Estimates of Mortality in Conflict Situations

1. Estimates of Mortality in Conflict Situations Global Health Fellowship, St. Lukes-Roosevelt Hospital Center

2. Changing Profiles of Conflict

3. Changing Profile of Conflicts Most conflicts within nat’l borders Poor countries Civil wars with poorly trained armies Asymmetric wars Civilians and soldiers affected Disease/malnutrition > violence

4. Deaths in Major Recent Wars

5. Deaths in Major Recent Wars: Epidemiological Estimates

6. Biafra • Dr Karl Western from CDC conducted a convenience sample • Ran around countryside where security would permit • Estimated fraction of children in enclave with small pox vaccination scar • CDC estimated # living, know small pox doses administered • Estimated 1 million deaths • Estimated 30% of population suffering from famine edema, 2/3rds with dangerous loss of weight

7. Bangladesh Monitored 120,000 people in Matlab Birth rate went down to 7% in year of war CMR went from 15 to 21/1000/year Assuming population of 70 million, approx 500,000 excess deaths from war (Curlin GT, et al)

8. Liberia through ‘92 MSF did survey in part of country Applied rate to entire country Estimated 150,000 excess deaths

9. DRC 1999 IRC survey in Katana, estimates 15,000 deaths 2000 NYTimes estimates 50,000 excess deaths IRC does 5 surveys, estimates 1.7 million deaths 2001 IRC 6 surveys estimating 2.5 million 2002 IRC 20 surveys estimating 3.3 million 2004 IRC nationwide survey 3.8 million

10. Estimates of Mortality Crude mortality rate (CMR)-total # of deaths that occurred in a population of known size that is at risk of death in a certain period of time Under 5 mortality rate (U5MR)-age-specific mortality rate with above definition

11. Crude Mortality Rate CMR=(number of deaths)/(midterm population at risk X duration of time period) x 10,000 persons

12. Under 5 Mortality U5M=(number of deaths)/(midterm population of U5s at risk X duration of time period) X 10,000 persons

13. Estimates of Mortality The numerical difference between a “crisis CMR” and the “baseline CMR” is termed the excess mortality Used for estimating the magnitude of the emergency and monitoring the humanitarian response

14. Estimates of Mortality Direct deaths—those caused by war-related injuries and attacks Indirect deaths—those caused by the worsening of social, economic, and health conditions in the conflict-affected region

15. Estimating Mortality: direct deaths Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey Gilbert Burnham, Riyadh Lafta, Shannon Doocy, Les Roberts Mortality before and after the 2003 invasion of Iraq: cluster sample survey Les Roberts, Riyadh Lafta, Richard Garfield, Jamal Khudhairi, Gilbert Burnham **100,000 excess deaths, violent

16. Estimating Mortality: indirect deaths Mortality in the Democratic Republic of Congo: a nationwide survey Benjamin Coghlan, Richard J Brennan, Pascal Ngoy, David Dofara, Brad Otto, Mark Clements, Tony Stewart **600,000 excess deaths, 38,000/month, easily preventable and treatable illnesses

17. Indirect deaths Potentially holding political and military leaders responsible Collaboration between epidemiologists, statisticians, human rights organizations Bolstering of public health system

18. Challenges of collecting data • Security • Breakdown of health infrastructure • Relapsing fever southern Sudan 1998 • Marburg hemorrhagic fever in Angola 2005 • Hospital records and death certificates to verify death often absent; alternative verbal autopsy • No baseline data • Especially in chronic emergency settings • How to attribute indirect deaths to indirect impacts of conflict

19. How to estimate mortality in a conflict situation • Retrospective mortality surveys • Prospective surveillance • Analysis of multiple data sources • **Combination of above

20. Retrospective Mortality Surveys • The HH is the sampling unit; many of the methods we’ve discussed • Simple random sampling • Systemic random sampling • Cluster sampling—geographic similarity • Usually try to differentiate violent vs non-violent • As with all surveys, bias; survivor and recall

21. Retrospective Mortality Surveys: Examples War and mortality in Kosovo, 1998–99: an epidemiological testimony Paul B Spiegel, Peter Salama Two stage cluster survey of 1197 HHs, 8605 people 67 (64%) of 105 deaths from war-related trauma corresponding to 12,000 deaths in total population CMR increased 2.3 times from pre-conflict Highest age-specific mortality men 15-49, and older than 50

22. Retrospective Mortality Surveys: Examples Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey Gilbert Burnham, Riyadh Lafta, Shannon Doocy, Les Roberts Cross-sectional cluster survey of 1849 HHs and 12, 801 people 601,027 (92%) of 654,965 deaths violent CMR 2.6 times pre-invasion levels Most violent deaths men aged 15-59 Most non-violent persons > 60 yo, children, women 15-59 yo

23. Retrospective Mortality Surveys: Examples Mortality in the Democratic Republic of Congo: a nationwide survey Benjamin Coghlan, Richard J Brennan, Pascal Ngoy, David Dofara, Brad Otto, Mark Clements, Tony Stewart Stratified three-stage cluster survey of 19,500 HHs CMR of 2.1 deaths/1000 40% higher than the sub-Saharan region 38,000 excess deaths/month Most deaths due to preventable causes, malnutrition and IDs

24. Prospective Mortality Surveillance Through HIS, targeting health facilities and death registries Superior, but usually HIS is weak Ad-hoc systems can be put in place by NGOs, etc, but often not a priority in humanitarian response Refugee settings—UNHCRs passive HIS Cause of death, like RMS, difficult INDEPTH: sub-national demographic surveillance systems (19 developing countries)

25. Analysis of Multiple Data Sources • Reconstruction of mortality profiles using sources of statistics before, during, and after conflict by statisticians and demographers • Prospective surveillance and previously-conducted RMS • Database of human rights violations and interviews • Census of public graves • Exhumations • Missing persons from HHs in surveys • Health surveys and census data • Focus on the quality of the data sources

27. Politically controversial?