Exploring Social, Environmental, and Health Correlates of Lung and Bronchus Cancer Incidence in the Houston Methodist Neal Cancer Center Catchment Area

Exploring Social, Environmental, and Health Correlates of Lung and Bronchus Cancer Incidence in the Houston Methodist Neal Cancer Center Catchment Area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Social, Environmental, and Health Correlates of Lung and Bronchus Cancer Incidence in the Houston Methodist Neal Cancer Center Catchment Area Arica Brandford, Weishan Bai, King David Oware, Monica Hernandez, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9544681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Few studies have examined intra-metropolitan variation of cancer incidence patterns within large urban regions. We characterize and evaluate community-level drivers of lung and bronchus cancer incidence within the eight-counties of the Houston Methodist Neal Cancer Center (HMNCC) catchment area. Methods County-level cancer incidence (2018–2022) was collected through U.S. Cancer Statistics. Sociodemographic, clinical, behavioral, and environmental factors were obtained from CDC PLACES (2024) and ACS 5-year data (2019–2023). GIS mapping identified the spatial patterns across eight HMNCC counties. Weighted correlations and linear regressions of log transformed incidence assessed key associations. Results The population-weighted median incidence of lung and bronchus cancer in the eight counties was 51.45 per 100,000 (IQR: 44.90–58.78), with the county-wide rate ranging from 32.5 to 77.6 per 100,000. GIS analyses identified Liberty, Galveston, and Chambers counties as areas with consistently elevated lung and bronchus cancer incidence. Several factors exhibited strong positive correlations with incidence, including obesity (r = 0.998) and poor mental health (r = 0.984), and binge drinking (r = 0.976) (all raw p ≤ 0.05). In weighted univariate regression, statistically significant modifiable risk factors included poor mental health (β = 0.102, SE = 0.013, p < 0.001), obesity (β = 0.078, SE = 0.016, p = 0.003), and binge drinking (β = 0.129, SE = 0.032, p = 0.007). Cigarette smoking (p = 0.161) was not significant likely reflecting ecological fallacy. Conclusion Across the HMNCC catchment, lung cancer incidence clusters geographically align closely with modifiable behavioral and clinical risk indicators, especially poor mental health, obesity, and binge drinking. Impact These findings highlight priority communities and population-level factors for targeted prevention, early detection, and community-engaged interventions aimed at reducing inequities in lung cancer burden. Lung cancer incidence Community-level drivers Geographic Information System (GIS) mapping Health disparities Figures Figure 1 Figure 2 Figure 3 Background Lung and bronchus cancer remains the leading cause of cancer-related mortality in the United States, accounting for more deaths than breast, colorectal, and prostate cancers combined ( 1 , 2 ). Although national lung cancer incidence and mortality rates have declined over time, these aggregate trends mask substantial geographic variation in disease burden ( 3 , 4 ). Spatial disparities in lung cancer incidence reflect the uneven distribution of social, screening, environmental and other health factors. The Houston metropolitan region, one of the largest and most diverse urban areas in the United States, exhibits marked heterogeneity in population density, socioeconomic conditions, industrial activity, environmental exposures, and access to healthcare services ( 5 ). Houston Methodist Neal Cancer Center (HMNCC) is the cancer care and research arm of Houston Methodist, providing comprehensive cancer services across multiple locations throughout the Greater Houston area. HMNCC catchment area includes eight counties: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Chambers, and Jefferson. Lung and bronchus cancer drives cancer mortality in the HMNCC catchment area, with profound geographic and racial disparities threatening health equity. The eight-county catchment area exhibits stark variations in demographics, industrial exposures, and health resource access. As such, counties within the HMNCC catchment area have been the focus of recent environmental and cancer cluster investigations with profound geographic and racial disparities threatening health equity. Geographic diversity within the HMNCC catchment area may contribute to spatial clustering of lung and bronchus cancer incidence across counties. For example, certain areas of the Houston region have a high concentration of petrochemical plants, refineries, ports, and heavy industry. Communities in East Harris County, located near major shipping channels and industrial zones, may experience a higher cumulative environmental burden compared with other counties or suburban areas. East Harris County faces a disproportionate burden, with lung and bronchus cancer incidence 17% above the Texas average as per recently published 2013–2021 data from the Texas Department of State Health Services (DSHS). While lung cancer patterns have been extensively described at national and state levels, fewer studies have examined intra-metropolitan variation of these patterns within large urban regions. County-level analyses can identify localized areas of elevated cancer burden that may be obscured in broader geographic assessments and can reveal spatial inequities relevant for targeted cancer control planning ( 6 ). Therefore, in this study we aim to characterize and evaluate community-level drivers of lung and bronchus cancer incidence within the eight-counties of the Houston Methodist Neal Cancer Center (HMNCC) catchment area. In highly heterogeneous regions such as Houston, examining lung and bronchus cancer incidence at the county level provides an opportunity to characterize intra-metropolitan spatial disparities and community-level factors associated with cancer burden. Such findings can support data-driven, geography-based interventions (e.g., prevention, screening, and resource allocation strategies) to reduce lung cancer incidence and improve outcomes among high-risk populations across the eight county Houston region ( 7 ). Methods Study Design This cross-sectional ecological study examined the geographical patterns and community-level correlates of lung and bronchus cancer incidence across the eight counties within the HMNCC area. The unit of analysis was the county. Our analysis of secondary data was conducted using de-identified, publicly available aggregated data and did not require ethics approval from the Houston Methodist Institutional Review Board (IRB). Data sources All data for this study were obtained from Cancer InFocus, a publicly available data and visualization platform developed by the University of Kentucky Markey Cancer Center ( 8 ). Cancer InFocus integrates county-level cancer incidence and mortality data with socio-demographic, behavioral, clinical, environmental, and social vulnerability indicators. Cancer InFocus cancer incidence estimates are based on the US Cancer Statistics Program (USCS) data integrating population surveillance data from the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI) ( 9 ). Social and structural vulnerability indicators are integrated from the CDC and the Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI). Socio-demographic and socio-economic indicators are taken from the American Community Survey (ACS) five-year estimates. Cancer InFocus sources behavioral and clinical health indicators from CDC Population Level Analysis and Community Estimates (CDC PLACES) data. Environmental exposure indicators are derived from the U.S. Environmental Protection Agency (EPA) EJScreen database. For this study, variables were obtained from the Cancer InFocus database containing 2024 EPA EJScreen, CDC PLACES, and ATSDR SVI data; 2019–2023 ACS 5-Year estimates; and USCS 2018–2022 county-level cancer incidence data ( 10 , 11 , 12 , 13 ). Vector-based shapefile datasets were obtained from the U.S. Census Bureau, via the U.S. government's open geospatial data portal, to display the state and county boundaries of Texas. The shapefile dataset can be directly accessed under the following link: https://catalog.data.gov/dataset/tiger-line-shapefile-2024-state-texas-tx-county-subdivision . Variables and Measures Outcome Measure The outcome of interest was the incidence of lung and bronchus cancer at the county level. The incidence rates were age-adjusted to the US 2000 population and expressed per 100,000 persons. The rates represent the mean annual incidence for the period 2018–2022. The outcome was modeled as a continuous measure in our descriptive, correlational, and regression analyses. Community-Level Characteristics The characteristics at the community level were selected a priori based on the previously cited literature on cancer disparities and the availability of data in Cancer InFocus. The variables were classified into several domains including demographic composition, socioeconomic status, behavioral risk factors, clinical health indicators, features of the built environment, and overall vulnerability indicators. All variables were aggregated at the county level. Demographic domain variables included the age structure, racial and ethnic composition, and were expressed as a percentage of the total population. Education variables were captured as percentage of the highest level of education attained. Socioeconomic domain variables included median household income, the Gini index for income inequality, percentage of the population below the federal poverty level, prevalence of lacking health insurance, and Medicaid enrollment. Behavioral and clinical domain variables included the population prevalence of cigarette smoking, obesity, physical inactivity, binge drinking, and distress in mental health, as well as the presence of asthma, chronic obstructive pulmonary disease, and coronary heart disease. Measures of the built environment domain captured urbanicity and access to transportation. Social vulnerability was examined through the socioeconomic status component and the overall composite score of the Social Vulnerability Index. Table 1 provides the variables included in the descriptive summaries. A more extensive range of community-level variables were used in the initial exploratory analyses (not shown), but for the descriptive, correlational, or