CoChat
Sign Up
Cited by 120Data-Driven Disease SurveillanceGeographic Information Systems StudiesPublic Health and Environmental Issues

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

Open access
Nøhr, C., Sørensen, E. M., Gudes, O., Geraghty, E. M., Shaw, N. T., Bivona-Tellez, C., Lyseen, A. K., the IMIA Health GIS Working Group·2014·Yearbook of Medical Informatics
DOI 10.15265/iy-2014-0008PMC PMC4287070
Open this paper in CoChat — free accountOpen PDF

Abstract

Summary: Objectives: The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. Method: The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. Results: A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Conclusion: Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.15265/iy-2014-0008

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Within research communities, datasets on land cover, land use and associated characteristics are widely used to identify areas with increased risk.

    “Within research communities, datasets on land cover, land use and associated characteristics (such as vegetation indices or land surface temperatures) are widely used to identify areas with increased risk [104–108].”

    From Achieving global malaria eradication in changing landscapes (2021) · cites this paper as [105]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Part of this paper’s reference list could not be split into single entries.

Flagged first, then by citations

Research works printed but not linked: 78

  1. Barrett FA. Finke's 1792 map of human dis- eases: The first world disease map? Soc Sci Med.2000;50(7-8):915-21.
  2. Koch T. Disease Maps: Epidemics on the Ground. Chicago: University of Chicago Press; 2011.
  3. Levêque A, Humblet PC, Lagasse R. Atlas of 'avoidable mortality' in Belgium 1985-1989. Arch Public Health 1999;57(1-2):1-87.
  4. Albrecht J, Ramasubramanian L. The moving target: A geographic index of relative wellbeing. J Med Syst 2004;28(4):371-84.
  5. Meade MS, Emch M. Medical geography. The Guilford Press;2010.
  6. Nykiforuk CI, Flaman LM. Geographic infor- mation systems (GIS) for health promotion and public health: A review. Health Promot Pract 2011;12(1):63-73.
  7. Blatt AJ. Maps, geography libraries, and health outcomes: Gazing into the future of medical geography. J Map Geogr Libr 2011;7(1):2-12.
  8. Senkowski A, Colonna M, Bicout D-. Inframu- nicipality distribution of lung cancer in the urban area of Grenoble. Rev Epidemiol Sante Publ 2008;56(5):315-21.
  9. Jarup L. Health and environment information systems for exposure and disease mapping, and risk assessment. Environ Health Perspect 2004;112(9):995-7.
  10. Polack S, Brooker S, Kuper H, Mariotti S, Mabey D, Foster A. Mapping the global distribution of trachoma. Bull WHO 2005;83(12):913-9.
  11. Wright Willis A, Evanoff BA, Lian M, Criswell SR, Racette BA. Geographic and ethnic variation in Parkinson disease: A population-based study of us Medicare beneficiaries. Neuroepidemiology 2010;34(3):143-51.
  12. Peng ZH, Cheng YJ, Reilly KH, Wang L, Qin QQ, Ding ZW, et al. Spatial distribution of HIV/AIDS in Yunnan province, People's Republic of China. Geospatial Health 2011;5(2):177-82.
  13. Murad AA. Creating a GIS-based epidemiological application for Jeddah city. Int J Healthc Technol Manage 2008;9(5-6):540-51.
  14. Walsh SJ, Dechello LM. Geographical variation in mortality from systemic lupus erythematosus in the United States. Lupus.2001;10(9):637-46.
  15. Jeefoo P, Tripathi NK, Souris M. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. Int J Environ Res Public Health 2011;8(1):51-74.
  16. Murray EJ, Marais BJ, Mans G, Beyers N, Ayles H, Godfrey-Faussett P, et al. A multidisciplinary method to map potential tuberculosis transmission 'hot spots' in high-burden communities. Int J Tuberc Lung Dis 2009;13(6):767-74.
  17. Lee SS, Wong NS. The clustering and transmission dynamics of pandemic influenza A (H1N1) 2009 cases in Hong Kong. J Infect 2011;63(4):274-80.
  18. Sui DZ. Tobler's first law of geography: A big idea for a small world? Ann Assoc Am Geogr 2004;94(2):269-77.
