that may influence their use. METHODS: We conducted interviews with 19 participants between December 2022 and March 2023. Individuals self-identified as BIPOC (Black, Indigenous, People of Color), were over 21 years of age, spoke English or Spanish, resided in California, and had used cannabis during pregnancy in the last 0-2 years. Using qualitative, constructivist grounded theory methods, we analyzed the contexts that contributed to participants' lived experiences surrounding cannabis use behaviors during pregnancy. RESULTS: Participants reported making conscious decisions to responsibly manage their cannabis use during pregnancy to minimize potential harm to the fetus. Participants prioritized making what they perceived to be safer adjustments to their use of cannabis: (1) changing the amount of cannabis used, (2) changing the types of cannabis products used, and (3) changing sources of cannabis procurement. DISCUSSION: Our findings show that pregnant individuals are seeking information about safe cannabis use beyond medical supervision and are open to altering their cannabis consumption patterns. However, they are unable to find trustworthy and evidence-based harm reduction practices which can be implemented to mitigate harm to their unborn children. A harm reduction approach is needed in the field of maternal cannabis use to promote positive maternal and fetal health outcomes. CONCLUSIONS: More data is needed on comprehensive harm reduction approaches to cannabis use during pregnancy. This requires implementation of education on these topics in healthcare settings presented by prenatal care clinicians.

Plant G × Microbial E: Plant Genotype Interaction with Soil Bacterial Community Shapes Rhizosphere Composition During Invasion.

It is increasingly recognized that different genetic variants of hosts can uniquely shape their microbiomes. Invasive species often evolve in their introduced ranges, but little is known about the potential for their microbial associations to change during invasion as a result. We asked whether host genotype (G), microbial environment (E), or their interaction (G × E) affected the composition and diversity of host-associated microbiomes in Centaurea solstitialis (yellow starthistle), a Eurasian plant that is known to have evolved novel genotypes and phenotypes and to have altered microbial interactions, in its severe invasion of CA, USA. We conducted an experiment in which native and invading plant genotypes were inoculated with native and invaded range soil microbial communities. We used amplicon sequencing to characterize rhizosphere bacteria in both the experiment and the field soils from which they were derived. We found that native and invading plant genotypes accumulated different microbial associations at the family level in each soil community, often counter to differences in family abundance between soil communities. Root associations with potentially beneficial Streptomycetaceae were particularly interesting, as these were more abundant in the invaded range field soil and accumulated on invading genotypes. We also found that bacterial diversity is higher in invaded soils, but that invading genotypes accumulated a lower diversity of bacteria and unique microbial composition in experimental inoculations, relative to native genotypes. Thus variation in microbial associations of invaders was driven by the interaction of plant G and microbial E, and rhizosphere microbial communities appear to change in composition in response to host evolution during invasion.

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California.

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model-Deepbiosphere-not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Not all healthcare inequities in diabetes are equal: a comparison of two medically underserved cohorts.

INTRODUCTION: Diabetes disparities exist based on socioeconomic status, race, and ethnicity. The aim of this study is to compare two cohorts with diabetes from California and Florida to better elucidate how health outcomes are stratified within underserved communities according to state location, race, and ethnicity. RESEARCH DESIGN AND METHODS: Two cohorts were recruited for comparison from 20 Federally Qualified Health Centers as part of a larger ECHO Diabetes program. Participant-level data included surveys and HbA1c collection. Center-level data included Healthcare Effectiveness Data and Information Set metrics. Demographic characteristics were summarized overall and stratified by state (frequencies, percentages, means (95% CIs)). Generalized linear mixed models were used to compute and compare model-estimated rates and means. RESULTS: Participant-level cohort: 582 adults with diabetes were recruited (33.0% type 1 diabetes (T1D), 67.0% type 2 diabetes (T2D)). Mean age was 51.1 years (95% CI 49.5, 52.6); 80.7% publicly insured or uninsured; 43.7% non-Hispanic white (NHW), 31.6% Hispanic, 7.9% non-Hispanic black (NHB) and 16.8% other. Center-level cohort: 32 796 adults with diabetes were represented (3.4% with T1D, 96.6% with T2D; 72.7% publicly insured or uninsured). Florida had higher rates of uninsured (p<0.0001), lower continuous glucose monitor (CGM) use (18.3% Florida; 35.9% California, p<0.0001), and pump use (10.2% Florida; 26.5% California, p<0.0001), and higher proportions of people with T1D/T2D>9% HbA1c (p<0.001). Risk was stratified within states with NHB participants having higher HbA1c (mean 9.5 (95% CI 8.9, 10.0) compared with NHW with a mean of 8.4 (95% CI 7.8, 9.0), p=0.0058), lower pump use (p=0.0426) and CGM use (p=0.0192). People who prefer to speak English were more likely to use a CGM (p=0.0386). CONCLUSIONS: Characteristics of medically underserved communities with diabetes vary by state and by race and ethnicity. Florida's lack of Medicaid expansion could be a factor in worsened risks for vulnerable communities with diabetes.