regression analyses, only the specified variables are presented. Table 1 Population Descriptive Statistics for Lung and Bronchus Cancer Incidence and Key Sociodemographic, Behavioral, and Environmental Predictors (n = 8 counties) Domain Variable Median [IQR] Minimum Maximum Cancer Outcome Lung & Bronchus Cancer Incidence (per 100,000, age-adjusted) 51.45 [44.90–58.78] 32.46 77.62 Demographic – Age Group % Population Under 18 Years 26.11 [25.25–27.22] 24.00 29.00 % Population Aged 18–64 Years 60.61 [60.05–61.24] 59.00 62.00 % Population Aged ≥ 65 Years 12.59 [12.06–14.33] 11.00 15.00 Demographic – Race/Ethnicity % Non-Hispanic White Population 47.36 [33.59–57.58] 27.38 63.10 % Non-Hispanic Black Population 13.78 [7.60–19.49] 5.67 32.89 % Hispanic Population 26.49 [24.85–34.15] 23.53 43.41 % Asian Population 3.52 [2.34–7.19] 0.53 21.65 % American Indian/Alaska Native Population 0.13 [0.10–0.15] 0.10 0.16 % Native Hawaiian/Pacific Islander Population 0.04 [0.02–0.06] 0.00 0.07 % Other Race or Multiracial Population 3.00 [2.67–3.61] 2.25 3.80 Education % Adults with < 9th Grade Education 5.36 [4.40–8.39] 4.00 10.00 % Adults with High School Diploma or Equivalent 84.56 [79.17–85.49] 71.00 89.00 % Adults with Some College or Associate Degree 32.92 [21.21–36.08] 11.00 49.00 % Adults with Bachelor’s or Higher Degree 11.68 [5.90–13.02] 3.00 20.00 Socioeconomic Indicators Median Household Income (USD) 90251.50 [68938.50–102690.00] 59,934.00 113,409.00 Gini Index of Income Inequality 0.45 [0.42–0.47] 0.38 0.50 % Population Below Federal Poverty Level 9.39 [6.50–13.77] 6.0 15.0 % Adults Aged 18–64 Without Health Insurance 15.50 [13.29–21.23] 12.0 26.0 % Population Enrolled in Medicaid 13.07 [11.73–18.75] 10.0 20.0 Behavioral and Clinical Risk Factors % Adults Who Currently Smoke Cigarettes 15.25 [13.45–17.40] 11.20 21.10 % Adults with Obesity (BMI ≥ 30 kg/m²) 38.55 [35.50–39.90] 33.70 40.20 % Adults Physically Inactive 26.20 [24.65–31.15] 22.50 34.80 % Adults Reporting Binge Drinking 17.90 [16.85–18.50] 15.20 20.10 % Adults Reporting ≥ 14 Days of Poor Mental Health per Month 22.15 [20.80–23.45] 18.00 26.10 % Adults with Asthma Diagnosis 9.80 [9.10–10.15] 8.60 11.00 % Adults with COPD 6.70 [6.05–8.05] 4.60 9.40 % Adults Ever Diagnosed with Coronary Heart Disease 6.50 [6.00–7.05] 5.10 7.80 Built Environment and Access % Households Without Reliable Transportation 8.25 [7.80–11.80] 7.70 12.20 % Population Living in Urban Areas 83.78 [61.51–95.57] 23.0 99.0 Summary Indices Social Vulnerability Index – Socioeconomic Status 0.63 [0.45–0.95] 0.38 0.98 Overall Social Vulnerability Index 0.69 [0.50–0.95] 0.45 0.97 Notes: Summaries show weighted medians with interquartile ranges across counties. Cancer incidence rates are age-adjusted per 100 000 population. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates. GIS-Based Data Processing and Mapping To better understand the spatial information of incident lung and bronchus cancer cases, several GIS-Based tools were utilized in this study for data processing and mapping. Specifically, the cancer incidence, community characteristics, and geographic information data were processed by using Geopandas and Matplotlib in Python, including coordinate transformation, layer overlay, and visualization. The ArcGIS Pro software was also used to create a series of choropleth maps, including adding legends and scale bars. With the help of those GIS-based tools, the data processing steps enable spatial consistency across datasets with different formats and coordinate systems, enabling accurate spatial overlay and analysis. The integration of Python-based scripting and ArcGIS Pro facilitated both reproducible geospatial workflows and high-quality cartographic representations. The resulting maps were used to visualize the spatial distribution of cancer incidence and explore its potential associations with demographic and socioeconomic variables. Statistical Analyses Descriptive statistics were initially calculated to obtain county-level cancer incidence and community characteristics. Due to the data being aggregated and the small number of counties, median and interquartile range (IQR), along with minimum and maximum values, were used to describe the variability across counties. These summaries are shown in Table 1 . Spearman rank-order correlation coefficients were used to assess the strength and direction of the association between community-level variables and the incidence of lung and bronchus cancers. During exploration, correlations were calculated for a broad set of 80 + candidate variables. Due to the nature of the analysis, Holm's correction method was used to adjust the p-values for the number of correlations calculated, and then the reported correlations were ranked to obtain the top 10 positive and negative correlations. These associations are shown in Tables 2 A and 2 B. To present the spatial patterns of lung and bronchus cancer incidence within the study area and explore their geographical associations with community characteristics, this study created three sets of choropleth maps at the county level (Figs. 1 – 3 ). These maps not only help identify spatial variations in incidence rates but also reveal the visual correspondence between high-risk areas and potential influencing factors. All maps use standardized color gradients and scales to ensure comparability between different maps. By visually displaying the spatial patterns of these key variables, this study provides crucial geographical information support for subsequent spatial modeling analysis and targeted public health interventions. To further evaluate the independent relationship between selected community-level factors and cancer incidence, univariate linear regression models were fitted with age-adjusted log-transformed lung and bronchus cancer incidence. Each model evaluates one explanatory variable. The regression coefficients, standard errors, t-statistics, p-values, and coefficients of determination (R-squared) are given in Table 3 . Statistical analysis was performed using R (version 4.5.1; the R Core Team, Vienna, Austria) Foundation for Statistical Computing. All statistical significance was evaluated by p < 0.05. Table 3 Weighted Univariate Linear Regressions of Log-Transformed Lung & Bronchus Cancer Incidence on Key Sociodemographic, Behavioral, and Environmental Predictors (n = 8 counties) Domain Predictor Variable β (Estimate) SE (β) Standardized β p-value R² Demographic – Age Group % Population Under 18 Years -7.446 7.913 -0.486 0.383 0.129 % Population Aged ≥ 65 Years 7.302 4.663 0.409 0.168 0.290 % Population Aged 18–64 Years -6.475 6.407 -0.280 0.351 0.145 % American Indian/Alaska Native Population 6.700 2.438 0.663 0.033 0.557 % Asian Population -0.024 0.007 -0.621 0.017 0.643 % Non-Hispanic White Population 0.009 0.004 0.496 0.060 0.472 % Non-Hispanic Black Population -0.014 0.011 -0.488 0.247 0.215 % Other Race or Multiracial Population -0.191 0.183 -0.415 0.338 0.153 % Native Hawaiian/Pacific Islander Population -4.051 4.896 -0.370 0.440 0.102 % Hispanic Population -0.002 0.008 -0.068 0.754 0.018 Education % Adults with Bachelor’s or Higher Degree -4.238 1.233 -0.872 0.014 0.663 % Adults with Some College or Associate Degree -1.727 0.659 -0.789 0.040 0.534 % Adults with High School Diploma or Equivalent -0.873 1.562 -0.192 0.596 0.050 % Adults with < 9th Grade Education -0.773 2.361 -0.072 0.754 0.018 Socioeconomic Indicators Median Household Income (USD) -0.000 0.000 -0.221 0.504 0.077 Gini Index of Income Inequality (0 = Equal, 1 = Unequal) -0.435 2.037 -0.063 0.838 0.008 % Adults Aged 18–64 Without Health Insurance 0.321 1.647 0.061 0.852 0.006 % Population Below Federal Poverty Level 0.360 2.092 0.054 0.869 0.005 % Population Enrolled in Medicaid 0.231 1.879 0.034 0.906 0.003 Behavioral and Clinical Risk Factors % Adults Reporting ≥ 14 Days of Poor Mental Health per Month 0.102 0.013 0.966 < 0.001 0.910 % Adults with Asthma Diagnosis 0.288 0.078 0.858 0.010 0.692 % Adults with COPD (Chronic Obstructive Pulmonary Disease) 0.130 0.047 0.790 0.032 0.561 % Adults with Obesity (BMI ≥ 30 kg/m²) 0.078 0.016 0.757 0.003 0.790 % Adults Reporting Binge Drinking 0.129 0.032 0.743 0.007 0.731 % Adults Ever Diagnosed with Coronary Heart Disease 0.202 0.082 0.668 0.049 0.502 % Adults Who Currently Smoke Cigarettes 0.047 0.029 0.567 0.161 0.298 % Adults Physically Inactive (no leisure-time activity) 0.021 0.023 0.358 0.388 0.126 Built Environment and Access % Population Living in Urban Areas -0.859 0.406 -0.907 0.079 0.428 % Households Without Reliable Transportation -0.006 0.034 -0.045 0.872 0.005 Summary Indices Overall Social Vulnerability Index (Composite Score) 0.131 0.340 0.112 0.713 0.024 Social Vulnerability Index – Socioeconomic Status Component 0.038 0.264 0.036 0.892 0.003 Outcome: log(age-adjusted incidence per 100,000). Models weighted by county population. SE: Standard error. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates. Results Population Characteristics Table 1 presents the descriptive characteristics for the eight-county HMNCC. The median incidence of lung and bronchus cancer was 51.45 per 100,000 [IQR: 44.90–58.78], with county-specific incidences ranging from 32.46 to 77.62 per 100,000. Adults between the ages of 18 and 64 (60.61%, 60.05–61.24) dominated the age structure, followed by those under 18 (26.11%, IQR: 25.25–27.22), and over 65 (12.59%, IQR: 12.06–14.33). The racial and ethnic make-up of the population was diverse, with non-Hispanic whites accounting for 47.36% (IQR: 33.59–57.58), Hispanics accounting for 26.49% (IQR: 24.85–34.15), and non-Hispanic blacks accounting for 13.78% (IQR: 7.60-19.49). High school diploma or equivalent was the predominant educational attainment (84.56%, IQR: 79.17–85.49), whereas the median of bachelor's degree or higher attainment was 11.68% (IQR: 5.90-13.02). The socioeconomic indicators revealed a median Gini coefficient of 0.45 (IQR: 0.42–0.47) and a median household income of USD 90,251.50 (IQR: USD 68,938.50–102,690.00). The catchment area was characterized by moderate prevalence rates for selected clinical conditions and behavioral risk factors, including obesity (38.55%; IQR: 35.50–39.90), asthma (9.80%, IQR: 9.10–10.15%), physical inactivity (26.20%, IQR: 24.65–31.15), and cigarette smoking (15.25%; IQR, 13.45–17.40). Most residents resided within urban counties (83.78%; IQR, 61.51 to 95.57). Correlation Analysis Table 2 shows the Spearman population-weighted correlation between the incidence of lung and bronchus cancer and community characteristics. Several factors were positively correlated with the incidence of lung cancer (Table 2 A). The correlation between adult obesity prevalence (ρ = 0.998, Holm-adjusted p < 0.001) and frequent reporting of poor mental health (ρ = 0.984, Holm-adjusted p < 0.001) was strongest, followed by adult binge drinking (ρ = 0.976, Holm-adjusted p < 0.001). These correlations remained statistically significant after adjustment for multiple testing by Holm's method. Additional positive correlations were observed for asthma (ρ = 0.893), COPD (ρ = 0.7214), and coronary artery disease (ρ = 0.688), but these associations did not retain statistical significance after adjustment. On the other hand, some population characteristics were negatively correlated with the incidence of lung and bronchus cancer (Table 2 B). Residents under 18 years of age (ρ = −0.839), non-Hispanic blacks (ρ = −0.767) and Asians (ρ = −0.723) were strong but negatively correlated with the incidence of the disease. These negative correlations did not remain statistically significant after Holm-adjustment for the correction of multiple testing. Table 2 A. Top 10 Positive Weighted Spearman Correlations with Lung and Bronchus Cancer Incidence Predictor Variable Weighted Spearman ρ Raw p-value Holm-Adjusted p % Adults with Obesity (BMI ≥ 30 kg/m²) 0.9981 0.0000 0.0000 % Adults Reporting ≥ 14 Days of Poor Mental Health per Month 0.9838 0.0000 0.0008 % Adults Reporting Binge Drinking 0.9758 0.0000 0.0027 % Adults with Asthma Diagnosis 0.8925 0.0029 0.2206 % Housing Units Lacking Complete Plumbing Facilities 0.7296 0.0399 1.0000 % Adults with COPD 0.7214 0.0434 1.0000 % Adults with Hearing Disability 0.7124 0.0474 1.0000 % Adults Ever Diagnosed with Coronary Heart Disease 0.6883 0.0591 1.0000 % American Indian / Alaska Native Population 0.6541 0.0785 1.0000 % Adults Ever Diagnosed with Any Cancer 0.5156 0.1910 1.0000 Notes: All correlations weighted by county population (n = 8). Significance determined using Holm-adjusted p < 0.05. ρ = Spearman correlation coefficient. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates. Table 2 B. Top 10 Negative Weighted Spearman Correlations with Lung and Bronchus Cancer Incidence Predictor Variable Weighted Spearman ρ Raw p-value Holm-Adjusted p % Population Under 18 Years -0.8393 0.0092 0.696 % Non-Hispanic Black Population -0.7669 0.0264 1.000 % Asian Population -0.7230 0.0427 1.000 % Adults with Bachelor’s or Higher Degree -0.6569 0.0768 1.000 Broadband Access 100–1000 Mbps (% Households) -0.6555 0.0776 1.000 Broadband Access 20–100 Mbps (% Households) -0.5608 0.1482 1.000 Annual Labor Force Participation Rate (%) -0.5173 0.1892 1.000 Social Vulnerability Index – Minority & Language Domain -0.5123 0.1942 1.000 5G Network Availability (3–35 Mbps) -0.5107 0.1959 1.000 % Population Aged 18–64 Years -0.4323 0.2848 1.000 Notes: All correlations weighted by county population (n = 8). Significance determined using Holm-adjusted p < 0.05. ρ = Spearman correlation coefficient. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates. GIS Mapping and Spatial Patterns Three sets of choropleth maps are presented at the county level (Figs. 1 – 3 ). Figure 1 shows the age-adjusted cancer incidence rates in eight counties within the HMNCC catchment area from 2018 to 2022, providing a spatial context for the overall trends. Figure 1 illustrates the spatial distribution of age-adjusted cancer incidence rates, with Liberty County showing the highest rate (77.6 per 100,000) and Fort Bend County at the lowest (32.5 per 100,000). Notably, a distinct concentration of higher incidence appears in the eastern counties, suggesting possible spatial clustering. Figures 2 and 3 further illustrate the county-level spatial distribution of variables identified in the correlation analysis as the strongest positive and negative correlates of lung and bronchus cancer incidence. Figure 2 maps the three strongest positive correlates, obesity, poor mental health, and binge drinking, all of which remained statistically supported in subsequent weighted univariate regression analyses. These maps show that counties with relatively elevated lung and bronchus cancer incidence, particularly in the eastern portion of the HMNCC catchment area (Fig. 1 ), also tended to have higher levels of these modifiable behavioral and health-related factors. In contrast, Fig. 3 displays the three strongest negative correlates, the proportion of the population under 18 years old, the proportion of non-Hispanic Black residents, and the proportion of Asian residents. Although these variables showed inverse patterns in the exploratory correlation analysis, they did not remain statistically significant after Holm adjustment, and their evidence was less consistent in subsequent regression analyses. Therefore, Fig. 3 should be interpreted primarily as illustrating geographic variation in demographic composition rather than stable inverse predictors of lung and bronchus cancer incidence. Overall, these figures provide descriptive spatial context for the observed associations rather than independent evidence of effect. Univariate Linear Regression Table 3 displays a weighted univariate regression analysis of log-transformed lung and bronchus cancer incidence and community characteristics. Several socio-demographic and health indicators showed significant associations with incidence at the county level. For example, obesity (β = 0.078, SE = 0.016; p = 0.003), binge drinking (β = 0.129, SE = 0.032; p = 0.007), asthma (β = 0.288; SE = 0.078; p = 0.010), COPD (β = 0.130; SE = 0.047; p = 0.032) and coronary heart disease (β = 0.202; SE = 0.082; p = 0.049) were associated with increased incidence of lung and bronchus cancer. Among the behavioral and clinical risk factors, a significant positive association was found for poor mental health status (β = 0.102, SE = 0.013, p < 0.001). On the other hand, educational attainment at any level was associated with reduced lung and bronchus cancer incidence. However, some college or associate degree (β = -1.727; SE = 0.659; p = 0.040) and those with a bachelor's degree or higher (β = -4.238, SE = 1.233, p = 0.014) showed significant negative associations. Race- and ethnic-specific associations varied. Pertaining to the Asian American population was associated with a significantly reduced incidence of the disease (β = -0.024, SE = 0.007, p = 0.017), whereas pertaining to the Indian American population showed a strong positive association (β = 6.700, SE = 2.438, p = 0.033). Discussion & Conclusions This study identified a significant association between the incidence of lung and bronchus cancer and modifiable behavioral and clinical risk factors (i.e., poor mental health, obesity and binge drinking). Notably, some factors widely confirmed in other studies to be highly correlated with lung and bronchus cancer incidence showed no significant correlation in the regression analysis conducted in this study, such as smoking cigarettes (p = 0.161). This may be an ecological fallacy, where aggregate-level information is incorrectly generalized to another aggregate level, potentially leading to inapplicable inferences ( 14 ). In the highly industrialized context of the Houston metropolitan area, smoking behavior may be highly spatially correlated with other factors in the study, such as income, age, social vulnerability, and binge drinking. This multicollinearity may reduce the statistical power of smoking as an independent risk factor in small-sample analyses. Furthermore, the study found a significant association between poor mental health and the incidence of lung and bronchus cancer. This suggests that lung and bronchus cancer incidence may have deeper social determinants. For example, people living in communities in eastern Harris County near major waterways and industrial areas may bear a higher cumulative environmental burden than those in other counties or suburbs. These factors can potentially exacerbate the psychological stress of residents. From the perspective of syndemic theory ( 15 ), residents living near the Houston metropolitan area are exposed to environmental stressors and economic instability, which interact with unhealthy behaviors such as alcohol abuse, thereby accelerating disease progression. This finding suggests that lung and bronchus cancer control strategies must move beyond traditional clinical screening models, integrating psychosocial support into interventions and addressing the underlying structural inequalities that drive spatial unfairness. This study used GIS technology for spatial analysis and mapping to illustrate the spatial distribution of cancer incidence and related factors. However, the analysis is limited by the spatial resolution of the available data and the sample size. The study was conducted at the county level, using only eight counties as the study area. This large scale and small sample size does not provide sufficient statistical power for robust spatial econometric models. This makes it unsuitable to use formal spatial autocorrelation indicators such as Moran's I to further explore the spatial relationship between cancer incidence and related factors ( 16 ). This limitation points to directions for future research: disaggregating data from the county level to census tracts (or even finer geographical scales) will allow the application of models such as geographically weighted regression to precisely quantify the impact of various factors on lung and bronchus cancer incidence ( 17 ). This refined analysis is crucial for identifying cancer clusters at a small scale and will provide an empirical basis for HMNCC to implement precise, data-driven prevention and screening programs in the catchment area. Declarations CRediT Author Contributions Arica Brandford: Conceptualization, Methodology, Investigation, Writing - original draft, Writing - reviewing and editing. Weishan Bai: Conceptualization, Methodology, Data curation, Formal analysis, Visualization, Writing – original draft. King David Oware : Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Resources, Writing- original draft. Monica Hernandez : Investigation, Data curation, Validation, Writing- reviewing and editing. Jennifer Cullen : Writing - reviewing and editing. Nestor Esnaola : Writing- reviewing and editing. Xinyue Ye : Conceptualization, Writing - reviewing and editing, Supervision. Gang Han : Conceptualization, Methodology, Data curation, Formal analysis, Writing - reviewing and editing, Supervision. Acknowledgements We would like to thank Preeti Sohoni for her contributions to this manuscript. Conflicts of Interes t No conflicts of interest are reported for this study. Data Availability Statement The authors confirm that the data supporting the findings of this study are available within the article. Additional requests for variable datasets and statistical analyses used in this study may be sent to Monica Hernandez at [email protected] . Funding Declaration The authors declare that no financial support or funding was received for the research, authorship, and/or publication of this manuscript. