Digital Health Initiative for HIV Testing Promotion in War-Affected Ukraine: Effectiveness Evaluation

Digital Health Initiative for HIV Testing Promotion in War-Affected Ukraine: Effectiveness Evaluation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Digital Health Initiative for HIV Testing Promotion in War-Affected Ukraine: Effectiveness Evaluation Hlib Aleksandrenko, Olga Chervak, Kateryna Krasnikova, Maryna Shevchenko, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6640828/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background : With approximately one-third of Ukrainians living with human immunodeficiency virus (HIV) unaware of their status, the ongoing military conflict has severely disrupted healthcare delivery, creating significant barriers for HIV testing and care engagement. Digital health initiatives have emerged in response to humanitarian crises, but their effectiveness still requires evaluation. This evaluates the effectiveness of a digital health intervention to promote HIV testing among war-affected Ukrainians. Methods : This study examined the outcomes and costs of TESTporuch ('TEST nearby'), a digital health intervention that integrates a messenger-based chatbot, a multi-landing website, and digital communication campaigns to evaluate its effectiveness. Data on user interactions with the chatbot, website visitors, and communication campaign metrics from August 2022 to January 2025 were analyzed along with project documentation. The conversion from TESTporuch to ARTporuch (a specialized chatbot that connects people living with HIV to antiretroviral therapy and related services) served as a proxy for HIV-positive cases, enabling the calculation of HIV positivity rate and cost-effectiveness for comparison with national estimates reflecting the overall effectiveness of HIV testing interventions nationwide. Results : The digital communication campaigns generated 32,982,883 impressions, while the multi-landing website engaged 179,072 visitors. Among 4,968 unique chatbot users, 93.4% completed the initial screening phase, HIV infection risk was identified in 50.8% of users, and 8.7% self-identified as key populations. Overall, the digital health intervention demonstrated a HIV positivity rate of 3.15%, exceeding the national average by 363%. The interventions surpassed the national average twice in engaging key populations, which also demonstrated conversion rates of 7.91%, surpassing the national average by 159%. The digital health intervention has a total cost of $879.63 per HIV case identified, 40% lower than the overall national estimates from HIV testing interventions. Digital communication campaigns increased conversion effectiveness up to 132% and boosted key population engagement compared to non-campaign periods. Conclusions : The TESTporuch digital health intervention demonstrated promising outcomes and cost-effectiveness in promoting HIV testing among war-affected Ukrainians, particularly engaging key populations at high risk. The findings suggest that similar digital health initiatives may constitute an effective component of healthcare delivery strategies in conflict-affected and resource-limited settings for vulnerable communities. digital health HIV testing health promotion chatbot cost-effectiveness resource-limited settings conflict-affected settings humanitarian crisis health system resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Digital health has emerged as a powerful approach to making health systems more efficient and sustainable, enabling the delivery of high-quality, affordable, and equitable care to individuals and communities [1] [2]. The digital health landscape has rapidly evolved in recent years, with an increasing recognition of its potential to transform healthcare delivery models, particularly in low- and middle-income countries (LMICs) [3] [4] [5]. The COVID-19 pandemic further accelerated the adoption of digital health technologies globally, as health systems were forced to adapt to lockdowns and social distancing measures [6]. For Ukraine, this transformation was compounded by the onset of the full-scale invasion by Russia in February 2022, leading to a humanitarian crisis, creating unprecedented challenges for the health system and particularly affecting vulnerable populations, including people living with human immunodeficiency virus (PLHIV) [7,8]. Internal displacement has affected approximately 5.1 million people and forced more than 6 million to seek refuge abroad, disrupting the healthcare infrastructure, with over 1,000 health facilities damaged by shelling, and more than 200 destroyed since the war began [13] [12]. These