  19. Jarup L, Best N, Toledano MB, Wakefield J, Elliott P. Geographical epidemiology of prostate cancer in Great Britain. Int J Cancer 2002;97(5):695-9.
  20. Wilhelm M, Meng Y-, Rull RP, English P, Balmes J, Ritz B. Environmental public health tracking of chidhood asthma using California health interview survey, traffic, and outdoor air pollution data. Environ Health Perspect 2008;116(9):1254-60.
  21. Wang Y, Gulliver J, McHugh C. In: Modeling the health impacts of air pollution exposures in London within the GENESIS system. 2011 international symposium on water resource and environmental protection, ISWREP 2011; 20 May 2011 through 22 May 2011; Xi'an; 2011. p. 2341-4.
  22. Jerrett M, Finkelstein MM, Brook JR, Arain MA, Kanaroglou P, Stieb DM, et al. A cohort study of traffic-related air pollution and mortality in To- ronto, Ontario, Canada. Environ Health Perspect 2009;117(5):772-7.
  23. Gilboa SM, Mendola P, Olshan AF, Harness C, Loomis D, Langlois PH, et al. Comparison of residential geocoding methods in population-based study of air quality and birth defects. Environ Res 2006;101(2):256-62.
  24. Nyberg F, Gustavsson P, Järup L, Bellander T, Berglind N, Jakobsson R, et al. Urban air pollu- tion and lung cancer in Stockholm. Epidemiology 2000;11(5):487-95.
  25. Chellini E, Cherubini M, Chetoni L, Seniori Costantini A, Biggeri A, Vannucchi G. Risk of respiratory cancer around a sewage plant in Prato, Italy. Arch Environ Health 2002;57(6):548-53.
  26. Goovaerts P, Jacquez GM. Detection of tempo- ral changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models. J Geogr Syst 2005;7(1):137-59.
  27. Greiling DA, Jacquez GM, Kaufmann AM, Rommel RG. Space-time visualization and analysis in the cancer atlas viewer. J Geogr Syst.2005;7(1):67-84.
  28. Biggeri A, Catelan D, Dreassi E. The epidemic of lung cancer in Tuscany (Italy): A joint analysis of male and female mortality by birth cohort. Spat Spatio-temporal Epidemiol 2009;1(1):31-40.
  29. Horst MA, Coco AS. Observing the spread of common illnesses through a community: Using geographic information systems (GIS) for surveil- lance. J Am Board Fam Med 2010;23(1):32-41.
  30. Hu W, Clements A, Williams G, Tong S. Spatial analysis of notified dengue fever infections. Epi- demiol Infect 2011;139(3):391-9.
  31. Emch M, Feldacker C, Sirajul SM, Ali M. Season- ality of cholera from 1974 to 2005: A review of global patterns. Int J Health Geogr 2008;7.
  32. Wagner SE, Burch JB, Bottai M, Puett R, Porter D, Bolick-Aldrich S, et al. Groundwater uranium and cancer incidence in south Carolina. Cancer Causes Control 2011;22(1):41-50.
  33. Beyer KMM, Comstock S, Seagren R. Disease maps as context for community mapping: A meth- odological approach for linking confidential health information with local geographical knowledge for community health research. J Community Health 2010;35(6):635-44.
  34. Fleming G, Merwe Mvd, McFerren G. Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa. Environ Model Softw 2007;22(4):442-8.
  35. Lewis MD, Pavlin JA, Mansfield JL, O'Brien S, Boomsma LG, Elbert Y, et al. Disease outbreak detection system using syndromic data in the greater Washington DC area. Am J Prev Med 2002;23(3):180-6.
  36. Guo JG, Vounatsou P, Cao CL, Utzinger J, Zhu HQ, Anderegg D, et al. A geographic information and remote sensing based model for prediction of oncomelania hupensis habitats in the Poyang Lake area, China. Acta Trop 2005;96(2-3):213-22.
  37. Ombok M, Adazu K, Odhiambo F, Bayoh N, Kiriinya R, Slutsker L, et al. Geospatial distribu- tion and determinants of child mortality in rural western Kenya 2002-2005. Trop Med Int Health 2010;15(4):423-33.
  38. Wu P-, Lay J-, Guo H-, Lin C-, Lung S-, Su H-. Higher temperature and urbanization affect the spatial patterns of dengue fever transmis- sion in subtropical Taiwan. Sci Total Environ 2009;407(7):2224-33.
  39. Yang G-, Vounatsou P, Zhou X-, Utzinger J, Tanner M. A review of geographic information system and remote sensing with applications to the epidemiol- ogy and control of schistosomiasis in China. Acta Trop 2005;96(2-3):117-29.
  40. Nakaya T, Nakase K, Osaka K. Spatio-temporal modelling of the HIV epidemic in Japan based on the national HIV/AIDS surveillance. J Geogr Syst Lyseen et al. 2005;7(3-4):313-36.
