1932

Abstract

The United Nations has called on all nations to take immediate actions to fight noncommunicable diseases (NCDs), which have become an increasingly significant burden to public health systems around the world. NCDs tend to be more common in developed countries but are also becoming of growing concern in low- and middle-income countries. Earth observation (EO) technologies have been used in many infectious disease studies but have been less commonly employed in NCD studies. This review discusses the roles that EO data and technologies can play in NCD research, including () integrating natural and built environment factors into NCD research, () explaining individual–environment interactions, () scaling up local studies and interventions, () providing repeated measurements for longitudinal studies including cohorts, and () advancing methodologies in NCD research. Such extensions hold great potential for overcoming the challenges of inaccurate and infrequent measurements of environmental exposure at the level of both the individual and the population, which is of great importance to NCD research, practice, and policy.

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2019-04-01
2024-04-25
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Literature Cited

  1. 1. 
    Abbott G, Backholer K, Peeters A, Thornton L, Crawford D, Ball K 2014. Explaining educational disparities in adiposity: the role of neighborhood environments. Obesity 22:2413–19
    [Google Scholar]
  2. 2. 
    Almanza E, Jerrett M, Dunton G, Seto E, Pentz MA 2012. A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place 18:46–54
    [Google Scholar]
  3. 3. 
    Anderson DJ, Rojas LF, Watson S, Knelson LP, Pruitt S et al. 2017. Identification of novel risk factors for community-acquired Clostridiumdifficile infection using spatial statistics and geographic information system analyses. PLOS ONE 12:e0176285
    [Google Scholar]
  4. 4. 
    Barnes TL, Colabianchi N, Hibbert JD, Porter DE, Lawson AB, Liese AD 2016. Scale effects in food environment research: implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of South Carolina. Appl. Geogr. 68:20–27
    [Google Scholar]
  5. 5. 
    Bastiaanssen WGM, Molden DJ, Makin IW 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manag. 46:137–55
    [Google Scholar]
  6. 6. 
    Beaglehole R, Bonita R, Alleyne G, Horton R, Li L et al. 2011. UN high-level meeting on non-communicable diseases: addressing four questions. Lancet 378:449–55
    [Google Scholar]
  7. 7. 
    Beaglehole R, Bonita R, Horton R, Adams C, Alleyne G et al. 2011. Priority actions for the non-communicable disease crisis. Lancet 377:1438–47
    [Google Scholar]
  8. 8. 
    Berge JM, Wall M, Larson N, Forsyth A, Bauer KW, Neumark-Sztainer D 2014. Youth dietary intake and weight status: healthful neighborhood food environments enhance the protective role of supportive family home environments. Health Place 26:69–77
    [Google Scholar]
  9. 9. 
    Bezold CP, Banay RF, Coull BA, Hart JE, James P et al. 2018. The relationship between surrounding greenness in childhood and adolescence and depressive symptoms in adolescence and early adulthood. Ann. Epidemiol. 28:213–19
    [Google Scholar]
  10. 10. 
    Bhavani P, Roy PS, Chakravarthi V, Kanawade VP 2017. Satellite remote sensing for monitoring agriculture growth and agricultural drought vulnerability using long-term (1982–2015) climate variability and socio-economic data set. PNAS India Sect. A Phys. Sci. 87:733–50
    [Google Scholar]
  11. 11. 
    Blackburn GA 2007. Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 58:855–67
    [Google Scholar]
  12. 12. 
    Brook JR, Setton EM, Seed E, Shooshtari M, Doiron D, Can. Urban Environ. Health Res. Consort. 2018. The Canadian Urban Environmental Health Research Consortium—a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health. BMC Public Health 18:114
    [Google Scholar]
  13. 13. 
    Burgoine T, Jones AP, Brouwer RJN, Neelon SEB 2015. Associations between BMI and home, school and route environmental exposures estimated using GPS and GIS: Do we see evidence of selective daily mobility bias in children?. Int. J. Health Geogr. 14:8
    [Google Scholar]
  14. 14. 
    CDC (Cent. Dis. Control Prev.) 2018. Nutrition, physical activity and obesity: data, trends and maps. Centers for Disease Control and Prevention https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html
  15. 15. 
