Table of Contents Author Guidelines Submit a Manuscript
Journal of Environmental and Public Health
Volume 2019, Article ID 6942787, 12 pages
https://doi.org/10.1155/2019/6942787
Research Article

Injury-Related Deaths according to Environmental, Demographic, and Lifestyle Factors

Department of Public Health, College of Life Sciences, Brigham Young University, Provo, UT, USA

Correspondence should be addressed to Ray M. Merrill; ude.uyb@llirrem_yar

Received 14 September 2018; Accepted 29 January 2019; Published 3 March 2019

Academic Editor: Pam R. Factor-Litvak

Copyright © 2019 Ray M. Merrill. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Environmental, demographic, and lifestyle variables have been associated with injury-related deaths. The current study identifies the simultaneous association of selected environmental, demographic, and lifestyle variables with deaths from homicide, unintentional injuries, and suicide. Materials and Methods. Analyses are based on county-level mortality data in the contiguous United States, 2011–15. Basic summary statistics and Poisson regression were used to evaluate the data. Results. The selected causes of death were impacted differently by age, sex, and race: for homicide, mortality rates were greater in ages 20–39, males, and blacks; for unintentional injuries, the rates increased with age, most noticeably in the oldest age group, and were highest among males and whites; and for suicide, the rates tended to increase with age and were greater in males and whites. Mortality rates from homicide were positively associated with poverty, cigarette smoking, air temperature, and leisure-time physical inactivity. They were negatively associated with precipitation and sunlight. Mortality rates from unintentional injuries were positively associated with altitude, cigarette smoking, air temperature, poverty, obesity, and precipitation. They were negatively associated with population density. Mortality rates from suicides were positively associated with altitude, cigarette smoking, obesity, air temperature, and precipitation and negatively associated with population density. Conclusion. The results confirm and extend previous research in which death from homicide, unintentional injuries, and suicide are distinctly associated with a combination of environmental, demographic, and lifestyle variables. The findings may be useful in developing strategies for reducing injury-related deaths.

1. Introduction

Injury-related situations explain most deaths in the age range 1–44 in the United States [1]. In 2015, deaths from unintentional injuries occurred 3.3 times more often than death from suicides and death from suicides occurred 2.5 times more often than death from homicides. Specifically, the number of deaths from unintentional injuries (e.g., motor vehicles, poisoning, fire, falling, suffocation, and drowning) was 146,571 (45.6 per 100,000), from suicides was 44,193 (13.7 per 100,000), and from homicides was 17,793 (5.5 per 100,000) [2].

Environmental factors such as ambient air temperature, altitude, and precipitation have been associated with injury-related deaths. For example, a recent study showed that increases in ambient air temperature explained 10% of the variance in violent crime in Finland [3]. The study further suggested that the effect of ambient air temperature on violent crime is mediated by the serotonergic system. Specifically, higher ambient air temperature is associated with changes in serotonin transporter density that, in turn, increases impulsivity and irritability. Other studies have linked increasing ambient air temperature with higher violent suicide rates, violence, and trauma [4, 5]. There is also evidence of greater risk-taking behavior with increased heat exposure [6]. Violent crime and risk-taking behavior may result in death from unintentional injury (e.g., motor vehicle crashes and accidental firearm discharge) or homicide (death inflicted by another person with the intent to harm).

Several studies have found a positive relationship between altitude and suicide rates, after adjusting for demographic characteristics, gun ownership, and population density [713]. Increase in altitude is associated with an exponential decrease in atmospheric pressure [14]. As atmospheric pressure decreases, the partial pressure of inspired oxygen falls and less oxygen is absorbed into the body, thus causing hypoxia [15]. Metabolic stress from hypoxia negatively affects mood and increases the risk of depressive symptoms, thereby increasing the risk of suicide [811, 1622].

Adverse effects of higher altitude on psychological mood (irritability, hostility, and depression) and cognitive function (mental skills, reaction time, psychomotor performance, memory, etc.) are well described [2326]. Hypoxic conditions explain the impairment in mood and cognition that occurs with higher altitude exposure [20, 27]. Hypoxia associated with higher altitude has been associated with greater occupational injuries among miners [28] and higher rates of errors [29, 30]. Psychological states such as depression have been associated with increased risk of subsequent unintentional injury [31, 32].

Rainfall and extreme precipitation events have been shown to increase the risk of automobile accidents [3335], through loss of contact between the tire and the road, impaired visibility, and strain on cognitive capacity [36, 37]. Weather conditions explained 22% of car accidents during 2005–14 [38]. Of those involved in these accidents, about 1.3% were killed. Precipitation can also lead to injuries and deaths from falls, drowning, and hypothermia.

