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Objective: To generate a global reference for caesarean section (CS) rates at health facilities.Design: Cross-sectional study.Setting: Health facilities from 43 countries.Population/sample: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing.Methods: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models.Main Outcome Measures: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate.Results: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/).Conclusions: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS.Tweetable Abstract: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. [ABSTRACT FROM AUTHOR]

Introduction: US drug overdose deaths are at historic levels. For every fatal drug overdose, there are many more non-fatal; however, minimal nationally representative data exist on trends in the ratio of fatal to non-fatal drug overdoses and how this differs by drug type., Methods: Data from the Centers for Disease Control and Prevention's National Vital Statistics System were used to assess the number of fatal overdoses; data from the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample database were used to estimate the number of non-fatal overdoses treated in emergency departments. Counts of fatal and non-fatal overdoses by drug type (all drug, all opioid, synthetic opioid, heroin, stimulant, and opioid and stimulant polysubstance) were calculated from 2010 to 2020 (for non-fatal synthetic opioid-involved overdoses, from 2016 to 2020 only). Trends in overdose counts and the ratio of fatal to non-fatal overdoses were assessed., Results: On average, counts of fatal overdoses increased quarterly among all drug types, and non-fatal overdoses increased among most drug types. Over the 11-year period, the greatest average quarterly percent change (AQPC) in fatal overdose counts was among synthetic opioid-involved overdoses (AQPC: 7.1%; 95% CI: 6.0 to 8.2) and for non-fatal overdoses was among heroin-involved overdoses (AQPC: 4.3%; 95% CI: 3.9 to 4.8). During 2010‒2020, there was approximately 1 fatal overdose per 15 non-fatal. The ratio of fatal to non-fatal drug overdoses increased among every drug type except heroin; ratio increases were driven by greater relative increases in fatal overdoses compared with non-fatal., Conclusions: Assessment of the ratio of fatal to non-fatal drug overdoses can be used to understand the lethality of different drugs and inform response and prevention efforts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Nonfatal drug overdoses (NFODs) are often attributed to individual behaviors and risk factors; however, identifying community-level social determinants of health (SDOH) associated with increased NFOD rates may allow public health and clinical providers to develop more targeted interventions to address substance use and overdose health disparities. CDC's Social Vulnerability Index (SVI), which aggregates social vulnerability data from the American Community Survey to produce ranked county-level vulnerability scores, can help identify community factors associated with NFOD rates. This study aims to describe associations between county-level social vulnerability, urbanicity, and NFOD rates., Methods: We analyzed county-level 2018-2020 emergency department (ED) and hospitalization discharge data submitted to CDC's Drug Overdose Surveillance and Epidemiology system. Counties were ranked in vulnerability quartiles based on SVI data. We used crude and adjusted negative binomial regression models, by drug category, to calculate rate ratios and 95% confidence intervals comparing NFOD rates by vulnerability., Results: Generally, as social vulnerability scores increased, ED and hospitalization NFOD rates increased; however, the magnitude of the association varied across drugs, visit type, and urbanicity. SVI-related theme and individual variable analyses highlighted specific community characteristics associated with NFOD rates., Conclusions: The SVI can help identify associations between social vulnerabilities and NFOD rates. Development of an overdose-specific validated index could improve translation of findings to public health action. The development and implementation of overdose prevention strategies should consider a socioecological perspective and address health inequities and structural barriers associated with increased risk of NFODs at all levels of the social ecology., Competing Interests: Declaration of Competing Interest No conflict declared., (Published by Elsevier B.V.)