  41. Khormi HM, Kumar L. Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Sci Total Environ 2011;409(22):4713-9.
  42. Møller-Jensen L. KRY. Exploiting available data sources: Location/allocation modeling for health service planning in rural Ghana. Geogr Tidsskr 2001;101:145-53.
  43. Rahman S-, Smith DK. Use of location-allo- cation models in health service development planning in developing nations. Eur J Oper Res 2000;123(3):437-52.
  44. Farahani RZ, Hekmatfar M. Facility location - concepts, models, algorithms and case studies. Heidelberg: Physica-verlag -A Springer Com- pany; 2009.
  45. Shortt NK, Moore A, Coombes M, Wymer C. Defining regions for locality health care plan- ning: A multidimensional approach. Soc Sci Med 2005;60(12):2715-27.
  46. Murad AA. Creating a geographical information systems-based spatial profile for exploring health services supply and demand. Am J Appl Sci 2011;8(6):644-51.
  47. Ong MEH, Tan EH, Yan X, Anushia P, Lim SH, Leong BS, et al. An observational study describing the geographic-time distribution of cardiac arrests in Singapore: What is the utility of geographic information systems for planning public access defibrillation? (PADS phase I). Resuscitation 2008;76(3):388-96.
  48. Gudes O, Pathak V, Kendall E, Yigitcanlar T. In: Thinking spatially, acting collaboratively: A GIS-based health decision support system for im- proving the collaborative health-planning practice. International conference on health informatics, HEALTHINF 2011; 26 January 2011 through 29 January 2011; Rome; 2011. p. 148-55.
  49. Gudes O, Kendall E, Yigitcanlar T, Pathak V, Baum S. Rethinking health planning: A frame- work for organising information to underpin collaborative health planning. Health Inf Manage 2010;39(2):18-29.
  50. Houghton F. Reflections on the science and art1 of using a GIS to locate a new national children's hospital in Ireland. Ir Geogr 2009;42(2):245-52.
  51. Lwasa S. Geospatial analysis and decision support for health services planning in Uganda. Geospat Health 2007;2(1):29-40.
  52. Brabyn L, Beere P. Population access to hospital emergency departments and the impacts of health reform in New Zealand. Health Informatics J 2006;12(3):227-37.
  53. Higgs G. The role of GIS for health utilization studies: Literature review. Health Serv Outcomes Res Methodol 2009;9(2):84-99.
  54. Chang Y-, Wen T-, Lai M-. Using geographic information systems (GIS) to identify the as- sociation between geographic accessibility and hospital-seeking behavior by hepatocellular carci- noma patients in Taiwan. Taiwan J Public Health 2009;28(6):517-29.
  55. Lee JE, Sung JH, Ward WB, Fos PJ, Lee WJ, Kim JC. Utilization of the emergency room: Impact of geographic distance. Geospat Health 2007;1(2):243-53.
  56. Leung YW, Brual J, Macpherson A, Grace SL. Geographic issues in cardiac rehabilitation utilization: A narrative review. Health Place 2010;16(6):1196-205.
  57. Zulian G, Donisi V, Secco G, Pertile R, Tansella M, Amaddeo F. How are caseload and service utilization of psychiatric services influenced by distance? A geographical approach to the study of community-based mental health services. Soc Psychiatry Psychiatr Epidemiol 2011;46(9):881-91.
  58. Henneman PL, Garb JL, Capraro GA, Li H, Smith- line HA, Wait RB. Geography and travel distance impact emergency department visits. J Emerg Med 2011;40(3):333-9.
  59. Brabyn L, Gower P. Comparing three GIS tech- niques for modelling geographical access to gener- al practitioners. Cartographica.2004;39(2):41-50.
  60. Fortney J, Rost K, Warren J. Comparing alternative methods of measuring geographic access to health services. Health Serv Outcomes Res Methodol 2000;1(2):173-84.
  61. Yang D-, Goerge R, Mullner R. Comparing GIS- based methods of measuring spatial accessibility to health services. J Med Syst 2006;30(1):23-32.
  62. Luo W, Qi Y. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health Place 2009;15(4):1100-7.
  63. McGrail MR, Humphreys JS. Measuring spatial ac- cessibility to primary care in rural areas: Improving the effectiveness of the two-step floating catchment area method. Appl Geogr 2009;29(4):533-41.
