Health differences between rural and non-rural Texas counties based on 2023 County Health Rankings

Abstract

Background

Place matters for health. In Texas, growing rural populations face a variety of structural, social, and economic disparities that position them for potentially worse health outcomes. The current study contributes to understanding rural health disparities in a state-specific context.

Methods

Using 2023 County Health Rankings data from the University of Wisconsin Population Health Institute, the study analyzes rural/non-rural county differences in Texas across six composite indexed domains of health outcomes (length of life, quality of life) and health factors (health behavior, clinical care, socioeconomic factors, physical environment) with a chi-square test of significance and logistic regression.

Results

Quartile ranking distributions of the six domains differed between rural and non-rural counties. Rural Texas counties were significantly more likely to fall into the bottom quartile(s) in the domains of length of life and clinical care and less likely to fall into the bottom quartile(s) in the domains of quality of life and physical environment. No differences were found in the domains of health behavior and socioeconomic factors. Findings regarding disparities in length of life and clinical care align with other studies examining disease prevalence and the unavailability of many health services in rural Texas. The lack of significant differences in other domains may relate to indicators that are not present in the dataset, given studies that find disparities relating to other underlying factors.

Conclusions

Texas County Health Rankings data show differences in health outcomes and factors between rural and non-rural counties. Limitations of findings relate to the study’s cross-sectional design and parameters of the secondary data source. Ultimately, results can help state health stakeholders, especially those in community or operational contexts with limited resources or access to more detailed health statistics, to use the CHR dataset to consider more relevant local interventions to address rural health disparities.

Background

Health disparities are health differences faced by groups of people due to individual characteristics and/or systematic social-institutional disadvantages, such as socioeconomic status, gender, race or ethnicity, and geographic location [1]. Across the United States, place-based disparities in health and well-being measures are well documented, particularly in rural populations [2, 3]. Although various health outcomes are improving nationally, trends in rural areas are progressing more slowly [4, 5]. Past research has linked this lag not only to the lack of availability of health services in rural communities [6], but also to specific environmental qualities, socioeconomic conditions, and demographic characteristics of individuals in these communities—often labeled as social determinants of health (SDOH) [7]—that shape their experiences of disease, as well as use of available services [8, 9].

A plethora of data exists to examine differences in how rural and non-rural populations fare in terms of health outcomes [10]. One such initiative, the University of Wisconsin Population Health Science Institute’s County Health Rankings (CHR) database [11], has been the source of many studies investigating place-based components of health behavior, care quality, and outcome inequity across the United States [12,13,14,15].

The primary objective of this study was to determine whether differences in CHR health outcome and health factor domains exist between rural and non-rural counties in Texas, a fast-growing, diverse state where concerning social, economic, environmental, and health disparities continue to emerge. A secondary objective was to apply a prior application of CHR data [16] in a state (vs. national) analytical context. As localized place-based interventions often have more meaningful impact on improving health disparities [17], the study's findings can help state health stakeholders, especially those in settings with limited resources or access to detailed health statistics, to use CHR data and consider relevant potential entry points to better address locality-based health needs.

Health disparities and County Health Rankings

Numerous complex factors exacerbate place-based health disparities in health status and outcomes [18]. Drawing from SDOH principles [19], the CHR dataset presents place-based indices that demonstrate how different contributing factors (across individual, environmental, and institutional levels) affect population health. Across six composite domains of health outcomes and social/environmental health factors, the CHR ranks nearly all U.S. counties based on selecting specific indicator measures from each domain and weighing them according to their determined level of importance (Table 1). The dataset also includes other variables that do not contribute to weights or rankings but add additional context and analytical capacity [20]. The purpose of the CHR is not to be a national repository of detailed health statistics, but rather to demonstrate place-based differences in health, draw attention to core problems by assessing a collection of interrelated factors and ultimately support community-oriented efforts to improve them [21]. The CHR offers health stakeholders—especially those working in rural, community, or local policy contexts with limited resources or capacity to analyze complex statistical data [22]—a consolidated and efficient way to assess various locality-based indicators of health [23]. As increased local investment in municipal social services and health infrastructure has been associated with improved health outcomes and factors as measured by the CHR, it is positioned as a relevant tool to consider entry points for local interventions that might have lasting impacts on population health [24].