Along with environmental factors, injury-related deaths may be influenced by poverty, smoking, obesity, and other demographic and lifestyle variables. For example, research has associated higher poverty with greater levels of homicide [39], suicide [40, 41], and deaths from unintentional injuries [42, 43]; cigarette smoking with suicide [4446] and deaths from unintentional injuries [47]; and obesity with suicide [48] and death from motor vehicle collision [49]. However, a recent systematic review and meta-analysis showed an inverse association between obesity and suicide [50].

The relative contribution of environmental, demographic, and lifestyle variables on injury-related deaths has not been previously assessed. The purpose of the current study was to identify the association between injury-related deaths and selected environmental, demographic, and lifestyle variables and injury-related deaths. The association between the environmental, demographic, and lifestyle variables will also be considered.

2. Materials and Methods

Analyses were based on county-level mortality data for homicide and legal intervention (homicide), unintentional injuries, and suicide in the contiguous United States [51]. Federal Information Processing Standard (FIPS) codes identify counties in the United States. Mortality groupings are based on the International Classification of Diseases (ICD). Mortality data cover 2011 through 2015. Other variables included in this study, measured on the county level, were average daily sunlight (kJ/m2), altitude (m), average maximum daily temperature (F), average fine particulate matter (µg/m³), average daily precipitation (mm), percent living in poverty, percent of adults who smoke cigarettes, percent urban residents, percent obese (body mass index ≥ 30), and percent leisure-time physical inactivity. The number of counties with available information on these environmental, demographic, and lifestyle variables ranged from 3108 to 3142, as described in Table 1.

Table 1: Selected county-specific environmental, demographic, and lifestyle variables in the contiguous United States.

Average daily sunlight (kJ/m2), daily air temperature (F), and daily precipitation (mm) values are from the North America Land Data Assimilation System through the CDC Wonder database, 2007–11 [52]. Fine particulate matter (µg/m³) values are from the CDC wonder database, 2007–11 [52]. Altitude (meters) values are from the Geographic Names Information System from the United States Geological Survey [53].

Poverty values for 2013 are from the United States Census Bureau [54]. This measure is based on estimates of the level of income required to cover basic needs. Poverty involves those people residing in households with income below what is needed to cover basic needs. Cigarette-smoking prevalence estimates (age-adjusted 2000 US standard population) in adults, 1996–2012, are taken from a previous report [55]. The percent of the population living in urban areas was obtained from 2010 individual state reports from the 2010 Census of Population and Housing [56]. The population density per square mile of land area for 2010 is from the United States Census Bureau [57]. Prevalence values for obesity and leisure-time physical inactivity (age-adjusted 2000 US standard population) for 2013 were obtained from the Centers for Disease Control and Prevention [58].

The environmental, demographic, and lifestyle variables considered in this study were described using summary statistical values (mean, median, standard deviation, minimum, and maximum) across the counties. Poisson regression was used to assess the association between the mortality rates from homicide, unintentional injuries, and suicide with the environmental, demographic, and lifestyle variables. Variables were added to the models, one at a time, and dropped if they were not statistically significant at the 0.05 level (or close to unity but significant merely because of large numbers). Age, sex, and race were retained in each model. Resulting mortality rate ratios were adjusted for age, sex, race, and other variables that significantly contributed to the model. Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA, 2012).

3. Results

Relative mortality rates of homicide, unintentional injuries, and suicide in the United States are presented according to age, sex, and race for the years 2011–15 in Table 2. Age, sex, and race impacted these causes of death differently. For homicide, the rates were greater in ages 20–39, males, and blacks. There was no difference in rates between people in the youngest and oldest age groups. Other racial groups (Asian/Pacific Islander and American Indian/Alaska Native) had the lowest rates. For unintentional injuries, the rates increased with age, most noticeably in the oldest age group, and were highest among males and whites. Rates were similar across ages 20 through 79 and lowest among those in the other racial group. For suicide, the rates were greater in ages 20 years and older, males, and whites. Rates were similar across ages 20–79 and between blacks and those in the other racial group.

Table 2: Relative mortality rates from homicide and legal intervention, accidents and adverse effects, and suicide and self-inflicted injury in the contiguous United States according to age, sex, and race, 2011–15.

Summary statistics for selected county-specific environmental, demographic, and lifestyle variables are shown in Table 1. The variables are normally distributed, with the exceptions of altitude and population density, which are positively skewed. Each variable shows large variation across the counties.

Relative mortality rates of homicide in the United States are presented according to selected environmental, demographic, and lifestyle variables for 2011–15 in Table 3. Relative mortality rates from homicide are adjusted for age, sex, and race or fully adjusted for the variables with results appearing in the table. Counties with higher average daily temperature, poverty, adult smoking, and physical inactivity have higher mortality rates from homicide. Counties with higher average daily sunlight, average daily precipitation, and population density have lower mortality rates from homicide. In general, the lowest rates of death from homicide are in counties with higher average daily sunlight, lower average daily air temperature (bottom 25%), higher average daily precipitation, lower poverty (bottom 25%), lower adult smoking (bottom 25%), higher population density, and lower leisure-time physical inactivity (bottom 25%).

