Why is High Altitude Living Associated with Lower Premature Death and Better Quality of Life? An Ecologic Study Involving US Counties

Merrill RM

Brigham Young University, Department of Public Health, College of Life Sciences

Article
Article Info
Author Info
Figures & Data

Abstract

The current study identifies direct and indirect associations between altitude and premature death and poor quality of life health outcomes. An ecologic study design was used, involving 3,108 counties in the contiguous US. Statistical measures estimated the association between county-level altitude and selected health outcomes. Higher altitude was associated with less premature death and better quality of life, but these associations were explained by higher average daily sunlight and lower average daily maximum air temperature, PM2.5 and precipitation, which correlate with higher altitude. After accounting for the environmental variables, higher altitude was then associated with more premature death and poorer quality of life. In an adjusted model, the amount of variation in a summary health outcomes index explained by altitude was 3.9%, average daily sunlight was 2.1%, maximum air temperature was 21.0%, and precipitation was 3.5%. The effect of PM2.5 was negligible. The model was extended to also include smoking status, income, insurance status, children in poverty, income ratio, single-parent households, violent crime rate, and injury death rate, whereupon the amount of variation in the health outcomes index explained by altitude was 0.7%, average daily sunlight was 0.6%, maximum air temperature was 0.9%, and precipitation was 0.5%. Taken together, better health is associated with lower altitude and average daily maximum air temperature, PM2.5, and precipitation, but higher average daily sunlight. The effects of these environmental variables on the health outcomes index are largely explained by the mediating influence of the social and economic variables considered.

Introduction

Years of potential life lost is a summary measure of premature death. This health indicator provides a way to weight deaths that occur in younger ages, quantifying the social and economic loss stemming from premature death. Other health outcomes that have social and economic implications include poor or fair health, poor physical health days, poor mental health days, and low birthweight. Poor physical and mental health contributes to on-the-job productivity loss and unemployment [1-5]. Low birthweight has potential social and economic consequences, as these children are more likely to have disabilities, hospitalizations, brain damage, impaired language development, require special education classes, and to experience chronic health problems and have a greater risk of being in lower economic classes later in life [6].

Several studies have shown that premature death and quality of life health outcomes are impacted by education, income, health insurance, poverty, income ratio, single-parent households, violent crime and injury [7-12]. In addition, the natural environment may influence premature death and quality of life health outcomes, both directly and indirectly through their effect on these variables (i.e., education, income, etc.). However, little is known about the role the natural environment plays in this context. An understanding of this role may better inform policy and intervention programs and, thereby, lower premature death and improve quality of life health outcomes.

The purpose of this study was to identify direct and indirect associations between altitude and premature death and poor quality of life health outcomes.

Materials and Methods

The current study involved an ecologic design in which the variables being assessed represented 3,108 counties in the contiguous United States. Analyses were performed on measurements taken on the county-level data. The study was intended to generate hypotheses that may be assessed more definitively with longitudinal data. IRB approval was not required as the study used publicly available datasets.

County-level premature death, quality of life, and social and economic data were compiled by a Robert Wood Johnson Foundation program called County Health Rankings & Reports: Building a Culture of Health, County by County [13]. The study also involved county-level environmental data, available through the Environmental Public Health Tracking Network, the United States Geological Survey’s National Elevation Dataset programs and the Wonder Online Databases supported by the Centers for Disease Control and Prevention [13–15].

Premature Death and Poor Quality of Life Health Outcomes

Five health outcomes were considered in this study: premature death, poor or fair health, poor physical health days, poor mental health days, and low birthweight. Premature death was measured as years of potential life lost before age 75 years per 100,000 populations, available from the National Center for Health Statistics mortality files, 2016-2018. Poor or fair health represented the percentage of adults reporting fair or poor health; poor physical health days represented the average number of physically unhealthy days reported in the past 30 days; and poor mental health days represented the average number of mentally unhealthy days reported in the past 30 days. Each of these measures were age-adjusted and was available from the Behavior Risk Factor Surveillance System, 2017. Age-adjustment involved weighting the age-group specific estimates to the 2000 US population [16]. Low birthweight represented the percentage of live births with low birthweight (< 2,500 grams), and was available from the National Center for Health Statistics –Natality Files, 2012-2018.

A health outcomes index was created to provide a summary of the five health outcomes. To calculate the index, each measure was first standardized using z-scores. This allowed for combining multiple measures of different scales. The index score was then a weighting of the standardized scores for the five variables. The county with the lowest aggregated z-score was healthiest. The weights were based on those used by states in 2020 County Health Ranks: Ranked Measure Sources and Years of Data: 0.5 for premature death, 0.1 for poor or fair health, 0.1 for poor physical health days, 0.1 for poor mental health days, and 0.2 for low birthweight [13].