circumstances have created significant challenges for continuous HIV care, as thousands of PLHIV have been disconnected from their regular healthcare providers and treatment [14]. Ukraine has been facing significant challenges in HIV care, with alarming clinical indicators compared to other European nations. In 2021, Ukraine had the second-highest rate of newly diagnosed HIV infections in the WHO European Region, with 37.1 cases per 100,000 population [10]. Vulnerable communities, such as key populations, are disproportionately affected by HIV and continue to face stigma and discrimination, which additionally contributes to their being underserved by conventional health systems [11]. Non-governmental organizations (NGOs) play a critical role in Ukraine's HIV response, particularly in reaching key populations at higher risk of HIV with testing and prevention services at the community level. A recent cost-effectiveness analysis demonstrated that NGOs’ community-based interventions targeting key populations in Ukraine have prevented considerable new HIV infections and are highly cost-effective [31]. However, of the approximately 245,000 PLHIV in Ukraine (as of early 2022) [10], about one-third are still unaware of their HIV status [9] [26]. This represents a significant public health challenge, especially in the context of war-related disruptions to health services. Digital health can serve as a critical bridge to maintaining continuity of care in these challenging circumstances [15,16]. Research indicates that digital health interventions (DHIs) can improve HIV prevention, testing uptake, linkage to care, and treatment adherence among vulnerable populations [17]. These interventions are especially valuable in contexts where traditional healthcare delivery is compromised, such as conflict-affected regions [18]. Still, information about implementing DHI in war-affected settings is minimal [45]. Despite the potential benefits, implementing DHIs in resource-limited and conflict settings faces numerous challenges, including digital literacy barriers, limited internet connectivity, and concerns about data privacy and security [19,20]. In Ukraine specifically, the war has created additional obstacles, such as electricity outages, destruction of digital infrastructure, and disruption of mobile networks [21]. Evaluating DHI in resource-limited and conflict settings is essential for understanding its effectiveness and potential to strengthen fragile health systems [22, 23]. Assessing both the outcomes and cost-effectiveness of these interventions is crucial, especially in resource-constrained environments where optimizing limited resources is imperative [24, 25]. Such evaluations offer valuable insights for evidence-informed decision-making in health policy and programs aiming to implement effective digital health initiatives in emergency and resource-limited settings. This study aims to evaluate the effectiveness of a digital health intervention to promote HIV testing among war-affected Ukrainians. Methods Study Design This study evaluates the outcomes and costs of the DHI named TESTporuch ("TEST nearby"), designed to promote HIV testing in wartime Ukraine between August 2022 and January 2025. The World Bank's Framework for the Economic Evaluation of Digital Health Interventions informed the study design [47]. DHI Description The DHI was developed by the State Enterprise Center of Public Health of the Ministry of Health of Ukraine (the CPH of the MoH of Ukraine) with the Community Action for HIV Control Project support [48]. Following digital marketing strategy principles, the DHI integrated three synergistic elements, each serving a distinct role in the HIV testing promotion funnel (Figure 1): Communication campaigns to provide outreach through targeted multi-channel digital marketing; Multi-landing website to engage visitors with dynamic, population-specific interfaces tailored to different key populations; and Telegram-based chatbot to facilitate linkage to services through personalized HIV risk assessment and connections to HIV testing service delivery points. This comprehensive approach created a seamless user journey specifically designed to promote HIV testing uptake among hard-to-reach populations in Ukraine during wartime conditions. Communication Campaign A communication campaign was implemented as a component of the intervention. In addition, information about the chatbot and multi-landing launch was disseminated through communication materials through official government and community stakeholder channels [49, 50]. Two sequential information campaigns were implemented during 2023-2024 within the framework of a