EVALUATING THE ECONOMIC AND HEALTH IMPACTS OF INVESTING IN LABORATORIES IN EAST AFRICA

Transcription

1 Public Disclosure Authorized Public Disclosure Authorized EVALUATING THE ECONOMIC AND HEALTH IMPACTS OF INVESTING IN LABORATORIES IN EAST AFRICA D I S C U S S I O N P A P E R M A Y Simone Peart Boyce Andrés A. Berruti Helen Connolly Miriam Schneidman Public Disclosure Authorized Public Disclosure Authorized

2

3 EVALUATING THE ECONOMIC AND HEALTH IMPACTS OF INVESTING IN LABORATORIES IN EAST AFRICA Development and Application of a Conceptual Framework Simone Peart Boyce, Andrés A. Berruti, Helen Connolly, Miriam Schneidman May 2015

4 Health, Nutrition and Population (HNP) Discussion Paper This series is produced by the Health, Nutrition, and Population Global Practice. The papers in this series aim to provide a vehicle for publishing preliminary results on HNP topics to encourage discussion and debate. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations or to members of its Board of Executive Directors or the countries they represent. Citation and the use of material presented in this series should take into account this provisional character. For information regarding the HNP Discussion Paper Series, please contact the Editor, Martin Lutalo at mlutalo@worldbank.org or Erika Yanick at eyanick@worldbank.org The International Bank for Reconstruction and Development / The World Bank 1818 H Street, NW Washington, DC All rights reserved. i

5 Health, Nutrition and Population (HNP) Discussion Paper Evaluating the Economic and Health Impacts of Investing in Laboratories in East Africa Development and Application of a Conceptual Framework Simone Peart Boyce a Andrés A. Berruti b Helen Connolly a Miriam Schneidman c a ICF International, Atlanta, Georgia, USA b Division of Global HIV/AIDS, Centers for Disease Control and Prevention, Atlanta, Georgia, USA c Eastern and Southern Africa, The World Bank, Washington, DC, USA Supported by joint funding from the World Bank and U.S. Centers for Disease Control and Prevention Abstract: Laboratories provide essential services to the health sector on the monitoring and treatment of disease. Routine implementation of new diagnostic techniques may be costly; therefore, understanding their clinical utility, impact, and cost-effectiveness are necessary to guide decisions as to whether and how such techniques should be implemented. In this study, the authors design a conceptual framework for examining the following: (1) optimal mix of laboratory services at different levels of the health system; (2) combination of resources required within laboratories to promote efficiency; and (3) potential for outsourcing to promote cost containment. The framework considers both the health and economic rationale for laboratory investments. The authors then use the conceptual framework to inform a decision analytics model that maps out the health and economic impact of laboratory investments, and to illustrate the model by investigating the best placement of a new technology (GeneXpert) for detecting multi-drug-resistant tuberculosis (MDR-TB). The illustrative application of the model shows that investment in a new diagnostic technology for MDR-TB is cost-effective regardless of placement in a district-level (satellite) or national-level (reference) laboratory. Placement of the GeneXpert system at the satellite laboratory results in patients tested for MDR-TB or TB at lower costs than the reference laboratory. Furthermore, if testing occurs at the satellite laboratory, more primary and secondary cases are treated and cured than if testing was conducted at the reference laboratory, leading to better outcomes. Overall, testing at the satellite laboratory results in more deaths averted and more disability life-adjusted years (DALYs) saved. Both facilities have average costs per DALY well below the WHO-suggested threshold for the per capita gross domestic product (GDP). However, the satellite laboratory saves more DALYs at a lower additional cost per DALY. Keywords: cost-effectiveness, laboratory, investment, tuberculosis, Xpert MTB/RIF Disclaimer: The findings, interpretations and conclusions expressed in the paper are entirely those of the authors, and do not represent the views of the World Bank, its Executive Directors, or the countries they represent. ii

6 Correspondence Details: Simone Peart Boyce, ICF International, 3 Corporate Square, Suite 370, Atlanta, GA, USA, (404) , (404) (fax), simone.boyce@icfi.com, iii

7 Table of Contents ACKNOWLEDGMENTS... VI LIST OF ABBREVIATIONS... VII PREFACE... VIII PART I INTRODUCTION... 1 PART II MODEL... 2 CONCEPTUAL FRAMEWORK... 2 MEASURING IMPACTS THROUGH COST-EFFECTIVENESS... 4 PART III AN APPLICATION OF THE COST-EFFECTIVENESS MODEL... 5 OVERVIEW... 5 STUDY POPULATION... 6 MODEL PERSPECTIVE... 6 TIME HORIZON... 6 MODEL ASSUMPTIONS... 6 Testing... 7 Laboratory Capacity... 7 Treatment and Transmission... 8 Population Testing data (Sensitivity and Specificity) Cost LIMITATIONS PART IV FINDINGS OUTCOMES COSTS COST-EFFECTIVENESS iv

