Associating nighttime lights with population health, with consideration of environmental and socio-economic confounders

Associating nighttime lights with population health, with consideration of environmental and socio-economic confounders | 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 Associating nighttime lights with population health, with consideration of environmental and socio-economic confounders Yixi Zhang, Rong Rong, Qiaochu Xu, Bingjie Qu, Xiang Shi, Kelvin P Jordan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5102937/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2025 Read the published version in Discover Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background: The relationship between nighttime lights and human health is being discussed in a growing number of studies, with a vast majority of results supporting an association. However, the impact of potential confounding has been insufficiently addressed in existing studies, undermining the reported associations. Objective: This study aimed to assess the relationship between exposure to nighttime lights and population health outcomes in the USA with a careful adjustment of potential confounders. Methods: This ecological study analyzed county-level data from 3104 counties in the USA. Nighttime light exposure data were derived from the DMSP-OLS Nighttime Lights Time Series. The outcomes were county-level health indicators including life expectancy at birth, age-specific mortality risk, and 21 cause-specific mortality rates. Covariates included county-level data on population demographics, socio-economics, healthcare, and environmental variables. Univariate regression and multivariate regression modeling were conducted to analyze the crude and confounding-adjusted association between the exposure and outcomes. Results: After adjusting for confounders, no significant associations were found between nighttime light exposure and the population health outcomes except mortality rates from mental and substance use disorders, and transport and unintentional injuries. Substantial changes in the direction and magnitude of the association estimates were identified before and after adjusting for the potential confounders, particularly the urbanization and socio-economic factors. Conclusions: This national study provides evidence that the relationship between nighttime light exposure and population health outcomes is complex and often confounded by environmental and socio-economic factors. Policy efforts should focus on mitigating unnecessary nighttime illumination to potentially reduce associated health risks. Artificial nighttime light Health disparity Confounding effect Ecological study Epidemiology Figures Figure 1 Figure 2 Introduction Nighttime lighting has become essential to modern life, fundamentally altering living patterns and extending activities into the night[ 1 – 3 ]. Today, 80% of the global population live under artificial nighttime lights, with its prevalence growing alongside industrial and population expansion[ 4 ]. However, the extensive use of artificial nighttime lights has led to a rapid increase in nighttime light pollution[ 5 ], with studies showing an average annual increase in sky brightness of 10%, outpacing the estimated 2% annual increase in light emissions captured by satellite data[ 6 ]. Beyond light pollution, nighttime lights can disrupt circadian rhythms and sleep cycles, potentially impairing immune responses and leading to various health issues[ 7 ]. Epidemiological evidence links nighttime lights to major behavioral and health problems, such as cancer[ 8 – 10 ], sleep problem[ 11 ], depressive symptom[ 12 , 13 ], hyperactivity[ 12 ], diabetes[ 14 ], and obesity[ 15 , 16 ]. Previous studies have also demonstrated associations between exposure to nighttime lights and increased mortality risk from many causes, including common non-communicable diseases such as cardiovascular disease, respiratory system disorder, and cancer[ 17 ]. However, given the close geographical relationship between nighttime light and a number of socio-demographic and socio-economic factors—such as population size and intensity, regional gross domestic product (GDP)[ 18 , 19 ], urbanization level, and human activity[ 20 ], which are well-established population health influencers[ 21 , 22 ]—it is important to note that the study of the association between nighttime light and human health could be easily confounded by these factors. The validity and relevance of previous studies, on the potential health effects of nighttime lights on human health, are frequently compromised by insufficient control of confounding factors. To address this potential deficiency, the current study aims to examine the association between nighttime light exposure and a range of population health indicators after accounting for a relatively comprehensive list of potential confounders, including population characteristics, socio-economic status, healthcare resources, residential environment, and geographical and climatic proxies. We hypothesize that there will be no significant association between nighttime light and human health after the adjustment of confounding effects. To further validate our hypothesis, we also investigated changes in the associations before and after adjustments, and identified the important confounding factors influencing the relationship between nighttime light and human health. Methods Study Design This ecological study included 3104 (98.9%) out of the 3140 counties in the USA, with county-level data analyzed. The exposure variable was the average annual level of artificial nighttime light at the county level. The outcome variables were county-level life expectancy at birth, age-specific mortality risk and cause-specific mortality rates. Additionally, the change in life expectancy at birth over a 10-year period from 1995 to 2014 was evaluated as the secondary outcome. Population characteristics, socio-economic factors, healthcare service, residential environment, and geographical and climatic proxies at the county level were collected as the potential confounding factors. Data Collection Nighttime lights data Nighttime lights data were sourced from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) Nighttime Lights Time Series (1992–2013)[ 23 ], which were the most extended historical Nighttime lights data available that could be combined with the collected health data in 2014[ 24 ]. The cleaned-up average visible–the 6-bit quantized image data rectified to a 30-arcsecond grid (equivalent to approximately 1 km 2 )–was used in this study. A digital number (DN) with a scaled value from 0 to 63 was assigned to each pixel to represents the frequency of nighttime light occurrence in a given year for that pixel, which had been normalized across multiple satellites[ 25 ]. The processed image data were further combined with the USA part of Database of Global Administrative Areas, which featured highly detailed boundaries to county-level municipal subdivisions. Spatial analysis was conducted with the ArcToolbox feature of ArcGIS software, averaging the pixel DNs within a 30-arcsecond grid. The merged data contained information about county-level pixel-averaged nighttime light intensity in 3148 counties from 1992 to 2013. The average annual frequency of nighttime light during that period derived for each county was used in the subsequent analyses. Population health data Population health data at the county level were obtained from the Institute for Health Metrics and Evaluation (IHME)[ 26 , 27 ], including life expectancy at birth in 2014, change in life expectancy at birth from 1995 to 2014, age-specific mortality risk in 2014, and cause-specific mortality rates in 2014. Specifically, the life expectancy at birth and age-specific mortality risks were estimated using small area estimation methods, which produced annual county-level life tables. These estimates utilized de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the Census Bureau, NCHS, and the Human Mortality Database[ 28 ]. The age-specific mortality risk was categorized into five age groups (i.e., 0–5, 5–25, 25–45, 45–65, and 65–85 years). For cause-specific mortality rates, redistribution of garbage codes and small area estimation methods were used on National Vital Statistics System data to estimate annual county-level mortality rates for 21 causes of death[ 29 ]. These mutually exclusive causes were communicable, maternal, neonatal and nutritional diseases, including 1) HIV/AIDS and tuberculosis, 2) diarrhea, lower respiratory and other common infectious diseases, 3) neglected tropical diseases and malaria, 4) maternal disorders, 5) neonatal disorders, 6) nutritional deficiencies, 7) other communicable, maternal, neonatal and nutritional diseases; non-communicable diseases, including 8) neoplasms, 9) cardiovascular diseases, 10) chronic respiratory diseases, 11) cirrhosis and other chronic liver diseases, 12) digestive diseases, 13) neurological disorders, 14) mental and substance use disorders, 15) diabetes, urogenital, blood and endocrine diseases, 16) musculoskeletal disorders, 17) other non-communicable diseases; and injuries, including 18) transport injuries, 19) unintentional injuries, 20) self-harm and interpersonal violence and 21) forces of nature, war and legal intervention 26 . Covariates Factors were chosen as potential confounders based on the literature or expert option. County-level information on population demographics (population size, gender, ethnicity, age), socio-economics (unemployment rate, median household income, poverty rates, gross domestic product per capita, educational level), healthcare services (medical insurance coverage, physicians per population), residential environment (Rural-Urban Continuum