Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors

BACKGROUND

Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022.

METHODS

We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates.

RESULTS

LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period.

CONCLUSION

Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Development and validation of a community-level social determinants of health index for drug overdose deaths in the HEALing Communities Study

INTRODUCTION

Social determinants of health (SDoH), such as socioeconomic status, education level, and food insecurity, are believed to influence the opioid crisis. While global SDoH indices such as the CDC's Social Vulnerability Index (SVI) and Area Deprivation Index (ADI) combine the explanatory power of multiple social factors for understanding health outcomes, they may be less applicable to the specific challenges of opioid misuse and associated outcomes. This study develops a novel index tailored to opioid misuse outcomes, tests the efficacy of this index in predicting drug overdose deaths across contexts, and compares the explanatory power of this index to other SDoH indices.

METHODS

Focusing on four HEALing Communities Study (HCS) states (Kentucky, Massachusetts, New York and Ohio; encompassing 4269 ZIP codes), we identified multilevel SDoH potentially associated with opioid misuse and aggregated publicly available data for each measure. We then leveraged a random forest model to develop a composite measure that predicts age-adjusted drug overdose mortality rates based on SDoH. We used this composite measure to understand HCS and non-HCS communities in terms of overdose risk across areas of varying racial composition. Finally, we compared variance in drug overdose deaths explained by this index to variance explained by the SVI and ADI.

RESULTS

Our composite measure included 28 SDoH measures and explained approximately 89 % percent of variance in age-adjusted drug overdose mortality across HCS states. Health care measures, including emergency department visits and primary care provider availability, were top predictors within the index. Index accuracy was robust within and outside of HCS communities and states. This measure identified high levels of overdose mortality risk in segregated communities.

CONCLUSIONS

Existing SDoH indices fail to explain much variation in area-level overdose mortality rates. Having tailored composite indices can help us to identify places in which residents are at highest risk based on their composite contexts. A comprehensive index can also help to develop effective community interventions for programs such as HCS by considering the context in which people live.

Construction of multilevel statistical models in health research: Foundations and generalities

This topic review aims to present a global vision of multilevel analysis models’ applicability to health research, explaining its theoretical, methodological, and statistical foundations. We describe the basic steps to build these models and examples of their application according to the data hierarchical structure. It ir worth noticing that before using these models, researchers must have a rationale for needing them, and a statistical evaluation accounting for the variance percentage explained by the observations grouping effect. The requirements to conduct this type of analysis depends on special conditions such as the type of variables, the number of units per level, or the type of hierarchical structure. We conclude that multilevel analysis models are a useful tool to integrate information, considering the complexity of the relationships and interactions involved in most health conditions, including the loss of independence between observation units.

The association between community social vulnerability and prescription opioid availability with individual opioid overdose

BACKGROUND

This study aims to assess the association of community social vulnerability and community prescription opioid availability with individual non-fatal or fatal opioid overdose.

METHODS

We identified patients 12 years of age or older from the Oregon All Payer Claims database (APCD) linked to other public health datasets. Community-level characteristics were captured in an exposure period (EP) (1/1/2018-12/31/2018) and included: census tract-level social vulnerability domains (socio-economic status, household composition, racial and ethnic minority status, and housing type and transportation), census tract-level prescriptions and community-level opioid use disorder (OUD) diagnoses per 100 capita binned into quartiles or quintiles. We employed Cox models to estimate the risk of fatal and non-fatal opioid overdoses events in the 12 months following the EP.

MAIN FINDINGS

We identified 1,548,252 individuals. Patients were mostly female (54%), White (61%), commercially insured (54%), and lived in metropolitan areas (81%). Of the total sample, 2485 (0.2%) experienced a non-fatal opioid overdose and 297 died of opioid overdose. There was higher hazard for non-fatal overdose in communities with greater OUD per 100 capita. We also found higher non-fatal and fatal hazards for opioid overdose among patients in communities with higher housing type and transportation-related vulnerability compared to the lowest quintile. Conversely, patients were at less risk of opioid overdose when living in communities with greater prevalence of the young or the elderly, the disabled, single parent families or low English proficiency.

CONCLUSION

These findings underscore the importance of the environmental context when considering public health policies to reduce opioid harms.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis

BACKGROUND

Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level.

METHODS

This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis.

RESULTS

By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level.

CONCLUSIONS

The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Opioid Use Disorder and Overdose in the First Year Postpartum: A Rapid Scoping Review and Implications for Future Research

OBJECTIVE

Opioid overdose is a leading cause of maternal mortality, yet limited attention has been given to the consequences of opioid use disorder (OUD) in the year following delivery when most drug-related deaths occur. This article provides an overview of the literature on OUD and overdose in the first year postpartum and provides recommendations to advance maternal opioid research.

