Active screening for tuberculosis in high-incidence Inuit communities: a cost-effectiveness analysis

Active screening for tuberculosis in high-incidence Inuit communities: a cost-effectiveness analysis

 

Aashna Uppal MScPH^{1, 2, 3}, Ntwali Placide Nsengiyumva MSc^{2, 3}, Céline Signor MSc⁴, Frantz Jean-Louis MPH⁴, Marie Rochette MD, MSc⁴, Hilda Snowball⁵, Sandra Etok⁶, David Annanack⁷, Julie Ikey⁸, Faiz Ahmad Khan MD, MPH^1,2,3, Kevin Schwartzman MD, MPH^{1, 2, 3}

 

AFFILIATIONS

 

¹ Montreal Chest Institute, Montreal, Quebec, Canada

² Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of McGill University Health Centre, Montreal, Quebec, Canada

³ McGill International Tuberculosis Centre, Montreal, Quebec, Canada

⁴ Régie Régionale de la Santé et des Services Sociaux du Nunavik, Quebec, Canada

⁵ Kativik Regional Government, Quebec, Canada

⁶ Ulluriaq School, Kangiqsualujjuaq, Quebec, Canada

⁷ Northern Village of Kangiqsualujjuaq, Quebec, Canada

⁸ Salluit Birth Center, Salluit, Quebec, Canada

 

Corresponding Author

Dr. Kevin Schwartzman, kevin.schwartzman [at] mcgill.ca

 

Author Contributions

Concept and Design: Schwartzman, Ahmad Khan, Jean-Louis

Acquisition, Analysis, Interpretation of Data: all authors

Drafting the Manuscript: Uppal, Schwartzman

Critical Revision of the Manuscript: all authors

Statistical Analysis: Uppal, Nsengiyumva, Signor, Jean-Louis, Ahmad Khan, Schwartzman

 

Ethics

Ethics approval was not required for this study.

 

Funding Statement

This work was funded by the Régie Régionale de la Santé et des Services Sociaux du Nunavik, Québec, Canada.

 

Role of the Funder/Sponsor

The funder itself had no role in the design, conduction, data collection, analysis, management, interpretation, or in manuscript preparation or in the decision to submit for publication. However, professional and community members of the Régie Régionale de la Santé et des Services Sociaux du Nunavik were authors and reviewed and approved the final manuscript accordingly.

 

Declaration of Competing Interests

None to declare.

 

Abstract Word Count: 244 Main Text Word Count: 2,587

Tables: 5 Figures: 1 References: 39

Supplemental Content: Appendix (34 pages)

Abstract (244 words)

 

Background: Active screening for tuberculosis (TB) involves systematic detection of previously undiagnosed TB disease and/or latent TB infection (LTBI). It may be an important step toward TB elimination among Canadian Inuit. We evaluated the cost-effectiveness of community wide active screening for TB infection and disease in two Inuit communities in Nunavik.

 

Methods: We incorporated screening data from the two communities into a decision analysis model. We predicted TB-related health outcomes over a 20-year time frame, beginning in 2019. We assessed the cost-effectiveness of active screening in the presence of varying outbreak frequency and intensity. We also considered scenarios involving variation in timing, impact and uptake of screening programs.

 

Results: Given a single large outbreak in 2019, one round of active screening reduced TB disease by 13% (95% uncertainty range: -3% to 27%) and was cost saving compared to no screening, over 20 years. In the presence of simulated large outbreaks every three years thereafter, a single round of active screening was cost saving, as was biennial active screening. Compared to a single round, biennial active screening reduced TB disease by 59% (52% to 63%) and was estimated to cost $6,430 (2019 Canadian dollars; -$29,131 to $13,658) per additional active TB case prevented. With smaller outbreaks or improved rates of treatment initiation and completion for persons with LTBI, biennial active screening remained reasonably cost-effective.

 

Interpretation: Active screening is a potentially cost saving approach to reducing disease burden in Inuit communities that experience frequent TB outbreaks.