Code), and geographical and climatic proxies (latitude, longitude, annual precipitation in millimeters, and temperature in degrees Celsius) were collected from the USA national official sources as potential confounding factors. Data sources and specific data files of these variables are listed in Supplementary Table 1. Statistical analysis Datasets of nighttime light, population health, and covariates were merged by county. Descriptive statistics, with county as the unit for analysis, were firstly reported on all the variables studied. The 21 death causes were also ranked based on the corresponding cause-specific mortality rate (per 100,000 population) (Supplementary Table 2). Data visualization was conducted to demonstrate the distribution of the varying level of nighttime light exposure as determined by DN values across the USA counties. Univariate linear regression analyses were initially performed to assess the bivariate association between the nighttime light exposure and each outcome variable. Next, multivariate linear regression analyses, which included the covariates, were conducted to adjust for potential confounding factors. Specifically, we used a backward selection method in multivariate regression modeling to filter out the statistically significant confounding factors while having considered other covariates. Furthermore, in examining the associations with cause-specific mortality rates, we applied the Change in Estimate (CIE) method to identify the most influential confounders by capturing the effect of each potential confounder on the association between exposure and outcomes[ 30 ]. The identified confounders varied across different causes of death. Subsequently, we progressively incorporated each of these confounders into the previous univariate regression model for each cause-specific mortality rate, documenting the changes in the effect of the exposure on each outcome. The effect modification was reflected in the magnitude and direction of the change in the coefficient estimate when adjusting for each identified confounder. After identifying the individual influential confounders, we employed a confounding assessment model that included all these confounders collectively to calculate their total influence on the associations between nighttime light exposure and cause-specific mortality rates. Within this framework, we focused our analysis, as examples, on the mortality rates of several causes of death, particularly those with the heaviest disease burden and those showing significant changes in association after adjustments. Concurrently, we reported the direct, indirect and total effects of the observed association from the intensity of nighttime lights to specific mortality rates. A two-tailed p-value < 0.001 (instead of 0.05) was considered statistically significant. This stringent criterion aims to enhance the validly of our findings by building upon earlier studies that were less thoroughly investigated and seeking more robust associations. All statistical analyses were conducted using R (version 4.3.1). Results The median annual DN value of nighttime light exposure during the study period among the 3104 counties was 5.43 (Interquartile range (IQR), (2.12, 10.44)). The geographical pattern of the distribution of nighttime lights is presented in Fig. S1 . At the county level, the descriptive statistics on the population characteristics, social-economics, healthcare service, residential environment, and geographical and climatic proxies are provided in Table 1 . The average life expectancy at birth was 77.75 years (standard deviation (SD), 2.37) for the 3104 USA counties included, and the average change of life expectancy from 1995 to 2014 was 2.26 years (SD, 0.84). The death cause with the highest average annual mortality rate was cardiovascular diseases, which accounted for 277.98 (SD, 58.73) deaths per 100,000 people (Supplementary Table 2). Table 1 County-level summary descriptive statistics in 3104 studied USA counties Mean (Standard deviation) Median (Interquartile range) Number (%) Exposure to nighttime lights Intensity, digital number 8.90 (11.16) 5.43 (2.12, 10.44) Population characteristics Size 101262 (326278) 25758 (11039, 67386) Gender, male 50.08% (2.27%) 49.61% (48.96%, 50.50%) Ethnicity, white alone 85.41% (15.96%) 92.16% (80.84%, 96.07%) Age, years 0–9 12.24% (2.13%) 12.14% (10.94%, 13.32%) 10–19 12.90% (1.69%) 12.87% (11.91%, 13.84%) 20–29 12.31% (3.30%) 11.71 (10.38%, 13.31%) 30–39 11.54% (1.65%) 11.43 (10.51%, 12.43%) 40–49 12.20% (1.48%) 12.22 (11.22%, 13.17%) 50–59 14.61% (1.66%) 14.71 (13.74%, 15.56%) 60–69 12.27% (2.46%) 12.04 (10.78%, 13.41%) 70–79 7.42% (2.01%) 7.27 (6.15%, 8.45%) 80 and over 4.51% (1.51%) 4.31 (3.50%, 5.25%) Socio-economics Unemployment rate. Age < 65 6.23% (2.26%) 6.00% (4.60%, 7.50%) Median Household Income (annual, US dollar) 47064 (11987) 45209 (38911, 52492) Poverty rate 16.84% (6.42%) 15.80% (12.10%, 20.30%) Gross domestic product per capita (annual, US dollar) 57078 (445096.8) 36447 (26732, 49378) Education level, 25 years and over Less than a high school diploma 13.41% (6.34%) 12.10% (8.80%, 17.20%) A high school diploma only 34.33% (7.17%) 34.60% (29.90%, 39.30%) Completing some college or associate degree 30.74% (5.20%) 20.60% (27.30%, 34.23%) A bachelor’s degree or higher 21.51% (9.35%) 19.20% (15.00%, 25.50%) Healthcare service Health insurance coverage, age < 65 (%) 85.59% (5.16%) 86.00% (82.30%, 89.50%) Physicians per 1,000 population 1.19 (1.59) 0.76 (0.39, 1.47) Residential environment (Rural-Urban Continuum Code) 1 (Metro areas, 1 million population or more) 424 (13.66%) 2 (Metro areas, 250 thousand to 1 million population) 376 (12.11%) 3 (Metro areas, population fewer than 250 thousand) 352 (11.34%) 4 (Urban population of 20 thousand or more, adjacent to a metro area) 212 (6.83%) 5 (Urban population of 20 thousand or more, not adjacent to a metro area) 89 (2.88%) 6 (Urban population of 2,500 to 19,999, adjacent to a metro area) 586 (18.88%) 7 (Urban population of 2,500 to 19,999, not adjacent to a metro area) 430 (13.85%) 8 (Completely rural or < 2,500 urban population, adjacent to a metro area) 219 (7.06%) 9 (Completely rural or < 2,500 urban population, not adjacent to a metro area) 416 (13.40%) Geographic and climatic proxies Longitude -91.90 -90.24 (-98.12, -83.29) Latitude 38.23 38.23 (34.41, 41.47) Annual Precipitation, millimeter 11626.7 (4529.6) 12128.0 (8301.2, 14411.7) Temperature, o C 11.8 (4.8) 11.8 (8.2, 15.5) In univariate linear regression, exposure to nighttime lights showed a significant positive association with life expectancy at birth in 2014 (Supplementary Table 3). However, no significant association was found for the change in life expectancy at birth from 1995 to 2014 (Supplementary Table 3). In multivariate linear regression analyses, after adjusting for confounding factors, no significant relationship was found between exposure to nighttime lights and life expectancy, nor for the change in life expectancy. These results suggested that the impact of confounding factors far exceeded the intrinsic association between nighttime lights and the county-level life expectancy at birth (Table 2 ). Table 2 Multivariate linear regression analyses for exposure to nighttime lights on life expectancy Life expectancy at birth Change of life expectancy Regression coefficient (99.9% CIs) |R 2 Model 0.720 0.474 Exposure to nighttime lights Intensity, digital number (per 10) -0.040 (-0.156, 0.076) -0.043 (-0.099, 0.013) Population Population size (per 1,000,000) 0.654 (0.378, 0.929) 0.410 (0.277, 0.543) Male (%) 0.142 (0.105, 0.179) 0.069 (0.051, 0.087) White alone (%) 0.021 (0.014, 0.027) -0.015 (-0.018, -0.012) Age \(\:\ge\:\) 70 (%) 0.156 (0.125, 0.187) 0.054 (0.039, 0.069) Socio-economics A bachelor’s degree or higher 0.109 (0.095, 0.123) 0.031 (0.025, 0.038) Median household income (per 1,000 $ ) 0.040 (0.024, 0.055) 0.018 (0.010, 0.025) Unemployment rate, age < 65 (%) -0.044 (-0.090, 0.003) 0.055 (0.033, 0.078) Poverty rate (%) -0.061 (-0.089, -0.032) -0.020 (-0.034, -0.006) GDP per capita (per 1000 $ ) -0.160 (-0.329, 0.009) -0.027 (-0.109, 0.054) Healthcare service Health insurance coverage, age < 65 (%) -0.018 (-0.039, 0.002) -0.037 (-0.047, -0.027) Physicians per 1000 population -0.051 (-0.113, 0.011) 0.021 (-0.009, 0.051) Residential environment Rural-Urban Continuum Code 2 (vs. 1) 0.287 (-0.033, 0.607) -0.107 (-0.261, 0.048) 3 (vs. 1) 0.465 (0.117, 0.813) -0.044 (-0.212, 0.124) 4 (vs. 1) 0.440 (0.044, 0.837) -0.060 (-0.252, 0.131) 5 (vs. 1) 0.260 (-0.271, 0.790) -0.260 (-0.517, -0.004) 6 (vs. 1) 0.232 (-0.115, 0.580) -0.124 (-0.292, 0.044) 7 (vs. 1) 0.035 (-0.339, 0.409) -0.257 (-0.438, -0.077) 8 (vs. 1) 0.115 (-0.317, 0.548) -0.166 (-0.375, 0.043) 9 (vs. 1) 0.037 (-0.369, 0.444) -0.217 (-0.414, -0.020) Geographic and climatic proxies Longitude 0.001 (-0.008, 0.010) 0.011 (0.007, 0.015) Latitude 0.080 (0.060, 0.100) 0.046 (0.036, 0.056) Precipitation (per 1,000) -0.049 (-0.070, -0.029) -0.012 (-0.022, -0.003) CIs, confidence intervals; GDP, gross domestic product; Based on 3104 studied USA counties. For age-specific mortality risks, univariate linear regression analyses initially indicated that the increased intensity of nighttime lights was significantly associated with decreased mortality risks across all age groups. However, after adjusting for confounders, this association remained statistically significant only for the 5–25 years age group (regression coefficient (99.9% confidence intervals (CIs), -0.031 (-0.046, -0.015), Table 3 ), which suggested a significant decrease by 0.031 in mortality risk for people aged 5–25 years associated with one unit increase in the DN value of nighttime light exposure. Table 3 Univariate analyses and multivariate analyses for exposure