Table 3: Relative mortality rates of death from homicide and legal intervention according to selected environmental, demographic, and lifestyle variables in the contiguous United States, 2011–15.

Relative mortality rates of unintentional injuries in the United States are presented according to selected environmental, demographic, and lifestyle variables for the time period 2011–15 in Table 4. Counties with higher altitude, average daily temperature, average daily precipitation, poverty, adult smoking, and obesity have higher mortality rates from unintentional injuries. Counties with higher population density have lower mortality rates from unintentional injuries. In general, the lowest rates of death from unintentional injuries are in counties with lower altitude (bottom 25%), lower average daily air temperature (bottom 75%), lower average daily precipitation (bottom 25%), lower poverty (bottom 75%), lower adult smoking (bottom 25%), higher population density (top 75%), and lower obesity (bottom 25%).

Table 4: Relative mortality rates of death from accidents and adverse events according to selected environmental, demographic, and lifestyle variables in the contiguous United States, 2011–15.

Relative mortality rates of suicide in the United States are presented according to selected environmental, demographic, and lifestyle variables for the time period 2011–15 in Table 5. Counties with higher altitude, average maximum daily temperature, average daily precipitation, adult smokers, and obesity have higher mortality rates from suicide. Counties with higher population density have lower mortality rates from suicide. In general, the lowest rates of death from suicide are in counties with low altitude (bottom 25%), low average maximum daily air temperature (bottom 50%), lower average daily precipitation (bottom 25%), low adult smoking (bottom 25%), higher poverty (top 50%), higher population density (top 75%), and lower obesity (bottom 25%).

Table 5: Relative mortality rates of death from suicide and self-inflicted injury according to selected environmental, demographic, and lifestyle variables in the contiguous United States, 2011–15.

Average fine particulate matter above the median was significantly associated with higher levels of homicide, accidents, and suicide after adjusting for age, sex, and race. However, with the addition of adult smoking, average fine particulate matter became insignificant in each of the models. Higher urban residency was significantly associated with greater levels of homicide, accidents, and suicide after adjusting for age, sex, and race. Yet, when population density per square mile of land was also added to the models, urban residency became insignificant. Finally, strong correlation between obesity and physical inactivity resulted in the dominant of the two variables being retained in each of the models.

4. Discussion

This study explored the association between injury-related mortality rates and selected environmental, demographic, and lifestyle variables in the United States. Mortality rates from homicide, unintentional injuries, and suicide according to age, sex, and race are consistent with a report from the National Center for Health Statistics, 2016 [59]. Each of these causes of death was simultaneously associated with a combination of environmental, demographic, and lifestyle variables.

Deaths rates from homicide were greater in counties with higher average daily temperature, poverty, adult smoking, and physical inactivity. Rates were lower in areas with higher average daily sunlight, average daily precipitation, and population per square mile. Other studies have associated homicide with higher temperature and poverty [3, 5, 6, 39]. The result on population density is unclear. It does not appear that other research has assessed the association between homicide and smoking, precipitation, and sunlight. It is not known whether the associations in the current study are explained by other covariates, not included in our study. Higher temperature may be associated with more homicide deaths because greater temperature causes people to spend more time outside their homes [60]. In a similar manner, we found that more precipitation was related to lower homicide mortality rates. The observed positive association between leisure-time physical inactivity and homicide may be because people in places with greater violent crime are less inclined to go outdoors to exercise. One study found that adult walking was deterred in areas with higher violent crime [61]. Greater sunlight exposure is associated with higher levels of serotonin, which correlates with better mood, lower anxiety, and depression [62], and possibly lower risk-taking behavior [63]. Sunlight is also a source of vitamin D. Deficiency of vitamin D has been associated with mood disorders and depression [64, 65]. Research has identified increased risk of violent crime among individuals with mood disorders, anxiety, or depression [6668].

Mortality rates from unintentional injuries are higher in counties with higher altitude, average daily temperature, average daily precipitation, poverty, adult smoking, and obesity. There is an inverse association between mortality rates from unintentional injuries and population density. The association with altitude may be because higher altitude is associated with places where deaths are more likely to occur from injuries (e.g., falls from mountain climbing or trauma from ski crashes). Acute mountain sickness, which occurs when the body does not get enough oxygen [15], has symptoms like dizziness and muscle aches that can increase unintentional injuries.