APPROACH

A rapid scoping review of peer-reviewed research (2010-2021) on OUD and overdose in the year following delivery was conducted in PubMed, PsycINFO, and Web of Science databases. This article discusses existing research, remaining knowledge gaps, and methodological considerations needed.

RESULTS

Seven studies were included. Medication for OUD (MOUD) was the only identified factor associated with a reduction in overdose rates. Key literature gaps include the role of mental health disorders and co-occurring substance use, as well as interpersonal, social, and environmental contexts that may contribute to postpartum opioid problems and overdose.

CONCLUSION

There remains a limited understanding of why women in the first year postpartum are particularly vulnerable to opioid overdose. Recommendations include: (1) identifying subgroups of women with OUD at highest risk for postpartum overdose, (2) assessing opioid use, overdose, and risks throughout the first year postpartum, (3) evaluating the effect of co-occurring physical and mental health conditions and substance use disorders, (4) investigating the social and contextual determinants of opioid use and overdose after delivery, (5) increasing MOUD retention and treatment engagement postpartum, and (6) utilizing rigorous and multidisciplinary research methods to understand and prevent postpartum overdose.

Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.

An external exposome-wide association study of opioid use disorder diagnosed during pregnancy in Florida

The prevalence of opioid use disorder (OUD) during pregnancy has quadrupled in recent years and widely varies geographically in the US. However, few studies have examined which environmental factors are associated with OUD during pregnancy. We conducted an external exposome-wide association study (ExWAS) to investigate the associations between external environmental factors and OUD diagnosed during pregnancy. Data were obtained from a unique, statewide database in Florida comprising linked individual-level birth and electronic health records. A total of 255,228 pregnancies with conception dates between 2012 and 2016 were included. We examined 82 exposome measures characterizing seven aspects of the built and social environment and spatiotemporally linked them to each individual record. A two-phase procedure was utilized for the external ExWAS. In Phase 1, we randomly divided the data into a discovery set (50 %) and a replication set (50 %). Associations between exposome measures (normalized and standardized) and OUD initially diagnosed during pregnancy were examined using logistic regression. A total of 15 variables were significant in both the discovery and replication sets. In Phase 2, multivariable logistic regression was used to fit all variables selected from Phase 1. Measures of walkability (the national walkability index, OR: 1.23, 95 % CI: 1.17, 1.29), vacant land (the percent vacant land for 36 months or longer, OR: 1.06, 95 % CI: 1.00, 1.12) and food access (the percentage of low food access population that are seniors at 1/2 mile, OR: 1.47, 95 % CI: 1.38, 1.57) were each associated with diagnosis of OUD during pregnancy. This is the first external ExWAS of OUD during pregnancy, and the results suggest that low food access, high walkability, and high vacant land in under-resourced neighborhoods are associated with diagnosis of OUD during pregnancy. These findings could help develop complementary tools for universal screening for substance use and provide direction for future studies.

The salience of social context, opioid antagonist use, and prior opioid exposure as determinants of fatal and non-fatal opioid overdoses

This study examines the salience of social context for opioid overdoses in Boston from 2014 to 2019. Longitudinal negative binomial models with random effects indicated that higher levels of concentrated disadvantage, residential instability, and illicit drug activity increased annual block group counts of opioid overdoses. Logistic hierarchical and cross-classified random effects models indicated that the use of Narcan and greater exposure to drugs through previous opioid overdose and contextual lillicit drug crime activity reduced the odds of fatal opioid overdose relative to non-fatal opioid overdose. The findings suggest that the accurate tracking of both fatal and non-fatal overdoses, and a consideration of the broader social context, can facilitate effective public health resource allocation to reduce opioid overdoses.

Drug overdoses, geographic trajectories, and the influence of built environment and neighborhood characteristics

Much research has analyzed the spatial patterns of drug overdose events and identified features of the environment associated with heightened overdose levels. Generally absent from the literature are studies that analyze how unique trajectories of overdoses vary over time. We address this gap in the literature through an analysis of drug overdoses occurring in Passaic County, New Jersey from 2015 through 2019. A group-based trajectory analysis classifies block groups according to their overdose trends. A mixed-effects panel negative binomial regression model then examines the built environment and neighborhood characteristics associated with overall overdose levels. Results indicate that Passaic County block groups can be classified across three groups based upon their overdose levels over the study period: low and stable, low with moderate increase, and elevated and increasing. While the largest effects were observed for concentrated disadvantage in the regression analysis, most variables positively associated with overdose levels were built environment measures.

Supply-side predictors of fatal drug overdose in the Washington/Baltimore HIDTA region: 2016-2020

BACKGROUND

Rising rates of fentanyl- and polydrug-involved drug overdose deaths have prompted inquiry into the role of drug supply in fatal overdose outcomes in the United States. To date, however, there have been few empirical investigations of drug enforcement strategies on fatal overdose rates, despite knowledge that both drug use and supply are often geographically distributed. To address this limitation, we examined measures of drug enforcement as predictors of next-year fatal overdose rates in the Washington/Baltimore High Intensity Drug Trafficking Area (W/B HIDTA).

