Cost-effectiveness analysis of a digital health-enabled non-communicable disease management intervention: Evidence from Ghana

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Technology- driven interventions present great potential solutions. The Akoma Pa initiative which integrates digital health solutions to improve early screening, management, and patient support was piloted. This study evaluates its cost-effectiveness, providing evidence to guide resource allocation and policy decisions for sustainable NCD care in Ghana. Methods: This study performed a cost-effectiveness analysis (CEA) of the Akoma Pa program versus conventional method of managing diabetes and hypertension in Christian Health Facilities in Ghana. The evaluation was conducted retrospectively from January to December 2023. Using mixed methods, data from 705 hypertensive and diabetic patients across 16 health facilities was analysed. Costs, health outcomes, and incremental cost-effectiveness ratios (ICERs) were assessed, with one-way sensitivity analyses to test the robustness of the results. Results: The Akoma Pa intervention significantly improved patient outcomes and demonstrated strong cost-effectiveness. Compared to conventional care, it achieved higher follow-up rates (89% vs. 36.5%), greater reductions in HbA1c (-2.10% vs. -1.68%), fasting blood glucose (-2.21 mmol/L vs. -1.89 mmol/L), and systolic blood pressure (-54.38 mmHg vs. -51.88 mmHg). Negative ICER values confirmed cost savings, including HbA1c (-$51,043.79), fasting blood glucose (-$66,994.97), and systolic blood pressure (-$8,575.36). Sensitivity analysis reinforced the robustness of these findings. Conclusion: The Akoma Pa program shows that digital health interventions are not only clinically effective but also economically beneficial. These findings support adoption of digital health solutions to improve health outcomes and promote economic sustainability in healthcare systems. Cost-effectiveness digital health non-communicable disease Akoma Pa Figures Figure 1 Figure 2 Figure 3 Background Diabetes and hypertension are the most prevalent non-communicable diseases (NCDs) in Ghana, significantly contributing to the country’s health burden. According to a World Health Organization (WHO) report, 3.2 million Ghanaians aged 30–79 years had been diagnosed with hypertension by 2019; with only 19% controlled [ 1 ]. The prevalence of diabetes has also risen sharply to about 9% as of 2022. Ghana Health Service records an average of 200,000 cases of diabetes reported to health facilities annually [ 2 ]. This trend is expected to continue. These figures highlight the pressing need for effective strategies to manage NCDs in Ghana. The economic burden of diabetes and hypertension in Ghana extends beyond healthcare, significantly impacting households and the broader economy. Studies reveal that managing these chronic conditions leads to substantial out-of-pocket expenditures, worsening financial risk protection for families [ 3 , 4 ]. Households with members living with NCDs face 49% higher healthcare costs compared to healthier households [ 5 ]. The average monthly healthcare management cost for type 2 diabetes patients with complications is estimated at US $ 38.68, with direct costs constituting 94% of total expenses [ 6 ]. Conventional approaches to NCD management often focus on reactive care, such as glucose regulation, insulin control, and medication adherence [ 7 ]. While these strategies provide some benefits, they may fall short of addressing the broader metabolic and psychosocial challenges associated with NCDs [ 8 , 9 ]. Moreover, these methods rely heavily on paper-based records, in-person consultations, and patient self- discipline, which can be particularly challenging in resource-limited settings. Barriers such as frequent hospital visits, high healthcare costs, inefficiencies, ineffectiveness, low value for money, delay in care delivery, and difficulty adhering to prescribed lifestyle changes further complicate NCD management [ 10 ]. Digital health technologies offer a promising alternative to conventional approaches. These technologies can enhance NCD management by reducing costs through fewer complications, hospitalizations, and avoid unnecessary consultations; improving adherence through personalized feedback, reminders, and educational materials; and expanding access to care, particularly in remote areas [ 11 ]. Beyond clinical management, digital health can promote health literacy and empower patients to take active roles in their care [ 12 ]. Also, digital health strategies offer significant economic advantages in healthcare. They can lead to cost savings through streamlined processes and optimized resource allocation [ 13 ]. However, despite these potential benefits, questions remain regarding the cost-effectiveness of these interventions compared to conventional methods. Cost-effectiveness analysis (CEA) is essential for informed decision-making in healthcare, particularly in resource-constrained settings like Ghana. CEA systematically evaluates the relative costs and effects of interventions. By bringing clinical and economic analyses together, CEA ensures comprehensive analysis and support multidisciplinary consultations to guide policymakers for evidence-based and evidence informed allocation of resources efficiently and sustainably [ 14 , 15 ]. In Ghana, the cost-saving potential of telemedicine for basic healthcare delivery, has been demonstrated with an ICER of - $ 453.70 for each avoidable unnecessary referral [ 16 ]. However, the cost-effectiveness of digital health interventions tailored specifically to NCD management remains underexplored. The need for effective and sustainable healthcare solutions is further underscored by Ghana’s systemic health funding challenges. Health sector allocation as a percentage of government expenditure has declined from 8.1% in 2019 to a projected 6.4% in 2025, failing to meet the Abuja Declaration target of 15% [ 17 ]. Volatile donor funding and reduced contributions from oil revenues exacerbate these challenges, leaving Ghana with health spending as a percentage of GDP below the lower-middle-income country average threshold of 2.3%. This financial instability highlights the importance of cost-effective interventions that can optimize limited resources while improving health outcomes. This study sought to conduct a cost- effectiveness analysis of Akoma Pa, a digital health-enabled NCD management program versus conventional management of NCDs to inform large scale roll out of the Akoma Pa Digital Health initiative in Ghana. Methods Health Economic Analysis A health economic analysis plan was developed prior to the study to guide the evaluation of the Akoma Pa program’s cost-effectiveness. The plan outlined the methods for cost analysis, outcome measurement, and the calculation of the Incremental Cost-Effectiveness Ratio (ICER). It also detailed the sensitivity analysis to assess the robustness of the findings. The plan was aligned with the study’s objectives and adhered to standard health economic evaluation frameworks. Description of the Akoma Pa Intervention The Akoma Pa program, launched in 2021 by CHAG, Novartis, Medtronic LABS, and GIZ, aims to address critical gaps in conventional non-communicable disease (NCD) care by expanding access to integrated diabetes and hypertension management across 85 faith-based healthcare facilities in nine regions of Ghana [ 18 ]. The program targets low-income and underserved populations, enhancing early screening, clinical management, and patient support through digital health solutions and capacity-building for 2,231 healthcare professionals. Medtronic LABS supports digital patient tracking and remote monitoring, while Novartis SSA provides fully subsidized medications, reducing financial barriers to care [ 18 ]. In this study, eight health facilities in the Central Region were purposively selected for their participation in the Akoma Pa program, with the remaining facilities serving as control sites for comparative analysis. The study compared the Akoma Pa program, an integrated diabetes and hypertension management initiative, with standard care provided at Christian Health Association of Ghana (CHAG) facilities. The Akoma Pa program included tele-counselling, prescription refill reminders, medication adherence counselling, and patient support groups, which were absent in standard care. The comparator was chosen to evaluate the incremental benefits and costs of the Akoma Pa program relative to conventional care practices. Data Collection and Discounting The study adopted a healthcare system perspective, focusing on costs incurred by healthcare providers and the system, including direct medical, direct non-medical, and indirect