Use, limitations, and future directions of mixtures approaches to understand the health impacts of weather- and climate change-related exposures, an under-studied aspect of the exposome.

The exposome concept aims to account for the comprehensive and cumulative effects of physical, chemical, biological, and psychosocial influences on biological systems. To date, limited exposome research has explicitly included climate change-related exposures. We define these exposures as those that will intensify with climate change, including direct effects like extreme heat, tropical cyclones, wildfires, downstream effects like air pollution, power outages, and limited or contaminated food and water supplies. These climate change-related exposures can occur individually or simultaneously. Here, we discuss the concept of a climate mixture, defined as three or more simultaneous climate change-related exposures, in the context of the exposome. In a motivating climate mixture example, we consider the impact of a co-occurring tropical cyclone, power outage, and flooding on respiratory hospitalizations. We identify current gaps and future directions for assessing the effect of climate mixtures on health. Mixtures methods allow us to incorporate climate mixtures into exposomics.

Tampons as a source of exposure to metal(loid)s.

BACKGROUND: Between 52-86% of people who menstruate in the United States use tampons-cotton and/or rayon/viscose 'plugs'-to absorb menstrual blood in the vagina. Tampons may contain metals from agricultural or manufacturing processes, which could be absorbed by the vagina's highly absorptive tissue, resulting in systemic exposure. To our knowledge, no previous studies have measured metals in tampons. OBJECTIVES: We evaluated the concentrations of 16 metal(loid)s in 30 tampons from 14 tampon brands and 18 product lines and compared the concentrations by tampon characteristics. METHODS: About 0.2 - 0.3 g from each tampon (n = 60 samples) were microwave-acid digested and analyzed by inductively coupled plasma mass spectrometry (ICP-MS) to determine concentrations of arsenic, barium, calcium, cadmium, cobalt, chromium, copper, iron, manganese, mercury, nickel, lead, selenium, strontium, vanadium, and zinc. We compared concentrations by several tampon characteristics (region of purchase, organic material, brand type) using median quantile mixed models. RESULTS: We found measurable concentrations of all 16 metals assessed. We detected concentrations of several toxic metals, including elevated mean concentrations of lead (geometric mean [GM] = 120 ng/g), cadmium (GM = 6.74 ng/g), and arsenic (GM = 2.56 ng/g). Metal concentrations differed by region of tampon purchase (US versus European Union/United Kingdom), by organic versus non-organic material, and for store- versus name-brand tampons. Most metals differed by organic status; lead concentrations were higher in non-organic tampons while arsenic was higher in organic tampons. No categoriy had consistently lower concentrations of all or most metals. DISCUSSION: Tampon use is a potential source of metal exposure. We detected all 16 metals in at least one sampled tampon, including some toxic metals like lead that has no "safe" exposure level. Future research is needed to replicate our findings and determine whether metals can leach out of tampons and cross the vaginal epithelium into systemic circulation.

Power Outages: An Underappreciated Risk Factor for Children's Carbon Monoxide Poisoning.

Children's risk of exposure to carbon monoxide (CO) increases after disasters, likely due to improper generator use during power outages. Here, we evaluate the impact of outages on children's CO-related emergency department (ED) visits in New York State (NYS). We leveraged power outage data spanning 2017-2020 from the NYS Department of Public Service for 1,865 power operating localities (i.e., communities) and defined all-size and large-scale power outage hours. All-size outage hours affected ≥1% of customers, and large-scale outage hours affected ≥20%. We identified CO poisoning using diagnostic codes among those aged <18 between 2017 and 2020 using the Statewide Planning and Research Cooperative System (SPARCS), an all-payer reporting system in NYS. We linked community power outage exposure to patients using the population-weighted centroid of their block group of residence. We estimated the impact of power outages on CO poisoning using a time-stratified case-crossover study design with conditional logistic regression, controlling for daily relative humidity, mean temperature, and total precipitation. Analyses were stratified by urban and rural communities. From 2017-2020, there were 917 pediatric CO poisoning ED visits in NYS. Most cases (83%) occurred in urban region of the state. We observed an association statewide between all-size and large-scale outages and CO ED visits on the index day and the following two days before a return to baseline on lag day 3. Four hours without power increased the odds of a pediatric CO poisoning ED visit by ≥50% for small-scale and ≥150% for large-scale outages, and associations were stronger in urban versus rural areas. While CO poisoning is a relatively rare cause of pediatric ED visits in NYS, it can be deadly and is also preventable. Expanded analyses of the health impacts of outages and advocacy for reliable energy access are needed to support children's health in a changing climate.

