Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation
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{"title"=>"Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation", "type"=>"journal", "authors"=>[{"first_name"=>"Nicolas A.", "last_name"=>"Menzies", "scopus_author_id"=>"26027942300"}, {"first_name"=>"Ted", "last_name"=>"Cohen", "scopus_author_id"=>"7202415780"}, {"first_name"=>"Hsien Ho", "last_name"=>"Lin", "scopus_author_id"=>"39061859200"}, {"first_name"=>"Megan", "last_name"=>"Murray", "scopus_author_id"=>"35479179200"}, {"first_name"=>"Joshua A.", "last_name"=>"Salomon", "scopus_author_id"=>"56745638600"}], "year"=>2012, "source"=>"PLoS Medicine", "identifiers"=>{"pui"=>"366156170", "sgr"=>"84870266092", "issn"=>"15491277", "pmid"=>"23185139", "scopus"=>"2-s2.0-84870266092", "doi"=>"10.1371/journal.pmed.1001347", "isbn"=>"1549-1676"}, "id"=>"ab6b869b-7191-3d4f-adfc-a6bf94243340", "abstract"=>"BACKGROUND: The Xpert MTB/RIF test enables rapid detection of tuberculosis (TB) and rifampicin resistance. The World Health Organization recommends Xpert for initial diagnosis in individuals suspected of having multidrug-resistant TB (MDR-TB) or HIV-associated TB, and many countries are moving quickly toward adopting Xpert. As roll-out proceeds, it is essential to understand the potential health impact and cost-effectiveness of diagnostic strategies based on Xpert.\\n\\nMETHODS AND FINDINGS: We evaluated potential health and economic consequences of implementing Xpert in five southern African countries--Botswana, Lesotho, Namibia, South Africa, and Swaziland--where drug resistance and TB-HIV coinfection are prevalent. Using a calibrated, dynamic mathematical model, we compared the status quo diagnostic algorithm, emphasizing sputum smear, against an algorithm incorporating Xpert for initial diagnosis. Results were projected over 10- and 20-y time periods starting from 2012. Compared to status quo, implementation of Xpert would avert 132,000 (95% CI: 55,000-284,000) TB cases and 182,000 (97,000-302,000) TB deaths in southern Africa over the 10 y following introduction, and would reduce prevalence by 28% (14%-40%) by 2022, with more modest reductions in incidence. Health system costs are projected to increase substantially with Xpert, by US$460 million (294-699 million) over 10 y. Antiretroviral therapy for HIV represents a substantial fraction of these additional costs, because of improved survival in TB/HIV-infected populations through better TB case-finding and treatment. Costs for treating MDR-TB are also expected to rise significantly with Xpert scale-up. Relative to status quo, Xpert has an estimated cost-effectiveness of US$959 (633-1,485) per disability-adjusted life-year averted over 10 y. Across countries, cost-effectiveness ratios ranged from US$792 (482-1,785) in Swaziland to US$1,257 (767-2,276) in Botswana. Assessing outcomes over a 10-y period focuses on the near-term consequences of Xpert adoption, but the cost-effectiveness results are conservative, with cost-effectiveness ratios assessed over a 20-y time horizon approximately 20% lower than the 10-y values.\\n\\nCONCLUSIONS: Introduction of Xpert could substantially change TB morbidity and mortality through improved case-finding and treatment, with more limited impact on long-term transmission dynamics. Despite extant uncertainty about TB natural history and intervention impact in southern Africa, adoption of Xpert evidently offers reasonable value for its cost, based on conventional benchmarks for cost-effectiveness. However, the additional financial burden would be substantial, including significant increases in costs for treating HIV and MDR-TB. Given the fundamental influence of HIV on TB dynamics and intervention costs, care should be taken when interpreting the results of this analysis outside of settings with high HIV prevalence.", "link"=>"http://www.mendeley.com/research/population-health-impact-costeffectiveness-tuberculosis-diagnosis-xpert-mtbrif-dynamic-simulation-ec-4", "reader_count"=>36, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>4, "Student > Master"=>12, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>4, "Student > Master"=>12, "Student > Bachelor"=>3, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Nursing and Health Professions"=>3, "Biochemistry, Genetics and Molecular Biology"=>5, "Medicine and Dentistry"=>11, "Agricultural and Biological Sciences"=>2, "Neuroscience"=>1, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>2, "Immunology and Microbiology"=>2, "Economics, Econometrics and Finance"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>11}, "Neuroscience"=>{"Neuroscience"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"United Kingdom"=>1, "South Africa"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/539217"], "description"=>"<p>For each ratio, the diamond indicates the point estimate (mean incremental costs divided by mean incremental DALYs averted), and the bar indicates the width of the 95% posterior interval. <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001347#s3\" target=\"_blank\">Results</a> based on US$30 Xpert per-test cost.</p>", "links"=>[], "tags"=>["xpert", "compared", "quo", "african", "countries"], "article_id"=>209709, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_of_Xpert_strategy_compared_to_status_quo_strategy_in_five_southern_African_countries_2011_US_dollars_/209709", "title"=>"Cost-effectiveness of Xpert strategy compared to status quo strategy in five southern African countries (2011 US dollars).