Context: The Centers for Disease Control and Prevention (CDC) developed a syndrome definition for detection of suspected nonfatal cocaine-involved overdoses. The definition can be used to monitor trends and detect anomalies in emergency department (ED) syndromic surveillance data at the national, state, and local levels., Objective: This study describes the development of the nonfatal, unintentional/undetermined intent cocaine-involved overdose (UUCOD) definition and analysis of trends over time., Design/setting: CDC developed the UUCOD definition to query ED data in CDC's National Syndromic Surveillance Program (NSSP). Data between 2018 and 2021 were analyzed from 29 states sharing data access in the Drug Overdose Surveillance and Epidemiology (DOSE) System via NSSP. Using Joinpoint regression, trends were analyzed for UUCOD overall, by sex and age group, and for UUCOD co-involving opioids., Measures: Time trends between 2018 and 2021 were analyzed by examining average monthly percentage change. Individual trend segments and trend inflection points were analyzed by examining monthly percentage change., Results: During 2018-2021, a total of 27 240 UUCOD visits were identified by the syndrome definition. Analyses identified different patterns in trends for males and females, with largely similar trends for persons aged 15 to 44 years and 45 years or older. Analyses also identified seasonal patterns with increases in spring/summer months in UUCOD overall and UUCOD co-involving opioids and declines for both in fall/winter months., Conclusion: This UUCOD syndrome definition will be useful for ongoing monitoring of suspected nonfatal overdoses involving cocaine and co-involving cocaine and opioids. Ongoing assessment of cocaine-involved overdose trends might identify anomalies requiring further investigation and inform deployment of resources., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Limited data exist on risk factors for illicit stimulant use, including associations between prescription stimulant use/nonmedical use (NMU) and illicit stimulant use., Methods: We used 2017-2021 data from adults assessed for substance use disorder (SUD) treatment using the National Addictions Vigilance Intervention and Prevention Program Addiction Severity Index-Multimedia Version® tool. Multivariable Poisson regression models analyzed associations between past 30-day prescription stimulant use as prescribed or NMU and past 30-day illicit stimulant use. Separate models examined past 30-day illicit stimulant, methamphetamine, and cocaine use. We explored problem severity across seven biopsychosocial domains (e.g., drug, psychiatric, family) by past 30-day prescription stimulant use/NMU and illicit stimulant use., Results: Among 218,981 assessments, 1.8% reported prescription stimulant NMU; 1.6% reported use as prescribed. Past 30-day prescription stimulant NMU (vs. no use) was associated with past 30-day illicit stimulant use (adjusted prevalence ratio [aPR] [95% CI]: 2.67 [2.59, 2.75]), methamphetamine use (aPR: 2.81 [2.71, 2.92]), and cocaine use (aPR: 3.53 [3.33, 3.74]). Prescription stimulant use as prescribed (vs. no use) was associated with lower prevalence of past 30-day illicit stimulant use. Assessments reporting prescription stimulant NMU (vs. no use, or use as prescribed) appeared more likely to have moderate-to-extreme problem scores across biopsychosocial domains, indicating greater need for treatment or assistance. Assessments reporting prescription stimulant use as prescribed or NMU frequently reported opioids, alcohol, or other substances as their primary substance problem., Conclusions: Adults using illicit stimulants/nonmedically using prescription stimulants may benefit from care addressing polysubstance use, mental health, social, and recovery support services., Competing Interests: Taryn Dailey Govoni and Jody L. Green are employees of Inflexxion, a division of Uprise Health. Inflexxion contracts with FDA and companies with interests in prescription stimulant products, which are evaluated as a class in this article. The authors have no other conflicts of interest or relevant financial relationships to disclose., (Published by Elsevier B.V.)