Cigarette smoking has been identified as a leading cause of fire disaster and resulting death, fatal automobile accidents, and injuries at work and other unintentional injuries [36, 69, 70]. Reasons for the association between smoking and injuries may be because of distraction, direct toxicity, and confounding factors (smoking-related medical conditions, personality, and behavior characteristics) [47].

The association between air temperature and unintentional injuries is consistent with other research showing that, at hotter temperatures, individuals perceive the same risky behavior as less risky and participate in more risk-taking behavior [6].

The association between higher poverty and death from unintentional injuries is consistent with other research [42, 43]; the association between obesity and death from unintentional injuries is consistent with previous research [49], and the positive association between precipitation and unintentional injuries is consistent with other research [3338]. Other research has found that urban settings have lower levels of death from unintentional injuries, possibly because rural areas have higher levels of motor vehicle trauma, and exposure to hazardous firearms, farm machinery, falls, poisoning, and open water [71, 72]. The relative lack of emergency care and emergency care resources in rural areas may further explain higher deaths from unintentional injuries in lower population density areas [73].

Mortality rates from suicide are higher in counties with higher altitude, average maximum daily temperature, average daily precipitation, adult smokers, and obesity have higher mortality rates from suicide. The observed association between suicide and altitude is consistent with previous research [713]. The mechanism may involve hypoxia [15], wherein hypoxia increases the risk of depressive symptoms and risk of suicide [811, 1622]. The observed association between suicide and smoking has also been observed previously [4446]. The positive association observed between suicide and obesity was seen in another recent study [48] but not identified in a review study [50]. Consistent with the current finding, higher ambient air temperature has been previously associated with greater violent suicide rates [4]. It is not clear why precipitation was positively related to suicide. It may be that greater precipitation limits sun synthesized vitamin D, increases feelings of isolation, and adversely affects other feelings and possible behaviors associated with suicide. The negative association observed between suicide and population density is consistent with other research [74].

Average fine particulate matter was positively associated with suicide, after adjusting for age, sex, and race. This result is consistent with a study finding a positive association between exposure to nitrogen dioxide, particulate matter, and sulfur dioxide and suicide [75]. However, we found that further adjustment for other variables resulted in no association between air pollution and suicide, as consistent with another study adjusting for potential confounders [76].

The primary limitation of this study is that the measures are on the county level rather than the individual level. Thus, ecologic fallacy may play a role in our assessment of associations. Nevertheless, the environmental variables are similarly experienced among people within each county, for the most part. The large sample size tended to produce statistically significant results that may not be of practical importance. The environmental variables considered are largely not modifiable and may be less clinically useful for physicians and public health officials. Although the study focuses on the influence of environmental variables, a limited number of other variables were considered. This list does not include all variables known to influence injury (e.g., alcohol use). Finally, because the data are not longitudinal, drawing conclusions about causal directions is problematic (e.g., between obesity and smoking and smoking and poverty). Yet, for some of the variables, the causal direction is clear (e.g., higher temperature leads to more leisure-time physical activity, obesity, and poverty).

5. Conclusion

The findings in this study show the relative contribution of environmental, demographic, and lifestyle variables on injury-related deaths. Several databases were combined to provide a clearer understanding of factors associated with injury-related deaths. Mortality rates from homicide are positively associated with poverty, cigarette smoking, air temperature, and leisure-time physical inactivity and negatively associated with precipitation and sunlight; mortality rates from unintentional injuries are positively associated with altitude, cigarette smoking, air temperature, poverty, obesity, and precipitation and negatively associated with population density; and suicides are positively associated with altitude, cigarette smoking, obesity, air temperature, and precipitation and negatively associated with population density. Future study may determine more clearly the impact of the environmental variables assessed in this study on mortality rates from homicide, unintentional injuries, and suicide by considering seasonality.

Abbreviations

FIPS:Federal Information Processing Standard
ICD:International Classification of Diseases
kJ/m2:Kilojoule/square meter
m:Meter
mm:Millimeters
F:Fahrenheit
µg/m³:Microgram per cubic meter.

Data Availability

The data used in this study are in the public domain, with references to these data provided in the paper.

Conflicts of Interest

The author declares that there are no conflicts of interest.

Acknowledgments

This study was supported through funding from Brigham Young University.

