Cost-effectiveness of integrating paediatric tuberculosis services into child healthcare services in Africa: a modelling analysis of a cluster-randomised trial

Discussion

Our modelling suggests that integrating TB services into child healthcare services would improve TB diagnosis and treatment initiation in children under 5 years of age compared with SoC. In addition, improvements in such services would increase treatment completion and reduce TB-related mortality. The intervention is likely to be cost-effective from a health systems perspective compared with the SoC at a willingness to pay threshold of US$750/DALY averted in Cameroon. For Kenya, the lack of improvements in TB diagnosis and treatment initiation compared with the increased costs means the intervention, as implemented, is unlikely to be cost-effective at typical threshold choices (US$910/DALY averted representing 0.5×GDP per capita).

These findings suggest that the cost-effectiveness of integrating TB services into child healthcare services is likely to differ by setting and may reflect varying levels of baseline integration and/or decentralisation, the different health systems and policy/operational contexts. These differences also highlight the importance of considering context when planning and implementing interventions. Large improvements in TB diagnosis and treatment initiation observed in Cameroon suggest that there was a substantial baseline case detection gap for TB in children. Although similar improvements were not achieved in Kenya, the intervention improved treatment outcomes leading to a reduction in mortality and DALYs.

Several factors can explain the lack of intervention effect observed in Kenya. Although active case finding was already occurring as SoC, TB diagnosis in children was mostly clinical and overdiagnosis was possible. Improvements brought in by the CAP-TB intervention potentially reduced overdiagnosis resulting in the observed no effect. The widespread healthcare worker strikes experienced in Kenya during the INPUT study adversely impacted the intervention. These resulted in fewer children being screened, diagnosed and subsequently treated. The enrolment period for the INPUT study overlapped with the COVID-19 pandemic which could have influenced intervention performance. During the COVID-19 pandemic, TB notifications decreased, with larger decreases for children, due to disruptions in health services and attendance, increased stigma around respiratory symptoms and fear of infection. The magnitude of this impact differed across countries.

These differences in detection and treatment success were the major determinants of cost-effectiveness in this analysis. Although the observed 10-fold increase in case detection in Cameroon significantly increased resource use and costs, the associated huge increases in life years saved resulted in the intervention being cost-effective. These results were robust to different assumptions including case fatality rate (see online supplemental appendix table S12).

Despite significantly improved treatment outcomes in Kenya, the observed lack of improvements in case detection meant the intervention was not cost-effective there. Cost-effectiveness results for Kenya were sensitive to different assumptions, due to this lack of change in detection. Assuming systematic review-based case fatality rates with no improvements in treatment outcomes resulted in the intervention being dominated by the SoC there (became more costly and less effective). These results suggest that interventions that improve case detection are likely to be cost-effective in settings where there is the most scope for improvement.

Our study draws major strength from the use of primary data collected alongside a randomised control study to inform pathways of care, resource use and intervention effects. The diverse nature of the two settings in terms of service integration and decentralisation, and the organisation of national TB control and TB care suggest these findings may be generalisable to other settings. However, context-specific adaptation of such interventions, based on existing levels of integration and decentralisation, is required to improve cost-effectiveness. Intervention costs were estimated using comprehensive country-specific budget and expenditure data for implementing the intervention activities. These approximated the additional costs that a public health system would incur to improve the utilisation of paediatric TB services (through increased access and improved services), beyond what the system currently provides. Therefore, these costs provide an estimate of programmatic costs required for scaling up the intervention such as costs for training and supervision, logistics or investment in resources beyond what is available to the healthcare system in these settings.

This study has some limitations worth highlighting. Data on the number of children systematically screened for TB symptoms on seeking care at health facilities were not available. Without these data, we used approximations based on data from the CaP-TB pre/post study done in parallel to the INPUT study across nine sub-Saharan African countries including Cameroon and Kenya.18 While the assumptions better approximate screening under the intervention where systematic TB screening for all children seeking care was actively promoted, they may be less appropriate under the SoC. Our cost estimates may, therefore, be an underestimate or an overestimate. Our sensitivity analyses show our results are robust to these assumptions in Cameroon, but more sensitive to them for Kenya, where a lower effect of the intervention was found.