Natural Environmental Variables

Five natural environmental variables were included in this study: weighted altitude (m), average daily sunlight (KJ/m2), average daily maximum air temperature (F), average fine particulate matter (ug/m3), and average daily precipitation (mm). Altitude was estimated in meters above sea level for each county, based on data from the Geographic Names Information System from the United States Geological Survey [17]. In some counties with mountainous areas, most people live in the valleys. To obtain a measure of altitude reflecting where most of the people live, a weighted estimate of altitude was derived. Weighted altitude has been used in a previous study relating altitude to suicide [18]. Average county-level daily sunlight, maximum air temperature and precipitation represent the combined years 2007-2011. These data were obtained through the North American Land Data Assimilation System, available through the CDC Wonder database [CDC WONDER [Internet] [15]. Average county-level daily density of fine particulate matter in micrograms per cubic meter (PM2.5) cover 2011-2014, obtained from the Environmental Public Health Tracking network [19].

Social and Economic Variables

The county-level social and economic factors considered in this study were % adults with some college, median household income, % uninsured, % children in poverty, income ratio (ratio of household income at the 80th percentile to income at the 20th percentile), % single-parent households, violent crime rate, and injury death rate. The Robert Wood Johnson program obtained these data from various sources. Percent some college, income ratio, and % single-parent households of adults data was obtained from the American Community Survey, 5-year estimates, 2014-2018; median household income and % children in poverty was obtained from Small Area Income and Poverty Estimates, 2018; % uninsured was obtained from Small Area Health Insurance Estimates, 2017; violent crime rate was obtained from uniform crime reporting, FBI, 2016; and injury death rates were obtained from National Center for Health Statistics mortality files, 2014-2018.

Statistical Techniques

County-level data was described for each variable using summary statistical measures (mean, standard deviation, median, minimum, and maximum). The health outcome index was correlated with the natural environmental and social and economic variables using the Pearson Correlation Coefficient. Correlations were also estimated between each of the five premature death and quality of life health outcomes and social and economic factors with the natural environmental variables. The assumption of linearity between variables was assessed graphically (data not shown). Unadjusted and adjusted correlation coefficients were estimated. Statistical significance was based on two-sided hypothesis tests at the 0.05 level. Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA, 2012). Graphs were created in Microsoft Excel, 2016.

Results

Summary statistics for premature death and quality of life, environmental, social and economic variables are presented in Table 1.

Table 1. Summary Information for Premature Death and Quality of Life Health Outcomes, Environmental, Social, and Economic Factors for Counties in the Contiguous United States

 

No.

Mean

SD

Median

Min

Max

Correlation with the Health Outcome Index

Premature Death and Quality of Life Health Outcomes

 

 

 

 

 

 

 

Years of potential life lost before age 75 per 100,000 population (age-adjusted)

2827

8574

2584

8328

2731

29138

0.941

Percentage of adults reporting fair or poor health (age-adjusted)

3108

17.95

4.74

17.00

8.00

41.00

0.844

Average number of physically unhealthy days reported in past 30 days (age-adjusted)

3108

3.99

0.70

3.90

2.40

6.60

0.856

Average number of mentally unhealthy days reported in past 30 days (age-adjusted)

3108

4.17

0.60

4.20

2.50

6.30

0.805

Percentage of live births with low birthweight (< 2,500 grams)

3011

8.16

2.06

8.00

3.00

24.00

0.731

Health Outcomes Index

2825

0.02

0.86

-0.06

-1.82

4.68

1.000

Natural Environmental Factors

 

 

 

 

 

 

 

Weighted Altitude (m)

3106

414.3

487.4

263.1

-9.5

3471.4

-0.143

Average Daily Sunlight (KJ/m2)

3106

16398.3

1605.0

16102.9

12689.0

21191.1

0.292

Average Daily Maximum Air Temperature (F)

3106

65.4

9.3

64.8

38.4

87.5

0.458

Average PM2.5 (ug/m3)

3108

9.0

2.0

9.4

3.0

19.7

0.208

Average Daily Precipitation (mm)

3106

2.7

0.9

3.0

0.2

7.1

0.278

Social and Economic Factors

 

 

 

 

 

 

 

% Some College

3108

57.89

11.83

58.00

15.00

100.00

-0.618

Median household income

3108

52659

13822

50514

25385

140382

-0.723

% Uninsured, everyone

3108

11.42

5.11

11.00

2.00

34.00

0.327

% Children in Poverty

3108

21.20

8.90

20.00

3.00

68.00

0.833

Income Ratio

3106

4.52

0.76

4.40

2.50

12.00

0.537

% Single-Parent Households

3107

32.33891

10.65447

32

0

87

0.658

Violent Crime Rate

2935

251.3874

191.8668

204

0

1820

0.331

Injury Death Rate

3015

86.63184

25.10892

84

22

320

0.616

Rates were age-adjusted to the 2000 US adult population. To calculate the Health Outcomes Index, each measure was first standardized using z-scores. This allowed for combining multiple measures of different scales. The index score was then a weighting of the standardized scores for the five variables. The county with the lowest aggregated z-score is healthiest. The weights were based on those used by states in 2020 County Health Ranks: Ranked Measure Sources and Years of Data: 0.5 for premature death, 0.1 for poor or fair health, 0.1 for poor physical health days, 0.1 for poor mental health days, and 0.2 for low birthweight [13]. The summary natural environmental variable statistics have been reported previously [20].