to nighttime lights on age-specific mortality risk Univariate analyses (without adjustment) Multivariate analyses (with confounding adjustment) Age groups Regression coefficient (99.9% CIs) R 2 Regression coefficient (99.9% CIs) 0–5 years -0.036 (-0.047, -0.025) 0.035 -0.006 (-0.016, 0.004) 5–25 years -0.086 (-0.101, -0.071) 0.107 -0.031 (-0.046, -0.015) 25–45 years -0.171 (-0.218, -0.125) 0.045 -0.020 (-0.063, 0.02) 45–65 years -0.401 (-0.561, -0.241) 0.021 0.103 (-0.042, 0.247) 65–85 years -0.494 (-0.809, -0.178) 0.008 0.214 (-0.123, 0.550) CIs, confidence intervals; Adjustment for all the studied population characteristics, socio-economics, healthcare service, residential environment, and geographical and climatic proxies. In univariate regression analyses, a majority (18 out of 21) of the cause-specific mortality rates demonstrated significant associations with nighttime light exposure, with negative associations comprising a substantial proportion. However, after adjusting for all confounding factors, only the mortality rate of six causes retained a significant association with nighttime lights. Specifically, there was a positive association between nighttime light exposure and mortality from mental and substance use disorders. Additionally, mortality rates from unintentional and transport injuries showed a significant negative relationship with nighttime light exposure. Our results indicated that most cause-specific mortality rates had no significant association with nighttime light exposure when accounting for confounding factors, including major health concerns such as cardiovascular diseases, cancers, and neurological disorders (Fig. 1 ). The CIE method was applied, as examples, to nine representative causes of mortality: neoplasms, cardiovascular diseases, chronic respiratory diseases, cirrhosis and other chronic liver diseases, digestive diseases, neurological disorders, mental and substance use disorders, diabetes, and self-harm and interpersonal violence. The percentage change in the estimated association between nighttime light exposure and the selected cause-specific mortality rates were reported in Supplementary Table 4. The education level (i.e., the percentage of population with a bachelor’s degree or higher) emerged as the primary confounder, accounting for 50–100% of the change in the estimated association between nighttime light exposure and the mortality rates of all the studied causes except neurological disorders. Besides, adjusting for educational level often led to a reversal in the direction of the association, such as from a positive to a negative association. Median household income and poverty rate also exerted considerable influence on the targeted associations. For neurological disorders, all the examined confounders attenuated the estimated association between the exposure and outcome, with the residential environment having the greatest impact. The regression coefficient of nighttime light exposure decreased from 4.211 before adjustment to 1.166 after adjustment. To further demonstrate the influence of confounding factors on the association between nighttime lights and cause-specific mortality rates, we conducted a confounding assessment model focusing on the mortality rates of cardiovascular diseases, neurological disorders, and mental and substance use disorders (Fig. 2 ). For cardiovascular diseases and neurological disorders, the direct effect of nighttime light exposure on their mortality rates was significant. However, after adjusting for confounding factors, the total effect was no longer significant, indicating that the indirect effect played a dominant role. For mental and substance use disorders, the total effect was significant, while the direct effect was not, suggesting that the significant association between nighttime light exposure and mortality from mental and substance use disorders was affected by the confounding factors. Although the mortality rates of these three causes ultimately showed varying adjusted relationships with nighttime light exposure, they all demonstrated the dominant influence of confounding factors. Discussion Except associations with higher mortality rate due to mental and substance disorders and lower mortality rate from transport and unintentional injuries, our results revealed no significant effect of artificial nighttime light exposure on other population-level health outcomes including life expectancy at birth, change in life expectancy at birth, age-specific mortality risks, or mortality rates for most causes of death after adjusting for confounding factors. Even in cases where a true association between exposure to nighttime lights and cause-specific mortality rate exists (e.g., mental and substance use disorders), the relationship was still largely obscured by confounding factors, indicating that the association between exposure to nighttime lights and specific causes of death may be more nuanced than it apparently presented. The significant changes in the relationship between nighttime lights and population health outcomes before and after confounding adjustment implied that these confounding factors contributed more to the explanation of variations in health outcomes than exposure to nighttime lights. A significant negative association has been identified between nighttime light exposure and transport and unintentional injuries in this study. This could be attributed to the benefits of exposure to nighttime lights, such as aiding in stage adaptation[ 31 ], improving alertness and workplace safety[ 32 ], and reducing nocturnal falls among older adults[ 33 ]. Outdoor nighttime lights also enhance perceptions of safety in dark areas. However, excessive nighttime illumination beyond optimal levels may be unnecessary and potentially harmful. It can adversely affect mental health by disrupting sleep, increasing medication use, and exacerbating symptoms of depression, suicidal tendencies, and anxiety[ 34 – 37 ]. According to our analyses, this suspected positive association between nighttime light exposure and mortality from mental and substance use disorders was influenced by confounders linked to urbanization and modern life. Although previous studies have highlighted the impact of nighttime light exposure on both mental and physical health, the potential concurrent influencers such as urbanization and socio-economic factors have not been thoroughly examined. Some epidemiological evidence suggests that prolonged exposure to nighttime lights increases the risk of cancer[ 8 , 9 , 34 ], diabetes[ 15 , 16 , 38 ], cardiovascular disease[ 39 ], obesity[ 40 – 42 ], and atopic diseases[ 43 ]. For example, a study reported a significant association between prolonged exposure to high-intensity outdoor nighttime light and an increased risk of impaired glucose balance and a higher diabetes prevalence[ 44 ]. However, our findings indicated that after taking into account a series of confounders, these associations were no longer significant for a vast majority of mortality causes. Instead, factors such as education level, household income, poverty rate, and urbanization level played a predominant role in most of these associations. Nighttime light intensity often coincides with the extent and intensity of human activity, as well as the infrastructural continuum from the countryside to city center[ 20 , 45 ]. Meanwhile, highly urbanized areas typically correlate with higher availability of employment, education, healthcare, and cultural opportunities[ 46 ]. Consistent with our results, the unignorably effect of urbanization and socio-economic factors on population health has been verified in a few studies. A stratified study of different socioeconomic groups in China revealed that residing in highly urbanized areas increased the risk of chronic diseases, particularly among high-income populations[ 47 ]. In addition, education attainment is closely related to life expectancy, morbidity, and health behaviors[ 48 ]. Adults with higher levels of education often lead healthier and longer lives compared to those with lower levels of education, and this disparity is still widening[ 49 ]. To the best of our knowledge, this is the first study to dismantle the proposed association between nighttime light exposure and human health by carefully considering the potential impact of confounding effects. Our analyses were adjusted for a wide range of potential confounders, including population characteristics, socio-economics, healthcare services, residential environment, and geographical and climatic proxies, which have often been under-considered in previous studies. This study was enabled by a large database, providing a substantial sample size and extensive geographical coverage across the USA. However, some limitations of this study should be noted. First, other possible important covariates, such as air quality, were not included in the study. Second, instead of using disease prevalence data, disease-specific mortality data were investigated. However, many influence of nighttime light on health may be not necessarily related to pre-mature death. Additionally, potential biases due to population migration and reporting inconsistencies across counties may be relevant, as this is an ecological study. The mechanisms underlying the observed associations between nighttime light exposure and population health were not explored. Future research, introducing better design, such as longitudinal and experimental studies, is required to further investigate the following debate, what is the real influence of nighttime light exposure on various mental and physical conditions? Our study contributes to the growing evidence of the relationship between exposure to nighttime lights and public health. These nationwide findings may inform public health strategies aimed at reducing the disease burden associated with nighttime light and other exposures, and advocate for policy development to address these issues. Declarations Acknowledgments This work was supported by Xi’an Jiaotong-Liverpool University [Research Development Fund-22-01-012]. The authors thank all the colleagues at the Wisdom Lake Academy of Pharmacy, Xi’an Jiaotong-Liverpool University for their academic and administrative supports. Funding This work was supported by Xi’an Jiaotong-Liverpool University [Research Development Fund-22-01-012]. Competing Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data availability Please find the Supplementary information (Supplementary table 1) and corresponding references for details of raw data sources. Code availability Not applicable Author Contributions YC conceived the study, YZ, QX, BQ and XS analyzed the data, and YZ, RR, YC and KJ drafted the initial manuscript. References James SR, Dennell RW, Gilbert AS, Lewis HT, Gowlett J a. J, Lynch TF, et al. Hominid Use of Fire in the Lower and Middle Pleistocene: A Review of the Evidence [and Comments and Replies]. Current Anthropology. 