Ozone as an environmental driver of influenza.

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Power outages and pediatric unintentional injury hospitalizations in New York State.

Background: In the past decade, electrical power disruptions (outages) have increased in the United States, especially those attributable to weather events. These outages have a range of health impacts but are largely unstudied in children. Here, we investigated the association between outages and unintentional injury hospitalizations, a leading cause of childhood morbidity. Methods: The study setting was New York State (NYS) from 2017 to 2020. Outage exposure was defined as ≥10%, ≥20%, and ≥50% of customers from a power operating locality without power, ascertained from NYS Department of Public Service records and stratified by rural, urban non-New York City (NYC), and NYC regions. Outcome daily block group-level pediatric injury hospitalization data was from the Statewide Planning and Research Cooperative System (SPARCS). We leveraged a case-crossover study design with logistic conditional regression. Results: We identified 23,093 unintentional injury hospitalizations in children <18 years with complete block group and exposure data. Most hospitalizations occurred in urban regions (90%), whereas outages were more likely in rural than urban areas. In urban non-NYC regions, outages ≥4 hours were associated with 30% increased odds of all-cause unintentional injury hospitalizations when ≥50% of customers were without power. Analyses by injury subtype revealed increasing point estimates as the proportion of customers exposed increased. These results, however, had wide confidence intervals. Conclusions: Outage exposure differed significantly across rural, urban non-NYC, and NYC regions across New York. Especially at the highest outage threshold, we observed an increased risk of pediatric unintentional injury hospitalizations.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

The association between temperature and alcohol- and substance-related disorder hospital visits in New York State.

BACKGROUND: Limited evidence exists on how temperature increases are associated with hospital visits from alcohol- and substance-related disorders, despite plausible behavioral and physiological pathways. METHODS: In the present study, we implemented a case-crossover design, which controls for seasonal patterns, long-term trends, and non- or slowly-varying confounders, with distributed lag non-linear temperature terms (0-6 days) to estimate associations between daily ZIP Code-level temperature and alcohol- and substance-related disorder hospital visit rates in New York State during 1995-2014. We also examined four substance-related disorder sub-causes (cannabis, cocaine, opioid, sedatives). RESULTS: Here we show that, for alcohol-related disorders, a daily increase in temperature from the daily minimum (-30.1 °C (-22.2 °F)) to the 75th percentile (18.8 °C (65.8 °F)) across 0-6 lag days is associated with a cumulative 24.6% (95%CI,14.6%-34.6%) increase in hospital visit rates, largely driven by increases on the day of and day before hospital visit, with an association larger outside New York City. For substance-related disorders, we find evidence of a positive association at temperatures from the daily minimum (-30.1 °C (-22.2 °F)) to the 50th percentile (10.4 °C (50.7 °F)) (37.7% (95%CI,27.2%-48.2%), but not at higher temperatures. Findings are consistent across age group, sex, and social vulnerability. CONCLUSIONS: Our work highlights how hospital visits from alcohol- and substance-related disorders are currently impacted by elevated temperatures and could be further affected by rising temperatures resulting from climate change. Enhanced social infrastructure and health system interventions could mitigate these impacts.

