Methodological issues from 16 mortality surveys in DRC

1. Methodological issues from 16mortality surveys in DRC Surveys Conducted by: Charles Hail IRC DRC Roselidah Ondeko Laura Coby Fethi Belyadoumi Pascal Ngoy Karen Kassen MSF Kisangani Les Roberts IRC New York

2. Background Genocide in Rwanda 1994 Genocidaires flee and take haven in Zaire 1996 Rwanda & Uganda invade Eastern Zaire, country crumbles in front of them. Rwanda & Uganda have falling out with government they put in place. August 1998 – Rwanda & Uganda invade again

3. Objective Measure CMR over a range of areas in DRC Specific goal: Get U.S. Secretary of State to believe very bad things are happening in DRC and to enter the fray.

4. Legend: C.A.R. SUDAN DR CONGO: Mortality Survey Sites 2000 and 2001 Health zone limit controlled by the RCD ORIENTALE EQUATEUR Ocean or lake Surveyed in 2000 only UGANDA Kisangani Surveyed in both 2000 and 2001 Surveyed in 2001 only NORTH KIVU REPUBLIC OF CONGO Bunyakiri Katana Kalima KINSHASA SOUTH KIVU Kabare MANIEMA Demba Lusambo BANDUNDU Kananga Bilomba KASAI OCCIDENTALE BAS CONGO Kalemie Moba KATANGA KASAI ORIENTALE TANZANIA ANGOLA Note: Map is not to scale.

5. What is different here? • Spatial sampling – population location • often unknown, GPS used in HH selection. • 2) Current HH census – we discussed yesterday: • :recall < 2 year dubious • :allows for slept in your HH • confirmation. • :no contingency thinking. • 3) Four freakin’ years of high mortality – allows • for demographic issues to unfold.

6. Methods Two-stage cluster survey: Clusters assigned systematically, proportional to population. Cluster of 10 houses chosen as a random point in space.

7. Methods: Random Pt. Selection 1) Superimpose rectangle on clinic area N

8. Methods: Random Pt. Selection 2) Superimpose grid on rectangle N

9. Methods: Random Pt. Selection 3) Choose a position on grid. N 6 4 2 * 7,3 0 1 2 3 4 5 6 7 8 9 10

10. Methods: Random Pt. Selection 4) Estimate Dist. and Dir. to point Angle = 45, Compass heading of 135 Dist. = 5.66 6 4 2 * 7,3 N 0 1 2 3 4 5 6 7 8 9 10

11. Methods: Household Selection * 7,3

12. Methods: Interview Asked: Who (gender, age) is in your HH? Have any HH members died since Christmas 2000? Did any HH member die during 2000? If yes => age, gender, month, suspected cause, did they seek healthcare?

13. Methods: Interview cont. Person needed to sleep in HH last night to be in denominator. Person needed to sleep in HH last living night to be in numerator. This addresses people wandering in to HH. Stillbirths discarded.

15. Methodological Issues • HH information not verified. • (w/in HH – lying, external – survivor bias) • “Rural Bias” of spatial sampling. • Maybe deaths “cluster.”

22. Methodological Issues • HH information not verified. • 2) Rural Bias of spatial sampling. • We measured distance to 10th (5th) house. • Chi-squared for trend negative for 3. • Data show no trend. • 3) Maybe deaths “cluster.”

23. Methodological Issues • 2) Rural Bias of spatial sampling. • Data show no trend. • Radius Katana’99 Katana ’00 • <100m 4.2 1.4 • 100-200 3.4 3.4 • 201-300 2.7 3.3 • 301-400 6.7 2.1 • >400 2.2 2.8

24. Methodological Issues • HH information not verified. • Rural Bias of spatial sampling. • Maybe deaths “cluster.” • Design effect measured (.9 – 2.0) • Not like CDC Balkans Survey. • Over 400 clusters in this data.

25. Lessons that have made IRC SMARTer Having spatial information can be useful.

26. Katana Ville Kalahe Ville

27. C.A.R. SUDAN CMR: Deaths / 1000 / mo. ORIENTALE EQUATEUR 3 UGANDA NORTH KIVU REPUBLIC OF CONGO 5 6 4 7 KINSHASA SOUTH KIVU 4 MANIEMA 3 BANDUNDU 2 11 BAS CONGO 3 KASAI OCCIDENTALE 12 KATANGA KASAI ORIENTALE TANZANIA ANGOLA Note: Map is not to scale.

28. C.A.R. SUDAN Rates of Violent Deaths, Killings / 1000 / mo. ORIENTALE EQUATEUR .03 UGANDA NORTH KIVU REPUBLIC OF CONGO .31 1.9 .17 .21 KINSHASA SOUTH KIVU 0 MANIEMA 0 BANDUNDU 0 2.1 BAS CONGO 0 KASAI OCCIDENTALE .65 KATANGA KASAI ORIENTALE TANZANIA ANGOLA Note: Map is not to scale.

29. Lessons that have made IRC SMARTer Having spatial information can be useful. Even crude cause of death data has value. (In the land of the blind…..)

31. Lessons that have made IRC SMARTer Having spatial information can be useful. Even crude cause of death data has value. (In the land of the blind…..) Data like this have a lot of influence, but maybe not on the right people.

32. Deaths in Major Recent Wars* ’66-’70 Biafra 1.0 – 3.1 million 1971 Bangladesh 300,000-3 mil. ’70-’75 Cambodia 2-600,000 ’75- ’80 “ “ Pol Pot 1.2-3.3 million ’74 – ‘94 Ethiopia 1 – 2 million ’75–’93 Mozambique 600,000 – 1mil. ’79 - Afghanistan 1- 2 million ’83 –’98 Sudan 1.3 – 2 mil. ’80 – ‘88 Iran-Iraq 1 million Angola ?????????? ’91 – ’98 Somalia 4 – 500,000 ’91 Gulf War 150 – 200,000 ’92-’95 Bosnia 160 - 250,000 *see hppt://users.erols.com/mwhite28/warstat3.htm

33. Epidemiological Est. Made ’66-’70 Biafra 1.0 – 3.1 million ~2M 1971 Bangladesh 300,000-3 mil. 1.3 M ’70-’75 Cambodia 2-600,000 ’75- ’80 “ “ Pol Pot 1.2-3.3 million ’74 – ‘94 Ethiopia 1 – 2 million ’75–’93 Mozambique 600,000 – 1 mil. ’79 - Afghanistan 1- 2 million ’83 –’98 Sudan 1.3 – 2 mil. ’80 – ‘88 Iran-Iraq 1 million Angola ?????????? ’91 – ’98 Somalia 4 – 500,000 ’91 Gulf War 150 – 200,000 ’92-’95 Bosnia 160 - 250,000 *see http://users.erols.com/mwhite28/warstat3.htm

34. TB & HIV?????? HH w/ Deaths There was TB (36) or HIV (5) There was not Singleton Multiple Singleton Multiple

35. OR of another HH death given that 1 was reported as HIV or TB Multiple Singleton TB 22 18 Not TB 168 469 OR= 3.4 (95% 1.7 – 6.8)

38. Les, sit down and be quiet!