Cost effectiveness analysis of expanding tuberculosis preventive therapy to household contacts aged 5-14 years in the Philippines

doi:10.21203/rs.3.rs-2217226/v1

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Cost effectiveness analysis of expanding tuberculosis preventive therapy to household contacts aged 5-14 years in the Philippines

https://doi.org/10.21203/rs.3.rs-2217226/v1

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Background: Children aged 5-14 years who are household contacts of index pulmonary TB cases have limited coverage for TB preventive therapy (TPT) due to variable uptake of the national guideline recommendations in the Philippines. We conducted a cost effectiveness analysis evaluating the expansion of latent TB testing and treatment among pediatric (5-14 years) household contacts of index TB patients in the Philippines.

Methods: Using a Markov state transition model, eligible household contacts (HHCs) age 5-14 years are screened for latent TB infection (LTBI) with either the tuberculin skin test (TST) or interferon gamma release assay (IGRA). Those who test positive are then simulated to receive one of the following TPT strategies: 6 months of daily isoniazid (6H), 3 months of weekly isoniazid and rifapentine (3HP), 3 months of daily isoniazid plus rifampicin (3HR) and the current practice of no testing or treatment for LTBI (NTT). The analysis assesses the projected cost and quality-adjusted life years (QALY) gained for every strategy from the perspective of the Philippines public healthcare system over a time horizon of 20 years. The total cost and gain in QALYs are presented as an incremental cost-effectiveness ratio (ICER) comparing cost per QALY gained for each strategy over NTT.

Results: Our model estimates that expanding TPT coverage to HHCs aged 5-14 years would be cost effective with incremental cost-effectiveness ratios (ICERs) ranging from 1,024 $/QALY gained when using TST and 6H (Uncertainty range: 497 - 2,334) to 2,293 $/QALY gained when IGRA and 3HR are used (Uncertainty range: 1,140 – 5,203). IGRA cost would have to drop to $5.50 to achieve similar results to strategies using TST. These findings were robust to sensitivity analyses over a wide range of parameter values.

Conclusion: Expanding TPT coverage to HHCs aged 5-14 years is cost effective when using TST and 6H closely followed by a strategy combining TST and 3HP. IGRA cost will require significant reduction to achieve results similar to TST.

Tuberculosis

pediatric TB

TB prevention

Tuberculosis (TB) remains one of the infectious diseases with the heaviest burdens worldwide infecting one fourth of the world’s population and causing 1.6 million deaths in 2021 (1). The United Nations included TB elimination as one of its sustainable development goals and the World Health Organization (WHO) has emphasized TB prevention for individuals at high risk including household contacts (HHCs) of people with TB (2, 3). However, implementing TB preventive therapy (TPT) for HHCs in general and children contacts in particular remains limited in high burden countries with finite resources and competing health priorities, especially in light of the COVID-19 pandemic (4). In 2020, more than 272,000 people in the Philippines fell ill with TB and a significant burden occurred in children (5). Over recent years, increasing attention has been given to prevention efforts, including in high TB burden setting. For example, in their 2020 Guidelines, the Philippines National TB Program (NTP) adopted guidance from the WHO to expand TPT to all HHC, rather than just those < 5 years of age, but these guidelines have had variable uptake since their publication (5, 6). Many studies have shown that shorter regimens for TPT are cost effective compared to the standard isoniazid-only regimens, but these analyses targeted either different age groups or different strategies to the planned expansion of TPT in the Philippines (7–9). Consequently, this cost-effectiveness analysis was conducted in partnership with the Philippines NTP and PEER-USAID (Partnerships for Enhanced Engagement in Research-United States Agency for International Development) to explore the cost effectiveness of three recommended regimens: 6 months of daily isoniazid (6H), 3 months of weekly isoniazid plus rifapentine (3HP) and 3 months of daily isoniazid and rifampicin (3HR). All three regimens where selected for evaluation by the Philippines NTP and have been included in the WHO and national TB guidelines for TPT.

