The Importance of Implementation Strategy in Scaling Up Xpert MTB/RIF for Diagnosis of Tuberculosis in the Indian Health-Care System: A Transmission Model
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{"title"=>"The Importance of Implementation Strategy in Scaling Up Xpert MTB/RIF for Diagnosis of Tuberculosis in the Indian Health-Care System: A Transmission Model", "type"=>"journal", "authors"=>[{"first_name"=>"Henrik", "last_name"=>"Salje", "scopus_author_id"=>"16553354700"}, {"first_name"=>"Jason R.", "last_name"=>"Andrews", "scopus_author_id"=>"15058787200"}, {"first_name"=>"Sarang", "last_name"=>"Deo", "scopus_author_id"=>"34067539700"}, {"first_name"=>"Srinath", "last_name"=>"Satyanarayana", "scopus_author_id"=>"35732163300"}, {"first_name"=>"Amanda Y.", "last_name"=>"Sun", "scopus_author_id"=>"55969830500"}, {"first_name"=>"Madhukar", "last_name"=>"Pai", "scopus_author_id"=>"14054425400"}, {"first_name"=>"David W.", "last_name"=>"Dowdy", "scopus_author_id"=>"6603069678"}], "year"=>2014, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491676", "scopus"=>"2-s2.0-84905381454", "pmid"=>"25025235", "doi"=>"10.1371/journal.pmed.1001674", "isbn"=>"1549-1676 (Electronic)\\n1549-1277 (Linking)", "pui"=>"373686452", "sgr"=>"84905381454"}, "id"=>"ef545fb7-b63a-32f9-b941-f51900dea680", "abstract"=>"BACKGROUND: India has announced a goal of universal access to quality tuberculosis (TB) diagnosis and treatment. A number of novel diagnostics could help meet this important goal. The rollout of one such diagnostic, Xpert MTB/RIF (Xpert) is being considered, but if Xpert is used mainly for people with HIV or high risk of multidrug-resistant TB (MDR-TB) in the public sector, population-level impact may be limited.\\n\\nMETHODS AND FINDINGS: We developed a model of TB transmission, care-seeking behavior, and diagnostic/treatment practices in India and explored the impact of six different rollout strategies. Providing Xpert to 40% of public-sector patients with HIV or prior TB treatment (similar to current national strategy) reduced TB incidence by 0.2% (95% uncertainty range [UR]: -1.4%, 1.7%) and MDR-TB incidence by 2.4% (95% UR: -5.2%, 9.1%) relative to existing practice but required 2,500 additional MDR-TB treatments and 60 four-module GeneXpert systems at maximum capacity. Further including 20% of unselected symptomatic individuals in the public sector required 700 systems and reduced incidence by 2.1% (95% UR: 0.5%, 3.9%); a similar approach involving qualified private providers (providers who have received at least some training in allopathic or non-allopathic medicine) reduced incidence by 6.0% (95% UR: 3.9%, 7.9%) with similar resource outlay, but only if high treatment success was assured. Engaging 20% of all private-sector providers (qualified and informal [providers with no formal medical training]) had the greatest impact (14.1% reduction, 95% UR: 10.6%, 16.9%), but required >2,200 systems and reliable treatment referral. Improving referrals from informal providers for smear-based diagnosis in the public sector (without Xpert rollout) had substantially greater impact (6.3% reduction) than Xpert scale-up within the public sector. These findings are subject to substantial uncertainty regarding private-sector treatment patterns, patient care-seeking behavior, symptoms, and infectiousness over time; these uncertainties should be addressed by future research.\\n\\nCONCLUSIONS: The impact of new diagnostics for TB control in India depends on implementation within the complex, fragmented health-care system. Transformative strategies will require private/informal-sector engagement, adequate referral systems, improved treatment quality, and substantial resources. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/importance-implementation-strategy-scaling-up-xpert-mtbrif-diagnosis-tuberculosis-indian-healthcare", "reader_count"=>105, "reader_count_by_academic_status"=>{"Unspecified"=>8, "Librarian"=>3, "Researcher"=>30, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>4, "Student > Master"=>23, "Other"=>9, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>8, "Librarian"=>3, "Researcher"=>30, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>4, "Student > Master"=>23, "Other"=>9, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Nursing and Health Professions"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>59, "Agricultural and Biological Sciences"=>9, "Business, Management and Accounting"=>6, "Psychology"=>1, "Social Sciences"=>8, "Immunology and Microbiology"=>3, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>59}, "Social Sciences"=>{"Social Sciences"=>8}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>9}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>11}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>2, "Brazil"=>1, "United Kingdom"=>1, "Mexico"=>1, "Nigeria"=>1, "India"=>4}, "group_count"=>6}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/1593094"], "description"=>"a<p>95% uncertainty ranges provided in square brackets.</p>b<p>Assuming second-line treatment is available for those diagnosed with MDR-TB.