Presentation on theme: "Modelling the cost and the impact of the TB Global Plan Carel Pretorius 29 October 2014 Stop TB Partnership, TB MAC, Futures Institute, WHO Global TB Program,"— Presentation transcript:
Modelling the cost and the impact of the TB Global Plan Carel Pretorius 29 October 2014 Stop TB Partnership, TB MAC, Futures Institute, WHO Global TB Program, UNAIDS Reference Group, Gates Foundation, USAID Country groups, Post 2015 Targets Strategy, TIME, Costing
Acknowledgments Stop TB Partnership TB Modeling and Analysis Consortium (TB MAC) WHO Global TB Program (GTB) UNAIDS Reference Group for HIV Estimates and Projection Bill and Melinda Gates Foundation USAID
Overview Give overview of the three-phased approach to modeling TB Global Plan Phase 1: Country classification/groups Phase 2: Produce global TB impact estimates in relation to Post 2015 Targets framework and Global Plan intervention packages Phase 3: Cost country TB plans and produce global price tag of TB Global Plan
1. Country groups and classification
Overview: Country groups Data collection and resulting multivariate dataset Countries can be clustered/grouped/classified in many ways: be clear about purpose Hierarchical clustering based on being above or below thresholds of key variables K-means clustering and principles components analysis (PCA) Comments and recommendations
Country groups: Data sources GTB: TB burden, notification, treatment outcomes, MDR burden, treatment outcomes World Development Indictors (WDI): measures of wealth, health access and coverage Millennium Development Goals (MDG): measures of development such as coverage of child vaccinations UnPop: Population data including population size and TFR UNDP: Human development indices FFP: Fragile state indices UNAIDS: HIV data including ART coverage and PMTCT coverage WHO Health Systems data, focusing on Health Financing
Country groups: Data collection Developed routines to run through the large datasets, such as WDI and MDG databases, and list the number of countries with data for each indicator. Focused on key indicators from the subset of ‘well- represented’ indicators that are thought to be relevant to TB. Additionally incorporated several variables recommended by Secretariat (particularly for HS and fragile state indices). Performed initial PCA to identify co-linear variables and reduce list further. Finalized list at 17 variables available for 120 countries.
Country groups: Hierarchical classification Each country clustering/grouping/classification should serve a clearly defined purpose. One approach is communicated in “Post-2015 Global TB Strategy and targets: process and vision”: pre-elimination, concentrated or endemic TB, with high HIV and high MDR. Generalized this approach to classify countries as falling above or below threshold using average values of variables. The 16 permutations of four variables is a convenient classification method and allows countries to easily identify their situation and recommended strategy.
Country groups: K-means clustering PCA analysis is used to transform the dataset into new variables, which are independent and successively accounts for most variance in the original dataset. Thus, PC 1 explains most of the variance, PC 2 second most, and so on. Multivariate dataset is then re-projected into the space spanned by the PCs. K-means clustering performed on transformed data.
Country groups: PCA
Country groups: K-means clustering Statistical method to find a specified number of clusters (i.e. K) so that the sum, over all clusters, of the within-cluster sums of point- to-cluster-centroid distances is minimized. We performed K-means clustering on the PCA transformed dataset, experimenting with the number of clusters K. K=6 to 9 works well in terms of generating meaningful groups. More than 9, but depending on variables included, give clusters that are outliers rather than meaningful clusters in terms of the analysis.
Country groups: K-means, k=9 GroupTBincTBnotiTBincHP TBmort ALLTBmortHNTBmortHP TBm dr NEWS PtsCDR , , ,
Country groups: K-means, k=9 GroupImunARTcov GNIperca pHDIFS HExpGD P GovVsTot al PerCapHe alth , , , , , , , , , , ,
Comments and recommendations The 120 countries in the MV dataset represent > 95% of world population. The list of 17 variables is the most representative we could find. But we can still add specific variables for specific sub-groups, e.g. to highlight a TB-related Health Systems issue for a group of countries. Country groups should allow countries to identify their TB context and recommended strategies in terms of the most important variables for their context. In particular, should maximally inform a corresponding set of ‘Targets’ strategies for each group.
