Background

As of July 2018, 45 United States (US) states and the District of Columbia have enacted an overdose Good Samaritan law (GSL). These laws, which provide limited criminal immunity to individuals who request assistance during an overdose, may be of importance in the current wave of the overdose epidemic, which is driven primarily by illicit opioids including heroin and fentanyl. There are substantial differences in the structures of states' GSL laws which may impact their effectiveness. This study compared GSLs which have legal provisions protecting from arrest and laws which have more limited protections.

Methods

Using national county-level overdose mortality data from 3109 US counties, we examined the association of enactment of GSLs with protection from arrest and GSLs with more limited protections with subsequent overdose mortality between 2013 and 2018. Since GSLs are often enacted in conjunction with Naloxone Access Laws (NAL), we examined the effect of GSLs separately and in conjunction with NAL. We conducted these analyses using hierarchical Bayesian spatiotemporal Poisson models.

Results

GSLs with protections against arrest enactment in conjunction with a NAL were associated with 7% lower rates of all overdose deaths (rate ratio (RR): 0.93% Credible Interval (CI): 0.89-0.97), 10% lower rates in opioid overdose deaths (RR: 0.90; CI: 0.85-0.95) and 11% lower rates of heroin/synthetic overdose mortality (RR: 0.89; CI: 0.82-0.96) two years after enactment, compared to rates in states without these laws. Significant reductions in overdose mortality were not seen for GSLs with protections for charge or prosecution.

Conclusion

GSLs with more expansive legal protections combined with a NAL, were associated with lower rates of overdose deaths, although these risk reductions take time to manifest. Policy makers should consider enacting and implementing more expansive GSLs with arrest protections to increase the likelihood people will contact emergency services in the event of an overdose.

Aims

To determine (1) whether prescription opioid poisoning (PO) hospital discharges spread across space over time, (2) the locations of 'hot-spots' of PO-related hospital discharges, (3) how features of the local environment contribute to the growth in PO-related hospital discharges and (4) where each environmental feature makes the strongest contribution.

Design

Hierarchical Bayesian Poisson space-time analysis to relate annual discharges from community hospitals to postal code characteristics over 10 years.

Setting

California, USA.

Participants

Residents of 18 517 postal codes in California, 2001-11.

Measurements

Annual postal code-level counts of hospital discharges due to PO poisoning were related to postal code pharmacy density, measures of medical need for POs (i.e. rates of cancer and arthritis-related hospital discharges), economic stressors (i.e. median household income, percentage of families in poverty and the unemployment rate) and concentration of manual labor industries.

Findings

PO-related hospital discharges spread from rural and suburban/exurban 'hot-spots' to urban areas. They increased more in postal codes with greater pharmacy density [rate ratio (RR) = 1.03; 95% credible interval (CI) = 1.01, 1.05], more arthritis-related hospital discharges (RR = 1.08; 95% CI = 1.06, 1.11), lower income (RR = 0.85; 95% CI = 0.83, 0.87) and more manual labor industries (RR = 1.15; 95% CI = 1.10, 1.19 for construction; RR = 1.12; 95% CI = 1.04, 1.20 for manufacturing industries). Changes in pharmacy density primarily affected PO-related discharges in urban areas, while changes in income and manual labor industries especially affected PO-related discharges in suburban/exurban and rural areas.

Conclusions

Hospital discharge rates for prescription opioid (PO) poisoning spread from rural and suburban/exurban hot-spots to urban areas, suggesting spatial contagion. The distribution of age-related and work-place-related sources of medical need for POs in rural areas and, to a lesser extent, the availability of POs through pharmacies in urban areas, partly explain the growth of PO poisoning across California, USA.

Background

Cannabis outlets may affect health and health disparities. Local governments can regulate outlets, but little is known about the effectiveness of local policies in limiting outlet densities and discouraging disproportionate placement of outlets in vulnerable neighborhoods.

Methods

For 241 localities in California, we measured seven policies pertaining to density or location of recreational cannabis outlets. We geocoded outlets using web-scraped data from the online finder Weedmaps between 2018 and 2020. We applied Bayesian spatiotemporal models to evaluate associations of local cannabis policies with Census block group-level outlet counts, accounting for confounders and spatial autocorrelation. We assessed whether associations differed by block group median income or racial-ethnic composition.

Results

Seventy-six percent of localities banned recreational cannabis outlets. Bans were associated with fewer outlets, particularly in block groups with higher median income, fewer Hispanic residents, and more White and Asian residents. Outlets were disproportionately located in block groups with lower median income [posterior RR (95% credible interval): 0.76 (0.70, 0.82) per $10,000], more Hispanic residents [1.05 (1.02, 1.09) per 5%], and fewer Black residents [0.91 (0.83, 0.98) per 5%]. For the six policies in jurisdictions permitting outlets, two policies were associated with fewer outlets and two with more; two policy associations were uninformative. For these policies, we observed no consistent heterogeneity in associations by median income or racial-ethnic composition.