Table 1 CHR domains, indicators, weighting, and data sources

National rural health disparities across domains of the CHR

National examination of CHR data shows domain-based differences between rural and non-rural areas [25,26,27]. Other work finds more general evidence for differences in rural and non-rural areas across the health outcomes (length of life, quality of life) and factors (health behavior, clinical care, socioeconomic factors, physical environment) identified in the CHR.

With respect to length of life, heightened premature mortality rates in rural areas remain persistent [28,29,30]. Premature and/or excessive death in these areas may be related to rural residents being more likely to die from heart disease, cancer, unintentional injury, chronic lower respiratory disease, and stroke than urban residents [31]. Similarly, although urban and rural death rates differed regionally during the COVID-19 pandemic, these differences underscored that rurality can exacerbate mortality disparities [32].

Quality of life has been shown in some instances to be worse in rural communities [4, 33, 34]. For example, reduced quality of life caused by chronic conditions such as obesity, diabetes, and heart disease may relate to lack of availability of amenities such as recreational facilities and grocery stores [29, 33]. Suicide rates are higher in some rural counties compared to those in urban counties [33], possibly related to the social isolation experienced by those living in rural areas, the stigmatization of mental health concerns, the accessibility of firearms, and the general lack of health services [35]. Certain groups of individuals in rural communities, such as veterans [36], migrants [14], pregnant teens [37], and very low-birth weight infants [38] have been shown to be particularly prone to quality of life disparities.

Disparities related to health factors and behaviors vary regionally and encompass a wide range of activities, indicators and drivers. Rural residents in some areas are more likely to smoke cigarettes [39] and suffer from obesity [31], but other studies show this effect in urban areas [40]. The prevalence of food deserts—areas that lack access to a variety of healthy and cost-efficient food options—may be more closely tied to an area’s poverty as opposed to rurality [41], although the intersection of those living in poverty in rural communities may be at an elevated risk of suffering from food insecurity [42], leading to numerous chronic health issues [33, 43]. National trends suggest that rural and non-rural residents tend to consume alcohol and use illicit drugs at similarly high rates, but rural young adults have higher illicit drug use than nonrural young adults [33]. Specific disparities in rural areas also emerge when conducting region- and drug-specific analyses [33, 44, 45]. Sexual behavior patterns such as sexually transmitted disease and teen birth rates often vary between rural and urban locations due to several factors including socioeconomic conditions, opioid use, and local support and education policies [46], although teen birth rates in rural counties nationwide are generally higher [47].

Regarding clinical care, provider and service availability are more clearly problematic in rural areas [1, 31, 34, 48]. In all but four US states, rural residents are less likely to be insured than urban residents [48]. Inadequate availability of family medicine practitioners [48], dental care [49], prenatal care [50, 51], and mental health providers [52, 53] have been demonstrated specifically in many rural areas. Access to and use of preventative services such as cancer screenings [31] and seasonal vaccines [54] may be less prevalent in rural communities. In turn, this may be contributing to nationally observed disparities in higher preventable emergency room visits and hospital admissions in rural as opposed to urban areas [55].

Social and economic factors contributing to rural health disparities are similarly extensive but can vary regionally. In some rural areas, individuals are less likely to go to or graduate from college, are faced with fewer employment opportunities, and experience higher rates of poverty compared to those in urban areas [1, 4]. As such, the inability to pay for care, compounded with reduced rates of insurance coverage, can create logistical blocks to healthcare access and link rural poverty to poor health outcomes [56,57,58]. Other socioeconomic factors—such as children in poverty, income inequality, or familial demographics—have been shown to have more prominent effects in rural as opposed to urban areas [1, 4, 29, 59]. Home- and community-based services, as well as long-term social supports, may be lacking in rural communities to overcome these socioeconomic challenges [60].

