Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2025 Jan 31;20(1):e0316001. doi: 10.1371/journal.pone.0316001

A prospective model of the potential clinical and economic impact of cervical cancer screening supported by a mobile phone app

Fredrick Chite Asirwa 1, Brian W Bresnahan 2,3, Faith Yego 4, Dana Duncan 5, James K Karichu 6, Louis P Garrison Jr 3,7,*
Editor: Edward Adekola Oladele8
PMCID: PMC11785268  PMID: 39888944

Abstract

Introduction

Cervical cancer is a preventable and highly curable disease when detected early and adequately treated, yet it remains the leading cause of cancer-related death in women in Kenya due to low screening coverage and treatment. Implementing World Health Organization screening guidelines for human papillomavirus (HPV) is challenging due to the complex logistics of result return and follow-up requiring multiple clinic visits. Increasing the use of mobile technology can support follow-up care in cervical cancer screening programs.

Methods

We developed a prospective clinico-economic model to assess the potential impact of a mobile phone-based application (“app”) communicating laboratory results and recommendations to improve follow-up care for cervical cancer screening in Kenya. The model is structured to simulate a three-visit pathway for HPV-based screening used in a clinical trial of the app and based on epidemiological data, clinical guideline-based workflow, and patient-based behavioral pathways. Published literature, expert elicitation, and time-and-motion observations were used to estimate clinical data, care pathways, and visit-related costs. This analysis was conducted from a base-case healthcare system perspective with a scenario from a “limited” societal perspective.

Results

In a simulated cohort of women using the app-based intervention compared to conventional care, with 10,000 women in each arm, use of the app is projected to increase healthcare costs by $12.53 per enrolled woman during the trial period and to detect and treat an additional 247 women—229 with precancerous cervical lesions and 18 with cervical cancer. The incremental cost-effectiveness ratio of the app versus conventional care was $174 per case detected and treated. This would be cost-saving given the average lifetime cost per cervical cancer case of $1,000–$3,000.

Conclusion

Use of a mobile phone-based app is costlier than conventional screening but by improving visit compliance, it can be a cost-effective and cost-saving strategy to enhance detection and treatment in cervical cancer screening programs.

Introduction

Cervical cancer is the fourth most common cause of cancer among women worldwide and has wide geographical variation in incidence and mortality. Over 90% of cervical cancer deaths occur in low- and middle-income countries [1]. Measures for eliminating cervical cancer exist but are not widely implemented in regions of the world where the disease burden is highest. In 2020, the World Health Organization (WHO) set a 2030 target to screen 70% of women with a high-performance cervical cancer test by 35 and 45 years of age and to treat 90% of women identified with cervical precancer or cancer [2,3]. Achieving these targets is challenging for many countries with a high disease burden due to the multiple clinic visits required to complete screening and treatment. Alternative screening approaches are being evaluated—with and without DNA testing [4]. In any approach, effective systems for increasing patient compliance with follow-up care are critical to achieving the WHO goal of cervical cancer elimination.

Mobile phone technology holds immense potential to increase visit compliance. With mobile phone coverage of around 90%, most women in Kenya may benefit from a mobile-phone application (app)-based intervention to increase the effectiveness of cervical cancer screening programs [5,6]. Mobile phone telecommunication using text messaging has been used effectively in cervical cancer screening programs to provide reminders for follow-up care, education, and electronic specimen tracking [7,8]. Mobile app-based interventions offer the advantage over text messages of tailoring content to match the needs and preferences of users and provide multimedia content to enhance participation and further motivate behavior change [9–11]. When strategically integrated into cervical cancer screening programs, mobile apps may increase completion rates for screening and treatment.

The clinical impact and cost-effectiveness of using innovative apps to increase visit compliance in cervical cancer screening programs in low-and middle-income countries (LMICs) is largely unknown. However, there has been important research on developing and implementing mobile apps for cervical cancer screening in Kenya, in particular [12–15]. We modeled the potential clinical and economic value of a mobile app to boost adherence and treatment by women enrolled in a cervical cancer screening program relative to the estimated cost. This model can be used to evaluate the potential clinical and economic impact of an app used for cervical cancer screening in Western Kenya. The results of this analysis can support policy and program implementation discussions for using mobile app technology in cervical cancer screening programs in Kenya and other high-burden settings.

Methods

Overview

We developed a prospective clinico-economic model alongside an ongoing randomized-controlled trial (RCT) to assess the potential economic impact on cervical cancer screening in clinics in western and eastern Kenya supported by an innovative mobile phone-based app used to communicate care outcomes and recommendations to patients. Our model was based on the clinical protocol used in an RCT study (ITH-HPV-548; RD006031) assessing the clinical consequences associated with using a mobile app compared to standard of care to aid communication to women about human papillomavirus (HPV) testing results, follow-up care recommendations, and cervical cancer screening outcomes. The timeframe for this prospective model included a basic three-visit schedule (usually completed in less than six months), aligned with guideline-based care. The model is structured to simulate expected pathways in Kenya for HPV-based cervical cancer screening and subsequent care based on biologic information (epidemiology), clinical guideline-based workflow pathways, and patient-based behavioral pathways. These alternative pathways influence the proportion of patients experiencing different clinical events in a simulated flow for a cohort of patients using a cost-consequences framework. Key consequences include intermediate endpoints (such as test results) and final endpoints (viz., pre-cancerous and cancer cases detected and treated). The best available relevant evidence influenced our parameter values for base-case values and ranges for sensitivity analyses.

Multiple sources of information were used, including data from: (a) published literature and publicly-available websites reporting on cervical cancer screening, treatments, costs, and published guidelines, (b) expert elicitation, primarily from “subject matter experts” (SMEs) representing a not-for-profit private cancer center in Western Kenya conducting an HPV-screening RCT, as well as experts in other regions of Kenya; (c) a time-and-motion study capturing time and resources involved in standard cervical cancer screening visits and follow-up care (private clinic and public hospital settings); and (d) testing-related and procedure-related cost estimates collected through key-informant interviews (multiple observations). The software acquisition cost of the mobile app was not included in the model.

This prospective clinico-economic analysis was conducted from two perspectives—a base-case healthcare system perspective and a scenario considering a “limited” societal perspective [16]. The latter includes estimates of patient opportunity costs associated with expected incremental differences in reduced time required to attend clinic visits for the intervention and control groups. We begin with a cost-consequences approach since there are multiple intermediate and final outcomes being generated from the RCT and projected real-world pathways, such as differences in patients receiving post-screening follow-up care, including: recommended visit attendance, receiving colposcopy and/or biopsy for higher-risk cases, and receiving guideline-consistent pre-cancerous lesions and cancer-related treatment. The consequences for patients and expected patient flow processes result in patients following distinct care pathways subsequent to HPV-based screening result availability. Our model structure also allows for the calculation of some specific cost-per-consequence metrics (i.e., cost-effectiveness measures such as cost-per-case detected and cost-per-cancer-case avoided).