References

  1. Centers for Disease Control and Prevention, Injury Prevention and Control. Ten Leading Causes of Death and Injury, Centers for Disease Control and Prevention, Atlanta, GA, USA, 2018, https://www.cdc.gov/injury/wisqars/LeadingCauses.html.
  2. S. L. Murphy, J. Q. Xu, K. D. Kochanek, S. C. Curtin, and E. A. Deaths, Final Data for 2015. National Vital Statistics Reports, vol. 66, National Center for Health Statistics, Hyattsville, MD, USA, 2017.
  3. J. Tiihonen, P. Halonen, L. Tiihonen, H. Kautiainen, M. Storvik, and J. Callaway, “The association of ambient temperature and violent crime,” Scientific Reports, vol. 7, no. 1, p. 6543, 2017. View at Publisher · View at Google Scholar · View at Scopus
  4. H. C. Lin, C. S. Chen, S. Xirasagar, and H. C. Lee, “Seasonality and climatic associations with violent and nonviolent suicide: a population-based study,” Neuropsychobiology, vol. 57, no. 1-2, pp. 32–37, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. S. J. Michel, H. Wang, S. Selvarajah et al., “Investigating the relationship between weather and violence in Baltimore, Maryland, USA,” Injury, vol. 47, no. 1, pp. 272–276, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. C.-H. Chang, T. E. Bernard, and J. Logan, “Effects of heat stress on risk perceptions and risk taking,” Applied Ergonomics, vol. 62, pp. 150–157, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Ha and W. Tu, “An ecological study on the spatially varying relationship between county-level suicide rates and altitude in the United States,” International Journal of Environmental Research & Public Health, vol. 15, no. 4, p. 671, 2018. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Brenner, D. Cheng, S. Clark, and C. A. Camargo Jr., “Positive association between altitude and suicide in 2584 U.S. counties,” High Altitude Medicine & Biology, vol. 12, no. 1, pp. 31–35, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. R. S. Huber, H. Coon, N. Kim, P. F. Renshaw, and D. G. Kondo, “Altitude is a risk factor for completed suicide in bipolar disorder,” Medical Hypotheses, vol. 82, no. 3, pp. 377–381, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. C. A. Haws, D. D. Gray, D. A. Yurgelun-Todd, M. Moskos, L. J. Meyer, and P. F. Renshaw, “The possible effect of altitude on regional variation in suicide rates,” Medical Hypotheses, vol. 73, no. 4, pp. 587–590, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Kim, N. Choi, Y.-J. Lee et al., “High altitude remains associated with elevated suicide rates after adjusting for socioeconomic status: a study from South Korea,” Psychiatry Investigation, vol. 11, no. 4, pp. 492–494, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Alameda-Palacios, M. Ruiz-Ramos, and B. García-Robredo, “Mortalidad por suicidio en Andalucía: distribución geográfica y relación con el uso de antidepresivos, la altitud y desigualdades socioeconómicas,” Revista Española de Salud Pública, vol. 89, no. 3, pp. 283–293, 2015. View at Publisher · View at Google Scholar
  13. M. Helbich, V. Blüml, M. Leitner, and N. D. Kapusta, “Does altitude moderate the impact of lithium on suicide? A spatial analysis of Austria,” Geospatial Health, vol. 7, no. 2, pp. 209–218, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Wallace and P. Hobbs, Atmospheric Science: An Introductory Survey, Elsevier Academy Press, Amsterdam, Netherlands, 2nd edition, 2006.
  15. A. J. Peacock, “ABC of oxygen: oxygen at high altitude,” BMJ, vol. 317, no. 7165, pp. 1063–1066, 1998. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Cheng, “Higher suicide death rate in rocky mountain states and a correlation to altitude,” Wilderness & Environmental Medicine, vol. 21, no. 2, pp. 177-178, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. J. L. Gamboa, R. Caceda, and A. Arregui, “Is depression the link between suicide and high altitude?” High Altitude Medicine & Biology, vol. 12, no. 4, pp. 403-404, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. K. DelMastro, T. Hellem, N. Kim, D. Kondo, Y. H. Sung, and P. F. Renshaw, “Incidence of major depressive episode correlates with elevation of substate region of residence,” Journal of Affective Disorders, vol. 129, no. 1–3, pp. 376–379, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Kanekar, O. V. Bogdanova, P. R. Olson, Y.-H. Sung, K. E. D’Anci, and P. F. Renshaw, “Hypobaric hypoxia induces depression-like behavior in female Sprague-Dawley rats, but not in males,” High Altitude Medicine & Biology, vol. 16, no. 1, pp. 52–60, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Li, X. Wu, C. Fu, X. F. Shen, Y. H Wu, and T. Wang, “Effects of acute mild and moderate hypoxia on human mood state,” Space Medicine & Medical Engineering, vol. 13, no. 1, pp. 1–5, 2000. View at Google Scholar