costs. This perspective was chosen to capture the full economic impact of the Akoma Pa program on the healthcare system and to inform decision-making for scaling up the intervention. The time horizon for the study was one year (January to December 2023), reflecting the period over which data was collected retrospectively. This timeframe was deemed appropriate to capture short- to medium- term health outcomes and costs associated with the intervention. A discount rate of 3% was applied to costs and outcomes, consistent with standard health economic evaluation practices. A sensitivity analysis was conducted using a 5% discount rate to test the robustness of the results. Outcomes The primary outcomes included clinical measures such as reductions in HbA1c, fasting blood sugar (FBS), systolic blood pressure (SBP), and body mass index (BMI). Secondary outcomes included patient follow- up rates, medication adherence, and quality-adjusted life years (QALYs). These outcomes were selected to capture both the clinical effectiveness and broader health benefits of the intervention. Clinical outcomes were measured using electronic health records and primary data collected via the Kobo Toolbox. Health indicators such as BMI, FBS, SBP, and HbA1c levels were recorded at baseline and follow-up. Patient engagement and follow-up rates were tracked using digital health tools, including tele- counselling and reminders. Outcomes were valued using standard health economic methods. QALYs were estimated based on improvements in clinical outcomes and patient-reported quality of life. The population used for valuation was the study cohort, with methods aligned with WHO guidelines for cost-effectiveness analysis. Cost (Effect) Analysis Costs were categorized into direct medical (e.g., medications, diagnostics, healthcare visits), direct non- medical (e.g., transportation, caregiver expenses), and indirect costs (e.g., productivity losses calculated using the human capital approach). Costs were measured retrospectively from healthcare records and program expenditure reports. Costs were reported in US dollars (USD) based on 2023 prices. Currency conversion was performed using the average exchange rate for the study period. No modelling was used in this study. The analysis was based on empirical data collected from the intervention and control groups. Descriptive statistics were used to summarize sociodemographic and clinical data. The ICER was calculated to compare incremental costs and health outcomes between the intervention and control groups. Sensitivity analysis was conducted to test the robustness of the results under varying assumptions, such as different discount rates. Estimation of Cost-Effectiveness The Incremental Cost-Effectiveness Ratio (ICER) was calculated to compare the incremental costs of the Akoma Pa program against its incremental health benefits. The ICER was derived by dividing the difference in costs between the intervention and control groups by the difference in health outcomes. This metric provided insights into the program’s cost-effectiveness relative to standard care. Sensitivity Analysis Uncertainty was addressed through one-way sensitivity analysis, which tested the impact of varying key parameters (e.g., cost estimates, discount rates) on the results. The analysis confirmed the robustness of the findings across different scenarios. Results Sociodemographic characteristics of patients The study included 367 patients from the Akoma Pa intervention sites and 338 patients from the comparator CHAG sites. The average age of patients at the Akoma Pa facilities was 53 years, with 25% aged over 65, compared to an average age of 61 years at the comparator sites, where 42% were over 65. The majority of patients were female at both sites (51% at Akoma Pa and 77% at comparator sites). Employment status varied, with 47% of Akoma Pa patients employed, compared to 51% retired at the comparator sites. Educational attainment was higher in the comparator group, with 48% having completed tertiary education, compared to 28% at Akoma Pa facilities (see Table 1 ). Table 1 Sociodemographic characteristics of patients Variable Akoma Pa n = 367 (%) Conventional n = 338 (%) Age 53 (29, 79) 61 (30, 90) 24–35 55 (15) 35 (10) 36–45 68 (19) 28 (8) 46–55 74 (20) 62 (18) 56–65 77 (21) 72 (21) Above 65 93 (25) 141 (42) Gender Female 186 (51) 261 (77) Male 181 (49) 77 (23) Highest level of education Primary 97 (26) 68 (20) Secondary 81 (22) 84 (25) Tertiary 100 (28) 163 (48) No formal education 89 (24) 23 (7) Marital status Married 87 (24) 87 (26) Divorced 96 (26) 91 (27) Single 96 (26) 84 (25) Widowed 88 (24) 76 (22) Current employment status Employed 173 (47) 94 (28) Retired 30 (9) 174 (51) Self-employed 76 (20) 0 (0) Unemployed 88 (24) 70 (21) Clinical characteristics of patients At the Akoma Pa sites, 62% of patients were diagnosed with hypertension alone, while 5% had diabetes only, and 3% had both conditions. In contrast, the comparator group had 45% hypertensives, 7% diabetics, and 35% with both conditions. All patients in the Akoma Pa group received tele-counselling, prescription refill reminders, and medication adherence counselling, which were absent in the comparator group. Additionally, 82% of Akoma Pa patients participated in patient support groups (see Table 2 ). Table 2 Clinical characteristics of patients Variable Akoma Pa (n = 367) Conventional (n = 338) Number of Hypertensives only 229 174 Number of Diabetics only 18 29 Both Hypertension and Diabetes 12 135 Received Tele counselling 367 0 Prescription Refill reminders 367 0 Medication Adherence Counselling 367 0 Patient support group 300 0 Cost Analysis The total cost of implementing the Akoma Pa intervention across eight facilities was $ 508,443.89 (see Table 3), compared to $ 529,882.28 for the comparator sites (see Table 4). The primary cost drivers for the Akoma Pa intervention included initial technology investments, personnel training, and operational costs related to tele-counselling services. However, these costs were offset by reductions in hospitalization rates, fewer in-person clinic visits, and lower complication rates among patients. [insert Tables 3 and 4 here] Effectiveness and cost effectiveness Clinical effectiveness The Akoma Pa intervention significantly improved diabetes and hypertension management outcomes. Participants achieved notable reductions in HbA1c levels (mean reduction of 2.10% vs. 1.68% in the comparator group) and fasting blood glucose (mean reduction of 2.21 mmol/L vs. 1.89 mmol/L). Systolic blood pressure reduction was also greater in the intervention group (54.38 mmHg vs. 51.88 mmHg). The intervention group showed a higher percentage of patients with controlled hypertension (58.9% vs. 23.2% in the comparator group) and better glycaemic control (85.4% vs. 31.7%) (see Table 5 ). Table 5 Health effects per health outcome (12 months follow-up period) Health outcome Intervention group Comparator group Mean systolic reduction 54.38mmHg 51.88mmHg Mean fasting blood sugar reduction 2.21mmol/L 1.89mmol/L Mean hba1c reduction 2.10% 1.68% Mean BMI reduction 4.42kg/m2 0.61kg/m2 Percentage of patients followed up 89% 36.5% Diastolic blood pressure reduction 0mmHg 27.1mmHg Cost-effectiveness The ICER for the Akoma Pa intervention demonstrated cost-effectiveness across multiple health outcomes. The ICER for systolic blood pressure reduction was - $ 8,575.36, for fasting blood sugar reduction was - $ 66,994.97, and for HbA1c reduction was - $ 51,043.79. The intervention also showed cost savings in terms of body mass index (BMI) reduction (ICER: - $ 5,626.87) and patient follow-up rates (ICER: - $ 40,835.03) (see Fig. 1 ). The program’s cost per Quality-Adjusted Life Year (QALY) gained was below the WHO threshold of $ 50,000. Sensitivity analysis A one-way sensitivity analysis confirmed the robustness of the cost-effectiveness results. The ICER values remained consistent across different discount rates (3% and 5%), indicating that the intervention’s cost- effectiveness was not significantly affected by changes in discounting assumptions. The most cost-effective scenario was observed for fasting blood sugar reduction, with an ICER of - $ 66,994.97 (see Fig. 2 ). ICER was recalculated after applying the discount rates. This analysis helps to identify which scenarios result in the highest or lowest cost-effectiveness of the intervention. The more negative the ICER, the more cost- effective the intervention is under that scenario. Digital Technology and Follow-Up Rates and Patient Engagement The Akoma Pa intervention significantly improved patient follow-up rates, with 89% of patients in the intervention group completing follow-up visits, compared to 36.5% in the comparator group. This improvement was attributed