These between-group differences—those using the mobile app or not—are expected to be influenced by differential follow-up visit compliance rates, which is due presumably to the improved ability to notify and communicate with patients using the mobile app. Our prior expectation was that there are likely to be incremental cost-savings associated with the intervention group due to a reduced number of clinical follow-up visits required to receive “normal” results, i.e., negative high-risk human papillomavirus (hrHPV) test results. In addition, given the improved communication opportunities with the mobile app, we expect a higher prevalence of attendance for recommended, “clinically necessary” follow-up visits. Due to better follow-up visit compliance, we also expect higher total costs for patients with the mobile app for visits associated with performing colposcopies and biopsies to reveal cervical intraepithelial neoplasia (CIN) status or cancer diagnosis and to make pre-cancer and cancer-related treatment decisions. Likewise, we expect higher eventual-treatment costs consistent with clinical guideline-based care in the intervention group, due to the relative ease of patient contact via the mobile application. Our model allowed for a per-patient cost for navigation or phone follow-up for each group.

We developed a decision-tree model structure mirroring the guideline-based clinical practices and generally expected patient and decision-making flow in the RCT for obtaining and disseminating cervical cancer screening consequences and recommendations. We constructed an Excel-based model calculating expected incremental costs and consequences within and between the randomized groups. To inform model structure and to parameterize the model, we conducted a targeted assessment of the cost-related and cost-effectiveness-related literature, as well as select clinical and epidemiologic literature and clinical agency websites. Our aim was to identify the prevalence of cervical cancer, clinical outcomes, and costs for cervical cancer screening, diagnosis, and treatment. For the literature assessment, the following databases were searched (PubMed, OVID, Embase, ProQuest, and Google Scholar). The search strategy applied to all databases used the following keywords (“cost-effectiveness”, “cervical cancer,” and “Kenya”). We also used specific keywords such as “cervical cancer screening in Kenya;” “mobile applications and cervical cancer in Kenya;” “economics of cervical cancer screening in Kenya;” “consequences of cervical cancer in Kenya;” and “scaling up cervical cancer in Kenya.” We focused on literature specific to Kenya from 2010–2023. We utilized only articles that were in English. All abstracts meeting the search criteria were assessed and reviewed. Full-text reviews were conducted for publications or reports deemed relevant to informing model parameterization.

We also relied on the informed judgment of the study team to finalize the model parameters with assumptions based on the literature and on interviews with site staff. Thus, we were able to define clinical pathways and a clinical-outcomes structure, and to generate a range of base-case estimates derived from relevant probabilities and costs. In addition, the literature-based parameters were supplemented with estimates by SMEs in Kenya. We (see supplemental Inclusivity in Global Research Questionnaire) collaborated with clinical and clinic administration professionals and study investigators to refine estimates for the model clinical parameters, specify ranges of parameter estimates, and develop plausible cost estimates for services and interventions. Ethics approval for the expert elicitation was obtained from KEMRI (Protocol #4217), and verbal informed consent was obtained from participants prior to conducting the interviews (between May 15, 2023, and August 15, 2023). For the verbal consent process, the study was explained to the participants who were selected based on their expertise in the study topic, and once they agreed to participate, the consent was documented and the interview conducted. All informed consenting processes were approved by the IRB.

Our primary base-case estimates are structured to first report healthcare process metrics, such as the number of patients screened, the number of visits attended, and counts of patients receiving various interventions (colposcopies, biopsies, pre-cancer, and cancer treatment), accounting for preliminary loss-to-follow-up (LTFU) assumptions. We also report group-related aggregate cost estimates for each treatment arm, associated with the use of these services and visits, as well as per-patient cost estimates. Since the time horizon for the primary analysis is short-term (i.e., less than one year to complete the 3 visits), there is no strong need to perform discounting of costs or outcomes. Given the high likelihood of patients not returning for follow-up visits, we specifically report statistics related to visit compliance. Lastly, we conducted selected one-way sensitivity analyses—altering one key model parameter at a time—to provide information about the expected impact on results due to changing different variables related to probabilities, costs, or follow-up rates. Additional information describing the three-visit study protocol and what tests or interventions were administered to patients at specific visits is provided in the Supporting information section.

Results

Our prospective clinico-economic model structure aligned with an RCT based on standard clinical practice in Kenya. Fig 1 presents the trial clinical pathways prescribed in the protocol.

Fig 1. Clinical trial workflow protocol.

Fig 1

Open in a new tab

Table 1 presents model input parameters for probabilities, including the expected likelihood of attending recommended follow-up visits. On the first visit, visual inspection with acetic acid (VIA) or visual inspection with Lugol’s iodine (VILI) and HPV sample collection for testing are performed. The probabilities provide estimates for HPV test findings, for the likelihood of receiving follow-up care at a second healthcare encounter, and for receiving pre-cancer treatment at a third encounter as needed. Based on the literature and expert clinical judgment, the vast majority (80%) of patients are expected to be VIA- and HPV- and thus have no clinical reason to physically attend a second healthcare visit. The remainder of the patients are categorized into four groups with their expected percentages: VIA+ & hrHPV- (& no tumor) (3%), VIA-/VIA+ and/or hrHPV+ (non 16/18 & no tumor) (6%), VIA-/VIA+ and/or hrHPV+ (16/18 & no tumor) (9%), and suspicious for CA (VIA+ with Gross Tumor) (2%).

Table 1. Probabilities of clinical events.