Finally, conditions of the physical environment differ in rural and non-rural areas and have varied effects on rural health outcomes. For example, rural areas often have lower air pollution, but they experience greater exposure to agriculture-related pollution, including poor water quality [16, 61]. Housing-, driving-, and traffic-related conditions related to health can also vary. Affordable housing options are often limited in urban areas [5], but rural areas may be faced with substandard rental options with inadequate heating or plumbing systems, leaks, or pest infestations [4]. Driving long distances to services, especially medical services, is often more prevalent in rural communities and impacts health outcomes negatively [62], although the impacts of long commute times and driving to work alone vary in rural and urban areas based on different intervening socioeconomic characteristics [63].

Demographic qualities and rural health disparities in Texas

Given its influential economy, sociodemographic diversity, and large rural population, Texas represents a relevant case study to examine growing rural health disparities in a state-based context [64]. As the 8th largest economy in the world, Texas has a labor force of over 15 million individuals, produces 9% of U.S. GDP, and accounts for over 22% of U.S. exports [65]. With the fastest-growing population of any U.S. state [65] and second-highest Hispanic population [66], Texas also has the largest rural population of any U.S. state, with around 16% (4.7 million) of its 30.5 million people living in rural areas [67].

Indicators of health outcomes are worsening in rural Texas populations [68]. Deaths from cancer, heart disease, respiratory disease, and unintentional injury are higher in rural Texas than Texas overall [69]. Relatedly, rural areas tend to show higher prevalence and severity of chronic health issues, such as heart disease [70], COPD [71], diabetes [72], and substance abuse [73].

Similarly, reduced access to and availability of clinical care in rural areas is of acute concern [74, 75]. Nearly three times more rural counties are designated as primary care shortage areas than urban counties [76], and rural hospital closures have also been increasing since 2005 [77]. Additional potential access barriers to healthcare in rural Texas may relate to SDOH factors such as higher rates of unemployment and lack of high school completion [78, 79], as well as reduced insurance coverage [77] (Fig. 1) and greater poverty [80] (Fig. 2). Figure 1 shows clusters of rural counties with higher percentages of their population lacking health insurance. Specifically, 76 counties have an uninsured rate exceeding 20%, and 60 (79%) of these counties are rural, primarily located across northern to southwestern Texas. Out of 53 counties in which over 20% of the population experiences poverty, 43 counties (81%) are rural. Whether at the level of state policy in terms of program availability (e.g., Medicaid expansion); local health systems in terms of services and offerings; or individuals in terms of their possession of insurance, money, information, education, or transportation, health-related resource constraints contribute significantly to rural health differences in Texas [81].

Fig. 1

Uninsured individuals across rural and non-rural counties in Texas. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. Health insurance data is sourced from the Census and retrieved via SimplyAnalytics accessed through an institutional license. RUCC classification is sourced from the USDA [82]

Fig. 2

Poverty distribution across rural and non-rural counties in Texas. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. Poverty data is sourced from the Census and retrieved via SimplyAnalytics accessed through an institutional license. RUCC classification is sourced from the USDA [82]

While some prior works examine CHR data in Texas [83,84,85,86,87], none emphasize rural disparities, and none have been published using recent CHR data. Ultimately, more locality-based information is needed to provide relevant population- and service-oriented interventions to prevent existing disparities from worsening [75].

Methods

Only a few studies have used recent CHR data to examine rural health disparities nationally [25, 26]. None have examined Texas specifically. As CHR data changes annually, ongoing examinations of its applications across a variety of contexts help to support its use as a tool to track, identify, and examine locality-based health issues and entry points for community intervention support [21, 23]. The current study adds to this repository on CHR data use, as well as rural health disparities, by broadly quantifying rural health disparities in Texas demonstrated in other literature, as well as by examining updated CHR data in a state setting.