  64. Spencer J, Angeles G. Kernel density estimation as a technique for assessing availability of health services in Nicaragua. Health Serv Outcomes Res Methodol 2007;7(3-4):145-57.
  65. Loh C-A, Cobb SC, Johnson CK. Potential and actual accessibility to hospital and hospital services in northeast Florida. Southeast Geogr 2009;49(2):171-84.
  66. Higgs G. A literature review of the use of GIS-based measures of access to health care services. Health Serv Outcomes Res Methodol 2004;5(2):119-39.
  67. Acharya AB, Nyirenda JC, Higgs GB, Bloomfield MS, Cruz-Flores S, Connor LT, et al. Distance from home to hospital and thrombolytic utilization for acute ischemic stroke. J Stroke Cerebrovasc Dis 2011;20(4):295-301.
  68. Simões PP, Almeida RMVR. Geographic accessi- bility to obstetric care and maternal mortality in a large metropolitan area of Brazil. Int J Gynecol Obstet 2011;112(1):25-9.
  69. Cudnik MT, Schmicker RH, Vaillancourt C, Newgard CD, Christenson JM, Davis DP, et al. A geospatial assessment of transport distance and survival to discharge in out of hospital cardiac arrest patients: Implications for resuscitation centers. Resuscitation 2010;81(5):518-23.
  70. Yamashita T, Kunkel SR. The association between heart disease mortality and geographic access to hospitals: County level comparisons in Ohio, USA. Soc Sci Med 2010;70(8):1211-8.
  71. Jones AP, Haynes R, Sauerzapf V, Crawford SM, Zhao H, Forman D. Travel times to health care and survival from cancers in northern England. Eur J Cancer 2008;44(2):269-74.
  72. Pedigo AS, Odoi A. Investigation of disparities in geographic accessibility to emergency stroke and myocardial infarction care in east Tennessee using geographic information systems and network analysis. Ann Epidemiol 2010;20(12):924-30.
  73. Susi L, Mascarenhas AK. Using a geographical in- formation system to map the distribution of dentists in Ohio. J Am Dent Assoc 2002;133(5):636-42.
  74. Clark RA, Driscoll A, Nottage J, McLennan S, Coombe DM, Bamford EJ, et al. Inequitable pro- vision of optimal services for patients with chronic heart failure: A national geo-mapping study. Med J Aust 2007;186(4):169-73.
  75. Stuart B, Hinde A. Accessing genitourinary medi- cine clinics: Does it matter where you live? J Fam Plann Reprod Health Care 2011;37(3):152-7. 100. White P, James V, Ansell D, Lodhi V, Donovan KL. Equity of access to dialysis facilities in Wales. QJM 2006;99(7):445-52. 101. Dai D. Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health Place 2010;16(5):1038-52. 102. Fulcher CL, Kaukinen CE. Visualizing the infra- structure of US healthcare using internet GIS: A community health informatics approach for reducing health disparities. Medinfo 2004;11(Pt 2):1197-201. 103. Committee for the Study of the Future of Public Health. The Future of Public Health. Washington: National Academy Press; 1988. 225 p. 104. Alcaraz KI, Kreuter MW, Bryan RP. Use of GIS to identify optimal settings for cancer prevention and control in African American communities. Prev Med 2009;49(1):54-7. 105. Miner JW, White A, Lubenow AE, Palmer S. Geocoding and social marketing in Alabama's cancer prevention programs. Prev Chronic Dis 2005;2 Spec no. 106.
  76. Murphy E, King EA, Rice HJ. Estimating human exposure to transport noise in central Dublin, Ireland. Environ Int 2009;35(2):298-302. 135. Aspuru I, Vázquez M, Eguiguren JL, Tellado N. In: Nilson A., Boden H., editors. Environmental noise management system. integrated answer to the directive for urban noise management from GIS technologies and interim calculation methods. Proceedings of the tenth international congress on sound and vibration; 7 July 2003 through 10 July 2003; Stockholm; 2003. p. 2663-70. 136. Brink M. Parameters of well-being and subjective health and their relationship with residential traf- fic noise exposure -A representative evaluation in Switzerland. Environ Int 2011;37(4):723-33. 137. Eriksson C, Bluhm G, Hilding A, Östenson C-, Pershagen G. Aircraft noise and incidence of hypertension-gender specific effects. Environ Res. 2010;110(8):764-72. 