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        <title>IMC Journal of Medical Science</title>
        <link>https://admin.imcjms.com/ </link>
        <description>Ibrahim Medical College Journal of Medical Science</description>

                        <item>
                    <title><![CDATA[County-level
determinants of mental and physical health in the United States: comparative
analysis of environmental, socioeconomic, and demographic factors]]></title>
                    <author>Fang Fang*</author><author>Lusi Li</author>                    <link> https://admin.imcjms.com/registration/journal_full_text/605 </link>
                    <pubDate>2026-06-24 10:16:58</pubDate>
                    <category>Original Article</category>
                    <comments>January 2026; Vol. 20(1):009</comments>
                    <description>
                        <![CDATA[Abstract
Background
and objectives: Mental and physical health are influenced by a complex
interplay of environmental, socioeconomic, and demographic factors, yet
county-level determinants and their differential effects on these outcomes
remain underexplored. Accordingly, this study examines the associations between
a comprehensive set of environmental, socioeconomic, and demographic factors
and county-level mental and physical health outcomes in the U.S., with
particular attention to differences in their determinants.
Materials
and Methods: Data from 2145 counties were analyzed, focusing on the
percentage of adults experiencing frequent mental distress and frequent
physical distress, defined as 14 or more days of poor health per month.
Descriptive analyses summarized outcome distributions and their associations
with county characteristics, while multivariate ordinary least squares
regressions with state-clustered standard errors identified significant
predictors across living environment, socioeconomic, and demographic domains. 
Results:
Housing quality, insufficient sleep, and access to healthy
food were significantly associated with both mental and physical distress, with
mental health particularly sensitive to housing and sleep challenges.
Educational attainment and household income were negatively associated with
both outcomes, while poverty was positively associated with distress across
domains. Demographic factors showed outcome-specific patterns: female
population share was associated with both mental and physical health,
non-Hispanic white population share was significant only for mental health,
rurality only for physical health, and older age was negatively associated for
physical but not mental distress. 
Conclusion:
These findings highlight that county-level structural,
socioeconomic, and demographic characteristics were jointly associated with
mental and physical health, with both shared and outcome-specific effects,
offering guidance for targeted public health interventions, resource
allocation, and policy development.
January 2026; Vol. 20(1):009.  DOI: https://doi.org/10.55010/imcjms.20.009
*Correspondence:Fang
Fang, California State University, Los Angeles, 5151 State University Drive,
Los Angeles, U.S.,90032. Email: ffang2@calstatela.edu.
© 2026 The Author(s). This is an open access article distributed under
the terms of the Creative
Commons Attribution License(CC BY 4.0)
 
Introduction
Mental and physical health are fundamental indicators of
population well-being and are closely linked to quality of life, productivity,
and social functioning. Research has consistently shown that poor mental health
can increase the risk of chronic physical conditions, while physical health
problems can exacerbate psychological distress, highlighting the
interdependence of these outcomes. However, health outcomes are not solely
linked at the individual level; structural and contextual factors at the
community or county level play a critical role in shaping patterns of distress.
County-level characteristics such as housing quality, access to healthcare
providers, neighborhood safety, environmental exposures, and local food
environments can create systematic variations in both mental and physical
health across populations. Similarly, socioeconomic conditions—including
educational attainment, income levels, and social support—and demographic
composition, such as age, gender, race, and rurality, are further associated
with population health, potentially amplifying or mitigating health
disparities.
Despite growing recognition of the importance of
county-level determinants, existing research has often focused on either mental
or physical health outcomes in isolation or has examined individual-level risk
factors without accounting for broader structural contexts [1, 2, 3]. As a
result, it remains unclear how environmental, socioeconomic, and demographic
factors simultaneously relate to mental and physical health at the county
level, and whether the same factors are significant for both outcomes.
Understanding these differences is critical for designing targeted public
health interventions, allocating resources efficiently, and addressing health
disparities effectively.
This study aims to fill this gap by integrating two
publicly available datasets—the County Health Rankings National Data and
county-level poverty measures from the U.S. Department of Agriculture (USDA)—to examine the associations
between county-level living environment, socioeconomic conditions, and
demographic characteristics with the prevalence of frequent mental and physical
distress among adults. Mental distress and physical distress are defined as
experiencing 14 or more days of poor mental or physical health in a month,
allowing for a standardized measure of burden across counties. By combining
descriptive analyses with multivariate regression models, we provide a
comparative assessment of the correlates of mental versus physical health,
highlighting which county-level factors are significantly associated with each
outcome and offering evidence to guide management and policy decisions.
This study is anchored in the Social Determinants of Health
(SDoH) framework, which posits that population health is shaped by the
conditions in which people live and work. Rather than viewing health as a
purely biological outcome, this framework allows us to systematically
categorize county-level predictors into three critical domains: living
environment (e.g., housing and food access), socioeconomic status (e.g., income
and education), and demographic composition (e.g., age and rurality). By
applying this framework, we can theorize how structural inequities and resource
distribution are differentially linked to mental versus physical distress
across U.S. counties.
 