8 PART V DISCUSSION REFERENCES APPENDIX I ELEMENTS OF THE CONCEPTUAL FRAMEWORK APPENDIX II USE OF DECISION ANALYSIS TO ESTIMATE COST-EFFECTIVENESS Inputs: Laboratory Capacity Outputs: Laboratory Results Outcomes: Health DECISION ANALYSIS INDICATORS APPENDIX III STUDY DESIGN Economic Costs of Testing Cost Collection and Time Frame Categories for Analysis Test Performed Inputs Sources of Support Program Cost Categories DATA COLLECTION Administrative Data Cost Data APPENDIX IV ADMINISTRATIVE QUESTIONNAIRES APPENDIX V COST QUESTIONNAIRES v

9 ACKNOWLEDGMENTS The authors would like to thank all members of the East Africa Public Health Laboratory Networking Project for their participation and assistance in preparing this report. Without their generous contributions of time and effort, this study could not have been completed. This includes the staff members of the Ministry of Health, Division of Leprosy, TB and Lung Diseases (Kenya); Ministry of Health, National TB and Leprosy Program (Uganda); Kenya Medical Research Institute; and the National AIDS and STI Control Programme (Kenya). We appreciate the efforts of the staff members and clients at each of the reference laboratories: the National TB Reference Laboratories (Kenya and Uganda); Central Public Health Laboratories (Uganda); and the National HIV Reference Laboratory (Kenya). The staff of the four satellite district facilities visited, as well as the district and regional officers, provided invaluable information and assistance: Kitale District Hospital (Kenya), Malindi District Hospital (Kenya), St. Mary s Hospital Lacor (Uganda), and Gulu Regional Referral Hospital (Uganda). We would like to thank the management of the Global Health Practice of the Africa Region at the World Bank; and of the U.S. Centers for Disease Control and Prevention, Division of Global HIV/AIDS for their valuable support. The authors are grateful to the World Bank for publishing this report as an HNP Discussion Paper. vi

10 LIST OF ABBREVIATIONS DST EAPHLNP GDP ICER MDR-TB MIS SSM TB WHO Drug Susceptibility Testing East Africa Public Health Laboratory Networking Project Gross Domestic Product Incremental Cost-effectiveness Ratio Multi-drug Resistant Tuberculosis Management Information System Sputum Smear Microscopy Tuberculosis World Health Organization vii

11 PREFACE This project evolved from the growing recognition of the importance of laboratories in disease prevention and control. This occurred against the backdrop of increased investments in laboratories worldwide and in Africa, in particular East Africa, through the East Africa Public Health Laboratory Networking Project. There is a broad based recognition that weak laboratory systems can lead to misdiagnosis of diseases and a lack of etiological confirmation of disease outbreaks, which increases the economic costs of treatment, compromises patient care, and hampers the response to disease outbreaks. Hence, it is critical to improve understanding of how to invest more efficiently and effectively in laboratory systems and laboratory networks. This project helps to provide understanding about optimal investment strategies. viii

12 PART I INTRODUCTION 1. Well-equipped and effective laboratories are the cornerstone of a well-functioning health care system. Laboratories help to inform clinical decisions regarding diagnosis, treatment eligibility, treatment failures, and the timing of shifts to new treatment regimens. In addition, they perform key public health roles in monitoring disease trends, tracking outbreaks, and effectively diagnosing pathogens during epidemics. 2. The Maputo Declaration on Strengthening of Laboratory Systems, which resulted from the Consensus Meeting on Clinical Laboratory Testing, Harmonization and Standardization, held in Maputo, Mozambique, in 2008, recognized that limited laboratory capacity represents a major barrier to implementation and sustainability of prevention, treatment, and care programs for HIV, malaria, and tuberculosis (World Health Organization [WHO], 2008). In response to this growing recognition of the importance of laboratory medicine in addressing health issues in developing countries, in 2010 the World Bank approved the US$63.7 million East Africa Public Health Laboratory Networking Project (EAPHLNP). The project aims to establish a network of efficient, high quality, accessible public health laboratories for the diagnosis and surveillance of TB and other communicable diseases in the East African Community member states. The original group of countries included Kenya, Rwanda, Tanzania and Uganda, with Burundi (US$15 million) added in The laboratories serve as surveillance sites for monitoring and early detection and response to disease outbreaks; have introduced and tested new diagnostic technologies; and are conducting drug resistance monitoring. The project is strengthening laboratory capacity by improving the existing infrastructure, increasing the quality of human resources available, and updating information systems. 3. With the advent of new laboratory technologies and with increased investment in laboratory capacity in Africa, it becomes critically important to evaluate the impact, costeffectiveness, and value for money of strengthening laboratory systems and to determine the most efficient mix of services to be offered. Increasingly, WHO and other international agencies working on health and human development issues are relying on cost-effectiveness and value for money approaches in developing their guiding principles (WHO, 2009). Routine implementation of new diagnostic techniques may be costly; therefore, understanding their clinical utility, impact, and cost-effectiveness are necessary to guide decisions as to whether and how such techniques should be implemented. Such understanding is particularly important, given that the benefits of some diagnostic technologies, which are part of routine care in developed countries, often need to be weighted by their costs in resource-constrained countries in the developing world (Walensky, et al., 2010). 4. This study sought to understand the impact of investing in a network of public health laboratories on economic and health outcomes. The study team designed a methodology for examining three key issues: (1) optimal mix of laboratory services at different levels of the health system; (2) mix of resources required within laboratories to promote efficiency; and (3) potential for outsourcing to promote efficiency and cost containment. Specifically, the study lays out a conceptual framework that considers the health and economic rationale for laboratory investments; it uses the conceptual framework to inform a decision analytic model that maps out the health and economic impact of laboratory investments; and it illustrates the model by 1