The health outcomes index is a weighted overall measure of the premature death and quality of life variables. A lower index score represents better health. This measure is correlated separately with each of the variables in the table. The correlations indicate that counties experiencing better health (lower health outcomes index) have higher altitude and lower sunlight, temperature, PM2.5, and precipitation. Counties experiencing better health also have higher % some college and median household income and a lower % uninsured, % children in poverty, income ratio, % single-parent households, violent crime rate, and injury death rate.    

Health outcomes and social and economic factors are correlated with the environmental variables in Table 2.

Table 2. Premature Death and Quality of Life Health Outcomes and Social and Economic Factors According to Selected Environmental Measures

 

Pearson Correlation Coefficient – unadjusted (row 1) and adjusted (row 2)

Label

Weighted Altitude (m)

Average Daily Sunlight (KJ/m²)

Average Daily Max Air Temp (F)

Average Daily PM2.5

Average Daily Precipitation (mm)

Years of potential life lost before age 75 per 100,000 population (age-adjusted)

-0.10

0.20

0.35

0.12

0.35

0.21

-0.20

0.33

-0.07

0.17

Percentage of adults reporting fair or poor health (age-adjusted)

-0.20

0.42

0.59

0.28

0.24

0.22

-0.13

0.38

0.03

0.16

Average number of physically unhealthy days reported in past 30 days (age-adjusted)

-0.18

0.23

0.40

0.31

0.35

0.27

-0.13

0.28

0.10

0.29

Average number of mentally unhealthy days reported in past 30 days (age-adjusted)

-0.23

0.17

0.37

0.40

0.46

0.28

-0.09

0.23

0.17

0.38

Percentage of live births with low birthweight (< 2,500 grams)

-0.06

0.36

0.42

0.20

0.24

0.24

0.05

0.17

0.08

0.27

% Some College

0.11

-0.29

-0.40

-0.16

-0.14

 

-0.16

-0.10

-0.27

0.00

-0.10

Median household income

0.04

-0.15

-0.28

-0.02

-0.10

 

-0.21

0.24

-0.35

0.14

-0.12

% Uninsured, everyone

0.11

0.61

0.57

-0.14

-0.19

 

0.20

-0.08

0.44

-0.26

-0.10

% Children in Poverty

-0.14

0.36

0.46

0.14

0.19

 

0.14

-0.06

0.27

-0.08

0.18

Income Ratio

-0.18

0.28

0.35

0.15

0.19

 

-0.01

0.08

0.07

-0.03

0.15

% Single-Parent Households

-0.27

0.26

0.36

0.24

0.25

 

-0.10

0.12

0.02

0.02

0.13

Violent Crime Rate

-0.14

0.31

0.32

0.20

0.12

 

-0.04

0.19

-0.05

0.12

0.07

Injury Death Rate

0.22

0.05

0.01

-0.20

-0.04

 

0.25

-0.15

0.19

-0.18

0.15

Although better health is associated with higher altitude, after accounting for higher temperature and lower PM2.5 and precipitation, which are associated with higher altitude, higher altitude now correlates with poorer health, as reflected by each of the health indicators. The adjusted correlations also tend to show better health for each of the indicators in counties with more sun, lower average daily maximum air temperature, PM2.5, and precipitation.

Counties with higher altitude correlate with higher % some college, % uninsured, and injury death rate, but lower % children in poverty, income ratio, % single-parent households, and violent crime rate. However, after accounting for higher sunlight and lower temperature, PM2.5 and precipitation at higher altitude, higher altitude is associated with higher % uninsured, % children in poverty, and injury death rate, and lower % some college, median household income, % single-parent households, and violent crime. The primary environmental factor associated with these social and economic variables is average daily maximum air temperature. In the adjusted models, temperature has the largest association with % some college (negative), median household income (negative), % uninsured (positive), and % children in poverty (positive); precipitation has the largest association with income ratio (positive) and % single-parent households (positive); sunlight has the largest association with violent crime (positive); and altitude has the largest association with injury death rate (positive).