Materials
and methods
Two publicly available
datasets were merged. The first dataset was the County Health Rankings National
Data, obtained from the County Health Rankings & Roadmaps website (released
January 2025) [4]. The second dataset was the County-Level Poverty Data
(released January 31, 2025), obtained from the U.S. Department of Agriculture
(USDA) Economic Research Service [5].The County Health Rankings dataset
provided county-level information for 3,159 counties across the 50 U.S. states,
including two mental health outcome measures: the percentage of adults
experiencing frequent mental distress and the percentage of adults
experiencing frequent physical distress, defined as reporting 14 or more
days of poor mental and physical health per month. Additionally,
the dataset included a comprehensive set of county-level covariates capturing
living environment conditions (such as primary care physician density, mental
health provider density, the food environment index, the share of households
with severe housing problems, the prevalence of insufficient sleep, the
proportion of long-commute workers who drive alone, and average daily
particulate matter-PM2.5 concentrations), social and economic factors
(educational attainment, median household income, and the prevalence of
inadequate social and emotional support), and demographic characteristics (the
proportion of residents aged 65 and older, female, non-Hispanic white, and
living in rural areas). To account for broader socioeconomic context at the
state level, a state-level poverty indicator from the USDA was merged into the
county-level dataset.
Analysis focuses on county-level mental and physical health
by examining two outcome measures: the percentage of adults reporting frequent
mental distress and the percentage reporting frequent physical distress. Both
measures are defined as experiencing 14 or more days of poor mental or physical
health within a month. Missing data were addressed using listwise deletion to
ensure the consistency and integrity of the multivariate analysis. From the
initial 3,159 U.S. counties, observations with missing values in any of the
primary outcomes or the 15 covariates were excluded. This process resulted in a
final analytic sample of 2,145 counties, covering approximately 70% of all U.S.
counties. The use of listwise deletion ensures that all comparative analyses
between mental and physical health outcomes are based on an identical set of
observations, thereby enhancing the internal validity of the findings.
We first conducted descriptive analyses to assess the
distribution of each outcome across counties. Histogram-based visualizations
were used to summarize key distributional features, including central tendency,
dispersion, skewness, and the presence of extreme values. These descriptive
results provide an overview of cross-county variation in mental and physical
health and inform the subsequent empirical analysis.
To assess bivariate associations, we explored how
environmental, socioeconomic, and demographic characteristics vary with levels
of each health outcome. A series of visualizations (scatter plots) was employed
to examine the conditional distributions of key covariates across different
degrees of mental and physical health burden. This exploratory analysis offers
an intuitive depiction of potential relationships between predictors and
outcomes and helps identify patterns that motivate the multivariate regression
framework.
The explanatory variables comprise a broad set of
county-level indicators capturing living environment conditions, socioeconomic
circumstances, and population structure. Living environment measures include
primary care physician supply, mental health provider supply, the food
environment index, the proportion of households facing severe housing problems,
the prevalence of insufficient sleep, the share of workers with long commutes
who drive alone, and average daily concentrations of PM2.5. Socioeconomic variables
include educational attainment, median household income, poverty, and the
prevalence of inadequate social and emotional support. Demographic controls
include the proportion of the population aged 65 and older, female,
non-Hispanic White, and residing in rural areas.
Because counties are nested within states, we estimated
multivariate ordinary least squares (OLS) models with standard errors clustered
at the state level to account for within-state correlation arising from shared
policy environments, economic conditions, and institutional contexts. Separate
regressions were estimated for mental distress and physical distress, enabling
direct comparison of coefficient magnitudes and statistical significance across
the two outcomes.
Taken together, the descriptive and regression analyses
provide a comparative assessment of the correlates of mental and physical
health at the county level, highlighting how environmental, socioeconomic, and
demographic factors are differentially associated with each outcome. All
analyses were implemented in Python 3.12.12 using Google Colab.
 