Introduction (337 words)

 

Tuberculosis (TB) was the world’s deadliest infectious disease in 2019 [[1]]. The burden of TB is disproportionately borne by vulnerable and marginalized communities, including Canadian Inuit [[2]], where it reflects colonization and persistent socioeconomic inequities [[3]]. Canada is a low-TB-incidence country (defined as < 10 cases per 100,000 annually [[4]]), with an incidence rate of 4.9 per 100,000 in 2017 [[5]]. The overall incidence in Inuit communities was more than 40-fold higher [5]. In 2018, Inuit Tapiriit Kanatami, the national representative organization for Inuit in Canada, and the Government of Canada announced their goal to eliminate TB from Inuit regions by 2030 [3].

 

In 2019, the TB incidence in Nunavik, in Northern Quebec, was 495 per 100,000 [[6]]. The Nunavik Regional Board of Health and Social Services (NRBHSS) implemented community-wide active screening for active TB and latent TB infection (LTBI) in two villages, where repeated outbreaks were common. Outbreaks are considered to occur when either ≥ 2 contacts of a person with active TB are also diagnosed with active TB, or ≥ 2 persons who develop active TB within one year are epidemiologically linked [[7]]; the outbreaks in these villages have been much more extensive [[8]]. With active screening, persons with TB disease may be identified and treated, while minimally symptomatic and less contagious [[9]]. Persons with LTBI may be identified and treated before they develop active TB disease [[10],[11]]. Active screening is most often undertaken when other practices appear insufficient to interrupt transmission and reduce morbidity, and may be particularly relevant in remote settings [[12]]. It is pertinent to consider the benefits and costs of community-wide screening, within a TB elimination strategy that also addresses underlying health determinants [[13],[14]]. We used decision analysis modelling to project health outcomes and costs associated with active screening in these villages over a 20-year time frame. Our objectives were to evaluate the cost-effectiveness of the 2019 screening activities, and to assess potential cost-effectiveness of future screening.

 

Methods (1,038 words)

In 2019, the NRBHSS led active screening campaigns in Village 1 (population approximately 1,000) and Village 2 (population approximately 1,500). These campaigns were community-wide: they targeted anyone who was not already known to have active TB or LTBI, without age restrictions. Consequently, in Village 1, approximately 60% of the population was eligible for screening, and in Village 2, approximately 70% was eligible. Persons without a history of LTBI and without symptoms suggestive of active TB underwent tuberculin skin testing (TST), and persons with a TST result of at least five millimetres or a history of LTBI or active TB underwent chest radiography [[15]]. The NRBHSS worked with local staff as well as staff flown into the villages to organize these screening campaigns. Additional detail is provided in the Screening Campaigns section of the Supplementary Appendix.

 

To simulate these campaigns, we incorporated summary public health and cost data into a decision analysis model using TreeAge Pro software (TreeAge Software 2019, Williamstown, MA). Using simulated population cohorts reflecting the inhabitants of these villages, the model predicted TB-related health outcomes: persons with active TB, latent TB infection, and TB-related deaths, over a 20-year time frame, from 2019. The model also predicted direct health system costs, including those of managing active and latent TB, and of screening itself. We considered open cohorts [[16]], and used an annual discount rate of 1.5% for future outcomes and costs [[17]].

 

Figure 1 shows a simplified depiction of the model structure. We simulated an active screening campaign in 2019 (reflecting the campaigns that in fact occurred). The counterfactual scenario with no active screening shared the same model structure, but with lower probabilities for diagnosis and treatment of TB infection and disease.

 

Figure 1. Simplified depiction of the decision analysis model. Transitions between health states are experienced by cohort members each cycle. For example, each cycle, a number of newborn persons enter the Susceptible States. Here, they may acquire or reacquire infection, relapse with active TB, or remain susceptible. If infected or reinfected, cohort members move to the LTBI states, where the clinical pathway entails diagnosis and treatment. Probabilities of diagnosis and treatment are lower in strategies where there is no active screening. The clinical pathway for active TB states resembles that of LTBI states. Similarly, probabilities of diagnosis and treatment are lower in strategies where there is no active screening. Finally, there are death states, which include death from TB or other causes (i.e. background mortality).