    Cerin E, Frank LD, Sallis JF, Saelens BE, Conway TL et al. 2011. From neighborhood design and food options to residents’ weight status. Appetite 56:693–703
    [Google Scholar]
  16. 16. 
    Charreire H, Casey R, Salze P, Simon C, Chaix B et al. 2010. Measuring the food environment using geographical information systems: a methodological review. Public Health Nutr 13:1773–85
    [Google Scholar]
  17. 17. 
    Chen H-J, Wang Y 2016. Changes in the neighborhood food store environment and children's body mass index at peripuberty in the United States. J. Adolesc. Health 58:111–18
    [Google Scholar]
  18. 18. 
    Chen R, Wang C, Meng X, Chen H, Thach TQ et al. 2013. Both low and high temperature may increase the risk of stroke mortality. Neurology 81:1064–70
    [Google Scholar]
  19. 19. 
    Cole-Hunter T, Jayaratne R, Stewart I, Hadaway M, Morawska L, Solomon C 2013. Utility of an alternative bicycle commute route of lower proximity to motorised traffic in decreasing exposure to ultra-fine particles, respiratory symptoms and airway inflammation—a structured exposure experiment. Environ. Health 12:29
    [Google Scholar]
  20. 20. 
    Coombes E, Jones AP, Hillsdon M 2010. The relationship of physical activity and overweight to objectively measured green space accessibility and use. Soc. Sci. Med. 70:816–22
    [Google Scholar]
  21. 21. 
    Corrigan CE, Roberts GC, Ramana MV, Kim D, Ramanathan V 2007. Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmos. Chem. Phys. Discuss. 7:11429–63
    [Google Scholar]
  22. 22. 
    Crawford D, Cleland V, Timperio A, Salmon J, Andrianopoulos N et al. 2010. The longitudinal influence of home and neighbourhood environments on children's body mass index and physical activity over 5 years: the CLAN study. Int. J. Obes. 34:1177–87
    [Google Scholar]
  23. 23. 
    Crawford TW, Jilcott Pitts SB, McGuirt JT, Keyserling TC, Ammerman AS 2014. Conceptualizing and comparing neighborhood and activity space measures for food environment research. Health Place 30:215–25
    [Google Scholar]
  24. 24. 
    Dalal S, Beunza JJ, Volmink J, Adebamowo C, Bajunirwe F et al. 2011. Non-communicable diseases in sub-Saharan Africa: what we know now. Int. J. Epidemiol. 40:885–901
    [Google Scholar]
  25. 25. 
    Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P et al. 2017. Air pollution and mortality in the Medicare population. N. Engl. J. Med. 376:2513–22
    [Google Scholar]
  26. 26. 
    Doak CM, Adair LS, Bentley M, Monteiro C, Popkin BM 2005. The dual burden household and the nutrition transition paradox. Int. J. Obes. 29:129–36
    [Google Scholar]
  27. 27. 
    Dominici F, Peng RD, Bell ML, Pham L, McDermott A et al. 2006. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295:1127–34
    [Google Scholar]
  28. 28. 
    Duncan DT, Sharifi M, Melly SJ, Marshall R, Sequist TD et al. 2014. Characteristics of walkable built environments and BMI z-scores in children: evidence from a large electronic health record database. Environ. Health Perspect. 122:1359–65
    [Google Scholar]
  29. 29. 
    Evans GW 2003. The built environment and mental health. J. Urban Health 80:536–55
    [Google Scholar]
  30. 30. 
    Floury M, Usseglio-Polatera P, Ferreol M, Delattre C, Souchon Y 2013. Global climate change in large European rivers: long-term effects on macroinvertebrate communities and potential local confounding factors. Glob. Change Biol. 19:1085–99
    [Google Scholar]
  31. 31. 
    Fornace KM, Drakeley CJ, William T, Espino F, Cox J 2014. Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends Parasitol 30:514–19
    [Google Scholar]
  32. 32. 
    Friel S, Bowen K, Campbell-Lendrum D, Frumkin H, McMichael AJ, Rasanathan K 2011. Climate change, noncommunicable diseases, and development: the relationships and common policy opportunities. Annu. Rev. Public Health 32:133–47
    [Google Scholar]
  33. 33. 
