Downscaling NLDAS-2 daily maximum air temperatures using MODIS land surface temperatures.

We have developed and applied a relatively simple disaggregation scheme that uses spatial patterns of Land Surface Temperature (LST) from MODIS warm-season composites to improve the spatial characterization of daily maximum and minimum air temperatures. This down-scaling model produces qualitatively reasonable 1 km daily maximum and minimum air temperature estimates that reflect urban and coastal features. In a 5-city validation, the model was shown to provide improved daily maximum air temperature estimates in the three coastal cities, compared to 12 km NLDAS-2 (North American Land Data Assimilation System). Down-scaled maximum temperature estimates for the other two (non-coastal) cities were marginally worse than the original NLDAS-2 temperatures. For daily minimum temperatures, the scheme produces spatial fields that qualitatively capture geographic features, but quantitative validation shows the down-scaling model performance to be very similar to the original NLDAS-2 minimum temperatures. Thus, we limit the discussion in this paper to daily maximum temperatures. Overall, errors in the down-scaled maximum air temperatures are comparable to errors in down-scaled LST obtained in previous studies. The advantage of this approach is that it produces estimates of daily maximum air temperatures, which is more relevant than LST in applications such as public health. The resulting 1 km daily maximum air temperatures have great potential utility for applications such as public health, energy demand, and surface energy balance analyses. The method may not perform as well in conditions of strong temperature advection. Application of the model also may be problematic in areas having extreme changes in elevation.

Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data.

BACKGROUND: Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes. METHODS: We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression. RESULTS: We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria. CONCLUSION: Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.

Reconnoitering the linkage between cardiovascular disease mortality and long-term exposures to outdoor environmental factors in the USA using remotely-sensed data.

This ecological study aimed to assess the association between long-term exposures to outdoor environmental factors and mortality rate from cardiovascular disease (CVD) in a diverse and spatially distributed population from 3,094 counties within the U.S. (n > 3,780,000 CVD deaths) using satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were conducted to determine whether PM2.5, sunlight and maximum heat index were related to the odds of the total CVD death rate based on gender, race, and age taking into consideration the confounding risk factors of diabetes, obesity, leisure- time physical inactivity, smoking and socioeconomic status. The study has shown that elevated levels of PM2.5, sunlight and heat long-term exposures are significantly associated with an increase in the odds ratio of the total CVD mortality. The results suggest a 9.8% (95% CI = 6.3% - 13.4%), 0.9% (95% CI = 0.5% - 1.2%), and 0.7% (95% CI = 0.5% - 11.2%) increase in total CVD mortality associated with 10 µg/m3 increase in PM2.5 concentrations, 1,000 kJ/m2 increases in sunlight, and 1 oF increase in heat index, respectively. The odds ratios for the CVD death rate due to long-term exposures of PM2.5, sunlight, and heat index were significantly greater than 1.0 for all categories except for Asians, Hispanics, and American Indians, indicating that the effect of long-term exposures to particulate matter, sunlight radiation, and maximum heat on CVD mortality is trivial for Asians, Hispanics, and American Indians. Among the categories of age, the group of 65 years and older had the highest odds ratios, suggesting that the age group of 65 years and older are the most vulnerable group to the environmental exposures of PM2.5 (OR = 1.179, 95% CI = 1.124 - 1.237), sunlight (OR = 1.047, 95% CI = 1.041 - 1.053), and maximum heat (OR = 1.014, 95% CI = 1.011 - 1.016). The odds ratios of CVD mortality due to the environmental exposures were higher for Blacks than those for Whites. The odds ratios for all categories were attenuated with the inclusion of diabetes, obesity, leisure-time physical inactivity, smoking, and income covariates, reflecting the effect of other medical conditions, lifestyle, behavioral and socioeconomic factors on the CVD death rate besides the environmental factors.

Fine particulate matter and incident coronary heart disease in the REGARDS cohort.