Model Structure

Figure 1 shows the model structure and transition states. We used a decision tree followed by a Markov state transition model (10–12) to estimate the incremental cost-effectiveness of each scenario of expanded TPT compared to the current practice (e.g. pre-2020 guidelines), which does not include children age 5–14 years in TPT care. Individuals enter the model if they are HHCs aged 5–14 years of an index case of TB and do not have active TB. The expanded TPT scenarios include testing for latent TB infection (LTBI) by either tuberculin skin test (TST) or interferon gamma release assay (IGRA) followed by treatment with 6H, 3HP or 3HR for those who test positive. TST and IGRA has an estimated sensitivity of 71% and 79% and specificity of 89% and 99% in the base vase scenario, respectively (13). Individuals who test positive for LTBI (whether truly or falsely) are then modelled to undergo LTBI treatment with either 6H, 3HP or 3HR. After undergoing LTBI testing and treatment (if eligible), individual scenario trajectories were simulated using a Markov model with four transition states: (Eligible for screening for) LTBI, active TB, cure (of active TB) and death (related to TB or not related to TB).

The model first implements a decision tree among a hypothetical group representing the newly eligible household contacts for LTBI screening with either tuberculin skin test (TST) or interferon gamma release assay (IGRA). Those who screen positive are treated with 6 months of daily isoniazid (6H), 3 months of weekly isoniazid and rifapentine (3HP) or 3 months of daily isoniazid plus rifampicin (3HR) and every strategy is then compared to a scenario of no screening or treatment for LTBI (NTT). Each subgroup is determined by the outcome of screening then enters a Markov state transition model with four possible states: latent tuberculosis infection (LTBI), active tuberculosis (TB), cured from active TB (Cure) and death (TB and non-TB). This Markov process is simulated over a time horizon of 20 years.

Model Parameters and Target Population:

Table 1 shows all model parameters. The estimated burden of LTBI among children age 5–14 years was 53.1% based on a meta-analysis by Fox et al compiling data for LTBI prevalence among HHCs age 5–14 in low and middle income countries (14).

Table 1

Model Parameters
Parameter	Base Value	Distribution*	Low	High	Reference
LTBI prevalence	53.1%	Beta	47.79%	58.41%	(14)
Total number of HHCs to be screened	786,197	Random	707,578	864,817	(14–16)
Annual transition probability from LTBI to active without TPT	0.0205	Beta	0.0185	0.0226	(3)
Proportion Progressing to Active with 6H compared to NTT	0.40	Beta	0.36	0.44	(17)
Proportion Progressing to Active with 3HP compared to NTT	0.36	Beta	0.32	0.40	(17)
Proportion Progressing to Active with 3HR compared to NTT	0.33	Beta	0.30	0.36	(17)
Annual transition probability from active TB to death	0.02	Beta	0.018	0.028	(15)
Annual transition probability from active TB to cure	0.84	Beta	0.76	0.92	(15)
6H Drug Cost (180 tablets of 300mg) in USD	3.18	Gamma	2.86	3.49	(18)
3HP Drug Cost (36 tabs of 300–300) in USD	15.00	Gamma	13.50	16.50	(18)
3HR Drug Cost (6x84 doses of RH 75-50mg) in USD	23.70	Gamma	21.33	26.07	(18)
Cost per LTBI outpatient visit in USD	2.55	Gamma	2.31	2.81	(19)
Total cost for one case of active TB in USD	113.82	Gamma	102.44	125.21	(15, 19, 20)
Utility-LTBI	1.00	-	1.00	1.00	(21)
Utility-Active TB	0.67	Beta	0.60	0.73	(21)
Utility-Cure	0.95	Beta	0.85	1.00	(22)
TST cost in USD	3.39	Gamma	3.05	3.73	(19)
TST sensitivity	0.71	Beta	0.64	0.78	(13)
TST specificity	0.89	Beta	0.80	0.98	(13)
IGRA cost in USD	18.45	Gamma	16.61	20.30	(23)
IGRA sensitivity	0.79	Beta	0.71	0.87	(13)
IGRA specificity	0.99	Beta	0.89	0.99	(13)
Discount rate (for cost and effect)	3%				(24, 25)
* Refers to the statistical distribution used to vary the model value between the low and high values.

