Decarbonizing Water: The Potential to Apply the Voluntary Carbon Market toward Global Water Security.

The increase in global water insecurity is one of the first perceivable effects of climate change. Two billion people are now without access to safe drinking water, and four billion experience water stress at least once a year, primarily in low per-capita emission countries. This nexus between climate change and water insecurity has significant implications for the global economy, with the water sector contributing 10% of global emissions. Though traditionally a local issue, climate finance mechanisms like the voluntary carbon market (VCM) present opportunities for a global, sustainable, performance-based funding stream to address water insecurity. Since 2010, water-related carbon projects have yielded over 45 million emission reduction credits. Our analysis estimates a global potential of over 1.6 billion tCO2e per year across various water project subsectors. At a $10 per credit average, this could attract over $160 billion in investments over the next decade, enhancing global water security. However, barriers like high intervention costs and limited technologies hinder widespread implementation, creating a tension between standardized and bespoke credits. We present case studies, spanning drinking water initiatives to the wastewater treatment sector that illustrate VCM's role in channeling private sector capital for water security in climate-vulnerable regions.

Disaggregating Latino nativity in equity research using electronic health records.

OBJECTIVE: To develop and validate prediction models for inference of Latino nativity to advance health equity research. DATA SOURCES/STUDY SETTING: This study used electronic health records (EHRs) from 19,985 Latino children with self-reported country of birth seeking care from January 1, 2012 to December 31, 2018 at 456 community health centers (CHCs) across 15 states along with census-tract geocoded neighborhood composition and surname data. STUDY DESIGN: We constructed and evaluated the performance of prediction models within a broad machine learning framework (Super Learner) for the estimation of Latino nativity. Outcomes included binary indicators denoting nativity (US vs. foreign-born) and Latino country of birth (Mexican, Cuban, Guatemalan). The performance of these models was compared using the area under the receiver operating characteristics curve (AUC) from an externally withheld patient sample. DATA COLLECTION/EXTRACTION METHODS: Census surname lists, census neighborhood composition, and Forebears administrative data were linked to EHR data. PRINCIPAL FINDINGS: Of the 19,985 Latino patients, 10.7% reported a non-US country of birth (5.1% Mexican, 4.7% Guatemalan, 0.8% Cuban). Overall, prediction models for nativity showed outstanding performance with external validation (US-born vs. foreign: AUC = 0.90; Mexican vs. non-Mexican: AUC = 0.89; Guatemalan vs. non-Guatemalan: AUC = 0.95; Cuban vs. non-Cuban: AUC = 0.99). CONCLUSIONS: Among challenges facing health equity researchers in health services is the absence of methods for data disaggregation, and the specific ability to determine Latino country of birth (nativity) to inform disparities. Recent interest in more robust health equity research has called attention to the importance of data disaggregation. In a multistate network of CHCs using multilevel inputs from EHR data linked to surname and community data, we developed and validated novel prediction models for the use of available EHR data to infer Latino nativity for health disparities research in primary care and health services research, which is a significant potential methodologic advance in studying this population.

A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination risk in drinking water: Design, characterization and field validation.