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):17–48. Alberg AJ, Brock MV, Samet JM. Epidemiology of lung cancer. Chest. 2013;143(5 Suppl):eS1–29. Henley SJ, Thomas CC, Lewis DR et al. Annual report to the nation on the status of cancer. J Natl Cancer Inst. 2020;112(12):1227–1239. Turner MC, Krewski D, Diver WR, et al. Ambient air pollution and cancer mortality. Environ Health Perspect. 2017;125(8):087013. Cogliano VJ, Baan R, Straif K, et al. Preventable exposures associated with human cancers. J Natl Cancer Inst. 2011;103(24):1827–39. Pickle LW, Feuer EJ, Edwards BK. Geographic patterns of cancer incidence. Am J Prev Med. 2002;22(2):75–81. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose CT screening. N Engl J Med. 2011;365(5):395–409. Burus JT, Park L, McAfee CR, Wilhite NP, Hull PC. Cancer InFocus: tools for cancer center catchment area geographic data collection and visualization. Cancer Epidemiol Biomarkers Prev. 2023;32(7):OF1–5. 10.1158/1055-9965.EPI-22-1319 . U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool [Internet]. Atlanta (GA): Centers for Disease Control and Prevention, National Cancer Institute., 2022. Available from: https://www.cdc.gov/united-states-cancer-statistics/dataviz/index.html U.S. Environmental Protection Agency. EJScreen technical documentation [Internet]. Washington (DC): U.S. Environmental Protection Agency. 2024. Available from: https://www.epa.gov/ejscreen/ejscreen-technical-documentation U.S. Census Bureau. American Community Survey 5-Year Estimates, 2019–2023 [Internet]. Washington (DC): U.S. Census Bureau. 2023. Available from: https://data.census.gov/ Centers for Disease Control and Prevention. PLACES: Local data for better health [Internet]. Atlanta (GA): Centers for Disease Control and Prevention. 2024. Available from: https://www.cdc.gov/places Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry. Social Vulnerability Index [Internet]. Atlanta (GA): Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry. 2024. Available from: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html Piantadosi S, Byar DP, Green SB. The ecological fallacy. Am J Epidemiol. 1988;127(5):893–904. Tsai AC. Syndemics: a theory in search of data or data in search of a theory? Soc Sci Med. 2018;206:117–22. Goovaerts P, Jacquez GM. Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models. J Geogr Syst. 2005;7(1):137–59. Gilbert A, Chakraborty J. Using geographically weighted regression for environmental justice analysis: cumulative cancer risks from air toxics in Florida. Soc Sci Res. 2011;40(1):273–86. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 May, 2026 Editor assigned by journal 29 Apr, 2026 Submission checks completed at journal 29 Apr, 2026 First submitted to journal 27 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9544681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636255881,"identity":"f4c4c33a-e470-486d-9fa6-93430f69b874","order_by":0,"name":"Arica Brandford","email":"","orcid":"","institution":"Houston Methodist Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Arica","middleName":"","lastName":"Brandford","suffix":""},{"id":636255882,"identity":"477c42cb-fde5-484c-9f0e-71a45362c615","order_by":1,"name":"Weishan Bai","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Weishan","middleName":"","lastName":"Bai","suffix":""},{"id":636255883,"identity":"f6269bee-97fe-46b3-b535-5c6f2fbe4df6","order_by":2,"name":"King David Oware","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"King","middleName":"David","lastName":"Oware","suffix":""},{"id":636255884,"identity":"a1c9216a-3986-4ea2-96f6-e19adc6984f0","order_by":3,"name":"Monica Hernandez","email":"data:image/png;base64,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","orcid":"","institution":"Houston Methodist Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Monica","middleName":"","lastName":"Hernandez","suffix":""},{"id":636255885,"identity":"0f7e7bbf-bb3f-4bc6-b751-67b69a850dec","order_by":4,"name":"Jennifer Cullen","email":"","orcid":"","institution":"The Methodist Hospital Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Cullen","suffix":""},{"id":636255886,"identity":"1430997e-41f0-4306-9401-425f35b8505f","order_by":5,"name":"Nestor Esnaola","email":"","orcid":"","institution":"Houston Methodist Academic Institute","correspondingAuthor":false,"prefix":"","firstName":"Nestor","middleName":"","lastName":"Esnaola","suffix":""},{"id":636255887,"identity":"9175f8bf-f80e-407b-887c-c4ff1edf8a81","order_by":6,"name":"Xinyue Ye","email":"","orcid":"","institution":"The University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Ye","suffix":""},{"id":636255888,"identity":"7fd7d88c-e3df-4a46-9f07-8279475f7efa","order_by":7,"name":"Gang Han","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2026-04-27 17:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9544681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9544681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108992164,"identity":"a2554e17-1175-41c0-bb4f-cc7609960b5a","added_by":"auto","created_at":"2026-05-11 13:37:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":891393,"visible":true,"origin":"","legend":"\u003cp\u003eCounty-Level Distribution of Lung and Bronchus Cancer Incidence\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe map shows the age-adjusted incidence rates of lung and bronchus cancer per 100,000 population across eight counties within the HMNCC catchment area between 2018 and 2022. Rates range from 32.5 per 100,000 in Fort Bend County to 77.6 per 100,000 in Liberty County. Darker shades indicate higher cancer incidence. A 50 km scale bar and an inset map of Texas highlight the regional location of the study area. County names and corresponding cancer incidence values are labeled directly on the map.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9544681/v1/f1591ded6fc5ce1035b71b3f.png"},{"id":108992165,"identity":"36fd4825-5cbd-441a-8975-5b0b815798bc","added_by":"auto","created_at":"2026-05-11 13:37:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":432223,"visible":true,"origin":"","legend":"\u003cp\u003eCounty-Level Distribution of the Three Positive Correlates of Lung and Bronchus Cancer Incidence\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePanel a, b, and c respectively show the spatial distributions of obesity, poor mental health, and binge drinking among adults across the eight-county HMNCC catchment area. These three variables were identified as the most positively associated predictors of lung and bronchus cancer incidence in weighted univariate regression models. County-specific values are labeled on the map, and all maps include a consistent 50 km scale bar and color ramps standardized across counties to allow visual comparison.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9544681/v1/f68a23c05538eff79745d5e2.png"},{"id":108992166,"identity":"8b612fe9-3f9a-4d51-9f76-1018910f57b2","added_by":"auto","created_at":"2026-05-11 13:37:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373223,"visible":true,"origin":"","legend":"\u003cp\u003eCounty-Level Distribution of the Three Strongest Negative Correlates of Lung and Bronchus Cancer Incidence\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePanel a, b, and c show the spatial distribution of demographic variables that demonstrated the strongest negative associations with lung and bronchus cancer incidence across the eight-county HMNCC catchment area. County-level percentages are labeled, and a consistent 50 km scale bar is included for reference.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9544681/v1/e9cc389d251ca3f8d82dab24.png"},{"id":109081442,"identity":"4b71e2bc-7b94-4ea3-8def-d5f01ac6d028","added_by":"auto","created_at":"2026-05-12 12:18:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2101748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9544681/v1/5a5e7c7d-3e65-41f6-97d0-f424c8d2ee2b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Social, Environmental, and Health Correlates of Lung and Bronchus Cancer Incidence in the Houston Methodist Neal Cancer Center Catchment Area","fulltext":[{"header":"Background","content":"\u003cp\u003eLung and bronchus cancer remains the leading cause of cancer-related mortality in the United States, accounting for more deaths than breast, colorectal, and prostate cancers combined (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although national lung cancer incidence and mortality rates have declined over time, these aggregate trends mask substantial geographic variation in disease burden (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Spatial disparities in lung cancer incidence reflect the uneven distribution of social, screening, environmental and other health factors. The Houston metropolitan region, one of the largest and most diverse urban areas in the United States, exhibits marked heterogeneity in population density, socioeconomic conditions, industrial activity, environmental exposures, and access to healthcare services (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHouston Methodist Neal Cancer Center (HMNCC) is the cancer care and research arm of Houston Methodist, providing comprehensive cancer services across multiple locations throughout the Greater Houston area. HMNCC catchment area includes eight counties: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Chambers, and Jefferson. Lung and bronchus cancer drives cancer mortality in the HMNCC catchment area, with profound geographic and racial disparities threatening health equity. The eight-county catchment area exhibits stark variations in demographics, industrial exposures, and health resource access. As such, counties within the HMNCC catchment area have been the focus of recent environmental and cancer cluster investigations with profound geographic and racial disparities threatening health equity.