    Gan WQ, Davies HW, Koehoorn M, Brauer M 2012. Association of long-term exposure to community noise and traffic-related air pollution with coronary heart disease mortality. Am. J. Epidemiol. 175:898–906
    [Google Scholar]
  34. 34. 
    GBD (Glob. Burd. Dis.) DALYs Hale Collab., Murray CJ, Barber RM, Foreman KJ et al. 2015. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. Lancet 386:2145–91
    [Google Scholar]
  35. 35. 
    Giardina F, Franke J, Vounatsou P 2015. Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery. Geospat. Health 10:333
    [Google Scholar]
  36. 36. 
    Glob. Burd. Dis. Cancer Collab., Fitzmaurice C, Dicker D, Pain A, Hamavid H et al. 2015. The global burden of cancer 2013. JAMA Oncol 1:505–27
    [Google Scholar]
  37. 37. 
    Goetz SJ, Prince SD, Small J 2000. Advances in satellite remote sensing of environmental variables for epidemiological applications. Adv. Parasitol. 47:289–307
    [Google Scholar]
  38. 38. 
    Goodchild MF 2007. Citizens as sensors: the world of volunteered geography. GeoJournal 69:211–21
    [Google Scholar]
  39. 39. 
    Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202:18–27
    [Google Scholar]
  40. 40. 
    Guite HF, Clark C, Ackrill G 2006. The impact of the physical and urban environment on mental well-being. Public Health 120:1117–26
    [Google Scholar]
  41. 41. 
    Haines A, Amann M, Borgford-Parnell N, Leonard S, Kuylenstierna J, Shindell D 2017. Short-lived climate pollutant mitigation and the Sustainable Development Goals. Nat. Climate Change 7:863–69
    [Google Scholar]
  42. 42. 
    Hajat S, Haines A, Atkinson RW, Bremner SA, Anderson HR, Emberlin J 2001. Association between air pollution and daily consultations with general practitioners for allergic rhinitis in London, United Kingdom. Am. J. Epidemiol. 153:704–14
    [Google Scholar]
  43. 43. 
    Hay SI 2000. An overview of remote sensing and geodesy for epidemiology and public health application. Adv. Parasitol. 47:1–35
    [Google Scholar]
  44. 44. 
    Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW 2004. The global distribution and population at risk of malaria: past, present, and future. Lancet Infect. Dis. 4:327–36
    [Google Scholar]
  45. 45. 
    Hay SI, Omumbo JA, Craig MH, Snow RW 2000. Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Adv. Parasitol. 47:173–215
    [Google Scholar]
  46. 46. 
    Hegde G, Ahamed JM, Hebbar R, Raj U 2014. Urban land cover classification using hyperspectral data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40:751
    [Google Scholar]
  47. 47. 
    Herbreteau V, Salem G, Souris M, Hugot JP, Gonzalez JP 2007. Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health Place 13:400–3
    [Google Scholar]
  48. 48. 
    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25:1965–78
    [Google Scholar]
  49. 49. 
    Hilker T, Wulder MA, Coops NC, Linke J, McDermid G et al. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing Environ 113:1613–27
    [Google Scholar]
  50. 50. 
    Hoek G, Beelen R, De Hoogh K, Vienneau D, Gulliver J et al. 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 42:7561–78
    [Google Scholar]
  51. 51. 
    Holben BN, Eck TF, Slutsker I, Tanré D, Buis JP et al. 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66:1–16
    [Google Scholar]
  52. 52. 
    Hotez PJ, Kamath A 2009. Neglected tropical diseases in sub-Saharan Africa: review of their prevalence, distribution, and disease burden. PLOS Negl. Trop. Dis. 3:e412
    [Google Scholar]
  53. 53. 
    Hu Z, Liebens J, Rao KR 2008. Linking stroke mortality with air pollution, income, and greenness in northwest Florida: an ecological geographical study. Int. J. Health Geogr. 7:20
    [Google Scholar]
  54. 54. 