to the use of digital health tools, including tele-counselling and automated reminders, which enhanced patient engagement and adherence to treatment plans (see Fig. 3 ). Discussion Diabetes and hypertension are among the most prevalent non-communicable diseases (NCDs) globally, with a disproportionate burden in low- and middle-income countries (LMICs) such as Ghana. These conditions contribute significantly to morbidity, mortality, and healthcare expenditures, exacerbating the strain on already resource-constrained health systems. Despite the growing prevalence of diabetes and hypertension, management remains suboptimal in many LMICs, characterized by low control rates, fragmented care, and high out-of-pocket expenditures for patients. This study evaluated the effectiveness and cost-effectiveness of the Akoma Pa program, a digital health intervention designed to improve the management of hypertension and diabetes in Ghana. By leveraging digital tools such as telemonitoring and continuous patient engagement, the program aims to enhance clinical outcomes while reducing healthcare costs. The findings offer valuable insights for scaling digital health interventions in LMICs, with implications for global NCD management strategies. The incremental cost-effectiveness ratio (ICER) analysis of the Akoma Pa program reveals significant economic and clinical benefits, which are particularly relevant in resource-limited settings. The ICER for systolic blood pressure reduction, indicates cost savings rather than additional expenditure. This suggests that each millimetre of mercury reduction in systolic blood pressure achieved through the Akoma Pa program is more cost-effective than conventional methods. The negative ICER underscores the program’s dual advantage: it not only improves clinical outcomes but also reduces the need for higher-cost interventions such as hospitalizations or emergency care. This finding aligns with global evidence demonstrating the potential of digital health interventions to enhance hypertension management while optimizing resource allocation in healthcare systems [ 19 ]. Similarly, the ICER for HbA1c reduction highlights the program’s role in improving diabetes management. HbA1c, a key indicator of long-term blood glucose control, is critical for preventing diabetes-related complications. The substantial reduction in HbA1c at a lower cost suggests that the Akoma Pa program facilitates more effective diabetes management, potentially reducing reliance on costly medications and interventions for complications. This finding is consistent with global studies that have demonstrated the therapeutic efficacy of digital health solutions in managing diabetes [ 20 – 22 ]. Furthermore, economic evaluations of digital health interventions, including telemonitoring, SMS-based interventions, and remote consultations, have consistently shown positive impacts on both clinical outcomes and cost savings [ 23 , 24 ]. These results underscore the potential of digital health interventions to address the growing burden of diabetes in LMICs, where access to specialized care is often limited. The ICER for body mass index (BMI) reduction introduces another critical dimension to the program’s impact. Given the high comorbidity of obesity with both diabetes and hypertension, effective weight management is essential for improving overall health outcomes. The negative ICER for BMI reduction suggests that the Akoma Pa program is more cost-effective in managing weight compared to conventional methods, likely due to enhanced patient engagement with dietary and exercise counselling facilitated by digital tools. This finding is supported by a growing body of evidence demonstrating the positive effects of telemonitoring and tele-screening on glycaemic control, weight management, and physical activity [ 25 – 27 ]. These results highlight the potential of digital health interventions to address multiple risk factors simultaneously, offering a holistic approach to NCD management. The ICER for patient follow-up further underscores the program’s ability to sustain long-term patient engagement, a critical factor for the successful management of chronic conditions. The Akoma Pa program not only supports continuous patient interaction but also offers substantial economic advantages by reducing healthcare costs. This finding is particularly relevant in LMICs, where patient retention and adherence to treatment plans are often challenging due to financial, geographic, and systemic barriers. Previous studies have consistently highlighted the therapeutic benefits of digital health interventions in improving patient adherence and health outcomes, ultimately contributing to better disease management and reduced long-term healthcare expenditures [ 20 – 22 ]. The findings of this study have significant implications for global health, particularly in LMICs grappling with the dual burden of infectious diseases and NCDs. The Akoma Pa program’s cost-effectiveness and clinical effectiveness demonstrate that scaling up digital health interventions could alleviate the economic burden of NCDs while improving patient outcomes. Policymakers in LMICs should consider integrating digital health solutions into national NCD management strategies, particularly in settings where healthcare resources are limited. Investments in digital infrastructure, capacity building for healthcare providers, and patient education could further enhance the impact of such interventions. Moreover, the success of the Akoma Pa program offers a replicable model for other LMICs facing similar challenges in NCD management. By leveraging widely accessible technologies such as mobile phones and telemonitoring, digital health interventions can bridge gaps in healthcare delivery, particularly in rural and underserved areas. Global health organizations and funding agencies should prioritize support for the development and implementation of digital health solutions as part of comprehensive NCD control strategies. Strengths and limitations This study offers valuable evidence on the effectiveness and cost-effectiveness of digital health interventions for chronic disease management. This study was limited to the Central Region of Ghana, which may not fully represent other populations. The use of retrospective data collection could have introduced the potential for reporting biases. The study’s findings’ generalizability is inhibited by the purposive sampling strategy used. Despite these limitations, the study offers valuable evidence on the effectiveness of digital health interventions for chronic disease management. Conclusion The Akoma Pa program represents a promising approach to addressing the growing burden of diabetes and hypertension in Ghana and other LMICs. Its cost-effectiveness, coupled with its ability to improve clinical outcomes, underscores the potential of digital health interventions to transform NCD management in resource-constrained settings. As the global health community strives to achieve universal health coverage and the Sustainable Development Goals (SDGs), scaling up innovative solutions like the Akoma Pa program will be critical to reducing the burden of NCDs and improving health equity worldwide. Abbreviations BMI - Body Mass Index CEA - Cost Effectiveness Analysis CHAG - Christian Health Association of Ghana FBS - Fasting Blood Sugar ICER - Incremental Cost Effectiveness Ratio LMIC - Low- and Middle-Income country NCDs - Non-Communicable Diseases QALYs - Quality-Adjusted Life Years SBP - Systolic Blood Pressure SDGs - Sustainable Development Goals WHO - World Health Organization Declarations Ethical approval and consent to participate The study received ethical approval from the Committee on Human Research Publication and Ethics (CHRPE/AP/695/24) at Kwame Nkrumah University of Science and Technology. The methodology employed in this study followed the principles of the Helsinki Declaration. Participant anonymity and confidentiality were preserved throughout the study. Informed consent was obtained from all individual participants included in the study. Consent for publication Not applicable. Data availability statement All data generated or analysed during this study are included in this article. Competing interest The authors declare that they have no competing interests. Funding No funding was received for conducting this study. Author Contributions Conceptualization: AAO and RAB; Methodology: AAO, RAB and PN; Data Curation: AAO and TYAE; Formal analysis: AAO, RAB and TYAE; Writing initial draft: RAB; Reviewing and editing: AAO, RAB, TYAE, PN and PAB; Supervision: PAB. All authors read and approved the final manuscript. Acknowledgement We acknowledge the support of the management of CHAG and the management of the various health facilities in the Central region that were involved in the study. With their permission, we have recognized their contributions in this manuscript. Approach to Engagement The study engaged patients, healthcare providers, and stakeholders in the design and implementation of the Akoma Pa program. 