Objectives: To monitor stimulant-involved overdose (SOD) trends, the Centers for Disease Control and Prevention (CDC) developed and evaluated the validity of a syndromic surveillance definition for suspected nonfatal, unintentional/undetermined intent stimulant-involved overdose (UUSOD)., Methods: We analyzed all emergency department (ED) visits in CDC's surveillance system that met the UUSOD syndrome definition (January 2018-December 2019). We classified visits as true positive, possible, or not UUSODs after reviewing diagnosis codes and chief complaints. We first assessed whether visits were acute SODs, subsequently classifying acute SODs by intent. The percentage of true-positive UUSODs did not include intentional or possibly intentional visits. We considered all visits with UUSOD diagnosis codes to be acute SODs and reviewed them for intent. We manually reviewed and double-coded a 10% random sample of visits without UUSOD diagnosis codes using decision rules based on signs and symptoms. The overall percentage of true-positive UUSODs was a weighted average of the percentage of true-positive UUSODs based on diagnosis codes and the percentage of true-positive UUSODs determined by manually reviewing visits without codes., Results: During 2018-2019, 40 045 ED visits met the syndrome definition for UUSOD. Approximately half (n = 18 793; 46.9%) of 40 045 visits had UUSOD diagnosis codes, indicating acute SOD; of these, 98.6% (n = 18 534) were true-positive UUSODs. Of 2125 manually reviewed visits without UUSOD diagnosis codes, 32.6% (n = 693) were true-positive UUSODs, 54.2% (n = 1151) were possible UUSODs, and 13.2% (n = 281) were not UUSODs. Overall, 63.6% of visits were true-positive UUSODs, 29.3% were possible UUSODs, and 7.1% were not UUSODs., Practice Implications: CDC's UUSOD definition may assist in surveillance efforts with further refinement to capture data on SOD clusters and trends.

The number of nonfatal opioid-involved overdoses treated by health care providers has risen in the United States; the median number of emergency department (ED) visits for these overdoses was significantly higher during 2020 than during 2019 (1). ED visit data can underestimate nonfatal opioid-involved overdose incidence because, increasingly, persons experiencing a nonfatal opioid overdose are refusing transport to EDs by emergency medical services (EMS) (2). A study in Kentucky found that during a 6-month period, 19.8% of persons treated by EMS for an opioid overdose refused transport to an ED (2). Thus, EMS encounter data involving suspected nonfatal opioid-involved overdoses complement ED data and also allow for near real-time analysis (3). This report describes trends in rates of EMS encounters for nonfatal opioid-involved overdoses per 10,000 total EMS encounters (rates) by selected patient- and county-level characteristics during January 2018–March 2022 in 491 counties from 21 states using data from biospatial, Inc.* During this period, the nonfatal opioid-involved overdose rate increased, on average, 4.0% quarterly. Rates increased for both sexes and for most age groups. Rates were highest among non-Hispanic White (White) and non-Hispanic Native Hawaiian or other Pacific Islander (NH/OPI) persons, and increases were largest among non-Hispanic Black (Black), followed by Hispanic or Latino (Hispanic) persons. Rates increased in both urban and rural counties and for all quartiles of county-level characteristics (i.e., unemployment, education, and uninsured), except in counties with the lowest percentage of uninsured persons. Rates were highest and rate increases were largest in urban counties and counties with higher unemployment rates. This analysis of nonfatal opioid-involved overdose trends in EMS data highlights the utility of these data and the importance of addressing inequities that contribute to disproportionate overdose risk, such as through focused outreach to racial and ethnic minority groups, who disproportionately experience these inequities, and communities with higher levels of unemployment. EMS providers are in a unique position to engage in postoverdose response protocols and promote evidence-based overdose education and facilitate linkage to care and harm reduction services.†,§, Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Josh Walters reports that he owns employee stock options in biospatial, Inc. No other potential conflicts of interest were disclosed.

Introduction: Nonfatal and fatal drug overdoses have recently increased. There are limited data describing the range of illicit, prescribed, and over-the-counter drugs involved in overdoses presenting to U.S. emergency departments (EDs)., Methods: Using 2018 Healthcare Cost and Utilization Project (HCUP) Nationwide ED Sample (NEDS) data, we calculated weighted counts and percentages by drug among overdose-related ED visits. Overdose-related ED visits were those having an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) drug poisoning code falling under parent codes T36-T50 (codes involving alcohol were not explicitly queried). We identified the top 30 mutually exclusive polydrug combinations and compared characteristics of visits by polydrug status., Results: In 2018, 908,234 ED visits had a T36-T50 drug poisoning code. The most frequently reported drugs involved were opioids (30.3% of visits; heroin: 15.2%), benzodiazepines (11.0%), stimulants (7.9%), other/unspecified antidepressants (7.1%), 4-aminophenol derivatives (6.6%), and other/unspecified drugs, medicaments, and biological substances (11.8%). Overdose was uncommon for most other drug classes (e.g., antibiotics). Polydrug visits were more likely to involve females (prevalence ratio [PR]: 1.14, 95% confidence interval [CI]: 1.12-1.16), be coded intentional self-harm (PR: 1.81, 95% CI: 1.77-1.85), and result in hospitalization (PR: 1.84, 95% CI: 1.79-1.89) or death (PR: 1.37, 95% CI: 1.22-1.53) compared to single-drug overdose-related visits. Benzodiazepines, opioids, and/or stimulants were most frequently involved in polydrug overdoses., Conclusion: Opioids, benzodiazepines, and stimulants were most commonly reported in both single-drug and polydrug overdose-involved ED visits. Other drugs involved in overdoses included antidepressants and 4-aminophenol derivatives. Jurisdictions can use data on drugs involved in overdoses to better tailor prevention strategies to emerging needs., (Published by Elsevier Ltd.)