Chronic exposure to fine particulate matter (PM2.5) is accepted as a causal risk factor for coronary heart disease (CHD). However, most of the evidence for this hypothesis is based upon cohort studies in whites, comprised of either only males or females who live in urban areas. It is possible that many estimates of the effect of chronic exposure to PM2.5 on risk for CHD do not generalize to more diverse samples. METHODS: Therefore, we estimated the relationship between chronic exposure to PM2.5 and risk for CHD in among participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort who were free from CHD at baseline (n=17,126). REGARDS is a sample of whites and blacks of both genders living across the continental United States. We fit Cox proportional hazards models for time to CHD to estimate the hazard ratio for baseline 1-year mean PM2.5 exposure, adjusting for environmental variables, demographics, and other risk factors for CHD including the Framingham Risk Score. RESULTS: The hazard ratio (95% CI) for a 2.7-µg/m3 increase (interquartile range) 1-year mean concentration of PM2.5 was 0.94 (0.83-1.06) for combined CHD death and nonfatal MI, 1.13 (0.92-1.40) for CHD death, and 0.85 (0.73-0.99) for nonfatal MI. We also did not find evidence that these associations depended upon overall CHD risk factor burden. CONCLUSIONS: Our results do not provide strong evidence for an association between PM2.5 and incident CHD in a heterogeneous cohort, and we conclude that the effects of chronic exposure to fine particulate matter on CHD require further evaluation.

Investigating the effects of environmental factors on autism spectrum disorder in the USA using remotely sensed data.

This study aimed to assess the association between exposures to outdoor environmental factors and autism spectrum disorder (ASD) prevalence in a diverse and spatially distributed population of 8-year-old children from the USA (n = 2,097,188) using the air quality index (AQI) of the US Environmental Protection Agency as well as satellite-derived data of PM2.5 concentrations, sunlight, and maximum heat index. Multivariable logistic regression analyses were performed to determine whether the unhealthy AQI, PM2.5, sunlight, and maximum heat index were related to the odds of ASD prevalence based on gender and race and taking into consideration the confounding factors of smoking and socioeconomic status. The logistic regression odds ratios for ASD per 10% increase in the unhealthy AQI were greater than 1 for all categories, indicating that unhealthy AQI is related to the odds of ASD prevalence. The odds ratio of ASD due to the exposure to the unhealthy AQI was higher for Asians (OR = 2.96, 95% CI = 1.11-7.88) than that for Hispanics (OR = 1.308, 95% CI = 0.607-2.820), and it was higher for Blacks (OR = 1.398, 95% CI = 0.827-2.364) than that for Whites (OR = 1.219, 95% CI = 0.760-1.954). The odds ratio of ASD due to the unhealthy AQI was slightly higher for males (OR = 1.123, 95% CI = 0.771-1.635) than that for females (OR = 1.117, 95% CI = 0.789-1.581). The effects of the unhealthy environmental exposures on the odds ratios of ASD of this study were inconclusive (i.e., statically insignificant; p value > 0.05) for all categories except for Asians. The odds ratios of ASD for Asians were increased by 5, 12, and 14% with increased levels of the environmental exposures of 10 µg/m3 of PM2.5, 1000 kJ/m2 of sunlight, and 1 °F of maximum heat index, respectively. The odds ratios of ASD prevalence for all categories, except for Asians, were increased with the inclusion of the smoking covariate, reflecting the effect of smoking on ASD prevalence besides the unhealthy environmental factors.

The association of remotely sensed outdoor fine particulate matter with cancer incidence of respiratory system in the USA.

This study aimed to assess the association between exposure to fine particulate matter (PM2.5) and respiratory system cancer incidence in the US population (n = 295,404,580) using a satellite-derived estimate of PM2.5 concentrations. Linear and logistic regression analyses were performed to determine whether PM2.5 was related to the odds of respiratory system cancer (RSC) incidence based on gender and race. Positive linear regressions were found between PM2.5 concentrations and the age-adjusted RSC incidence rates for all groups (Males, Females, Whites, and Blacks) except for Asians and American Indians. The linear relationships between PM2.5 and RSC incidence rate per 1 µg/m3 PM2.5 increase for Males, Females, Whites, Blacks, and all categories combined had slopes of, respectively, 7.02 (R2 = 0.36), 2.14 (R2 = 0.14), 3.92 (R2 = 0.23), 5.02 (R2 = 0.21), and 4.15 (R2 = 0.28). Similarly, the logistic regression odds ratios per 10 µg/m3 increase of PM2.5 were greater than one for all categories except for Asians and American Indians, indicating that PM2.5 is related to the odds of RSC incidence. The age-adjusted odds ratio for males (OR = 2.16, 95% CI = 1.56-3.01) was higher than that for females (OR = 1.50, 95% CI = 1.09-2.06), and it was higher for Blacks (OR = 2.12, 95% CI = 1.43-3.14) than for Whites (OR = 1.72, 95% CI = 1.23-2.42). The odds ratios for all categories were attenuated with the inclusion of the smoking covariate, reflecting the effect of smoking on RSC incidence besides PM2.5.