  21. B. Shukitt-Hale, L. E. Banderet, and H. R. Lieberman, “Elevation-dependent symptom, mood, and performance changes produced by exposure to hypobaric hypoxia,” International Journal of Aviation Psychology, vol. 8, no. 4, pp. 319–334, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Young, “Elevated incidence of suicide in people living at altitude, smokers and patients with chronic obstructive pulmonary disease and asthma: possible role of hypoxia causing decreased serotonin synthesis,” Journal of Psychiatry & Neuroscience, vol. 38, no. 6, pp. 423–426, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. S. Bahrke and B. Shukitt-Hale, “Effects of altitude on mood, behaviour and cognitive functioning,” Sports Medicine, vol. 16, no. 2, pp. 97–125, 1993. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Pun, V. Guadagni, K. M. Battauer et al., “Effect on cognitive function of acute, subacute and repeated exposures to high altitude,” Frontier in Physiology, vol. 9, p. 1131, 2018. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Yan, “Cognitive impairments at high altitudes and adaptation,” High Altitude Medicine & Biology, vol. 15, no. 2, pp. 141–145, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. B. L. Shukitt and L. E. Banderet, “Mood states at 1600 and 4300 m terrestrial altitude,” Aviation, Space, and Environmental Medicine, vol. 59, pp. 530–532, 1988. View at Google Scholar
  27. D. Asmaro, J. Mayall, and S. Ferguson, “Cognition at altitude: impairment in executive and memory processes under hypoxic conditions,” Aviation, Space, and Environmental Medicine, vol. 84, no. 11, pp. 1159–1165, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Vearrier and M. I. Greenberg, “Occupational health of miners at altitude: adverse health effects, toxic exposures, pre-placement screening, acclimatization, and worker surveillance,” Clinical Toxicology, vol. 49, no. 7, pp. 629–640, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. T. F. Hornbein, B. D. Townes, R. B. Schoene, J. R. Sutton, and C. S. Houston, “The cost to the central nervous system of climbing to extremely high altitude,” New England Journal of Medicine, vol. 321, no. 25, pp. 1714–1719, 1989. View at Publisher · View at Google Scholar · View at Scopus
  30. K. Davranche, L. Casini, P. J. Arnal, T. Rupp, S. Perrey, and S. Verges, “Cognitive functions and cerebral oxygenation changes during acute and prolonged hypoxic exposure,” Physiology & Behavior, vol. 164, pp. 189–197, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. K. J. Inder, E. G. Holliday, T. E. Handley et al., “Depression and risk of unintentional injury in rural communities–a longitudinal analysis of the Australian rural mental health study,” International Journal of Environmental Research and Public Health, vol. 14, no. 9, p. 1080, 2017. View at Publisher · View at Google Scholar · View at Scopus
  32. S. S. Alavi, M. R. Mohammadi, H. Souri, S. Mohammadi Kalhori, F. Jannatifard, and G. Sepahbodi, “Personality, driving behavior and mental disorders factors as predictors of road traffic accidents based on logistic regression,” Iran Journal of Medical Sciences, vol. 42, no. 1, pp. 24–31, 2017. View at Google Scholar
  33. J. B. Edwards, “The temporal distribution of road accidents in adverse weather,” Meteorological Applications, vol. 6, no. 1, pp. 59–68, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Qiu L and W. A. Nixon, “Effects of adverse weather on traffic crashes systematic review and meta-analsyis,” Transportation Research Record, vol. 2055, no. 1, pp. 139–146, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Liu, S. I. Soneja, C. Jiang et al., “Frequency of extreme weather events and increased risk of motor vehicle collision in Maryland,” Science of the Total Environment, vol. 580, pp. 550–555, 2017. View at Publisher · View at Google Scholar · View at Scopus
  36. L. Fridstrom, J. Ifver, S. Ingebrigtsen, R. Kulmala, and L. K. Thomsen, “Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts,” Accident Analysis & Prevention, vol. 27, no. 1, pp. 1–20, 1995. View at Publisher · View at Google Scholar
  37. R. Elvik, “Laws of accident causation,” Accident Analysis & Prevention, vol. 38, no. 4, pp. 742–747, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. After car accidents, Top 7 causes of car accidents: 2018 Statistics, http://www.after-car-accidents.com/car-accident-causes.html.
  39. C. Cubbin, F. B. LeClere, and G. S. Smith, “Socioeconomic status and injury mortality: individual and neighbourhood determinants,” Journal of Epidemiology and Community Health, vol. 54, no. 7, pp. 517–524, 2000. View at Publisher · View at Google Scholar · View at Scopus