1989;30(1):1–26. https://doi.org/10.1086/203705 Sanderson SW, Simons KL. 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Outdoor light at night and the prevalence of depressive symptoms and suicidal behaviors: A cross-sectional study in a nationally representative sample of Korean adults. J Affect Disord. 2018;227:199–205. https://doi.org/10.1016/j.jad.2017.10.039 Touitou Y, Reinberg A, Touitou D. Association between light at night, melatonin secretion, sleep deprivation, and the internal clock: Health impacts and mechanisms of circadian disruption. Life Sciences. 2017;173:94–106. https://doi.org/10.1016/j.lfs.2017.02.008 Sorensen TB, Wilson R, Gregson J, Shankar B, Dangour AD, Kinra S. Is night-time light intensity associated with cardiovascular disease risk factors among adults in early-stage urbanisation in South India? A cross-sectional study of the Andhra Pradesh Children and Parents Study. BMJ Open. 2020;10(11):e036213. https://doi.org/10.1136/bmjopen-2019-036213 Obayashi K, Saeki K, Iwamoto J, Okamoto N, Tomioka K, Nezu S, et al. Exposure to light at night, nocturnal urinary melatonin excretion, and obesity/dyslipidemia in the elderly: a cross-sectional analysis of the HEIJO-KYO study. J Clin Endocrinol Metab. 2013;98(1):337–44. https://doi.org/10.1210/jc.2012-2874 Zhang D, Jones RR, Powell-Wiley TM, Jia P, James P, Xiao Q. A large prospective investigation of outdoor light at night and obesity in the NIH-AARP Diet and Health Study. Environmental Health. 2020;19(1):74. https://doi.org/10.1186/s12940-020-00628-4 Lin L-Z, Zeng X-W, Deb B, Tabet M, Xu S-L, Wu Q-Z, et al. Outdoor light at night, overweight, and obesity in school-aged children and adolescents. Environmental Pollution. 2022;305:119306. https://doi.org/10.1016/j.envpol.2022.119306 Tang Z, Li S, Shen M, Xiao Y, Su J, Tao J, et al. Association of exposure to artificial light at night with atopic diseases: A cross-sectional study in college students. International Journal of Hygiene and Environmental Health. 2022;241:113932. https://doi.org/10.1016/j.ijheh.2022.113932 Zheng R, Xin Z, Li M, Wang T, Xu M, Lu J, et al. Outdoor light at night in relation to glucose homoeostasis and diabetes in Chinese adults: a national and cross-sectional study of 98,658 participants from 162 study sites. Diabetologia. 2023;66(2):336–45. https://doi.org/10.1007/s00125-022-05819-x Wu B, Huang H, Wang Y, Shi S, Wu J, Yu B. Global spatial patterns between nighttime light intensity and urban building morphology. International Journal of Applied Earth Observation and Geoinformation. 2023;124:103495. https://doi.org/10.1016/j.jag.2023.103495 Moore M, Gould P, Keary BS. Global urbanization and impact on health. International Journal of Hygiene and Environmental Health. 2003;206(4–5):269–78. https://doi.org/10.1078/1438-4639-00223 Miao J, Wu X. Urbanization, socioeconomic status and health disparity in China. Health & Place. 2016;42:87–95. https://doi.org/10.1016/j.healthplace.2016.09.008 The Lancet Public Health. Education: a neglected social determinant of health. The Lancet Public Health. 2020;5(7):e361. https://doi.org/10.1016/S2468-2667(20)30144-4 Zajacova A, Lawrence EM. The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health. 2018;39(1):273–89. https://doi.org/10.1146/annurev-publhealth-031816-044628 Additional Declarations No competing interests reported. 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11:38:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5102937/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5102937/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-00972-1","type":"published","date":"2025-09-20T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79543155,"identity":"6fdd6f67-4bfb-48cb-b623-a1cc887f36c1","added_by":"auto","created_at":"2025-03-31 04:21:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495685,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for the associations of exposure to nighttime lights with cause-specific mortality rates\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5102937/v1/3ba7515a0c72247134b17c80.png"},{"id":79543156,"identity":"36d80aed-dd47-4c0c-bd23-f18d563b8072","added_by":"auto","created_at":"2025-03-31 04:21:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":504161,"visible":true,"origin":"","legend":"\u003cp\u003eConfounding assessment model for the association of exposure to nighttime lights with cardiovascular diseases, neurological disorders, and mental and substance use disorders\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5102937/v1/3ac6ccd17a9b254cf35daaab.png"},{"id":91890029,"identity":"b0344a8b-cb36-4de8-8757-9fdaa1bf8fa3","added_by":"auto","created_at":"2025-09-22 16:03:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1831943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5102937/v1/443254d1-cae4-4904-b0e3-e6949e578acf.pdf"},{"id":79543157,"identity":"7ebff357-5d0f-4b16-9c14-7277467d1022","added_by":"auto","created_at":"2025-03-31 04:21:20","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":3178231,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5102937/v1/6eea7e56162a1015f7642784.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associating nighttime lights with population health, with consideration of environmental and socio-economic confounders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNighttime lighting has become essential to modern life, fundamentally altering living patterns and extending activities into the night[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Today, 80% of the global population live under artificial nighttime lights, with its prevalence growing alongside industrial and population expansion[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the extensive use of artificial nighttime lights has led to a rapid increase in nighttime light pollution[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], with studies showing an average annual increase in sky brightness of 10%, outpacing the estimated 2% annual increase in light emissions captured by satellite data[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond light pollution, nighttime lights can disrupt circadian rhythms and sleep cycles, potentially impairing immune responses and leading to various health issues[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Epidemiological evidence links nighttime lights to major behavioral and health problems, such as cancer[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], sleep problem[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], depressive symptom[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], hyperactivity[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], diabetes[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and obesity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies have also demonstrated associations between exposure to nighttime lights and increased mortality risk from many causes, including common non-communicable diseases such as cardiovascular disease, respiratory system disorder, and cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, given the close geographical relationship between nighttime light and a number of socio-demographic and socio-economic factors\u0026mdash;such as population size and intensity, regional gross domestic product (GDP)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], urbanization level, and human activity[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which are well-established population health influencers[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u0026mdash;it is important to note that the study of the association between nighttime light and human health could be easily confounded by these factors.\u003c/p\u003e \u003cp\u003eThe validity and relevance of previous studies, on the potential health effects of nighttime lights on human health, are frequently compromised by insufficient control of confounding factors. To address this potential deficiency, the current study aims to examine the association between nighttime light exposure and a range of population health indicators after accounting for a relatively comprehensive list of potential confounders, including population characteristics, socio-economic status, healthcare resources, residential environment, and geographical and climatic proxies. We hypothesize that there will be no significant association between nighttime light and human health after the adjustment of confounding effects. To further validate our hypothesis, we also investigated changes in the associations before and after adjustments, and identified the important confounding factors influencing the relationship between nighttime light and human health.