13 investigating the best placement of a new technology (GeneXpert) for detecting multi-drugresistant tuberculosis (MDR-TB). This decision analytic model is flexible enough to be applied to different scenarios when considering investments in laboratories, especially in the context of limited data. 5. The illustrative application of the model shows that investment in a new diagnostic technology for MDR-TB is cost-effective regardless of placement in a satellite (district-level) or reference (usually at the national level) laboratory. Placement of the GeneXpert system in the satellite laboratory results in fewer patients tested for MDR-TB or TB at lower costs than the reference laboratory. The model further indicates that if testing occurs at the satellite laboratory, more primary and secondary cases are treated and cured than if testing is conducted at the reference laboratory, leading to higher total treatment costs if testing occurs at the satellite laboratory. Overall, testing at the satellite laboratory results in more deaths averted and more disability life-adjusted years (DALYs) saved. Both facilities have average costs per DALY well below the WHO-suggested threshold for the per capita gross domestic product (GDP). However, the satellite laboratory saves more DALYs at a low additional cost per DALY. 6. Part II describes the conceptual framework. Part III illustrates the applicability of the conceptual framework to testing for MDR-TB, explains the assumptions used to test the conceptual framework, and includes limitations of the analysis. Part IV explains the findings from the illustration of the model. Part V summarizes the main findings. PART II MODEL CONCEPTUAL FRAMEWORK 7. The EAPHLNP is strengthening laboratory capacity, which is expected to contribute to improvements in health outcomes. Two key areas of investment include laboratory infrastructure and human resources capacity, both of which had been neglected over the recent decades. Within laboratories, weak infrastructure can stem from, for instance, inadequate equipment and supplies and antiquated diagnostic methods; and from insufficient quality and quantity of personnel to detect and diagnose diseases (Martinez-Guarneros et al., 2003; Addo et al., 2006; Bates & Maitland, 2006; Petti et al., 2006; Ridderhof et al., 2007; Sarkinfada et al., 2009; Addo et al., 2010; Funjungo et al., 2012). Limitations in either laboratory infrastructure or human capacity undermine the reliability of disease detection and diagnoses, which may contribute to misdiagnosis, presumptive or incorrect treatment, further spread of disease, and/or an increase in drug-resistant strains. 8. Strengthening laboratory capacity improves diagnostic capabilities that will reduce the incidence of misdiagnosis and better align treatment options for patients. These factors contribute to better health outcomes, which in turn, lead to enhanced economic outcomes for the population. Fewer people may be burdened with the effects of the disease or may die from the disease. Transfiguring the health status of a population may then result in higher economic 2

14 growth as seen by declines in worker absenteeism and increases in labor productivity and ultimately in gross national product. Figure A: Conceptual Framework 9. Figure A illustrates the pathway between investments in laboratory capacity strengthening, improved delivery of quality laboratory services, and key health and economic outcomes. Underpinning this pathway are external factors that are assumed to be outside the immediate control of the networking project, but which will affect outcomes. These external 3

15 factors impact both the demand for laboratory services and the cost of testing. 1 In the framework, inputs (that is, laboratory capacity investments) lead to better outputs (that is, improved delivery of quality laboratory services) and consequently improve health and economic outcomes. Details on various elements of the conceptual framework are included in Appendix I. MEASURING IMPACTS THROUGH COST-EFFECTIVENESS 10. The conceptual framework provides the basis of a model to determine the costeffectiveness of investing in various aspects of the laboratory network. The model combines the costs of investing in laboratories and the resulting health and/or economic outcomes. 11. The input measures capture the types of investments in laboratory capacity that the project supports (for example, equipment, supplies, personnel, and laboratory management information systems (LMIS). By investing in these components, laboratories will be able to improve the quality of their services, including improved accuracy of the tests performed (that is, higher specificity and sensitivity of tests), delivery of timely test results, and improved communication to health care providers (Salinas et al., 2010; Bell et al., 2006; Kay et al., 2006). 12. Outcomes can encompass measures of individual health, disease containment, and individual and/or public economic benefits (that is, employment, wages, GDP). To determine if laboratory investment strategies are cost-effective, the model weighs the effectiveness of the investments on outcome measures against the costs of these investments. In this way, alternative investment strategies can be compared to determine which strategy delivers the highest impact for the least cost. 13. This conceptual framework is used to guide the development of the decision analytic model that incorporates measures of effectiveness and costs in a unified framework. This approach can be used to determine and compare the expected impact and cost-effectiveness of alternative investment strategies, when data are limited. It can also be used to estimate future costs based on newly implemented strategies or medical interventions. Appendix II provides details of the decision analytic model used to estimate the cost-effectiveness of laboratory investments under this framework. 1 The application of the conceptual framework will assume these factors are fixed in the short-run and already reflected in the outcomes measurement in the model. 4

