Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract-level prevalence estimates of 27 measures for the 500 largest US cities. To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code-level estimates for the city of Boston, Massachusetts. By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available.

In response to the need of more rigorous data on medical cannabis and chronic pain, we conducted a 3-month prospective study incorporating ecological momentary assessment (EMA) to examine the effects of medical cannabis on pain, anxiety/depression, sleep, and quality of life. Data were collected from 46 adults (Mean age=55.7±11.9, 52.2% male) newly initiating medical cannabis treatment for chronic pain. Participants completed a baseline survey, EMA for approximately 1 week pre- and up to 3 weeks post- medical cannabis treatment, and a 3-month follow-up survey. The self-reported EMA data (2535 random and 705 daily assessments) indicated significant reductions in momentary pain intensity (b = -16.5, p < .001, 16.5 points reduction on 0-100 visual analog) and anxiety (b = -0.89, p < .05), and significant increase in daily sleep duration (b = 0.34, p < .01) and sleep quality (b = 0.32, p <.001) after participants initiated medical cannabis for a few weeks. At 3 months, self-reported survey data showed significantly lower levels of worst pain (t = -2.38, p < .05), pain interference (t = -3.82, p < .05), and depression (t = -3.43, p < .01), as well as increased sleep duration (t = 3.95, p < .001), sleep quality (t = -3.04, p < .01), and quality of life (t = 4.48, p < .001) compared to baseline. In our sample of primarily middle-aged and older adults with chronic pain, medical cannabis was associated with reduced pain intensity/inference, lower anxiety/depression, and improved sleep and quality of life.