Based on estimated annual TB cases in the Philippines and the prevalence of active TB among HHCs of

TB patients in low and middle income countries, we estimated that starting with 595,000 index cases results in 797,360 HHCs aged 5–14 years. Among these pediatric HHCs, 11,163 (1.40%) will be diagnosed with TB disease and 786,197 (98.60%) will be eligible for LTBI evaluation; of those eligible for evaluation 417,471 (53.1%) will have LTBI (14–16, 26).

Annual transition probabilities from LTBI to active TB without LTBI treatment was assumed to decline steadily between years 2 and 5 and assumed to be constant after year 5 based on published literature (14, 27). Annual transition probabilities from LTBI to active TB in the setting of LTBI treatment were based on data from a meta-analysis reflecting real world practice of TB preventive therapy and thus treatment completion rates were not explicitly modelled (17). Annual transition probabilities from active TB to cure and death were based on WHO data, including pediatric-specific data when available, on reported TB outcomes averaged from 2019 and 2020 (15). Annual probability of death in the absence of active TB was based on published lifetables for the Philippines (28).

The cost of LTBI treatment regimens was based on procurement estimates provided by the Stop TB Partnership and corroborated through the Philippines NTP (18). To estimate the cost of active TB, we used the reimbursement rate for directly-observed-therapy short-course (DOTS) package in the Philippines of $111.03 and added a cost of hospitalization assuming a 5-day length of stay on average and 3% chance of hospitalization based on WHO reporting on drug sensitive TB for all ages in the Philippines (15, 20). The cost of IGRA was based on the reported cost of QuantiFERON-TB Gold Plus as reported by the Stop TB Partnership (23). The cost of outpatient visits, TSTs, and hospitalizations were based on the average values at the community health unit for outpatient care and primary hospital level for inpatient care as reported by the Value TB study (19). All costs were measured in 2021 US Dollars after adjusting for inflation in the following ways: Philippines specific inflation calculator was used when cost was estimated from local sources; consumer price index was used for inflation adjustment when cost was derived from international supplier; official exchange rates from the Philippines central bank were used when cost estimates were provided in Philippine Pesos (29–31).

Effectiveness was estimated as the number of quality adjusted life years (QALYs) gained, assuming that utility for the LTBI state was similar to that of the underlying general population estimated to be 1.0, whereas the utility for active TB was assumed to be 0.67 based on published literature (21, 32). Based on literature regarding post-TB sequelae, we also assumed a decrement in health utility of 0.053 following successful treatment of active TB (22, 32). Utilities were applied on an annual basis. The total costs and total effects were used to generate an incremental cost effectiveness ratio (ICER) by dividing the incremental cost for every strategy compared to the current pre-2020 guideline strategy of no testing or treatment for LTBI (NTT) by the incremental QALY gain for every strategy compared to NTT. We also provide a second set of ICERs comparing each strategy to the next most cost-effective strategy. Both costs and effectiveness were discounted by 3% per year based on the recommendations of the U.S. Second Panel on Cost-Effectiveness in Health and Medicine and WHO Guide to Cost-effectiveness Analysis, we also explored the effects of 0% and 5% discount rates on the results (24, 25). The model used a time horizon of 20 years and was performed from the perspective of the Philippines public healthcare system which covers TB care (20). Willingness to pay threshold explored in this analysis relied upon the Philippines GDP per capita as estimated by the World Bank and based on the recommendations of the WHO Guide to Cost-Effectiveness Analysis (24, 25, 33). For additional details on model parameters, please refer to S1 Supplementary Appendix.

Model Assumptions:

We assumed that, in the absence of progression or reactivation to active TB, individuals with LTBI have no risk of TB-related death. We assumed the cost of LTBI treatment to include an initial outpatient visit for consultation followed by a monthly outpatient visit for follow-up and refill over the duration of treatment per the Philippines TB guidelines (6). We assumed that LTBI treatment would be delivered per the same guidelines by self-administered therapy and thus excluded costs of directly observed therapy.