16 PART III AN APPLICATION OF THE COST-EFFECTIVENESS MODEL OVERVIEW 14. In this analysis, we implement the cost-effectiveness model in the specific context of estimating the clinical outcomes associated with improved TB laboratory services in Africa. The model includes assessment of various parameters that could be modified in the near term to improve the testing procedures for TB. To illustrate how the model is implemented, we provide a specific example of the decision analysis process, testing for first-line drug resistance to TB. This specific illustrative example of the model measures the impact of using testing devices at the local level (satellite) versus sending specimens for testing at a reference laboratory. In evaluating this component, the model is determining the impact of the speed of testing on patient retention, diagnosis, and treatment rates. 15. Drug susceptibility testing (DST) has been historically performed in national or regional reference laboratories due to the lack of capacity and biosafety levels of the smaller satellite laboratories. Health facilities often collect sputum specimen from suspected TB patients and then transport the specimen to reference laboratories for DST. The most widely used phenotypic method for DST requires growing organisms in drug-containing and drug-free control media, a process that can take up to six weeks (Fonseca, Moore, and Durier, 2011). This does not include the transportation time between the laboratories. 16. Xpert MTB/RIF drastically reduces this wait time, allowing the detection of TB and resistance to rifampicin (a first-line TB drug) in a matter of hours. Use of Xpert MTB/RIF also does not require highly skilled staff. In 2011, WHO approved Xpert MTB/RIF for use in initial testing for TB and resistance to first-line drugs amongst individuals suspected of being multidrug resistant to TB (MDR-TB) or having HIV-associated TB (WHO, 2011c). WHO recommended using Xpert MTB/RIF in districts or at placement points closest to the patient (WHO, 2010c). Xpert MTB/RIF reduces the diagnostic time and consequently, provides an opportunity to immediately start TB patients on a drug regimen, thereby reducing patient loss to follow-up and transmission rates. However, Xpert MTB/RIF is only recommended for disease detection and not treatment monitoring. Sputum Smear Microscopy (SSM) and culture remain the recommended methods for determining treatment outcomes among TB patients. In the model, treatment costs of MDR-TB include the use of these two methods. 17. The model is equipped to estimate cost-effectiveness of a variety of outcomes, including both public and private health and economic outcomes. We concentrate on the health differences that result from testing for MDR-TB within and across laboratories and based on the speed and accuracy of results provided through each facility. The model accounts for both the direct effects of testing and treatment as well as the indirect effects of disease transmission. 18. We calculate the number of patients tested, which include secondary cases arising from uncured and untreated cases, and the average cost per patient tested across the two laboratory 5

17 types (satellite/district and national/reference laboratories), given the existing cost of all inputs into the test and laboratory capacity. Using these estimates we then calculate the average cost of cases diagnosed and treated, including treatment of incorrectly diagnosed patients, and compare those against changes in lives saved (as measured in disability adjusted life-years (DALYs). DALYs are reported as the number of years of life lost to poor health, disability, or death. They measure the extent of the public health burden of disease. 2 STUDY POPULATION 19. The population investigated for this project is the group of patients with symptoms of TB presenting at satellite facilities. The comparison comes from measuring the effects of sending specimens to a reference laboratory (represented by the National Tuberculosis Regional Laboratory in Kampala, Uganda) or providing testing at a satellite facility (represented by the Kitale District Hospital in Kitale, Kenya). 3 At the time of data collection for the study, the reference laboratory tests for TB was using SSM, mycobacterial culture, and recently began using Xpert MTB/RIF. The satellite laboratory initially used only sputum smear microscopy (SSM) to test for TB. MODEL PERSPECTIVE 20. Data were collected to analyze the cost differences from the perspective of the public health system. These costs include personnel, training and supervision, staff travel (as relevant to performing their duties), laboratory supplies and equipment, contracted services, infrastructure (both existing and new), utilities, and other supplies and equipment. 21. The costs do not include patient out-of-pocket costs or time spent getting to or waiting at the facility. However, these factors contribute to the model outcome in terms of the probability that a patient returns for follow-up. TIME HORIZON 22. The study time horizon is a two-year window to align with minimum duration of treatment with standardized second-line drugs (Kenya Ministry of Public Health and Sanitation, 2009). MODEL ASSUMPTIONS 23. A simple decision analytic model (as described in detail in Appendix II) was produced using the Stata 12. We expounded on the decision analytic model to further capture the specifics of testing for TB in Kenya, including the probability of the patient returning for treatment. The 2 Additionally, it is possible to monetize DALYs if a measure of cost-effectiveness is desired or in order to assign an economic cost to the disease burden. 3 Using laboratories in two different countries may potentially overstate the incremental cost between the satellite and reference laboratories if there are significant cost differences across countries. 6