Correlations between the health outcomes index and the environmental variables are shown in Figure 1. Each of the correlated estimates change as we sequentially adjust for the environmental variables. For example, the correlation involving the altitude and the health outcomes index goes from negative to positive with the adjustment of temperature. In other words, better health experienced at higher altitude is primarily explained by cooler temperatures, which correspond with higher altitude. After accounting for sunlight, temperature, PM2.5, and precipitation, altitude now correlates with poorer health. In the adjusted model, sunlight is associated with better health. Higher temperature and precipitation remain correlated with poorer health in the fully adjusted models. PM2.5 shows little correlation with health in the fully adjusted models.

Figure 1. Pearson Correlation Coefficients Measuring the Association between the Health Outcomes Index and Natural Environmental Variables

A: weighted alititude (m); S: average daily sunlight (KJ/m2); T: average daily maximum air temperature (F); PM: average fine particulate matter (ug/m3); and PR: average daily precipitation (mm).

Correlation estimates between the health outcomes index and the social and economic factors are potentially confounded by the environmental factors. For example, since ambient air temperature correlates with % some college and independent of that relationship correlates with the health outcomes index, it is a possible confounder. Correlations between the health outcomes index and the social and economic factors are shown in Figure 2.

Figure 2. Pearson Correlation Coefficients Measuring the Association between the Health Outcomes Index and the Social and Economic Factors

Although adjustment for the environmental variables tend to dampen the correlation estimates between the health outcomes index and the social and economic factors, these estimates remain fairly strong, with the possible exception of % uninsured.

Finally, in a multiple regression model, the health outcomes index was regressed on the environmental variables. In the fully adjusted model, average daily PM2.5 was insignificant (p = 0.5872). Altitude, temperature, and precipitation positively correlated with the health outcomes index (poorer health) and sunlight negatively correlated with the health outcomes index (better health). The amount of variation in the health outcomes index explained by altitude was 3.9%, average daily sunlight was 2.1%, maximum air temperature was 21.0%, and precipitation was 3.5%. Assuming the social and economic factors mediate the association between the environmental factors and the health outcomes index, including these variables in the model should dampen the associations. Indeed, in the full model that includes the environmental, social and economic factors, the amount of variation in the health outcomes index explained by altitude was 0.7%, average daily sunlight was 0.6%, maximum air temperature was 0.9%, and precipitation was 0.5%.

Discussion

The current study described the effect of altitude on premature death and poor quality of life through its relationship with sunlight, ambient air temperature, PM2.5, and precipitation. The direct influence of the natural environmental variables on premature death and poor quality of life health outcomes persisted after adjustment for their simultaneous effects. However, almost all of their association with premature death and poor quality of life appears to be mediated by selected social and environmental factors.

Higher altitude was associated with lower premature death and better quality of life outcomes. Lower premature death and better quality of life outcomes at higher altitude may be due to biological changes that take place in the body at higher altitude and/or natural environmental factors associated with higher altitude, such as more moderate ambient air temperature. After accounting for average daily sunlight, maximum air temperature, PM2.5, and precipitation, higher altitude correlated with higher premature death and poorer quality of life outcomes. However, this does not mean that there are no health benefits to higher altitude living beyond the influence of the other environmental variables.

Some have hypothesized that there are health benefits to higher altitude living associated with positive adjustments the body makes to hypoxia [21-25]. Hypoxia is a condition where the body or part of the body has inadequate oxygen supply at the tissue level. Hypoxia occurs at altitudes above sea level, but physiological responses are small below 1,500 meters (0-5,000 feet) for healthy individuals, compromised because of memory issues from 1,500 to 3,500 meters (5,000 -11,400 feet), and visual sensitivity at night falls 10% at 1,500 meters and 30% at 3,000 meters [26]. Chronic hypoxia alters serotonin metabolism and brain bioenergetics [27]; certain illnesses may impair the body’s ability to absorb lower levels of oxygen into the bloodstream [28]; and hypoxia can cause fatigue, shortness of breath, and, in extreme cases loss of consciousness, which increase the risk of serious injury [29]. Hypoxia among women at altitudes ≥ 2500 meters decreases birth weight by altering maternal glucose and lipid metabolism [30].

Higher average daily sunlight was associated with more premature death and poorer quality of life outcomes. However, sunlight strongly correlates with altitude, ambient air temperature, and precipitation. After accounting for altitude, ambient air temperature, PM2.5, and precipitation, higher average daily sunlight then correlated with lower premature death, poor or fair health days, and physically and mentally unhealthy days. Although public health messages tend to focus on the hazards of too much sun (i.e., destroying vitamin A in the skin such that skin aging is accelerated, increasing the risk of skin cancer, causing cataracts, and reactivating some viruses), sunlight exposure boosts the body’s vitamin D supply and it also has other beneficial health effects (i.e., increase endorphins, decrease autoimmune diseases, increase gene repair, helps treat skin disorders like psoriasis, etc.) [31]. Although a World Health Organization (WHO) report linked 0.1% of the global burden of disease measured using disability-adjusted life years (DALYs) to excessive exposure to ultraviolet radiation, the report also indicated that 3.3 billion DALYs globally is attributed to very low levels of exposure to ultraviolet radiation [32].