We investigated the relationship between temperature and hospital visits related to alcohol and other drugs including cannabis, cocaine, opioids, and sedatives in New York State. We found that higher temperatures resulted in more hospital visits for alcohol. For other drugs, higher temperatures also resulted in more hospital visits but only up to a certain temperature level. Our findings suggest that rising temperatures, including those caused by climate change, may influence hospital visits for alcohol and other drugs, emphasizing the need for appropriate and proportionate social and health interventions, as well as highlighting potential hidden burdens of climate change.

Methods in Public Health Environmental Justice Research: a Scoping Review from 2018 to 2021.

PURPOSE OF REVIEW: The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods. RECENT FINDINGS: We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

The role of insurance status in the association between short-term temperature exposure and myocardial infarction hospitalizations in New York State.

Myocardial infarction (MI) is a leading cause of morbidity and mortality in the United States and its risk increases with extreme temperatures. Climate change causes variability in weather patterns, including extreme temperature events that disproportionately affect socioeconomically disadvantaged communities. Many studies on the health effects of extreme temperatures have considered community-level socioeconomic disadvantage. Objectives: To evaluate effect modification of the relationship between short-term ambient temperature and MI, by individual-level insurance status (insured vs. uninsured). Methods: We identified MI hospitalizations and insurance status across New York State (NYS) hospitals from 1995 to 2015 in the New York Department of Health Statewide Planning and Research Cooperative System database, using International Classification of Diseases codes. We linked short-term ambient temperature (averaging the 6 hours preceding the event [MI hospitalization]) or nonevent control period in patient residential zip codes. We employed a time-stratified case-crossover study design for both insured and uninsured strata, and then compared the group-specific rate ratios. Results: Over the study period, there were 1,095,051 primary MI admissions, 966,475 (88%) among insured patients. During extremely cold temperatures (<5.8 °C) insured patients experienced reduced rates of MI; this was not observed among the uninsured counterparts. At warmer temperatures starting at the 65th percentile (15.7 °C), uninsured patients had higher rates than insured patients (e.g., for a 6-hour pre-event average temperature increase from the median to the 75th percentile, the rate of MI increased was 2.0% [0.0%-4.0%] higher in uninsured group). Conclusions: Uninsured individuals may face disproportionate rates of MI hospitalization during extreme temperatures.

Spatiotemporal distribution of power outages with climate events and social vulnerability in the USA.

Power outages threaten public health. While outages will likely increase with climate change, an aging electrical grid, and increased energy demand, little is known about their frequency and distribution within states. Here, we characterize 2018-2020 outages, finding an average of 520 million customer-hours total without power annually across 2447 US counties (73.7% of the US population). 17,484 8+ hour outages (a medically-relevant duration with potential health consequences) and 231,174 1+ hour outages took place, with greatest prevalence in Northeastern, Southern, and Appalachian counties. Arkansas, Louisiana, and Michigan counties experience a dual burden of frequent 8+ hour outages and high social vulnerability and prevalence of electricity-dependent durable medical equipment use. 62.1% of 8+ hour outages co-occur with extreme weather/climate events, particularly heavy precipitation, anomalous heat, and tropical cyclones. Results could support future large-scale epidemiology studies, inform equitable disaster preparedness and response, and prioritize geographic areas for resource allocation and interventions.

Impact of a Statewide Livestock Antibiotic Use Policy on Resistance in Human Urine Escherichia coli Isolates: A Synthetic Control Analysis.