Likely Low High Source
Visit #1: Primary HPV/Screen App Registration
Initial Screen Result
1. VIA- & HPV- 0.8 0.64 0.96 [17]
2. VIA+ & hrHPV- (& no tumor) 0.03 0.024 0.036 [18]
3. VIA-/VIA+ and/or hrHPV+ (non 16/18 & no tumor) 0.06 0.048 0.072 [19]
4. VIA-/VIA+ and/or hrHPV+ (16/18 & no tumor) 0.09 0.072 0.108 [20,21]
5. Suspicious for CA (VIA+ with Gross Tumor) 0.02 0.016 0.024 Assumption—Expert Judgment/Elicitation
Conditional Probability of Return for Second Visit or Follow-up Visit
Control Arm (Return to find out HPV results)
1. VIA- & HPV- 0.6 0.48 0.72 [17]
2. VIA+ & hrHPV- (& no tumor) 0.6 0.48 0.72 [22]
3. VIA-/VIA+ and/or hrHPV+ (non 16/18 & no tumor) 0.6 0.48 0.72 [19]
4. VIA-/VIA+ and/or hrHPV+ (16/18 & no tumor) 0.6 0.48 0.72 [20,21]
5. Suspicious for CA (VIA+ with Gross Tumor) 0.75 0.6 0.9 Assumption—Expert Judgment/Elicitation
Mobile App Arm (Informed by App; If return, for Colposcopy)
1. VIA- & HPV- 0 0 0 [17]
2. VIA+ & hrHPV- (& no tumor) 0.9 0.72 0.95 [22]
3. VIA-/VIA+ and/or hrHPV+ (non 16/18 & no tumor) 0.9 0.72 0.95 [19]
4. VIA-/VIA+ and/or hrHPV+ (16/18 & no tumor) 0.9 0.72 0.95 [20,21]
5. [Suspicious for CA (VIA+ with Gross Tumor) [See below]] 0.85 0.68 0.95 Assumption—Expert Judgment/Elicitation
Visit #2: Colposcopy & Biopsy and/or Treatment
Visit Result (Conditional on Arm)
Subpop 1. VIA- & HPV-
A. Colposcopy Normal 0.28 0.224 0.336 [23]
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0.32 0.256 0.384 [17]
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0.33 0.264 0.396 [24]
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.06 0.048 0.072 [24]
E. Colposcopy Abnormal—Suspicious for CA 0.01 0.008 0.012 Per protocol
Subpop 2. VIA+ & hrHPV- (& no tumor)
A. Colposcopy Normal 0.28 0.224 0.336 [23]
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0.32 0.256 0.384 [17]
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0.33 0.264 0.396 [24]
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.06 0.048 0.072 [24]
E. Colposcopy Abnormal—Suspicious for CA 0.01 0.008 0.012 Per protocol
Subpop 3. VIA-/VIA+ and/or hrHPV+ (non 16/18 & no tumor)
A. Colposcopy Normal 0.28 0.224 0.336 [23]
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0.32 0.256 0.384 [17]
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0.33 0.264 0.396 [24]
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.06 0.048 0.072 [24]
E. Colposcopy Abnormal—Suspicious for CA 0.01 0.008 0.012 Per protocol
Subpop 4. VIA-/VIA+ and/or hrHPV+ (16/18 & no tumor)
A. Colposcopy Normal 0.28 0.224 0.336 [23]
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0.32 0.256 0.384 [17]
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0.33 0.264 0.396 [24]
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.06 0.048 0.072 [24]
E. Colposcopy Abnormal—Suspicious for CA (As % of All) 0.01 0.008 0.012 Per protocol
Subpop 5. Suspicious for CA (VIA+ with Gross Tumor)
F. First visit suspicious of CA—Biopsy CIN1 or less 0.1 0.08 0.12 Assumption—Expert Judgment/Elicitation
G. First visit suspicious of CA—Biopsy CIN2/CIN3 0.4 0.32 0.48 Assumption—Expert Judgment/Elicitation
H. First visit suspicious of CA—Cancer 0.5 0.4 0.6 Assumption—Expert Judgment/Elicitation
Visit #3: Diagnosis and/or Treatment (Pre-cancer or Cancer)
Conditional Probability (on Visit #2 Outcome) of RETURN for Visit 3
Control
A. Colposcopy Normal 0 0 0 Assumption—Expert Judgment/Elicitation
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0.5 0.4 0.575 Assumption—Expert Judgment/Elicitation
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0.5 0.4 0.575 Assumption—Expert Judgment/Elicitation
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.5 0.4 0.575 Assumption—Expert Judgment/Elicitation
E. Colposcopy Abnormal—Suspicious for CA (As % of All) 0.8 0.64 0.88 Assumption—Expert Judgment/Elicitation
Conditional Probability (on Visit #1 Outcome and of RETURN for Planning Visit 2)
F. First visit suspicious of CA—Biopsy CIN1 or less 0.8 0.64 0.96 Assumption—Expert Judgment/Elicitation
G. First visit suspicious of CA—Biopsy CIN2/CIN3 0.8 0.64 0.96 Assumption—Expert Judgment/Elicitation
H. First visit suspicious of CA—Cancer 0.9 0.72 0.99 Assumption—Expert Judgment/Elicitation
Mobile App
A. Colposcopy Normal 0 0 0 Assumption—Expert Judgment/Elicitation
B. Colposcopy Abnormal—Pre-cancer Treatment (As % of All) 0 0 0 Assumption—Expert Judgment/Elicitation
C. Colposcopy Abnormal—Biopsy Collection CIN1 or less (As % of All) 0 0 0 Assumption—Expert Judgment/Elicitation
D. Colposcopy Abnormal—Biopsy Collection CIN2/CIN3 (As % of All) 0.8 0.64 0.88 Assumption—Expert Judgment/Elicitation
E. Colposcopy Abnormal—Suspicious for CA (As % of All) 0.9 0.72 0.99 Assumption—Expert Judgment/Elicitation
Conditional Probability (on Visit #1 Outcome and of RETURN for Planning Visit 2)
F. First visit suspicious of CA—Biopsy CIN1 or less 0 0 0 Assumption—Expert Judgment/Elicitation
G. First visit suspicious of CA—Biopsy CIN2/CIN3 0.9 0.72 0.99 Assumption—Expert Judgment/Elicitation
H. First visit suspicious of CA—Cancer 0.9 0.72 0.99 Assumption—Expert Judgment/Elicitation
Treatment (Conditional on Visit #2 or Follow-up Visit Outcome)
Colposcopy Normal
 Pre-cancer Treatment 0 0 0 Protocol
 Cancer Treatment 0 0 0 Protocol
Colposcopy Abnormal—Pre-cancer Treatment
 Pre-cancer Treatment 1 1 1 Protocol
Colposcopy Abnormal—Biopsy Collection CIN1 or less
 Pre-cancer Treatment 0 0 0 Protocol
Colposcopy Abnormal—Biopsy Collection CIN2/CIN3
 Pre-cancer Treatment 1 1 1 Protocol
Colposcopy Abnormal—Biopsy Collection—Cancer
 Cancer Treatment 1 1 1 Protocol
Control
Screening visit suspicious of CA—Biopsy CIN1 or less
 Pre-cancer Treatment 0 0 0 Protocol
Screening visit suspicious of CA—Biopsy CIN2/CIN3
 Pre-cancer Treatment 1 1 1 Protocol
Screening visit suspicious of CA—Cancer
 Cancer Treatment 1 1 1 Protocol
Mobile App
Screening visit suspicious of CA—Biopsy CIN1 or less
 Pre-cancer Treatment 0 0 0 Protocol
Screening visit suspicious of CA—Biopsy CIN2/CIN3
 Pre-cancer Treatment 1 1 1 Protocol
Screening visit suspicious of CA—Cancer
 Cancer Treatment 1 1 1 Protocol
Open in a new tab

Source: Authors’ estimates.

Cost parameters used in the model are presented in Table 2, in estimated 2022 U.S. dollars. The costs for routine screening visits are included ($28.5), as well as the average cost of colposcopies ($35) and biopsies ($105) if subsequent diagnostic tests are deemed clinically necessary and performed after the HPV results are obtained. In addition, costs for the time of the clinical staff to support downloading the app at the screening visit are included ($3), making a screening visit $31.5 for the app arm. Average costs related to contacting patients if they do not attend a visit are included for completeness ($2 for the control arm and $0 for the app arm). The estimates for pre-cancer treatment were $348 per episode (generally cryotherapy or loop electrosurgical excision procedure (LEEP). Cancer treatment per patient was estimated to be $425 which currently only includes the additional cost of local lesion therapy.

Table 2. Input parameters—Costs.