Results
A descriptive assessment of the distributions of the two
health outcome measures: the percentage of adults experiencing frequent mental
distress and the percentage of adults experiencing frequent physical distress
was conducted. The purpose of this step was to determine where county-level
observations are most concentrated and to summarize the overall dispersion of
each outcome before proceeding to multivariate analysis. The histogram of
adults reporting frequent mental distress indicates that the largest share of
counties falls within the range of approximately 18% to 19% per month,
suggesting this interval reflects the modal level of mental health burden
across counties. In contrast, the distribution of adults reporting frequent
mental distress shows the greatest concentration between 12% to 13% per month.
Taken together, these patterns indicate that, at their most commonly observed
levels, counties report marginally higher frequencies of mental distress than
physical distress.
 
 
Figure-1: Distributions of mental and physical health outcome variables across
counties
 
To examine bivariate associations, we assessed the
distributions of key covariates conditional on levels of mental and physical
health outcomes, providing insight into how county characteristics vary with
the intensity of health burden. Figure-2 presents the distributions of county-level
covariates across the share of adults reporting frequent mental distress.
Counties with higher prevalence of insufficient sleep, larger rural
populations, and weaker social and emotional support tend to exhibit more
mentally unhealthy days. In contrast, more favorable food environments, higher
educational attainment, and greater median household income are associated with
fewer days of mental distress.
 
 
Figure-2: Distributions of county-level covariates across
percentage of people experiencing frequent mental distress
 
Figure-3 illustrates the distributions of county-level
covariates across levels of frequent physical distress and reveals patterns
that closely mirror those observed for mentally unhealthy days. Counties with
higher prevalence of insufficient sleep, greater rural population shares, and
higher levels of inadequate social and emotional support tend to report more
physically unhealthy days. By contrast, more favorable food environments,
higher educational attainment, and higher median household income are
associated with fewer physically unhealthy days.
 
 
Figure-3: Distributions of county-level covariates
across percentage of people experiencing frequent physical distress
 
To assess how county-level characteristics relate to mental
and physical health, we estimated multivariate ordinary least squares (OLS)
regression models with standard errors clustered at the state level. When
examining the percentage of adults experiencing frequent mental distress,
several predictors exhibit statistically significant positive relationships.
Counties facing more severe housing challenges tend to experience higher levels
of mental distress (coefficient = 0.057, p = 0.015). Similarly, a higher
prevalence of insufficient sleep is associated with an increase in the
percentage of experiencing frequent mental distress (coefficient = 0.189, p<
0.001). Mental distress is also more pronounced in counties with larger shares
of non-Hispanic white residents (coefficient = 0.042, p < 0.001),
higher poverty rates (coefficient = 0.184, p = 0.035), and greater
proportions of female residents (coefficient = 0.103, p< 0.001).
In contrast, several socioeconomic indicators are inversely
related to the percentage of adults experiencing frequent mental distress.
Higher educational attainment is associated with lower levels of reported
mental distress (coefficient
= −0.055, p < 0.001),
as is the food environment index (coefficient = −0.263, p = 0.005).
Median household income likewise shows a negative association with mentally
unhealthy days, although the magnitude of this relationship is relatively
modest (coefficient = −0.00001, p = 0.006).
The pattern of associations changes when focusing on
physical health outcomes. Several county-level characteristics are positively
and significantly related to the share of adults reporting frequent physical
distress. Counties with more severe housing problems tend to have higher levels
of physical distress (coefficient = 0.084, p< 0.001). A higher
prevalence of insufficient sleep is likewise associated with a greater
proportion of adults experiencing frequent physical distress (coefficient =
0.148, p< 0.001). Physical distress is also more prevalent in
counties with larger female populations (coefficient = 0.043, p= 0.010),
higher poverty rates (coefficient = 0.128, p= 0.009), and a greater share
of residents living in rural areas (coefficient = 0.009, p< 0.001).
In contrast, several factors are inversely associated with
frequent physical distress. Higher levels of educational attainment are linked
to a lower percentage of adults reporting physical distress (coefficient =
−0.061, p< 0.001), and median household income shows a similar
negative relationship, although the magnitude is relatively small (coefficient
= −0.00003, p< 0.001). In addition, more favorable food environments
are associated with reduced physical distress (coefficient = −0.404, p<
0.001), as is a higher proportion of residents aged 65 and older (coefficient =
−0.049, p< 0.001).
 