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Lu HY, Ding X, Hirst JE, Yang Y, Yang J, Mackillop L, et al. Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes. IEEE Rev Biomed Eng. 2023;17:98–117. Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Cost Effectiveness and Resource Allocation → Version 1 posted Editorial decision: Revision requested 03 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 23 May, 2025 Editor assigned by journal 10 May, 2025 Submission checks completed at journal 10 May, 2025 First submitted to journal 01 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6574366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461071648,"identity":"329f7524-5a38-4764-9b42-65a6db8d7831","order_by":0,"name":"Appiah Akwasi Obeng","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Appiah","middleName":"Akwasi","lastName":"Obeng","suffix":""},{"id":461071649,"identity":"e123ced8-138c-4117-8f13-bedeba76e2ad","order_by":1,"name":"Richard Abeiku Bonney","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAwhlgyQkAcQPQOIH8GpJQ+KDtCQQ1nIYXQsDbi3m7L3HHnxsOy/PP+3sMckfDH8SG6Sbj31IKLjDwHe8AasWy55z6YYz224bzridlybNw2CQ2CBzLHlGgsEzBskz2K0xuJFjJs3bdjuB4TaQAeQn7r+RYwz0y2GgVAJuLX/bziXIA7UAHQa0RSL/M0TL/Qe4tTC2HUgwAGqRADtMIocZagt271v2nDGT7DmXbLjxdl6yNY+BsTHQL2CH8Uiewe4wc/YeM4kfZXbycrdzD978USEnCwyxxwwf/hyW4zuO3ftgwMgGInkY4NEE4+IBf4hQMwpGwSgYBSMXAADL9l5Sd6Ai7AAAAABJRU5ErkJggg==","orcid":"","institution":"Technische Universität Berlin","correspondingAuthor":true,"prefix":"","firstName":"Richard","middleName":"Abeiku","lastName":"Bonney","suffix":""},{"id":461071650,"identity":"74313d63-e6aa-4e3d-8445-e8a7539a8ec5","order_by":2,"name":"Thomas Yaw Ayensu Essel","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"Yaw Ayensu","lastName":"Essel","suffix":""},{"id":461071651,"identity":"503d0772-36a1-466d-ac37-97f9dee4338f","order_by":3,"name":"Paulina Nyamekye","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Nyamekye","suffix":""},{"id":461071652,"identity":"2852c3ef-7843-488d-9c3a-ba7b5c092758","order_by":4,"name":"Peter Agyei-Baffour","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Agyei-Baffour","suffix":""}],"badges":[],"createdAt":"2025-05-01 22:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6574366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6574366/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12962-026-00733-0","type":"published","date":"2026-02-27T15:59:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83478993,"identity":"fdf73f4c-cbff-4395-a1a1-cd8ddeb2fff3","added_by":"auto","created_at":"2025-05-27 06:07:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSensitivity analysis of health outcomes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6574366/v1/128c353459be5813028064df.jpg"},{"id":83478997,"identity":"a09e31bd-73fb-4362-83f4-b0fe7a415f29","added_by":"auto","created_at":"2025-05-27 06:07:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSensitivity analysis at 3% and 5% discount rate\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6574366/v1/85f4f3627facce1048548f75.jpg"},{"id":83478994,"identity":"2b1d7f3f-059c-44df-ba07-8a95ad9bb644","added_by":"auto","created_at":"2025-05-27 06:07:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMonthly follow-up rates comparison\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6574366/v1/69093080d8400da0d2dcdb1a.jpg"},{"id":103766371,"identity":"206c9dd0-fa2e-4b2b-b1a5-f80c46f6181c","added_by":"auto","created_at":"2026-03-02 16:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1047692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6574366/v1/4455d842-8399-40b0-94e6-27a039b23b6a.pdf"},{"id":83479651,"identity":"9348b649-7232-4ac0-aa0f-bb368c97498c","added_by":"auto","created_at":"2025-05-27 06:23:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":221918,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6574366/v1/a49350ba49038ff2fd1575cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-effectiveness analysis of a digital health-enabled non-communicable disease management intervention: Evidence from Ghana","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes and hypertension are the most prevalent non-communicable diseases (NCDs) in Ghana, significantly contributing to the country\u0026rsquo;s health burden. According to a World Health Organization (WHO) report, 3.2\u0026nbsp;million Ghanaians aged 30\u0026ndash;79 years had been diagnosed with hypertension by 2019; with only 19% controlled [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of diabetes has also risen sharply to about 9% as of 2022. Ghana Health Service records an average of 200,000 cases of diabetes reported to health facilities annually [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This trend is expected to continue. These figures highlight the pressing need for effective strategies to manage NCDs in Ghana. The economic burden of diabetes and hypertension in Ghana extends beyond healthcare, significantly impacting households and the broader economy. Studies reveal that managing these chronic conditions leads to substantial out-of-pocket expenditures, worsening financial risk protection for families [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Households with members living with NCDs face 49% higher healthcare costs compared to healthier households [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The average monthly healthcare management cost for type 2 diabetes patients with complications is estimated at US\u003cspan\u003e$\u003c/span\u003e38.68, with direct costs constituting 94% of total expenses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional approaches to NCD management often focus on reactive care, such as glucose regulation, insulin control, and medication adherence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While these strategies provide some benefits, they may fall short of addressing the broader metabolic and psychosocial challenges associated with NCDs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, these methods rely heavily on paper-based records, in-person consultations, and patient self- discipline, which can be particularly challenging in resource-limited settings. Barriers such as frequent hospital visits, high healthcare costs, inefficiencies, ineffectiveness, low value for money, delay in care delivery, and difficulty adhering to prescribed lifestyle changes further complicate NCD management [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDigital health technologies offer a promising alternative to conventional approaches. These technologies can enhance NCD management by reducing costs through fewer complications, hospitalizations, and avoid unnecessary consultations; improving adherence through personalized feedback, reminders, and educational materials; and expanding access to care, particularly in remote areas [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Beyond clinical management, digital health can promote health literacy and empower patients to take active roles in their care [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Also, digital health strategies offer significant economic advantages in healthcare. They can lead to cost savings through streamlined processes and optimized resource allocation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, despite these potential benefits, questions remain regarding the cost-effectiveness of these interventions compared to conventional methods.\u003c/p\u003e \u003cp\u003eCost-effectiveness analysis (CEA) is essential for informed decision-making in healthcare, particularly in resource-constrained settings like Ghana. CEA systematically evaluates the relative costs and effects of interventions. By bringing clinical and economic analyses together, CEA ensures comprehensive analysis and support multidisciplinary consultations to guide policymakers for evidence-based and evidence informed allocation of resources efficiently and sustainably [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Ghana, the cost-saving potential of telemedicine for basic healthcare delivery, has been demonstrated with an ICER of -\u003cspan\u003e$\u003c/span\u003e453.70 for each avoidable unnecessary referral [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the cost-effectiveness of digital health interventions tailored specifically to NCD management remains underexplored.