Healthcare System Perspective
Cost per Visit: Likely Low High Source
Visit #1: Primary HPV/Screen App Registration
 Control Arm $28.50 $22.80 $34.20 [25]; Author calculations
 Mobile App Arm $31.50 $25.20 $37.80 HPV test [25,26]; plus time costs
Post-Visit 1: Navigation/Notification
 Control Arm $2.00 $1.60 $2.40 Assumption—Expert Judgment/Elicitation
 Mobile App Arm $0.00 $0.00 $0.00 Assumption—Expert Judgment/Elicitation
Visit #2: Colposcopy & Biopsy and/or Treatment
 Cost of Information-Only Visit $5.00 $4.00 $6.00 Assumption—Expert Judgment/Elicitation
 Base Encounter Cost $28.50 $22.80 $34.20 Assumption—Expert Judgment/Elicitation
 Colposcopy (No biopsy) $35.00 $28.00 $42.00 Assumption—Expert Judgment/Elicitation
 Biopsy Incremental Cost $105.00 $84.00 $126.00 Assumption—Expert Judgment/Elicitation
Post-Visit 2: Navigation/Notification
 Control Arm $2.00 $1.60 $2.40 Assumption—Expert Judgment/Elicitation
 Mobile App Arm $0.00 $0.00 $0.00 Assumption—Expert Judgment/Elicitation
Visit for Treatment Initiation: Diagnosis and/or Treatment (Pre-cancer or Cancer)
 Follow-up on Visit 1 Biopsy for Treatment Planning $15.00 $12.00 $18.00 Assumption—Expert Judgment/Elicitation
 Pre-cancer Treatment $348.00 $278.40 $417.60 Assumption—Expert Judgment/Elicitation
 Cancer Treatment $425.00 $40.00 $510.00 Assumption—Expert Judgment/Elicitation
Societal Perspective
Patient Travel Cost and Time Cost
 All visits—Mean patient time cost (missed work) 3 2.4 3.6 https://www.fke-kenya.org/Minimumwages
 All visits—Mean Total Travel Cost per Visit (round trip) 3 2.4 3.6 https://www.fke-kenya.org/Minimumwages
 Total Societal Cost per Visit 6 4.8 7.2 Calculated
 All visits—Mean select out-of-pocket costs (childcare and meals) 2 1.6 2.4 [27] (semi-integrated visit); Author calculations
Open in a new tab

Source: Authors’ estimates.

Table 3 summarizes the visit and cost results of the model projection. In a simulated cohort of 10,000 patients in each arm, it is projected that the app-based intervention would generate increased healthcare costs of $125,278 (or $12.53 per enrolled patient) over the three-visit study period. Based on an epidemiological assumption, 80% of screened patients are VIA- and HPV-, and follow-up is not required. Unlike the intervention group, patients in the control arm need to return for a second visit to find out their status. This generates many visits for the VIA-/HPV- patients: we assume the cost is $5 for on-site notification alone vs. $64 for a full return visit with colposcopy. Factoring in per-protocol colposcopy for the patients VIA+ or hrHPV+ , overall, this results in an additional 4,257-second visits in the control arm, but the net effect is that the intervention arm costs $6,206 more. The increased cost due to the intervention is projected, however, to produce potential health gains in the cohort in that, as shown in Table 4, an additional 229 patients who are at high risk of cancer are detected and receive pre-cancer treatment. Furthermore, 18 more patients are detected as having cancer and will receive treatment.

Table 3. Summary of key cost impacts.

Control Arm Mobile App Difference
Number Total Number Total
First visit—Screening and App Loading Costs 10,000 $285,000 10,000 $315,000 $30,000
Mobile phone notification of controls post-Visit 1 10,000 $20,000 0 $0.00 $−20000
Second Visit—Non-suspicious with Colposcopy 1,080 $68,580 1,620 $102,870 $34,290
Second Visit—Non-suspicious without Colposcopy 4,800 $24,000 0 $0.00 $−24000
Biopsies 632 $66,360 848 $89,040 $22,680
Mobile call notification of controls post-Visit 2 632 $1,264 0 $0.00 $−1264
Return for V2 Treatment Initiation for V1 Ca Suspicious 132 $1,973 149 $228 $255
Return for V3 CIN Status (CIN1) 1 $5.00 0 $0.00 $−4.50
Pre-Cancer Treatments (Visit 3) 444 $154,442 673 $234,211 $79,769
Cancer Treatments (V3) 70 $29,835 84 $35,611 $5,776
Tota Detection Costs – $463,940 – $506,910 $42,970
Trial Period Total Detection and Treatment Costs – $651,454 – $776,732 $125,278
Open in a new tab

Source: Authors’ estimates.

Table 4. Clinical consequences—Detection and treatment.

Control Mobile App Difference
Total Receiving Pre-Cancer Treatment Cases Cases Cases
CIN2/CIN3 following Vl Biopsy 64 72 8
Colposcopy Abnormal-Pre-CA Tx 346 518 173
V2 Colposcopy Abnormal-CIN2/CIN3 32 78 45
V2 Suspicious Ca—CIN2/3 1.8 4.9 3.1
Total Detected Cases with Pre-CA Tx 444 673 229
Total Receiving Cancer Treatment
V2 Colposcopy Abnormal-Suspicious for CA 3 7 5
Screening visits (suspicious of CA-Cancer) 68 77 9
V2 Suspicious for Ca—Cancer 3 7 5
Total Detected Cases with Cancer Tx 73 91 18
Cases Dx& Tx 517 764 247
Open in a new tab

Source: Authors’ estimates.

From a cost-effectiveness perspective, we can calculate (Table 5) the incremental detection cost up to the point of either pre-cancer treatment or cancer treatment. Thus, excluding the projected treatment costs from the total, the mobile app arm generates $42,970 in additional costs to detect and treat an additional 247 patients. The incremental cost-effectiveness ratio for this is $174 per case detected and treated for pre-cancer.

Table 5. Incremental cost-effectiveness analysis.

Trial period Estimates
Total Detection and Treatment Costs
Total $125,278
Incremental (Per Screened Member) $12.53
Incremental Cost of Screening and Detection
Total $42,970
Incremental (Per Screened Member) $4.30
Incremental Cases Detected 247
Incremental Cost Per Case Detected $174
Open in a new tab

Source: Authors’ estimates.

While the clinical trial protocol calls for all returning patients with VIA-/HPV- results to have a colposcopy at the second visit, we also conducted a scenario analysis where fewer women receive colposcopy. If we assume that only women with high-risk HPV genotypes 16 or 18 and a positive or negative VIA result have a colposcopy done, this further reduces the detection cost in the intervention arm. The average cost per patient screened falls from $12.5 to $5.95, but only 140 additional pre-cancer or cervical cancer cases are detected and treated compared to control, rather than the additional 247 in the higher biopsy use scenario. The incremental cost-per-case detected and treated falls to $103, so, in the short-term, it would be cost-saving for the 140 patients, but cost-increasing for the health system for the 107 untreated patients.

In terms of a limited societal perspective (Table 6), if we assume the cost of the patient time at the clinic plus transportation costs of $6 per visit, the higher number of visits (4,394) in the control arm would imply an additional aggregate cost of $26,365. From a societal perspective, this would offset some of the additional $125,000 in healthcare costs incurred for patients using the mobile application.