\u003c/p\u003e \u003cp\u003eGeographic diversity within the HMNCC catchment area may contribute to spatial clustering of lung and bronchus cancer incidence across counties. For example, certain areas of the Houston region have a high concentration of petrochemical plants, refineries, ports, and heavy industry. Communities in East Harris County, located near major shipping channels and industrial zones, may experience a higher cumulative environmental burden compared with other counties or suburban areas. East Harris County faces a disproportionate burden, with lung and bronchus cancer incidence 17% above the Texas average as per recently published 2013\u0026ndash;2021 data from the Texas Department of State Health Services (DSHS).\u003c/p\u003e \u003cp\u003eWhile lung cancer patterns have been extensively described at national and state levels, fewer studies have examined intra-metropolitan variation of these patterns within large urban regions. County-level analyses can identify localized areas of elevated cancer burden that may be obscured in broader geographic assessments and can reveal spatial inequities relevant for targeted cancer control planning (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, in this study we aim to characterize and evaluate community-level drivers of lung and bronchus cancer incidence within the eight-counties of the Houston Methodist Neal Cancer Center (HMNCC) catchment area.\u003c/p\u003e \u003cp\u003eIn highly heterogeneous regions such as Houston, examining lung and bronchus cancer incidence at the county level provides an opportunity to characterize intra-metropolitan spatial disparities and community-level factors associated with cancer burden. Such findings can support data-driven, geography-based interventions (e.g., prevention, screening, and resource allocation strategies) to reduce lung cancer incidence and improve outcomes among high-risk populations across the eight county Houston region (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional ecological study examined the geographical patterns and community-level correlates of lung and bronchus cancer incidence across the eight counties within the HMNCC area. The unit of analysis was the county. Our analysis of secondary data was conducted using de-identified, publicly available aggregated data and did not require ethics approval from the Houston Methodist Institutional Review Board (IRB).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eAll data for this study were obtained from Cancer InFocus, a publicly available data and visualization platform developed by the University of Kentucky Markey Cancer Center (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Cancer InFocus integrates county-level cancer incidence and mortality data with socio-demographic, behavioral, clinical, environmental, and social vulnerability indicators. Cancer InFocus cancer incidence estimates are based on the US Cancer Statistics Program (USCS) data integrating population surveillance data from the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Social and structural vulnerability indicators are integrated from the CDC and the Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI). Socio-demographic and socio-economic indicators are taken from the American Community Survey (ACS) five-year estimates. Cancer InFocus sources behavioral and clinical health indicators from CDC Population Level Analysis and Community Estimates (CDC PLACES) data. Environmental exposure indicators are derived from the U.S. Environmental Protection Agency (EPA) EJScreen database.\u003c/p\u003e \u003cp\u003eFor this study, variables were obtained from the Cancer InFocus database containing 2024 EPA EJScreen, CDC PLACES, and ATSDR SVI data; 2019\u0026ndash;2023 ACS 5-Year estimates; and USCS 2018\u0026ndash;2022 county-level cancer incidence data (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Vector-based shapefile datasets were obtained from the U.S. Census Bureau, via the U.S. government's open geospatial data portal, to display the state and county boundaries of Texas. The shapefile dataset can be directly accessed under the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://catalog.data.gov/dataset/tiger-line-shapefile-2024-state-texas-tx-county-subdivision\u003c/span\u003e\u003cspan address=\"https://catalog.data.gov/dataset/tiger-line-shapefile-2024-state-texas-tx-county-subdivision\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eVariables and Measures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Measure\u003c/h2\u003e \u003cp\u003eThe outcome of interest was the incidence of lung and bronchus cancer at the county level. The incidence rates were age-adjusted to the US 2000 population and expressed per 100,000 persons. The rates represent the mean annual incidence for the period 2018\u0026ndash;2022. The outcome was modeled as a continuous measure in our descriptive, correlational, and regression analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCommunity-Level Characteristics\u003c/h3\u003e\n\u003cp\u003eThe characteristics at the community level were selected a priori based on the previously cited literature on cancer disparities and the availability of data in Cancer InFocus. The variables were classified into several domains including demographic composition, socioeconomic status, behavioral risk factors, clinical health indicators, features of the built environment, and overall vulnerability indicators. All variables were aggregated at the county level. Demographic domain variables included the age structure, racial and ethnic composition, and were expressed as a percentage of the total population. Education variables were captured as percentage of the highest level of education attained. Socioeconomic domain variables included median household income, the Gini index for income inequality, percentage of the population below the federal poverty level, prevalence of lacking health insurance, and Medicaid enrollment. Behavioral and clinical domain variables included the population prevalence of cigarette smoking, obesity, physical inactivity, binge drinking, and distress in mental health, as well as the presence of asthma, chronic obstructive pulmonary disease, and coronary heart disease. Measures of the built environment domain captured urbanicity and access to transportation. Social vulnerability was examined through the socioeconomic status component and the overall composite score of the Social Vulnerability Index.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the variables included in the descriptive summaries. A more extensive range of community-level variables were used in the initial exploratory analyses (not shown), but for the descriptive, correlational, or regression analyses, only the specified variables are presented.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation Descriptive Statistics for Lung and Bronchus Cancer Incidence and Key Sociodemographic, Behavioral, and Environmental Predictors (n\u0026thinsp;=\u0026thinsp;8 counties)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung \u0026amp; Bronchus Cancer Incidence (per 100,000, age-adjusted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.45 [44.90\u0026ndash;58.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDemographic \u0026ndash; Age Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Under 18 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.11 [25.25\u0026ndash;27.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Aged 18\u0026ndash;64 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.61 [60.05\u0026ndash;61.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Aged\u0026thinsp;\u0026ge;\u0026thinsp;65 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.59 [12.06\u0026ndash;14.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eDemographic \u0026ndash; Race/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Non-Hispanic White Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.36 [33.59\u0026ndash;57.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Non-Hispanic Black Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.78 [7.60\u0026ndash;19.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Hispanic Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.49 [24.85\u0026ndash;34.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Asian Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.52 [2.34\u0026ndash;7.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% American Indian/Alaska Native Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13 [0.10\u0026ndash;0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Native Hawaiian/Pacific Islander Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04 [0.02\u0026ndash;0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Other Race or Multiracial Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.67\u0026ndash;3.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with \u0026lt;\u0026thinsp;9th Grade Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.36 [4.40\u0026ndash;8.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with High School Diploma or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.56 [79.17\u0026ndash;85.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Some College or Associate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.92 [21.21\u0026ndash;36.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Bachelor\u0026rsquo;s or Higher Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.68 [5.90\u0026ndash;13.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSocioeconomic Indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian Household Income (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90251.50 [68938.50\u0026ndash;102690.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59,934.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113,409.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGini Index of Income Inequality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45 [0.42\u0026ndash;0.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Below Federal Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.39 [6.50\u0026ndash;13.