We designed and validated a sensitive, continuous, in-situ, remotely reporting tryptophan-like fluorescence sensor and coupled it with a machine learning model to predict high-risk fecal contamination in water (>10 colony forming units (CFU)/100mL E. coli). We characterized the sensor's response to multiple fluorescence interferents with benchtop analysis. The sensor's minimum detection limit (MDL) of tryptophan dissolved in deionized water was 0.05 ppb (p <0.01) and its MDL of the correlation to E. coli present in wastewater effluent was 10 CFU/100 mL (p <0.01). Fluorescence response declined exponentially with increased water temperature and a correction factor was calculated. Inner filter effects, which cause signal attenuation at high concentrations, were shown to have negligible impact in an operational context. Biofouling was demonstrated to increase the fluorescence signal by approximately 82% in a certain context, while mineral scaling reduced the sensitivity of the sensor by approximately 5% after 24 hours with a scaling solution containing 8 times the mineral concentration of the Colorado River. A machine learning model was developed, with TLF measurements as the primary feature, to output fecal contamination risk levels established by the World Health Organization. A training and validation data set for the model was built by installing four sensors on Boulder Creek, Colorado for 88 days and enumerating 298 grab samples for E. coli with membrane filtration. The machine learning model incorporated a proxy feature for fouling (time since last cleaning) which improved model performance. A binary classification model was able to predict high risk fecal contamination with 83% accuracy (95% CI: 78% - 87%), sensitivity of 80%, and specificity of 86%. A model distinguishing between all World Health Organization established risk categories performed with an overall accuracy of 64%. Integrating TLF measurements into an ML model allows for anomaly detection and noise reduction, permitting contamination prediction despite biofilm or mineral scaling formation on the sensor's lenses. Real-time detection of high risk fecal contamination could contribute to a major step forward in terms of microbial water quality monitoring for human health.

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action.

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 km2. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June-September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Mobility and social deprivation on primary care utilisation among paediatric patients with asthma.

OBJECTIVE: Asthma care is negatively impacted by neighbourhood social and environmental factors, and moving is associated with undesirable asthma outcomes. However, little is known about how movement into and living in areas of high deprivation relate to primary care use. We examined associations between neighbourhood characteristics, mobility and primary care utilisation of children with asthma to explore the relevance of these social factors in a primary care setting. DESIGN: In this cohort study, we conducted negative binomial regression to examine the rates of primary care visits and annual influenza vaccination and logistic regression to study receipt of pneumococcal vaccination. All models were adjusted for patient-level covariates. SETTING: We used data from community health centres in 15 OCHIN states. PARTICIPANTS: The sample included 23 773 children with asthma aged 3-17 across neighbourhoods with different levels of social deprivation from 2012 to 2017. We conducted negative binomial regression to examine the rates of primary care visits and annual influenza vaccination and logistic regression to study receipt of pneumococcal vaccination. All models were adjusted for patient-level covariates. RESULTS: Clinic visit rates were higher among children living in or moving to areas with higher deprivation than those living in areas with low deprivation (rate ratio (RR) 1.09, 95% CI 1.02 to 1.17; RR 1.05, 95% CI 1.00 to 1.11). Children moving across neighbourhoods with similarly high levels of deprivation had increased RRs of influenza vaccination (RR 1.13, 95% CI 1.03 to 1.23) than those who moved but stayed in neighbourhoods of low deprivation. CONCLUSIONS: Movement into and living within areas of high deprivation is associated with more primary care use, and presumably greater opportunity to reduce undesirable asthma outcomes. These results highlight the need to attend to patient movement in primary care visits, and increase neighbourhood-targeted population management to improve equity and care for children with asthma.

Role of social deprivation on asthma care quality among a cohort of children in US community health centres.

OBJECTIVE: Social deprivation is associated with worse asthma outcomes. The Social Deprivation Index is a composite measure of social determinants of health used to identify neighbourhood-level disadvantage in healthcare. Our objective was to determine if higher neighbourhood-level social deprivation is associated with documented asthma care quality measures among children treated at community health centres (CHCs). METHODS SETTING, PARTICIPANTS, OUTCOME MEASURES: We used data from CHCs in 15 states in the Accelerating Data Value Across a National Community Health Center Network (ADVANCE). The sample included 34 266 children with asthma from 2008 to 2017, aged 3-17 living in neighbourhoods with differing levels of social deprivation measured using quartiles of the Social Deprivation Index score. We conducted logistic regression to examine the odds of problem list documentation of asthma and asthma severity, and negative binomial regression for rates of albuterol, inhaled steroid and oral steroid prescription adjusted for patient-level covariates. RESULTS: Children from the most deprived neighbourhoods had increased rates of albuterol (rate ratio (RR)=1.22, 95% CI 1.13 to 1.32) compared with those in the least deprived neighbourhoods, while the point estimate for inhaled steroids was higher, but fell just short of significance at the alpha=0.05 level (RR=1.16, 95% CI 0.99 to 1.34). We did not observe community-level differences in problem list documentation of asthma or asthma severity. CONCLUSIONS: Higher neighbourhood-level social deprivation was associated with more albuterol and inhaled steroid prescriptions among children with asthma, while problem list documentation of asthma and asthma severity varied little across neighbourhoods with differing deprivation scores. While the homogeneity of the CHC safety net setting studied may mitigate variation in diagnosis and documentation of asthma, enhanced clinician awareness of differences in community risk could help target paediatric patients at risk of lower quality asthma care.