BACKGROUND: On 1 January 2018, California implemented Senate Bill 27 (SB27), banning, for the first time in the United States, routine preventive use of antibiotics in food-animal production and any antibiotic use without a veterinarian's prescription. OBJECTIVES: Our objective was to assess whether SB27 was associated with decreased antimicrobial resistance among E. coli isolated from human urine. METHODS: We used U.S. nationwide monthly state-level data from BD Insights Research Database (Becton, Dickinson, and Co.) spanning 1 January 2013 to 30 June 2021 on antibiotic-resistance patterns of 30-d nonduplicate E. coli isolated from urine. Tested antibiotic classes included aminoglycosides, extended-spectrum cephalosporins (ESC), fluoroquinolones, and tetracyclines. Counts of tested and not-susceptible (resistant and intermediate, hereafter resistant) urine isolates were available by sex, age group (<65, 65+ year), month, and state. We applied a synthetic control approach to estimate the causal effect of SB27 on resistance patterns. Our approach created a synthetic California based on a composite of other states without the policy change and contrasted its counterfactual postpolicy trends with the observed postpolicy trends in California. FINDINGS: We included 7.1 million E. coli urine isolates, 90% among women, across 33 states. From 2013 to 2017, the median (interquartile range) resistance percentages in California were 11.9% (7.4, 17.6), 13.8% (5.8, 20.0), 24.6% (9.6, 36.4), 7.9% (2.1, 13.1), for aminoglycosides, ESC, fluoroquinolones, and tetracyclines, respectively. SB27 was associated with a 7.1% reduction in ESC resistance (p-value for joint null: <0.01), but no change in resistance to aminoglycosides, fluoroquinolones, or tetracyclines. DISCUSSION: Further research is needed to determine the role of SB27 in the observed reduction in ESC resistance E. coli in human populations, particularly as additional states implement similar legislation. https://doi.org/10.1289/EHP11221.

Unintended impacts of the Open Streets program on noise complaints in New York City.

BACKGROUND: During the COVID-19 pandemic, several cities allocated more public spaces for physical activity and recreation instead of road transport through Open Streets. This policy locally reduces traffic and provides experimental testbeds for healthier cities. However, it may also generate unintended impacts. For instance, Open Streets may impact the levels of exposure to environmental noise but there are no studies assessing these unintended impacts. OBJECTIVES: Using noise complaints from New York City (NYC) as a proxy of annoyance caused by environmental noise, we estimated associations at the census tract level between same-day proportion of Open Streets in a census tract and noise complaints in NYC. METHODS: Using data from summer 2019 (pre-implementation) and summer 2021 (post-implementation), we fit regressions to estimate the association between census tract-level proportion of Open Streets and daily noise complaints, with random effects to account for within-tract correlation and natural splines to allow non-linearity in the estimated association. We accounted for temporal trends and other potential confounders, such as population density and poverty rate. RESULTS: In adjusted analyses, daily street/sidewalk noise complaints were nonlinearly associated with an increasing proportion of Open Streets. Specifically, compared to the mean proportion of Open Streets in a census tract (0.11%), 5% of Open Streets had a 1.09 (95% CI: 0.98, 1.20) and 10% had a 1.21 (95% CI: 1.04, 1.42) times higher rate of street/sidewalk noise complaints. Our results were robust to the choice of data source for identifying Open Streets. CONCLUSION: Our findings suggest that Open Streets in NYC may be linked to an increase in street/sidewalk noise complaints. These results highlight the necessity to reinforce urban policies with a careful analysis for potential unintended impacts to optimize and maximize the benefits of these policies.

Wildfire smoke exposure and emergency department visits for headache: A case-crossover analysis in California, 2006-2020.