</p>c<p>Estimated population of India by 2019 is 1.3 billion.</p>d<p>Assumes 20% of Xpert tests are performed on individuals with TB for scenario 1, 10% for scenarios 2–4, and 5% for scenario 5.</p>e<p>Assumes four runs per day per module and each machine has four modules and operates 300 d/y.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "xpert", "rollout", "tb", "incidence"], "article_id"=>1104059, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.t005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Effect_of_Xpert_rollout_on_annual_TB_incidence_and_mortality_after_5_/1104059", "title"=>"Effect of Xpert rollout on annual TB incidence and mortality after 5", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593076"], "description"=>"<p>Reduction in total annual incidence and MDR-TB incidence per 100,000 individuals from a rollout of 100 Xpert machines. The scenarios are as described in the <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001674#s3\" target=\"_blank\">Methods</a>. Rollout of 100 Xpert machines in the private sector has substantially greater impact than a similar rollout in the public sector, but only if high treatment success can be assured. If treatment is poor, use of Xpert machines in the private sector has no epidemiological benefit.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "100", "xpert", "systems", "rolled", "sectors"], "article_id"=>1104041, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_100_Xpert_systems_rolled_out_in_different_sectors_after_5_y_/1104041", "title"=>"Impact of 100 Xpert systems rolled out in different sectors after 5 y.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593093"], "description"=>"a<p>Sensitivity analysis conducted by varying the transmission rate such that annual incidence changed by ±25%. In the multiway analysis, parameter combinations that resulted in more than a 25% change in baseline incidence were discarded.</p>b<p>“Highly infectious” means “diagnosable by smear.” Individuals with highly infectious TB are assumed to be less infectious until seeking diagnosis. “Less infectious” means “not diagnosable by smear.”</p>c<p>In multiway sensitivity analyses, some parameter values were made to correlate with each other so they either both increase or both decrease from their base value: (1) the proportion of infections that are highly infectious in those HIV− and those HIV+; (2) the proportion of individuals that progress rapidly in those HIV− and HIV+; (3) losses to follow-up between culture, Xpert, and smear; (4) endogenous activation of TB for those HIV− and HIV+; (5) the self-cure rate for highly infectious and less infectious TB.</p>d<p>Transmission rate varied so that incidence remained constant.</p>e<p>Where current diagnostic attempt is made in the informal sector, sensitivity range for next visit being to the qualified private sector is 0.25–0.75. The movement to the public sector is unchanged at 0.03, and remaining in the informal sector is the balancing figure (022–0.72). Where current diagnostic attempt is made in the qualified private sector, sensitivity range for next visit being to the public sector is 0.4–0.8. The movement to the informal sector is unchanged at 0.03, and remaining in the private sector is the balancing figure (0.17–0.57).</p>f<p>Individuals with a history of TB treatment had an increased probability of diagnosis of half the difference between one and the probability of diagnosis for first-time infections (model assumption).</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104058, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.t002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_parameters_/1104058", "title"=>"Model parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593074"], "description"=>"<p>Percentage reduction in annual incidence, prevalence, mortality, and MDR-TB incidence from an Xpert rollout after 5 y in six different scenarios. The final set represents an alternative scenario where there is an increase in referrals from the informal sector to the public sector to 20% with no Xpert rollout.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "xpert"], "article_id"=>1104039, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_Xpert_after_5_y_/1104039", "title"=>"Impact of Xpert after 5 y.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593087"], "description"=>"<p>We explored the impact of increasing referrals from the informal and qualified private sectors to the public sector following broad access to Xpert in that sector (scenario 2). The figure shows the impact on incidence of a 50% increase in the referral rate from qualified private and informal providers to the public sector where Xpert is broadly available in the public sector but not available in the private sector (scenario 2).