Table 6. Costs-limited societal perspective.

Control arm Mobile app Difference
Number Total Number Total
Cost Cost
Total Direct Medical Costs – $651,454 – $776,732 $125,278
Indirect/Time Costs
First Visits 10,000 10,000
Second Visits 5,880 1,620
Third visits (incl Tx) 296 162
Total 16,176 $97,056 11,782 $70,691 $26,365
Total Societal Costs $748,510 $763,143 $14,633
Open in a new tab

Source: Authors’ estimates.

Discussion

This prospective clinico-economic model projects the potential impact of a mobile app for cervical cancer screening on health system costs and clinical outcomes and aims to inform policy and program decision-making in Kenya and other high-burden settings. The overall projection is that for a cohort of 10,000 women enrolled in an HPV-based cervical cancer screening program, an app-based intervention would incur $125,278 more ($12.53 per patient) during a three-visit screening process. The health consequences would be that an additional 247 women would be detected early and receive pre-cancer treatment and an additional 18 women would be detected and receive cancer treatment.

The cost-consequences assessment was able to model patient flow through a standard clinical workflow process in Kenya, accounting for screening visits, follow-up visits, and expected costs of care influenced by assumptions related to lost-to-follow-up rates. The use of a mobile phone app is estimated to result in fewer “unnecessary” post-screening follow-up visits due to the ability to inform patients about normal or negative test findings, complemented with educational messages and subsequent follow-up testing recommendations. Due to an assumed higher likelihood of app-using patients returning for cervical cancer-related second and third healthcare encounters, more patients who have confirmed HPV+ results in the mobile application arm were expected to receive guideline-recommended colposcopy, biopsy, and eventual pre-cancer or cancer treatment. Although there were differential impacts between the arms in the expected clinical workflow, there were also higher overall costs for using the mobile app. However, these higher costs are due to more patients receiving clinically appropriate care, guided by the ease of patient-level communication through the app. Although not modeled specifically, there are likely secondary benefits for a busy clinic in the reduction of congestion from women who can avoid returning to the clinic to learn that they have negative/normal cervical cancer screening results.

While this analysis was conducted primarily as a short-term cost-consequences projection, we were also able to calculate a cost-effectiveness ratio for diagnostic costs generated up to the point of pre-cancer treatment. The calculations imply that the incremental cost per patient detected and treated is $174, and $103 if only hrHPV (16/18) patients receive a colposcopy. Is this good value for money from a health system perspective? To answer this, it must be compared with the lifetime costs and survival for a woman who progresses to invasive cervical cancer. Our analysis did not estimate this nor could we find specific Kenyan estimates for these two outcomes although we were able to use more aggregated Kenyan cost data for cancer screening and treatment for comparison purposes and to support our parameter values and cost ranges. A study in Tanzania estimated per-patient lifetime discounted cervical cancer treatment costs ranging from $1,700 (for Stage 4) to $3,000 (for Stage 1) [28]. The study estimated that Stage 1 patients lost about 1.1 years of life and Stage 4 patients lost about 6 years of life. Several Kenyan cost studies suggest that these treatment cost estimates would be comparable in Kenya. Studies have often reported “patient out-of-pocket” costs as the source of payment for services in Kenya [29] which is different than estimating limited-societal costs related to “producing or delivering” a healthcare visit or care episode—the focus of our analysis. Studies assessing patient costs in several cancer types estimated that cervical cancer treatment costs vary and increase by cancer stage severity. Treatment costs were reported to range from approximately $1,000 to several thousands of dollars, with higher payments (costs) for care at private clinics compared to public facilities [23,30]. If these ranges of estimates were to apply in Kenya, the impact of the mobile app would be judged to be not only cost-saving but also “dominant” in economic terminology—i.e., lower cost and better outcomes. That would represent good value for money from a healthcare system perspective and is likely to be favorably viewed by health ministries and funders. The basic intuition behind this extrapolation is that the mobile app arm generates $42,970 in additional aggregate costs during a shorter-term diagnostic phase to detect 247 additional women with cervical cancer, but, for example, with an average lifetime cost per case of $2,000, this would save almost $600,000 in cancer treatment costs even without accounting for the value of the additional life years gained.

Several studies have examined the economic aspects of incorporating digital tools into cervical cancer screening in Kenya [23,31]. The cost-related studies have mostly reported on “costs per woman screened” and other micro-costs associated with community-based and clinic-based screening strategies in Kenya [32,33]. Those studies focused on community health campaigns to improve screening and compared screening effects and costs for these short-term campaigns (30 days total for outreach, screening, and notification/referral) to outcomes in government health clinics, assessing HPV-based screening using self-collection strategies in these settings [23,31]. This study complements the previous work, focusing on a new intervention to improve screening efficacy and efficiency. Our analysis used data collected in public and private clinic-based settings to model a more comprehensive process of VIA and HPV-based testing, followed by subsequent diagnoses for persons based on their screening results (i.e., colposcopy and biopsy when appropriate), as well as delivery of pre-cancer and cancer treatments based on patient’s health profiles through this guideline-based testing regimen. Our prospective model is consistent with recommended processes starting from screening and carrying through to treatment initiation for patients identified as having pre-cancerous lesions or diagnosed with cervical cancer.

Other cost-effectiveness studies have focused on expanding HPV-based cervical cancer screening and treatment strategies; however, study participants were often patients with ongoing care at an HIV-related clinic [32,34]. Our modeling study of the potential value provided by a cervical cancer screening phone-based app enabled the testing of various scenarios, including our base-case scenario and other select sensitivity analyses. We were able to utilize parameter estimates and cost ranges from the published screening-based and treatment literature, and to supplement these estimates with direct data collection of targeted time, resource needs, cost, and probabilities associated with our model structure of cervical-cancer patient flow and clinician workflow in Kenya.

This prospective economic modeling study has several limitations. First, as a preliminary analysis, it is hindered by the use of “best expert judgment” or “best literature-based estimate” informing parameter values in the model. The purpose of prospective modeling is to forecast scenarios and likely outcomes, but a drawback is that the model has limited study-based parameter values to use. A related limitation is that modelers must rely on literature-based assumptions for clinical and economic parameters, and there is generally substantial heterogeneity in the types of studies reported in medical literature. Studies vary in the types of patients assessed—e.g., HIV populations or specialty clinics in the case of cervical cancer—or geographic location of patients, and the types of interventions tested and/or compared in published studies. That said, there are several high-quality publications related to cervical cancer screening in Kenya, and we used available HPV-based test outcomes reported, clinical status classifications, follow-up rates, and cost estimates provided by the research community. Our model structure focused on a specific three-visit trial design using laboratory-based HPV-DNA testing which was consistent with guidelines-based practice in Kenya (i.e., screen, further diagnose if risk-signal present, treat when appropriate). In this short-term model, we did not model longer-term costs or effects related to multi-year visit schedules, and subsequent progression of cancer cases missed in diagnosis or not treated, nor did we estimate mortality reduction or lifetime morbidity effects for the mobile application compared to standard of care approaches. Nor did we model different regions in Kenya (urban vs. rural or western Kenya vs. eastern Kenya) or assess potential cost-consequence differences in public vs. private clinics or via alternative screening campaign strategies.