Assimilation of SMOS Retrievals in the Land Information System.

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm3 cm-3. These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.

The public health exposome: a population-based, exposure science approach to health disparities research.

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures "get under the skin". The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.

Environmental Public Health Applications Using Remotely Sensed Data.

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Sunlight exposure and cardiovascular risk factors in the REGARDS study: a cross-sectional split-sample analysis.

BACKGROUND: Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status. METHODS: Residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine previous-year sunlight radiation exposure for 17,773 black and white participants aged 45+ from the US. Exploratory and confirmatory analyses were performed by randomly dividing the sample into halves. Logistic regression models were used to examine relationships with cardiovascular risk factors. RESULTS: The lowest, compared to the highest quartile of insolation exposure was associated with lower high-density lipoprotein levels in adjusted exploratory (-2.7 mg/dL [95% confidence interval: -4.2, -1.2]) and confirmatory (-1.5 mg/dL [95% confidence interval: -3.0, -0.1]) models. The lowest, compared to the highest quartile of insolation exposure was associated with higher systolic blood pressure levels in unadjusted exploratory and confirmatory, as well as the adjusted exploratory model (2.3 mmHg [95% confidence interval: 0.8, 3.8]), but not the adjusted confirmatory model (1.6 mg/dL [95% confidence interval: -0.5, 3.7]). CONCLUSIONS: The results of this study suggest that lower long-term sunlight exposure has an association with lower high-density lipoprotein levels. However, all associations were weak, thus it is not known if insolation may affect cardiovascular outcomes through these risk factors.

The relationship between long-term sunlight radiation and cognitive decline in the REGARDS cohort study.

Sunlight may be related to cognitive function through vitamin D metabolism or circadian rhythm regulation. The analysis presented here sought to test whether ground and satellite measures of solar radiation are associated with cognitive decline. The study used a 15-year residential history merged with satellite and ground monitor data to determine sunlight (solar radiation) and air temperature exposure for a cohort of 19,896 cognitively intact black and white participants aged 45+ from the 48 contiguous United States. Exposures of 15, 10, 5, 2, and 1-year were used to predict cognitive status at the most recent assessment in logistic regression models; 1-year insolation and maximum temperatures were chosen as exposure measures. Solar radiation interacted with temperature, age, and gender in its relationships with incident cognitive impairment. After adjustment for covariates, the odds ratio (OR) of cognitive decline for solar radiation exposure below the median vs above the median in the 3rd tertile of maximum temperatures was 1.88 (95 % CI: 1.24, 2.85), that in the 2nd tertile was 1.33 (95 % CI: 1.09, 1.62), and that in the 1st tertile was 1.22 (95 % CI: 0.92, 1.60). We also found that participants under 60 years old had an OR = 1.63 (95 % CI: 1.20, 2.22), those 60-80 years old had an OR = 1.18 (95 % CI: 1.02, 1.36), and those over 80 years old had an OR = 1.05 (0.80, 1.37). Lastly, we found that males had an OR = 1.43 (95 % CI: 1.22, 1.69), and females had an OR = 1.02 (0.87, 1.20). We found that lower levels of solar radiation were associated with increased odds of incident cognitive impairment.

Fine particulate matter and incident cognitive impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort.

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

Short- and long-term sunlight radiation and stroke incidence.