Table-1: OLS
regression results for mental and physical health outcomes
 
 
The multivariate OLS models demonstrate high explanatory
power for both health outcomes. The model for frequent physical distress
achieved an of 0.795
(adjusted  = 0.793), while the
model for frequent mental distress yielded an  of 0.717 (adjusted =
0.715), indicating that the environmental, socioeconomic, and
demographic predictors account for a substantial portion of the variance in county-level
health. The F-statistics for both the physical health model ( = 344.8, p<
0.001) and the mental health model ( = 131.1, p<
0.001) were highly significant, confirming the overall
statistical validity of the models. To address potential heteroscedasticity and
within-state correlation, state-clustered standard errors were employed,
ensuring the robustness of the reported significance levels.
To ensure the statistical rigor of our multivariate models,
we conducted a formal diagnostic assessment for multicollinearity across all
predictors. The Variance Inflation Factor (VIF) analysis revealed that all
individual VIF values range from 1.231 to 3.364, well below the conservative
threshold of 5, indicating that the estimated OLS coefficients are stable and
reliable. Furthermore, a Pearson correlation matrix was examined to inspect
pairwise relationships between socioeconomic, environmental, and demographic
factors. No pairwise correlations between independent variables exceeded the
0.80 threshold. These results confirm that while the predictors are
theoretically linked under the SDoH framework, they remain statistically
distinct, allowing for a robust comparative analysis of mental and physical
health determinants.
 