According to the Philippines TB guidelines, chest x-rays are used for all HHCs over the age of 5 as part of active TB disease evaluation and thus we assumed their cost to be equal in all strategies. Individuals with false-positive test results were assumed to have no risk of progression to active TB. Reinfection with TB after the testing and treatment period was assumed to occur equally in all groups and was thus not explicitly modeled. We also assumed all cases of active TB to be drug sensitive and occurring in individuals without HIV co-infection since there are no current guideline recommendations for TB preventive therapy among HHC of people with MDR/RR TB and the prior inclusion of people living with HIV in TPT recommendations (6, 15). Based on published literature, the probability of severe adverse events that would require medical attention in the context of LTBI treatment in this age group is exceedingly rare and thus we did not explicitly include incremental adverse events in the model (17, 34, 35). Finally, we assumed that the ratio between reported and estimated TB cases to be constant across age groups and that TB outcomes remain constant during the time horizon of the analysis.

Base Case and Sensitivity Analyses:

Our base case starts with a population of 786,197 HHCs age 5–14, which is the estimated number of individuals eligible for the intervention in the Philippines. To determine the parameters that had the most effect on model outputs, we performed a one-way deterministic sensitivity analysis by varying each parameter by +/-10% of the base value without violating logical boundaries (e.g. 0–1 for probabilities). We also performed multivariate probabilistic sensitivity analysis (PSA) based on 1000 trials. In each trial, parameters were sampled following a uniform statistical distribution (Gamma distribution for cost parameters, random selection for HHCs eligible and beta distribution for all other parameters) to account for parameter uncertainty. The PSA was used to generate a cost effectiveness plane comparing different TPT strategies to NTT. In addition, we used the PSA to quantify the probability of cost effectiveness for each regimen based on either testing strategy by employing a net monetary benefit approach (10, 11). We used our PSA to calculate uncertainty ranges at the 2.5th and 97.5th percentile for each reported value; we provided these ranges within parentheses when presenting results. We also explored price points under which IGRA would achieve similar results to TST. All analyses were performed using Microsoft Excel 2016 (Microsoft; Redmond, Washington, USA).

Institutional review board (IRB) approval was not required according to guidelines from the University of Virginia IRB office, as this research did not involve human subjects.

Table 2 shows the base case scenario results when using TST to test for LTBI. Among the 786,197 HHCs screened for TB using TST, 336,964 (42.86%) would be eligible for LTBI treatment; of those 296,404 (87.96%) are truly positive for LTBI. In this scenario, using 6H achieved the lowest ICER estimated to be 1,024 USD/QALY gained (Uncertainty range: 497 – 2,334) closely followed by 3HP with an estimated ICER of 1,105 USD/QALY gained (Uncertainty range: 537 – 2,503). In addition, using TST and treating those who test positive with 6H compared to NTT results in 6,593 (Uncertainty range: 5,024– 8,467) fewer active TB cases and 166 (Uncertainty range: 119 – 226) fewer TB-related deaths. This strategy yields an estimated gain in QALYs of 8,674 (Uncertainty range: 3,705 – 17,845) at an estimated additional cost of 8.88 million USD (Uncertainty range: 7.11 million – 10.74 million). On the other hand, treating those who test positive with 3HP compared to NTT results in 7,081 (Uncertainty range: 5,682– 9,454) fewer active TB cases and 177 (Uncertainty range: 128 – 240) fewer deaths. This strategy yields an estimated gain in QALYs of 9,256 (Uncertainty range: 3,959 – 18,903) at an estimated additional cost of 10.23 million USD (Uncertainty range: 8.18 million – 12.34 million). Table 2 also shows results for 3HR. Table 3 shows the base-case results when IGRA is used as the screening test for LTBI. Given the higher proportion of children who are truly positive for LTBI, 3HP achieved the lowest ICER. However, ICERs for all three strategies were almost twice as high compared to strategies using TST but still below the Philippines GDP per capita.