Data

This study follows the approach of a prior study [16] examining rural health differences using the CHR dataset. At the time of its publication, the study [16] was one of the first to use CHR data to examine differences in health outcomes and health factors in rural and non-rural counties across the United States. However, since 2013, the CHR dataset has undergone a variety of changes [88]. These changes namely include insertion and deletion of some indicators that represent health factors and outcomes, as well as their categorization and weighting within the overall index [20]. Although many are retained consistently, indicators are evaluated annually to ensure they reflect specific components of population health that might be improved with community-oriented interventions, but also to balance considerations such as scholarly emphasis and data availability, access cost, validity, and coverage, to promote enhanced quality and relevance of the CHR over time [20].

The prior work [16] relied on comparing individual domain rankings because this comparison allowed for more specificity in determining where health differences were situated. To take the same approach, the study used 2023 data because 2024 data did not provide publicly available state-derived z-scores across each individual domain. Complete details on the 2023 indicator selection and weighting process are available on the CHR website [89].

The current study uses data from Texas counties only (n = 244; 10 counties did not receive a ranking due to insufficient data). Texas data from the 2023 dataset show a substantial improvement in availability from prior studies, in which up to 31 counties were missing [16, 84]. Rankings for each domain were grouped into quartiles, such that the first quartile represented counties in Texas with the top 25% of rankings and the fourth quartile represented those with the bottom 25% of rankings. Rurality was labeled for each county using the US Department of Agriculture’s 2023 Rural–Urban County Continuum Codes (RUCC) [82]. The purpose of RUCC is to distinguish rural and non-rural counties by metro-area population size, degree of urbanization, and proximity to neighboring metro-areas. Although rurality can be difficult or contentious to define [90], RUCC is a common measure of rurality used across disciplines [91, 92], using nine categories of gradation (with 1 being the least and 9 being the most rural). Each county was also given a rural/non-rural designation based on RUCC groupings of categories 1 to 3 as non-rural and categories 4 to 9 as rural [93]. Figure 3 displays the geographic distribution of rural and non-rural counties across Texas; 66% (168) of all counties, representing about 69% of Texas’ landmass, are designated rural.

Fig. 3

Non-rural (RUCC 1 – 3) and rural (RUCC 4 – 9) counties across Texas in 2023. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. RUCC classification is sourced from the USDA [82]

Analysis approach

Following the prior work [16], two methods of analysis were used to examine differences in health outcomes and health factors in rural and non-rural Texas counties. First, a chi-squared test (with significance level set at p ≤ 0.05) assessed whether there were general differences in rural and non-rural counties across the quartile distributions of the CHR domains of length of life, quality of life, health behaviors, clinical care, social and economic factors, and physical environment. Second, logistic regression assessed the likelihood of a county being rural given its quartile ranking in each CHR domain. Quartile rankings in each CHR domain were the independent variables and rurality (rural/non-rural) was the dependent variable. Non-ranked counties were excluded from analysis. All statistical analyses were conducted using SPSS 28 (SPSS, Chicago, Illinois). Maps showing geographic distributions of classifications and CHR domain quartile rankings were produced using ArcGIS Pro 3.2.2 (Esri, Redlands, California).

The study did not meet the definition of human subjects research per the IRB of the authors’ institution (IRB #FWA00000191); therefore there are no ethics considerations to disclose.

Results

The results revealed differences between rural and non-rural Texas counties in health outcomes and factors. Table 2 shows the percentage of quartile rankings for each domain in rural and non-rural Texas counties; Figs. 4, 5 and 6 show the geographic distribution. Figure 4 shows clear clustering of the highest rank for length of life in the areas near main cities in central, eastern and northeastern Texas, highlighted by the yellow areas on the map. Smaller clusters are also identified along the southern edge and other parts of the state. The clusters of the highest rank for quality of life are similarly distributed in the central and eastern areas near cities, as well as across the northern part of the state.