Higher average daily maximum air temperature had the strongest association with greater premature death and lower quality of life outcomes among the natural environmental variables. Research has shown that hot temperatures are related to more deaths each year in the United States than from any other weather condition [33]. According to a 30 year average (1989-2018), there are approximately 4.5 more heat-related deaths than cold-related deaths [33]. A literature review identified several studies associating higher ambient air temperature with more overall and cause-specific morbidity (e.g., cardiovascular and respiratory diseases) [34]. A multi-country study estimated that 7.7% of mortality is attributed to non-optimal ambient air temperature [35]. One review found that high ambient air temperatures resulted in greater risk of mental health-related admissions, emergency department visits, and suicides [36]. Another review confirmed that hot weather increases the risk of unintentional injuries and accidents in high-income countries [37]. Research has further linked hotter ambient air temperature with increased risk for injuries, violent crime, suicide, and low birth weight infants [38, 39].

Compared with the other environmental factors considered in this study, PM2.5 tended to have a small influence on premature death and poor quality of life outcomes. However, its negative association with the average number of physically and mentally unhealthy days reported in the past 30 days, more so for the latter, is worth noting. Previous research has associated particulate matter pollution with morbidity from respiratory diseases, coronary obstructive pulmonary disease, asthma, and pneumonia [40]. Research has also shown that high levels of fine particulate matter in the air correlate with higher rates of death from homicide and legal intervention, unintended accidents, and suicide [38]. Further, there is some research linking exposure to fine particulate matter with lower cognition and well-being [41, 42].

After adjustment for altitude, average daily maximum air temperature and PM2.5, precipitation correlated with more premature death, poor or fair health days, physically and mentally unhealthy days, and live births with low birthweight. Extreme precipitation events may increase the risk of automobile accidents, unintentional injuries, and suicide [38, 43-46]. Extreme precipitation can also compromise water quality (i.e., runoffs that may include heavy metals, pesticides, nitrogen, and phosphorus), hurting ecosystems and the public’s health [47]. Although precipitation positively correlated with low birth weight infants, in countries with relatively low precipitation, where more precipitation is particularly helpful for crops, precipitation has been shown to decrease low birth weight infants [39, 48].

The associations between the natural environmental variables (altitude, sunlight, ambient air temperature, and precipitation) and the health outcomes index became negligible after including the social and economic variables in the model. Because these variables tended to be associated with both the environmental variables and the health outcome index, they are potential mediators. For example, the amount of variation in the health outcomes index explained by average daily maximum air temperature was 21.0%. However, temperature was also associated with several variables that influence premature death and quality of life (e.g., education, income, children in poverty, single-parent households, violent crime, and injury). Indeed, when these variables were included in the model, only 0.9% of the amount of variation in the health outcomes index was explained by a direct effect of temperature.

A limitation of this study is that the data reflects county-level measurements rather than individual level information. Hence, ecologic fallacy may exist in our estimated correlations. In addition, to fully capture the effect of extreme ambient air temperatures on people’s health is impossible because of our climate-controlled homes, cars, and workplaces. However, the environmental variables are experienced at similar levels among individuals within counties.

We should also note that the large number of counties assessed produced generally significant results, some of which may not be of practical importance. In addition, the natural environmental variables are generally not modifiable, hence may be less clinically important. Finally, the data are not longitudinal such that we are not able to draw conclusions about causal-effect relationships. Yet, for some variables the causal direction is clear.

Conclusion

This study attempted to explain why higher altitude living correlates with better health. The beneficial effect of altitude on the selected health indicators considered in this study is primarily explained by higher altitude being associated with lower temperature, PM2.5, and precipitation. Sunlight is positively associated with better health after adjusting for the other environmental variables. The study further considered select social and economic variables, which were found to be associated with both the environmental and health outcome variables. After accounting for these variables, associations between the environmental and health outcome measures became insignificant. Hence, the social and economic variables considered fully explain the association between the environmental variables related to altitude (temperature, PM2.5, precipitation, and sunlight) and the health outcome measures.

Funding Statement

None

Data Availability

All relevant data within the paper can be accessed through the public domain, with references provided.