18 decision analytic model is shown in Figure II-D in the Appendix. To operationalize the model, we make several assumptions that are summarized below (Table 1-4) Testing 24. The baseline model assumes that both the reference and satellite laboratories test all presenting cases who previously received TB treatment or had contact with MDR-TB with the GeneXpert system using the Xpert MTB/RIF assay. However, only 97 percent of all transported sputum arrives at the reference and satellite laboratories with an appropriate quality for testing due to inefficiencies in sputum collection and delivery. Furthermore, Xpert MTB/RIF testing volume at the reference laboratory, which tests samples from multiple facilities, is independent of the testing volume received from the project-supported facility. We assume the volume of Xpert MTB/RIF tests at the reference laboratory is roughly equal to half the volume of SSMs currently conducted. For simplification, the model assumes that positive test results indicate drug resistance to rifampicin and negative test results indicate TB without drug resistance to rifampicin. Laboratory Capacity 25. Table 1 shows the assumptions concerning laboratory capacity of the satellite and reference laboratories. Scores from the Stepwise Laboratory Improvement Process Towards Accreditation (SLIPTA) are used as a proxy for testing quality/accuracy. We acknowledge that high SLIPTA scores on laboratory processes do not necessarily translate to testing accuracy, which can be better measured by external quality assessments. With this caveat in mind, however, we assume that laboratories with high capacity, as indicated by the SLIPTA scores, are assumed to have higher testing quality. This is reflected in the laboratory-specific values for specificity and sensitivity for Xpert MTB/RIF tests. We also assume that the probability of the laboratory correctly interpreting the test result is 100 percent for both laboratories because interpretation of the Xpert MTB/RIF results is independent of laboratory capacity and relies solely on the GeneXpert technology. Hence, better interpretation of test results, as depicted in the generic decision analytic model, is not relevant for this analysis. 7

19 Table 1: Laboratory Capacity Assumptions 4 Description Reference Laboratory Satellite Laboratory Source Probability of viable specimen 96.6% 96.6% Assumption (SL);SLIPTA (RL) Probability of available equipment 100% 100% Calculations from primary data collection Probability of high quality equipment 97% 77% SLIPTA Probability of adequate supplies and inventory control 100% 70% SLIPTA Probability of adequate staff 67% 100% Calculations from primary data collection Probability of trained and mentored staff 67% 100% Calculations from primary data collection Probability of adequate Management Information System 100% 57% SLIPTA Probability of timely test results 65% 100% Assumption (SL); SLIPTA (RL) Probability of correct interpretation of results 100% 100% Assumption Note: Based on laboratory capacity of the Kitale District Hospital (Kitale, Kenya) and the National Tuberculosis Reference Laboratory (Kampala, Uganda). RL refers to reference laboratory; SL refers to satellite laboratory. Treatment and Transmission 26. Treatment occurs at the local health facility and is affected only by the availability of test results, not where the test was performed. Once the test results are determined, the patient must return to the local health facility for treatment. It is assumed that patient follow-up is affected by the time it takes to return the test results. Therefore, the shorter distance from the patient to the satellite laboratory relative to the reference laboratory increases the probability of follow-up, which is reflected in the model. 27. There are three different treatment options included in the model: (1) treat with standardized second-line drugs; (2) treat with the retreatment regimen; or (3) no treatment. Specific treatment options depend on the results of the Xpert MTB/RIF test. Positive test results 4 We assume that reliance on part-time or volunteer labor to conduct testing, specifically Xpert MTB/RIF, reflects insufficient staffing. The reference laboratory has a greater reliance on part-time or volunteer labor than the satellite laboratory. 8

20 (that is, diagnosed with drug resistance to rifampicin) lead to treatment options (1) or (2). These options occur because while the test detects drug resistance to rifampicin, medical providers may prescribe the retreatment regimen rather than treatment with second-line drugs due to factors such as shortages and/or costs of second-line drugs. On the other hand, negative test results (that is, diagnosed with non-resistant TB) lead to treatment options (2) or (3). Medical providers may ignore the results of the test and prescribe treatment empirically. Each of these treatment options is related to a probability of the patients being cured, dying, defaulting (dropping out from treatment), or not responding to treatment as shown in Table 2. Table 2: Treatment Outcome Parameters Description Cure Rate Death Rate Default/ Nonresponse Rate Source MDR-TB: TB: Retreatment regimen 35% 10% 55% Resch et al. (2006); Suarez et al. (2002) Standardized second-line regimen 50% 5% 45% Resch et al. (2006); Suarez et al. (2002) Untreated patients 1% 65% 34% Suarez et al. (2002); Acuna-Villaorduna et al. (2008) Retreatment regimen 68% 5% 27% Resch et al. (2006) Standardized second-line regimen 68% 5% 27% Resch et al. (2006) Untreated patients 20% 30% 50% Resch et al. (2006) Note: The Default/Nonresponse Rate for untreated patients indicates the percent of remaining patients who have neither been cured nor have died. 28. As seen in Table 3, transmission of TB occurs at a rate of eight cases per year. The transmission period of TB depends on the effectiveness of treatment. Specifically, patients who are correctly diagnosed with MDR-TB or TB and are treated with second-line drugs or on a retreatment regimen, respectively, and are cured, transmit only during the first month of treatment. Similarly, the model assumes that all patients who die after correct diagnosis and treatment do so after the first month of treatment and transmit during this first month. All other cases (that is, false-negatives, false-positives treated with second-line drugs, defaulters/nonresponders, and cases who do not return for treatment) remain infectious over the entire two-year period. 29. We assume that primary cases transmit the disease strain of their mycobacteria (that is, TB patients transmit TB and those with MDR-TB transmit MDR-TB). All secondary cases are 9