Improving rural health care reduces illegal logging and conserves carbon in a tropical forest.

Tropical forest loss currently exceeds forest gain, leading to a net greenhouse gas emission that exacerbates global climate change. This has sparked scientific debate on how to achieve natural climate solutions. Central to this debate is whether sustainably managing forests and protected areas will deliver global climate mitigation benefits, while ensuring local peoples' health and well-being. Here, we evaluate the 10-y impact of a human-centered solution to achieve natural climate mitigation through reductions in illegal logging in rural Borneo: an intervention aimed at expanding health care access and use for communities living near a national park, with clinic discounts offsetting costs historically met through illegal logging. Conservation, education, and alternative livelihood programs were also offered. We hypothesized that this would lead to improved health and well-being, while also alleviating illegal logging activity within the protected forest. We estimated that 27.4 km2 of deforestation was averted in the national park over a decade (∼70% reduction in deforestation compared to a synthetic control, permuted P = 0.038). Concurrently, the intervention provided health care access to more than 28,400 unique patients, with clinic usage and patient visitation frequency highest in communities participating in the intervention. Finally, we observed a dose-response in forest change rate to intervention engagement (person-contacts with intervention activities) across communities bordering the park: The greatest logging reductions were adjacent to the most highly engaged villages. Results suggest that this community-derived solution simultaneously improved health care access for local and indigenous communities and sustainably conserved carbon stocks in a protected tropical forest.

Assessment of Opioid Prescribing Patterns in a Large Network of US Community Health Centers, 2009 to 2018.

Importance: Understanding opioid prescribing patterns in community health centers (CHCs) that disproportionately serve low-income patients may help to guide strategies to reduce opioid-related harms. Objective: To assess opioid prescribing patterns between January 1, 2009, and December 31, 2018, in a network of safety-net clinics serving high-risk patients. Design, Setting, and Participants: Cross-sectional study of 3 227 459 opioid prescriptions abstracted from the electronic health records of 2 129 097 unique primary care patients treated from 2009 through 2018 at a network of CHCs that included 449 clinic sites in 17 states. All age groups were included in the analysis. Main Outcomes and Measures: The following measures were described at the population level for each study year: (1) percentage of patients with at least 1 prescription for an opioid by age and sex, (2) number of opioid prescriptions per 100 patients, (3) number of long-acting opioid prescriptions per 100 patients, (4) mean annual morphine milligram equivalents (MMEs) per patient, (5) mean MME per prescription, (6) number of chronic opioid users, and (7) mean of high-dose opioid users. Results: The study population included 2 129 097 patients (1 158 413 women [54.4%]) with a mean (SD) age of 32.2 (21.1) years and a total of 3 227 459 opioid prescriptions. The percentage of patients receiving at least 1 opioid prescription in a calendar year declined 67.4% from 15.9% in 2009 to 5.2% in 2018. Over the 10-year study period, a greater percentage of women received a prescription (13.1%) compared with men (10.9%), and a greater percentage of non-Hispanic White patients (18.1%) received an opioid prescription compared with non-Hispanic Black patients (9.5%), non-Hispanic patients who self-identified as other races (8.0%), and Hispanic patients (6.9%). The number of opioid prescriptions for every 100 patients decreased 73.7% from 110.8 in 2009 to 29.1 in 2018. The number of long-acting opioids for every 100 patients decreased 85.5% during the same period, from 22.0 to 3.2. The MMEs per patient decreased from 1682.7 in 2009 to 243.1 in 2018, a decline of 85.6%. Conclusions and Relevance: In this cross-sectional study, the opioid prescribing rate in 2009 in the CHC network was higher than national population estimates but began to decline earlier and more precipitously. This finding likely reflects harm mitigation policies and efforts at federal, state, and clinic levels and strong clinical quality improvement strategies within the CHCs.