 

 

We simulated secondary transmission, using observed data [8,20,27]. We used a ratio of 1.82 secondary active TB cases per index TB case in Village 1, reflecting pooled data from outbreak and non-outbreak years [20]. A ratio of 0.67 persons with new LTBI per index case was used [8,27]. The relatively low number of persons with incident LTBI reflects the high proportion already infected at baseline (48% in Village 1 and 33% in Village 2) versus those susceptible to infection (49% in Village 1 and 66% in Village 2) (Table S1). During simulated outbreaks, we increased the probabilities of progression, reactivation and transmission as observed in these villages; see the “Simulating Outbreaks” section of the Supplementary Appendix for details.

 

Epidemiologic parameters

Epidemiologic parameters fell into three categories. The first included parameters related to TB pathogenesis and treatment [[18],[19]]. These came from published literature. The second category included parameters related to the LTBI or active TB treatment cascade. These parameters came from Nunavik TB program data [[20]] and were vetted by regional experts and community members. Active screening was considered to increase diagnosis and treatment initiation among individuals with LTBI, and to increase diagnosis among individuals with active TB [[21]]. The specific impact of active screening on these parameters reflected program data from both communities in 2019; details are provided in Table S2 in the Supplementary Appendix. The third category included other parameters, such as duration of hospitalization for TB disease. These were primarily informed by local data [20]. Table 1 outlines key epidemiologic parameters and their data sources.

 

Table 1. Key epidemiologic parameters used in the decision analysis model

Description	Value	Source
Parameters related to TB pathogenesis
Probability of progression to active TB after recent infection	0.05-0.265*	[22]
Probability of reactivation to active TB after remote infection	0.0005-0.075*	22,[23]
Annual risk of infection	0.0095	22
Probability of cure following complete active TB treatment	0.928	[24]
Probability of LTBI cure following complete LTBI treatment	0.9	[25]
Probability of dying of untreated TB if smear negative	0.02	[26]
Probability of dying of untreated TB if smear positive	0.07	26
Probability of adverse event during active TB treatment	0.051	18
Probability of adverse event during LTBI treatment	0.003	19
Average number of new LTBI per index TB case	0.67	8,27
Average number of secondary active TB cases per index TB case	1.82	20
Other epidemiologic parameters
Annual birth rate	0.019-0.023†	16
Probability of non-TB-related death (background mortality)	0.014-0.021†	16,[27]
Number of days hospitalized if smear negative††	14	20
Number of days hospitalized if smear positive††	60	20
TB cascade parameters in the absence of active screening§
Active TB
Proportion of individuals with active TB who are diagnosed	0.82§§	Calculated
Proportion of individuals diagnosed who start treatment	1	20
Proportion of individuals started on treatment who complete it	0.99	20
LTBI
Proportion of individuals with LTBI who are diagnosed	0.83§§	Calculated
Proportion of individuals diagnosed who start treatment	0.70§§	20
Proportion of individuals started on treatment who complete it	0.75	20

* The probabilities of progression and reactivation changed over time in the model, starting at their high values (0.265 and 0.075 respectively), then declining over time. This reflected the presence of an outbreak at the beginning of the model, with a subsequent decline in transmission. In scenarios where repeated outbreaks were simulated, these parameters were adjusted accordingly (in addition to the annual risk of infection parameter); this process is described in detail in the “Simulating Outbreaks” section of the Supplementary Appendix.

** The values shown reflect the cascade in absence of active screening

^† These values change year over year in the model to reflect changing birth and death rates in the region
^†† Standard TB management in the region requires all persons with active pulmonary TB to be hospitalized [20]

^§These cascade parameters are specific to Village 1. Those pertaining to Village 2 are provided in the Supplementary Appendix, Table S2.

^§§ The value of these cascade parameters increases when active screening is added

 

 

Cost parameters

All costs were considered from the health system perspective and adjusted to 2019 Canadian Dollars [[28]]. Cost inputs fell into two categories. The first category included costs related to active screening. These costs came from Nunavik program data and reflected the steps needed to conduct active screening activities in both communities in 2019 [21]. Notably, all screening campaign costs were incurred by the health system, including lodging and transportation costs for staff who had to be flown into the villages. The second category included costs related to standard TB care. Wherever possible, these costs came from Nunavik, or Nunavut when necessary. Where such information was unavailable, costs came from published literature, but were confirmed with regional experts. Table 2 highlights key cost parameters, which are further described in Table S1 in the Supplementary Appendix.