21 tested for MDR-TB using Xpert MTB/RIF and treatment for their particular strain of the mycobacteria occurs at rates similar to the primary cases. This analysis focuses only on primary and secondary cases, although the model allows for the inclusion of further cases generated by secondary cases (that is, tertiary cases and beyond). Table 3: Treatment Assumptions Variable Value Source Probability of patient loss to follow-up 10% Keeler et al. (2006) Transmission rate of untreated MDR-TB or TB 8 cases/year Conversation with expert consultant Probability of treatment with second-line drugs given detection of MDR-TB Probability of treatment with retreatment regimen given detection of TB 61% Global Fund (2012) 82% WHO (2011a) Population 30. This analysis focuses on testing and treatment outcomes in Kitale, Kenya. Population assumptions used to define the catchment area are shown in Table 4. Table 4: Population Assumptions Variable Value Source Catchment population (Kitale) 496,390 Primary data collection Incidence of SSM positive tests 0.14% WHO (2007) Percent of SSM+ tests given treatment and requiring retreatment 20% WHO (1997) Probability of treatment given SSM+ 82% WHO (2011a) Incidence of MDR-TB among previously retreated cases 7.9% WHO (2007) Testing data (Sensitivity and Specificity) 31. We use the sensitivity and specificity of Xpert MTB/RIF to rifampicin as indicated in Boehme, et al. (2010), where patients in Peru, Azerbaijan, South Africa and India were tested for TB and resistance to rifampicin. The authors determined that Xpert MTB/RIF correctly diagnosed resistant TB (sensitivity) 97.6 percent of the time and correctly determined no resistance to rifampicin (specificity) in 98.3 percent of cases. 10

22 Cost 32. At the time of data collection for this study, the laboratories had either not yet or just recently started using the GeneXpert system. For purposes of this analysis, we assume that testing with Xpert MTB/RIF had been implemented from the start of the study period and estimate costs using actual and imputed costs of all elements of operations needed to perform the test. We assume that inputs into Xpert MTB/RIF testing are fixed in the short-term (that is, labor, materials and equipment, staff travel, utilities and rent, and investments in new infrastructure), and that variable costs include the test-specific supplies (that is, specimen collection containers, pipettes, and Xpert MTB/RIF cartridges needed for each sample) Costs were assessed in 2012 US dollars. Cost data were collected from a satellite facility in Kenya and a reference laboratory in Uganda, two of the countries participating in the East Africa Public Health Laboratory Networking Project (EAPHLNP). Data were collected on the costs of testing for TB (that is, SSM, mycobacterial culture, and Xpert MTB/RIF) over a one-year period (May 1, 2011, to April 30, 2012). 6 The costs of laboratory testing include costs incurred for labor, materials and equipment, staff travel, utilities and rent, and infrastructure. 34. Labor costs are calculated from annual salaries and pro-rated according to the time spent on each of the tests. Rent per year is calculated based on floor space and equivalent monthly rental values. Annualized equipment costs are calculated using the assumption of a 5-year life expectancy and a discount rate of 3 percent. Investments in new infrastructure are also annualized at a similar discount rate but over a 30-year life expectancy. Sources of data for these cost components include interviews with staff and key officials, and administrative and project records. Detailed information regarding the study design and data collection method can be found in Appendix III. LIMITATIONS 35. We recognize important potential limitations that must be considered when interpreting the findings, especially considering that this is only an illustration of how to apply the conceptual model. First, due to limitations around data collection, we were unable to collect complete cost data on the reference laboratory in Kenya. As a result, we use the reference laboratory in Uganda as a proxy for a reference laboratory in Kenya under the assumption of similar laboratory capacity and operating environments across the two laboratories. We recognize that these laboratories may indeed have different characteristics and potentially costs that may confound the findings. Depending on how these differences translate into cost discrepancies, this assumption may overstate the incremental cost between the reference and satellite laboratories. 5 Both the reference and satellite laboratories incur transportation costs for the delivery of tests. The model includes only the incremental cost between the two facilities and subsumes this cost within the fixed costs of the reference laboratory. The reference laboratory contracts with a private courier service that transports the specimen between laboratories. 6 Costs were also collected for the testing and diagnosis of HIV, but for the application of the model, we do not separately model the HIV-related outcomes. 11

23 36. Second, the application of the model relies on treatment costs and parameters of treatment outcomes derived from the literature, in particular, studies conducted in Peru, a middle-income country with a strong TB control program. None of the other studies reviewed that would be more applicable to low-income countries clearly stated or included treatment costs or parameters on treatment outcomes. This assumption, however, will not influence the main findings as the treatment costs and outcome parameters are the same regardless of whether testing occurs at the reference or satellite laboratory. 37. Finally, the results of this application may not be generalizable to all settings due to a possible correlation between testing performance and epidemiological setting (for example, prevalence of TB and MDR-TB). Because the testing environment may influence the sensitivity and specificity of the test, misdiagnosis and inadequate treatment may occur, especially if treatment prescription is based solely on the result of Xpert MTB/RIF. This is especially true in low TB prevalent areas. A recent study evaluated the performance of Xpert MTB/RIF in: (1) low versus high TB prevalent settings; (2) HIV-positive and HIV-negative populations; and (3) different sputum collection methods (Luetkemeyer et al., 2014). The study found that the performance of Xpert MTB/RIF did not significantly vary with changes in these parameters and suggests that confirmatory DST may not be necessary for treatment in these settings. However, confirmatory DST may still be necessary for instance, in populations with increasing rifampicin monoresistance; costs for confirmatory testing have not been included in this application but can be easily incorporated into the conceptual framework. 12