Table 2: Base case results using TST as the screening test:

Strategy

People Treated

(Thousands)

Active TB

TB-Related Deaths

Cost (3% discounted, thousands

USD)

QALY (3% discounted, thousands)

ICER (Strategy-next best strategy)

(3% discounted,

USD/QALY))

NTT

15,671

(12518, 19221)

393

(294, 511)

2,055

(1533, 2680)

12,089

(10935, 13254)

337

(263, 412)

9,078

(7053, 11476)

226

(165, 302)

10,935

(8908, 13026)

12,097

(10944, 13264)

1,024

(497, 2334)

3HP

337

(263, 412)

8,590

(6345, 10471)

215

(157, 287)

12,282

(9979, 14656)

12,098

(10944, 13264)

146

(-28868, 28635)

3HR

337

(263, 412)

8,254

(6638, 10863)

207

(150, 277)

15,170

(12211, 18224)

12,098

(10944, 13264)

6,603

(-82470, 84700)

Incremental (Strategy-NTT)

People Treated

(Thousands)

Active TB

TB-Related Deaths

Cost (3% discounted, thousands)

QALY (3% discounted, thousands)

ICER (Discounted)

USD/QALY)

6H-NTT

337

(263, 412)

-6,593

(-8,467, -5,024)

-166

(-226, -119)

8,880

(7111, 10743)

8.67

(3.71, 17.85)

1,024

(497, 2334)

3HP-NTT

337

(263, 412)

-7,081

(-9,454, -5,682)

-177

(-240, -128)

10,227

(8177, 12340)

9.26

(3.96, 18.90)

1,105

(537, 2503)

3HR-NTT

337

(263, 412)

-7,417

(-9,063, -5,404)

-186

(-251, -134)

13,115

(10369, 15980)

9.69

(4.18, 19.73)

1,353

(661, 3046)

Table 3: Base case results using IGRA as the screening Test:

Strategy

People Treated

(Thousands)

Active TB

TB-Related Deaths

Cost (3% discounted, thousands

USD)

QALY (3% discounted, thousands)

ICER (Strategy-next best strategy)

(3% discounted,

USD/QALY))

NTT

15,671

(12522, 19140)

393

(295, 511)

2,055

(1531, 2680)

12,089

(10935, 13255)

333

(271, 412)

8,335

(6367, 10574)

208

(150, 277)

22,604

(19357, 26177)

12,098

(10942, 13265)

2,046

(-26776, 26776)

3HP

333

(271, 412)

7,792

(5563, 9492)

195

(140, 262)

23,930

(20487, 27679)

12,099

(10943, 13266)

2,124

(1050, 4839)

3HR

333

(271, 412)

7,419

(5868, 9950)

186

(133, 250)

26,783

(22894, 31197)

12,099

(10943, 13267)

3,683

(1086, 35162)

Incremental (Strategy-NTT)

People Treated

(Thousands)

Active TB

TB-Related Deaths

Cost (3% discounted, thousands)

QALY (3% discounted, thousands)

ICER (Discounted)

USD/QALY)

6H-NTT

333

(271, 412)

-7,336

(-9495, -5538)

-185

(-250, -133)

20,549

(17529, 23886)

9.65

(4.13, 19.96)

2,129

(1052, 4900)

3HP-NTT

333

(271, 412)

-7,879

(-10545, -6307)

-197

(-266, -143)

21,875

(18659, 25394)

10.30

(4.41, 21.16)

2,124

(1050, 4839)

3HR-NTT

333

(271, 412)

-8,252

(-10087, -6034)

-207

(-278, -150)

24,728

(21079, 28873)

10.79

(4.63, 22.18)

2,293

(1140, 5203)

Figures 2 and 3 show the results of one-way sensitivity analysis where utility of cure and annual transition probability from LTBI to active TB without TPT were the parameters that produced the most variability in ICERs. For example, as seen in Figure 3A, a 10% decrease in the total cost of IGRA led to a 7% improvement in ICER in the IGRA+6H strategy. Figure 4 shows the effect of changing the total cost of IGRA per individual on the ICER of each of regimen strategy. For IGRA strategies to achieve ICERs similar to those achieved with TST, the total cost of IGRA to the Philippines healthcare system would need to drop by approximately 70%.