OBJECTIVE: To evaluate the association of short-term exposure to overall fine particulate matter of <2.5 µm (PM2.5 ) and wildfire-specific PM2.5 with emergency department (ED) visits for headache. BACKGROUND: Studies have reported associations between PM2.5 exposure and headache risk. As climate change drives longer and more intense wildfire seasons, wildfire PM2.5 may contribute to more frequent headaches. METHODS: Our study included adult Californian members (aged ≥18 years) of a large de-identified commercial and Medicare Advantage claims database from 2006 to 2020. We identified ED visits for primary headache disorders (subtypes: tension-type headache, migraine headache, cluster headache, and "other" primary headache). Claims included member age, sex, and residential zip code. We linked daily overall and wildfire-specific PM2.5 to residential zip code and conducted a time-stratified case-crossover analysis considering 7-day average PM2.5 concentrations, first for primary headache disorders combined, and then by headache subtype. RESULTS: Among 9898 unique individuals we identified 13,623 ED encounters for primary headache disorders. Migraine was the most frequently diagnosed headache (N = 5534/13,623 [47.6%]) followed by "other" primary headache (N = 6489/13,623 [40.6%]). For all primary headache ED diagnoses, we observed an association of 7-day average wildfire PM2.5 (odds ratio [OR] 1.17, 95% confidence interval [CI] 0.95-1.44 per 10 µg/m3 increase) and by subtype we observed increased odds of ED visits associated with 7-day average wildfire PM2.5 for tension-type headache (OR 1.42, 95% CI 0.91-2.22), "other" primary headache (OR 1.40, 95% CI 0.96-2.05), and cluster headache (OR 1.29, 95% CI 0.71-2.35), although these findings were not statistically significant under traditional null hypothesis testing. Overall PM2.5 was associated with tension-type headache (OR 1.29, 95% CI 1.03-1.62), but not migraine, cluster, or "other" primary headaches. CONCLUSIONS: Although imprecise, these results suggest short-term wildfire PM2.5 exposure may be associated with ED visits for headache. Patients, healthcare providers, and systems may need to respond to increased headache-related healthcare needs in the wake of wildfires and on poor air quality days.

The 2021 Texas Power Crisis: distribution, duration, and disparities.

BACKGROUND: Precipitated by an unusual winter storm, the 2021 Texas Power Crisis lasted February 10 to 27 leaving millions of customers without power. Such large-scale outages can have severe health consequences, especially among vulnerable subpopulations such as those reliant on electricity to power medical equipment, but limited studies have evaluated sociodemographic disparities associated with outages. OBJECTIVE: To characterize the 2021 Texas Power Crisis in relation to distribution, duration, preparedness, and issues of environmental justice. METHODS: We used hourly Texas-wide county-level power outage data to estimate geographic clustering and association between outage exposure (distribution and duration) and six measures of racial, social, political, and/or medical vulnerability: Black and Hispanic populations, the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), Medicare electricity-dependent durable medical equipment (DME) usage, nursing homes, and hospitals. To examine individual-level experience and preparedness, we used a preexisting and non-representative internet survey. RESULTS: At the peak of the Texas Power Crisis, nearly 1/3 of customers statewide (N = 4,011,776 households/businesses) lost power. We identified multiple counties that faced a dual burden of racial/social/medical vulnerability and power outage exposure, after accounting for multiple comparisons. County-level spatial analyses indicated that counties where more Hispanic residents resided tended to endure more severe outages (OR = 1.16, 95% CI: 1.02, 1.40). We did not observe socioeconomic or medical disparities. With individual-level survey data among 1038 respondents, we found that Black respondents were more likely to report outages lasting 24+ hours and that younger individuals and those with lower educational attainment were less likely to be prepared for outages. SIGNIFICANCE: Power outages can be deadly, and medically vulnerable, socioeconomically vulnerable, and marginalized groups may be disproportionately impacted or less prepared. Climate and energy policy must equitably address power outages, future grid improvements, and disaster preparedness and management.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice.

BACKGROUND: Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. METHODS: We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. RESULTS: For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. CONCLUSIONS: Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.

Methods for managing miscarriage: a network meta-analysis.

