1. COSTS AND COST EFFECTIVENESS OF TRAINING TRADITIONAL BIRTH ATTENDANTS TO REDUCE NEONATAL MORTALITY IN THE LUFWANYAMA NEONATAL SURVIVAL STUDY Christopher J. Gill MD MS Center for Global Health and Development Department of International Health Boston University School of Public Health ICIUM 2012, Antalya Turkey

2. Background Problem: Neonatal Mortality accounts for ~40% of ‘Under Five’ deaths 75% due to perinatal conditions: birth asphyxia, hypothermia and sepsis In areas with limited access to health services, Traditional Birth Attendants are a common source of basic obstetrical care Response: The Lufwanyama Neonatal Survival Project (LUNESP) assessed the effectiveness of training TBAs in skills targeting birth asphyxia, hypothermia and sepsis. Question: What is the cost effectiveness of this strategy?

3. Setting and Methods Setting: Lufwanyama, Zambia Low population density: 6.4 persons/square kilometer High neonatal mortality: ~40/1000 live births Methods: Cluster randomized and controlled effectiveness trial 120 TBAs randomized to intervention/control Control TBAs continued standard of care Intervention TBAs trained in two skill sets: Neonatal resuscitation protocol Antibiotics with Facilitated Referral Primary endpoint: mortality by day 28 among live-born infants Endpoints captured on ~3500 deliveries (97.9% of total enrolled) Lufwanyama facts: 12 health posts/centers No physicians No hospitals

4. Results of main study Results from main trial 1 death averted per 56 deliveries attended Relative risk reduction 0.55 ( 95% CI 0.33 to 0.90) Absolute risk reduction of 18 deaths / 1000 live births Death Rate on Day of Delivery: 19.9/1000 births (control) vs. 7.8/1000 births (intervention) RR = 0.4, 95% CI 0.19-0.83 Key Question: But is it cost effective?

5. Cost effectiveness analysis Costs and effectiveness data taken directly from the trial Cost effectiveness assessed from three perspectives 1. Financial – actual costs incurred during LUNESP 2. Economic – factors in additional costs from a societal perspective 3. 10-year forecasted economic analysis – models the cost effectiveness of the LUNESP interventions if applied programmatically All costs adjusted for inflation, expressed in constant dollars Discount rate of 3% Key Outcomes: 1. Cost per delivery attended 2. Cost effectiveness: per life saved 3. Cost effectiveness: per DALY averted

6. Results: Costs Assumed main features of LUNESP except 100% task shifting: 1.TBA training 2.Program management

7. Results: Cost effectiveness

8. Multivariate sensitivity analyses Parameters varied in Monte Carlo: 1.Effect size 2.Average No. deliveries/month/TBA 3.Training workshop logistic costs 4.Costs Monitoring and Supervision

9. Conclusions WHO classification of cost effectiveness of interventions: ‘Cost effective’ if a DALY averted is less than three times per capita GDP ‘Highly cost effective’ if less than per capita GDP Zambia’s 2010 per capital GDP was 1500 dollars LUNESP’s interventions were ‘highly cost effective’ - even under most conservative assumptions Intervention will be maximally cost effective in settings where TBAs are busier, and where local ownership of program is complete. This approach can be recommended as high value for money.

10. Acknowledgements Our team Lora Sabin David Hamer Anna B Knapp Nicholas Guerina Grace Mazala Joshua Kasimba William MacLeod Our Funders USAID NIH/NIAID AAP UNICEF

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12. Overview of LUNESP study design 12 Randomization of TBAs Intervention TBAs: Trained in AFR + NRP Control TBAs: Existing standard of care Deliveries Stillbirths Live births Week One Week Four Death Data collector assessments Verbal autopsies Statistical analysis STUDY OVERVIEW

13. Assumptions for scenario analyses ParameterBase caseHigh ImpactConservative # of TBAs trained together 608060 Time for refresher workshop 212 Annual trainings needed 334 Births/TBA/Month1.293.341.21 Effect size of intervention 17.9/1000 live births 13.4/1000 live births (25% drop) MonitoringMonthlyEvery other monthMonthly

14. Results: Costs for the three models

15. One-way Sensitivity analyses: key drivers of CE

16. Multivariate sensitivity analyses