In a probabilistic uncertainty analysis shown in Figure 5, all simulations projected a gain in QALYs for all regimens using either TST or IGRA. When using TST (panel A) almost all simulations resulted in an ICER lower than the Philippines’ GDP per capita and the remaining simulations resulted in ICER between 1-3 times GDP per capita for the Philippines. The analysis also showed significant overlap between the three regimens for gain in QALYs but 3HR had the highest cost in most simulations. When using IGRA (Panel B) many simulations resulted in an ICER below the Philippines’ GDP per capita, with the remainder of simulations producing an ICER between 1-3 GDP per capita. The analysis showed significant overlap among regimens for both incremental cost and effect, as the cost of IGRA was the main driver of overall cost in this scenario.

Figure 6 shows the probability of cost effectiveness (based on achieving the highest net monetary benefit) for every strategy when using TST (panel A) compared to IGRA (panel B) over a range of willingness to pay thresholds. As the willingness to pay increased with both testing strategies, the probability of NTT being cost effective fell. When using TST and a willingness to pay threshold of the Philippines GDP per capita, the NTT has a projected probability of cost effectiveness of 15% compared to 42% for 6H and 46% for 3HP.

Variation in discount rate had limited effect on the results of our analysis as shown in Table 6. A lower discount rates of 0% resulted in slight improvement in cost effectiveness for all strategies while increasing discount rate to 5% resulted in slight worsening of cost-effectiveness across all strategies. However, the effect was small in either direction. Results are shown in Tables S1-S4 in Supplementary Appendix.

Our analysis found the expansion of TPT to HHCs age 5–14 years to be cost effective when using both TST and IGRA with strategies using TST achieving ICERs in the low 1,000’s USD/QALY gained, all regimens were likely to be cost effective compared to NTT even when using IGRA as the screening test, which has significant importance for implementation in this high burden country.

Our findings are in line with published literature on pediatric TPT when studied in various settings. For example, Mandalakas et al evaluated isoniazid preventive therapy for children in South Africa and found it to be cost effective without a screening test when compared to various testing modalities (36). However, their analysis which was conducted in 2013 focused on those under the age of 5 years. In a more similar example to our analysis, Jo et al found household contact screening, with treating TB cases as well as providing preventive treatment to those who do not have active TB, to be cost effective. Jo et al reported cost per disability-adjusted life years (DALY) averted ranging from 102 USD/DALY-averted in Malawi to 1,600 $/DALY-averted in Brazil for pediatric HHCs. Their analysis evaluated the cost effectiveness of a more comprehensive strategy that included screening and treatment all forms of TB among children 0–15 years of age, along with a home-based care program, but the analysis did not include the Philippines (37).

When choosing a cost-effectiveness threshold we chose to focus on the Philippines GDP per capita as a reasonable threshold, which is considered as highly cost-effective in the WHO guidance on CEAs (24). Since the original document was published in 2003 many subsequent publications argued that using 3 times a country’s GDP as a threshold for cost-effectiveness would make it difficult for decision makers to choose interventions that can be truly implemented in a relevant setting (38). For example, Woods et al estimated that a middle income country such as El Salvador with a GDP per capita similar to that of the Philippines should have a cost-effectiveness threshold of $422 to $1967 which is 11–51% of its GDP per capita (39). In another example, Horton et al listed the most cost-effective health-related interventions in childhood most of which had an ICER under 1,000 USD per QALY gained/DALY averted (40).

The major strengths of our analysis are the data provided directly by the Philippines NTP and the multitude of scenarios it explored. In addition, we used NTT scenario as the comparator for all other strategies as well as comparing every strategy to the next most cost-effective alternative to facilitate decision making by the Philippines NTP (10).