national initiative to enhance demand and access to HIV services across Ukraine (excluding temporarily occupied territories), coordinated by the CPH of the MoH of Ukraine. Both campaigns utilized a combination of digital (≥70%) and offline (≤30%) channels. The present study analyzes only the digital component of these campaigns designed to facilitate user engagement with the multi-landing website. Campaign messaging consistently emphasized accessibility, privacy, and cost-free aspects of HIV testing services through communication strategies tailored to specific audience segments (see Supplementary Materials 1 and 2 for campaign messages and creative examples). The first campaign, conducted from April to July 2023, targeted "infantile" (the general population who do not test for HIV despite risky behaviors). This campaign aimed to increase awareness about community-based HIV testing services provided by NGOs and to drive demand for these services. Digital channels included dating sites, adult websites, gossip and lifestyle news portals, and marketplaces. Promotion tactics employed Facebook and Instagram advertising, YouTube video campaigns, Google Display Network banner advertising, Vpoint programmatic advertising, and contextual targeting. The second campaign, implemented from March to May 2024, focused on targeting specific key populations: female sex workers (FSW), men who have sex with men (MSM), and persons who inject drugs (PWID). Digital channels included platforms similar to the first campaign, with refined targeting. Promotion technologies utilized included Google Display Network, Facebook and Instagram advertising, Performance Max automated campaigns, DemandGen (demand generation) tactics, and Vpoint programmatic targeting. Additional promotion methods included blogger engagement, mobile application advertising, and contextual advertising on popular regional websites. Geotargeting strategies were employed to reach PWID in specific territories. Multi-landing Website A multi-landing website was launched in April 2023 following the chatbot deployment. The website was designed for specific key populations, including PWID, MSM, and FSW, as well as the general population at risk of HIV infection (see Appendix 3 for the multi-landing website design and content). The multi-landing platform featured four distinct user interfaces, each offering population-specific content and messaging while maintaining a unified technical infrastructure. Each interface presented targeted information about HIV, testing procedures, an open interactive map with HIV testing service delivery points, and direct integration with the chatbot. The website architecture employed responsive design principles to ensure functionality across various devices, with particular optimization for mobile access. The website's launch was postponed due to a government prohibition on providing open information about the locations of health facilities, citing the threat of an attack on them. Chatbot Launched in August 2022, the TESTporuch chatbot, beginning with privacy assurances, offers personalized HIV risk evaluation through a structured algorithm of initial screening and in-depth risk assessment questions with privacy assurances. It also provides location-based referrals to HIV testing service delivery points across Ukraine with personalized recommendations based on user profiles (see Appendix 4 for algorithm specifications). Additional functionalities include connections to the National HIV/AIDS Hotline, information on pre- and post-exposure prophylaxis, free oral test ordering for HIV self-testing, a personal user account with stored search history, and reminders for HIV testing. The system was adopted to operate within Ukraine's wartime constraints, incorporating security features to protect the location information of HIV services delivery points from potential targeting. Development occurred between June and August 2022, using a low-code platform to minimize costs while ensuring compliance with the national security requirements. The chatbot’s messenger selection criteria included widespread adoption among target populations and perceived privacy features, making it an accessible channel for reaching key populations in Ukraine. The chatbot development platform was selected based on formal registration in Ukraine and compliance with the General Data Protection Regulation (GDPR). DHI Classification According to the WHO Classification of Digital Interventions, Services and Applications in Health (CDISAH) [46], the TESTporuch DHI addresses multiple health system challenges: communication roadblocks (1.4), lack of access to information (1.5), poor experience of persons (3.1), low demand for services (5.1), loss to follow-up (5.4), insufficient person engagement (8.1), and inadequate representation (9.2). The targeted primary users for this intervention are persons at risk of HIV infection, for whom it transmits targeted health information based on health status or demographics (1.1.2), targeted alerts and reminders (1.1.3), look-up of information on health and health services (1.6.1), and simulated human-like conversations (1.6.2). These capabilities are delivered through communication systems (A1), decision support systems (A3), and telehealth systems (A9). Data Collection and Preparation The study utilized multiple data sources for a comprehensive evaluation. First, anonymized user interaction data were extracted from two interconnected Telegram-based chatbots: TESTporuch, which focused on HIV testing promotion, and ARTporuch, which aims to reconnect war-affected Ukrainian PLHIV to antiretroviral therapy, as well as other medical and social services in Ukraine and abroad. A left join operation was performed between the TESTporuch and ARTporuch users’ data tables in databases, utilizing unique Telegram identifiers (UID) as the primary key for database integration and ensuring the availability of registration timestamps within the study period (Figure 2). As a next step, specific inclusion criteria were established to identify TESTporuch users who subsequently registered in ARTporuch as a proxy for HIV positive cases. Users were included in the further statistical analysis if they: (1) registered in both TESTporuch and ARTporuch platforms; (2) completed TESTporuch initial screening; and (3) had a TESTporuch registration timestamp preceding or coinciding with ARTporuch registration. Data preprocessing and integration were conducted using Microsoft Power BI with standardized validation procedures to ensure data integrity and compatibility between the chatbot’s databases. Data cleaning procedures included removing duplicate entries, managing missing data, and verifying data completeness. Timestamps were converted to a uniform date format to enable proper chronological analysis of user registration and activity patterns. The resulting integrated dataset, with all personal identifiers removed, is available in Appendix 5. Second, project documentation, including technical specifications, implementation reports, and financial records, was systematically reviewed to characterize the TESTporuch DHI features and determine cost structures. Implementation reports from two sequential digital communication campaigns (2023-2024) were analyzed to present the target population's reach. Also, Google Analytics data for the multi-landing website provided supplementary insights on user engagement patterns between April 2023 and January 2025. Statistical Analysis Data were analyzed using the R statistical software package (version 4.1.2). Descriptive statistics were applied to analyze user data across all three intervention components (chatbot, multi-landing website, and communication campaign), characterizing the user population through frequencies and proportions for categorical variables. For the TESTporuch chatbot, user interaction data were analyzed based on predefined categories, with detailed descriptions of all metrics provided in Table 1. For the analysis of conversion from TESTporuch to ARTporuch, we calculated the conversion rate defined as the percentage of potential HIV-positive cases relative to the total number of users who completed initial screening. Pearson's chi-square test with Yates' continuity correction was used to assess the statistical significance of differences in conversion rates between user groups. Fisher's exact test was used to calculate odds ratios (OR) with 95% confidence intervals. Relative risk (RR) was calculated using the epitools package in R. A p-value < 0.05 was considered statistically significant for all analyses. Table 1. Key Metrics and Definitions for TESTporuch Chatbot Analysis Metric/User Category Description Total users Number of unique users who accessed the chatbot Initial screening Users who completed the preliminary sorting stage of the chatbot algorithm Referred for HIV testing Users who utilized the chatbot's HIV testing service delivery point search function Identified HIV risk Number of users with any identified HIV risk factors based on responses to screening questions Risky behavior Users who reported behaviors associated with increased HIV transmission risk Recent potential HIV exposure Users who reported recent (within 48 hours) potential HIV exposure requiring urgent intervention Key population member