Table 2 2023 CHR domain quartiles in Texas counties by location type

Fig. 4

2023 CHR domain quartile distribution for length and quality of life across Texas. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. Quartile data is author-generated from CHR rankings [11]. RUCC classification is sourced from the USDA [82]

Figure 5 illustrates the spatial distribution of quartile ranks concerning socioeconomic and physical environments. Socioeconomic disadvantage is more pronounced in rural counties in the south and west, with smaller clusters in the eastern and northern panhandle parts of the state. Low physical environment rankings appear to be a more significant issue in rural counties across northeastern to southeastern Texas, as well as in the counties surrounding cities. Low rankings in both these domains are less apparent in central Texas. Figure 6 shows a noticeable cluster of rural counties in east Texas struggling with health behavior rankings, while the lowest ranks for clinical care are more concentrated in the south and across northwest to western Texas.

Fig. 5

2023 CHR domain quartile distribution for socioeconomic factors and physical environment across Texas. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. Quartile data is author-generated from CHR rankings [11]. RUCC classification is sourced from the USDA [82]

Fig. 6

2023 CHR domain quartile distribution for health behavior and clinical care across Texas. Map is author-generated. The Human Geography Basemap is sourced from ArcGIS Pro 3.3.2., a product of Esri. Quartile data is author-generated from CHR rankings [11]. RUCC classification is sourced from the USDA [82]

For all domains except physical environment (20.0% rural counties vs. 4.1% non-rural counties), a higher percentage of non-rural county rankings fell into the first quartile. In all domains, significant differences were found between rural and non-rural counties, with larger percentages of rural counties’ rankings falling into the fourth (worst) quartile. Specifically, 19.7% of rural counties (vs. 4.1% of non-rural counties) fell into the fourth quartile for length of life; 16.8% (vs. 7.0%) for quality of life; 20.1% (vs. 3.7%) for health behavior; 18.4% (vs. 5.3%) for clinical care; 18.4% (vs. 5.3%) for socioeconomic factors; and 12.3% (vs. 11.5%) for physical environment. Generally, an increasing gradient effect was seen for rural counties across domains, such that the percentage of counties increased in each quartile from 1 to 4; a decreasing gradient effect was seen for non-rural counties.

Table 3 shows the logistic regression results for rural counties across six domains. For length of life (estimated OR = 5.743, 95% CI 1.847, 18.845) and clinical care (estimated OR = 5.194, 95% CI 1.673, 17.052), rural counties were at significantly greater odds of being in the worst quartile. However, rural counties were at significantly decreased odds of being in the worst quartile in the domains of quality of life (estimated OR = 0.197, 95% CI 0.044, 0.797) and physical environment (estimated OR = 0.184, 95% CI 0.064, 0.495). No significant differences were found in the domains of health behavior or socioeconomic factors.

Table 3 Logistic regression results for rural location of Texas counties by 2023 CHR domain quartiles

Discussion

Overall, the results show evidence for both health outcome and health factor disparities in rural Texas counties. Quartile ranking distributions of all domains differed between rural and non-rural counties. Specifically, rural Texas counties were more likely to fall into the bottom quartiles in the length of life and clinical care domains and less likely to fall into the bottom quartiles in the quality of life and physical environment domains. No statistically significant differences were found in the health behavior and socioeconomic factor domains. Generally, with the exception of physical domain rankings, counties in the top quartiles across domains tended to cluster around major cities (i.e., Austin, Dallas, Houston) in central and eastern Texas. Counties in the lowest quartiles across domains tended to cluster in the peripheral regions, especially along the eastern Louisiana border, the southern Mexico border and into the northern panhandle.