Our analysis does have the following limitations: we chose the maximum possible dose for every regimen for this age group and an upper end estimate for HHCs who would be eligible for screening and treatment to account for the highest possible cost to the Philippines healthcare system. This approach would underestimate the overall cost effectiveness and increase the implementation cost of TPT expansion respectively. In addition, this analysis used the cost of regimens that are available to the Philippines NTP at their request, this includes a dispersible formulation of 3HR which is likely more desirable when treating young children but also more expensive, but non-dispersible tablet formulations for 6H and 3HP. Other short course options that may be endorsed by the WHO such as 4 months of daily rifampicin (4R) or 1 month of daily isoniazid and rifapentine (1HP), have been left out of this analysis due to the lack of local endorsement within the Philippines national guidelines. Also, this analysis was performed from the perspective of the Philippines public healthcare system and therefore did not incorporate the significant direct and indirect costs incurred by patients and families(41). We also did not account for secondary transmission which would be significant in a high burden setting and likely to improve the cost-effectiveness of LTBI testing and treatment, particularly over a 20 year horizon. The analysis also used a generic range for PSA parameters due to the limited availability of uniform confidence intervals in the published literature; this approach is supported by CEA methodology and allows the analysis to avoid combining confidence intervals derived from non-uniform statistical methods. Finally, the analysis did not account for strategy-specific upfront costs such as personnel training or the differential needs for ongoing case management of children using a traditional 6-month regimen compared to shorter-course regimens as these costs were beyond the scope of this work.

In conclusion and based on the findings of this analysis, a HHC management strategy that incorporates TST-based screening for LTBI followed by 6H treatment for those who test positive is likely to be the most cost-effective strategy. A strategy that combines TST testing with 3HP treatment achieves very similar results and would be a reasonable alternative. We also found that the cost of IGRA would need to drop to $5.50 to achieve results similar to TST.

3HP: 3 months of weekly isoniazid plus rifapentine

3HR: 3 months of daily isoniazid and rifampicin

6H: 6 months of daily isoniazid

CEA: Cost-effectiveness analysis

DOTS: directly-observed-therapy short-course

GDP: Gross domestic product

HHC: household contact

ICER: incremental cost effectiveness ratio

IGRA: interferon gamma release assay

IRB: Institutional review board

LTBI: latent TB infection

NTP: National TB Program

NTT: scenario of no screening or treatment for LTBI

PEER-USAID: Partnerships for Enhanced Engagement in Research-United States Agency for International Development

PSA: probabilistic sensitivity analysis

QALY: quality adjusted life year

TB: Tuberculosis

TPT: TB preventive therapy

TST: tuberculin skin test

USD: US Dollars

WHO: World Health Organization

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests: The authors declare that they have no competing interests.

Funding: The study was funded by National Institute of Health (Bethesda, MD, USA) training grant 5T32AI007046 (G.I.) and the USAID Partnerships for Enhanced Engagement in Research (PEER) Cycle 5 Project-173 (USAID AID-OAA-A-11-00012 to F.B and T.A.T.).

Authors’ Contributions:

GI contributed significantly to conceptualizing the analysis question. GI also designed the cost-effectiveness model, analyzed the results of the analysis and wrote the final manuscript.

JKM contributed significantly to designing the cost-effectiveness model, analyzing the results and significant revisions to the final manuscript

RT: contributed significantly to conceptualizing the analysis question, verifying model parameters and reviewing the final manuscript

AFR: contributed significantly to conceptualizing the analysis question, verifying model parameters and reviewing the final manuscript

FRB: contributed significantly to conceptualizing the analysis question, verifying model parameters and reviewing the final manuscript

TAT: contributed significantly to conceptualizing the analysis question, analyzing the results and revising the final manuscript.

All authors read and approved the final manuscript and stand by the accuracy and integrity of the results presented in this manuscript.

Acknowledgements:

The authors would like to acknowledge the generous help we received from the Philippines office for the United States Agency for International Development represented by Mr. Tito Rodrigo for the time and effort they expanded to organize the several meetings we have held with stakeholders throughout this study, as well as the consistent institutional support offered by President Ricmar P. Aquino at Isabela State University.

The Philippines NTP, represented by Dr. Celine Garfin, Dr. Clarice Sabido, Dr. Winston Palasi and Dr. Allan Fabella, helped clarify certain model parameters, regimen and testing choices that are relevant to implementation in the Philippines. Any opinion, findings, conclusion, or recommendation expressed in this article are those of the authors alone, and do not necessarily reflect the views of NIH, PEER-USAID or the institutions of the Authors.

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Cost effectiveness analysis of expanding tuberculosis preventive therapy to household contacts aged 5-14 years in the Philippines

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