References

  1. Stepanek MJahanshahi KMillard F (2019) Individual, workplace, and combined effects modeling of employee productivity loss. J Occup Environ Med 61: 469-478. [Crossref]
  2. Pohling RBuruck G, Jungbauer KL, et al. (2016) Work related factors of presenteeism: The mediating role of mental and physical health. J Occup Health Psychol 21: 220-234. [Crossref]
  3. Merrill RM, Aldana SG, Pope JE, et al. (2012) Presenteeism according to health behaviors, physical health, and work environment. Popul Health Manag 15: 293-301. [Crossref]
  4. Olesen SC, Butterworth P, Leach LS, et al. (2013) Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study. BMC Psychiatry 13: 144. [Crossref]
  5. Butterworth P, Leach LS, Pirkis J, et al. (2012) Poor mental health influences risk and duration of unemployment: a prospective study. Soc Psychiatry Psychiatr Epidemiol 47: 1013-1021. [Crossref]
  6. Kogan MD (1995) Social causes of low birth weight. J R Soc Med 88: 611-615. [Crossref]
  7. Hummer RA, Henandez EM (2013) The effects of educational attainment on adult mortality in the United States. Popul Bull 68: 1-16. [Crossref]
  8. Kaplan RM, Howard VJ, Safford MM, et al. (2015) Educational attainment and longevity: Results from the REGARDS U.S. national cohort study of blacks and whites. Ann Epidemiol 25: 323-328. [Crossref]
  9. Kaplan RM, Milstein A (2019) Contributions of health care to longevity: A review of 4 estimation methods. Ann Fam Med 17: 267-272. [Crossref]
  10. Rousou E, Kouta C, Middleton N, et al. (2019) Mental health among single mothers in Cyprus: a cross-sectional descriptive correlational study. BMC Women’s Health 19: 67. [Crossref]
  11. Herrera-Escobar JP, Seshadri AJ, Rivero R, et al. (2019) Lower education and income predict worse long-term outcomes after injury. J Trauma Acute Care Surg 87: 104-110. [Crossref]
  12. Roy B, Kiefe CI, Jacobs DR, et al. (2020) Education, race/ethnicity, and causes of premature mortality among middle-aged adults in 4 US urban communities: Results from CARDIA, 1985-2017. Am J Public Health 110: 530-536. [Crossref]
  13. University of Wisconsin Population Health Institute (2020) County Health Rankings State Report 2020. [Crossref]
  14. U.S. Geology Survey. [Crossref]
  15. (2017) Centers for Disease Control and Prevention. [Crossref]
  16. Klein RJ, Schoenborn CA (2001) Age adjustment using the 2000 projected U.S. population. Healthy People 2010 Stat Notes 20: 1-10. [Crossref]
  17. (2017) Ground Surface Elevation - 30m. ArcGIS.
  18. Fructos AM, Sloan C, Merrill RM (2018) Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression. PloS One 13: e0206992. [Crossref]
  19. Centers for Disease Control and Prevention (2020) Daily Census Tract-Level PM2.5 Concentrations, 2011-2014. [Crossref]
  20. Merrill RM (2020) Explaining the inverse association between altitude and obesity. J Obes 1946723. [Crossref]
  21. Ezzati M, Horwitz ME, Thomas DS (2012) Altitude, life expectancy and mortality from ischaemic heart disease, stroke, COPD and cancers: national population-based analysis of US counties. J Epidemiol Community Health 66: e17. [Crossref]
  22. Judd SE, Tangpricha V (2009) Vitamin D deficiency and risk for cardiovascular disease. Am J Med Sci 338: 40-44. [Crossref]
  23. Thiersch M, Swenson ER (2018) High altitude and cancer mortality. High Alt Med Biol 2018; 19: 116-123. [Crossref]
  24. Woolcott OO, Ader M, Bergman RN (2015) Glucose homeostasis during short-term and prolonged exposure to high altitudes. Endocr Rev 36: 149-173. [Crossref]