METHODS

We conducted mixed-effects models to examine the role of drug seizures and disruption in drug trafficking organizations (DTOs) and money laundering organizations (MLOs) on fatal overdose rates over a 5-year period (2016-2020) across 45 local jurisdictions in the W/B HIDTA region. Outcomes included any, opioid-involved, and fentanyl-involved fatal overdose.

RESULTS

Adjusting for covariates, both the total number of drug seizures and amount of cocaine seized (in dosage units per capita) positively predicted next-year opioid- and fentanyl-involved fatal overdose rates. Disruption to DTO and MLO operations did not significantly predict next-year fatal overdose rates for any outcome.

CONCLUSION

Supply-side enforcement activities alone may have limited impact on reducing fatal overdose rates, but may serve as important markers to identify communities at high risk of fatal overdose and facilitate targeted intervention. Our findings underscore the importance of comprehensive law enforcement approaches that extend beyond drug enforcement to integrate prevention, linkage to treatment, and harm reduction strategies as needed to address the overdose epidemic.

Endogenous opiates and behavior: 2020

This paper is the forty-third consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2020 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

Drug overdose mortality by race/ethnicity across US-born and immigrant populations

BACKGROUND

The present study examined racial/ethnic differences in US drug overdose mortality among US-born and foreign-born men and women.

METHODS

In this cross-sectional analysis of 2010-2019 data from the National Center for Health Statistics, Bayesian hierarchical models predicted drug overdose mortality based on the interaction of race/ethnicity, nativity, and sex, adjusting for age, for 518,553 drug overdose deaths among individuals ages 15-74 identified as Non-Hispanic (NH) White, NH Black, Hispanic, or NH Asian/Pacific Islander (PI). Rate ratios with 95% Highest Posterior Density Intervals (HPDIs) were examined by race/ethnicity and nativity.

RESULTS

In the US-born population, 2017-2019 estimated overdose mortality rates were higher for NH Black than NH White men (ratio 1.48 [95% HPDI 1.28-1.72]), similar between NH Black and NH White women (ratio 1.03 [95% HPDI 0.89-1.20]), similar between Hispanic and NH White men (ratio 0.96 [95% HPDI 0.82-1.10]), and lower for NH Asian/PI than NH White men and women. In the foreign-born population, both for men and women, estimated overdose mortality rates were lower in every racial/ethnic group relative to the NH White group. For men and women of all racial/ethnic groups examined, estimated overdose mortality rates were higher in US-born than foreign-born subpopulations, yet the extent of this nativity differential was least pronounced in the NH White group.

CONCLUSIONS

In the US-born population, NH Black men experienced the highest recent rates of overdose mortality; in the foreign-born population, the highest rates of overdose mortality were observed among NH White men and women.

Neighborhood disadvantage and prescription drug misuse in low-income urban mothers

BACKGROUND

Prescription drug misuse remains a persistent problem in the United States. Residents living in disadvantaged neighborhoods are at greater risk of substance abuse such as alcohol, tobacco, or drugs. However, whether neighborhood disadvantage affects prescription drug misuse remains underexplored.

METHODS

This study uses data on 3444 mothers from the Fragile Families and Child Wellbeing Study to examine the role of neighborhood disadvantage in prescription drug misuse. In addition, we examine whether social support and neighborhood collective efficacy (social cohesion and social control) explain this relationship. The analysis uses multivariate logistic regression and delineated between the following neighborhoods: affluent (3% poverty), low poverty (3-10%), moderate poverty (10-20%), and high poverty neighborhoods (20% or more).

RESULTS

Mothers living in moderately poor neighborhoods were more than twice as likely (odds = 2.17, 95% CI: 1.43-3.27) to misuse prescription drugs than mothers living in neighborhoods with high poverty. Mothers living in neighborhoods with high poverty did not have a statistically significant difference in prescription drug misuse than those living in affluent or low poverty neighborhoods. Social support and neighborhood collective efficacy did not explain these associations. The association between moderate poverty and prescription drug misuse was mostly direct and there was no indirect association.

CONCLUSION

The study highlights the higher risk of prescription drug misuse among mothers living in neighborhoods with moderate poverty. Interventions aimed at reducing opioid misuse should focus on demographic groups that are more vulnerable such as low-income mothers living in disadvantaged neighborhoods.