\u003c/p\u003e "},{"header":"Methods","content":"\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis ecological study included 3104 (98.9%) out of the 3140 counties in the USA, with county-level data analyzed. The exposure variable was the average annual level of artificial nighttime light at the county level. The outcome variables were county-level life expectancy at birth, age-specific mortality risk and cause-specific mortality rates. Additionally, the change in life expectancy at birth over a 10-year period from 1995 to 2014 was evaluated as the secondary outcome. Population characteristics, socio-economic factors, healthcare service, residential environment, and geographical and climatic proxies at the county level were collected as the potential confounding factors.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eNighttime lights data\u003c/h2\u003e \u003cp\u003eNighttime lights data were sourced from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) Nighttime Lights Time Series (1992\u0026ndash;2013)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which were the most extended historical Nighttime lights data available that could be combined with the collected health data in 2014[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The cleaned-up average visible\u0026ndash;the 6-bit quantized image data rectified to a 30-arcsecond grid (equivalent to approximately 1 km\u003csup\u003e2\u003c/sup\u003e)\u0026ndash;was used in this study. A digital number (DN) with a scaled value from 0 to 63 was assigned to each pixel to represents the frequency of nighttime light occurrence in a given year for that pixel, which had been normalized across multiple satellites[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe processed image data were further combined with the USA part of Database of Global Administrative Areas, which featured highly detailed boundaries to county-level municipal subdivisions. Spatial analysis was conducted with the ArcToolbox feature of ArcGIS software, averaging the pixel DNs within a 30-arcsecond grid. The merged data contained information about county-level pixel-averaged nighttime light intensity in 3148 counties from 1992 to 2013. The average annual frequency of nighttime light during that period derived for each county was used in the subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation health data\u003c/h3\u003e\n\u003cp\u003ePopulation health data at the county level were obtained from the Institute for Health Metrics and Evaluation (IHME)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], including life expectancy at birth in 2014, change in life expectancy at birth from 1995 to 2014, age-specific mortality risk in 2014, and cause-specific mortality rates in 2014. Specifically, the life expectancy at birth and age-specific mortality risks were estimated using small area estimation methods, which produced annual county-level life tables. These estimates utilized de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the Census Bureau, NCHS, and the Human Mortality Database[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The age-specific mortality risk was categorized into five age groups (i.e., 0\u0026ndash;5, 5\u0026ndash;25, 25\u0026ndash;45, 45\u0026ndash;65, and 65\u0026ndash;85 years). For cause-specific mortality rates, redistribution of garbage codes and small area estimation methods were used on National Vital Statistics System data to estimate annual county-level mortality rates for 21 causes of death[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These mutually exclusive causes were communicable, maternal, neonatal and nutritional diseases, including 1) HIV/AIDS and tuberculosis, 2) diarrhea, lower respiratory and other common infectious diseases, 3) neglected tropical diseases and malaria, 4) maternal disorders, 5) neonatal disorders, 6) nutritional deficiencies, 7) other communicable, maternal, neonatal and nutritional diseases; non-communicable diseases, including 8) neoplasms, 9) cardiovascular diseases, 10) chronic respiratory diseases, 11) cirrhosis and other chronic liver diseases, 12) digestive diseases, 13) neurological disorders, 14) mental and substance use disorders, 15) diabetes, urogenital, blood and endocrine diseases, 16) musculoskeletal disorders, 17) other non-communicable diseases; and injuries, including 18) transport injuries, 19) unintentional injuries, 20) self-harm and interpersonal violence and 21) forces of nature, war and legal intervention\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eFactors were chosen as potential confounders based on the literature or expert option. County-level information on population demographics (population size, gender, ethnicity, age), socio-economics (unemployment rate, median household income, poverty rates, gross domestic product per capita, educational level), healthcare services (medical insurance coverage, physicians per population), residential environment (Rural-Urban Continuum Code), and geographical and climatic proxies (latitude, longitude, annual precipitation in millimeters, and temperature in degrees Celsius) were collected from the USA national official sources as potential confounding factors. Data sources and specific data files of these variables are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDatasets of nighttime light, population health, and covariates were merged by county. Descriptive statistics, with county as the unit for analysis, were firstly reported on all the variables studied. The 21 death causes were also ranked based on the corresponding cause-specific mortality rate (per 100,000 population) (Supplementary Table\u0026nbsp;2). Data visualization was conducted to demonstrate the distribution of the varying level of nighttime light exposure as determined by DN values across the USA counties.\u003c/p\u003e \u003cp\u003eUnivariate linear regression analyses were initially performed to assess the bivariate association between the nighttime light exposure and each outcome variable. Next, multivariate linear regression analyses, which included the covariates, were conducted to adjust for potential confounding factors. Specifically, we used a backward selection method in multivariate regression modeling to filter out the statistically significant confounding factors while having considered other covariates. Furthermore, in examining the associations with cause-specific mortality rates, we applied the Change in Estimate (CIE) method to identify the most influential confounders by capturing the effect of each potential confounder on the association between exposure and outcomes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The identified confounders varied across different causes of death. Subsequently, we progressively incorporated each of these confounders into the previous univariate regression model for each cause-specific mortality rate, documenting the changes in the effect of the exposure on each outcome. The effect modification was reflected in the magnitude and direction of the change in the coefficient estimate when adjusting for each identified confounder.\u003c/p\u003e \u003cp\u003eAfter identifying the individual influential confounders, we employed a confounding assessment model that included all these confounders collectively to calculate their total influence on the associations between nighttime light exposure and cause-specific mortality rates. Within this framework, we focused our analysis, as examples, on the mortality rates of several causes of death, particularly those with the heaviest disease burden and those showing significant changes in association after adjustments. Concurrently, we reported the direct, indirect and total effects of the observed association from the intensity of nighttime lights to specific mortality rates.\u003c/p\u003e \u003cp\u003eA two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (instead of 0.05) was considered statistically significant. This stringent criterion aims to enhance the validly of our findings by building upon earlier studies that were less thoroughly investigated and seeking more robust associations. All statistical analyses were conducted using R (version 4.3.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe median annual DN value of nighttime light exposure during the study period among the 3104 counties was 5.43 (Interquartile range (IQR), (2.12, 10.44)). The geographical pattern of the distribution of nighttime lights is presented in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAt the county level, the descriptive statistics on the population characteristics, social-economics, healthcare service, residential environment, and geographical and climatic proxies are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average life expectancy at birth was 77.75 years (standard deviation (SD), 2.37) for the 3104 USA counties included, and the average change of life expectancy from 1995 to 2014 was 2.26 years (SD, 0.84). The death cause with the highest average annual mortality rate was cardiovascular diseases, which accounted for 277.98 (SD, 58.73) deaths per 100,000 people (Supplementary Table\u0026nbsp;2).\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\u003eCounty-level summary descriptive statistics in 3104 studied USA counties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(Standard deviation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e(Interquartile range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to nighttime lights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity, digital number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.90 (11.