A more complete economic assessment would include formal longer-term cost-effectiveness modeling and potential consideration of impacts on clinic operations and of regional or national scale-up of the intervention [35,36]. Further prospective population-level modeling should be considered for future studies to understand broader lifetime expected population health profiles, and potential impacts of digital health interventions to support cervical cancer screening programs on resource-constrained health systems.

Our model was aligned with an RCT study aiming to assess the clinical consequences associated with using a mobile app compared to standard of care to aid clinicians and patients in disseminating and receiving information, respectively, about cervical cancer screening outcomes related to HPV testing and follow-up care recommendations. Plausible parameter ranges informed this analysis, and it will be important to reanalyze the intervention with trial-based results for critical parameters. The most critical ones are those related to whether women receiving the app-based mobile intervention are more likely to return and receive appropriate follow-up care.

Conclusion

This prospective clinico-economic model, constructed in conjunction with an ongoing HPV-based cervical cancer screening trial, aimed to assess the potential clinical and economic impact of an innovative mobile phone-based app to communicate outcomes and recommendations to patients in western and eastern Kenya. The analysis suggested that under plausible assumptions about patient behavior, disease epidemiology, treatment patterns, and costs from prior literature and expert elicitation, the proposed intervention would be cost-increasing in the short term but result in greater detection and treatment. Given the projected increase in detection and treatment, the mobile app intervention is expected—over an average woman’s lifetime—to be cost-saving for the health system while significantly improving patients’ life expectancy. Thus, a mobile-app-based intervention could be cost-effective and an economically “dominant” intervention(i.e., improving outcomes and reducing costs) and support achieving WHO cervical cancer disease elimination goals.

Supporting information

S1 Appendix. Screening visit and follow-up visit structure for prospective clinico-economic model.

(DOCX)

pone.0316001.s001.docx (26.6KB, docx)
S1 File. Supporting inclusivity in global research questionnaire.

(DOCX)

pone.0316001.s002.docx (65.6KB, docx)

Acknowledgments

We would like to thank and acknowledge the contributions of Kennedy Olweny and David Muyodi of the International Cancer Institute in Eldoret, Kenya for assistance with facilitating data collection, and Dr. Joseph Babigumira for helpful discussions related to the economic modeling approach and cancer screening processes in Africa.

Data Availability

All relevant data needed to replicate the modeling results in this paper are in the Table 1 (probabilities) and Table 2 (costs).

Funding Statement

Roche Diagnostics funded the economic analysis through a contract with Veritech Corporation, providing support for LPG, BWB, and FY.