 

Table 2. Key cost parameters used in the decision analysis model

Description	Value (2019 CAD)	Source
Costs related to active screening*
Total cost of active screening per person (in 2019**)	$1,952	21
a) Total cost of human resources	$776	21
b) Total cost of lodging and transport	$1,102	21
c) Total cost of communication & mobilization	$5	21
d) Total cost of training & workshops	$2	21
e) Total cost of supplies	$49	21
f) Total cost of amenities	$18	21
Costs related to management of active TB and latent TB infection
Cost of medication for active TB	$674	[29]
Cost of medication for latent TB	$114	29
Cost of visits to manage active TB treatment	$436	[30],31
Cost of visits to manage LTBI treatment	$42	30,[31],32
Cost of adverse event due to active TB treatment	$16,364	18
Cost of adverse event during LTBI treatment	$782	[32]
Cost of medical evacuation	$6,713	[33]
Cost of hospitalization per day	$2,050	[34]

All cost values included in this table are per-person. There were 604 persons screened in Village 1.

* These values are specific to Village 1. Those that pertain to Village 2 are provided in the Supplementary Appendix, Table S1.

** Construction costs are included in the lodging and transport costs above. The village required an extra structure to be built to accommodate screening activities, which is what comprises the construction costs. In subsequent years, if active screening was repeated, we removed costs related to construction so the cost of active screening per person was cheaper. The total cost of active screening is equal to the sum of a, b, c, d, e, and f.

 

 

Screening strategies

We simulated the following strategies, given a single outbreak in 2019, with no subsequent outbreaks:

 

1. No active screening: We estimated what most likely would have occurred had no active screening been introduced in 2019. We used background rates of diagnosis, treatment initiation and treatment completion for TB disease and LTBI, informed by community data during 2017 and 2018, when there was no active screening. Screening close contacts of persons with TB disease is standard practice.

 

2. Community wide active screening in 2019 only: Both Village 1 and Village 2 had active, community-based screening programs in 2019. This strategy incorporated program data to reflect increased rates of diagnosis, treatment initiation, and treatment completion, compared to Strategy A.

 

We then simulated an outbreak in 2019 and every three years thereafter, as these villages have experienced TB outbreaks every two to three years since 2011 [20]. We considered an additional strategy in this context:

3. Community wide active screening every two years for twenty years: Local public health authorities did not think annual screening was feasible, but wished to consider biennial screening.

 

Secondary Analyses. We considered additional strategies, involving variations in screening frequency and target groups. We also considered several scenarios to explore variations in key model parameters: increased rates of LTBI treatment initiation and completion, decreased rates of LTBI diagnosis, use of local staff (to decrease lodging and transportation costs), reduced adherence to active screening, and variations in outbreak intensity. Varying outbreak intensity involved lower peaks for the progression and reactivation parameters during outbreaks. See “Incorporation of Additional Strategies” and “Scenario Analyses” sections in the Supplementary Appendix for detailed descriptions.

 

Finally, one-way sensitivity analyses and probabilistic sensitivity analysis (PSA) assessed the impact of variation in input parameters on predicted outcomes. PSA involved 10,000 model runs, sampling parameters from pre-specified ranges listed in the Supplementary Appendix, Table S1.

 

As the analysis was initiated by the NRBHSS and used only aggregate program data, ethics review board approval was not required. However, results were first shared with community members of the NRBHSS, and community leaders’ and members’ approval of this manuscript was obtained before submission.

 

Results (655 words)

The 2019 active screening campaigns took place in the two villages over 6-11 weeks. They detected 52 people with previously unknown LTBI and 13 persons with previously undiagnosed active TB [21]. For simplicity, we focus on results for Village 1, which had experienced more extensive outbreaks.

 

Given a single outbreak in 2019: Results are summarized in Table 3, with strategies ordered from least to most expensive. Compared to no active screening, adding community-wide active screening in 2019 was estimated to reduce the number of persons with active TB by 13% (95% uncertainty range: -3% to 27%) over 20 years, and was less expensive (dominant), saving $355 (-$273 to $1,055) per person.