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43 (2.12, 10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101262 (326278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25758 (11039, 67386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.08% (2.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.61% (48.96%, 50.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, white alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.41% (15.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.16% (80.84%, 96.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.24% (2.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.14% (10.94%, 13.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.90% (1.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.87% (11.91%, 13.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.31% (3.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.71 (10.38%, 13.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.54% (1.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.43 (10.51%, 12.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.20% (1.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.22 (11.22%, 13.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.61% (1.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.71 (13.74%, 15.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.27% (2.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.04 (10.78%, 13.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.42% (2.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27 (6.15%, 8.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.51% (1.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.31 (3.50%, 5.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate. Age\u0026thinsp;\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.23% (2.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.00% (4.60%, 7.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Household Income (annual, US dollar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47064 (11987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45209 (38911, 52492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.84% (6.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.80% (12.10%, 20.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross domestic product per capita (annual, US dollar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57078 (445096.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36447 (26732, 49378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, 25 years and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than a high school diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.41% (6.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.10% (8.80%, 17.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA high school diploma only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.33% (7.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.60% (29.90%, 39.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleting some college or associate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.74% (5.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.60% (27.30%, 34.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA bachelor\u0026rsquo;s degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.51% (9.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.20% (15.00%, 25.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage, age\u0026thinsp;\u0026lt;\u0026thinsp;65 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.59% (5.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.00% (82.30%, 89.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicians per 1,000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.39, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential environment (Rural-Urban Continuum Code)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (Metro areas, 1\u0026nbsp;million population or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424 (13.66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (Metro areas, 250 thousand to 1\u0026nbsp;million population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e376 (12.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (Metro areas, population fewer than 250 thousand)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (11.34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (Urban population of 20 thousand or more, adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (6.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (Urban population of 20 thousand or more, not adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (2.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 (Urban population of 2,500 to 19,999, adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e586 (18.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 (Urban population of 2,500 to 19,999, not adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e430 (13.85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 (Completely rural or \u0026lt;\u0026thinsp;2,500 urban population, adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219 (7.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 (Completely rural or \u0026lt;\u0026thinsp;2,500 urban population, not adjacent to a metro area)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e416 (13.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic and climatic proxies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-91.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-90.24 (-98.12, -83.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.23 (34.41, 41.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Precipitation, millimeter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11626.7 (4529.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12128.0 (8301.2, 14411.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8 (8.2, 15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariate linear regression, exposure to nighttime lights showed a significant positive association with life expectancy at birth in 2014 (Supplementary Table\u0026nbsp;3). However, no significant association was found for the change in life expectancy at birth from 1995 to 2014 (Supplementary Table\u0026nbsp;3). In multivariate linear regression analyses, after adjusting for confounding factors, no significant relationship was found between exposure to nighttime lights and life expectancy, nor for the change in life expectancy. These results suggested that the impact of confounding factors far exceeded the intrinsic association between nighttime lights and the county-level life expectancy at birth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression analyses for exposure to nighttime lights on life expectancy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife expectancy at birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChange of life expectancy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegression coefficient (99.9% CIs) |R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to nighttime lights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity, digital number (per 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.040 (-0.156, 0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.043 (-0.099, 0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation size (per 1,000,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.654 (0.378, 0.929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.410 (0.277, 0.543)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142 (0.105, 0.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069 (0.051, 0.087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite alone (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021 (0.014, 0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015 (-0.018, -0.012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 70 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.156 (0.125, 0.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054 (0.039, 0.069)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA bachelor\u0026rsquo;s degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109 (0.095, 0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031 (0.025, 0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian household income (per 1,000\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040 (0.024, 0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018 (0.010, 0.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment rate, age\u0026thinsp;\u0026lt;\u0026thinsp;65 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.044 (-0.090, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055 (0.033, 0.078)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.061 (-0.089, -0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020 (-0.034, -0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP per capita (per 1000\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.160 (-0.329, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.027 (-0.109, 0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage, age\u0026thinsp;\u0026lt;\u0026thinsp;65 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018 (-0.039, 0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.037 (-0.047, -0.027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicians per 1000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.051 (-0.113, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021 (-0.009, 0.051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural-Urban Continuum Code\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.287 (-0.033, 0.