</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104052, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_behavioral_changes_/1104052", "title"=>"Impact of behavioral changes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593082"], "description"=>"<p>The parameters were changed in turn to the maximum (red) and minimum (green) values from <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001674#pmed-1001674-t002\" target=\"_blank\">Table 2</a>. The effect of Xpert after 5 y on MDR-TB incidence and overall incidence in scenarios 1 ([A] and [B]) and 2 ([C] and [D]) was recorded for each new value. The five parameters to which the model is most sensitive are shown in the diagrams. In both cases, the most important parameters in one-way sensitivity analysis reflected aspects of the existing health-care system in India, not characteristics of the diagnostic assay itself.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "parameter"], "article_id"=>1104049, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_One_way_analysis_of_parameter_sensitivity_/1104049", "title"=>"One-way analysis of parameter sensitivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593078"], "description"=>"<p>The qualified private sector represents a wide range of operations, many of which are believed to provide poor levels of treatment. To explore the potential impact of a rollout of Xpert access for 20% of patients seeking care in the qualified private sector where the treatment provided in the qualified private sector is poorer than that provided in the public sector, we ran three sensitivity analyses. Sensitivity A represents the main analysis with no difference between the private and public sectors. In Sensitivity B, patients put on treatment in the private sector have twice the probability of developing MDR-TB as those put on treatment in the public sector, and lower levels of treatment success. In Sensitivity C, patients put on treatment in the private sector have five times the probability of developing MDR-TB as those put on treatment in the public sector, and lower levels of treatment success.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "differential"], "article_id"=>1104043, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Impact_of_differential_treatment_failure_between_public_and_private_providers_/1104043", "title"=>"Impact of differential treatment failure between public and private providers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593095"], "description"=>"<p>The table shows the diagnostic algorithm used for each scenario to diagnose TB among individuals with respiratory symptoms in whom a diagnosis of TB is being considered. We do not consider active screening in this model.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104060, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.t004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scenario_overview_/1104060", "title"=>"Scenario overview.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593092"], "description"=>"<p>The reported values represent the estimated burden of TB in India. The adjusted values reflect the adult-only rates for pulmonary TB (the reported values represent all individuals and include pulmonary and extrapulmonary TB, whereas our model is an adult-only model of pulmonary TB). The fitted value represents the value we obtained in our model following our calibration exercise. Adjusted values were calculated using the fact that individuals aged 15 y and under represent 2% of notified cases and 30% of the population and that 85% of TB cases are pulmonary TB <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001674#pmed.1001674-World1\" target=\"_blank\">[1]</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104057, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_calibration_/1104057", "title"=>"Model calibration.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593090"], "description"=>"<p>Where TB infected individuals initially go to seek diagnosis and the location of subsequent visits <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001674#pmed.1001674-Kapoor1\" target=\"_blank\">[12]</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104055, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Movement_between_health_care_providers_/1104055", "title"=>"Movement between health-care providers.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593071"], "description"=>"<p>Diagram of the compartments in the model. Not shown, but present in the model, are parallel structures by (a) HIV status and (b) MDR-TB status.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control"], "article_id"=>1104037, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001674.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_schematic_/1104037", "title"=>"Model schematic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-15 03:09:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1593098", "https://ndownloader.figshare.com/files/1593099", "https://ndownloader.figshare.com/files/1593100", "https://ndownloader.figshare.com/files/1593101", "https://ndownloader.figshare.com/files/1593102", "https://ndownloader.figshare.com/files/1593103", "https://ndownloader.figshare.com/files/1593104"], "description"=>"<div><p>Background</p><p>India has announced a goal of universal access to quality tuberculosis (TB) diagnosis and treatment. A number of novel diagnostics could help meet this important goal. The rollout of one such diagnostic, Xpert MTB/RIF (Xpert) is being considered, but if Xpert is used mainly for people with HIV or high risk of multidrug-resistant TB (MDR-TB) in the public sector, population-level impact may be limited.