The Percentage of Patients Experiencing Financial Strain Depends on the Screening Measure: Evidence From a Cross-Sectional Survey of Adult Members of an Integrated Healthcare Delivery System.

OBJECTIVES: Financial strain has important consequences for patients, providers, and health care systems. However, there is currently no gold standard measure to screen for financial strain. This study compared the performance of 3 single-item screeners using a composite measure of financial strain as a "gold standard." METHODS: We conducted a secondary analysis of unweighted data from a 2021 survey of Kaiser Permanente Northern California health plan members comparing the percentages of adults who experienced financial strain based on 3 general single-item screeners, a screener specific to medical and dental health care use, and a composite financial strain measure. The study sample was comprised of 2734 non-Medicaid insured adults who answered all financial strain questions. Kappa statistics evaluating agreement of the 3 general screeners with the composite measure were calculated for the sample overall, by age group, and within age group, by 4 levels of income and 4 racial/ethnic subgroups. RESULTS: Among 947 adults aged 35 to 65, 30.7% had just enough money or not enough money to make ends meet, 23.3% had a somewhat hard or hard time paying for basics, 18.8% had trouble paying for ≥1 type of expense, 20.5% had delayed/used less medical/dental care, and 41.5% had experienced financial strain based on the composite measure. Among 1787 adults aged 66 to 85, the percentages who screened positive on these measures were 22.7%, 19.4%, 12.9%, 19.8%, and 34.4%, respectively. Across the sample, by income categories and racial/ethnic groups, the making ends meet screener identified higher percentages of adults experiencing financial strain and performed better when compared with the composite measure than the hard to pay for the very basics and trouble paying for expenses screeners. Overall, substantial decreases in the percentages of adults who screened positive on the financial strain measures were seen as level of income increased. Within income categories, middle-aged adults were more likely than older adults to have experienced financial strain based on the composite and general single-item screeners. CONCLUSIONS: As social risk screening becomes part of the standard of care, it will be important to assess how well different brief screeners for financial strain perform with diverse patient populations.

Rental Housing Deposits and Health Care Use.

Importance: Housing deposits and tenancy supports have become new Medicaid benefits in multiple states; however, evidence on impacts from these specific housing interventions is limited. Objective: To evaluate the association of rental housing deposits and health care use among Medicaid beneficiaries receiving social needs case management as part of a Whole-Person Care (Medicaid 1115 waiver) pilot program in California. Design, Setting, and Participants: This cohort study compared changes in health care use among a group of adults who received a housing deposit between October 2018 and December 2021 along with case management vs a matched comparison group who received case management only in Contra Costa County, California, a large county in the San Francisco Bay Area. All participants were enrolled in health and social needs case management based on elevated risk of acute care use. Data analysis took place from March 2023 to June 2024. Exposure: Rental housing deposit funds that covered 1-time moving transition costs. Funds averaged $1750 per recipient. Main Outcomes and Measures: Changes in hospitalizations, emergency department visits, primary care visits, specialty care visits, behavioral health visits, psychiatric emergency services, or detention intakes during the 6 months before vs 6 months after deposit receipt. Changes 12 months before and after deposit receipt were examined as a sensitivity analysis. Results: Of 1690 case management participants, 845 received a housing deposit (362 [42.8%] <40 years old; 422 [49.9%] male) and 845 received case management only (367 [43.4%] <40 years old; 426 [50.4%] male). In adjusted analyses, deposit recipients had no statistically significant differential changes in health care use for any measure compared to participants who received case management alone. Twelve-month sensitivity analyses yielded consistent results. Conclusions and Relevance: In this cohort study, compared to case management only, housing deposits with case management were not associated with short-term changes in health care use. There may be other unmeasured health benefits or downstream benefits from greater case management engagement. States considering housing deposits as an expanded Medicaid benefit may need to temper expectations about short-term health care use impacts.