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Aged 18\u0026ndash;64 Without Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.50 [13.29\u0026ndash;21.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Enrolled in Medicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.07 [11.73\u0026ndash;18.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eBehavioral and Clinical Risk Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Who Currently Smoke Cigarettes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.25 [13.45\u0026ndash;17.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.55 [35.50\u0026ndash;39.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Physically Inactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.20 [24.65\u0026ndash;31.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Reporting Binge Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.90 [16.85\u0026ndash;18.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Reporting\u0026thinsp;\u0026ge;\u0026thinsp;14 Days of Poor Mental Health per Month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.15 [20.80\u0026ndash;23.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Asthma Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.80 [9.10\u0026ndash;10.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with COPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70 [6.05\u0026ndash;8.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Ever Diagnosed with Coronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.50 [6.00\u0026ndash;7.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBuilt Environment and Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Households Without Reliable Transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.25 [7.80\u0026ndash;11.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Living in Urban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.78 [61.51\u0026ndash;95.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSummary Indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Vulnerability Index \u0026ndash; Socioeconomic Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63 [0.45\u0026ndash;0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Social Vulnerability Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69 [0.50\u0026ndash;0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Summaries show weighted medians with interquartile ranges across counties. Cancer incidence rates are age-adjusted per 100 000 population. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018\u0026ndash;2022; CDC PLACES; ACS 5-Year Estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGIS-Based Data Processing and Mapping\u003c/h2\u003e \u003cp\u003eTo better understand the spatial information of incident lung and bronchus cancer cases, several GIS-Based tools were utilized in this study for data processing and mapping. Specifically, the cancer incidence, community characteristics, and geographic information data were processed by using Geopandas and Matplotlib in Python, including coordinate transformation, layer overlay, and visualization. The ArcGIS Pro software was also used to create a series of choropleth maps, including adding legends and scale bars. With the help of those GIS-based tools, the data processing steps enable spatial consistency across datasets with different formats and coordinate systems, enabling accurate spatial overlay and analysis. The integration of Python-based scripting and ArcGIS Pro facilitated both reproducible geospatial workflows and high-quality cartographic representations. The resulting maps were used to visualize the spatial distribution of cancer incidence and explore its potential associations with demographic and socioeconomic variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics were initially calculated to obtain county-level cancer incidence and community characteristics. Due to the data being aggregated and the small number of counties, median and interquartile range (IQR), along with minimum and maximum values, were used to describe the variability across counties. These summaries are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSpearman rank-order correlation coefficients were used to assess the strength and direction of the association between community-level variables and the incidence of lung and bronchus cancers. During exploration, correlations were calculated for a broad set of 80\u0026thinsp;+\u0026thinsp;candidate variables. Due to the nature of the analysis, Holm's correction method was used to adjust the p-values for the number of correlations calculated, and then the reported correlations were ranked to obtain the top 10 positive and negative correlations. These associations are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eTo present the spatial patterns of lung and bronchus cancer incidence within the study area and explore their geographical associations with community characteristics, this study created three sets of choropleth maps at the county level (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These maps not only help identify spatial variations in incidence rates but also reveal the visual correspondence between high-risk areas and potential influencing factors. All maps use standardized color gradients and scales to ensure comparability between different maps. By visually displaying the spatial patterns of these key variables, this study provides crucial geographical information support for subsequent spatial modeling analysis and targeted public health interventions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the independent relationship between selected community-level factors and cancer incidence, univariate linear regression models were fitted with age-adjusted log-transformed lung and bronchus cancer incidence. Each model evaluates one explanatory variable. The regression coefficients, standard errors, t-statistics, p-values, and coefficients of determination (R-squared) are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Statistical analysis was performed using R (version 4.5.1; the R Core Team, Vienna, Austria) Foundation for Statistical Computing. All statistical significance was evaluated by p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted Univariate Linear Regressions of Log-Transformed Lung \u0026amp; Bronchus Cancer Incidence on Key Sociodemographic, Behavioral, and Environmental Predictors (n\u0026thinsp;=\u0026thinsp;8 counties)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (Estimate)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic \u0026ndash; Age Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Under 18 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Aged\u0026thinsp;\u0026ge;\u0026thinsp;65 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Aged 18\u0026ndash;64 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% American Indian/Alaska Native Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Asian Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Non-Hispanic White Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Non-Hispanic Black Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Other Race or Multiracial Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Native Hawaiian/Pacific Islander Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Hispanic Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Bachelor\u0026rsquo;s or Higher Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Some College or Associate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with High School Diploma or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with \u0026lt;\u0026thinsp;9th Grade Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic Indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian Household Income (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGini Index of Income Inequality (0\u0026thinsp;=\u0026thinsp;Equal, 1\u0026thinsp;=\u0026thinsp;Unequal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Aged 18\u0026ndash;64 Without Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Below Federal Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Enrolled in Medicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral and Clinical Risk Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Reporting\u0026thinsp;\u0026ge;\u0026thinsp;14 Days of Poor Mental Health per Month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Asthma Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with COPD (Chronic Obstructive Pulmonary Disease)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults with Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Reporting Binge Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Ever Diagnosed with Coronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Who Currently Smoke Cigarettes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Adults Physically Inactive (no leisure-time activity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt Environment and Access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Population Living in Urban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Households Without Reliable Transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary Indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Social Vulnerability Index (Composite Score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Vulnerability Index \u0026ndash; Socioeconomic Status Component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOutcome: log(age-adjusted incidence per 100,000). Models weighted by county population. SE: Standard error. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018\u0026ndash;2022; CDC PLACES; ACS 5-Year Estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive characteristics for the eight-county HMNCC. The median incidence of lung and bronchus cancer was 51.45 per 100,000 [IQR: 44.90–58.78], with county-specific incidences ranging from 32.46 to 77.62 per 100,000. Adults between the ages of 18 and 64 (60.61%, 60.05–61.24) dominated the age structure, followed by those under 18 (26.11%, IQR: 25.25–27.22), and over 65 (12.59%, IQR: 12.06–14.33). The racial and ethnic make-up of the population was diverse, with non-Hispanic whites accounting for 47.36% (IQR: 33.59–57.58), Hispanics accounting for 26.49% (IQR: 24.85–34.15), and non-Hispanic blacks accounting for 13.78% (IQR: 7.60-19.49). High school diploma or equivalent was the predominant educational attainment (84.56%, IQR: 79.17–85.49), whereas the median of bachelor's degree or higher attainment was 11.68% (IQR: 5.90-13.02). The socioeconomic indicators revealed a median Gini coefficient of 0.45 (IQR: 0.42–0.47) and a median household income of USD 90,251.50 (IQR: USD 68,938.50–102,690.00). The catchment area was characterized by moderate prevalence rates for selected clinical conditions and behavioral risk factors, including obesity (38.55%; IQR: 35.50–39.90), asthma (9.80%, IQR: 9.10–10.15%), physical inactivity (26.20%, IQR: 24.65–31.15), and cigarette smoking (15.25%; IQR, 13.45–17.40). Most residents resided within urban counties (83.78%; IQR, 61.51 to 95.57).