Background: Deaths involving illicitly manufactured fentanyl (IMF) have increased since 2013 in the United States. Little research has examined individuals using IMF. This study aims to explore the characteristics of US adults who used IMF, heroin, or misused prescription opioids and examine the associations between demographic, clinical, psychosocial characteristics and IMF use., Methods: A convenience sample of adults aged ≥ 18 years being assessed for substance use disorder (SUD) treatment was collected between January-December 2019 using the Addiction Severity Index-Multimedia Version instrument. We used a multivariable logistic regression model to examine the associations between demographic, clinical, psychosocial characteristics and IMF use., Results: Adults reporting IMF as their primary lifetime substance use problem also reported using other substances-most often alcohol or heroin-both in the past 30 days and during their lifetime. Characteristics associated with increased odds of reporting IMF as the primary lifetime substance use problem included age 18-24 years (adjusted odds ratio (aOR) = 1.68; 95% confidence interval (CI) = 1.18-2.38) versus 45-54 years, non-Hispanic Black persons (aOR = 1.44; 95% CI = 1.11-1.85) versus non-Hispanic White persons, being assessed in Northeast (aOR = 15.46; 95% CI = 8.67-27.56) versus West, and having a history of at least one lifetime overdose (1 overdose (aOR = 1.91; 95% CI = 1.49-2.44); 2 overdoses (aOR = 1.95; 95% CI = 1.48-2.58); 3 or more overdoses (aOR = 2.27; 95% CI = 1.82-2.82))., Conclusions: These findings provide new insights into this high-risk population and help identify strategies to address increasing overdose death rates involving IMF. Opportunities for intervention include expanding naloxone distribution and harm reduction programs and connecting individuals with nonfatal overdoses to SUD treatment., (Published by Elsevier B.V.)

Background: High-dose vitamin A (VA) supplements (VAS) can temporarily affect VA status. Hence, micronutrient surveys might need to be timed around VAS campaigns to accurately estimate VA deficiency (VAD) prevalence. Little is known about optimal timing of micronutrient surveys when the modified-relative-dose-response (MRDR) is used as a VA indicator., Objectives: We evaluated the association between days since the end of a VAS campaign and MRDR values in children aged 12-23 mo in Uganda., Methods: We pooled data from 2 cross-sectional, population-based surveys in eastern Uganda conducted in 2015-2016 (n = 118 children). We estimated the prevalence of VAD (MRDR ≥0.060). Days since the end of a VAS campaign ("days since VAS") was calculated as the interview date minus the end date of the VAS campaign. The MRDR value was assessed using HPLC. We excluded children whose MRDR values were below the limit of detection (<0.007). We used linear regression to evaluate the association between days since VAS and log-transformed MRDR. In adjusted analyses, we controlled for potential confounders. Statistical analyses accounted for the surveys' complex design., Results: The prevalence of VAD was 5.2% (95% CI: 1.1%, 9.3%). Mean days since VAS was 54.1 d (range 39-68 d). Days since VAS was not associated with log-transformed MRDR in unadjusted analyses ($\hat{\beta } = \ $0.0055; 95% CI: -0.009, 0.020; P = 0.45) or adjusted analyses ($\hat{\beta } = $ -0.0073; 95% CI: -0.024, 0.010; P = 0.39)., Conclusions: MRDR measurement through a nutrition survey began as early as 1.3 mo after the end of a VAS campaign in eastern Uganda. Days since the end of a VAS campaign was not associated with MRDR in Ugandan children aged 12-23 mo. Future studies should consider longitudinal designs and evaluate time since VAS and MRDR in children of different ages and in regions with higher VAD prevalence., (Published by Oxford University Press on behalf of the American Society for Nutrition 2021.)