References

  • 1.Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, et al. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health. 2020;8(2):e191–203. doi: 10.1016/S2214-109X(19)30482-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.World Health Organization. Cervical cancer country profiles 2021. Kenya Cervical Cancer Profile. [Google Scholar]
  • 3.Campos NG, Sharma M, Clark A, Lee K, Geng F, Regan C, et al. The health and economic impact of scaling cervical cancer prevention in 50 low- and lower-middle-income countries. Int J Gynaecol Obstet. 2017;138(Suppl 1):47–56. doi: 10.1002/ijgo.12184 [DOI] [PubMed] [Google Scholar]
  • 4.Lobin C, Orang’o EO, Were E, Muthoka K, Singh K, De Allegri M. Cost-effectiveness analysis of alternative screening strategies for the detection of cervical cancer among women in rural areas of Western Kenya. Int J Cancer. 2024;155(7):1257–67. doi: 10.1002/ijc.35036 [DOI] [PubMed] [Google Scholar]
  • 5.Page CM, Ibrahim S, Park LP, Huchko MJ. Patient factors affecting successful linkage to treatment in a cervical cancer prevention program in Kenya: a prospective cohort study. PLoS ONE. 2019;14(9):e0222750. doi: 10.1371/journal.pone.0222750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wanyoro A, Kabiru E. Use of mobile phone short text message service to enhance cervical cancer screening at Thika Level 5 hospital, Kiambu County, Kenya: a randomised controlled trial. Res Obst Gyn. 2017;5(1):10–20. [Google Scholar]
  • 7.Huchko MJ, Ibrahim S, Blat C, Cohen CR, Smith JS, Hiatt RA, et al. Cervical cancer screening through human papillomavirus testing in community health campaigns versus health facilities in rural western Kenya. Int J Gynaecol Obstet. 2018;141(1):63–9. doi: 10.1002/ijgo.12415 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Huchko MJ, Olwanda E, Choi Y, Kahn JG. HPV-based cervical cancer screening in low-resource settings: maximizing the efficiency of community-based strategies in rural Kenya. Int J Gynaecol Obstet. 2020;148(3):386–91. doi: 10.1002/ijgo.13090 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Salmani H, Ahmadi M, Shahrokhi N. The impact of mobile health on cancer screening: a systematic review. Cancer Inform. 2020;19:1176935120954191. doi: 10.1177/1176935120954191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chen J, Chu J, Marsh S, Shi T, Bullen C. Smartphone app-based interventions to support smoking cessation in smokers with mental health conditions: a systematic review. Psych. 2023;5(4):1077–100. doi: 10.3390/psych5040072 [DOI] [Google Scholar]
  • 11.National Institute for Clinical Excellence. Available from: https://www.nice.org.uk/guidance/ng183/chapter/Rationale-and-impact
  • 12.Osei E, Kuupiel D, Vezi PN, Mashamba-Thompson TP. Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa: a scoping review. BMC Med Inform Decis Mak. 2021;21(1):11. doi: 10.1186/s12911-020-01381-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Stocks J, Ibrahim S, Park L, Huchko M. Mobile phone ownership and use among women screening for cervical cancer in a community-based setting in western Kenya: observational study. JMIR Public Health Surveill. 2022;8(6):e28885. doi: 10.2196/28885 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Isaacson S, Adewumi K, Smith JS, Novak C, Oketch S, Huchko MJ. A qualitative exploration of barriers to treatment among HPV-positive women in a cervical cancer screening study in Western Kenya. Oncologist. 2023;28(1):e9–18. doi: 10.1093/oncolo/oyac208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Choi Y, Ibrahim S, Park LP, Bukusi EA, Huchko MJ. The impact of text message reminders on cryotherapy uptake among women testing positive for HPV in western Kenya: a prospective cohort study. BMC Womens Health. 2024;24(1):32. doi: 10.1186/s12905-023-02842-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Garrison LP Jr, Mansley EC, Abbott TA 3rd, Bresnahan BW, Hay JW, Smeeding J. Good research practices for measuring drug costs in cost-effectiveness analyses: a societal perspective: the ISPOR Drug Cost Task Force report—part II. Value Health. 2010;13(1):8–13. doi: 10.1111/j.1524-4733.2009.00660.x [DOI] [PubMed] [Google Scholar]
  • 17.Ministry of Health Kenya. Kenya national cancer screening guidelines 2019-2030. Nairobi; 2018. Nov. [Google Scholar]
  • 18.Karani LW, Musyoki S, Orina R, Nyamache AK, Khayeka-Wandabwa C, Nyagaka B. Human papillomavirus genotype profiles and cytological grades interlinkages in coinfection with HIV. Pan Afr Med J. 2020;35:67. Epub 2020/06/17. doi: 10.11604/pamj.2020.35.67.21539 ; PMCID: PMC7250203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Orang’o O, Tonui P, Muthoka K, Kiptoo S, Maina T, Agosa M, et al. A community-based approach to cervical cancer prevention in western Kenya: an AMPATH feasibility project. SAGE Open Med. 2022;10:20503121221102111. doi: 10.1177/20503121221102111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Sweet K, Bosire C, Sanusi B, Sherrod CJ, Kwatampora J, Waweru W, et al. Prevalence, incidence, and distribution of human papillomavirus types in female sex workers in Kenya. Int J STD AIDS. 2020;31(2):109–18. doi: 10.1177/0956462419884454 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ngugi CW, Schmidt D, Wanyoro K, Boga H, Wanzala P, Muigai A, et al. p16(INK4a)/Ki-67 dual stain cytology for cervical cancer screening in Thika district, Kenya. Infect Agent Cancer. 2015;10(1):25. doi: 10.1186/s13027-015-0020-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Orang’o EO, Were E, Rode O, Muthoka K, Byczkowski M, Sartor H, et al. Novel concepts in cervical cancer screening: a comparison of VIA, HPV DNA test and p16INK4a/Ki-67 dual stain cytology in Western Kenya. Infect Agent Cancer. 2020;15(1):57. doi: 10.1186/s13027-020-00323-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Huchko MJ, Saduma I, Blat C, Oketch S, Bukusi EA. How providing cervical cancer screening results via cell phone affects patient follow-up rates in Western Kenya. J Glob Oncol. 2019;5:1–8. doi: 10.1200/JGO.18.00264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Huchko MJ, Leslie H, Sneden J, Maloba M, Abdulrahim N, Bukusi EA, et al. Risk factors for cervical precancer detection among previously unscreened HIV-infected women in Western Kenya. Int J Cancer. 2014;134(3):740–5. doi: 10.1002/ijc.28401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Olwanda EE, Kahn JG, Choi Y, Islam JY, Huchko M. Comparison of the costs of HPV testing through community health campaigns versus home-based testing in rural Western Kenya: a microcosting study. BMJ Open. 2020;10(10):e033979. doi: 10.1136/bmjopen-2019-033979 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Shen J, Olwanda E, Kahn JG, Huchko MJ. Cost of HPV screening at community health campaigns (CHCs) and health clinics in rural Kenya. BMC Health Serv Res. 2018;18(1):378. doi: 10.1186/s12913-018-3195-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Vodicka EL, Babigumira JB, Mann MR, Kosgei RJ, Lee F, Mugo NR, et al. Costs of integrating cervical cancer screening at an HIV clinic in Kenya. Int J Gynaecol Obstet. 2017;136(2):220–8. doi: 10.1002/ijgo.12025 [DOI] [PubMed] [Google Scholar]
  • 28.Nelson S, Kim J, Wilson FA, Soliman AS, Ngoma T, Kahesa C, et al. Cost-effectiveness of screening and treatment for cervical cancer in Tanzania: implications for other sub-Saharan African countries. Value Health Reg Issues. 2016;10:1–6. doi: 10.1016/j.vhri.2016.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Subramanian S, Gakunga R, Kibachio J, Gathecha G, Edwards P, Ogola E, et al. Cost and affordability of non-communicable disease screening, diagnosis and treatment in Kenya: patient payments in the private and public sectors. PLoS ONE. 2018;13(1):e0190113. doi: 10.1371/journal.pone.0190113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Atieno OM, Opanga S, Martin A, Kurdi A, Godman B. Pilot study assessing the direct medical cost of treating patients with cancer in Kenya; findings and implications for the future. J Med Econ. 2018;21(9):878–87. doi: 10.1080/13696998.2018.1484372 [DOI] [PubMed] [Google Scholar]
  • 31.Stocks J, Choi Y, Ibrahim S, Huchko M. Iterative development of a mobile phone app to support community health volunteers during cervical cancer screening in western Kenya: qualitative study. JMIR Form Res. 2022;6(2):e27501. doi: 10.2196/27501 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Zimmermann MR, Vodicka E, Babigumira JB, Okech T, Mugo N, Sakr S, et al. Cost-effectiveness of cervical cancer screening and preventative cryotherapy at an HIV treatment clinic in Kenya. Cost Eff Resour Alloc. 2017;15(1):13. doi: 10.1186/s12962-017-0075-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Olwanda E, Shen J, Kahn JG, Bryant-Comstock K, Huchko MJ. Comparison of patient flow and provider efficiency of two delivery strategies for HPV-based cervical cancer screening in Western Kenya: a time and motion study. Glob Health Action. 2018;11(1):1451455. doi: 10.1080/16549716.2018.1451455 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Vodicka EL, Chung MH, Zimmermann MR, Kosgei RJ, Lee F, Mugo NR, et al. Estimating the costs of HIV clinic integrated versus non-integrated treatment of pre-cancerous cervical lesions and costs of cervical cancer treatment in Kenya. PLoS ONE. 2019;14(6):e0217331. doi: 10.1371/journal.pone.0217331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Canfell K. Towards the global elimination of cervical cancer. Papillomavirus Res. 2019;8:100170. doi: 10.1016/j.pvr.2019.100170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.World Health Organization. WHO framework for strengthening and scaling-up of services for the management of invasive cervical cancer. Geneva: Licence: CC BY-NC-SA 3.0 IGO. 2020. [Google Scholar]

Decision Letter 0

Edward Adekola Oladele

1 Oct 2024

PONE-D-24-32009A Prospective Model of the Potential Clinical and Economic Impact of

Cervical Cancer Screening Supported by a Mobile Phone AppPLOS ONE

Dear Dr. Garrison,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 15 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Edward Adekola Oladele, MD, MPH, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include a complete copy of PLOS’ questionnaire on inclusivity in global research in your revised manuscript. Our policy for research in this area aims to improve transparency in the reporting of research performed outside of researchers’ own country or community. The policy applies to researchers who have travelled to a different country to conduct research, research with Indigenous populations or their lands, and research on cultural artefacts. The questionnaire can also be requested at the journal’s discretion for any other submissions, even if these conditions are not met.  Please find more information on the policy and a link to download a blank copy of the questionnaire here: https://journals.plos.org/plosone/s/best-practices-in-research-reporting . Please upload a completed version of your questionnaire as Supporting Information when you resubmit your manuscript.

3. In the ethics statement in the Methods, you have specified that verbal consent was obtained. Please provide additional details regarding how this consent was documented and witnessed, and state whether this was approved by the IRB

4. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse.

5. Thank you for stating the following financial disclosure: Roche Diagnostics funded the economic analysis through a contract with Veritech Corporation, providing support for LPG, BWB, and FY.   

Please state what role the funders took in the study.  If the funders had no role, please state: ""The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."" 