Allelochemicals determine competition and grazing control in Alexandrium catenella.

The production of allelochemicals by the toxigenic dinoflagellate Alexandrium catenella is one of the suggested mechanisms to facilitate its bloom formation and persistence by outcompeting other phototrophic protists and reducing grazing pressure. In Southern California, toxic events caused by A. catenella and paralytic shellfish toxins (PSTs) regularly impact coastal ecosystems; however, the trophic interactions and mechanisms promoting this species in a food web context are still not fully understood. In the present study, we combined a dynamical mathematical model with laboratory experiments to investigate potential toxic and allelochemical effects of an A. catenella strain isolated off the coast of Los Angeles, Southern California, on competitors and a common zooplankton consumer. Experiments were conducted using three toxigenic strains of A. catenella, comparing the new Californian isolate (Alex Cal) to two strains previously described from the North Sea, a lytic (Alex2) and non-lytic (Alex5) strain, testing for donor density-dependent effects on two phytoplankton species (Rhodomonas salina, Tetraselmis sp.) and on the rotifer Brachionus plicatilis. Bioassays revealed a steep decline in competitor and consumer populations with increasing Alex Cal concentrations, indicating an intermediate lytic activity compared to the North Sea strains (lytic Alex2 and non-lytic Alex5). The rotifer fed and grew well on the PST- toxic, but non-lytic Alex5 strain, while its survival significantly decreased with increasing concentrations of the two lytic strains Alex Cal and Alex 2, indicating that negative effects on the rotifer were mediated by allelochemicals rather than PST-toxins. Mixed culture experiments including both competitors and consumers demonstrated that the intensity of allelochemical effects not only depended on the A. catenella density but also on the target density. Negative effects on grazers were alleviated by co-occurring competitors with a lower sensitivity to allelochemicals, thus reducing harmful compounds and allowing grazing control on the dinoflagellate to come into effect again. Results from mixed culture experiments were supported by the mathematical approach used in this study which was calibrated with data from simple monoculture growth, pairwise competition and predator-prey experiments, demonstrating the applicability of this model approach to predict the outcome of more complex food web dynamics at the community level.

Spatiotemporal distribution of environmental microbiota around animal farms adjacent to produce fields in central coast California.

This study aimed to identify different environmental microbiota in animal farms adjacent to produce fields and to understand their potential flow pattern. Soil and water samples were collected from 16 locations during the winter, spring, summer, and fall seasons. In addition, a high-resolution digital elevation model helped to create a stream network to understand the potential flow of the microbiome. Metagenomic analysis of the 16 S rRNA gene revealed that soil and water samples from the four seasons harbor diverse microbiome profiles. The phylogenetic relationship of operational taxonomic units (OTUs) is separated by a maximum of 0.6 Bray-Curtis distance. Similarly, the Principal Component Analysis (P = 0.001) demonstrated the soil and water microbiome clustering across different locations and seasons. The relative abundance of Proteobacteria, Bacteroidetes, and Firmicutes was higher in the water samples than in the soil samples. In contrast, the relative abundance of Actinobacteria and Chloroflexi was higher in the soil compared to the water samples. Soil samples in summer and water samples in spring had the highest abundance of Bacteroidetes and Firmicutes, respectively. A unique microbial community structure was found in water samples, with an increased abundance of Hydrogenophaga and Solirubrobacter. Genera that were significantly abundant at a 1% false discovery rate (FDR) among seasons and soil or water samples, include Nocardioides, Gemmatimonas, JG30-KF-CM45, Massilia, Gaiellales, Sphingomonas, KD4-96, Bacillus, Streptomyces, Gaiella, and Gemmatimonadaceae. The relative abundance of pathogenic genera, including Mycobacterium, Bacteroides, Nocardia, Clostridium, and Corynebacterium, were significantly (at 1% FDR) affected by seasons and environmental type. The elevation-based stream network model suggests the potential flow of microbiomes from the animal farm to the produce fields.