\u003c/p\u003e \u003cp\u003eThe need for effective and sustainable healthcare solutions is further underscored by Ghana\u0026rsquo;s systemic health funding challenges. Health sector allocation as a percentage of government expenditure has declined from 8.1% in 2019 to a projected 6.4% in 2025, failing to meet the Abuja Declaration target of 15% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Volatile donor funding and reduced contributions from oil revenues exacerbate these challenges, leaving Ghana with health spending as a percentage of GDP below the lower-middle-income country average threshold of 2.3%. This financial instability highlights the importance of cost-effective interventions that can optimize limited resources while improving health outcomes. This study sought to conduct a cost- effectiveness analysis of Akoma Pa, a digital health-enabled NCD management program versus conventional management of NCDs to inform large scale roll out of the Akoma Pa Digital Health initiative in Ghana.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHealth Economic Analysis\u003c/h2\u003e \u003cp\u003eA health economic analysis plan was developed prior to the study to guide the evaluation of the Akoma Pa program\u0026rsquo;s cost-effectiveness. The plan outlined the methods for cost analysis, outcome measurement, and the calculation of the Incremental Cost-Effectiveness Ratio (ICER). It also detailed the sensitivity analysis to assess the robustness of the findings. The plan was aligned with the study\u0026rsquo;s objectives and adhered to standard health economic evaluation frameworks.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescription of the Akoma Pa Intervention\u003c/h3\u003e\n\u003cp\u003eThe Akoma Pa program, launched in 2021 by CHAG, Novartis, Medtronic LABS, and GIZ, aims to address critical gaps in conventional non-communicable disease (NCD) care by expanding access to integrated diabetes and hypertension management across 85 faith-based healthcare facilities in nine regions of Ghana [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The program targets low-income and underserved populations, enhancing early screening, clinical management, and patient support through digital health solutions and capacity-building for 2,231 healthcare professionals. Medtronic LABS supports digital patient tracking and remote monitoring, while Novartis SSA provides fully subsidized medications, reducing financial barriers to care [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, eight health facilities in the Central Region were purposively selected for their participation in the Akoma Pa program, with the remaining facilities serving as control sites for comparative analysis.\u003c/p\u003e \u003cp\u003eThe study compared the Akoma Pa program, an integrated diabetes and hypertension management initiative, with standard care provided at Christian Health Association of Ghana (CHAG) facilities. The Akoma Pa program included tele-counselling, prescription refill reminders, medication adherence counselling, and patient support groups, which were absent in standard care. The comparator was chosen to evaluate the incremental benefits and costs of the Akoma Pa program relative to conventional care practices.\u003c/p\u003e\n\u003ch3\u003eData Collection and Discounting\u003c/h3\u003e\n\u003cp\u003eThe study adopted a healthcare system perspective, focusing on costs incurred by healthcare providers and the system, including direct medical, direct non-medical, and indirect costs. This perspective was chosen to capture the full economic impact of the Akoma Pa program on the healthcare system and to inform decision-making for scaling up the intervention.\u003c/p\u003e \u003cp\u003eThe time horizon for the study was one year (January to December 2023), reflecting the period over which data was collected retrospectively. This timeframe was deemed appropriate to capture short- to medium- term health outcomes and costs associated with the intervention.\u003c/p\u003e \u003cp\u003eA discount rate of 3% was applied to costs and outcomes, consistent with standard health economic evaluation practices. A sensitivity analysis was conducted using a 5% discount rate to test the robustness of the results.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes included clinical measures such as reductions in HbA1c, fasting blood sugar (FBS), systolic blood pressure (SBP), and body mass index (BMI). Secondary outcomes included patient follow- up rates, medication adherence, and quality-adjusted life years (QALYs). These outcomes were selected to capture both the clinical effectiveness and broader health benefits of the intervention.\u003c/p\u003e \u003cp\u003eClinical outcomes were measured using electronic health records and primary data collected via the Kobo Toolbox. Health indicators such as BMI, FBS, SBP, and HbA1c levels were recorded at baseline and follow-up. Patient engagement and follow-up rates were tracked using digital health tools, including tele- counselling and reminders.\u003c/p\u003e \u003cp\u003eOutcomes were valued using standard health economic methods. QALYs were estimated based on improvements in clinical outcomes and patient-reported quality of life. The population used for valuation was the study cohort, with methods aligned with WHO guidelines for cost-effectiveness analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCost (Effect) Analysis\u003c/h2\u003e \u003cp\u003eCosts were categorized into direct medical (e.g., medications, diagnostics, healthcare visits), direct non- medical (e.g., transportation, caregiver expenses), and indirect costs (e.g., productivity losses calculated using the human capital approach). Costs were measured retrospectively from healthcare records and program expenditure reports.\u003c/p\u003e \u003cp\u003eCosts were reported in US dollars (USD) based on 2023 prices. Currency conversion was performed using the average exchange rate for the study period.\u003c/p\u003e \u003cp\u003eNo modelling was used in this study. The analysis was based on empirical data collected from the intervention and control groups.\u003c/p\u003e \u003cp\u003eDescriptive statistics were used to summarize sociodemographic and clinical data. The ICER was calculated to compare incremental costs and health outcomes between the intervention and control groups. Sensitivity analysis was conducted to test the robustness of the results under varying assumptions, such as different discount rates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstimation of Cost-Effectiveness\u003c/h3\u003e\n\u003cp\u003eThe Incremental Cost-Effectiveness Ratio (ICER) was calculated to compare the incremental costs of the Akoma Pa program against its incremental health benefits. The ICER was derived by dividing the difference in costs between the intervention and control groups by the difference in health outcomes. This metric provided insights into the program\u0026rsquo;s cost-effectiveness relative to standard care.\u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eUncertainty was addressed through one-way sensitivity analysis, which tested the impact of varying key parameters (e.g., cost estimates, discount rates) on the results. The analysis confirmed the robustness of the findings across different scenarios.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics of patients\u003c/h2\u003e \u003cp\u003eThe study included 367 patients from the Akoma Pa intervention sites and 338 patients from the comparator CHAG sites. The average age of patients at the Akoma Pa facilities was 53 years, with 25% aged over 65, compared to an average age of 61 years at the comparator sites, where 42% were over 65. The majority of patients were female at both sites (51% at Akoma Pa and 77% at comparator sites). Employment status varied, with 47% of Akoma Pa patients employed, compared to 51% retired at the comparator sites. Educational attainment was higher in the comparator group, with 48% having completed tertiary education, compared to 28% at Akoma Pa facilities (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAkoma Pa n\u0026thinsp;=\u0026thinsp;367 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConventional n\u0026thinsp;=\u0026thinsp;338 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e53 (29, 79)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e61 (30, 90)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261 (77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest level of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent employment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of patients\u003c/h2\u003e \u003cp\u003eAt the Akoma Pa sites, 62% of patients were diagnosed with hypertension alone, while 5% had diabetes only, and 3% had both conditions. In contrast, the comparator group had 