The length of life disparity found in rural Texas is in line with both Texas [69] and national data [28,29,30]. The disparity with regards to lack of clinical care is also in line with many observable declines in rural healthcare provisioning in Texas—which include hospital closures, provider shortages, lack of Medicaid expansion, reduced social programs, and budget reductions—that various academic, medical, and policy stakeholders have identified as serious concerns [68, 94,95,96,97]. Provider shortages across various fields are a state-wide problem projected to increase through at least 2032 [74]. Rural areas are likely to suffer more from these shortages however, as more medical doctors, nurse practitioners, dentists and physician assistants continue to work in urban areas [98,99,100,101], and rural counties remain more likely to be designated as health professional shortage areas [102].

Concerningly, the significant disparity in these two domains highlights the broader connection research finds between higher mortality rates and lack of clinical care found both nationally [103] and specifically in Texas [102]. This result underscores the acute need to continue to implement and evaluate solutions that alleviate care shortages in rural areas to increase care use and reduce mortality gaps over time. Solutions attempted in Texas include offering rural medicine residency programs [68, 104], establishing community-based micro-hospitals [105] and relying on community health workers [72, 106] to deliver in-person and virtual health services to rural populations [68, 107, 108]. These solutions have demonstrated varying degrees of short-term efficacy and require further commitments to implementation and longitudinal investigation to understand their potential impact on reducing both care- and mortality-related rural disparities.

Somewhat unexpectedly, despite these clear differences in mortality and clinical care, the results do not show differences in health behaviors or sociodemographic factors, nor negative differences with respect to quality of life (related to disease or health condition). This is not wholly in line with other Texas literature, which suggests that rates of various diseases, conditions, and behavioral risk factors may be higher in rural areas [69, 109, 110]. However, this finding may be due to the composition of CHR indicators across various domains. Quality of life, for example, is derived from BRFFS data, which has shown some conflicting reporting patterns of rural residents perceiving their health better than urban residents, even when their objective measures of disease are worse [111]. Additionally, rurality itself interacts in complex manners with other physical and social characteristics (e.g., obesity, age, income, education level, social capital) to affect perceptions of health [111, 112]. Many of these factors have been shown to have effects on health outcomes and care utilization across Texas more generally, not just in rural areas [64, 70]. Additionally, outcomes related to these factors can be mediated by social and physical characteristics of rural or urban neighborhoods such as social cohesion, trust, and an orderly physical environment [64], not necessarily accounted for in the CHR scheme. Although some of these types of indicators are accounted for in the CHR scheme overall, they may be separated across domains, making it difficult to assess interacting effects. This may also help explain why the CHR’s food access indicators did not necessarily drive rural differences; in various studies of food access, a much more diverse set of indicators than included in the CHR data have been used to explore food access inequalities present in rural Texas communities [27, 113]. In general, the difficulties of assessing the interactions and effects of sociodemographic factors on health behaviors are well documented, and future work should explore ways to design studies that better test and account for their influences on one another [114], especially in state and local contexts.

Similarly, it is possible that activities or behaviors in rural Texas related to varying disease rates and the burdens of these diseases may also be associated with or exacerbated by activities or conditions not included in the CHR. Measures such as use of (non-mammography) cancer screening services [115, 116] or unnecessary ER visits [117] show disparities in rural Texas areas, but are not accounted for in the CHR. The availability of insurance and providers may contribute to reduced use of some types of preventative or screening services in rural Texas communities [118], although more investigation is needed to understand the influence of intervening demographic factors such as age and race on these types of health behaviors [119]. As such, stakeholders should take caution in interpreting quality of life, socioeconomic, and health behavior data, in which unaccounted for differences may in fact exist. They should also not neglect more apparent measurable differences in disease, treatment, and recovery statistics in rural areas that could benefit from potential solutions related to socioeconomic or behavioral interventions.