Increasing Identification and Follow-Up of Cognitive Impairment in Primary Care: A Quality Improvement Pilot Study.

INTRODUCTION: Beyond our population growing older and living longer, there is an increased risk of developing a cognitive disorder. Standardized screening during a routine visit in primary care may be ideal for early detection of mild cognitive impairment (MCI) and follow-up for cognitive changes. AIM: This quality improvement (QI) project aimed to determine the impact of implementing the Mini-Cog© quick screening for early dementia detection to identify and follow up on the cognitive impairment of older adults in a primary care clinic setting. METHODS: Implementation started in February 2024 in a primary care clinic in Southern California. Data was collected for this project over a total of 16 weeks. This QI project implemented a routine cognitive screening using the Mini-Cog©. Cognitive impairment was identified, and if indicated by the Mini-Cog© scores, follow-up for a cognitive assessment and care plan services were initiated. Data were obtained from the project site's electronic medical record on a total sample size of 471 participants (n = 382 in the pre-implementation group and n = 89 in the post-implementation group). RESULTS: Pearson's chi-square test indicated a statistically significant improvement in the identification rate of cognitive impairment, increasing from 11.8% (n = 45 out of 382) at pre-implementation to 34.8% (n = 31 out of 89) at post-implementation, and specifically, mild cognitive impairment increased from zero identified in pre-implementation to 12.4% (n = 11 out of 89) post-implementation. Lastly, follow-up rates improved from 91.1% (n = 41 out of 45) to 100% (n = 31 out of 31) in post-implementation, and clinical significance was evident based on the phi-coefficient (φ = 0.196), indicating a small effect size and a 100% follow-up rate. CONCLUSIONS: The findings of this project suggest older adults should receive cognitive screenings to help identify early cognitive impairment and increase follow-up for further evaluation, treatment, and advanced care planning.

Evaluation of Socioeconomic and Racial/Ethnic Disparities in All-Cause Mortality in Adolescents and Young Adults With Cancer.

BACKGROUND: Adolescent and young adult (AYA) patients with cancer have historically been understudied. Few studies have examined survival disparities associated with racial/ethnic and socioeconomic status (SES) and do not account for the influence of insurance status and access to care. We evaluated the association of SES and race/ethnicity with overall mortality for AYA patients who were members of an integrated health system with relatively equal access to care. METHODS: AYA patients diagnosed with the 15 most common cancer types during 2010 through 2018 at Kaiser Permanente Southern California were included. Neighborhood Deprivation Index (NDI) quartile (Q1: least deprived; Q4: most deprived) was used as a measure of SES. Mortality rate per 1,000 person-years was calculated for each racial/ethnic and NDI subgroup. Multivariable Cox model was used to estimate hazard ratios (HRs) for all-cause mortality adjusting for sex, age and stage at diagnosis, cancer type, race/ethnicity, and NDI. RESULTS: Data for 6,379 patients were tracked for a maximum of 10 years. Crude mortality rates were higher among non-White racial/ethnic patients compared with non-Hispanic (NH)-White patients. In the Cox model, Hispanic (HR, 1.31; P=.004) and NH-Black (HR, 1.34; P=.05) patients experienced significantly higher all-cause mortality risk compared with NH-White patients. Patients from more deprived neighborhoods had higher mortality risk. In the Cox model, there was no significant difference in all-cause mortality between Q1 and Q2 through Q4 (Q2: HR, 0.88; P=.26, Q3: HR, 0.94; P=.56, and Q4: HR, 0.95; P=.70). CONCLUSIONS: For AYAs with cancer with similar access to care, Hispanic and NH-Black patients have higher risk of all-cause mortality than NH-White patients, whereas no significant SES-associated survival disparities were observed. These findings warrant further investigation, awareness, and intervention to address inequities in cancer care among vulnerable populations.

