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Objectives. To estimate the annual prevalence of opioid use disorder (OUD) in Massachusetts from 2011 to 2015. Methods. We performed a multisample stratified capture–recapture analysis to estimate OUD prevalence in Massachusetts. Individuals identified from 6 administrative databases for 2011 to 2012 and 7 databases for 2013 to 2015 were linked at the individual level and included in the analysis. Individuals were stratified by age group, sex, and county of residence. Results. The OUD prevalence in Massachusetts among people aged 11 years or older was 2.72% in 2011 and 2.87% in 2012. Between 2013 and 2015, the prevalence increased from 3.87% to 4.60%. The greatest increase in prevalence was observed among those in the youngest age group (11–25 years), a 76% increase from 2011 to 2012 and a 42% increase from 2013 to 2015. Conclusions. In Massachusetts, the OUD prevalence was 4.6% among people 11 years or older in 2015. The number of individuals with OUD is likely increasing, particularly among young people.

Objectives. We evaluated the overall and sociodemographic disparities in trends in prevalence of childhood overweight and obesity in Massachusetts public school districts between 2009 and 2014. Methods. In 2009, Massachusetts mandated annual screening of body mass index for students in grades 1, 4, 7, and 10. This was part of the statewide Mass in Motion prevention programs. We assessed trends in the prevalence of overweight and obesity between 2009 and 2014 by district, gender, grade, and district income. Results. From 2009 to 2014, prevalence decreased 3.0 percentage points (from 34.3% to 31.3%) statewide. The 2014 district-level rates ranged from 13.9% to 54.5% (median = 31.2%). When stratified by grade, the decreasing trends were significant only for grades 1 and 4. Although rates of districts with a median household income greater than $37 000 improved notably, rates of the poorest remain unchanged and were approximately 40%. Conclusions. Although overall prevalence began to decrease, the geographic and socioeconomic disparities in childhood obesity are widening and remain a public health challenge in Massachusetts. Special efforts should be made to address the needs of socioeconomically disadvantaged districts and to narrow the disparities in childhood obesity.

Electronic health record systems contain clinically detailed data from large populations of patients that could significantly enrich public health surveillance. Clinical practices’ security, privacy, and proprietary concerns, however, have limited their willingness to share these data with public health agencies. We describe a novel distributed network for public health surveillance called MDPHnet. The system allows the Massachusetts Department of Public Health (MDPH) to initiate custom queries against participating practices’ electronic health records while the data remain behind each practice’s firewall. Practices can review proposed queries before execution and approve query results before releasing them to the health department. MDPH is using the system for routine surveillance for priority conditions and to evaluate the impact of public health interventions.

Objectives. We developed a method to evaluate geographic and temporal variations in community-level risk factors and prevalence estimates, and used that method to identify communities in Massachusetts that should be considered high priority communities for smoking interventions. Methods. We integrated individual-level data from the Behavioral Risk Factor Surveillance System from 1999 to 2005 with community-level data in Massachusetts. We used small-area estimation models to assess the associations of adults’ smoking status with both individual- and community-level characteristics and to estimate community-specific smoking prevalence in 398 communities. We classified communities into 8 groups according to their prevalence estimates, the precision of the estimates, and temporal trends. Results. Community-level prevalence of current cigarette smoking among adults ranged from 5% to 36% in 2005 and declined in all but 16 (4%) communities between 1999 and 2005. However, less than 15% of the communities met the national prevalence goal of 12% or less. High smoking prevalence remained in communities with lower income, higher percentage of blue-collar workers, and higher density of tobacco outlets. Conclusions. Prioritizing communities for intervention can be accomplished through the use of small-area estimation models. In Massachusetts, socioeconomically disadvantaged communities have high smoking prevalence rates and should be of high priority to those working to control tobacco use.

Objectives. We used electronic health record (EHR) data to determine rates and patient characteristics in offering cessation interventions (counseling, medications, or referral) and initiating quit attempts. Methods. Ten community health centers in New York City contributed 30 months of de-identified patient data from their EHRs. Results. Of 302 940 patients, 40% had smoking status recorded and only 34% of documented current smokers received an intervention. Women and younger patients were less likely to have their smoking status documented or to receive an intervention. Patients with comorbidities that are exacerbated by smoking were more likely to have status documented (82.2%) and to receive an intervention (52.1%), especially medication (10.8%). Medication, either alone (odds ratio [OR] = 1.9; 95% confidence interval [CI] = 1.5, 2.3) or combined with counseling (OR = 1.8; 95% CI = 1.5, 2.3), was associated with higher quit attempts compared with no intervention. Conclusions. Data from EHRs demonstrated underdocumentation of smoking status and missed opportunities for cessation interventions. Use of data from EHRs can facilitate quality improvement efforts to increase screening and intervention delivery, with the potential to improve smoking cessation rates.