The results show positive differences with respect to the physical environment, which has not been a significant predictor of health outcomes in prior Texas work [87]. Air and water pollution components may affect urban areas of Texas more due to factors such as traffic volume, building density [120], and urban sprawl [121], although water quality issues may be more prevalent in rural areas depending on the measure used [122]. Although air and other types of pollution have been associated with manufacturing and oil refineries in Texas, such pollution is often geographically concentrated near these industries as opposed to affecting rural areas more uniformly [123, 124]. Housing shortages and affordability issues as monitored by state policy efforts are also not necessarily directly tied to urban–rural divides, but intersecting sociodemographic characteristics such as income, poverty status, migration status, age, and proximity to the border [125], again underscoring the need for additional research examining these interaction effects on health and interventions that take them into account. Although rural road traffic fatality rates are higher than urban ones [126], other research suggests long driving times to health services in rural Texas contribute to poor health outcomes [127, 128]. The CHR dataset does not include these as transit indicators, and instead includes drive time to work and driving alone to work. As these factors are more prevalent in urban areas of Texas [129, 130], potential rural differences here may not be wholly captured due to the dataset composition. Stakeholders looking to better understand the influences of rural physical environments on health behaviors and outcomes may thus need to consider focusing on studying and addressing indicators external to the CHR dataset.

Ultimately, the results highlight indicators of rural health factors and outcomes that may be of more pressing concern than others, although they also emphasize the need to expand examinations to a variety of indicators in comprehensive evaluations of care access, utilization, and outcome issues locally in rural communities. More generally, the results underscore rurality’s role in exacerbating various psychological, social, economic, and geographic risk determinants that collectively contribute to poorer health outcomes in rural populations [131].

Limitations

As in the model study [16], this study’s primary limitation relates to its reliance on secondary data, limiting the confirmation of causality and control over data selection and quality. Data is not available for every county, which might influence the reliability of results generated, as criticized in prior work [84, 132, 133]. Similarly, the indicator selection, weighting, and rankings reflect preferences and judgments in CHR’s methodology, which not only change over time, but may be disputed conceptually in terms of what ought to comprise the multidimensional nature of health [134, 135]. As previously discussed with respect to disparities in Texas, CHR is unable to capture all possible indicators related to health disparities and presents only one compiled view of health assessment. As such, a lack of differences shown in one domain does not necessarily mean that differences do not exist; instead, they may require further examination with different approaches or indicators. Nonetheless, CHR does not claim to be a source for exhaustive health data that proves causal trends; rather, it aims to provide a consolidated locality-based assessment of health that can help generate calls to action and overcome broad challenges in data availability, access, and analysis [22, 136]. Its overall methodology, which includes data compiled from a variety of nationally validated sources [89], has been recently empirically evaluated as a broadly sound framework [87] to examine healthcare inequities and their relationship to place [15]. As such, the use of CHR data does not diminish the general alignment of the study’s findings with previous research [137] on the impact of place on health, nor observations about differences appearing in rural areas that can be considered and further examined in proposals of health policies and interventions.

Conclusion

Overall, the results highlight key differences in health outcomes and factors between rural and non-rural counties in Texas. Further research, especially in county and community-based contexts, should investigate the specific local patterns of disease more directly contributing to premature death, as well as how expansion of facilities and care access could improve management of these diseases in rural areas. Stakeholders such as those from local governments, community health organizations, and hospital systems may play a role in examining these patterns and possible interventions at the local level, given their proximity to and knowledge of the populations at hand. Given that many of the most prominent disparities are clustered in the same areas, scaling solutions regionally—especially with regards to expanding clinical care—may be a viable course of action. State policy makers can note specifically differences in rural mortality rate and clinical care indicators as they consider funding and structuring policies related to care access, such as benefits provision and eligibility, insurance expansion, and health program funding. As policy intersects with locality to shape SDOH, this study highlights the need for additional, place-based policy and research to better understand the general impact of socioeconomic, behavioral, and environmental factors influencing health, especially those not captured in the CHR dataset, but documented in other health disparity research. Stakeholders from other states can also utilize the approaches from this study to reach this aim. Ultimately, greater insights into the conditions and service offerings within localities serve as an entry point to establishing solutions that better address the specific health issues of those populations and reduce health disparities in the long term.