    James P, Bertrand KA, Hart JE, Schernhammer ES, Tamimi RM, Laden F 2017. Outdoor light at night and breast cancer incidence in the Nurses' Health Study II. Environ. Health Perspect. 125:087010
    [Google Scholar]
  55. 55. 
    James P, Hart JE, Banay RF, Laden F 2016. Exposure to greenness and mortality in a nationwide prospective cohort study of women. Environ. Health Perspect. 124:1344–52
    [Google Scholar]
  56. 56. 
    James P, Hart JE, Hipp JA, Mitchell JA, Kerr J et al. 2017. GPS-based exposure to greenness and walkability and accelerometry-based physical activity. Cancer Epidemiol. Biomark. Prev. 26:525–32
    [Google Scholar]
  57. 57. 
    Jerrett M, Turner MC, Beckerman BS, Pope CA, van Donkelaar A et al. 2017. Comparing the health effects of ambient particulate matter estimated using ground-based versus remote sensing exposure estimates. Environ. Health Perspect. 125:552–59
    [Google Scholar]
  58. 58. 
    Jia P 2018. Integrating kindergartener-specific questionnaires with citizen science to improve child health. Front. Public Health 6:236
    [Google Scholar]
  59. 59. 
    Jia P 2019. Spatial lifecourse epidemiology. Lancet Planet. Health. 3:2 In press
    [Google Scholar]
  60. 60. 
    Jia P, Anderson JD, Leitner M, Rheingans R 2016. High-resolution spatial distribution and estimation of access to improved sanitation in Kenya. PLOS ONE 11:e0158490
    [Google Scholar]
  61. 61. 
    Jia P, Cheng X, Xue H, Wang Y 2017. Applications of geographic information systems (GIS) data and methods in obesity-related research. Obes. Rev. 18:400–11
    [Google Scholar]
  62. 62. 
    Jia P, Gaughan AE 2016. Dasymetric modeling: a hybrid approach using land cover and tax parcel data for mapping population in Alachua County, Florida. Appl. Geogr. 66:100–8
    [Google Scholar]
  63. 63. 
    Jia P, Joyner A 2015. Human brucellosis occurrences in inner Mongolia, China: a spatio-temporal distribution and ecological niche modeling approach. BMC Infect. Dis. 15:36
    [Google Scholar]
  64. 64. 
    Jia P, Joyner A, Sun Y 2014. Short-term associations between accumulated rainfall and atmospheric moisture during landfall of three Atlantic hurricanes. Geogr. Bull. 55:49–62
    [Google Scholar]
  65. 65. 
    Jia P, Nie Y, Song G 2010. Detection of underground remains by remote sensing and geophysics. 2010 18th International Conference on Geoinformatics1–6 New York: IEEE
    [Google Scholar]
  66. 66. 
    Jia P, Nie Y, Yang L 2010. Recognition and extraction of the ancient sites covered by thick vegetation in Hainan Province of China. 2010 IEEE International Geoscience Remote Sensing Symposium3898–901 New York: IEEE
    [Google Scholar]
  67. 67. 
    Jia P, Sankoh O, Tatem AJ 2015. Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network. Health Place 36:88–96
    [Google Scholar]
  68. 68. 
    Jia P, Stein A 2017. Using remote sensing technology to measure environmental determinants of non-communicable diseases. Int. J. Epidemiol. 46:1343–44
    [Google Scholar]
  69. 69. 
    Jia P, Wang F, Xierali IM 2017. Delineating hierarchical hospital service areas in Florida. Geogr. Rev. 107:608–23
    [Google Scholar]
  70. 70. 
    Jia P, Wang F, Xierali IM 2017. Using a Huff-based model to delineate hospital service areas. Prof. Geogr. 69:522–30
    [Google Scholar]
  71. 71. 
    Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H et al. 2017. Climatologies at high resolution for the earth's land surface areas. Sci. Data 4:170122
    [Google Scholar]
  72. 72. 
    Kerr JT, Ostrovsky M 2003. From space to species: ecological applications for remote sensing. Trends Ecol. Evol. 18:299–305
    [Google Scholar]
  73. 73. 
    Leiter U, Garbe C 2008. Epidemiology of melanoma and nonmelanoma skin cancer—the role of sunlight. Adv. Exp. Med. Biol. 624:89–103
    [Google Scholar]
  74. 74. 