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:41:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/539424"], "description"=>"<p>All costs are given in 2011 US dollars. <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001347#s3\" target=\"_blank\">Results</a> are based on US$30 Xpert per-test cost. Range in parentheses represents the 95% posterior interval for each estimate.</p>", "links"=>[], "tags"=>["programmatic", "outcomes", "costs", "10"], "article_id"=>209915, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.t002", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_programmatic_outcomes_and_costs_over_10_y_following_choice_of_strategy_/209915", "title"=>"Average programmatic outcomes and costs over 10 y following choice of strategy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-20 02:45:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/288949", "https://ndownloader.figshare.com/files/289011", "https://ndownloader.figshare.com/files/289083", "https://ndownloader.figshare.com/files/289148", "https://ndownloader.figshare.com/files/289319", "https://ndownloader.figshare.com/files/289355"], "description"=>"<div><h3>Background</h3><p>The Xpert MTB/RIF test enables rapid detection of tuberculosis (TB) and rifampicin resistance. The World Health Organization recommends Xpert for initial diagnosis in individuals suspected of having multidrug-resistant TB (MDR-TB) or HIV-associated TB, and many countries are moving quickly toward adopting Xpert. As roll-out proceeds, it is essential to understand the potential health impact and cost-effectiveness of diagnostic strategies based on Xpert.</p> <h3>Methods and Findings</h3><p>We evaluated potential health and economic consequences of implementing Xpert in five southern African countries—Botswana, Lesotho, Namibia, South Africa, and Swaziland—where drug resistance and TB-HIV coinfection are prevalent. Using a calibrated, dynamic mathematical model, we compared the status quo diagnostic algorithm, emphasizing sputum smear, against an algorithm incorporating Xpert for initial diagnosis. Results were projected over 10- and 20-y time periods starting from 2012. Compared to status quo, implementation of Xpert would avert 132,000 (95% CI: 55,000–284,000) TB cases and 182,000 (97,000–302,000) TB deaths in southern Africa over the 10 y following introduction, and would reduce prevalence by 28% (14%–40%) by 2022, with more modest reductions in incidence. Health system costs are projected to increase substantially with Xpert, by US$460 million (294–699 million) over 10 y. Antiretroviral therapy for HIV represents a substantial fraction of these additional costs, because of improved survival in TB/HIV-infected populations through better TB case-finding and treatment. Costs for treating MDR-TB are also expected to rise significantly with Xpert scale-up. Relative to status quo, Xpert has an estimated cost-effectiveness of US$959 (633–1,485) per disability-adjusted life-year averted over 10 y. Across countries, cost-effectiveness ratios ranged from US$792 (482–1,785) in Swaziland to US$1,257 (767–2,276) in Botswana. Assessing outcomes over a 10-y period focuses on the near-term consequences of Xpert adoption, but the cost-effectiveness results are conservative, with cost-effectiveness ratios assessed over a 20-y time horizon approximately 20% lower than the 10-y values.</p> <h3>Conclusions</h3><p>Introduction of Xpert could substantially change TB morbidity and mortality through improved case-finding and treatment, with more limited impact on long-term transmission dynamics. Despite extant uncertainty about TB natural history and intervention impact in southern Africa, adoption of Xpert evidently offers reasonable value for its cost, based on conventional benchmarks for cost-effectiveness. However, the additional financial burden would be substantial, including significant increases in costs for treating HIV and MDR-TB. Given the fundamental influence of HIV on TB dynamics and intervention costs, care should be taken when interpreting the results of this analysis outside of settings with high HIV prevalence.</p> <p> <em>Please see later in the article for the Editors' Summary</em></p> </div>", "links"=>[], "tags"=>["cost-effectiveness", "tuberculosis", "xpert", "simulation", "evaluation"], "article_id"=>116908, "categories"=>["Cancer", "Medicine", "Biotechnology"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001347.s001", "https://dx.doi.org/10.1371/journal.pmed.1001347.s002", "https://dx.doi.org/10.1371/journal.pmed.1001347.s003", "https://dx.doi.org/10.1371/journal.pmed.1001347.s004", "https://dx.doi.org/10.1371/journal.pmed.1001347.s005", "https://dx.doi.org/10.1371/journal.pmed.1001347.s006"], "stats"=>{"downloads"=>15, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Population_Health_Impact_and_Cost_Effectiveness_of_Tuberculosis_Diagnosis_with_Xpert_MTB_RIF_A_Dynamic_Simulation_and_Economic_Evaluation__/116908", "title"=>"Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-20 01:55:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/538955"], "description"=>"<p>Epidemiologic outcomes in Xpert and status quo scenarios, 2012–2032.</p>", "links"=>[], "tags"=>["outcomes", "xpert", "quo"], "article_id"=>209453, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Epidemiologic_outcomes_in_Xpert_and_status_quo_scenarios_2012_8211_2032_/209453", "title"=>"Epidemiologic outcomes in Xpert and status quo scenarios, 2012–2032.