Users who identified themselves as belonging to at least one key population group at higher risk for HIV Unprotected sex Users who reported sexual contact with a partner whose HIV status was unknown Chemsex experience Users who reported using psychoactive substances during sexual activity STI history Users who reported history of sexually transmitted infections (STIs) PWID partner Users who reported sexual contact with PWID OST (Opioid Substitution Therapy) experience Users who reported participation in OST MSM Users who self-identified as MSM PLHIV partner Users who self-identified as sexual partners of PLHIV FSW Users who self-identified as FSW PWID Users who self-identified as PWID Transgender people Users who self-identified as transgender Former prisoners Users who self-identified as former prisoners Conversion to ARTporuch Users who met criteria for potential HIV positive case Cost Analysis Methodology Cost analysis was conducted employing the author's DHI lifecycle framework that categorized expenditures across two main stages: Creation and Maintenance. The Creation stage encompassed three phases: Preparation, Development, and Launch. The Maintenance stage included the Operation and Update phases. This model allowed for a structured assessment of both capital expenditures (CAPEX) and operational expenses (OPEX) throughout the DHI lifecycle (see Figure 3). For each phase, costs were categorized by type (Human Resources, Software, Services) and unit of measurement (number of hours or items). All costs were documented in US dollars based on the exchange rate at the time of the transaction. Effectiveness Evaluation Methodology A structured approach was established to evaluate the DHI's two key effectiveness metrics, HIV positivity rates and cost-effectiveness, compared to national estimates that represent the overall effectiveness of HIV testing interventions nationwide. The HIV positivity rate was defined as the conversion rate derived from the statistical analysis. Comparative data for national estimates were obtained from official statistical reports published by the CPH of the MoH of Ukraine for 2022-2024. Averages from years’ reports spanning this timeframe were calculated to ensure appropriate comparison with our intervention period (August 2022 through January 2025). For the cost-effectiveness evaluation, we calculated the cost per HIV case identified by dividing the total DHI cost by the number of potential HIV positive cases. These figures were then compared with modeled national estimates for 2022-2024, projected based on publicly available data from 2021. Since the DHI does not include the physical testing component, the cost of HIV testing services is added to ensure a fair comparison with the national estimates. Results DHI User Data Analysis Communication Campaign Combining both campaigns, 32,982,883 impressions were delivered, generating 192,511 clicks across all target audiences. As shown in Table 2 , key populations were reached, particularly emphasizing MSM and FSW. MSM was the most responsive audience segment in the first campaign, while the FSW segment represented about 40% of the campaign's reach, and the infantile audience showed less responsiveness. The second campaign showed more balanced results for FSW and MSM, while PWID with geotargeting showed the lowest reach with equal clicks. Table 2 Digital Communication Campaign Performance Metrics Campaign Audience Impressions Clicks Campaign 1 (2023) Subtotal 20,689,456 116,406 Campaign 2 (2024) FSW 4,736,627 25,744 MSM 4,514,834 24,345 PWID 2,041,966 25,016 Subtotal 11,293,427 75,105 Total 31,982,883 191,511 Multi-landing Website Performance Google Analytics data for the multi-landing website presented in Table 3 showed 195,496 users from April 2023 to January 2025. In total, key population-specific interfaces attracted 63.7% of total website traffic, with the MSM interface accounting for the most significant proportion. Available age data revealed that key populations (MSM and FSW) had higher engagement among the 55–64 and 35–44 age groups, while the general population interface showed stronger representation across all age groups, with no age data available for PWID users. Table 3 Multi-landing Website Users by Key Population Interface and Age Group Interface Age Group Active Users Percentage MSM 18–24 493 0.6% 25–34 979 1.2% 35–44 2,139 2.6% 45–54 1,256 1.5% 55–64 2,436 3.0% 65+ 1,636 2.0% Unknown 71,977 88.5% Subtotal 81,302 41.6% FSW 18–24 343 0.8% 25–34 706 1.7% 35–44 1,306 3.1% 45–54 1,011 2.4% 55–64 1,430 3.4% 65+ 1,015 2.4% Unknown 36,982 87.1% Subtotal 42,475 21.7% PWID Subtotal 728 0.4% General population 18–24 2402 3.40% 