45% hypertensives, 7% diabetics, and 35% with both conditions. All patients in the Akoma Pa group received tele-counselling, prescription refill reminders, and medication adherence counselling, which were absent in the comparator group. Additionally, 82% of Akoma Pa patients participated in patient support groups (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAkoma Pa (n\u0026thinsp;=\u0026thinsp;367)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConventional (n\u0026thinsp;=\u0026thinsp;338)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Hypertensives only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Diabetics only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Hypertension and Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Tele counselling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrescription Refill reminders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication Adherence Counselling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient support group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCost Analysis\u003c/h2\u003e \u003cp\u003e The total cost of implementing the Akoma Pa intervention across eight facilities was \u003cspan\u003e$\u003c/span\u003e508,443.89 (see Table\u0026nbsp;3), compared to \u003cspan\u003e$\u003c/span\u003e529,882.28 for the comparator sites (see Table\u0026nbsp;4). The primary cost drivers for the Akoma Pa intervention included initial technology investments, personnel training, and operational costs related to tele-counselling services. However, these costs were offset by reductions in hospitalization rates, fewer in-person clinic visits, and lower complication rates among patients.\u003c/p\u003e \u003cp\u003e[insert Tables\u0026nbsp;3 and 4 here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffectiveness and cost effectiveness\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eClinical effectiveness\u003c/h2\u003e \u003cp\u003eThe Akoma Pa intervention significantly improved diabetes and hypertension management outcomes. Participants achieved notable reductions in HbA1c levels (mean reduction of 2.10% vs. 1.68% in the comparator group) and fasting blood glucose (mean reduction of 2.21 mmol/L vs. 1.89 mmol/L). Systolic blood pressure reduction was also greater in the intervention group (54.38 mmHg vs. 51.88 mmHg). The intervention group showed a higher percentage of patients with controlled hypertension (58.9% vs. 23.2% in the comparator group) and better glycaemic control (85.4% vs. 31.7%) (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth effects per health outcome (12 months follow-up period)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntervention group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean systolic reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.38mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.88mmHg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean fasting blood sugar reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89mmol/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean hba1c reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean BMI reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.42kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of patients followed up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1mmHg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCost-effectiveness\u003c/h2\u003e \u003cp\u003eThe ICER for the Akoma Pa intervention demonstrated cost-effectiveness across multiple health outcomes. The ICER for systolic blood pressure reduction was -\u003cspan\u003e$\u003c/span\u003e8,575.36, for fasting blood sugar reduction was -\u003cspan\u003e$\u003c/span\u003e66,994.97, and for HbA1c reduction was -\u003cspan\u003e$\u003c/span\u003e51,043.79. The intervention also showed cost savings in terms of body mass index (BMI) reduction (ICER: -\u003cspan\u003e$\u003c/span\u003e5,626.87) and patient follow-up rates (ICER: -\u003cspan\u003e$\u003c/span\u003e40,835.03) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The program\u0026rsquo;s cost per Quality-Adjusted Life Year (QALY) gained was below the WHO threshold of \u003cspan\u003e$\u003c/span\u003e50,000.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eA one-way sensitivity analysis confirmed the robustness of the cost-effectiveness results. The ICER values remained consistent across different discount rates (3% and 5%), indicating that the intervention\u0026rsquo;s cost- effectiveness was not significantly affected by changes in discounting assumptions. The most cost-effective scenario was observed for fasting blood sugar reduction, with an ICER of -\u003cspan\u003e$\u003c/span\u003e66,994.97 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ICER was recalculated after applying the discount rates. This analysis helps to identify which scenarios result in the highest or lowest cost-effectiveness of the intervention. The more negative the ICER, the more cost- effective the intervention is under that scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDigital Technology and Follow-Up Rates and Patient Engagement\u003c/h2\u003e \u003cp\u003eThe Akoma Pa intervention significantly improved patient follow-up rates, with 89% of patients in the intervention group completing follow-up visits, compared to 36.5% in the comparator group. This improvement was attributed to the use of digital health tools, including tele-counselling and automated reminders, which enhanced patient engagement and adherence to treatment plans (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetes and hypertension are among the most prevalent non-communicable diseases (NCDs) globally, with a disproportionate burden in low- and middle-income countries (LMICs) such as Ghana. These conditions contribute significantly to morbidity, mortality, and healthcare expenditures, exacerbating the strain on already resource-constrained health systems. Despite the growing prevalence of diabetes and hypertension, management remains suboptimal in many LMICs, characterized by low control rates, fragmented care, and high out-of-pocket expenditures for patients. This study evaluated the effectiveness and cost-effectiveness of the Akoma Pa program, a digital health intervention designed to improve the management of hypertension and diabetes in Ghana. By leveraging digital tools such as telemonitoring and continuous patient engagement, the program aims to enhance clinical outcomes while reducing healthcare costs. The findings offer valuable insights for scaling digital health interventions in LMICs, with implications for global NCD management strategies.\u003c/p\u003e \u003cp\u003eThe incremental cost-effectiveness ratio (ICER) analysis of the Akoma Pa program reveals significant economic and clinical benefits, which are particularly relevant in resource-limited settings. The ICER for systolic blood pressure reduction, indicates cost savings rather than additional expenditure. This suggests that each millimetre of mercury reduction in systolic blood pressure achieved through the Akoma Pa program is more cost-effective than conventional methods. The negative ICER underscores the program\u0026rsquo;s dual advantage: it not only improves clinical outcomes but also reduces the need for higher-cost interventions such as hospitalizations or emergency care. This finding aligns with global evidence demonstrating the potential of digital health interventions to enhance hypertension management while optimizing resource allocation in healthcare systems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, the ICER for HbA1c reduction highlights the program\u0026rsquo;s role in improving diabetes management. HbA1c, a key indicator of long-term blood glucose control, is critical for preventing diabetes-related complications. The substantial reduction in HbA1c at a lower cost suggests that the Akoma Pa program facilitates more effective diabetes management, potentially reducing reliance on costly medications and interventions for complications. This finding is consistent with global studies that have demonstrated the therapeutic efficacy of digital health solutions in managing diabetes [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, economic evaluations of digital health interventions, including telemonitoring, SMS-based interventions, and remote consultations, have consistently shown positive impacts on both clinical outcomes and cost savings [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These results underscore the potential of digital health interventions to address the growing burden of diabetes in LMICs, where access to specialized care is often limited.