Post-marketing safety study to evaluate pregnancy outcomes among recipients of hepatitis B vaccines.

HepB-CpG is a licensed adjuvanted two-dose hepatitis B vaccine for adults, with limited data on exposure during pregnancy. We assessed the risk of pregnancy outcomes among individuals who received HepB-CpG or the 3-dose HepB-alum vaccine ≤28 d prior to conception or during pregnancy at Kaiser Permanente Southern California (KPSC). The pregnancy cohort included KPSC members aged ≥18 y who received ≥1 dose of hepatitis B vaccine (HepB-CpG or HepB-alum) at KPSC outpatient family or internal medicine departments from August 2018 to November 2020. We followed these individuals through electronic health records from the vaccination date until the end of pregnancy, KPSC health plan disenrollment, or death, whichever came first. Among 81 and 125 eligible individuals who received HepB-CpG and HepB-alum, respectively, live births occurred in 84% and 74%, spontaneous abortion occurred in 7% and 17% (adjusted relative risk [aRR] 0.40, 95% CI: 0.16-1.00), and preterm birth occurred in 15% and 14% of liveborn infants (aRR 0.97, 95% CI 0.47-1.99). No major birth defects were identified through 6 months of age. The study found no evidence of adverse pregnancy outcomes for recipients of HepB-CpG in comparison to HepB-alum.

Email Nudges Increased Eligibility Verification And Subsidy Receipt In California's ACA Marketplace.

In the Affordable Care Act (ACA) Marketplaces, enrollees must periodically demonstrate their eligibility to receive income-linked health insurance premium subsidies. Marketplaces can verify eligibility using existing records, but only with consumers' consent, which must be renewed at specified times. In a randomized experiment in September 2020, we tested the effect of email nudges reminding consumers to provide consent for verification of their continued eligibility for premium subsidies in California's ACA Marketplace. More than 20,000 households that had applied for subsidies but whose consent for eligibility verification would soon expire were sent one, two, or three emails reminding them to renew consent. Sending three emails increased consent updates by 1.9 percentage points (3.2 percent) and increased receipt of subsidies by 2.0 percentage points (4.0 percent). However, nearly 40 percent of households receiving three emails did not update their consent by the end of the open enrollment period, thus preventing their continued receipt of subsidies. To improve the affordability of Marketplace coverage, new policies and structural changes may be needed to reduce administrative barriers that can inhibit access to subsidies.

Dwelling proximity to wildfire and spina bifida risk in offspring.

OBJECTIVE: To evaluate the association between wildfire exposure in pregnancy and spina bifida risk. METHODS: This retrospective cohort study used the California Office of Statewide Health Planning and Development Linked Birth File with hospital discharge data between 2007 and 2010. The Birth File data were merged with the California Department of Forestry and Fire Protection data of the same year. Spina bifida was identified by its corresponding ICD-9 code listed on the hospital discharge of the newborn. Wildfire exposure was determined based on the zip code of the woman's home address. Pregnancy was considered exposed to wildfire if the mother lived within 15 miles of a wildfire during the pregnancy or within 30 days prior to pregnancy. RESULTS: There were 2,093,185 births and 659 cases of spina bifida between 2007 and 2010. The births were analyzed using multivariable logistic regression models and adjusted for potential confounders. Exposure to wildfire in the first trimester was associated with higher odds of spina bifida (aOR= 1.43 [1.11-1.84], p-value = 0.01). Wildfire exposure 30 days before the last menstrual period and during the second and third trimesters were not associated with higher spina bifida risk. CONCLUSION: Wildfire exposure has shown an increased risk of spina bifida during the early stages of pregnancy.

