1 London School of Hygiene and Tropical Medicine 2 Centers for Disease Control and Prevention

1. A new method to estimate mortality based on community informants: Validity and feasibility Bayard Roberts1, Oliver Morgan2, Francesco Checchi1 Presented by Keith Sabin, WHO 1 London School of Hygiene and Tropical Medicine 2 Centers for Disease Control and Prevention Collaborators: Ministry of Public Health, Afghanistan; United Nations High Commissioner for Refugees; Médecins Sans Frontières France; Aide Médicale Internationale

2. Rationale • Mortality surveillance is costly and deteriorates rapidly without supervision • Retrospective mortality surveys have a number of limitations • Various selection and response biases • No time to explore causes and circumstances of deaths • Can’t really get real-time estimates (unfeasibly high sample sizes) • Hypothesis: key community informants know about a very high proportion of recent deaths in their community • Often assumed by people doing rapid, rough assessments • Needed to test this formally against an acceptable gold standard measure of mortality • Meant mainly for crisis settings • Operational questions

3. How the method works (1) • Divide community into sectors based on obvious administrative boundaries • Rapid qualitative work to identify two key informants • Focus group discussion with community members • Topic guide, rapid analysis • There must be one of each in each sector • Exhaustive search process • Key informants refer investigators to all the households they know of in the sector where someone died in the last two months • Households are also asked to refer team to other households (snowball) • Sector is exhausted when informants cannot refer to any more households EM = Exhaustive Method

4. How the method works (2) • Household questionnaire • Multiple questions to clearly establish date • Actually, restrict analysis for the last month only (or a similar well-defined period) • Questionnaire about variables of interest (optional: Verbal Autopsy) • Denominator for mortality rate = person-time • Time: defined recall period • Person: population size estimated through rapid methods (e.g. structure count + mini-survey of structure occupancy), if not already available through registration/census activities

5. Methods Specific to Malawi • August-September 2008 • Focus group discussions with community • identify key community informants • EM - capture deaths over a two month recall period • Key informants = village headmen & village sage-women (fumukazi) • Population estimations • Capture-recapture analysis used to measure sensitivity • WHO standard verbal autopsy & analyses

6. Malawi results • Verbal autopsies feasible • Among 50 deaths for which a cause of death was attributed, • 48.8% were due to infectious causes • children under 5 years - 45.3% neonatal causes. • HIV/AIDS - (16.6%), • Mortality rate of 0.05 (95% CI 0.01-0.10) deaths per 10,000 person-days

7. Field testing (July-October 2008) • Urban district of Kabul city, Afghanistan (MoPH) • Extremely chaotic layout • Lots of population movement, short-term renters • Mae La refugee camp, Thailand (AMI) • Burmese Karen refugees • Well-established but with recent movement due to new arrivals and third-country emigration • Chiradzulu district, Malawi (MSF-France) • Rural, very scattered (spatial sampling of a fraction of the villages) • High HIV/AIDS burden • Lugufu and Mtabila camps, Tanzania (UNHCR) • DRC, Rwanda, Burundi, Sudanese refugees • Very well-established, well-serviced

8. Validation and feasibility Validation: • Capture-recapture analysis / Multiple Systems Estimation on three lists • Gold standard measure of the true number of deaths (denominator for sensitivity) • List 1: from the method itself • Lists 2 and 3 from other sources • Bayesian Model Averaging Feasibility: • Kept track of costs and time inputs • Compared to expected inputs for a SMART mortality survey • Considered ethical implications

9. Results: attrition • Rapidly canvassed large populations exhaustively

10. Results: yield of the method • Few referrals from households (no snowball process) • Contrary to preliminary observations in Darfur • Need sufficient number of events?

11. Results: sensitivity

12. Results: sensitivity for deaths <5y • District 1, Kabul: 52.6% (60d period), ≤20% (30d) • Mae La camp, Thailand: ≤12.5% (60d), 0% (30d) • Chiradzulu district, Malawi: 66.7% (60d), ≤80.0% (30d) • Tanzania camps: 53.7% (60d), 47.1% (30d) • Key informants inappropriate for children, except Malawi • But low numbers

13. Results: feasibility (financial inputs)

14. Results: feasibility (time inputs)

15. Major problems noted • Uninformative qualitative work • Difficult to get away from community leaders • “We know about every death!” • “If a family goes to bury their child somewhere, how am I supposed to know?” • Sectors a bit too big • Wouldn’t expect key informants to be able to cover them • No household chain referral process • Events too rare?

16. Appraisal of the method Advantages: • Cheap and rapid data collection • No sampling bias • Data entry and analysis can be done by non-epidemiologists without specialised software • Assuming population estimate is available • Facilitates in-depth exploration of circumstances (of death in our case) Disadvantages: • Low sensitivity, esp. for children (but may be improved by adding informants) • Especially for deaths of young children • Sensitivity is probably context-specific • Need good qualitative skills to identify key informants • May need to do population estimation (unlike surveys)

17. Acknowledgements • FANTA/AED funding • Megan Deitchler Co-investigators: • Mark Myatt (Brixton Health) • Daniel Chandramohan, Egbert Sondorp, Mohammed Ghaus Sultani (LSHTM) • Sunday Rwebangila (UNHCR) • Peter Nyasulu (MSF-France) Report now available: http://www.fantaproject.org/publications/EM_method.shtml