Smoking Assessment and Current Smoking Status Among Adolescents in Primary Care Settings.

INTRODUCTION: Initiating tobacco use in adolescence increases the risk of nicotine dependence and continued use into adulthood. Primary care visits provide opportunities for the assessment and treatment of tobacco use; however, little is known about prevalence and correlates of assessing smoking status and current use among adolescents in these settings. AIMS AND METHODS: Using electronic health record data from the OCHIN network, we identified adolescents with greater than or equal to one primary care visit to a study clinic (n = 366 clinics from 15 US states) during January 1, 2016 to December 31, 2017. We estimated odds ratios of smoking assessment and current smoking status by patient covariates. RESULTS: Of 140 887 patients, 87.4% were assessed for smoking. Being Latino or Black (adjusted odds ratio = 1.22, 95% confidence interval: 1.13-1.32; adjusted odds ratio = 1.17, 95% confidence interval: 1.07-1.29, respectively, vs. non-Hispanic White), publicly insured, having more visits, and having an asthma diagnosis or other respiratory symptoms were associated with higher odds of assessment. Odds were lower if the patient was male and uninsured. Of those assessed, 1.6% identified as current smokers. Being older, having more visits, an asthma diagnosis, other respiratory symptoms, and lower household income was associated with higher odds of being a current smoker. Latinos and Blacks had lower odds than non-Hispanic Whites. CONCLUSIONS: Although some commonly reported tobacco-related disparities were not present, smoking assessment and current smoking status differed significantly by most patient demographics. Implementation of adolescent tobacco assessment protocols and the development of interventions to target subpopulations of adolescents with higher rates of smoking could mitigate disparate rates of assessment and smoking, respectively. IMPLICATIONS: Clinical guidelines recommend screening adolescents for tobacco use in primary care settings. We found that most adolescents seen in US safety-net primary care clinics were assessed for smoking. We also found that smoking assessment and current smoking status differed significantly by most patient demographics. Implementing tobacco assessment protocols specific to adolescents could mitigate disparate rates of assessment and ensure accurate documentation of all forms of tobacco use, given the evolution of alternative tobacco products and poly use among adolescents. Interventions to target subpopulations of adolescents with higher smoking rates are needed to prevent the negative health effects of continued smoking.

Disparities in Biomarkers for Patients With Diabetes After the Affordable Care Act.