Residential mobility for young adults in extended foster care.

BACKGROUND: Placement stability is tied to well-being among children living in foster care, yet residential mobility remains largely unexplored for transition-age youth residing in extended foster care (EFC) between ages 18 and 21. During the time young adults in EFC can remain in foster homes, child welfare agencies offer living arrangements tailored to the transition into adulthood. In supervised independent living placements (SILPs), residents receive monthly stipends to cover their housing expenses and are responsible for covering food, utilities, and other basic needs-related costs. Transitional housing placements (THPs) offer light-touch caregiving during the transition to independence to ensure that residents have their basic needs met. OBJECTIVE: The current study is the first comprehensive examination of factors associated with residential mobility in a statewide population of young adults residing in EFC. PARTICIPANTS: Using California administrative Child Protective Services records, we identified young adults who turned 18 between 2016 and 2019 and lived in care for at least 1 week (N = 10,517). METHODS: Negative binomial regression evaluated the association between residential characteristics and per-year EFC moves. RESULTS: We found that young adults had a median number of 1.0 moves during EFC, with a median rate of 0.67 moves per year in EFC (i.e. roughly one move every 18 months). SILPs and THPs were the most common primary residence types among the study population (41.6 % and 26.0 %, respectively). The average duration of stay in EFC was two years, which varied by primary residence type. Regression results showed that predominant residence type in EFC was associated with EFC mobility, with young adults in SILPs being more likely to experience slightly higher mobility than those primarily living in other residence types (0.82 versus 0.75 moves per year in EFC). CONCLUSIONS: Findings suggest that housing for EFC residents tends to be long-lasting and relatively stable in California. Modest variations in mobility by residential type and by experiences in foster care before age 18 underscore the importance of integrating housing and psychosocial resources during independent living planning processes. The commonality of out-of-county residence should be considered when determining housing and transportation expenses for residents, given its potential implications for access to medical and behavioral health care, academic institutions, and employment opportunities for young adults receiving EFC services.

Climatic variation allows montane willows to escape an adaptive trade-off.

Adaptive responses to climate change, based on heritable variation in stress tolerance, may be important for plant population persistence. It is unclear which populations will mount the strongest future adaptive responses. It may be fruitful to identify populations that have escaped trade-offs among performance traits, which can hinder adaptation. Barring strong genetic constraints, the extent of trade-offs may depend on spatial relationships among climate variables shaping different traits. Here, we test for climate-driven ecotypic variation and trade-offs among drought and freezing sensitivity, and growth, for Lemmon's willow (Salix lemmonii) in a common garden study of 90 genotypes from 38 sites in the Sierra Nevada, USA. Salix lemmonii exhibits ecotypic variation in leaf turgor loss point, a measure of drought sensitivity, from -0.95 to -0.74 MPa along a gradient of spring snowpack. We also find variation in spring freezing sensitivity with minimum May temperature. However, we find no trade-off, as the climatic gradients shaping these traits are spatially uncorrelated in our study region, despite being negatively correlated across the Sierra Nevada. Species may escape adaptive trade-offs in geographic regions where climate variables are spatially decoupled. These regions may represent valuable reservoirs of heritable adaptive phenotypic variation.

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate systematic gaps. This paper, organized into two halves, underscores the importance of vetting algorithms for bias when used for questions of environmental science and justice. In the first half, we present a case study of using machine learning for environmental justice-motivated research: prediction of drinking water quality. While performance varied across models and contaminants, some performed well. Multiple models had overall accuracy rates at or above 90 % and F2 scores above 0.60 on their respective test sets. In the second half, we dissect this algorithmic approach to examine how modeling decisions affect modeling outcomes - and not only how these decisions change whether the model is correct or incorrect, but for whom. We find that multiple decision points in the modeling process can lead to different predictive outcomes. More importantly, we find that these choices can result in significant differences in demographic characteristics of false negatives. We conclude by proposing a set of practices for researchers and policy makers to follow (and improve upon) when applying machine learning to questions of environmental science, management, and justice.