Introduction: The use of multiple substances heightens the risk of overdose. Multiple substances, including alcohol, are commonly found among people who experience overdose-related mortality. However, the associations between alcohol use and the use of a range of other substances are often not assessed. Therefore, this study examines the associations between drinking patterns (e.g., binge drinking) and other substance use in the U.S., the concurrent use of alcohol and prescription drug misuse, and how other substance use varies by binge-drinking frequency., Methods: Past 30-day alcohol and other substance use data from the 2016-2018 National Survey on Drug Use and Health were analyzed in 2020 among 169,486 U.S. respondents aged ≥12 years., Results: The prevalence of other substance use ranged from 6.0% (nondrinkers) to 24.1% (binge drinkers). Among people who used substances, 22.2% of binge drinkers reported using substances in 2 additional substance categories. Binge drinking was associated with 4.2 (95% CI=3.9, 4.4) greater adjusted odds of other substance use than nondrinking. Binge drinkers were twice as likely to report concurrent prescription drug misuse while drinking as nonbinge drinkers. The prevalence of substance use increased with binge-drinking frequency., Conclusions: Binge drinking was associated with other substance use and concurrent prescription drug misuse while drinking. These findings can guide the implementation of a comprehensive approach to prevent binge drinking, substance misuse, and overdoses. This might include population-level strategies recommended by the Community Preventive Services Task Force to prevent binge drinking (e.g., increasing alcohol taxes and regulating alcohol outlet density)., (Published by Elsevier Inc.)

Introduction: Data on the prevalence and predictors of high blood pressure among children and non-pregnant women of reproductive age are sparse in Guatemala. Our objective was to identify the prevalence and predictors of high blood pressure among women of reproductive age and children in Guatemala., Methods: We analyzed data on blood pressure among 560 children aged 10 to 14 years and 1,182 non-pregnant women aged 15 to 49 from a cross-sectional, nationally representative household survey, SIVESNU (Sistema de Vigilancia Epidemiológica de Salud y Nutrición). We defined high blood pressure among children by using 2004 and 2017 US pediatric guidelines. We defined high blood pressure among women by using 1999 World Health Organization (WHO) and 2017 American College of Cardiology/American Heart Association (ACC/AHA) guidelines. We used multivariable logistic regression to identify significant predictors of high blood pressure. A base model included key covariates (age, ethnicity, socioeconomic index, anthropometric indicators) and accounted for complex sampling. We used backward elimination to identify additional candidate predictor variables., Results: High blood pressure was prevalent among 8.0% (95% confidence interval [CI], 5.4%-10.7%) and 14.0% (95% CI, 10.6%-17.5%) of children using 2004 and 2017 guidelines, respectively; and among 12.7% (95% CI, 10.7%-14.8%) and 41.1% (95% CI, 37.7%-44.4%) of women using 1999 WHO and 2017 ACC/AHA guidelines, respectively. Levels of awareness, treatment, and control of high blood pressure were low in women. Among children, significant predictors of high blood pressure were obesity, overweight, and indigenous ethnicity. Among women, significant predictors of high blood pressure included obesity, overweight, and diabetes., Conclusion: The prevalence of high blood pressure was high among Guatemalan women and children. Overweight and obesity were strong risk factors for high blood pressure. Increasing obesity prevention and control programs may help prevent high blood pressure, and expanding high blood pressure screening and treatment could increase awareness and control of high blood pressure in Guatemala.