BACKGROUND: Racial and ethnic minorities are disproportionately affected by diabetes and at greater risk of experiencing poor diabetes-related outcomes compared with non-Hispanic whites. The Affordable Care Act (ACA) was implemented to increase health insurance coverage and reduce health disparities. OBJECTIVE: Assess changes in diabetes-associated biomarkers [hemoglobin A1c (HbA1c) and low-density lipoprotein] 24 months pre-ACA to 24 months post-ACA Medicaid expansion by race/ethnicity and insurance group. RESEARCH DESIGN: Retrospective cohort study of community health center (CHC) patients. SUBJECTS: Patients aged 19-64 with diabetes living in 1 of 10 Medicaid expansion states with ≥1 CHC visit and ≥1 HbA1c measurement in both the pre-ACA and the post-ACA time periods (N=13,342). METHODS: Linear mixed effects and Cox regression modeled outcome measures. RESULTS: Overall, 33.5% of patients were non-Hispanic white, 51.2% Hispanic, and 15.3% non-Hispanic black. Newly insured Hispanics and non-Hispanic whites post-ACA exhibited modest reductions in HbA1c levels, similar benefit was not observed among non-Hispanic black patients. The largest reduction was among newly insured Hispanics versus newly insured non-Hispanic whites (P<0.05). For the subset of patients who had uncontrolled HbA1c (HbA1c≥9%) within 3 months of the ACA Medicaid expansion, non-Hispanic black patients who were newly insured gained the highest rate of controlled HbA1c (hazard ratio=2.27; 95% confidence interval, 1.10-4.66) relative to the continuously insured group. CONCLUSIONS: The impact of the ACA Medicaid expansion on health disparities is multifaceted and may differ across racial/ethnic groups. This study highlights the importance of CHCs for the health of minority populations.

Associations between Psychiatric Disorders and Cannabis-Related Disorders Documented in Electronic Health Records.

Data from a large network of community health centers connected via a single electronic health record (EHR) system examined associations between psychiatric disorders and documentation of a cannabis-related disorder among patients with reported cannabis use. Participants were adults who had at least one ambulatory visit at a clinic in three states between 1/1/2012 and 12/31/2016 and had either 1) a documented cannabis-related disorder indicated by an ICD-9/10 code on the problem list or encounter list or 2) documentation of cannabis use in the EHR social history section. Clinics included 101,405 patients with either cannabis use recorded in the social history of the EHR (n = 71,660) or a cannabis-related disorder documented in the encounter or problem list (n = 29,745). GEE logistic regression modeling estimated adjusted odds ratios (aOR). Odds of patients having cannabis-related disorder recorded on the encounter or problem list were higher for individuals with depression (aOR = 1.08, 95% CI: 1.04-1.13), anxiety (aOR = 1.16, CI: 1.11-1.21) and bipolar disorder (aOR = 1.16, CI: 1.10-1.23). A diagnosis of schizophrenia increased the odds of a cannabis-related disorder by 62% (aOR = 1.62, CI: 1.48- 1.78). Primary care providers should routinely screen for and document cannabis-related disorders and psychiatric disorders.

Oral corticosteroid use, obesity, and ethnicity in children with asthma.

Objective: Comorbid asthma and obesity leads to poorer asthma outcomes, partially due to decreased response to controller medication. Increased oral steroid prescription, a marker of uncontrolled asthma, may follow. Little is known about this phenomenon among Latino children. Our objective was to determine whether obesity is associated with increased oral steroid prescription for children with asthma, and to assess potential disparities in these associations between Latino and non-Hispanic white children.Methods: We examined electronic health record data from the ADVANCE national network of community health centers. The sample included 16,763 children aged 5-17 years with an asthma diagnosis and ≥1 ambulatory visit in ADVANCE clinics across 22 states between 2012 and 2017. Poisson regression analysis was used to examine the rate of oral steroid prescription overall and by ethnicity controlling for potential confounders.Results: Among Latino children, those who were always overweight/obese at study visits had a 15% higher rate of receiving an oral steroid prescription than those who were never overweight/obese [rate ratio (RR) = 1.15, 95% CI 1.05-1.26]. A similar effect size was observed for non-Hispanic white children, though the relationship was not statistically significant (RR = 1.10, 95% CI: 0.92-1.33). The interactions between body mass index and ethnicity were not significant (sometimes overweight/obese p = 0.95, always overweight/obese p = 0.58), suggesting a lack of disparities in the association between obesity and oral steroid prescription by ethnicity.Conclusions: Children with obesity received more oral steroid prescriptions than those at a healthy weight, which may be indicative of worse asthma control. We did not observe significant ethnic disparities.