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.107 (-0.261, 0.048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.465 (0.117, 0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.044 (-0.212, 0.124)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.440 (0.044, 0.837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060 (-0.252, 0.131)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.260 (-0.271, 0.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.260 (-0.517, -0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.232 (-0.115, 0.580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.124 (-0.292, 0.044)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035 (-0.339, 0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.257 (-0.438, -0.077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.115 (-0.317, 0.548)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.166 (-0.375, 0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 (vs. 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037 (-0.369, 0.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.217 (-0.414, -0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic and climatic proxies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 (-0.008, 0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011 (0.007, 0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080 (0.060, 0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046 (0.036, 0.056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation (per 1,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.049 (-0.070, -0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.012 (-0.022, -0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCIs, confidence intervals; GDP, gross domestic product; Based on 3104 studied USA counties.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor age-specific mortality risks, univariate linear regression analyses initially indicated that the increased intensity of nighttime lights was significantly associated with decreased mortality risks across all age groups. However, after adjusting for confounders, this association remained statistically significant only for the 5\u0026ndash;25 years age group (regression coefficient (99.9% confidence intervals (CIs), -0.031 (-0.046, -0.015), Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which suggested a significant decrease by 0.031 in mortality risk for people aged 5\u0026ndash;25 years associated with one unit increase in the DN value of nighttime light exposure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analyses and multivariate analyses for exposure to nighttime lights on age-specific mortality risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analyses (without adjustment)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate analyses (with confounding adjustment)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression coefficient (99.9% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegression coefficient (99.9% CIs)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.036 (-0.047, -0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.006 (-0.016, 0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.086 (-0.101, -0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.031 (-0.046, -0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171 (-0.218, -0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020 (-0.063, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.401 (-0.561, -0.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.103 (-0.042, 0.247)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;85 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.494 (-0.809, -0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.214 (-0.123, 0.550)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCIs, confidence intervals; Adjustment for all the studied population characteristics, socio-economics, healthcare service, residential environment, and geographical and climatic proxies.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariate regression analyses, a majority (18 out of 21) of the cause-specific mortality rates demonstrated significant associations with nighttime light exposure, with negative associations comprising a substantial proportion. However, after adjusting for all confounding factors, only the mortality rate of six causes retained a significant association with nighttime lights. Specifically, there was a positive association between nighttime light exposure and mortality from mental and substance use disorders. Additionally, mortality rates from unintentional and transport injuries showed a significant negative relationship with nighttime light exposure. Our results indicated that most cause-specific mortality rates had no significant association with nighttime light exposure when accounting for confounding factors, including major health concerns such as cardiovascular diseases, cancers, and neurological disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CIE method was applied, as examples, to nine representative causes of mortality: neoplasms, cardiovascular diseases, chronic respiratory diseases, cirrhosis and other chronic liver diseases, digestive diseases, neurological disorders, mental and substance use disorders, diabetes, and self-harm and interpersonal violence. The percentage change in the estimated association between nighttime light exposure and the selected cause-specific mortality rates were reported in Supplementary Table\u0026nbsp;4. The education level (i.e., the percentage of population with a bachelor\u0026rsquo;s degree or higher) emerged as the primary confounder, accounting for 50\u0026ndash;100% of the change in the estimated association between nighttime light exposure and the mortality rates of all the studied causes except neurological disorders. Besides, adjusting for educational level often led to a reversal in the direction of the association, such as from a positive to a negative association. Median household income and poverty rate also exerted considerable influence on the targeted associations. For neurological disorders, all the examined confounders attenuated the estimated association between the exposure and outcome, with the residential environment having the greatest impact. The regression coefficient of nighttime light exposure decreased from 4.211 before adjustment to 1.166 after adjustment.\u003c/p\u003e \u003cp\u003eTo further demonstrate the influence of confounding factors on the association between nighttime lights and cause-specific mortality rates, we conducted a confounding assessment model focusing on the mortality rates of cardiovascular diseases, neurological disorders, and mental and substance use disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For cardiovascular diseases and neurological disorders, the direct effect of nighttime light exposure on their mortality rates was significant. However, after adjusting for confounding factors, the total effect was no longer significant, indicating that the indirect effect played a dominant role. For mental and substance use disorders, the total effect was significant, while the direct effect was not, suggesting that the significant association between nighttime light exposure and mortality from mental and substance use disorders was affected by the confounding factors. Although the mortality rates of these three causes ultimately showed varying adjusted relationships with nighttime light exposure, they all demonstrated the dominant influence of confounding factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExcept associations with higher mortality rate due to mental and substance disorders and lower mortality rate from transport and unintentional injuries, our results revealed no significant effect of artificial nighttime light exposure on other population-level health outcomes including life expectancy at birth, change in life expectancy at birth, age-specific mortality risks, or mortality rates for most causes of death after adjusting for confounding factors. Even in cases where a true association between exposure to nighttime lights and cause-specific mortality rate exists (e.g., mental and substance use disorders), the relationship was still largely obscured by confounding factors, indicating that the association between exposure to nighttime lights and specific causes of death may be more nuanced than it apparently presented. The significant changes in the relationship between nighttime lights and population health outcomes before and after confounding adjustment implied that these confounding factors contributed more to the explanation of variations in health outcomes than exposure to nighttime lights.\u003c/p\u003e \u003cp\u003eA significant negative association has been identified between nighttime light exposure and transport and unintentional injuries in this study. This could be attributed to the benefits of exposure to nighttime lights, such as aiding in stage adaptation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], improving alertness and workplace safety[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and reducing nocturnal falls among older adults[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Outdoor nighttime lights also enhance perceptions of safety in dark areas. However, excessive nighttime illumination beyond optimal levels may be unnecessary and potentially harmful. It can adversely affect mental health by disrupting sleep, increasing medication use, and exacerbating symptoms of depression, suicidal tendencies, and anxiety[\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. According to our analyses, this suspected positive association between nighttime light exposure and mortality from mental and substance use disorders was influenced by confounders linked to urbanization and modern life.