Table-2: VIF results for independent variables
 
 
Discussion
Our county-level analyses reveal that environmental,
socioeconomic, and demographic factors collectively shape mental and physical
health outcomes across U.S. counties. Comparing these outcomes highlights both
shared determinants and outcome-specific significance, providing insights into
how structural and contextual factors are linked to population well-being.
Recognizing which factors are significantly associated with mental versus
physical health is essential for guiding targeted public health strategies and
management interventions.
Living environment factors, including housing quality,
sleep patterns, and access to a healthy food environment, are consistently
associated with health outcomes. Severe housing problems and insufficient sleep
are significantly associated with both mental and physical distress, though
mental health appears particularly sensitive [6,7]. Similarly, more favorable
food environments are significantly protective for both outcomes [8]. These
findings suggest that interventions to improve housing stability, promote
adequate sleep, and expand access to nutritious foods could benefit both mental
and physical health. Public health agencies could prioritize mental health
services in areas with severe housing or sleep challenges, while partnering
with community organizations and local authorities to address food access and
housing conditions.
Socioeconomic factors also demonstrate important
associations. Higher educational attainment is significantly protective for
both mental and physical health. Median household income similarly shows
significant protective effects. These results underscore the importance of
upstream interventions that address social determinants, including education
and income support programs [9, 10]. Health managers can leverage these
insights to design integrated strategies that simultaneously improve mental and
physical well-being, such as combining community education programs with social
support initiatives in high-poverty areas.
Demographic characteristics show outcome-specific
significance. Female population share is associated with both mental and
physical distress, reflecting social and caregiving roles that may increase
overall vulnerability [11, 12]. Non-Hispanic white population share is
significant only for mental health, possibly due to psychosocial stressors or
cultural factors influencing psychological well-being, while rurality is
significant only for physical health, likely because limited healthcare access
and preventive services are directly associated physical outcomes [13]. This
result about non-Hispanic white population aligns with the well-documented
framework of “deaths of despair”, which demonstrates that socioeconomically
distressed white communities in the U.S. have experienced severe declines in
mental well-being, driven by economic stagnation, erosion of social
infrastructure, and the opioid epidemic[14]. Conversely, counties with higher
shares of racial and ethnic minority populations may benefit from localized
cultural protective factors. Sociological literature suggests that
minority-concentrated communities often exhibit high levels of social cohesion,
deep-seated religious capital network structures, and robust kinship support
systems [15, 16, 17].Although aging is typically linked to increased chronic
illness, our finding that a higher share of residents aged 65 and older
correlates with reduced county-level physical distress may be explained by the
universal healthcare coverage and financial security provided by Medicare
eligibility at age 65 [18]. This trend likely reflects survivorship bias, where
the remaining elderly population represents a more resilient demographic,
alongside higher preventive care utilization patterns (e.g., wellness visits)
that facilitate more effective chronic disease management at the community
level. These patterns highlight the importance of tailoring interventions to
demographic context, such as gender-sensitive mental health programs, improved
rural healthcare access, and age-appropriate preventive services.
Overall, our findings indicate that structural,
socioeconomic, and demographic factors collectively shape health outcomes, with
some determinants linked with both mental and physical health and others
showing outcome-specific significance. Understanding these patterns can help
public health officials prioritize interventions, allocate resources
effectively, and develop programs that improve overall population health.
Second, the use of listwise deletion resulted in the exclusion of approximately
30% of U.S. counties from the initial pool due to missing values across the 15
covariates. It is possible that these excluded counties are not missing at
random, introducing selection bias. While our findings highlight significant
predictors, we cannot rule out the possibility of reverse causality or residual
confounding from unmeasured factors. Future research should further explore
causal pathways and interactions among determinants to refine management
strategies and reduce health disparities across counties; future studies using
longitudinal or panel data could better capture temporal relationships and
policy effects. Additionally, county-level analyses may mask within-county
heterogeneity, suggesting that more granular data at the neighborhood or
individual level could provide additional insights into localized health
disparities.
Our findings indicate that county-level environmental,
socioeconomic, and demographic characteristics collectively shape mental and
physical health outcomes, with some determinants influencing both outcomes and
others exhibiting outcome-specific significance. Factors in living environment
such as housing quality, sleep patterns, and access to nutritious food are
significantly associated with both mental and physical distress, while
socioeconomic resources including education and income provide protective
effects across outcomes. Demographic factors demonstrate more nuanced patterns,
with female population share associated with both outcomes, non-Hispanic white
population share significant only for mental health, rurality significant only
for physical health, and older age protective for physical but not mental
distress. These results underscore the importance of tailoring public health
interventions to local context, addressing environmental stressors, enhancing
socioeconomic support, and targeting demographic-specific vulnerabilities. By
identifying which county-level factors are significantly associated with mental
versus physical health, policymakers and public health managers can prioritize
resources, design integrated interventions, and implement strategies that
reduce health disparities and improve overall population well-being. Future
research should explore the causal pathways and interactions among these
determinants to further inform effective, context-specific management
approaches.
 
Funding
The
study was self- funded.
 
Conflict
of interest
The authors declared that they have no financial, personal, or
institutional conflicts of interest that could have influenced the preparation
or outcomes of this study.
 
Ethics approval 
The data that support the findings of this study are openly
available in County Health
Release National Data and County-level Data Sets- Poverty at https://www.countyhealthrankings.org/health-data/california/data-and-resources.
[Accessed in January 2026] and https://data.ers.usda.gov/
reports.aspx?ID=4040. [Accessed in January 2026], reference number [4, 5].
 
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Cite this article
as:
Fang F, Li L.County-level
determinants of mental and physical health in the United States: comparative
analysis of environmental, socioeconomic, and demographic factors. IMC J Med Sci. 2026; 20(1): 009.
DOI: https://doi.org/10.55010/imcjms.20.009]]>
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