    Li Y, Robinson LE, Carter WM, Gupta R 2015. Childhood obesity and community food environments in Alabama's Black Belt region. Child Care Health Dev 41:668–76
    [Google Scholar]
  75. 75. 
    Lin G, Spann S, Hyman D, Pavlik V 2007. Climate amenity and BMI. Obesity 15:2120–27
    [Google Scholar]
  76. 76. 
    Liu J, Han Y, Tang X, Zhu J, Zhu T 2016. Estimating adult mortality attributable to PM2.5 exposure in China with assimilated PM2.5 concentrations based on a ground monitoring network. Sci. Total Environ. 568:1253–62
    [Google Scholar]
  77. 77. 
    Lu Y, Jin H 2005. Statistical methods in osteoporosis research. Current Topics in Osteoporosis H-W Deng, Y-Z Liu 201–60 Hackensack, NJ: World Sci. Publ.
    [Google Scholar]
  78. 78. 
    Mayosi BM, Flisher AJ, Lalloo UG, Sitas F, Tollman SM, Bradshaw D 2009. The burden of non-communicable diseases in South Africa. Lancet 374:934–47
    [Google Scholar]
  79. 79. 
    Mingji C, Onakpoya IJ, Perera R, Ward AM, Heneghan CJ 2015. Relationship between altitude and the prevalence of hypertension in Tibet: a systematic review. Heart 101:1054–60
    [Google Scholar]
  80. 80. 
    Muka T, Imo D, Jaspers L, Colpani V, Chaker L et al. 2015. The global impact of non-communicable diseases on healthcare spending and national income: a systematic review. Eur. J. Epidemiol. 30:251–77
    [Google Scholar]
  81. 81. 
    Myers SS, Smith MR, Guth S, Golden CD, Vaitla B et al. 2017. Climate change and global food systems: potential impacts on food security and undernutrition. Annu. Rev. Public Health 38:259–77
    [Google Scholar]
  82. 82. 
    Nieuwenhuijsen MJ 2016. Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities. Environ. Health 15:Suppl. 138
    [Google Scholar]
  83. 83. 
    Phillips SJ, Dudík M, Schapire RE 2004. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning655–62 New York: Assoc. Comput. Mach.
    [Google Scholar]
  84. 84. 
    Pickard BR, Daniel J, Mehaffey M, Jackson LE, Neale A 2015. EnviroAtlas: a new geospatial tool to foster ecosystem services science and resource management. Ecosyst. Serv. 14:45–55
    [Google Scholar]
  85. 85. 
    Pöllänen R, Toivonen H, Peräjärvi K, Karhunen T, Ilander T et al. 2009. Radiation surveillance using an unmanned aerial vehicle. Appl. Radiat. Isot. 67:340–44
    [Google Scholar]
  86. 86. 
    PRISM Climate Group, Or. State Univ. 2015. Parameter-elevation regressions on independent slopes model (PRISM) dataset. Data Catalog https://catalog.data.gov/dataset/parameter-elevation-regressions-on-independent-slopes-model-prism-dataset
  87. 87. 
    Rabkin M, El-Sadr WM 2011. Why reinvent the wheel? Leveraging the lessons of HIV scale-up to confront non-communicable diseases. Glob. Public Health 6:247–56
    [Google Scholar]
  88. 88. 
    Raviglione M, Marais B, Floyd K, Lönnroth K, Getahun H et al. 2012. Scaling up interventions to achieve global tuberculosis control: progress and new developments. Lancet 379:1902–13
    [Google Scholar]
  89. 89. 
    Rogers DJ, Randolph SE, Snow RW, Hay SI 2002. Satellite imagery in the study and forecast of malaria. Nature 415:710–15
    [Google Scholar]
  90. 90. 
    Rohleder N 2016. Chronic stress and disease. Insights to Neuroimmune Biology I Berczi 201–14 Amsterdam: Elsevier, 2nd ed..
    [Google Scholar]
  91. 91. 
    RTI Int 2015. The Neighborhood Map of U.S. Obesity. RTI International http://synthpopviewer.rti.org/obesity/
  92. 92. 