Conclusions

Some local cannabis policies in California are associated with lower cannabis outlet densities, but are unlikely to deter disproportionate placement of outlets in racial-ethnic minority and low-income neighborhoods.

Objectives. To assess whether cannabis control policies that may protect public health were adopted evenly across California localities with differing sociodemographic compositions. Methods. From November 2020 to January 2021, we measured cannabis control policies for 241 localities across California and linked them to data on the characteristics of the communities affected by these policies. We evaluated whether disadvantaged communities were more likely to allow cannabis businesses and less likely to be covered by policies designed to protect public health. Results. Localities with all-out bans on cannabis businesses (65% of localities) were disproportionately high-education (55.8% vs 50.5% with any college) and low-poverty (24.3% vs 34.2%), with fewer Black (4.4% vs 6.9%) and Latinx (45.6% vs 50.3%) residents. Among localities that allowed retail cannabis businesses (28%), there were more cannabis control policies in localities with more high-income and Black residents, although the specific policies varied. Conclusions. Cannabis control policies are unequally distributed across California localities. If these policies protect health, inequities may be exacerbated. Public Health Implications. Uniform adoption of recommended cannabis control policies may help limit any inequitable health impacts of cannabis legalization. (Am J Public Health. 2022;112(11):1640-1650. https://doi.org/10.2105/AJPH.2022.307041).

BACKGROUND:Prescription drug monitoring program are designed to reduce harms from prescription opioids; however, little is known about what populations benefit the most from these programs. We investigated how the relation between implementation of online prescription drug monitoring programs and rates of hospitalizations related to prescription opioids and heroin overdose changed over time, and varied across county levels of poverty and unemployment, and levels of medical access to opioids. METHODS:Ecologic county-level, spatiotemporal study, including 990 counties within 16 states, in 2001-2014. We modeled overdose counts using Bayesian hierarchical Poisson models. We defined medical access to opioids as the county-level rate of hospital discharges for noncancer pain conditions. RESULTS:In 2010-2014, online prescription drug monitoring programs were associated with lower rates of prescription opioid-related hospitalizations (rate ratio 2014 = 0.74; 95% credible interval = 0.69, 0.80). The association between online prescription drug monitoring programs and heroin-related hospitalization was also negative but tended to increase in later years. Counties with lower rates of noncancer pain conditions experienced a lower decrease in prescription opioid overdose and a faster increase in heroin overdoses. No differences were observed across different county levels of poverty and unemployment. CONCLUSIONS:Areas with lower levels of noncancer pain conditions experienced the smallest decrease in prescription opioid overdose and the faster increase in heroin overdose following implementation of online prescription drug monitoring programs. Our results are consistent with the hypothesis that prescription drug monitoring programs are most effective in areas where people are likely to access opioids through medical providers.

Background

Prescription opioid overdose (POD) and heroin overdose (HOD) rates have quadrupled since 1999. Community-level socioeconomic characteristics are associated with opioid overdoses, but whether this varies by urbanicity is unknown.

Methods

In this serial cross-sectional study of zip codes in 17 states, 2002-2014 (n = 145,241 space-time units), we used hierarchical Bayesian Poisson space-time models to analyze the association between zip code-level socioeconomic features (poverty, unemployment, educational attainment, and income) and counts of POD or HOD hospital discharges. We tested multiplicative interactions between each socioeconomic feature and zip code urbanicity measured with Rural-Urban Commuting Area codes.

Results

Percent in poverty and of adults with ≤ high school education were associated with higher POD rates (Rate Ratio [RR], 5% poverty: 1.07 [95% credible interval: 1.06-1.07]; 5% low education: 1.02 [1.02-1.03]), while median household income was associated with lower rates (RR, $10,000: 0.88 [0.87-0.89]). Urbanicity modified the association between socioeconomic features and HOD. Poverty and unemployment were associated with increased HOD in metropolitan areas (RR, 5% poverty: 1.12 [1.11-1.13]; 5% unemployment: 1.04 [1.02-1.05]), and median household income was associated with decreased HOD (RR, $10,000: 0.88 [0.87-0.90]). In rural areas, low educational attainment alone was associated with HOD (RR, 5%: 1.09 [1.02-1.16]).

Conclusions

Regardless of urbanicity, elevated rates of POD were found in more economically disadvantaged zip codes. Economic disadvantage played a larger role in HOD in urban than rural areas, suggesting rural HOD rates may have alternative drivers. Identifying social determinants of opioid overdoses is particularly important for creating effective population-level interventions.

Background

Prescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.

Methods

We measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.

Results

Adoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).

Conclusions

State adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.

BACKGROUND:Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016. METHODS:Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death. RESULTS:After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98). CONCLUSION:There is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.