Background: There is limited information on potentially modifiable risk factors for stillbirth, such as gestational weight gain (GWG). Our purpose was to explore the association between GWG and stillbirth using the GWG z-score., Methods: We analyzed 479 stillbirths and 1601 live births from the Stillbirth Collaborative Research Network case-control study. Women with triplets or monochorionic twins were excluded from analysis. We evaluated the association between GWG z-score (modeled as a restricted cubic spline with knots at the 5th, 50th, and 95th percentiles) and stillbirth using multivariable logistic regression with generalized estimating equations, adjusting for pre - pregnancy body mass index (BMI) and other confounders. In addition, we conducted analyses stratified by pre - pregnancy BMI category (normal weight, overweight, obese)., Results: Mean GWG was 18.95 (SD 17.6) lb. among mothers of stillbirths and 30.89 (SD 13.3) lb. among mothers of live births; mean GWG z-score was - 0.39 (SD 1.5) among mothers of cases and - 0.17 (SD 0.9) among control mothers. In adjusted analyses, the odds of stillbirth were elevated for women with very low GWG z-scores (e.g., adjusted odds ratio (aOR) and 95% Confidence Interval (CI) for z-score - 1.5 SD versus 0 SD: 1.52 (1.30, 1.78); aOR (95% CI) for z-score - 2.5 SD versus 0 SD: 2.36 (1.74, 3.20)). Results differed slightly by pre - pregnancy BMI. The odds of stillbirth were slightly elevated among women with overweight BMI and GWG z-scores ≥1 SD (e.g., aOR (95% CI) for z-score of 1.5 SD versus 0 SD: 1.84 (0.97, 3.50))., Conclusions: GWG z-scores below - 1.5 SD are associated with increased odds of stillbirth.

Electronic cigarettes (e-cigarettes), also called vapes, e-hookas, vape pens, tank systems, mods, and electronic nicotine delivery systems (ENDS), are electronic devices that produce an aerosol by heating a liquid typically containing nicotine, flavorings, and other additives; users inhale this aerosol into their lungs (1). E-cigarettes also can be used to deliver tetrahydrocannabinol (THC), the principal psychoactive component of cannabis (1). Use of e-cigarettes is commonly called vaping. Lung injury associated with e-cigarette use, or vaping, has recently been reported in most states (2-4). CDC, the Food and Drug Administration (FDA), state and local health departments, and others are investigating this outbreak. This report provides data on patterns of the outbreak and characteristics of patients, including sex, age, and selected substances used in e-cigarette, or vaping, products reported to CDC as part of this ongoing multistate investigation. As of September 24, 2019, 46 state health departments and one territorial health department had reported 805 patients with cases of lung injury associated with use of e-cigarette, or vaping, products to CDC. Sixty-nine percent of patients were males, and the median age was 23 years (range = 13-72 years). To date, 12 deaths have been confirmed in 10 states. Among 514 patients with information on substances used in e-cigarettes, or vaping products, in the 30 days preceding symptom onset, 76.9% reported using THC-containing products, and 56.8% reported using nicotine-containing products; 36.0% reported exclusive use of THC-containing products, and 16.0% reported exclusive use of nicotine-containing products. The specific chemical exposure(s) causing the outbreak is currently unknown. While this investigation is ongoing, CDC recommends that persons consider refraining from using e-cigarette, or vaping, products, particularly those containing THC. CDC will continue to work in collaboration with FDA and state and local partners to investigate cases and advise and alert the public on the investigation as additional information becomes available., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

On September 6, 2019, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr). As of August 27, 2019, 215 possible cases of severe pulmonary disease associated with the use of electronic cigarette (e-cigarette) products (e.g., devices, liquids, refill pods, and cartridges) had been reported to CDC by 25 state health departments. E-cigarettes are devices that produce an aerosol by heating a liquid containing various chemicals, including nicotine, flavorings, and other additives (e.g., propellants, solvents, and oils). Users inhale the aerosol, including any additives, into their lungs. Aerosols produced by e-cigarettes can contain harmful or potentially harmful substances, including