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Spatial Science & Health

Spatial Science & Health. Health data Issues Dr Mark Cresswell. Topics. Who compiles health data? Medical ethics Anonymised case records Disease summary statistics Problems: human Problems: technical Examples of public-access health data. Who compiles Health Data?.

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Spatial Science & Health

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  1. Spatial Science& Health Health data Issues Dr Mark Cresswell

  2. Topics • Who compiles health data? • Medical ethics • Anonymised case records • Disease summary statistics • Problems: human • Problems: technical • Examples of public-access health data

  3. Who compiles Health Data? • General practitioners • Patient records • Hospitals • Mortality, morbidity and rehabilitation • Local health authorities • National health ministry (NHS in UK) • World Health Organization • Regional and World

  4. UK Health Data • UK National Health Service was set up in 1948 • It is the largest organisation in Europe • The NHS is funded by the taxpayer and managed by the Department of Health, which sets overall policy on health issues

  5. NHS structure in England (Source: NHS, 2006)

  6. Global Health Data? • World Health Organization (WHO) • Established in 1948 • Based in Geneva • Part of the United Nations Infrastructure The attainment by all peoples of the highest possible level of health Health is defined in WHO's Constitution as a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.

  7. Global Health Data? • The WHO is governed by 192 states which together make up the World Health Assembly • Main tasks for the assembly are to: • Approve the WHO programme • Monitor and approve the WHO budget • Decide key policy issues

  8. General WHO Administrative Structure

  9. Who compiles Health Data? • Universities • Medical research • Census and health economics • Drug (pharmaceutical) companies • NGOs (Médecins sans Frontières and IRC) • Charitable donors (B&M Gates Foundation)

  10. Who compiles Health Data? • Non Governmental Organisations may operate under special circumstances • Third World countries • Areas affected by natural disasters or war • Diseases and conditions poorly under resourced • Action paid for by existing medical charities • Politics!

  11. Medical Ethics • Personal data must be regarded as strictly confidential at all times (GP data) • Even today, data is transferred to/from hospitals by taxi (no digital system yet) UK • Studies may use anonymised case data • Unless drug trials • Unless prior consent in focus study

  12. Anonymised case records • Data may be released to scientific community for research purposes • Personally identifiable portions of GP/hospital records is stripped out (name, address and maybe date of birth) • Most records contain standard record information (age, sex, prior history, illness, duration of illness, location, date etc)

  13. Disease Summary Statistics • Specific diseases may be characterised by collating and summarising case data • Many diseases are grouped by theme and coded • Medical statistics are employed to help analyse spatial and temporal change in disease • Prevalence, morbidity, mortality etc are the common currency of epidemiologists

  14. Disease Summary Statistics This is the number of new cases in a particular time period: INCIDENCE I= Incidence N = Number of new cases in a given time period P = Person years at risk during same time period Note that person years at risk means the total amount of time (in years) that each member of the population being studied (the study population) is at risk of the disease during the period of interest.

  15. Disease Summary Statistics This is the proportion of current cases in a population at a given point in time: PREVALENCE P = Prevalence Nc = Number of cases in the population at a given point in time P = Total population at the same point in time

  16. Disease Summary Statistics The probability of having a disease, for those individuals who were exposed to a risk factor. ABSOLUTE RISK Ra = Absolute Risk Ne = Number of cases of disease in those exposed Ie = Number of individuals exposed

  17. Disease Summary Statistics This is an indication of the risk of developing a disease in a group of people who were exposed to a risk factor, relative to a group who were not exposed to it. RELATIVE RISK RR = Relative Risk Ie = Disease incidence in exposed group In = Disease incidence in non-exposed group

  18. Problems: Human • Social inequality and marginalised people • poverty or caste/class/race divides • Taboo and embarrassment • STDs and prostate/breast cancers • Mis-diagnosis (poor equipment or training) • Similarity of symptoms & no lab facilities • Poor reporting or surveillance infrastructure

  19. Problems: Human WHO vaccination campaign Burkina Faso Epidemiological Surveillance

  20. Problems: Technical • Diagnostics • Time consuming • Lab-on-chip technology expensive • Complex vector dynamics • E.g. Bird flu (migratory patterns) • Refugee and nomadic populations • Use of satellites to track people • Loss of medical records by displaced people

  21. Saharan dust storm March 2004 (MODIS)

  22. Disaster Monitoring Constellation (DMC) is a new series of geostationary satellites developed specifically for disaster applications including health. DMC: 26 or 32m resolution. Sun-synchronous A relatively cloud free image was available for the period 10:12 GMT on 13/6/05. A water body was identified (LAT: 13.054N, LON: 2.070W). All three spectral channels (NIR, RED and GREEN) were sampled across a transect line bisecting the water body

  23. Interpolation of sparse station readings is undesirable – so we must look to: • Remote sensing • Model output ABOVE: Model grid representation LEFT: Meteosat weather satellite

  24. European health data • The WHO regional office for Europe provides access to country-specific health and disease statistics via the centralized information system for infectious diseases (CISID). This is available from http://data.euro.who.int/cisid/. • Health for All database http://www.euro.who.int/hfadb

  25. European health data CISID data example

  26. European health data HFA data example

  27. African Malaria Data MARA data

  28. African Malaria Data MARA data

  29. African Malaria Data MARA data

  30. African Malaria Data MARA data

  31. African Meningitis Data MSF and MALSAT Spatial Distribution Meningitis Epidemics 1841-1999 (n = c.425) 1 1 Molesworth A.M., Thomson M.C., Connor S.J., Cresswell M.P., Morse A.P., Shears P., Hart C.A., Cuevas L.E. (2002) Where is the Meningitis Belt?, Transactions of the Royal Society of Hygiene and Tropical Medicine, 96, 242-249.

  32. WHO compile case statistics reports on a regular basis in a standard format

  33. WHO compile case statistics reports on a regular basis in a standard format

  34. WHO compile case statistics reports on a regular basis in a standard format

  35. ANY QUESTIONS

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