24 PART IV FINDINGS 38. The population tested is derived from the population in the catchment area of the satellite laboratory and the assumptions presented in Table 4. We calculate the number of retreatment patients (that is, those patients most likely to have developed drug resistance) presenting at the satellite laboratory with symptomatic TB from the catchment area population and disease probabilities (n=116 possible cases, of which 9 individuals possibly have MDR-TB and the remaining 107 cases have TB). OUTCOMES 39. Figure B shows how the differences in tests performed at each of the laboratories translate into differences in treatment outcomes at each of the laboratories. Overall, testing at the satellite laboratory results in more deaths averted through both cases cured and secondary cases averted due to reduced transmission (688 cases due to satellite testing versus 581 cases through the reference laboratory, or 58 percent versus 49 percent of all cases). Additionally, fewer cases default (do not return for treatment) and more cases respond to treatment when tested at satellite laboratory due to quicker turnaround of laboratory results. Figure B: Treatment Outcomes Accounting for Transmission, by Laboratory Type 13

25 40. Table 5 shows that the satellite laboratory will test slightly fewer cases (n=1,132 cases) than the reference laboratory (n=1,193 cases) since closer proximity of the satellite laboratory to the patient reduces the transmission rate of TB. This results in fewer patients (primary and secondary cases) acquiring MDR-TB or TB and fewer tests are required. COSTS 41. The unit cost of testing at the satellite laboratory under the model assumptions is lower than the reference laboratory. The satellite laboratory performs an Xpert MTB/RIF test for around US$35 per test, while the reference laboratory costs almost US$44, primarily because of higher facility costs at the reference laboratory. Table 5: Cost of Testing Item Reference Laboratory Satellite Laboratory Number Tests performed 1,193 1,132 Cost (US$) Total testing cost $51,931 $39,394 Cost per test performed $44.00 $35.00 Note: Assumes a transmission rate of 8 new cases per year per infected patient. The transmission period is 1 month for effective treatment and 24 months for ineffective treatment. 42. Table 6 shows the average cost of treatment for TB and MDR-TB patients. While the satellite laboratory tests slightly fewer patients (n=1,132; Table 5), more patients are treated (n=907; Table 6). In other words, the satellite facility refers 80 percent of diagnosed cases for treatment. On the other hand, the reference laboratory loses 197 patients who after testing never return to the health clinic for treatment (Table 6). Primarily as a result of this patient loss to treatment, the reference laboratory refers only 783 of the 1,193 cases tested for treatment, or 66 percent of diagnosed patients (see Table 5 and Table 6). Both the reference and satellite laboratories fail to refer for treatment 214 and 225 cases diagnosed with TB, respectively, as a result of negative test results. 7 Neither the cases lost to follow-up nor the cases not treated incur treatment costs. This is primarily due to the increased transmission that occurs from a higher number of untreated patients tested at the reference laboratory. While treatment rates are assumed to be the same at all facilities in this model (see Table 3), the number of untreated patients lost is larger at the reference laboratory leading to a larger number of patients transmitting the disease for longer periods of time. This could be further refined if data were available identifying actual treatment rates for each facility and/or disease strain. 7 Numbers may not add due to rounding. 14

26 43. The satellite laboratory s higher referral load for treatment subsequently leads to higher treatment costs. Results from the reference laboratory resulted in the treatment of 783 patients at a cost of $1,782 per patient treated. The satellite laboratory tests resulted in the treatment of 907 patients at a cost of $1,871 per patient treated, approximately $90 per patient higher than the results from the reference laboratory. The difference in treatment costs stems primarily from the additional 82 cases treated with the higher cost treatment regimen, using second line drugs. The average cost per patient diagnosed at the reference laboratory and satellite laboratories and subsequently treated is $1,848 and $1,915, respectively. 44. Missing from these calculations are the savings achieved from averting additional cases of MDR-TB or TB. Because the satellite laboratory is able to provide almost instantaneous results, patients testing positive for MDR-TB or TB are not lost to follow-up. In fact, immediate treatment of existing cases results in 61 secondary individuals who otherwise would have contracted MDR-TB or TB remaining healthy. The savings in both testing ($2,123) and expected treatment costs ($54,074) 8 results in a total savings of $56,197 or $921 per secondary case averted. These results are delineated in Table 6. On the other hand, testing in the reference laboratory results in 12 primary and 185 secondary cases (Total =197) lost to follow-up. This means that, not only are the individuals not being treated, they continue to spread the disease to an additional 8 individuals annually for up to two years. Since the costs of tertiary cases and beyond are not calculated here, the model is very conservative in its estimate of the impact on patients lost due to both costs and clinical outcomes. 8 The expected cost of treatment is calculated as [prevalence rate of MDR-TB*the number of secondary cases averted*cost of 2 nd line treatment] + [(1-prevalence rate of MDR-TB)*the number of secondary cases*cost of retreatment regimen]. 15