2. Targets framework and GP intervention packages
Overview: Targets Framework TIME care and control cascade Targets Framework Adaptation of Targets Framework to country groups Example: Application of Targets Framework to South Africa TB investment case Comments and recommendations
Intervention packages: TIME parameters Diagnostic sensitivity Relative diagnosis for smear negative cases Diagnostic rate as probability per year of being detected Diagnostic rate for HIV-negative and HIV-positive cases Linkage to care Probability of being linked to care once diagnosed Treatment success For HIV-negative For HIV-positive not on ART For HIV-positive on ART
Intervention packages: Targets Framework Increase access to high quality TB services Improve high quality TB services-post diagnosis Xpert replaces completely or partially smear as first laboratory test in high quality TB services Active Case Finding in general population Active Case Finding in general population and Preventative Therapy Continuous IPT for all HIV positive population Combination of all
Global Plan Targets TIME To relate the Post 2015 Targets framework to TIME we have to quantify and make an assessment of: Access to care Current diagnostic algorithm Relative rate of diagnosis in high and service quality of care Linkage to and quality of subsequent care So that we can adjust in TIME baseline models Detection rate, linkage to care and treatment outcomes To relate the GP to the Post 2015 Targets framework we have to relate country groups to Targets scenarios: Assess and quantify variables in terms of how well they measure access to and quality of care Focus on group classification variables related to treatment cascade as a function of quality of care
Targets applied to South Africa IC
TB Targets applied to South Africa IC TB Targets and most aggressive HIV IC scenario still lead to 0.2% incidence (200 per 100,000) ART at 95% coverage, CD4 eligibility at 500 TB diagnostics currently predominantly based on Xpert - thus not much diagnostic gain from rolling out Xpert Linkage to care and treatment success to be > 85% ACF to be 25% of general population IPT coverage for HIV+ cases to be 85%
Global Impact of GP: country models TIME Estimates first estimates TB ‘risk of disease’ for HIV negative cases F(HIV-negative)(t) = I - (t)/ P(t) Then formulates risk of disease for HIV-positive CD4 categories: F(c) = F(HIV-negative)∙p(1)∙p(2) dc where c is a CD4 category and dc a unit of 100 CD4 decline relative to CD4 500 category Use TIME Impact to update F, and produce incidence trends using the same CD4-incidence relationship determined by p(1) and p(2) Can then produce TB incidence and mortality trends for each country by modifying official projections via modified F Can impose ‘realistic’ and ‘advocacy’ version of global HIV strategy and its impact will be reflected in TB-HIV split.
Prioritization to high risk groups There is a general limitation in TIME in that risk groups are not directly modeled. Consequences include: No risk groups means no movement between them The risk groups have differential impact mon transmission which is crudely presented by ‘average’ approach. We make the assumptions that risk groups have the same average risk for TB and progression of TB, since the will have the same internal (to TIME) risk structure in terms of age, CD4, HIV and ART status. Have to decide how severe this limitation is, and how to frame an approximation as either an upper or lower bound to true expected impact of targeting to high risk groups.
PT prioritization to high risk groups HIV or high-risk of HIV Intravenous drug users known to be HIV-negative Had close contacts with newly diagnosed and TST negative children had close contact with newly diagnosed case Recent converters based on TST criteria Persons with abnormal chest radiographs showing old TB Persons with special medical conditions In addition, perhaps prioritized by age: Previously low-served population, e.g. low access to care Residents of facilities for long-term care, e.g., correctional institutions
ACF prioritization to high risk groups Could apply similar considerations to PT prioritization, namely calculate average coverage and effects of ACF and apply to special populations such as: HIV or high-risk of HIV Intravenous drug users known to be HIV-negative Previously low-served and currently marginalized population, e.g. low access to care Correctional institutions
Comments and recommendations Targets is a well-developed framework for developing GP Intervention Packages For each group need to quantify levels of access and quality of care, which is then related to TIME parameters Impact will be estimated for one representative country for each group through direct TIME Impact modeling. Impact will be ‘transferred’ to projections for TB burden within the TIME Estimates model to obtain country-specific estimates of Result is a direct impact on the global TB incidence trend currently produced by GTB
3. Costing the Global Plan
Overview: Costing Global Plan Discuss different approaches to costing the plan as well as the triangulation of different approaches: ‘Top-down’ approach based on GTB budget reports ‘Bottom-up’ approach based on One Health/TIME TB costing Literature reviews of unit costs of key cost inputs, focusing on key and perhaps all of the high burden countries Operational insights, e.g. Xpert rollout coordinated/funded by USAID Have to take a normative approach to costing program support structures Can produce a cost estimate at country level, based on GTB notification trends
Costing Global Plan: Basic approach Discuss utility of different costing data sources, in particular GTB and GF budget estimates A top-down approach based on extrapolating these estimates using projected notification trends should provide a reasonable benchmark for a global TB price tag of the TB GP Can supplement with a process of collecting country-specific cost data Can apply costing platform to country-specific impact projections
Costing Global Plan: Country data Two options A- Supplement current cost estimates with adjustments based on country visits or consultations B- Develop a costing workbook that countries are asked to fill out, in a process that will be supported with webinars and such In each case we can apply PPP corrections to obtain estimates for countries with no direct estimates from countries with direct ones
Costing Global Plan: Cost template We have prepared a template with the following structure Epidemiology – notification and its breakdown by case type Unit costs – sheets for diagnosis, treatment and patient support Program support - a normative program support costing approach Total cost - a sheet calculating total cost of the TB program
Cost template – country support Countries can be trained on the layout of the cost template and provided with instructions on how to fill it out Countries can be support online The 22 high burden countries should receive special attention The templates will be validated and serve as the baseline cost, and then be modified with TIME notification projections applied to the country workbook
Comments and recommendations A decision must be made if a country approach should be followed – regional costing also possible We suggest the use of multiple approaches which allows for triangulation The global price tag should be based on aggregating country- specific cost projections A cost-workbook approach is only feasible if WHO and GF take leading role in dissemination, collection and validation