  25. Malyshev IY, Wiegant FA, Mashina SY (2005) Possible use of adaptation to hypoxia in Alzheimer’s disease: a hypothesis. Med Sci Monit 11: HY31-HY38. [Crossref]
  26. SKYbrary. Hypoxia. [Crossref]
  27. Kious B, Kondo D, Renshaw PF (2018) Living high and feeling low: Altitude, suicide and depression. Harv Rev Psychiatry 26: 43-56. [Crossref]
  28. Moschovis PP, Banajeh S, MacLeod WB (2013) Childhood Anemia at High Altitude: Risk Factors for Poor Outcomes in Severe Pneumonia. Pediatrics 132: e1156-1162. [Crossref]
  29. Duan Z, Xu J, Ru H, et al. (2019) Classification of driving fatigue in high altitude areas. Sustainability 11: 817.
  30. Määttä JSissala NDimova EY, et al. (2018) Hypoxia causes reductions in birth weight by altering maternal glucose and lipid metabolism. Sci Rep 8: 13583.
  31. Mead MN (2008) Benefits of sunlight: A bright spot for human health. Environ Health Perspect 116: A160–A167. [Crossref]
  32. Lucas R, McMichael T, Smith W, et al. (2006) Solar ultraviolet radiation: Global burden of disease from solar ultraviolet radiation. Environmental Burden of Disease Series, No. 13. World Health Organization, Geneva.
  33. National Weather Service (2018) Natural Hazard Statistics. Weather Fatalities.
  34. Ye X, Wolff R, Yu W, et al. (2012) Ambient temperature and morbidity: A review of epidemiological evidence. Environ Health Perspect 120: 19-28. [Crossref]
  35. Gasparrini A, Guo Y, Hashizume M (2015) Mortality risk attributable to high and low ambient temperature: A multicountry observational study. Lancet 386: 369-375. [Crossref]
  36. Thompson R, Hornigold R, Page L, et al. (2018) Associations between high ambient temperatures and heat waves with mental health outcomes: a systematic review. Public Health 161: 171-191. [Crossref]
  37. Otte im Kampe E, Kovats S, Hajat (2016) SImpact of high ambient temperature on unintentional injuries in high-income countries: a narrative systematic literature review. BMJ Open 6: e010399.
  38. Merrill RM (2019) Injury-related deaths according to environmental, demographic, and lifestyle factors. J Environ Public Health 2019: 6942787.
  39. Grace K, Davenport F, Hanson H, et al. (2015) Linking climate change and health outcomes: Examining the relationship between temperature, precipitation and birth weight in Africa. Global Environ Change 35: 125-137.
  40. Zhao YWang SLang L, et al. (2017) Ambient fine and coarse particulate matter pollution and respiratory morbidity in Dongguan, China. Environ Pollut 222: 126-131.
  41.  Weuve J, Puett RC, Schwartz J, et al. (2012) Exposure to particulate air pollution and cognitive decline in older women. Arch Intern Med 172: 219-227. [Crossref]
  42. Tonne CElbaz ABeevers S, et al. (2014) Traffic-related air pollution in relation to cognitive function in older adults. Epidemiology 25: 674-681. [Crossref]
  43. Edwards JB (2006) The temporal distribution of road accidents in adverse weather. Meteorol Appl 2006; 6: 59-68.
  44. Qiu LL, Nixon WA (2008) Effects of adverse weather on traffic crashes systematic review and meta-analsyis. Transp Res Rec 2055: 139-146.
  45. Liu A, Soneja SI, Jiang C (2017) Frequency of extreme weather events and increased risk of motor vehicle collision in Maryland. Sci Total Environ 580: 550-555. [Crossref]
  46. After car accidents (2018) Top 7 causes of car accidents: 2018 Statistics.
  47. Center for Climate and Energy Solutions. Extreme precipitation and climate change.
  48. Bakhtsiyarava M, Grace K, Nawrotzki RJ (2018) Climate, Birth Weight, and Agricultural Livelihoods in Kenya and Mali. Am J Public Health 108: S144-S150. [Crossref]
Editorial Information