  40. W. C. Kerr, M. S. Kaplan, N. Huguet, R. Caetano, N. Giesbrecht, and B. H. McFarland, “Economic recession, alcohol, and suicide rates: comparative effects of poverty, foreclosure, and job loss,” American Journal of Preventive Medicine, vol. 52, no. 4, pp. 469–475, 2017. View at Publisher · View at Google Scholar · View at Scopus
  41. C. Coope, D. Gunnell, W. Hollingworth et al., “Suicide and the 2008 economic recession: who is most at risk? Trends in suicide rates in England and Wales 2001-2011,” Social Science in Medicine, vol. 117, pp. 76–85, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. R. A. Karb, S. V. Subramanian, and E. W. Fleegler, “County poverty concentration and disparities in unintentional injury deaths: a fourteen-year analysis of 1.6 million U.S. fatalities,” PLoS One, vol. 11, no. 5, Article ID e0153516, 2016. View at Publisher · View at Google Scholar · View at Scopus
  43. K. Steenland, W. Halperin, S. Hu, and J. T. Walker, “Deaths due to injuries among employed adults: the effects of socioeconomic class,” Epidemiology, vol. 14, no. 1, pp. 74–79, 2003. View at Publisher · View at Google Scholar · View at Scopus
  44. S. Park and J. Kim, “Association between smoking and suicidal behaviors among adolescents in the Republic of Korea,” Journal of Addictions Nursing, vol. 26, no. 4, pp. 175–183, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. L. Balbuena and R. Tempier, “Independent association of chronic smoking and abstinence with suicide,” Psychiatric Services, vol. 66, no. 2, pp. 186–192, 2015. View at Publisher · View at Google Scholar · View at Scopus
  46. J. Poorolajal and N. Darvishi, “Smoking and suicide: a meta-analysis,” PLoS One, vol. 11, no. 7, Article ID e0156348, 2016. View at Publisher · View at Google Scholar · View at Scopus
  47. J. J. Sacks and D. E. Nelson, “Smoking and injuries: an overview,” Preventive Medicine, vol. 23, no. 4, pp. 515–520, 1994. View at Publisher · View at Google Scholar · View at Scopus
  48. B. Wagner, G. Klinitzke, E. Brähler, and A. Kersting, “Extreme obesity is associated with suicidal behavior and suicide attempts in adults: results of a population-based representative sample,” Depression and Anxiety, vol. 30, no. 10, pp. 975–981, 2013. View at Publisher · View at Google Scholar · View at Scopus
  49. J. P. Donnelly, R. L. Griffin, N. Sathiakumar, and G. McGwin Jr., “Obesity and vehicle type as risk factors for injury caused by motor vehicle collision,” Journal of Trauma and Acute Care Surgery, vol. 76, no. 4, pp. 1116–1121, 2014. View at Publisher · View at Google Scholar · View at Scopus
  50. S. Amiri and S. Behnezhad, “Body mass index and risk of suicide: a systematic review and meta-analysis,” Journal of Affective Disorders, vol. 238, pp. 615–625, 2018. View at Publisher · View at Google Scholar · View at Scopus
  51. Surveillance, epidemiology, and end results (SEER) program, http://www.seer.cancer.gov, SEER∗Stat Database: Mortality - All COD, Aggregated With County, Total U.S. (1969-2015) <Katrina/Rita Population Adjustment> - Linked To County Attributes - Total U.S., 1969-2016 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released December 2017. Underlying mortality data provided by NCHS (http://www.cdc.gov/nchs).
  52. Centers for Disease Control and Prevention, Wonder, Centers for Disease Control and Prevention, Atlanta, GA, USA, 2018, https://wonder.cdc.gov/.
  53. USGS, Geographic Names Information System, USGS, Reston, VA, USA, 2018, https://www2.usgs.gov/faq/categories/9865/7021.
  54. United States Census Bureau, All Ages in Poverty, United States Census Bureau, Suitland, MD, USA, 2013, https://wwprod/data-tools/demo/saipe/saipe.html?s_appName=saipe&menu=grid_proxy.
  55. L. Dwyer-Lindgren, A. Mokdad, T. Srebotnjak, A. Flaxman, G. Hansen, and C. Murray, “Cigarette smoking prevalence in US counties: 1996–2012,” Population Health Metrics, vol. 12, no. 1, p. 5, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. United States Summary, Population and Housing Unit Counts, United States Census Bureau, Suitland, MD, USA, 2010, https://www.census.gov/prod/cen2010/cph-2-1.pdf; https://www2.census.gov/library/publications/cen2010/cph-2-1.pdf.
  57. United States Census Bureau, Population, Housing Units, Area, and Density: 2010-United States–County by State; and for Puerto Rico, 2010 Census Summary File 1, United States Census Bureau, Suitland, MD, USA, 2010, https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk.
