Michael William Flores1,2, Benjamin Lê Cook1,2, Brian Mullin1, Gabriel Halperin-Goldstein3, Aparna Nathan4, Kertu Tenso1,5, Zev Schuman-Olivier2,6. 1. Health Equity Research Laboratory, Cambridge Health Alliance, Cambridge, MA, USA. 2. Department of Psychiatry, Harvard Medical School, Boston, MA, USA. 3. Haverford College, Haverford, PA, USA. 4. Department of Bioinformatics, Harvard Medical School, Boston, MA, USA. 5. Boston University School of Public Health, Boston, MA, USA. 6. Addiction Services, Cambridge Health Alliance, Cambridge, MA, USA.
Abstract
AIM: To identify associations between opioid-related mortality and neighborhood-level risk factors. DESIGN: Cross-sectional study. SETTING: Massachusetts, USA. PARTICIPANTS: Using 2011-14 Massachusetts death certificate data, we identified opioid-related (n = 3089) and non-opioid-related premature deaths (n = 8729). MEASUREMENTS: The independent variables consisted of four sets of neighborhood-level factors: (1) psychosocial, (2) economic, (3) built environment and (4) health-related. At the individual level we included the following compositional factors: age at death, sex, race/ethnicity, marital status, education, veteran status and nativity. The primary outcome of interest was opioid-related mortality. FINDINGS: Multi-level models identified number of social associations per 10 000 [odds ratio (OR) = 0.84, P = 0.002, 95% confidence interval (CI) = 0.75-0.94] and number of hospital beds per 10 000 (OR = 0.78, P < 0.001, 95% CI = 0.68-0.88) to be inversely associated with opioid-related mortality, whereas the percentage living in poverty (OR = 1.01, P = 0.008, 95% CI = 1.00-1.01), food insecurity rate (OR = 1.21, P = 0.002, 95% CI = 1.07-1.37), number of federally qualified health centers (OR = 1.02, P = 0.028, 95% CI = 1.02-1.08) and per-capita morphine milligram equivalents of hydromorphone (OR = 1.05, P = 0.003, 95% CI = 1.01-1.08) were positively associated with opioid-related mortality. CONCLUSIONS: Opioid-related deaths between 2011 and 2014 in the state of Massachusetts appear to be positively associated with the percentage living in poverty, food insecurity rate, number of federally qualified health centers and per-capita morphine milligram equivalents of hydromorphone, but inversely associated with number of social associations per 10 000 and number of hospital beds per 10 000.
AIM: To identify associations between opioid-related mortality and neighborhood-level risk factors. DESIGN: Cross-sectional study. SETTING: Massachusetts, USA. PARTICIPANTS: Using 2011-14 Massachusetts death certificate data, we identified opioid-related (n = 3089) and non-opioid-related premature deaths (n = 8729). MEASUREMENTS: The independent variables consisted of four sets of neighborhood-level factors: (1) psychosocial, (2) economic, (3) built environment and (4) health-related. At the individual level we included the following compositional factors: age at death, sex, race/ethnicity, marital status, education, veteran status and nativity. The primary outcome of interest was opioid-related mortality. FINDINGS: Multi-level models identified number of social associations per 10 000 [odds ratio (OR) = 0.84, P = 0.002, 95% confidence interval (CI) = 0.75-0.94] and number of hospital beds per 10 000 (OR = 0.78, P < 0.001, 95% CI = 0.68-0.88) to be inversely associated with opioid-related mortality, whereas the percentage living in poverty (OR = 1.01, P = 0.008, 95% CI = 1.00-1.01), food insecurity rate (OR = 1.21, P = 0.002, 95% CI = 1.07-1.37), number of federally qualified health centers (OR = 1.02, P = 0.028, 95% CI = 1.02-1.08) and per-capita morphine milligram equivalents of hydromorphone (OR = 1.05, P = 0.003, 95% CI = 1.01-1.08) were positively associated with opioid-related mortality. CONCLUSIONS: Opioid-related deaths between 2011 and 2014 in the state of Massachusetts appear to be positively associated with the percentage living in poverty, food insecurity rate, number of federally qualified health centers and per-capita morphine milligram equivalents of hydromorphone, but inversely associated with number of social associations per 10 000 and number of hospital beds per 10 000.
Authors: Andrea Fleisch Marcus; Sandra E Echeverria; Bart K Holland; Ana F Abraido-Lanza; Marian R Passannante Journal: Am J Community Psychol Date: 2015-09