heavy metals such as lead, volatile organic compounds, ultrafine particles, cancer-causing chemicals, or other agents such as chemicals used for cleaning the device (1). E-cigarettes also can be used to deliver tetrahydrocannabinol (THC), the principal psychoactive component of cannabis, or other drugs; for example, "dabbing" involves superheating substances that contain high concentrations of THC and other plant compounds (e.g., cannabidiol) with the intent of inhaling the aerosol. E-cigarette users could potentially add other substances to the devices. This report summarizes available information and provides interim case definitions and guidance for reporting possible cases of severe pulmonary disease. The guidance in this report reflects data available as of September 6, 2019; guidance will be updated as additional information becomes available., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Problem: Chronic conditions and disorders (e.g., diabetes, cardiovascular diseases, arthritis, and depression) are leading causes of morbidity and mortality in the United States. Healthy behaviors (e.g., physical activity, avoiding cigarette use, and refraining from binge drinking) and preventive practices (e.g., visiting a doctor for a routine check-up, tracking blood pressure, and monitoring blood cholesterol) might help prevent or successfully manage these chronic conditions. Monitoring chronic diseases, health-risk behaviors, and access to and use of health care are fundamental to the development of effective public health programs and policies at the state and local levels., Reporting Period: January-December 2015., Description of the System: The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to and use of health care, and use of preventive health services related to the leading causes of death and disability. This report presents results for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico (Puerto Rico), and Guam and for 130 metropolitan and micropolitan statistical areas (MMSAs) (N = 441,456 respondents) for 2015., Results: The age-adjusted prevalence estimates of health-risk behaviors, self-reported chronic health conditions, access to and use of health care, and use of preventive health services varied substantially by state, territory, and MMSA in 2015. Results are summarized for selected BRFSS measures. Each set of proportions refers to the median (range) of age-adjusted prevalence estimates for health-risk behaviors, self-reported chronic diseases or conditions, or use of preventive health care services by geographic jurisdiction, as reported by survey respondents. Adults with good or better health: 84.6% (65.9%-88.8%) for states and territories and 85.2% (66.9%-91.3%) for MMSAs. Adults with ≥14 days of poor physical health in the past 30 days: 10.9% (8.2%-17.2%) for states and territories and 10.9% (6.6%-19.1%) for MMSAs. Adults with ≥14 days of poor mental health in the past 30 days: 11.3% (7.3%-15.8%) for states and territories and 11.4% (5.6%-20.5%) for MMSAs. Adults aged 18-64 years with health care coverage: 86.8% (72.0%-93.8%) for states and territories and 86.8% (63.2%-95.7%) for MMSAs. Adults who received a routine physical checkup during the preceding 12 months: 69.0% (58.1%-79.8%) for states and territories and 69.4% (57.1%-81.1%) for MMSAs. Adults who ever had their blood cholesterol checked: 79.1% (73.3%-86.7%) for states and territories and 79.5% (65.1%-87.3%) for MMSAs. Current cigarette smoking among adults: 17.7% (9.0%-27.2%) for states and territories and 17.3% (4.5%-29.5%) for MMSAs. Binge drinking among adults during the preceding 30 days: 17.2% (11.2%-26.0%) for states and territories and 17.4% (5.5%-24.5%) for MMSAs. Adults who reported no leisure-time physical activity during the preceding month: 25.5% (17.6%-47.1%) for states and territories and 24.5% (16.1%-47.3%) for MMSAs. Adults who reported consuming fruit less than once per day during the preceding month: 40.5% (33.3%-55.5%) for states and territories and 40.3% (30.1%-57.3%) for MMSAs. Adults who reported consuming vegetables less than once per day during the preceding month: 22.4% (16.6%-31.3%) for states and territories and 22.3% (13.6%-32.0%) for MMSAs. Adults who have obesity: 29.5% (19.9%-36.0%) for states and territories and 28.5% (17.8%-41.6%) for MMSAs. Adults aged ≥45 years with diagnosed diabetes: 15.9% (11.2%-26.8%) for states and territories and 15.7% (10.5%-27.6%) for MMSAs. Adults aged ≥18 years with a form of arthritis: 