Improving Patient Access to Malaria and other Essential Medicines in Zambia. Results of a Pilot Project

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1 Improving Patient Access to Malaria and other Essential Medicines in Zambia Results of a Pilot Project Prepared for Transport Week Learning Event Jed Friedman, DECPI with Monique Vledder, Mirja Sjoblom, Prashant Yadav, and others

2 Why was the pilot designed? Drugs are often unavailable in health facilities A 2006 survey shows that the key drug (ACTs) for malaria treatment were not available at time of visit in: 44% of urban facilities 29% of rural facilities 15% of hospitals Stock-outs on district level are less common indicating that distribution from districts to health facilities is the main bottleneck in the system

3 Stockout bottlenecks often lie between the district store and the facility SP (malaria prevention) DepoProvera (Contraceptive) CTX (Antibiotics) Amoxicillin (Antibiotics) DHO Health Facility ACT Adult (malaria treatment) ACT Pediatric (malaria treatment) 0% 10% 20% 30% 40% 50% 60%

4 Why is it difficult to deliver drugs?

5 Improving access to essential medicines in Zambia Objective Identify a cost-effective way to improve the availability of drugs through strengthening of the supply chain from center to districts and health facilities Approach The pilot compares the effectiveness of two different supply chain interventions to select one (or a combination/variation) that can be rolled-out nationally

6 Two interventions tested System A: Health centers (HCs) place orders to District Health Officer (DHO) who sends aggregated monthly orders to central stores (MSL) DHOs store commodities and supply HCs monthly Districts are responsible for assembling orders for the HCs and coordinating delivery between the district and HCs District logistic oversight conducted by new cadre, Commodity Planner (CP) System B: HCs place orders directly to MSL Orders are packed at MSL in sealed packages for each individual facility Districts only responsible for coordinating delivery or pick up of orders between the district and HCs, facilitated by CP

7 Pilot evaluation design Districts stratified and randomly selected from 52 peri-urban and rural districts in Zambia Total of 24 districts randomly assigned: 8 districts for system A, 8 districts for system B and 8 comparison districts Pilot implementation for a one-year period Baseline data collected from 250 facilities in Dec-Jan 2008/09 and follow-up data during the same period in 2009/10 Inventory and stock-out rates of tracer drugs measured at both baseline and endline Supplementary information stocking history, storage conditions also collected at endline

8 District Selection

9 Reduced stockouts in A system Comparison of baseline and endline values in A districts SP (malaria prevention) DepoProvera* (Contraceptive) CTX* (Antibiotics) Amoxicillin (Antibiotics) A baseline A endline ACT Adult* (malaria treatment) ACT Pediatric (malaria treatment) 0% 10% 20% 30% 40% 50% 60% 70% 80% *the reduction in stockout rate is statistically significant with respect to any observed change in control districts

10 Dramatically reduced stockouts in B system Comparison of baseline and endline values in B districts SP* (malaria prevention) DepoProvera* (Contraceptive) CTX* (Antibiotics) Amoxicillin* (Antibiotics) B baseline B endline ACT Adult* (malaria treatment) ACT Pediatric* (malaria treatment) 0% 10% 20% 30% 40% 50% 60% 70% 80% *the reduction in stockout rate is statistically significant with respect to any observed change in control districts

11 More people get their lifesaving drugs 40 in B districts Number of days of stockouts for the last quarter of comparison districts A districts B districts ACT Pediatric (malaria treatment) ACT Adult (malaria treatment) Amoxicillin (Antibiotics) CTX (Antibiotics) DepoProvera (Contraceptive) SP (malaria prevention)

12 Summary of results System B performs significantly better than system A and comparison districts in terms of availability There is a significant and large decrease in number of days of stockouts in B districts compared to control Hence unmet demand is significantly lower in B districts compared to A and control Reporting rates from district health offices to MSL have increased during the pilot period for both A and B districts to nearly 100%

13 Expected impact on malaria mortality If Model B were to be scaled up nationwide, projections indicate: 16,600 U5 deaths due to malaria could be averted by 2015, as well as 2,200 adult deaths Child and adult mortality due to malaria could be reduced by 21% and 25% respectively These gains focus only on increased availability of malaria drugs however widespread gains likely from increased availability of all essential drugs

14 Program costs The monthly recurrent costs for Model A is US$2832 per district and for Model B it is US$3325/district. Pilot was implemented in remote districts with higher transportation costs. Some net savings in B districts are not included National scale up of B would increase the supply chain operational cost from 4.1 percent to 8.5 percent of the total pharmaceutical budget Total current procurement budget for drugs (partners and MOH): approximately US$100 million/year

15 Model B is 4 times as cost-effective as Model A Cost per day of essential medicine stock-out averted: Model A reduces stock-out day of one tracer drug at a cost of $14.5 in additional operating costs Model B achieves the same stock-out reduction at a cost of $4.2 Focusing on possible malaria mortality averted: cost of $22 per YLL averted for a national scale-up of Version B over a 5 year period Compares favorably with many other public health interventions

16 What are the lessons for IE in infrastructure sectors? This intervention necessarily implemented at a highly aggregate level the district Common situation for infrastructure projects Even though the unit of observation is the HC (stock-outs), potential for much observational dependency within district The pilot only had funds to test 2 interventions in 8 districts, risk of underpowered study if within-district outcomes are highly correlated

17 Lessons (cont) From base-line data, power analysis suggested study can identify standardized effect of.49 Definition: standardized effect = (change in outcome of interest)/(standard deviation of outcome) Implication: study could not identify moderate gains that may have real health implications (i.e. reduction in stock-out rate to 20% from 40%) In this study model B is a smashing success: a standardized effect of.57 [Got Lucky!] however gains from A are often not statistically different from B or control

18 Lessons (cont) Need to ensure an adequately powered study First best solution is sufficient study scope and funding to observe adequate number of treatment units in an RCT When not possible, what can be done to help improve power? Pair-matched randomization (Imai, King, and Nall, 2009) Quasi-experimental methods (i.e. propensity score matched difference-in-difference) However these methods usually come at a cost stronger assumptions needed to identify causal impact No easy answer, but need to be aware of challenges at the start of project design stage

19 Additional slides

20 Another way to measure performance: Stockouts in Model B vs. Comparison Districts SP (malaria prevention) DepoProvera (Contraceptive) CTX (Antibiotics) Amoxicillin (Antibiotics) B districts comparison districts ACT Adult (malaria treatment) ACT Pediatric (malaria treatment) 0% 10% 20% 30% 40% 50% 60% 70% 80%

21 System A Commodity Planners Medical Stores Limited One pack per districts (for all health facilities) is compiled CP places orders to MSL Pull system, monthly delivery Health facilities place orders to CP Twice Monthly Health Facilities with limited storage space Districts Monthly CP receives stock from MSL and manages district stock in district store room and process and packs orders from health facilities Health Facilities with adequate storage space Health facilities receive facility packages from CP

22 System B Commodity Planners + Sealed Packages Medical Stores Limited One customized pack for each health facility is compiled Pull system, monthly delivery Health facilities place orders directly to MSL Districts CP receives facility packages from MSL; No stock kept at District Store Twice Monthly Health Facilities with limited storage space Monthly Health Facilities with adequate storage space Health facilities receive facility packages from CP