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Spearman population-weighted correlation between the incidence of lung and bronchus cancer and community characteristics. Several factors were positively correlated with the incidence of lung cancer (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The correlation between adult obesity prevalence (ρ = 0.998, Holm-adjusted p \u0026lt; 0.001) and frequent reporting of poor mental health (ρ = 0.984, Holm-adjusted p \u0026lt; 0.001) was strongest, followed by adult binge drinking (ρ = 0.976, Holm-adjusted p \u0026lt; 0.001). These correlations remained statistically significant after adjustment for multiple testing by Holm's method. Additional positive correlations were observed for asthma (ρ = 0.893), COPD (ρ = 0.7214), and coronary artery disease (ρ = 0.688), but these associations did not retain statistical significance after adjustment. On the other hand, some population characteristics were negatively correlated with the incidence of lung and bronchus cancer (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Residents under 18 years of age (ρ = −0.839), non-Hispanic blacks (ρ = −0.767) and Asians (ρ = −0.723) were strong but negatively correlated with the incidence of the disease. These negative correlations did not remain statistically significant after Holm-adjustment for the correction of multiple testing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA. Top 10 Positive Weighted Spearman Correlations with Lung and Bronchus Cancer Incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eWeighted Spearman ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eRaw p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHolm-Adjusted p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults with Obesity (BMI ≥ 30 kg/m²)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.9981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults Reporting ≥ 14 Days of Poor Mental Health per Month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.9838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults Reporting Binge Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.9758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults with Asthma Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.8925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.2206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Housing Units Lacking Complete Plumbing Facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.7296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults with COPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.7214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults with Hearing Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.7124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults Ever Diagnosed with Coronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.6883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% American Indian / Alaska Native Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.6541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults Ever Diagnosed with Any Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.5156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: All correlations weighted by county population (n = 8). Significance determined using Holm-adjusted p \u0026lt; 0.05. ρ = Spearman correlation coefficient. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eB. Top 10 Negative Weighted Spearman Correlations with Lung and Bronchus Cancer Incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eWeighted Spearman ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eRaw p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHolm-Adjusted p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Population Under 18 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.8393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Non-Hispanic Black Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.7669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Asian Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.7230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Adults with Bachelor’s or Higher Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.6569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBroadband Access 100–1000 Mbps (% Households)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.6555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBroadband Access 20–100 Mbps (% Households)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.5608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAnnual Labor Force Participation Rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.5173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSocial Vulnerability Index – Minority \u0026amp; Language Domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.5123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5G Network Availability (3–35 Mbps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.5107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e% Population Aged 18–64 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.4323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.2848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: All correlations weighted by county population (n = 8). Significance determined using Holm-adjusted p \u0026lt; 0.05. ρ = Spearman correlation coefficient. Counties included: Harris, Fort Bend, Montgomery, Brazoria, Galveston, Liberty, Jefferson, and Chambers Counties, Texas. Sources: Cancer in Focus 2018–2022; CDC PLACES; ACS 5-Year Estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGIS Mapping and Spatial Patterns\u003c/h2\u003e \u003cp\u003eThree sets of choropleth maps are presented at the county level (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the age-adjusted cancer incidence rates in eight counties within the HMNCC catchment area from 2018 to 2022, providing a spatial context for the overall trends. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the spatial distribution of age-adjusted cancer incidence rates, with Liberty County showing the highest rate (77.6 per 100,000) and Fort Bend County at the lowest (32.5 per 100,000). Notably, a distinct concentration of higher incidence appears in the eastern counties, suggesting possible spatial clustering.\u003c/p\u003e \u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e further illustrate the county-level spatial distribution of variables identified in the correlation analysis as the strongest positive and negative correlates of lung and bronchus cancer incidence. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e maps the three strongest positive correlates, obesity, poor mental health, and binge drinking, all of which remained statistically supported in subsequent weighted univariate regression analyses. These maps show that counties with relatively elevated lung and bronchus cancer incidence, particularly in the eastern portion of the HMNCC catchment area (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), also tended to have higher levels of these modifiable behavioral and health-related factors. In contrast, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the three strongest negative correlates, the proportion of the population under 18 years old, the proportion of non-Hispanic Black residents, and the proportion of Asian residents. Although these variables showed inverse patterns in the exploratory correlation analysis, they did not remain statistically significant after Holm adjustment, and their evidence was less consistent in subsequent regression analyses. Therefore, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e should be interpreted primarily as illustrating geographic variation in demographic composition rather than stable inverse predictors of lung and bronchus cancer incidence. Overall, these figures provide descriptive spatial context for the observed associations rather than independent evidence of effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Linear Regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays a weighted univariate regression analysis of log-transformed lung and bronchus cancer incidence and community characteristics. Several socio-demographic and health indicators showed significant associations with incidence at the county level. For example, obesity (β = 0.078, SE = 0.016; p = 0.003), binge drinking (β = 0.129, SE = 0.032; p = 0.007), asthma (β = 0.288; SE = 0.078; p = 0.010), COPD (β = 0.130; SE = 0.047; p = 0.032) and coronary heart disease (β = 0.202; SE = 0.082; p = 0.049) were associated with increased incidence of lung and bronchus cancer. Among the behavioral and clinical risk factors, a significant positive association was found for poor mental health status (β = 0.102, SE = 0.013, p \u0026lt; 0.001). On the other hand, educational attainment at any level was associated with reduced lung and bronchus cancer incidence. However, some college or associate degree (β = -1.727; SE = 0.659; p = 0.040) and those with a bachelor's degree or higher (β = -4.238, SE = 1.233, p = 0.014) showed significant negative associations. Race- and ethnic-specific associations varied. Pertaining to the Asian American population was associated with a significantly reduced incidence of the disease (β = -0.024, SE = 0.007, p = 0.017), whereas pertaining to the Indian American population showed a strong positive association (β = 6.700, SE = 2.438, p = 0.033).