\u003c/p\u003e \u003cp\u003eThe ICER for body mass index (BMI) reduction introduces another critical dimension to the program\u0026rsquo;s impact. Given the high comorbidity of obesity with both diabetes and hypertension, effective weight management is essential for improving overall health outcomes. The negative ICER for BMI reduction suggests that the Akoma Pa program is more cost-effective in managing weight compared to conventional methods, likely due to enhanced patient engagement with dietary and exercise counselling facilitated by digital tools. This finding is supported by a growing body of evidence demonstrating the positive effects of telemonitoring and tele-screening on glycaemic control, weight management, and physical activity [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These results highlight the potential of digital health interventions to address multiple risk factors simultaneously, offering a holistic approach to NCD management.\u003c/p\u003e \u003cp\u003eThe ICER for patient follow-up further underscores the program\u0026rsquo;s ability to sustain long-term patient engagement, a critical factor for the successful management of chronic conditions. The Akoma Pa program not only supports continuous patient interaction but also offers substantial economic advantages by reducing healthcare costs. This finding is particularly relevant in LMICs, where patient retention and adherence to treatment plans are often challenging due to financial, geographic, and systemic barriers. Previous studies have consistently highlighted the therapeutic benefits of digital health interventions in improving patient adherence and health outcomes, ultimately contributing to better disease management and reduced long-term healthcare expenditures [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of this study have significant implications for global health, particularly in LMICs grappling with the dual burden of infectious diseases and NCDs. The Akoma Pa program\u0026rsquo;s cost-effectiveness and clinical effectiveness demonstrate that scaling up digital health interventions could alleviate the economic burden of NCDs while improving patient outcomes. Policymakers in LMICs should consider integrating digital health solutions into national NCD management strategies, particularly in settings where healthcare resources are limited. Investments in digital infrastructure, capacity building for healthcare providers, and patient education could further enhance the impact of such interventions. Moreover, the success of the Akoma Pa program offers a replicable model for other LMICs facing similar challenges in NCD management. By leveraging widely accessible technologies such as mobile phones and telemonitoring, digital health interventions can bridge gaps in healthcare delivery, particularly in rural and underserved areas. Global health organizations and funding agencies should prioritize support for the development and implementation of digital health solutions as part of comprehensive NCD control strategies.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study offers valuable evidence on the effectiveness and cost-effectiveness of digital health interventions for chronic disease management. This study was limited to the Central Region of Ghana, which may not fully represent other populations. The use of retrospective data collection could have introduced the potential for reporting biases. The study\u0026rsquo;s findings\u0026rsquo; generalizability is inhibited by the purposive sampling strategy used. Despite these limitations, the study offers valuable evidence on the effectiveness of digital health interventions for chronic disease management.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Akoma Pa program represents a promising approach to addressing the growing burden of diabetes and hypertension in Ghana and other LMICs. Its cost-effectiveness, coupled with its ability to improve clinical outcomes, underscores the potential of digital health interventions to transform NCD management in resource-constrained settings. As the global health community strives to achieve universal health coverage and the Sustainable Development Goals (SDGs), scaling up innovative solutions like the Akoma Pa program will be critical to reducing the burden of NCDs and improving health equity worldwide.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eCEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cost Effectiveness Analysis\u003c/p\u003e\n\u003cp\u003eCHAG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Christian Health Association of Ghana\u003c/p\u003e\n\u003cp\u003eFBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fasting Blood Sugar\u003c/p\u003e\n\u003cp\u003eICER\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Incremental Cost Effectiveness Ratio\u003c/p\u003e\n\u003cp\u003eLMIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low- and Middle-Income country\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNCDs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-Communicable Diseases\u003c/p\u003e\n\u003cp\u003eQALYs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Quality-Adjusted Life Years\u003c/p\u003e\n\u003cp\u003eSBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eSDGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; - \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Committee on Human Research Publication and Ethics (CHRPE/AP/695/24) at Kwame Nkrumah University of Science and Technology. The methodology employed in this study followed the principles of the Helsinki Declaration. Participant anonymity and confidentiality were preserved throughout the study. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: AAO and RAB; Methodology: AAO, RAB and PN; Data Curation: AAO and TYAE; Formal analysis: AAO, RAB and TYAE; Writing initial draft: RAB; Reviewing and editing: AAO, RAB, TYAE, PN and PAB; Supervision: PAB. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support of the management of CHAG and the management of the various health facilities in the Central region that were involved in the study. With their permission, we have recognized their contributions in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproach to Engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study engaged patients, healthcare providers, and stakeholders in the design and implementation of the Akoma Pa program. Patient support groups and tele-counselling sessions facilitated direct engagement with patients, while training programs for healthcare professionals ensured their involvement in the intervention. Feedback from these groups informed the program’s design and evaluation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eW.H.O. Hypertension Ghana 2023 country profile. World Health Organization [Internet]. 2023. Available from: https://cdn.who.int/media/docs/default-source/country-profiles/hypertension/hypertension-2023/hypertension_gha_2023.pdf?sfvrsn=655f431b_4\u0026amp;download=true\u003c/li\u003e\n\u003cli\u003eMoH. 24 million Adults in Africa Are Currently Living with Diabetes [Internet]. Ministry of Health, Ghana. 2025. Available from: https://www.moh.gov.gh/24-million-adults-in-africa-are-currently-living-with-diabetes/\u003c/li\u003e\n\u003cli\u003eAmon S, Aikins M, Haghparast-Bidgoli H, Kretchy IA, Arhinful DK, Baatiema L, et al. Household economic burden of type-2 diabetes and hypertension comorbidity care in urban-poor Ghana: a mixed methods study. BMC Health Serv Res. 2024;24(1):1028. \u003c/li\u003e\n\u003cli\u003ede Graft Aikins A, Addo J, Ofei F, Bosu WK, Agyemang C. Ghana\u0026rsquo;s burden of chronic non-communicable diseases: future directions in research, practice and policy. Ghana Med J. 2012;46(2):1\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eTogoe HA. Household burden of chronic diseases in Ghana. Ghana Med J. 2012;46(2):54\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eAmon SK, Aikins MKS. Economic burden of type 2 diabetes mellitus complications among patients in the eastern region of Ghana: A descriptive cross-sectional cost-of-illness study. Diabetes Manag. 2017;7(5):367\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eJones M, Hendrick S. Neurogenic Bowel: Traditional Approaches and Clinical Pearls. Curr Phys Med Rehabil Reports. 2020;8:179\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eAmu H, Darteh EKM, Tarkang EE, Kumi-Kyereme A. Management of chronic non-communicable diseases in Ghana: a qualitative study using the chronic care model. BMC Public Health. 