138. Eriksson C, Rosenlund M, Pershagen G, Hild- ing A, Östenson C-, Bluhm G. Aircraft noise and incidence of hypertension. Epidemiology 2007;18(6):716-21. 139. Bodin T, Albin M, Ardö J, Stroh E, Stergren P-, Björk J. Road traffic noise and hypertension: Results from a cross-sectional public health survey in southern Sweden. Environ Health Global Access Sci Sour 2009;8(1). 140. Björk J, Ardö J, Stroh E, Lövkvist H, Östergren P-, Albin M. Road traffic noise in southern Swe- den and its relation to annoyance, disturbance of daily activities and health. Scand J Work Environ Health 2006;32(5):392-401. 141. Gundogdu IB. Applying linear analysis methods to GIS-supported procedures for preventing traffic accidents: Case study of Konya. Saf Sci 2010;48(6):763-9. 142. Ziari H, Khabiri MM. Applied GIS software for improving pedestrian & bicycle safety. Transport 2005;20(4):160-4. 143. Híjar M, Trostle J, Bronfman M. Pedestrian injuries in Mexico: A multi-method approach. Soc Sci Med 2003;57(11):2149-59. 144. Zhang W, Chen Q, McCubbin H, McCubbin L, Foley S. Predictors of mental and physical health: Individual and neighborhood levels of education, social well-being, and ethnicity. Health Place. 2011;17(1):238-47. 145. Prociow PA, Crowe JA. Towards personalised ambient monitoring of mental health via mobile technologies. Technol Health Care 2010;18(4- 5):275-84. 146. Lu X. In: Web GIS based information visualiza- tion for infectious disease prevention. 3rd inter- national symposium on intelligent information technology application, IITA 2009; 21 November 2009 through 22 November 2009; NanChang; 2009. p. 148-51. 147. Bonet M, Spiegel JM, Ibarra AM, Kouri G, Pintre Lic A, Yassi A. An integrated ecosystem approach for sustainable prevention and control of dengue in central Havana. Int J Occup Environ Health 2007;13(2):188-94. 148. De Souza Dias MCF, Dias GH, Nobre ML. The use of geographical information system (GIS) to improve active leprosy case finding campaigns in the municipality of Mossoro, riot Grande do note state, Brazil. Lepr Rev 2007;78(3):261-9. 149. Ulugtekin N, Alkoy S, Seker DZ. Use of a geographic information system in an epidemi- ological study in measles in Istanbul. J Int Med Res 2007;35(1):150-4. 150. Kamiya M, Trinipil Lagapa J, Oku Y. Research on targeting sources of alveolar echinococcosis in Japan. Comp Immunol Microbiol Infect Dis 2007;30(5-6):427-48. 151. Ali M, Emch M, Donnay JP, Yunus M, Sack RB. Identifying environmental risk factors for endemic cholera: A raster GIS approach. Health Place 2002;8(3):201-10. 152.
  77. Kligerman M, Sallis JF, Ryan S, Frank LD, Nader PR. Association of neighborhood design and recreation environment variables with physical activity and body mass index in adolescents. Am J Health Promot 2007;21(4):274-7. 153. Leslie E, Coffee N, Frank L, Owen N, Bauman A, Hugo G. Walkability of local communities: Using geographic information systems to objec- tively assess relevant environmental attributes. Health Place 2007;13(1):111-22. 154.
  78. Gabrysch S, Cousens S, Cox J, Campbell OMR. The influence of distance and level of care on delivery place in rural Zambia: A study of linked national data in a geographic information system. PLoS Med 2011;8(1). 171. Jones SG, Ashby AJ, Momin SR, Naidoo A. Spa- tial implications associated with using euclidean distance measurements and geographic centroid imputation in health care research. Health Serv Res 2010;45(1):316-27. 172. Zinszer K, Jauvin C, Verma A, Bedard L, Allard R, Schwartzman K, et al. Residential address errors in public health surveillance data: A description and analysis of the impact on geocoding. Spat Spatio-temporal Epidemiol 2010;1(2-3):163-8. 173. Zandbergen PA. Geocoding quality and impli- cations for spatial analysis. Geogr Compass 2009;3(2):647-80. 174. Zimmerman DL, Li J, Fang X. Spatial autocor- relation among automated geocoding errors and its effects on testing for disease clustering. Stat Med 2010;29(9):1025-36. 175. Vieira VM, Howard GJ, Gallagher LG, Fletcher T. Geocoding rural addresses in a community contaminated by PFOA: A comparison of meth- ods. Environ Health Global Access Sci Sour 2010;9(1). 176.