\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion \u0026 Conclusions","content":"\u003cp\u003eThis study identified a significant association between the incidence of lung and bronchus cancer and modifiable behavioral and clinical risk factors (i.e., poor mental health, obesity and binge drinking). Notably, some factors widely confirmed in other studies to be highly correlated with lung and bronchus cancer incidence showed no significant correlation in the regression analysis conducted in this study, such as smoking cigarettes (p = 0.161). This may be an ecological fallacy, where aggregate-level information is incorrectly generalized to another aggregate level, potentially leading to inapplicable inferences (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e). In the highly industrialized context of the Houston metropolitan area, smoking behavior may be highly spatially correlated with other factors in the study, such as income, age, social vulnerability, and binge drinking. This multicollinearity may reduce the statistical power of smoking as an independent risk factor in small-sample analyses.\u003c/p\u003e\u003cp\u003eFurthermore, the study found a significant association between poor mental health and the incidence of lung and bronchus cancer. This suggests that lung and bronchus cancer incidence may have deeper social determinants. For example, people living in communities in eastern Harris County near major waterways and industrial areas may bear a higher cumulative environmental burden than those in other counties or suburbs. These factors can potentially exacerbate the psychological stress of residents. From the perspective of syndemic theory (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e), residents living near the Houston metropolitan area are exposed to environmental stressors and economic instability, which interact with unhealthy behaviors such as alcohol abuse, thereby accelerating disease progression. This finding suggests that lung and bronchus cancer control strategies must move beyond traditional clinical screening models, integrating psychosocial support into interventions and addressing the underlying structural inequalities that drive spatial unfairness.\u003c/p\u003e\u003cp\u003eThis study used GIS technology for spatial analysis and mapping to illustrate the spatial distribution of cancer incidence and related factors. However, the analysis is limited by the spatial resolution of the available data and the sample size. The study was conducted at the county level, using only eight counties as the study area. This large scale and small sample size does not provide sufficient statistical power for robust spatial econometric models. This makes it unsuitable to use formal spatial autocorrelation indicators such as Moran's I to further explore the spatial relationship between cancer incidence and related factors (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). This limitation points to directions for future research: disaggregating data from the county level to census tracts (or even finer geographical scales) will allow the application of models such as geographically weighted regression to precisely quantify the impact of various factors on lung and bronchus cancer incidence (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). This refined analysis is crucial for identifying cancer clusters at a small scale and will provide an empirical basis for HMNCC to implement precise, data-driven prevention and screening programs in the catchment area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArica Brandford:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Writing - original draft, Writing - reviewing and editing. \u003cstrong\u003eWeishan Bai:\u003c/strong\u003e Conceptualization, Methodology, Data curation, Formal analysis, Visualization, Writing \u0026ndash; original draft. \u003cstrong\u003eKing David Oware\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Resources, Writing- original draft. \u003cstrong\u003eMonica Hernandez\u003c/strong\u003e: Investigation, Data curation, Validation, Writing- reviewing and editing. \u003cstrong\u003eJennifer Cullen\u003c/strong\u003e: Writing - reviewing and editing. \u003cstrong\u003eNestor Esnaola\u003c/strong\u003e: Writing- reviewing and editing. \u003cstrong\u003eXinyue Ye\u003c/strong\u003e: Conceptualization, Writing - reviewing and editing, Supervision. \u003cstrong\u003eGang Han\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Formal analysis, Writing - reviewing and editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Preeti Sohoni for her contributions to this manuscript. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflicts of Interes\u003c/strong\u003et\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest are reported for this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article. Additional requests for variable datasets and statistical analyses used in this study may be sent to Monica Hernandez at [email protected]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial support or funding was received for the research, authorship, and/or publication of this manuscript.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberg AJ, Brock MV, Samet JM. Epidemiology of lung cancer. Chest. 2013;143(5 Suppl):eS1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenley SJ, Thomas CC, Lewis DR et al. Annual report to the nation on the status of cancer. 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Cancer InFocus: tools for cancer center catchment area geographic data collection and visualization. Cancer Epidemiol Biomarkers Prev. 2023;32(7):OF1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.EPI-22-1319\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-22-1319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool [Internet]. Atlanta (GA): Centers for Disease Control and Prevention, National Cancer Institute., 2022. 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American Community Survey 5-Year Estimates, 2019\u0026ndash;2023 [Internet]. Washington (DC): U.S. Census Bureau. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.census.gov/\u003c/span\u003e\u003cspan address=\"https://data.census.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention. PLACES: Local data for better health [Internet]. Atlanta (GA): Centers for Disease Control and Prevention. 2024. 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The ecological fallacy. Am J Epidemiol. 1988;127(5):893\u0026ndash;904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai AC. Syndemics: a theory in search of data or data in search of a theory? Soc Sci Med. 2018;206:117\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoovaerts P, Jacquez GM. Detection of temporal changes in the spatial distribution of cancer rates using local Moran\u0026rsquo;s I and geostatistically simulated spatial neutral models. J Geogr Syst. 2005;7(1):137\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert A, Chakraborty J. Using geographically weighted regression for environmental justice analysis: cumulative cancer risks from air toxics in Florida. Soc Sci Res. 2011;40(1):273\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer incidence, Community-level drivers, Geographic Information System (GIS) mapping, Health disparities","lastPublishedDoi":"10.21203/rs.3.rs-9544681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9544681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFew studies have examined intra-metropolitan variation of cancer incidence patterns within large urban regions. We characterize and evaluate community-level drivers of lung and bronchus cancer incidence within the eight-counties of the Houston Methodist Neal Cancer Center (HMNCC) catchment area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCounty-level cancer incidence (2018–2022) was collected through U.S. Cancer Statistics. Sociodemographic, clinical, behavioral, and environmental factors were obtained from CDC PLACES (2024) and ACS 5-year data (2019–2023). GIS mapping identified the spatial patterns across eight HMNCC counties. Weighted correlations and linear regressions of log transformed incidence assessed key associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population-weighted median incidence of lung and bronchus cancer in the eight counties was 51.45 per 100,000 (IQR: 44.90–58.78), with the county-wide rate ranging from 32.5 to 77.6 per 100,000. GIS analyses identified Liberty, Galveston, and Chambers counties as areas with consistently elevated lung and bronchus cancer incidence. Several factors exhibited strong positive correlations with incidence, including obesity (r = 0.998) and poor mental health (r = 0.984), and binge drinking (r = 0.976) (all raw p ≤ 0.05). In weighted univariate regression, statistically significant modifiable risk factors included poor mental health (β = 0.102, SE = 0.013, p \u0026lt; 0.001), obesity (β = 0.078, SE = 0.016, p = 0.003), and binge drinking (β = 0.129, SE = 0.032, p = 0.007). Cigarette smoking (p = 0.161) was not significant likely reflecting ecological fallacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the HMNCC catchment, lung cancer incidence clusters geographically align closely with modifiable behavioral and clinical risk indicators, especially poor mental health, obesity, and binge drinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings highlight priority communities and population-level factors for targeted prevention, early detection, and community-engaged interventions aimed at reducing inequities in lung cancer burden.\u003c/p\u003e","manuscriptTitle":"Exploring Social, Environmental, and Health Correlates of Lung and Bronchus Cancer Incidence in the Houston Methodist Neal Cancer Center Catchment Area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 13:37:21","doi":"10.21203/rs.3.rs-9544681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-04T00:16:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T11:11:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T11:11:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2026-04-27T16:59:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c965a33b-a8de-45ee-ab3e-033f4dca04ec","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"9","date":"2026-05-04T00:16:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T11:11:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T11:11:02+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T13:37:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 13:37:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9544681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9544681","identity":"rs-9544681","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}