\u003c/p\u003e \u003cp\u003eAlthough previous studies have highlighted the impact of nighttime light exposure on both mental and physical health, the potential concurrent influencers such as urbanization and socio-economic factors have not been thoroughly examined. Some epidemiological evidence suggests that prolonged exposure to nighttime lights increases the risk of cancer[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], diabetes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], cardiovascular disease[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], obesity[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and atopic diseases[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For example, a study reported a significant association between prolonged exposure to high-intensity outdoor nighttime light and an increased risk of impaired glucose balance and a higher diabetes prevalence[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, our findings indicated that after taking into account a series of confounders, these associations were no longer significant for a vast majority of mortality causes. Instead, factors such as education level, household income, poverty rate, and urbanization level played a predominant role in most of these associations. Nighttime light intensity often coincides with the extent and intensity of human activity, as well as the infrastructural continuum from the countryside to city center[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Meanwhile, highly urbanized areas typically correlate with higher availability of employment, education, healthcare, and cultural opportunities[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Consistent with our results, the unignorably effect of urbanization and socio-economic factors on population health has been verified in a few studies. A stratified study of different socioeconomic groups in China revealed that residing in highly urbanized areas increased the risk of chronic diseases, particularly among high-income populations[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, education attainment is closely related to life expectancy, morbidity, and health behaviors[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Adults with higher levels of education often lead healthier and longer lives compared to those with lower levels of education, and this disparity is still widening[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to dismantle the proposed association between nighttime light exposure and human health by carefully considering the potential impact of confounding effects. Our analyses were adjusted for a wide range of potential confounders, including population characteristics, socio-economics, healthcare services, residential environment, and geographical and climatic proxies, which have often been under-considered in previous studies. This study was enabled by a large database, providing a substantial sample size and extensive geographical coverage across the USA. However, some limitations of this study should be noted. First, other possible important covariates, such as air quality, were not included in the study. Second, instead of using disease prevalence data, disease-specific mortality data were investigated. However, many influence of nighttime light on health may be not necessarily related to pre-mature death. Additionally, potential biases due to population migration and reporting inconsistencies across counties may be relevant, as this is an ecological study. The mechanisms underlying the observed associations between nighttime light exposure and population health were not explored. Future research, introducing better design, such as longitudinal and experimental studies, is required to further investigate the following debate, what is the real influence of nighttime light exposure on various mental and physical conditions?\u003c/p\u003e \u003cp\u003eOur study contributes to the growing evidence of the relationship between exposure to nighttime lights and public health. These nationwide findings may inform public health strategies aimed at reducing the disease burden associated with nighttime light and other exposures, and advocate for policy development to address these issues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Xi\u0026rsquo;an Jiaotong-Liverpool University [Research Development Fund-22-01-012]. The authors thank all the colleagues at the Wisdom Lake Academy of Pharmacy, Xi\u0026rsquo;an Jiaotong-Liverpool University for their academic and administrative supports.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Xi’an Jiaotong-Liverpool University [Research Development Fund-22-01-012].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlease find the Supplementary information (Supplementary table 1) and corresponding references for details of raw data sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYC conceived the study, YZ, QX, BQ and XS analyzed the data, and YZ, RR, YC and KJ drafted the initial manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJames SR, Dennell RW, Gilbert AS, Lewis HT, Gowlett J a. 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Health \u0026amp; Place. 2016;42:87\u0026ndash;95. https://doi.org/10.1016/j.healthplace.2016.09.008\u003c/li\u003e\n\u003cli\u003eThe Lancet Public Health. Education: a neglected social determinant of health. The Lancet Public Health. 2020;5(7):e361. https://doi.org/10.1016/S2468-2667(20)30144-4\u003c/li\u003e\n\u003cli\u003eZajacova A, Lawrence EM. The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health. 2018;39(1):273\u0026ndash;89. https://doi.org/10.1146/annurev-publhealth-031816-044628\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial nighttime light, Health disparity, Confounding effect, Ecological study, Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-5102937/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5102937/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The relationship between nighttime lights and human health is being discussed in a growing number of studies, with a vast majority of results supporting an association. However, the impact of potential confounding has been insufficiently addressed in existing studies, undermining the reported associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to assess the relationship between exposure to nighttime lights and population health outcomes in the USA with a careful adjustment of potential confounders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This ecological study analyzed county-level data from 3104 counties in the USA. Nighttime light exposure data were derived from the DMSP-OLS Nighttime Lights Time Series. The outcomes were county-level health indicators including life expectancy at birth, age-specific mortality risk, and 21 cause-specific mortality rates. Covariates included county-level data on population demographics, socio-economics, healthcare, and environmental variables. Univariate regression and multivariate regression modeling were conducted to analyze the crude and confounding-adjusted association between the exposure and outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e After adjusting for confounders, no significant associations were found between nighttime light exposure and the population health outcomes except mortality rates from mental and substance use disorders, and transport and unintentional injuries. Substantial changes in the direction and magnitude of the association estimates were identified before and after adjusting for the potential confounders, particularly the urbanization and socio-economic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This national study provides evidence that the relationship between nighttime light exposure and population health outcomes is complex and often confounded by environmental and socio-economic factors. Policy efforts should focus on mitigating unnecessary nighttime illumination to potentially reduce associated health risks.\u003c/p\u003e","manuscriptTitle":"Associating nighttime lights with population health, with consideration of environmental and socio-economic confounders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 04:21:15","doi":"10.21203/rs.3.rs-5102937/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-11T04:55:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-10T00:07:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-29T05:06:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67349738228392767967186437043372065632","date":"2025-01-18T22:17:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263084251716357856854682584894695763537","date":"2025-01-17T22:20:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82583710768957349784096804257529122880","date":"2024-10-17T04:45:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338693701894128961794710293083430081119","date":"2024-10-08T10:29:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-03T09:55:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-26T09:54:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-24T10:52:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2024-09-17T11:37:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db6c3532-412c-4355-8242-9b04a30f3081","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T16:03:09+00:00","versionOfRecord":{"articleIdentity":"rs-5102937","link":"https://doi.org/10.1186/s12982-025-00972-1","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-09-20 15:57:45","publishedOnDateReadable":"September 20th, 2025"},"versionCreatedAt":"2025-03-31 04:21:15","video":"","vorDoi":"10.1186/s12982-025-00972-1","vorDoiUrl":"https://doi.org/10.1186/s12982-025-00972-1","workflowStages":[]},"version":"v1","identity":"rs-5102937","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5102937","identity":"rs-5102937","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}