 

Table 3. Outcomes over 20 years in Village 1, given a single outbreak in 2019

Strategy*	Cost	Incident Active TB**	Incident LTBI**	TB-Related Deaths
B	$6,996,027 ($5,647,525 to $8,975,360)	90 (79 to 103)	38 (33 to 45)	0.6 (0.4 to 0.7)
A	$7,493,340 ($5,927,277 to $9,748,954)	103 (90 to 118)	42 (36 to 48)	0.9 (0.7 to 1.0)

Values in parentheses indicate 95% uncertainty ranges

*Strategy A: No active screening; Strategy B: Community wide active screening in 2019

** Incident LTBI includes new infections and reinfections. Incident active TB similarly includes cases due to primary progression or reactivation, as well as relapse. Both incident LTBI and incident active TB include secondary infections and active TB cases. Results in the Supplementary Appendix present secondary infections and secondary active TB cases separately.

 

 

 

Given an outbreak in 2019, and every three years thereafter: The results for Strategies A, B and C, in the presence of an outbreak every three years, are shown in Tables 4 and 5. Compared to no active screening, Strategy B and C substantially reduced the number of active TB cases. Strategy C, where community-wide active screening occurs every two years from 2019-2039, had the largest impact on TB morbidity and mortality, reducing active TB cases by 63% (57% to 67%), compared to Strategy A. Strategies B and C were cost saving compared to Strategy A.

 

Table 4. Outcomes over twenty years in Village 1 given an outbreak every three years, starting in 2019

Strategy*	Cost	Incident Active TB**	Incident LTBI**	TB-Related Deaths
B	$14,745,984 ($11,715,969 to $18,606,081)	249 (227 to 266)	87 (83 to 94)	1.5 (1.2 to 1.8)
C	$15,691,149 ($13,059,608 to $18,908,752)	102 (90 to 117)	30 (28 to 35)	0.3 (0.2 to 0.3)
A	$16,359,259 ($12,846,266 to $20,772,912)	276 (252 to 294)	94 (89 to 101)	1.9 (1.6 to 2.3)

Values in parentheses indicate 95% uncertainty ranges

*Strategy A: No active screening; Strategy B: Community wide active screening in 2019; Strategy C: Community wide active screening every two years from 2019-2039

**Incident LTBI includes new infections, reinfections, and secondary infections. Incident active TB similarly includes cases due to primary progression or reactivation, relapse, and secondary cases. Results in the Supplementary Appendix present secondary infections and secondary active TB cases separately.

 

 

Compared to no active screening, Strategies B and C were both dominant. Strategy C was more effective but likely more expensive compared to Strategy B.

 

Table 5. Incremental cost per active TB case averted in Village 1 given an outbreak every three years, starting in 2019

Strategy*	Incremental cost per person compared to preceding strategy	Incremental cost per active TB case averted compared to preceding strategy	Incremental cost per active TB case averted compared to Strategy A
B	--	--	Dominant**
C	$674 (-$1,427 to $2,808)	$6,430 (-$29,131 to $13,658)	Dominant**
A	$477 (-$1,827 to $2,865)	Dominated**	--

Values in parentheses indicate 95% uncertainty ranges. Incremental cost per active TB case averted is the difference in costs divided by the difference in active TB cases between two strategies. The population of Village 1 at the end of the simulation was 1402.

*Strategy A: No active screening; Strategy B: Community wide active screening in 2019; Strategy C: Community wide active screening every two years from 2019-2039

** Because Strategies B and C and was less costly and more effective than Strategy A at averting active TB cases, they were considered to be “dominant”, and Strategy A was dominated.

 

Village 2. In Village 2, active screening in 2019 alone appeared reasonably cost effective ($22,134 per active TB case averted), but not cost saving, compared to no active screening. Biennial active screening also appeared reasonably cost effective, given outbreaks every three years ($22,292 per active TB case averted), compared to no active screening. However, 95% uncertainty ranges were very wide. Detailed results are provided in Tables S4-S7 of the Supplementary Appendix.

 

Scenario Analyses. With a strengthened LTBI cascade, a single round of active screening remained cost saving compared to no active screening in the presence of a single outbreak, as well as in the presence of an outbreak every 3 years. Biennial active screening, however, was no longer cost saving in the presence of an outbreak every 3 years; the incremental cost per person, compared to no screening, was $392 (-$2,584 to $5,297).