  58. Centers for Disease Control and Prevention, County Data Indicators, Centers for Disease Control and Prevention, Atlanta, GA, USA, 2016, https://www.cdc.gov/diabetes/data/countydata/countydataindicators.html.
  59. National Center for Health Statistics, Health, United States, with Chartbook on Long-Term Trends in Health, National Center for Health Statistics, Hyattsville, MD, USA, 2016.
  60. S. Field, “The effect of temperature on crime,” British Journal of Criminology, vol. 32, no. 3, pp. 340–351, 1992. View at Publisher · View at Google Scholar · View at Scopus
  61. K. Janke, C. Propper, and M. A. Shields, “Assaults, murders and walkers: the impact of violent crime on physical activity,” Journal of Health Economics, vol. 47, pp. 34–49, 2016. View at Publisher · View at Google Scholar · View at Scopus
  62. M. N. Mead, “Benefits of sunlight: a bright spot for human health,” Environmental Health Perspectives, vol. 116, no. 10, pp. 160–167, 2008. View at Publisher · View at Google Scholar
  63. A. B. Long, C. M. Kuhn, and M. L. Platt, “Serotonin shapes risky decision making in monkeys,” Social Cognitive and Affective Neuroscience, vol. 4, no. 4, pp. 346–356, 2009. View at Publisher · View at Google Scholar · View at Scopus
  64. P. K. Murphy and C. L. Wagner, “Vitamin D and mood disorders among women: an integrative review,” Journal of Midwifery & Women’s Health, vol. 53, no. 5, pp. 440–446, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. M. Berk, K. M. Sanders, J. A. Paso et al., “Vitamin D deficiency may play a role in depression,” Medical Hypotheses, vol. 69, no. 6, pp. 1316–1319, 2007. View at Publisher · View at Google Scholar · View at Scopus
  66. S. Fazel, A. Wolf, Z. Chang, H. Larsson, G. M. Goodwin, and P. Lichtenstein, “Depression and violence: a Swedish population study,” Lancet Psychiatry, vol. 2, no. 3, pp. 224–232, 2015. View at Publisher · View at Google Scholar · View at Scopus
  67. T. Moberg, M. Stenbacka, A. Tengstrom, E. G. Jonsson, P. Nordstrom, and J. Jokinen, “Psychiatric and neurological disorders in late adolescence and risk of convictions for violent crime in men,” BMC Psychiatry, vol. 15, no. 1, p. 299, 2015. View at Publisher · View at Google Scholar · View at Scopus
  68. S. Fazel, J. Zetterqvist, H. Larsson, N. Langstrom, and P. Lichtenstein, “Antisychotics, mood stabilisers, and risk of violent crime,” Lancet, vol. 384, no. 9949, pp. 1206–1214, 2014. View at Publisher · View at Google Scholar · View at Scopus
  69. B. N. Leistikow, D. C. Martin, and C. E. Milano, “Fire injuries, disasters, and costs from cigarettes and costs from cigarettes and cigarette lights: a global overview,” Preventive Medicine, vol. 31, no. 1, pp. 91–99, 2000. View at Publisher · View at Google Scholar · View at Scopus
  70. J. Benton and EHS Safety News America, Top 10 List of the Most Deadliest Driving Distractions, EHS Safety News America, Chicago, IL, USA, 2013, https://ehssafetynewsamerica.com/2013/04/08/top-10-list-of-the-most-deadliest-driving-distractions/.
  71. M. Boland, A. Staines, P. Fitzpatrick, and E. Scallan, “Urban-rural variation in mortality and hospital admission rates for unintentional injury in Ireland,” Injury Prevention, vol. 11, no. 1, pp. 38–42, 2005. View at Publisher · View at Google Scholar · View at Scopus
  72. J. H. Coben, H. M. Tiesman, R. M. Bossarte, and P. M. Furbee, “Rural-urban differences in injury hospitalizations in the U.S., 2004,” American Journal of Preventive Medicine, vol. 36, no. 1, pp. 49–55, 2009. View at Publisher · View at Google Scholar · View at Scopus
  73. S. R. Myers, C. C. Branas, B. C. French et al., “Safety in numbers: are major cities the safest places in the United States?” Annals of Emergency Medicine, vol. 62, no. 4, pp. 408–418, 2013. View at Publisher · View at Google Scholar · View at Scopus
  74. National Advisory Committee on Rural Health and Human Services, Understanding the Impact of Suicide in Rural America, Policy Brief and Recommendations, Health Resources and Services Administration, Rockville, MD, USA, 2017, https://www.hrsa.gov/sites/default/files/hrsa/advisory-committees/rural/publications/2017-impact-of-suicide.pdf.
  75. A. V. Bakian, R. S. Huber, H. Coon et al., “Acute air pollution exposure and risk of suicide completion,” American Journal of Epidemiology, vol. 181, no. 5, pp. 295–303, 2015. View at Publisher · View at Google Scholar · View at Scopus
  76. J. A. Fernandez-Nino, C. I Astudillo-Garcia, L. A. Rodriguez-Villamizar, and V. A. Florez-Garcia, “Association between air pollution and suicide: a time series analysis of four Columbian cities,” Environmental Health, vol. 17, no. 1, p. 47, 2018. View at Publisher · View at Google Scholar · View at Scopus