A Geospatial Analysis of Health, Mental Health, and Stressful Community Contexts in Los Angeles County

Associations Between Patient Comfort with a Primary Care Provider and Three Measures of Behavioral Health Services Utilization

BACKGROUND

Behavioral health services (BHS) can help improve and treat mental and emotional health problems. Yet, attitudinal and/or structural barriers often prevent individuals from accessing and benefiting from these services. Positive provider-patient interactions in healthcare, encompassing patient comfort with a primary care provider (PCP), which is often enhanced by shared decision-making, may mitigate the stigma associated with seeing a mental health professional; this may improve BHS utilization among patients who need these services. However, few studies have examined how patient comfort with a PCP, often through shared decision-making, may influence patients' BHS utilization in the real world. This study sought to address this gap in practice.

METHOD

Multivariable regression analyses, using weighted data from an internet panel survey of Los Angeles County adults (n = 749), were carried out to examine the associations between patient comfort with a PCP and three measures of BHS utilization. Subsequent analyses were conducted to explore the extent to which shared decision-making moderated these associations.

RESULTS

Participants who reported an intermediate or high comfort level with a provider had higher odds of reporting that they were likely to see (aOR = 2.10 and 3.84, respectively) and get advice (aOR = 2.75 and 4.76, respectively) from a mental health professional compared to participants who reported a low comfort level. Although shared decision-making influenced participants' likelihood of seeing and getting advice from a mental health professional, it was not a statistically significant moderator in these associations.

CONCLUSION

Building stronger relationships with patients may improve BHS utilization, a provider practice that is likely underutilized.

Racial Differences in Self-Report of Mental Illness and Mental Illness Treatment in the Community: An Analysis of Jail Intake Data

Jails and prisons in the United States house people with elevated rates of mental health and substance use disorders. The goal of this cross-sectional study was to evaluate the frequency of racial/ethnic differences in the self-report of mental illness and psychiatric medication use at jail entry. Our sample included individuals who had been incarcerated between 2016 and 2020 at the Middlesex Jail & House of Correction, located in Billerica, MA. We used data from the "Offender Management System," the administrative database used by the jail containing data on people who are incarcerated, and COREMR, the electronic medical record (EMR) used in the Middlesex Jail & House of Correction. We evaluated two primary outcomes (1) self-reported mental illness history and (2) self-reported use of psychiatric medication, with the primary indicator of interest as race/ethnicity. At intake, over half (57%) of the sample self-reported history of mental illness and 20% reported the use of psychiatric medications. Among people who self-reported a history of mental illness, Hispanic (AOR: 0.73, 95% CI: 0.60-0.90), Black (AOR: 0.52, 95% CI: 0.43-0.64), Asian/Pacific Islander (Non-Hispanic) people (AOR: 0.31, 95% CI: 0.13-0.74), and people from other racial/ethnic groups (AOR: 0.33, 95% CI: 0.11-0.93) all had decreased odds of reporting psychiatric medications. Mental illness was reported in about one-half of people who entered jail, but only 20% reported receiving medications in the community prior to incarceration. Our findings build on the existing literature on jail-based mental illness and show racial disparities in self-report of psychiatric medications in people who self-reported mental illness. The timing, frequency, and equity of mental health services in both the community and the jail setting deserves further research, investment, and improvement.

Sociodemographic Correlates of Affordable Community Behavioral Health Treatment Facility Availability in Florida: A Cross-Sectional Study

Behavioral health disorders such as mental disorders (MD) and substance use disorders (SUD) are epidemics in the US; however, the availability of treatment and prevention services remains low. This study assessed neighborhood-level sociodemographic attributes to characterize the availability of behavioral health treatment facilities in Florida. The American Community Survey and SAMHSA's Behavioral Health Treatment Locator were used to identify behavioral health treatment facilities in Florida and calculate their density by census tract. Spatial lag regression models were used to assess census tract-level correlates of facility density for 390 MD treatment facilities, 518 SUD facilities, and subsets of affordable MD and SUD facilities. Behavioral health treatment facility density was negatively associated with rurality and positively associated with the proportion of non-Latino Black, Latino, insured, and college-educated populations. Stark rural-urban disparities in behavioral health treatment availability present opportunities to prioritize telehealth and mobile interventions and improve treatment utilization.