OBJECTIVE: Examine whether long- and short-term sunlight radiation is related to stroke incidence. METHODS: Fifteen-year residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine sunlight radiation (insolation) and temperature exposure for a cohort of 16,606 stroke and coronary artery disease-free black and white participants aged ≥45 years from the 48 contiguous United States. Fifteen-, 10-, 5-, 2-, and 1-year exposures were used to predict stroke incidence during follow-up in Cox proportional hazard models. Potential confounders and mediators were included during model building. RESULTS: Shorter exposure periods exhibited similar, but slightly stronger relationships than longer exposure periods. After adjustment for other covariates, the previous year's monthly average insolation exposure below the median gave a hazard ratio (HR) of 1.61 (95% confidence interval [CI], 1.15-2.26), and the previous year's highest compared to the second highest quartile of monthly average maximum temperature exposure gave an HR of 1.92 (95%, 1.27-2.92). INTERPRETATION: These results indicate a relationship between lower levels of sunlight radiation and higher stroke incidence. The biological pathway of this relationship is not clear. Future research will show whether this finding stands, the pathway for this relationship, and whether it is due to short- or long-term exposures.

Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression.

Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.

The association of remotely-sensed outdoor temperature with blood pressure levels in REGARDS: a cross-sectional study of a large, national cohort of African-American and white participants.

BACKGROUND: Evidence is mounting regarding the clinically significant effect of temperature on blood pressure. METHODS: In this cross-sectional study the authors obtained minimum and maximum temperatures and their respective previous week variances at the geographic locations of the self-reported residences of 26,018 participants from a national cohort of blacks and whites, aged 45+. Linear regression of data from 20,623 participants was used in final multivariable models to determine if these temperature measures were associated with levels of systolic or diastolic blood pressure, and whether these relations were modified by stroke-risk region, race, education, income, sex hypertensive medication status, or age. RESULTS: After adjustment for confounders, same-day maximum temperatures 20 °F lower had significant associations with 1.4 mmHg (95% CI: 1.0, 1.9) higher systolic and 0.5 mmHg (95% CI: 0.3, 0.8) higher diastolic blood pressures. Same-day minimum temperatures 20 °F lower had a significant association with 0.7 mmHg (95% CI: 0.3, 1.0) higher systolic blood pressures but no significant association with diastolic blood pressure differences. Maximum and minimum previous-week temperature variabilities showed significant but weak relationships with blood pressures. Parameter estimates showed effect modification of negligible magnitude. CONCLUSIONS: This study found significant associations between outdoor temperature and blood pressure levels, which remained after adjustment for various confounders including season. This relationship showed negligible effect modification.

Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance.

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 microm (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM,2. not only provides a more complete daily representation of PM,2. than either dataset alone would allow, but it also reduces the errors in the PM2.5-estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.

Effect of sunlight exposure on cognitive function among depressed and non-depressed participants: a REGARDS cross-sectional study.

BACKGROUND: Possible physiological causes for the effect of sunlight on mood are through the suprachiasmatic nuclei and evidenced by serotonin and melatonin regulation and its associations with depression. Cognitive function involved in these same pathways may potentially be affected by sunlight exposure. We evaluated whether the amount of sunlight exposure (i.e. insolation) affects cognitive function and examined the effect of season on this relationship. METHODS: We obtained insolation data for residential regions of 16,800 participants from a national cohort study of blacks and whites, aged 45+. Cognitive impairment was assessed using a validated six-item screener questionnaire and depression status was assessed using the Center for Epidemiologic Studies Depression Scale. Logistic regression was used to find whether same-day or two-week average sunlight exposure was related to cognitive function and whether this relationship differed by depression status. RESULTS: Among depressed participants, a dose-response relationship was found between sunlight exposure and cognitive function, with lower levels of sunlight associated with impaired cognitive status (odds ratio = 2.58; 95% CI 1.43-6.69). While both season and sunlight were correlated with cognitive function, a significant relation remained between each of them and cognitive impairment after controlling for their joint effects. CONCLUSION: The study found an association between decreased exposure to sunlight and increased probability of cognitive impairment using a novel data source. We are the first to examine the effects of two-week exposure to sunlight on cognition, as well as the first to look at sunlight's effects on cognition in a large cohort study.