22.7% (17.2%-33.6%) for states and territories and 23.2% (12.3%-33.9%) for MMSAs. Adults having had a depressive disorder: 19.0% (9.6%-27.0%) for states and territories and 19.2% (9.9%-27.2%) for MMSAs. Adults with high blood pressure: 29.1% (24.2%-39.9%) for states and territories and 29.0% (19.7%-41.0%) for MMSAs. Adults with high blood cholesterol: 31.8% (27.1%-37.3%) for states and territories and 31.4% (23.2%-42.0%) for MMSAs. Adults aged ≥45 years who have had coronary heart disease: 10.3% (7.2%-16.8%) for states and territories and 10.1% (4.7%-17.8%) for MMSAs. Adults aged ≥45 years who have had a stroke: 4.9% (2.5%-7.5%) for states and territories and 4.7% (2.1%-8.4%) for MMSAs., Interpretation: The prevalence of health care access and use, health-risk behaviors, and chronic health conditions varied by state, territory, and MMSA. The data in this report underline the importance of continuing to monitor chronic diseases, health-risk behaviors, and access to and use of health care in order to assist in the planning and evaluation of public health programs and policies at the state, territory, and MMSA level., Public Health Action: State and local health departments and agencies and others interested in health and health care can continue to use BRFSS data to identify groups with or at high risk for chronic conditions, unhealthy behaviors, and limited health care access and use. BRFSS data also can be used to help design, implement, monitor, and evaluate health-related programs and policies., Competing Interests: No conflicts of interest were reported.

Objective: To evaluate whether elective induction of labor between 39 through 41 weeks of gestation, as compared with expectant management, is associated with reduced cesarean delivery and other adverse outcomes among obese women and their offspring., Methods: We conducted a retrospective cohort study using the 2007-2011 California Linked Patient Discharge Data-Birth Cohort File of 165,975 singleton, cephalic, nonanomalous deliveries to obese women. For each gestational week (39-41), we used multivariable logistic regression models, stratified by parity, to assess whether elective induction of labor or expectant management was associated with lower odds of cesarean delivery and other adverse outcomes., Results: At 39 and 40 weeks of gestation, cesarean delivery was less common in obese nulliparous women who were electively induced compared with those who were expectantly managed (at 39 weeks of gestation, frequencies were 35.9% vs 41.0%, respectively [P<.05]; adjusted odds ratio [OR] 0.82, 95% CI 0.77-0.88). Severe maternal morbidity was less frequent among electively induced obese nulliparous patients (at 39 weeks of gestation, 5.6% vs 7.6% [P<.05]; adjusted OR 0.75, 95% CI 0.65-0.87). Neonatal intensive care unit admission was less common among electively induced obese nulliparous women (at 39 weeks of gestation, 7.9% vs 10.1% [P<.05]; adjusted OR 0.79, 95% CI 0.70-0.89). Patterns were similar among obese parous women at 39 weeks of gestation (crude frequencies and adjusted ORs [95% CIs] were as follows: for cesarean delivery, 7.0% vs 8.7% [P<.05] and 0.79 [0.73-0.86]; for severe maternal morbidity, 3.3% vs 4.0% [P<.05] and 0.83 [0.74-0.94]; for neonatal intensive care unit admission: 5.3% vs 7.4% [P<.05] and 0.75 [0.68-0.82]). Similarly, elective induction at 40 weeks of gestation was associated with reduced odds of cesarean delivery, maternal morbidity, and neonatal intensive care unit admission among both obese nulliparous and parous patients., Conclusion: Elective labor induction after 39 weeks of gestation was associated with reduced maternal and neonatal morbidity among obese women. Further prospective investigation is necessary.

Objective: To generate a global reference for caesarean section (CS) rates at health facilities., Design: Cross-sectional study., Setting: Health facilities from 43 countries., Population/sample: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing., Methods: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models., Main Outcome Measures: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate., Results: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal&#95;perinatal&#95;health/c-model/en/)., Conclusions: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS., Tweetable Abstract: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems., (© 2015 World Health Organization; licensed by John Wiley & Sons Ltd on behalf of Royal College of Obstetricians and Gynaecologists.)