Geospatial analysis of associations among mental health need, housing need, and involuntary psychiatric hospitalizations of people experiencing homelessness in Los Angeles County

The purpose of this study was to use geospatial indicators of mental health need and homelessness in Los Angeles County Service Planning Areas (SPAs) and a psychiatric sample of adults who were homeless to investigate 1) overlap between SPA level of mental health need and corresponding volume of involuntary psychiatric hospitalizations over time; 2) overlap between SPA level of unsheltered homelessness and corresponding volume of involuntary psychiatric hospitalizations over time; and 3) associations between SPA level of mental health need, SPA level of unsheltered homelessness, and initiation of a mental health conservatorship for grave disability. A sample of 373 adults who were homeless and hospitalized on an involuntary psychiatric hold from 2016 to 2018 were linked to data from the Greater Los Angeles Homeless Count on unsheltered homelessness and from the California Health Interview Survey on need for mental health services and suicidality, using admission zip codes to link variables at the SPA level. Geospatial mapping and bivariate tests were used to examine geographic overlap of SPA mental health need and unsheltered homelessness with volume of involuntary psychiatric admissions over the study period. Multiple logistic regression modeling was used to examine associations of SPA mental health need and unsheltered homelessness with conservatorship initiation. The volume of patients admitted from SPAs with higher levels of mental illness need grew from 2016 to 2018 (Tau = 0.27, P < 0.001; Tau = 0.40, P < 0.001), but there were fewer patients admitted from SPAs with higher levels of unsheltered homelessness over the same years (Tau of -0.33, P < 0.001). Being admitted from SPAs with the highest levels of unsheltered homelessness was associated with higher odds of conservatorship initiation (OR = 1.73, 95% CI = 1.82-16.74). Results suggest a need for targeted mental health and housing services to reach areas of highest need in Los Angeles County.

Investigating Health Context: Using Geospatial Big Data Ecosystem (Preprint)

Background

Objective

Methods

Results

Conclusions

What Are the Relationships between Psychosocial Community Characteristics and Dietary Behaviors in a Racially/Ethnically Diverse Urban Population in Los Angeles County?

To address existing gaps in public health practice, we used data from a 2014 internet panel survey of 954 Los Angeles County adults to investigate the relationships between psychosocial community characteristics (PCCs) and two key chronic disease-related dietary behaviors: fruit and vegetable (F+V) and soda consumption. Negative binomial regression models estimated the associations between 'neighborhood risks and resources' and 'sense of community' factors for each dietary outcome of interest. While high perceived neighborhood violence (p < 0.001) and perceived community-level collective efficacy (p < 0.001) were associated with higher F+V consumption, no PCCs were directly associated with soda consumption overall. However, moderation analyses by race/ethnicity showed a more varied pattern. High perceived violence was associated with lower F+V consumption among White and Asian/Native Hawaiian/Other Pacific Islander (ANHOPI) groups (p < 0.01). Inadequate park access and walking as the primary mode of transportation to the grocery store were associated with higher soda consumption among the ANHOPI group only (p < 0.05). Study findings suggest that current and future chronic disease prevention efforts should consider how social and psychological dynamics of communities influence dietary behaviors, especially among racially/ethnically diverse groups in urban settings. Intervention design and implementation planning could benefit from and be optimized based on these considerations.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Unmet health needs and associated factors among 1727 rural community-dwelling older adults: A cross-sectional study

AIM

To investigate the needed, unneeded and unmet health care services among rural community-dwelling older adults in China and examine the influencing factors, aiming to facilitate the global development of the home care system for a healthier world.

METHODS

This cross-sectional study investigated rural areas in three provinces in Western China. A total of 1727 rural community-dwelling older adults were enrolled. The needed, unneeded and unmet health care services were assessed by the Supply and Demand of Health Care Services (SD-HCS) questionnaire for older adults.

RESULTS

Respect (73%, 1265/1727) was the most needed. The other top 9 needed mainly belonged to the divisions of health monitoring and information regarding chronic diseases. Re-employment or part-time jobs (71%, 1230/1727) was the most unneeded. All five protection and safety items were the most unmet but needed. Religion was the main influencing factor of those health care services that were less unmet but needed.

CONCLUSIONS

Respect was basic for older adults, and chronic disease management was in great demand among rural community-dwelling older adults. Due to low willingness and the crisis workforce, a more flexible retirement policy is needed in rural China. It is urgent to improve the emergency care system in rural areas. Last but not least, more research is needed to explore the association between religion and health.

Beyond the 405 and the 5: Geographic variations and factors associated with SARS-CoV-2 positivity rates in Los Angeles County

Objectives

To highlight geographic differences and the socio-structural determinants of SARS-CoV-2 test positivity within Los Angeles County (LAC).

Methods

A geographic information system was used to integrate, map, and analyze SARS-CoV-2 testing data reported by LAC DPH, and data from the American Community Survey. Structural determinants included race/ethnicity, poverty, insurance status, education, population and household density. We examined which factors were associated with positivity rates, using a 5% test positivity threshold, with spatial analysis and spatial regression.

Results

Between 1 March and 30 June 2020 there were 843,440 SARS-CoV-2 tests and 86,383 diagnoses reported, for an overall positivity rate of 10.2% within the study area. Communities with high proportions of Latino/a residents, those living below the federal poverty line and with high household densities had higher crude positivity rates. Age- adjusted diagnosis rates were significantly associated with the proportion of Latino/as, individuals living below the poverty line, population, and household density.

Conclusions

There are significant local variations in test positivity within LAC and several socio-structural determinants contribute to ongoing disparities. Public health interventions, beyond shelter in place, are needed to address and target such disparities.