BACKGROUND: Miscarriage, defined as the spontaneous loss of a pregnancy before 24 weeks' gestation, is common with approximately 25% of women experiencing a miscarriage in their lifetime. An estimated 15% of pregnancies end in miscarriage. Miscarriage can lead to serious morbidity, including haemorrhage, infection, and even death, particularly in settings without adequate healthcare provision. Early miscarriages occur during the first 14 weeks of pregnancy, and can be managed expectantly, medically or surgically. However, there is uncertainty about the relative effectiveness and risks of each option. OBJECTIVES: To estimate the relative effectiveness and safety profiles for the different management methods for early miscarriage, and to provide rankings of the available methods according to their effectiveness, safety, and side-effect profile using a network meta-analysis. SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth's Trials Register (9 February 2021), ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) (12 February 2021), and reference lists of retrieved studies. SELECTION CRITERIA: We included all randomised controlled trials assessing the effectiveness or safety of methods for miscarriage management. Early miscarriage was defined as less than or equal to 14 weeks of gestation, and included missed and incomplete miscarriage. Management of late miscarriages after 14 weeks of gestation (often referred to as intrauterine fetal deaths) was not eligible for inclusion in the review. Cluster- and quasi-randomised trials were eligible for inclusion. Randomised trials published only as abstracts were eligible if sufficient information could be retrieved. We excluded non-randomised trials. DATA COLLECTION AND ANALYSIS: At least three review authors independently assessed the trials for inclusion and risk of bias, extracted data and checked them for accuracy. We estimated the relative effects and rankings for the primary outcomes of complete miscarriage and composite outcome of death or serious complications. The certainty of evidence was assessed using GRADE. Relative effects for the primary outcomes are reported subgrouped by the type of miscarriage (incomplete and missed miscarriage). We also performed pairwise meta-analyses and network meta-analysis to determine the relative effects and rankings of all available methods. MAIN RESULTS: Our network meta-analysis included 78 randomised trials involving 17,795 women from 37 countries. Most trials (71/78) were conducted in hospital settings and included women with missed or incomplete miscarriage. Across 158 trial arms, the following methods were used: 51 trial arms (33%) used misoprostol; 50 (32%) used suction aspiration; 26 (16%) used expectant management or placebo; 17 (11%) used dilatation and curettage; 11 (6%) used mifepristone plus misoprostol; and three (2%) used suction aspiration plus cervical preparation. Of these 78 studies, 71 (90%) contributed data in a usable form for meta-analysis. Complete miscarriage Based on the relative effects from the network meta-analysis of 59 trials (12,591 women), we found that five methods may be more effective than expectant management or placebo for achieving a complete miscarriage: · suction aspiration after cervical preparation (risk ratio (RR) 2.12, 95% confidence interval (CI) 1.41 to 3.20, low-certainty evidence), · dilatation and curettage (RR 1.49, 95% CI 1.26 to 1.75, low-certainty evidence), · suction aspiration (RR 1.44, 95% CI 1.29 to 1.62, low-certainty evidence), · mifepristone plus misoprostol (RR 1.42, 95% CI 1.22 to 1.66, moderate-certainty evidence), · misoprostol (RR 1.30, 95% CI 1.16 to 1.46, low-certainty evidence). The highest ranked surgical method was suction aspiration after cervical preparation. The highest ranked non-surgical treatment was mifepristone plus misoprostol. All surgical methods were ranked higher than medical methods, which in turn ranked above expectant management or placebo. Composite outcome of death and serious complications Based on the relative effects from the network meta-analysis of 35 trials (8161 women), we found that four methods with available data were compatible with a wide range of treatment effects compared with expectant management or placebo: · dilatation and curettage (RR 0.43, 95% CI 0.17 to 1.06, low-certainty evidence), · suction aspiration (RR 0.55, 95% CI 0.23 to 1.32, low-certainty evidence), · misoprostol (RR 0.50, 95% CI 0.22 to 1.15, low-certainty evidence), · mifepristone plus misoprostol (RR 0.76, 95% CI 0.31 to 1.84, low-certainty evidence). Importantly, no deaths were reported in these studies, thus this composite outcome was entirely composed of serious complications, including blood transfusions, uterine perforations, hysterectomies, and intensive care unit admissions. Expectant management and placebo ranked the lowest when compared with alternative treatment interventions. Subgroup analyses by type of miscarriage (missed or incomplete) agreed with the overall analysis in that surgical methods were the most effective treatment, followed by medical methods and then expectant management or placebo, but there are possible subgroup differences in the effectiveness of the available methods.  AUTHORS' CONCLUSIONS: Based on relative effects from the network meta-analysis, all surgical and medical methods for managing a miscarriage may be more effective than expectant management or placebo. Surgical methods were ranked highest for managing a miscarriage, followed by medical methods, which in turn ranked above expectant management or placebo. Expectant management or placebo had the highest chance of serious complications, including the need for unplanned or emergency surgery. A subgroup analysis showed that surgical and medical methods may be more beneficial in women with missed miscarriage compared to women with incomplete miscarriage. Since type of miscarriage (missed and incomplete) appears to be a source of inconsistency and heterogeneity within these data, we acknowledge that the main network meta-analysis may be unreliable. However, we plan to explore this further in future updates and consider the primary analysis as separate networks for missed and incomplete miscarriage.