Food insecurity, chronic pain, and use of prescription opioids

Chronic pain has been on the rise in recent decades in Canada. Accordingly, the use of prescription opioids (PO) in Canada increased drastically between 2005 and 2014, only starting to decrease in 2015. Both pain and PO use have serious public health repercussions, disproporionately affecting select socially disadvantaged populations. Food insecurity is a strong risk factor for mental disorders and suicidal outcomes, yet its relationship to chronic pain and PO use is largely unknown. Using two recent cycles from the population representative Canadian Community Health Survey (CCHS), we examined the association of household food insecurity status with chronic pain and PO use among Canadians 12 years and older, adjusting for health and sociodemographic characteristics. Compared to food-secure individuals, marginally, moderately, and severely food-insecure individuals had 1.31 (95% confidence interval [CI] 1.15-1.48), 1.89 (95% CI 1.71-2.08), and 3.29 (95% CI 2.90-3.74) times higher odds of experiencing chronic pain and 1.55 (95% CI 1.30-1.85), 1.77 (95% CI 1.54-2.04), and 2.65 (95% CI 2.27-3.09) times higher odds of using PO in the past year, respectively. The graded association with food insecurity severity was also found in severe pain experience and pain-induced activity limitations among chronic pain patients and, less consistently, in intensive, excess, and alternative use of PO and its acquisition through means other than medical prescription among past-year PO users. Food insecurity was a much more powerful predictor of chronic pain and PO use than other well-established social determinants of health like income and education. Policies reducing food insecurity may lower incidence of chronic pain and help contain the opioid crisis.

A Vulnerability Assessment for a Future HIV Outbreak Associated With Injection Drug Use in Illinois, 2017-2018

The current opioid crisis and the increase in injection drug use (IDU) have led to outbreaks of HIV in communities across the country. These outbreaks have prompted country and statewide examination into identifying factors to determine areas at risk of a future HIV outbreak. Based on methodology used in a prior nationwide county-level analysis by the US Centers for Disease Control and Prevention (CDC), we examined Illinois at the ZIP code level (n = 1,383). Combined acute and chronic hepatitis C virus (HCV) infection among persons <40 years of age was used as an outcome proxy measure for IDU. Local and statewide data sources were used to identify variables that are potentially predictive of high risk for HIV/HCV transmission that fell within three main groups: health outcomes, access/resources, and the social/economic/physical environment. A multivariable negative binomial regression was performed with population as an offset. The vulnerability score for each ZIP code was created using the final regression model that consisted of 11 factors, six risk factors, and five protective factors. ZIP codes identified with the highest vulnerability ranking (top 10%) were distributed across the state yet focused in the rural southern region. The most populous county, Cook County, had only one vulnerable ZIP code. This analysis reveals more areas vulnerable to future outbreaks compared to past national analyses and provides more precise indications of vulnerability at the ZIP code level. The ability to assess the risk at sub-county level allows local jurisdictions to more finely tune surveillance and preventive measures and target activities in these high-risk areas. The final model contained a mix of protective and risk factors revealing a heightened level of complexity underlying the relationship between characteristics that impact HCV risk. Following this analysis, Illinois prioritized recommendations to include increasing access to harm reduction services, specifically sterile syringe services, naloxone access, infectious disease screening and increased linkage to care for HCV and opioid use disorder.

Addressing Major Health Disparities Related to Coronavirus for People With Behavioral Health Conditions Requires Strength-Based Capacity Building and Intentional Community Partnership

Far from being an equalizer, as some have claimed, the COVID‐19 pandemic has exposed just how vulnerable many of our social, health, and political systems are in the face of major public health shocks. Rapid responses by health systems to meet increased demand for hospital beds while continuing to provide health services, largely via a shift to telehealth services, are critical adaptations. However, these actions are not sufficient to mitigate the impact of coronavirus for people from marginalized communities, particularly those with behavioral health conditions, who are experiencing disproportional health, economic, and social impacts from the evolving pandemic. Helping these communities weather this storm requires partnering with existing community‐based organizations and local governments to rapidly and flexibly meet the needs of vulnerable populations.

Law and mental health: A bridge between individual neurobiology and the collective organization of behaviors

Mental disorders (MD) or mental symptoms (MS) have multifactorial causes. Today we know much more about the variables that cause individual MD\MS, but in our opinion these characterizations, although essential, are not sufficient to account for the complexity in which we live. For example, they do not explain in a coherent and empirically verifiable way how the biological individual relates to the social architecture in which he lives. This article presents a hypothesis that connects social and organizational structures to the emergence of symptoms and mental disorders in the population. It is our belief that some of these structures fundamentally impact the distribution of MD/MS in a population and the medical and psychological communities must consider this impact seriously. Laws aim at directing the behavior of groups of people, whose behavior is strictly interdependent with their neurobiology. Given the ability of laws to direct the behaviors that regulate social interactions, traumatic factors may be considered capable of linking a non-material object (e.g., a law) to a real effect (e.g., MS/MD). We discuss, as a paradigmatic example, the laws that regulate the use of psychotropic substances.