27 Table 6: Total and Per-patient Cost of Treatment Item Reference Laboratory Satellite Laboratory Counts Total cases treated: Treated with 1st line retreatment regimen Treated with 2 nd -line treatment Cases not treated: Negative test result Lost to follow-up Secondary cases averted 0 61 Cost (US$) One round of first-line retreatment $676 Total cost of first-line retreatment $309,332 $337,846 One 18-month round of standardized second-line treatment $3,340 Total cost of second-line treatment $1,085,601 $1,359,548 Total Treatment Costs $1,394,932 $1,697,393 Cost per patient treated $1,782 $1,871 Sources for treatment costs: Suarez et al. (2002), Resch et al. (2006), Acuna-Villorduna et al (2008). (1) A negative test result indicates TB not resistant to rifampicin. 16

28 COST-EFFECTIVENESS 45. In order to better compare outcomes under the two scenarios, we look at these results in terms of disability-adjusted life years (DALYs). DALYs are outcome measures that capture both the mortality and morbidity of the disease, allowing for comparison across states (that is, death, cure, non-treatment). Estimates of DALYs account for the benefits of timely and correct treatment prescription and the corresponding reduced transmission. The lower treatment load and the resulting lower number of cases cured for the satellite laboratory results in a higher number of DALYs saved as a result of testing at the satellite laboratory (n=7,547) rather than the reference laboratory (n=5,923). 46. Combining both the costs of testing and treatment and the number of DALYs saved reveal that investment in a new diagnostic technology for MDR-TB is cost-effective regardless of placement in a satellite or reference laboratory. This can be seen in Table 7, which shows that the satellite laboratory, on average, spends $230 per DALY saved and the reference laboratory incurs $244 per DALY saved. Both average costs are well below $808, the per capita GDP of Kenya, and hence can both be considered very cost-effective strategies (World Bank, 2012). 17

29 Table 7: Incremental Cost-effectiveness Ratios Item Reference Laboratory Satellite Laboratory Cost (US$) Indicator Total Cost $1,446,869 $1,736,787 Incremental Cost comparator $289,917 Effectiveness Indicator Costeffectiveness Indicators Number of DALYs saved 5,923 7,547 Incremental number of DALYs saved comparator 1,623 Average Cost per DALY saved $244 $230 Incremental Cost per DALY saved $179 Note: Assumes a transmission rate of 8 people per year and a transmission period of 1 month for effective treatment and 24 months for ineffective treatment. 47. As both testing through the reference and satellite laboratories are cost-effective, Table 7 also shows the incremental cost-effectiveness ratios (ICERs). ICERs measure how much additional investment is required to save an additional DALY relative to the alternative (comparator) scenario, or the ratio of the incremental cost and the incremental number of DALYs saved. The resulting ICER indicates that by utilizing the satellite laboratory instead of the reference laboratory, an additional 1,623 DALYs can be saved at a cost of only $179 per DALY. 18

30 PART V DISCUSSION 48. The illustrated model describes a method for determining the most cost-effective facility for performing a specific test under the existing laboratory structures and costs. In addition to assessing the direct effects of changes to laboratory capacity, through the introduction of a new technology, on public health outcomes, this study also evaluated the reduction of transmission of infection as a result of the disease being successfully treated. 49. Our analysis looks at the use of Xpert MTB/RIF assays at sites closest to the patient population. Under conditions and assumptions outlined in the analysis, testing for MDR-TB among retreatment patients at the satellite laboratory is more cost-effective than performing the same test at a more-distant facility. This difference occurs partially because of lower test costs at the satellite laboratory, but also because of the number of patients lost to follow-up when required to wait for results from the reference laboratory. These patients continue to transmit the disease over the entire treatment window of 24 months, which leads to increases in secondary cases and the overall number of cases tested. 50. One tension in the model is that costs include both testing and treatment costs. Successful testing for MDR-TB results in more cases diagnosed. Treating MDR-TB is almost five times as expensive as the retreatment regimen for TB. Testing at the satellite and the reference laboratories results in a similar number of TB patients treated, but because patients fail to return for treatment after testing by the reference laboratory, a larger number of the more expensive MDR-TB patients tested at the satellite laboratory end up being treated. The end result is that overall treatment costs resulting from testing at the satellite laboratory are far higher than the costs associated with testing at the reference laboratory, negating any cost savings from testing even with the inclusion of costs saved from secondary cases averted. Focusing solely on treatment and testing costs fails to incorporate indirect costs from the increase in the burden of the disease in the population, which is a byproduct of the patients with MDR-TB or TB not receiving treatment and transmitting at an annual rate of 8 cases per patient. Incorporating the benefits of testing and treating with the inclusion of DALYs captures this (Table 7). As a result, testing at the satellite laboratory becomes a more cost-effective option than testing at the reference laboratory. 51. The model includes two pathways for patients to dropout: (1) during the diagnostic period ( loss to follow-up ); and (2) prior to treatment. The reference laboratory loses almost 200 cases to follow-up under the first pathway. These patients submit an initial specimen but fail to return. Previous studies have examined patient dropout during TB diagnosis. Although these studies focus more specifically on SSM diagnostics, many of the reasons remain applicable even with Xpert MTB/RIF, especially if patients must return to receive their test results. A study in Malawi found that the poor spent on average, 244 percent of their monthly income accessing TB diagnostic services despite free diagnostic services (Kemp et al. 2007). These costs stemmed from days lost of work, transportation, and food costs during care-seeking episodes. 19