2021 Jun;21(1):1120. \u003c/li\u003e\n\u003cli\u003eLaar AK, Adler AJ, Kotoh AM, Legido-Quigley H, Lange IL, Perel P, et al. Health system challenges to hypertension and related non-communicable diseases prevention and treatment: perspectives from Ghanaian stakeholders. BMC Health Serv Res. 2019 Oct;19(1):693. \u003c/li\u003e\n\u003cli\u003ede-Graft Aikins A, Kushitor M, Koram K, Gyamfi S, Ogedegbe G. Chronic non-communicable diseases and the challenge of universal health coverage: insights from community-based cardiovascular disease research in urban poor communities in Accra, Ghana. BMC Public Health. 2014;14:1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eRedfern J, Coorey G, Mulley J, Scaria A, Neubeck L, Hafiz N, et al. A digital health intervention for cardiovascular disease management in primary care (CONNECT) randomized controlled trial. NPJ Digit Med. 2020;3(1):117. \u003c/li\u003e\n\u003cli\u003eMurphy CM, Scott K, Colby SM, Yermash J, Evans EW, Wing RR, et al. \u0026ldquo;Healthier health in more ways than one\u0026rdquo;: Perspectives on a program for changing both smoking and obesity-related health behaviors. Eat Behav. 2024;53:101883. \u003c/li\u003e\n\u003cli\u003eMiller R, Fox D, Ioannou P. PUK27 THE ECONOMIC IMPACT OF DIGITAL HEALTH INTERVENTIONS IN THE MANAGEMENT OF TYPE 2 DIABETES: A SYSTEMATIC REVIEW. Value Heal. 2019;22:S918. \u003c/li\u003e\n\u003cli\u003eMuennig P, Bounthavong M. Cost-effectiveness analysis in health: a practical approach. John Wiley \u0026amp; Sons; 2016. \u003c/li\u003e\n\u003cli\u003eSanders GD, Maciejewski ML, Basu A. Overview of cost-effectiveness analysis. Jama. 2019;321(14):1400\u0026ndash;1. \u003c/li\u003e\n\u003cli\u003eOtsen B, Agyei-Baffour P. Cost-effectiveness analysis of telemedicine for primary healthcare delivery in the Amansie-west district, Ghana. African J Heal Econ. 2016;5:1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eUNICEF. Ghana Health Budget Brief [Internet]. 2022. Available from: https://www.unicef.org/ghana/media/4581/file/2022 Health Budget Brief .pdf\u003c/li\u003e\n\u003cli\u003eMedtronic LABS. Akoma Pa, Healthcare For Patients With Diabetes And Hypertension Launches In Ghana [Internet]. 2022 [cited 2023 Oct 12]. Available from: https://www.medtroniclabs.org/insights/akoma-pa-healthcare-for-patients-with-diabetes-and-hypertension-launches-in-ghana/\u003c/li\u003e\n\u003cli\u003eBlok S, van der Linden EL, Somsen GA, Tulevski II, Winter MM, Van den Born BJH. Success factors in high-effect, low-cost eHealth programs for patients with hypertension: a systematic review and meta-analysis. Eur J Prev Cardiol. 2021;28(14):1579\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eZhai Y, Zhao J, You H, Pang C, Yin L, Guo T, et al. Association of the rs11196218 polymorphism in TCF7L2 with type 2 diabetes mellitus in Asian population. Meta Gene. 2014;2:332\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eWang Q, Su M, Zhang M, Li R. Integrating digital technologies and public health to fight Covid-19 pandemic: key technologies, applications, challenges and outlook of digital healthcare. Int J Environ Res Public Health. 2021;18(11):6053. \u003c/li\u003e\n\u003cli\u003eChoi DW, Lee SA, Lee DW, Joo JH, Han KT, Kim S, et al. Effect of socioeconomic deprivation on outcomes of diabetes complications in patients with type 2 diabetes mellitus: a nationwide population-based cohort study of South Korea. BMJ Open Diabetes Res Care. 2020;8(1):e000729. \u003c/li\u003e\n\u003cli\u003eRinaldi G, Hijazi A, Haghparast-Bidgoli H. Cost and cost-effectiveness of mHealth interventions for the prevention and control of type 2 diabetes mellitus: A systematic review. Diabetes Res Clin Pract. 2020;162:108084. \u003c/li\u003e\n\u003cli\u003eMorris DR, Jones GT, Holmes M V, Bown MJ, Bulbulia R, Singh TP, et al. Genetic predisposition to diabetes and abdominal aortic aneurysm: a two stage Mendelian randomisation study. Eur J Vasc Endovasc Surg. 2022;63(3):512\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eBashshur RL, Shannon GW, Smith BR, Woodward MA. The empirical evidence for the telemedicine intervention in diabetes management. Telemed e-Health. 2015;21(5):321\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eKario K, Harada N, Okura A. Digital therapeutics in hypertension: evidence and perspectives. Hypertension. 2022;79(10):2148\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eLu HY, Ding X, Hirst JE, Yang Y, Yang J, Mackillop L, et al. Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes. IEEE Rev Biomed Eng. 2023;17:98\u0026ndash;117. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cost-effectiveness, digital health, non-communicable disease, Akoma Pa","lastPublishedDoi":"10.21203/rs.3.rs-6574366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6574366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDiabetes and hypertension are major contributors to Ghana’s health burden, yet conventional non-communicable disease (NCD) management remains reactive and resource-intensive. Technology- driven interventions present great potential solutions. The Akoma Pa initiative which integrates digital health solutions to improve early screening, management, and patient support was piloted. This study evaluates its cost-effectiveness, providing evidence to guide resource allocation and policy decisions for sustainable NCD care in Ghana.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study performed a cost-effectiveness analysis (CEA) of the Akoma Pa program versus conventional method of managing diabetes and hypertension in Christian Health Facilities in Ghana. The evaluation was conducted retrospectively from January to December 2023. Using mixed methods, data from 705 hypertensive and diabetic patients across 16 health facilities was analysed. Costs, health outcomes, and incremental cost-effectiveness ratios (ICERs) were assessed, with one-way sensitivity analyses to test the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The Akoma Pa intervention significantly improved patient outcomes and demonstrated strong cost-effectiveness. Compared to conventional care, it achieved higher follow-up rates (89% vs. 36.5%), greater reductions in HbA1c (-2.10% vs. -1.68%), fasting blood glucose (-2.21 mmol/L vs. -1.89 mmol/L), and systolic blood pressure (-54.38 mmHg vs. -51.88 mmHg). Negative ICER values confirmed cost savings, including HbA1c (-$51,043.79), fasting blood glucose (-$66,994.97), and systolic blood pressure (-$8,575.36). Sensitivity analysis reinforced the robustness of these findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThe Akoma Pa program shows that digital health interventions are not only clinically effective but also economically beneficial. These findings support adoption of digital health solutions to improve health outcomes and promote economic sustainability in healthcare systems.\u003c/p\u003e","manuscriptTitle":"Cost-effectiveness analysis of a digital health-enabled non-communicable disease management intervention: Evidence from Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 06:07:07","doi":"10.21203/rs.3.rs-6574366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-03T17:53:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T10:25:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T03:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172905488001211694185734643893138282729","date":"2026-01-03T02:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248984563888669702560933200418898714764","date":"2025-12-04T01:47:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247344887814558738253892710153941824934","date":"2025-11-29T10:22:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303032989942809912643786461442691466277","date":"2025-06-07T23:25:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T21:11:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24530193552964344747394509831072410403","date":"2025-06-02T21:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-23T14:55:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-10T04:35:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-10T04:32:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cost Effectiveness and Resource Allocation","date":"2025-05-01T21:53:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecb30a17-ef7c-4ad5-adce-c4b881b3fbb9","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:11:21+00:00","versionOfRecord":{"articleIdentity":"rs-6574366","link":"https://doi.org/10.1186/s12962-026-00733-0","journal":{"identity":"cost-effectiveness-and-resource-allocation","isVorOnly":false,"title":"Cost Effectiveness and Resource Allocation"},"publishedOn":"2026-02-27 15:59:51","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2025-05-27 06:07:07","video":"","vorDoi":"10.1186/s12962-026-00733-0","vorDoiUrl":"https://doi.org/10.1186/s12962-026-00733-0","workflowStages":[]},"version":"v1","identity":"rs-6574366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6574366","identity":"rs-6574366","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}