This study makes an important contribution to the existing literature on the programmatic costs associated with designing and implementing community-based HPV self-testing for cervical cancer screening in rural Cambodia. Through a comprehensive, activity-based micro-costing approach, the study findings provide important evidence to support policymakers in making informed resource allocation decisions and developing evidence-based policies for cervical cancer prevention in low-resource settings. The study found that the total cost of implementing community-based HPV self-sampling was US$204,507.14 over 2 years, reaching 7524 females across 60 communities in Siem Reap Province.

Training was the primary start-up cost driver (72.64% of start-up costs), consistent with previous studies showing that preparatory and capacity-building activities represent substantial early expenditures in LMIC screening programs [27, 38]. During the implementation phase, laboratory testing was the largest cost component, comprising 60.17% of implementation costs and 55.01% of total programmatic costs, aligning with earlier research identifying laboratory services as major cost drivers in HPV screening programs [23, 27, 38, 39]. The cost attributed to community-based HPV self-sampling (31.12% of total programmatic costs) also reflects trends observed in previous evaluations, emphasizing the resource-intensive nature of outreach and screening interventions [28, 29, 38, 41]. Minor cost components, such as transportation (0.36% of total programmatic costs) and monitoring and evaluation (0.67% of total programmatic costs) also mirror patterns reported in earlier cost analyses, where these activities comprised a small proportion of total implementation expenses [27, 38].

Differences in healthcare system contexts, national cervical cancer screening policies, baseline HPV prevalence, and implementation strategies are all known to influence the delivery and cost of primary HPV self-sampling programs [27,28,29]. Our study found that the cost per female screened was broadly consistent with findings from other LMICs, though some differences emerged due to the inclusion of opportunity costs in the total programmatic cost estimates. For example, a study in Peru reported a cost of $45.40 (2018 USD ≈ $56.70 in 2024 USD) per female screened, largely driven by opportunity costs associated with community health volunteers’ time and travel, which accounted for 36.90% of the total cost [27,28,29, 39]. A study in Kenya reported a higher cost of $37.70 (2018 USD ≈ $47.10 in 2024 USD), largely due to a lower number of females screened per campaign, which reduced cost efficiency by increasing the per-person share of fixed overheads [29]. These differences highlight the importance of scale in community-based screening efforts and the impact of contextual factors on programmatic costs.

Our programmatic cost estimates fall within the range reported across LMIC implementation studies; however, differences in costing perspective, included inputs (e.g., opportunity costs), scale, and delivery model limit direct comparability of absolute USD values across settings. The average cost per HPV-positive case detected in our study ($569.66 in 2024 USD) was substantially higher than the Peruvian study ($378.14 in 2018 USD ≈ $472.30 in 2024 USD), a disparity primarily driven by Cambodia’s lower screen-positive rate (4.77% compared with 12.00% in Peru) [27]. Further, our sensitivity analyses confirmed that the screen-positive rate is a major driver of cost efficiency. When varied within its 95% CI, lower screen-positive rates were associated with higher average costs per HPV-positive case ($632.11 in 2024 USD), while higher screen-positive rates were associated with lower average cost ($512.84 in 2024 USD). This underscores the importance of targeted screening strategies, particularly for high-risk sub-populations such as females aged 35–49 years, those aged 25–49 years living with HIV, and immunocompromised individuals. Prioritizing these groups could optimize resource allocation and improve cost effectiveness in similar low-prevalence settings [5, 42].

In terms of triage testing following a positive primary HPV screen test, our study found comparable costs to those reported in the literature, suggesting the feasibility of various triage options in LMIC contexts. The additional cost per female for VIA triage was US$3.69—consistent with evidence supporting VIA as the most cost-effective triage method in resource-constrained settings. HPV16/18 genotyping cost US$16.42 per female and falls within the reported range for molecular triage strategies, which, while more expensive, may offer improved specificity. Colposcopy remained the most expensive triage option at US$32.92 per female, reflecting the need for specialized personnel and infrastructure, which increases costs significantly, as shown in prior studies [40, 43].

Laboratory testing costs emerged as the most significant cost driver during implementation, and our sensitivity analysis confirmed its strong influence on average cost per female screened. Laboratory testing costs were primarily driven by several components, including capital investments in laboratory equipment and infrastructure, fixed overhead costs associated with laboratory set-up and quality assurance, recurring expenditures for assay reagents and consumables, and personnel costs required for sample processing and analysis. Potential opportunities to reduce laboratory costs include negotiating lower assay prices through pooled procurement or competitive tendering, fostering supplier competition, and committing to predictable testing volumes to achieve economies of scale [44]. In contrast, reducing costs for training, monitoring and evaluation, or transportation, had only marginal effects on average screening costs. Yet, adequate training, supervision, and monitoring are essential to maintain correct sample collection, testing procedures, and follow-up, while reliable transportation supports timely and appropriate handling of samples and referrals. Cutting funding on training, monitoring and evaluation, or transportation leads to the risks of undermined program quality and screening accuracy, thereby limiting the feasibility of scaling and sustaining national cervical cancer screening programs [5]. Therefore, cost-containment strategies should prioritize efficiency gains in high-impact cost drivers rather than reductions in training, monitoring and evaluation, or transportation functions.

Our results offer robust, real-world cost estimates and identify key cost drivers for community-based HPV self-sampling programs. Importantly, this study also captures the downstream costs of triage testing, including VIA, colposcopy, and HPV16/18 genotyping—integral elements of the WHO-recommended ‘screen-triage-treat’ model. While our findings provide essential inputs for policymakers, sustainable implementation will also require attention to financing mechanisms, pricing policies, reimbursement, and co-payment structures to ensure cervical cancer screening program accessibility and scalability [45, 46].

Our study has several limitations. While it aimed to provide a comprehensive analysis of resource use and cost data associated with the design and implementation of community-based primary HPV cervical cancer screening in rural Cambodia, the results of this study should be interpreted with some caution. First, despite our efforts to gather data from multiple sources, there is a potential inherent risk of incomplete data due to lack of detailed breakdown of cost data across categories, which may affect the comprehensiveness of our economic data. To mitigate this, we synthesized data from multiple data sources (quantitative costing tools designed for this micro-costing study and qualitative interviews with program implementers and other relevant stakeholders), ensuring a broader coverage and validation of the information collected. The pragmatic approach we adopted, combining program-level costs with selective micro-costing, introduced a trade-off between precision and feasibility. However, this hybrid approach allowed us to balance the need for detailed cost data against the constraints of time and resources, ultimately providing a practical and sufficiently accurate estimation of costs. Additionally, potential biases such as reporting, measurement, and recall biases were minimized through standardized data collection tools and prospective collection of clinical study data (females screened and referred for triage testing). Although we triangulated cost data using multiple sources, including program documents, direct observation, and semi-structured interviews, the number of health workers interviewed and observed was relatively small. Despite the limited number of key informants interviewed, we did not identify substantial variation in resource use reported by those informants, suggesting that within-site heterogeneity in resource use was limited. We sought to consult all relevant key personnel who had knowledge about resource use and costs for the program, to strengthen completeness of data inputs. Finally, the cost data were directly collected from females only in Siem Reap province in Cambodia. The sample population included females from villages in both remotely rural areas and suburban areas, providing a diverse sample to better represent the target population. Yet, when generalizing our results to other regions or countries with varying HPV prevalence, socio-economic status, religious beliefs, or access to healthcare, it is essential to consider the specific local context. These factors are known to significantly influence the applicability and relevance of micro-costing study findings, necessitating careful adaptation of our results to different healthcare settings.