    Sankar PL, Parker LS 2017. The Precision Medicine Initiative's All of Us Research Program: an agenda for research on its ethical, legal, and social issues. Genet. Med. 19:743–50
    [Google Scholar]
  93. 93. 
    Schmidt MI, Duncan BB, Azevedo e Silva G, Menezes AM, Monteiro CA et al. 2011. Chronic non-communicable diseases in Brazil: burden and current challenges. Lancet 377:1949–61
    [Google Scholar]
  94. 94. 
    Schootman M, Nelson EJ, Werner K, Shacham E, Elliott M et al. 2016. Emerging technologies to measure neighborhood conditions in public health: implications for interventions and next steps. Int. J. Health Geogr. 15:20
    [Google Scholar]
  95. 95. 
    Snider G, Weagle CL, Martin RV, van Donkelaar A, Conrad K et al. 2015. SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications. Atmos. Meas. Tech. 8:505–21
    [Google Scholar]
  96. 96. 
    Stern PC 2000. Toward a coherent theory of environmentally significant behavior. J. Soc. Issues 56:407–24
    [Google Scholar]
  97. 97. 
    Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RC, Kwok RK et al. 2017. Toward greater implementation of the exposome research paradigm within environmental epidemiology. Annu. Rev. Public Health 38:315–27
    [Google Scholar]
  98. 98. 
    Vincent RK 1997. Fundamentals of Geological and Environmental Remote Sensing Upper Saddle River, NJ: Prentice Hall
  99. 99. 
    Wade TJ, Calderon RL, Sams E, Beach M, Brenner KP et al. 2006. Rapidly measured indicators of recreational water quality are predictive of swimming-associated gastrointestinal illness. Environ. Health Perspect. 114:24–28
    [Google Scholar]
  100. 100. 
    Wang FH, Wen M, Xu YQ 2013. Population-adjusted street connectivity, urbanicity and risk of obesity in the US. Appl. Geogr. 41:1–14
    [Google Scholar]
  101. 101. 
    Wang J, Jia P, Cuadros DF, Xu M, Wang X et al. 2017. A remote sensing data based artificial neural network approach for predicting climate-sensitive infectious disease outbreaks: a case study of human brucellosis. Remote Sens 9:1018
    [Google Scholar]
  102. 102. 
    WHO (World Health Organ.). 2014. Noncommunicable diseases country profiles 2014 Rep., WHO, Geneva. https://www.who.int/nmh/publications/ncd-profiles-2014/en/
  103. 103. 
    WHO (World Health Organ.). 2015. Noncommunicable diseases prematurely take 16 million lives annually, WHO urges more action News release, Jan. 19, WHO, Geneva. http://www.who.int/mediacentre/news/releases/2015/noncommunicable-diseases/en/
  104. 104. 
    WHO (World Health Organ.). 2017. NCD mortality and morbidity. Global Health Observatory (GHO) Data http://www.who.int/gho/ncd/mortality_morbidity/en/
  105. 105. 
    Wild CP 2005. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomark. Prev. 14:1847–50
    [Google Scholar]
  106. 106. 
    Wulder MA, Coops NC 2014. Make Earth observations open access. Nature 513:30–31
    [Google Scholar]
  107. 107. 
    Yang J, Siri JG, Remais JV, Cheng Q, Zhang H et al. 2018. The Tsinghua-Lancet Commission on Healthy Cities in China: unlocking the power of cities for a healthy China. Lancet 391:10135
    [Google Scholar]
  108. 108. 
    Yiannakoulias N, Svenson LW, Schopflocher DP 2009. An integrated framework for the geographic surveillance of chronic disease. Int. J. Health Geogr. 8:69
    [Google Scholar]
  109. 109. 
    Yin L, Raja S, Li X, Lai Y, Epstein L, Roemmich J 2013. Neighbourhood for playing: using GPS, GIS and accelerometry to delineate areas within which youth are physically active. Urban Stud 50:2922–39
    [Google Scholar]
  110. 110. 
    Zhu J, Jia P, Nie Y 2010. Analysis of the ancient river system in Loulan Period in Lop Nur Region. Proc. SPIE 8203:820313
    [Google Scholar]
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