For pragmatic reasons, country-specific primary cost analyses were only performed to establish the additional costs associated with procedures introduced as part of the intervention. We applied unit costs for the core healthcare services available under the SoC estimated from publicly available sources. We made all necessary adjustments for costs derived from previous years or other countries, however, more recent and country-specific costs would closely reflect resource use and opportunity costs.

The INPUT study included some household child TB contacts who could have been initiated on TB preventive therapy after active disease was excluded. These were not followed in the study, therfore our analysis did not capture potential costs and benefits associated with TB preventive therapy. We did not model false positive TB, a potentially important factor in the context of the well-established challenges in the diagnosis of paediatric TB and the huge reliance on clinical diagnosis. False-positive TB can have potential adverse consequences including increased costs directly from unnecessary TB treatment and indirectly from second-line treatment for emerging drug-resistant TB. However, bacteriological testing (including Xpert) significantly increased under intervention although a statistically non-significant increase in bacteriological confirmation was observed (from 6/79 under SoC to 11/74 under intervention). Our analysis excluded drug-resistant TB which is associated with higher costs but is rare in children in these settings.

Evidence on the costs and cost-effectiveness of decentralised and family-centred, integrated models of TB care for children is very limited and has been highlighted as a research priority by the WHO Guideline Development Group on the management of TB in children and adolescents.14 Very few studies with a focus on children have been published to date. A systematic review by Alsdurf et al32 of studies providing cost and outcome data for systematic TB screening, found ICERs of between US$281 and US$698 per DALY averted among the general population, US$619/quality-adjusted life-year (QALY) gained among children and US$372–US$3718/DALY averted among close contacts. A few studies report ICERs of TB case-finding interventions in children. These include Mafirakureva et al33 who considered Xpert Ultra on stool and found ICERs of US$132 and US$94 per DALY averted in Ethiopia and Indonesia, respectively; Mupere et al34 who reported US$538 per QALY gained in Uganda; and Debes et al35, who found ICERs ranging from US$106 to US$184 per life-year gained in Uganda. In an analysis of a combined intervention of intensified case finding and strengthened household contact management and TPT provision across nine sub-Saharan African countries, Mafirakureva et al18 reported ICERs of between US$135 and US$6804/DALY averted. More recently, d’Elbée et al36 reported ICERs ranging between US$263 and US$342 per DALY averted for decentralising childhood TB diagnosis at district hospital compared with SoC. Our study is the first cost–utility analysis of TB case finding in children based on a randomised trial comparing the integration of paediatric TB services into child healthcare services (intervention) to current approaches to offering paediatric TB services (the SoC).

Judgements on whether an intervention represents good value for money are commonly based on comparing the estimated ICER to a cost-effectiveness threshold, and interventions with ICERs falling below the threshold are considered cost-effective. However, most countries, especially in the low-income and middle-income category, do not have explicit thresholds specified. In the absence of explicit cost-effectiveness thresholds in Cameroon and Kenya, as in most low-income and middle-income countries, we assumed a threshold of 0.5×GDP per capita for each country. This assumption generally aligns with empirically estimated thresholds based on health spending data for these countries.37–39 Specific estimates for these countries differ in their assumptions and results, including: for Cameroon, US$49–US$654,37 US$112–US$14038 and US$155–US$38640; and for Kenya, US$$32–US$519,37 US$491–US$64738 and US$270–US$671.40 However, the final choice of a cost-effectiveness threshold rests with decision-makers. Cost-effectiveness acceptability curves (figure 3) can be used to evaluate the probability of cost-effectiveness at any chosen threshold.

Integrating TB services into child healthcare services can potentially improve TB diagnosis and treatment initiation and may be cost-effective depending on existing levels of health services for paediatric TB and the choice of threshold. Baseline coverage of services and TB detection and treatment are likely to determine cost-effectiveness. Future empirical work could explore targeting specific elements of interventions that may improve cost-effectiveness in different settings.

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