1. Estimating need and coverage for chronic conditions through household surveys
Ties Boerma,
WHO, Geneva

2. Measuring the need for and coverage of treatment through surveys
Acute conditions in children
Diarrhea in the last 2 weeks
Cough and breathing difficulties in the last 2 weeks
Fever in the last 2 weeks
Treatment
Child taken to health facilities
Recall specific treatments
PLoS Medicine Collection 2012
Measurement poorer than thought, especially for suspected pneumonia

3. Measuring population needs for health care Prevalence of chronic conditions
Not all people with condition X are using health services  population survey is necessary
Survey questions: self-reported medical diagnosis of a condition in a survey is not enough
In high income countries service utilization is high and self reports are often taken as proxy for population prevalence
If many are not using the services it is not sufficient
Algorithms based on symptoms can provide an approximation of population unmet need for several conditions, but not all
How good: compare with medical records (gold standard)
Survey internal and external consistency  this presentation

4. WHO Study on Adult Health and Ageing (SAGE)
National samples: China, India, Mexico, Russia, Ghana, South Africa; Wave 1 completed 2010, Wave 2 going to the field; sample size 2,200-12,500
Collects data from respondents 50 years and older
Questionnaire modules (similar to World Health Survey ):
Chronic conditions: angina, arthritis, asthma & chronic obstructive lung disease, depression; diabetes, stroke
Multiple measures of health and functioning: self-rated health, activity limitations; health score based on 8 domains; ADL, IADL; WHODAS
Biological and clinical data collection
Performance tests: handgrip strength, rapid/normal walk, cognition
Anthropometry, blood pressure measurement, vision
Blood sample taken on filter papers: HbA1c, lipids, and others

5. Questions on chronic conditions in SAGE
Self-reported medical diagnosis
Yes
A: No treatment in last year
A1: Algorithm negative
A2: Algorithm positive
B: Treatment in last year No
C: Algorithm positive
D: Algorithm negative

6. Questions in SAGE: population prevalence
Self-reported medical diagnosis
Yes
No treatment in last year
Algorithm negative
B: Algorithm positive
A: Treatment in last year No
C: Algorithm positive
Prevalence = (A + B + C) / TOTAL
Undiagnosed = C / (A + B + C )
Coverage = B / (A + B + C)

7. Algorithms Condition Main question Additional Angina pectoris
Chest pain or discomfort (2)
Rose questions (+3): disappears upon rest, location pain
Arthritis
Joint pains (1)
Stiffness in morning, disappears upon exercise (+3 questions)
Asthma
Wheezing
Tightness in chest, shortness of breath without exercise (+4)
COPD
Shortness of breath, coughing/wheezing, coughing sputum / phlegm
Depression
Sad/depressed; lost interest in "life"; energy loss
15 additional questions
Diabetes
None
Cataract
Cloudy blurry vision; vision problems with light (glare)

8. Methods to assess plausibility of self-reports
Internal consistency
Data quality checks
Consistency with other indicators (e.g. arthritis with mobility score or pain score or clinical tests)
Comparison full algorithm with partial
Patterns by age, sex, and socioeconomic position
E.g. more common among older people, women, poor
Patterns among countries according to risk factors, health systems, level of development; trends over time using WHS

9. Angina: domain health score by diagnostic category Considerable loss of health for both SR & algorithm

10. Angina prevalence Patterns by background characteristics (excess fractions)

11. Angina treatment coverage Patterns by background characteristics (excess fractions)
Women / men / Poor40 / Best60 Urb / Rur

12. Angina pectoris - prevalence 3 Country patterns SAGE

13. Angina pectoris 3 Country patterns and over time

14. Angina pectoris – coverage intervention 3 Country patterns and over time

15. Arthritis 1 Internal consistency
Self-reports:
13-27% (median 19%) of those who reported a medical diagnosis did not have any treatment in the last year and did not meet the algorithm
Algorithm questions:
Chronic joint pains common: 47-75% (median 55%) of those did not meet the full algorithm;
Those with full algorithm did not have more functioning loss than those with joints pains only
Conclusion: could use either, affects prevalence

16. Arthritis: mobility score by diagnostic category Considerable loss of health for both SR & algorithm

17. Arthritis prevalence 2 Patterns by background characteristics (excess fractions)

18. Arthritis treatment coverage 2 Patterns by background characteristics (excess fractions)
Women / men / Poor40 / Best60 Urb / Rur

19. Arthritis - prevalence 3 Country patterns and over time

20. Arthritis 3 Country patterns and over time

21. Arthritis – coverage intervention 3 Country patterns and over time

22. Depression - prevalence 3 Country patterns and over time

23. Depression 3 Country patterns and over time

24. Depression – coverage intervention 3 Country patterns and over time

25. General service utilization Perceived needs and care received
Need is 50-70% of respondents in last 12 months
Over 90% received care in last 12 months, except Ghana 82%
It is likely that the care received figure includes self-treatment. It appears to be higher than admissions and OPD together.

26. Outpatient and Inpatient Utilization in the last 12 months
Admission to hospital or long term care facility in the last 12 months: 4-6%, except Russia
Ambulant care utilization 40-50% in the last year, with India as outlier

27. OPD visits per year Visits per person per year Mean OPD visits
% OPD use
Mean OPD visits
Visits per person per year
China
38.7
5.2
2.0
India
65.2
4.1
2.7
Russia
50.7
2.9
1.5
South Africa
44.2
6.3
2.8
Ghana
51.4
3.0
Visits per year seems on the low side, e.g. China is about 3-4 visits for general population in the NHSS 2008, but the population 50+ years should have higher utilization
Russia and China seem unlikely low

28. Excess fraction for self-perceived care needs in the last 12 months
Expected patterns are women > men, substantially higher than years and poorest > best-off quintile
Female excess as expected
Age effect in the expected direction but smaller than expected
Wealth effect in the opposite direction for 4/5 countries

29. Excess fraction for hospital admission in the last 12 months
Same expectations for sex-age as for self perceived need, except more admissions for q5
Female – male patterns variable
Older respondents more commonly admitted or in long term care facility in 4/5 countries, in three the difference is large as expected; South Africa and Ghana results puzzling
People in wealthiest households have higher admission rates in all countries, large difference in Ghana, China, South Africa, Russia (large confidence intervals)

30. Conclusions
Algorithm seems useful way to capture those who have no self reported medical diagnosis
The extent to which true population prevalence – coverage rates are determined remains a big question
Large and sometimes implausible differences between countries – seems more suitable for in-country assessment than comparable global measures
But consistency over time a concern (survey quality?)
General service utilization questions:
Cannot tell us much about unmet need/coverage of services
Hospital admissions may be most reliable piece of information
Much more work needed: on algorithms and on biomarkers