Cited by

Sorted by citations

Authors
Nøhr, C., Sørensen, E. M., Gudes, O., Geraghty, E. M., Shaw, N. T., Bivona-Tellez, C., Lyseen, A. K., & the IMIA Health GIS Working Group
Affiliations
  • Aalborg University (DK)
  • Curtin University (AU)
  • University of California, Davis (US)
  • Algoma University (CA)
  • Azusa Pacific University (US)
Corresponding author
Lyseen, A. K.
ORCID
Venue
Yearbook of Medical Informatics
Publisher
Georg Thieme Verlag KG
Published
2014-08
Volume
23
Issue
01
Pages
110-124
ISSN
  • 0943-4747 (ISSN-L)
  • 2364-0502
DOI
10.15265/iy-2014-0008
PMC
PMC4287070
OpenAlex
W2145244857
Cited by
120
References
4
Citations by year
2026: 2 · 2025: 7 · 2024: 5 · 2023: 12 · 2022: 14 · 2021: 21 · 2020: 19 · 2019: 5 · 2018: 11 · 2017: 8 · 2016: 8 · 2015: 8
FWCI
1.06
Type
journal article
Language
en
Open access
Locations
  • Yearbook of Medical Informatics (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eSpace (Curtin University) (submittedVersion)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Public Health and Environmental Issues
Keywords
health planning, health technology, research framework, infectious diseases, non-communicable diseases, malaria, schistosomiasis, cancer, asthma, GPS, smartphones, remote sensing
MeSH terms
Epidemiology, Health Planning, Health Services, Humans, Population Surveillance, Public Health, Geographic Information Systems, Spatial Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review