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:37:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/539394"], "description"=>"<p>All costs are given in 2011 US dollars.</p>a<p>As smear status is tracked in the model, the sensitivity of sputum smear for individuals classed as smear-negative and smear-positive is 0% and 100% (respectively) by construction.</p>b<p>As sputum culture is the gold standard for TB detection, the sensitivity is assumed to be 100%.</p>c<p>As the per-test cost of Xpert is of key interest to policy-makers (and potentially subject to price negotiation), the results of the analyses are presented for three separate values for the Xpert cost.</p>", "links"=>[], "tags"=>["parameter"], "article_id"=>209891, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.t001", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_model_parameter_values_and_ranges_/209891", "title"=>"Selected model parameter values and ranges.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-20 02:44:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/539127"], "description"=>"<p>Incremental costs of Xpert strategy (based on US$30 Xpert per-test cost) compared to status quo strategy, by cost category, 2012–2032 (2011 US dollars).</p>", "links"=>[], "tags"=>["costs", "xpert", "per-test", "compared", "quo"], "article_id"=>209620, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g004", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Incremental_costs_of_Xpert_strategy_based_on_US_30_Xpert_per_test_cost_compared_to_status_quo_strategy_by_cost_category_2012_8211_2032_2011_US_dollars_/209620", "title"=>"Incremental costs of Xpert strategy (based on US$30 Xpert per-test cost) compared to status quo strategy, by cost category, 2012–2032 (2011 US dollars).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:40:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/539310"], "description"=>"<p>Sensitivity analyses on the incremental cost per DALY averted (2011 US dollars) over a 10-y analytic horizon, assuming a US$30 Xpert per-test cost. In each one-way analysis, one parameter was varied ±1 standard deviation from its posterior mean, with all other variables fixed at their posterior means.</p>", "links"=>[], "tags"=>["univariate", "parameters", "cost-effectiveness", "xpert", "compared"], "article_id"=>209802, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g006", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_from_univariate_sensitivity_analyses_showing_the_ten_parameters_with_the_greatest_influence_on_the_cost_effectiveness_of_Xpert_compared_to_status_quo_South_Africa_/209802", "title"=>"Results from univariate sensitivity analyses, showing the ten parameters with the greatest influence on the cost-effectiveness of Xpert compared to status quo, South Africa.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:43:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/538847"], "description"=>"<p>Estimated and projected TB prevalence, TB incidence, and multidrug-resistant TB prevalence in southern Africa under status quo diagnostic algorithm, 1990–2032.</p>", "links"=>[], "tags"=>["projected", "tb", "multidrug-resistant", "prevalence", "africa", "quo", "diagnostic"], "article_id"=>209334, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g002", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimated_and_projected_TB_prevalence_TB_incidence_and_multidrug_resistant_TB_prevalence_in_southern_Africa_under_status_quo_diagnostic_algorithm_1990_8211_2032_/209334", "title"=>"Estimated and projected TB prevalence, TB incidence, and multidrug-resistant TB prevalence in southern Africa under status quo diagnostic algorithm, 1990–2032.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:35:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/539460"], "description"=>"<p>All costs are given in 2011 US dollars.</p>a<p>ICERs calculated using health system costs (including DOTS costs). Both costs and health outcomes discounted at 3%. Range in parentheses represents the 95% posterior interval for each estimate.</p>", "links"=>[], "tags"=>["xpert", "algorithm", "compared", "quo"], "article_id"=>209951, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.t003", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cost_effectiveness_results_for_Xpert_algorithm_compared_to_status_quo_algorithm_in_southern_Africa_/209951", "title"=>"Cost-effectiveness results for Xpert algorithm compared to status quo algorithm in southern Africa.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-20 02:45:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/538720"], "description"=>"<p>Model states, subdivisions, and transitions.</p>", "links"=>[], "tags"=>["public health and epidemiology", "Infectious diseases", "Non-clinical medicine"], "article_id"=>209215, "categories"=>["Biotechnology", "Medicine", "Infectious Diseases"], "users"=>["Nicolas A. Menzies", "Ted Cohen", "Hsien-Ho Lin", "Megan Murray", "Joshua A. Salomon"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001347.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_states_subdivisions_and_transitions_/209215", "title"=>"Model states, subdivisions, and transitions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-20 02:33:35"}

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Relative Metric

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