</p><p>Methods and Findings</p><p>We developed a model of TB transmission, care-seeking behavior, and diagnostic/treatment practices in India and explored the impact of six different rollout strategies. Providing Xpert to 40% of public-sector patients with HIV or prior TB treatment (similar to current national strategy) reduced TB incidence by 0.2% (95% uncertainty range [UR]: −1.4%, 1.7%) and MDR-TB incidence by 2.4% (95% UR: −5.2%, 9.1%) relative to existing practice but required 2,500 additional MDR-TB treatments and 60 four-module GeneXpert systems at maximum capacity. Further including 20% of unselected symptomatic individuals in the public sector required 700 systems and reduced incidence by 2.1% (95% UR: 0.5%, 3.9%); a similar approach involving qualified private providers (providers who have received at least some training in allopathic or non-allopathic medicine) reduced incidence by 6.0% (95% UR: 3.9%, 7.9%) with similar resource outlay, but only if high treatment success was assured. Engaging 20% of all private-sector providers (qualified and informal [providers with no formal medical training]) had the greatest impact (14.1% reduction, 95% UR: 10.6%, 16.9%), but required >2,200 systems and reliable treatment referral. Improving referrals from informal providers for smear-based diagnosis in the public sector (without Xpert rollout) had substantially greater impact (6.3% reduction) than Xpert scale-up within the public sector. These findings are subject to substantial uncertainty regarding private-sector treatment patterns, patient care-seeking behavior, symptoms, and infectiousness over time; these uncertainties should be addressed by future research.</p><p>Conclusions</p><p>The impact of new diagnostics for TB control in India depends on implementation within the complex, fragmented health-care system. Transformative strategies will require private/informal-sector engagement, adequate referral systems, improved treatment quality, and substantial resources.</p><p><i>Please see later in the article for the Editors' Summary</i></p></div>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "epidemiology", "Infectious diseases", "Bacterial diseases", "tuberculosis", "Infectious disease control", "scaling", "xpert", "indian"], "article_id"=>1104063, "categories"=>["Biological Sciences"], "users"=>["Henrik Salje", "Jason R. Andrews", "Sarang Deo", "Srinath Satyanarayana", "Amanda Y. Sun", "Madhukar Pai", "David W. Dowdy"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001674.s001", "https://dx.doi.org/10.1371/journal.pmed.1001674.s002", "https://dx.doi.org/10.1371/journal.pmed.1001674.s003", "https://dx.doi.org/10.1371/journal.pmed.1001674.s004", "https://dx.doi.org/10.1371/journal.pmed.1001674.s005", "https://dx.doi.org/10.1371/journal.pmed.1001674.s006", "https://dx.doi.org/10.1371/journal.pmed.1001674.s007"], "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Importance_of_Implementation_Strategy_in_Scaling_Up_Xpert_MTB_RIF_for_Diagnosis_of_Tuberculosis_in_the_Indian_Health_Care_System_A_Transmission_Model_/1104063", "title"=>"The Importance of Implementation Strategy in Scaling Up Xpert MTB/RIF for Diagnosis of Tuberculosis in the Indian Health-Care System: A Transmission Model", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-07-15 03:09:05"}

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  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"12", "full-text"=>"5", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"5", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"13", "full-text"=>"12", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"10", "full-text"=>"5", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"0", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"11", "full-text"=>"8", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"1", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"19", "full-text"=>"16", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"5", "full-text"=>"3", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Population biology", "average_usage"=>[312, 475]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[285]}, {"subject_area"=>"/Medicine and health sciences/Diagnostic medicine", "average_usage"=>[257]}, {"subject_area"=>"/People and places", "average_usage"=>[302]}, {"subject_area"=>"/People and places/Demography", "average_usage"=>[309]}]}
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Net::HTTPTooManyRequests

Source
Scopus
Time
2019-08-14 20:20:21 UTC
Target URL
https://api.elsevier.com/content/search/index:SCOPUS?query=DOI(10.1371%2Fjournal.pmed.1001674)
Trace

/app/models/concerns/networkable.rb:21:in `get_result'
/app/models/source.rb:165:in `get_data'
/app/models/retrieval_status.rb:47:in `perform_get_data'
/app/jobs/source_job.rb:52:in `block (2 levels) in perform'
/app/jobs/source_job.rb:51:in `block in perform'
/app/jobs/source_job.rb:35:in `each'
/app/jobs/source_job.rb:35:in `perform'