 

When the intensity of future outbreaks was reduced by 25% (peaks in progression and reactivation parameters reduced by 25%), Strategy B remained cost saving compared to Strategy A, but Strategy C became more expensive than Strategy A. The incremental cost per person of biennial active screening, compared to no active screening, was $577 (-$2,825 to $5,981). The same pattern was observed when the intensity of future outbreaks was reduced to a greater degree (see Supplementary Appendix, Table S8 and Figures S1 and S2 for further detail on these and all other scenario analyses).

 

Sensitivity Analyses. One-way sensitivity analysis suggested that per diem hospitalization costs for active TB, hospitalization duration if smear positive, the probability of progression to active TB, and the probability of cure following incomplete LTBI treatment were the most influential drivers of cost-effectiveness. Variations in these parameters changed the incremental savings per active TB case averted, but did not change conclusions (Figures S3 - S6, Supplementary Appendix).

 

Probabilistic sensitivity analyses are shown in Figures S7 - S10 of the Supplementary Appendix (and were used to derive the 95% uncertainty ranges in the tables above). These analyses demonstrated that, with a single outbreak, active screening averted more active TB cases than no active screening in 94% of simulations for Village 1. Active screening was cost saving (as well as effective) in 86% of simulations. This means that in most simulations, active screening was the dominant strategy. Similarly, with outbreaks every three years, biennial active screening was more effective (and more expensive) than a single round of screening in 75% of simulations. Nonetheless, biennial active screening averted more TB disease than no active screening and one-time screening in all simulations.

 

Interpretation (557 words)

 

In Inuit communities with very high TB incidence, active screening is likely reasonably cost-effective, and potentially cost-saving. Historically, community-wide screening has been implemented after outbreaks. The ideal screening program will prevent future outbreaks, but it is impossible to predict exactly when an outbreak will occur. The high cost of care for TB disease in Canada’s North makes preventive interventions more cost-effective. While there is no absolute threshold that defines cost-effectiveness in terms of cost per TB case averted, active screening was projected to be cost saving compared to no active screening in Village 1, given repeated large outbreaks. In Village 2 active screening was likely cost effective but not cost saving, reflecting a lower TB burden and screening yield: the 2019 screening campaign detected fewer persons with active TB than in Village 1, despite a larger population.

 

The Inuit TB Elimination Framework highlights key knowledge gaps with respect to active screening [3]. Our study builds on previous work from Nunavut and Nunavik [15,[35]] by projecting potential costs and cost savings, as well as health benefits. There is emerging literature suggesting the effectiveness and cost-effectiveness of active TB screening in such diverse settings as Cambodia, India, China, South Africa, Pakistan, and the South Pacific—particularly if repeated over the longer term [12,[36],[37],[38]].

 

It is essential that all TB care and prevention activities engage and mobilize communities in a culturally safe and appropriate manner. To that end, the NRBHSS has partnered with communities to develop and implement a regional plan for TB elimination, within the TB Elimination Framework created by Inuit Tapiriit Kanatami. This framework acknowledges the importance of strengthening local capacity and of the social determinants of health, including housing and food security [2,3]. We focused on community-wide screening as a stand-alone intervention; we did not address these fundamental upstream determinants here, but previous publications have explored them in the Nunavut context [13,14].

 

It is important to highlight several key assumptions. First, we assumed that costs related to construction would not recur, and that only operational costs would recur. Second, we initially assumed that adherence to repeated cycles of community-wide screening was 100%, though this was varied in scenario analysis. Third, we assumed that persons with LTBI identified by community screening would not otherwise have been found, and persons with active TB found with active screening would have otherwise been diagnosed at a later, more infectious stage. As well, the model was not stratified by age, so we could not assess whether benefits of active screening were higher in specific age groups – recognizing that the average age in Inuit communities is much younger than in Southern Canada [27].

 

Another limitation was the lack of region-specific data for certain model parameters. Wherever possible, we then used data from other Inuit regions. This was the case for estimating secondary transmission, for which we used both program and published data. When such data were not available, we used data from other settings (for the probability of treatment toxicity, for example), while epidemiologic parameters were vetted by the local public health authority.

 

Tuberculosis continues to exact a large and disproportionate burden on many Inuit communities across the North. We anticipate that community-based screening, supported by prompt and effective treatment of both active TB disease and latent infection, can play an important role in communities with the highest incidence.

 

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