Trends of temperature variability: Which variability and what health implications?

A large majority of climate change studies carried out to date are on changes in mean climate, which have comparatively downplayed variability. In terms of trend analysis or forecast, the scientific output and common knowledge for global warming are much more robust than for changes in temperature variability. Quantification of temperature variability adds another dimension of temporal scale, requiring immense labor and presenting great uncertainty. Regardless, this endeavor is necessary since changes in ambient temperature variabilities could also contribute to current and future human health burden besides changes in mean quantities. Here, we review the current literature on trends of surface air temperature variability defined at a range of timescales, aiming to tease out the welter of evidence and thus improving the scientific recognition of changes in air temperature variability in the context of climate change. The findings of reviewed studies from numerous regions differ substantially over various temporal scales. In general, the ambient temperature variability on short time scales (e.g., diurnal or inter-day) shows a downward trend, while it is increasing on longer time scales (e.g., inter-annual). We then move beyond the review and deliver an extended discussion of potential implications for future research related to ambient temperature variability. We highlight the need to consider the methodological choices, especially timescales of interest, in the trend analysis as well as health impact studies. Continued research focusing on temperature variability at multiple timescales, with concerted efforts from scientists of all relevant stripes, is meaningful in synthesizing knowledge and reducing uncertainties surrounding air temperature variability.

Acceptability of Naloxone Dispensing Among Pharmacists.

BACKGROUND: The San Francisco Department of Public Health initiated naloxone prescribing at 6 safety net clinics. We evaluated this intervention, demonstrating that naloxone prescribing from primary care clinics is feasible and acceptable. OBJECTIVE: To evaluate acceptability of naloxone dispensing to patients prescribed opioids among pharmacists serving clinics participating in a naloxone intervention. METHODS: We surveyed 58 pharmacists from November 2013 through January 2015 at pharmacies that serviced San Francisco safety net clinics. Surveys collected information on demographics, experiences in dispensing naloxone, and interest in prescriptive authority. We conducted descriptive analyses and assessed bivariate relationships. RESULTS: Most respondents were staff (56.9%) or supervising pharmacists (34.5%). Most (92.9%) were aware their pharmacy stocked naloxone and 86.8% felt it should be prescribed to some or all patients on long-term opioids. Most (82.1%) dispensed naloxone at least once in the past 12 months. More than half were comfortable providing naloxone education. Nearly half (43.4%) indicated they would want authority to furnish without a prescription. Over half (55.2%) reported no problems dispensing. The common problem was insufficient naloxone knowledge. Only 12% reported more than one problem in dispensing naloxone, which was associated with being uncomfortable with educating patients (P = .03). CONCLUSION: Naloxone dispensing was acceptable among pharmacists. Their most cited problem was insufficient naloxone education. This may be resolved with improved instructional materials, incentives for patient education, or mandatory training.