If this statement is not correct you must amend it as needed. 

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper does a good job explaining the limitations of this type of modeling tool while also highlighting the important role played by preliminary models like this one. You also highlighted some additional data that would be useful for future studies.

Reviewer #2: The authors have picked an interesting topic - the use of mobile technology in the followup of women being screened for Cervical Cancer, an important health problem. The study has been generally well conducted but I would like to address the following.

1. A clear definition of the time horizon over which costs and consequences were accrued is lacking, which is different from the study period.

2. A clear definition of the consequences being considered is not outlined. It is summarized as clinical, workflow and guideline pathways which is inadequate.

3. Also the phrase " standardized to 2022 cost" is not appropriate. This is because are cost are adjusted to a particular year to account for inflation and there are different approaches which can be used and the choice of approach should be specified.

4. Discounting is not mentioned and the rationale for not doing so should be provided.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: Yes:  Meghan Steel

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

PLoS One. 2025 Jan 31;20(1):e0316001. doi: 10.1371/journal.pone.0316001.r002

Author response to Decision Letter 0

3 Dec 2024

Response to Reviewers

Reviewer's Responses to Questions and Our Responses to Reviewers.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Authors' response:

Thanks! We address the four specific questions raised by Reviewer #2 below.

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

Authors' response:

Thanks!

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Authors' response:

Thanks! The data are provided in the manuscript.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Authors' response:

Thanks! We appreciate this. We have corrected some minor errors that are visible in the marked-up version.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper does a good job explaining the limitations of this type of modeling tool while also highlighting the important role played by preliminary models like this one. You also highlighted some additional data that would be useful for future studies.

Authors' response:

Thanks! We appreciate your understanding that this is a prospective clinico-economic evaluation using modeling.

Reviewer #2: The authors have picked an interesting topic - the use of mobile technology in the follow up of women being screened for Cervical Cancer, an important health problem.

The study has been generally well conducted but I would like to address the following.

Authors' response:

Thanks! We appreciate these questions:

1. A clear definition of the time horizon over which costs and consequences were accrued is lacking, which is different from the study period.

Authors' response:

Thanks! The analytic time horizon is the period (less than one year) for a typical patient to complete the three visits. It is shorter than the study period. From a practical perspective, our modeling approach assessed “events” occurring or not occurring (such as follow-up visits), rather than specific number of days or weeks/months. Timing of second or third visits, for example, varied. This is a realistic scenario, particularly in a low-to-middle-income country setting. We have added this language on page 5, line 98 to make it clear: "(usually completed in less that 6 months)"

2. A clear definition of the consequences being considered is not outlined. It is summarized as clinical, workflow and guideline pathways which is inadequate.

Authors' response:

Thanks for this comment. We have revised the text on page 6 line 104-5 to address this, adding: " "Key consequences include intermediate endpoints (such as test results) and final endpoints (viz., pre-cancerous and cancer cases detected and treated)." And on page 7, line 128-9, adding: " such as cost-per-case detected and cost-per-cancer-case avoided)'.

3. Also the phrase " standardized to 2022 cost" is not appropriate. This is because are cost are adjusted to a particular year to account for inflation and there are different approaches which can be used and the choice of approach should be specified.

Authors' response:

Thanks for this comment. We agree that the word "standardized' is unclear. We have changed it to "estimated costs in 2022," which more accurately reflects our approach.

Medical cost finding for economic evaluation from a (limited) societal perspective is difficult in both developed and developing counties if the aim is to use opportunity costs for clinical and economic policy making. Prices in medical "markets" are affected by government price setting and insurance--both public or private.

For this prospective modelling exercise, we needed to have plausible estimates or assumptions for the model parameters. By necessity, we used cost information from multiple sources to generate plausible base-case assumptions and ranges. Thus, our cost estimates were "triangulated" from multiple input sources, including: prospective data collection at sites, literature-based estimates, and expert opinion at the primary clinical site of the principal investigator in Kenya.

The prospective cost-related data was collected in 2022 in multiple Kenyan sites, including with hospital and clinic administrators (as IRB approved). Thus, we report our costs as 2022 U.S. dollars, converted from Kenyan shillings. We also used a time-and-motion estimation approach in 2022 to collect supporting data related to resources used during visits, time, and costs.

We did not apply a specific formulaic inflation adjustment to the literature-based costs estimates for visits, tests, or therapies, which were published in various years. We applied "face-validity testing" with experts (clinical and economic) to our 2022 base-case cost estimates and to our ranges of costs. We assessed how our estimates compared with recent published ranges for variables similar to our model inputs for amounts and ranges of cost.

A challenge is that literature-based costs varied in the inputs included in various studies, depending on the research question being asked and addressed, and other regional or national differences. Also, unlike the availability in the United States for having annual medical care price change percentages, there is not a similar precise "medical care inflation-adjustor." In Kenya, general inflation data is available through the Kenya National Bureau of Statistics (KNBS), and healthcare-related components might be included in the broader Consumer Price Index (CPI). However, these components typically lack the granularity or specificity of a dedicated "medical care inflation" index, making it more challenging to precisely adjust healthcare costs annually.

4. Discounting is not mentioned and the rationale for not doing so should be provided.

Authors' response:

Thanks for spotting this omission. We have added this sentence on page 9, lines 180-1, to make address this: "Since the time horizon for the primary analysis is short-term (i.e., less than one year to complete the 3 visits), there is no strong need to perform discounting of costs or outcomes."

Inquiry under Journal Requirements

3. In the ethics statement in the Methods, you have specified that verbal consent was obtained. Please provide additional details regarding how this consent was documented and witnessed, and state whether this was approved by the IRB.

Authors' Response:

We have added on page 9, lines 174-7: "For the verbal consent process, the study was explained to the participants who were selected based on their expertise in the study topic, and once they agreed to participate, the consent was documented and the interview conducted. All informed consenting processes were approved by the IRB."

Attachment

Submitted filename: Response to Reviewers.docx

pone.0316001.s003.docx (25.7KB, docx)

Decision Letter 1

Edward Adekola Oladele

5 Dec 2024

A Prospective Model of the Potential Clinical and Economic Impact of

Cervical Cancer Screening Supported by a Mobile Phone App

PONE-D-24-32009R1

Dear Dr. Garrison,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager®  and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Edward Adekola Oladele, MD, MPH, PhD

Academic Editor

PLOS ONE

Acceptance letter

Edward Adekola Oladele

PONE-D-24-32009R1

PLOS ONE

Dear Dr. Garrison Jr.,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Edward Adekola Oladele

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix. Screening visit and follow-up visit structure for prospective clinico-economic model.

    (DOCX)

    pone.0316001.s001.docx (26.6KB, docx)
    S1 File. Supporting inclusivity in global research questionnaire.

    (DOCX)

    pone.0316001.s002.docx (65.6KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0316001.s003.docx (25.7KB, docx)

    Data Availability Statement

    All relevant data needed to replicate the modeling results in this paper are in the Table 1 (probabilities) and Table 2 (costs).

    Articles from PLOS ONE are provided here courtesy of PLOS

    RESOURCES