Article Type

Research Article

Publication history

Received date: November 08, 2020
Accepted date: November 19, 2020
Published date: November 30, 2020

Copyright

©2020 Merrill RM. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Merrill RM. (2020) Why is High Altitude Living Associated with Lower Premature Death and Better Quality of Life? An Ecologic Study Involving US Counties. OSP J Health Car Med 1. HCM-1-117

Corresponding author

Ray M Merrill

Professor, Brigham Young University, 2063 Life Sciences Building Provo, Utah 84602. Ray_Merrill@byu.edu

Figure 1. Pearson Correlation Coefficients Measuring the Association between the Health Outcomes Index and Natural Environmental Variables

A: weighted a lititude (M); S: average daily sunlight (KJ/m2); T: average daily maximum air temperature (F); PM: average fine particulate matter (ug/m3); and PR: average daily precipitation (mm).

Figure 2. Pearson Correlation Coefficients Measuring the Association between the Health Outcomes Index and the Social and Economic Factors

Table 1. Summary Information for Premature Death and Quality of Life Health Outcomes, Environmental, Social, and Economic Factors for Counties in the Contiguous United States

 

No.

Mean

SD

Median

Min

Max

Correlation with the Health Outcome Index

Premature Death and Quality of Life Health Outcomes

 

 

 

 

 

 

 

Years of potential life lost before age 75 per 100,000 population (age-adjusted)

2827

8574

2584

8328

2731

29138

0.941

Percentage of adults reporting fair or poor health (age-adjusted)

3108

17.95

4.74

17.00

8.00

41.00

0.844

Average number of physically unhealthy days reported in past 30 days (age-adjusted)

3108

3.99

0.70

3.90

2.40

6.60

0.856

Average number of mentally unhealthy days reported in past 30 days (age-adjusted)

3108

4.17

0.60

4.20

2.50

6.30

0.805

Percentage of live births with low birthweight (< 2,500 grams)

3011

8.16

2.06

8.00

3.00

24.00

0.731

Health Outcomes Index

2825

0.02

0.86

-0.06

-1.82

4.68

1.000

Natural Environmental Factors

 

 

 

 

 

 

 

Weighted Altitude (m)

3106

414.3

487.4

263.1

-9.5

3471.4

-0.143

Average Daily Sunlight (KJ/m2)

3106

16398.3

1605.0

16102.9

12689.0

21191.1

0.292

Average Daily Maximum Air Temperature (F)

3106

65.4

9.3

64.8

38.4

87.5

0.458

Average PM2.5 (ug/m3)

3108

9.0

2.0

9.4

3.0

19.7

0.208

Average Daily Precipitation (mm)

3106

2.7

0.9

3.0

0.2

7.1

0.278

Social and Economic Factors

 

 

 

 

 

 

 

% Some College

3108

57.89

11.83

58.00

15.00

100.00

-0.618

Median household income

3108

52659

13822

50514

25385

140382

-0.723

% Uninsured, everyone

3108

11.42

5.11

11.00

2.00

34.00

0.327

% Children in Poverty

3108

21.20

8.90

20.00

3.00

68.00

0.833

Income Ratio

3106

4.52

0.76

4.40

2.50

12.00

0.537

% Single-Parent Households

3107

32.33891

10.65447

32

0

87

0.658

Violent Crime Rate

2935

251.3874

191.8668

204

0

1820

0.331

Injury Death Rate

3015

86.63184

25.10892

84

22

320

0.616

Rates were age-adjusted to the 2000 US adult population. To calculate the Health Outcomes Index, each measure was first standardized using z-scores. This allowed for combining multiple measures of different scales. The index score was then a weighting of the standardized scores for the five variables. The county with the lowest aggregated z-score is healthiest. The weights were based on those used by states in 2020 County Health Ranks: Ranked Measure Sources and Years of Data: 0.5 for premature death, 0.1 for poor or fair health, 0.1 for poor physical health days, 0.1 for poor mental health days, and 0.2 for low birthweight [13]. The summary natural environmental variable statistics have been reported previously [20].

Table 2. Premature Death and Quality of Life Health Outcomes and Social and Economic Factors According to Selected Environmental Measures

 

Pearson Correlation Coefficient – unadjusted (row 1) and adjusted (row 2)

Label

Weighted Altitude (m)

Average Daily Sunlight (KJ/m²)

Average Daily Max Air Temp (F)

Average Daily PM2.5

Average Daily Precipitation (mm)

Years of potential life lost before age 75 per 100,000 population (age-adjusted)

-0.10

0.20

0.35

0.12

0.35

0.21

-0.20

0.33

-0.07

0.17

Percentage of adults reporting fair or poor health (age-adjusted)

-0.20

0.42

0.59

0.28

0.24

0.22

-0.13

0.38

0.03

0.16

Average number of physically unhealthy days reported in past 30 days (age-adjusted)

-0.18

0.23

0.40

0.31

0.35

0.27

-0.13

0.28

0.10

0.29

Average number of mentally unhealthy days reported in past 30 days (age-adjusted)

-0.23

0.17

0.37

0.40

0.46

0.28

-0.09

0.23

0.17

0.38

Percentage of live births with low birthweight (< 2,500 grams)

-0.06

0.36

0.42

0.20

0.24

0.24

0.05

0.17

0.08

0.27

% Some College

0.11

-0.29

-0.40

-0.16

-0.14

 

-0.16

-0.10

-0.27

0.00

-0.10

Median household income

0.04

-0.15

-0.28

-0.02

-0.10

 

-0.21

0.24

-0.35

0.14

-0.12

% Uninsured, everyone

0.11

0.61

0.57

-0.14

-0.19

 

0.20

-0.08

0.44

-0.26

-0.10

% Children in Poverty

-0.14

0.36

0.46

0.14

0.19

 

0.14

-0.06

0.27

-0.08

0.18

Income Ratio

-0.18

0.28

0.35

0.15

0.19

 

-0.01

0.08

0.07

-0.03

0.15

% Single-Parent Households

-0.27

0.26

0.36

0.24

0.25

 

-0.10

0.12

0.02

0.02

0.13

Violent Crime Rate

-0.14

0.31

0.32

0.20

0.12

 

-0.04

0.19

-0.05

0.12

0.07

Injury Death Rate

0.22

0.05

0.01

-0.20

-0.04

 

0.25

-0.15

0.19

-0.18

0.15