The Economic and Mortality Impact of Stock-Outs of TB Medicines. Edmund Rutta, MD MPH SIAPS Bangkok, March 2-6, 2015

Transcription

1 The Economic and Mortality Impact of Stock-Outs of TB Medicines Edmund Rutta, MD MPH SIAPS Bangkok, March 2-6, 2015

2 Background A 2013 study in the Philippines found that: Only 7% of 583 MDR-TB patients completed treatment without interruption Patients who had longer interruptions with sporadic variability during the 6 12 month or the month treatment period had a significantly increased risk for poor outcomes compared to patients who had short, regular interruptions Patients that miss more consecutive days of treatment with sporadic interruption patterns or a greater proportion of treatment are at an increased risk for poor treatment outcomes Source: Podewils LJ, Gler MTS, Quelapio MI, Chen MP (2013) Patterns of Treatment Interruption among Patients with Multidrug-Resistant TB (MDR TB) and Association with Interim and Final Treatment Outcomes. PLoS ONE 8(7): e doi: /journal.pone

3 Impact of Reducing Stock-Outs Treatment interruption is likely to result in poor treatment outcomes and investment in reducing it may be very worthwhile An example of a poor treatment outcome is that interruption could lead a DS-TB patient to develop MDR-TB But treatment interruption also results in wasted resources For example, investments in lab services that are not followed up by successful treatment We often think that treatment interruption is a patient issue but it can also be due to stock-outs, which is a preventable problem

4 Treatment Interruption Impact Tool (TIIT) SIAPS is developing a tool to estimate the impact of stock-outs which can be used to advocate for greater investment in solving stock-out problems The tool can also be: Used to estimate the impact of TB patient nonadherence Adapted to ARVs, diseases that involve specific medicines, and laboratory supply stock-outs

5 The TIIT Tool Excel-based, open source, will be available online Now in prototype version Consultations with experts indicate that there is little knowledge on the impact of treatment interruption and it has not previously been costed Your feedback will be important and appreciated

6 How the Tool Works: Phases Separate algorithm for each of 4 treatment phases: Drug Susceptible TB (first-line): Initial phase Continuation phase Drug Resistant TB (second-line): Initial phase Continuation phase

7 Prototype Assumptions This initial version was intended to be as simple as possible but we can add other areas if they are important This version assumes 2 months of stock-out (the final version will include a 1 month and a 3 month model) It assumes stock-outs of all drugs needed, not one or two of the drugs in the package (which could be more serious) The health and treatment implications are based mainly on evolving expert judgment with some assumptions used to test the model (e.g. the percentage of persons who would use the private sector if the public sector has a stock-out) The unit costs are from Indonesia where we have conducted several costing studies

8 Example of the Data Entry and Costing Table MDR-TB Initial Phase Unit cost Total cost Key assumptions Length of stock-out in months 2 Patients affected by stock out 100 Number who buy drugs in private sector 25% Additional cost of medicines Additional monthly cost per patient $ 793 Total cost of medicines per patient $ 1,586 Total cost of medicines for all patients $ 39, Increased risk of XDR-TB due to poor quality drugs 50% 12.5 Cost per patient of diagnosing and treating MDR-TB $ 26,392 Total cost of patients who convert to XDR-TB due to poor quality medicines $ 329,900 Assumed good quality - no cost $ Increased risk of infecting other persons with MDR-TB Number of persons who could be infected 25 Number of persons who would develop MDR-TB 10% 2.5 Cost of treating one MDR-TB case 10,032 Total cost of treating persons who become infected with MDR-TB $ 25,080 Sub-total $ 394,626

9 Example of One of the Decision Trees 25% Number who buy drugs in private sector 50% Increased risk of XDR-TB due to poor quality 25 50%?? Die?? 70% Number who resume treatment and are cured MDRTB initial phase 100 0% Number who resume tre 20% Number who resume treatment but die 0 75% Number who go without drugs 75 10% Number who become XDR-TB cases 0% Number who will self-cure 100% Number who do not resume treatment % Number who die

10 Summary of Prototype Results for 100 People Without Access to Medicines for 2 Months (Results Not To Be Added) Phase US$ DS-TB initial phase 55,622 DS-TB continuation phase 355,039 MDR-TB initial phase 1,228,816 MDR-TB continuation phase 782,177

11 Summary of MDR-TB Initial Phase Stock-Out Impact by Component SUMMARY Initial Phase Assume 2 months stock-out for 100 Patients % # people Cost Number who buy drugs in private sector 25% 25 Additional cost of medicines $ 39,646 Increased risk of XDR-TB due to poor quality drugs $ 329,900 Increased risk of infecting other persons with MDR-TB $ 25,080 Sub-total $ 394,626 Number who go without drugs 75% 75 Number who resume treatment Number who resume treatment and are cured $ 33,989 Number who resume treatment but die $ 54,855 Number who become XDR-TB cases $ 123,620 Number who do not resume treatment $ 205,950 Number of new infections transmitted $ 183,285 Number of persons for whom sunken treatment costs were incurred $ 232,493 Sub-total $ 834,191 GRAND TOTAL $ 1,228,816

12 Next Steps Finalize prototype Internal review of prototype additions/changes Circulate to selected experts and review assumptions Show deaths? (DALYs too complex?) Cost partial packages? Conduct literature review to look for evidence Use tool to conduct analysis for target countries Kenya, Bangladesh, Tanzania and Philippines? Compare with costs of reducing stock-outs (investment case) For more information contact Edmund Rutta and David Collins

13 Thank you