25–34 2592 3.70% 35–44 4309 6.10% 45–54 3030 4.30% 55–64 2808 4.00% 65+ 1378 1.90% Unknown 54472 76.70% Subtotal 70991 36.3% Total 195,496 100% Chatbot Between August 2022 and January 2025, the TESTporuch chatbot was accessed by 4,968 unique users. Table 4 summarizes the key usage metrics, indicating that nearly all completed the initial screening process, half of the users were identified as having an HIV infection risk, and over two-thirds proceeded to utilize the HIV testing service delivery point search functionality. The most frequently reported specific risk factor was unprotected sex with a partner whose HIV status was unknown, and MSM represented the largest group among key populations. Table 4 Key Usage Metrics of the Chatbot Metric Count Percentage (%) Overall usage: Total users 4,968 100 Initial screening 4,640 93.4 Referred for HIV testing 3,404 68.5 HIV risk status : Identified HIV risk 2,523 50.8 Risky behavior 2,011 40.5 Recent potential HIV exposure 808 16.3 Key population member 430 8.7 Key population groups : MSM 239 4.8 PLHIV partner 104 2.1 FSW 70 1.4 PWID 37 0.7 Transgender people 25 0.5 Former prisoners 21 0.4 Risk factors : Unprotected sex 367 7.4 Chemsex experience 84 1.7 STI history 50 1% PWID partner 67 1.3 OST experience 19 0.4 The geographical distribution of users revealed notable variations across Ukraine, as illustrated in Fig. 4 . Usage patterns concentrated in central and western regions, with Kyiv city showing the highest engagement, followed by Lviv, Kyiv Oblast, and Dnipropetrovsk Oblast. In contrast, southern and eastern oblasts showed substantially lower usage, particularly those with the most temporarily occupied territories like Donetsk, Luhansk, and Kherson. This reveals a pattern of higher engagement in central and western regions compared to southern and eastern oblasts of Ukraine. Analysis of the transition from TESTporuch chatbot revealed that 3.15% (146 out of 4,640) of users who completed the initial screening converted to the ARTporuch chatbot, as detailed in Table 5 . This conversion rate varied by HIV risk status, with key population members exhibiting significantly higher conversion rates (7.91%), along with specific groups: former prisoners (14.29%), PWID (10.81%), MSM (9.62%), and PLHIV partners (7.69%). Table 5 Analysis of Conversion from TESTporuch to ARTporuch by User Categories User Category Total Users Converted Rate OR (95% CI) χ² p-value RR Initial screening 4,640 146 3.15% Reference - - 1 HIV risk status: With identified HIV risk 2,523 97 3.84% 1.23 (0.94–1.61) 2.22 0.136 1.14 Without identified HIV risk 2,117 49 2.31% 0.59 (0.41–0.85) 2.22 0.136 0.6 With recent potential HIV exposure 808 31 3.84% 1.23 (0.80–1.84) 0.83 0.361 1.19 Without recent potential HIV exposure 3,832 115 3.00% 0.78 (0.51–1.20) 0.83 0.361 0.78 With risky behavior 2,011 70 3.48% 1.11 (0.82–1.49) 0.4 0.528 1.07 Without risky behavior 2,629 76 2.89% 0.83 (0.59–1.17) 0.4 0.528 0.83 Key population members 430 34 7.91% 2.64 (1.74–3.92) 24.67 < 0.001 2.33 Non-key population members 4,210 112 2.66% 0.32 (0.21–0.49) 24.67 < 0.001 0.34 Key population groups: MSM 239 23 9.62% 3.28 (1.97–5.24) 26.61 < 0.001 2.97 PLHIV partners 104 8 7.69% 2.56 (1.06–5.39) 5.32 0.021 2.48 Sex workers 70 4 5.71% 1.87 (0.49–5.11) 0.76 0.384 1.84 PWID 37 4 10.81% 3.73 (0.95–10.69) 4.7 0.03 3.66 Transgender people 25 2 8.00% 2.68 (0.30–11.00) 0.65 0.419 2.65 Former prisoners 21 3 14.29% 5.13 (0.96–17.84) 5.17 0.023 5.05 Risk factors: Unprotected sex 367 18 4.90% 1.59 (0.90–2.64) 2.79 0.095 1.52 Chemsex experience 84 4 4.76% 1.54 (0.40–4.18) 0.27 0.601 1.52 STI history 50 0 0.00% - - - - PWID partners 67 2 2.99% 0.95 (0.11–3.62) 0 1 0.95 OST experience 19 2 10.53% 3.62 (0.40-15.48) 1.38 0.24 3.59 HIV testing referral: Referred 3,404 110 3.23% 1.03 (0.79–1.33) 0.02 0.881 1.02 Not referred 1,236 36 2.91% 0.90 (0.60–1.33) 0.02 0.881 0.9 Figure 5 illustrates the seasonal distribution of users and conversion rate, which revealed notable temporal patterns throughout the study period. Following initial engagement during the launch period (Summer 2022), user numbers showed cyclical variation with distinct seasonal patterns. Spring and summer periods consistently demonstrated higher metrics, with Spring 2024 recording the highest user acquisition (1,025 new users) and Summer 2023 achieving the peak conversion rate (4.7%). Communication campaign periods were associated with increased conversion rates. The first campaign period (April-July 2023) coincided with increasing conversion rates that reached 4.7% in Summer 2023, representing a 24% improvement from the pre-campaign Winter 2022–2023 period. The second campaign (March-June 2024) corresponded with even more substantial improvements in Spring-Summer 2024 (3.3–4.4% conversion rates), showing a 132% increase compared to Winter 2023–2024. Additionally, the campaigns substantially boosted key population engagement (17–48% increase) and, during the second campaign, increased HIV risk identification among users by 6%. The geographical distribution of conversions from TESTporuch to ARTporuch showed notable variations across Ukraine, as illustrated in Fig. 6 . Kyiv city demonstrated the highest absolute number of conversions with users, though its conversion rate was relatively low at 2.2%. Several eastern and southern oblasts of Ukraine showed higher conversion rates despite lower absolute numbers: Donetsk Oblast achieved a 10.5% rate, and Mykolaiv Oblast—9.3%. Cherkasy Oblast recorded substantial numbers and the highest rate among major regions (11.7%). Almost fully temporarily occupied, Luhansk Oblast reached a 100% conversion rate with only one HIV case. Western oblasts of Ukraine, like Lviv, had higher user numbers but moderate conversion rates (3.0%). These patterns indicate that while usage was concentrated in safer western and central regions, effectiveness in converting users to care varied substantially across the country. DHI Cost Analysis The total cost of implementing the TESTporuch DHI amounted to $ 94,962 over the observation period. As detailed in Table 6 , capital expenditures accounted for $ 10,682 (11.2%), while operational expenditures represented the majority at $ 84,280 (88.8%). Cost distribution analysis showed that most expenses occurred during the Maintenance stage, primarily within the operation phase ( $ 83,444, 87.9% of total costs). Communication campaigns represented the largest expense category ( $ 67,000, 70.6%), followed by project management activities ( $ 8,704, 9.2%). During the Creation stage, website development ( $ 4,000) and project management activities across all phases ( $ 4,352) constituted the major expenses. By resource type, technical services represented the largest expenditure (75.1%, $ 71,300), followed by human resources (24.3%, $ 23,112), with software subscriptions accounting for the smallest portion (0.6%, $ 550). Table 6 Cost Structure of the DHI TESTporuch by Lifecycle Stages and Phases Stage Phase Item Description Type Unit Cost per Unit, $ Quantity Total Cost, $ 1.Creation 1.Preparation Project Manager Project initiation, stakeholder and team coordination Human Resources Hour 17 40 680 Digital Specialist Architecture planning, consultative support Human Resources Hour 14 40 560 HIV/AIDS Consultants Consultative support for intervention planning and methods Human Resources Hour 10 16 160 2.Development No-code/Low-code Developer Chatbot creation Human Resources Hour 14 64 896 Chatbot Development Service Monthly subscription to the service for chatbot development and hosting Software Item 50 1 50 Project Manager Stakeholder and development team coordination, creating technical specifications for website Human Resources Hour 17 96 1632 Data Analyst Creating HIV testing points database, data collection tools development, data collection support Human Resources Hour 12 32 384 Website Development (multi-landing) Third-party contractors for website development according to technical specifications Services Item 4000 1 4000 3.Launch Project Manager Promotion organization, search and selection of promotion service providers Human Resources Hour 17 120 2040 No-code/Low-code Developer Bug fixes and functionality monitoring during launch Human Resources Hour 7 40 280 Chatbot Development Services Monthly subscription to chatbot development and hosting service during launch Software Item 50 1 50 Total CAPEX : 10682 2.Maintenance 4.Operation Promotion (digital marketing) Third-party contractors for communication campaign organization (2023–2024) Services Item 33500 2 67000 Chatbot Development Service Monthly subscription to the service for chatbot hosting Software Item 50 30 1500 Chatbot Administrator Chatbot operation support, minor bug fixes Human Resources Hour 12 260 3120 Website Administrator (multi-landing) Website operation support, minor bug fixes Human Resources Hour 12 260 3120 Project Manager Project maintenance coordination Human Resources Hour 17 512 8704 5.Update No-code/Low-code Developer Fixing significant chatbot issues, system simplification to reduce costs Human Resources Hour 14 24 336 Website Update Services Fixing significant website issues, UX/UI improvements implementation Services Item 500 1 500 Total OPEX : 84280 Total : 94962 DHI Effectiveness HIV Positivity Rates Comparison Figure 7 compares the TESTporuch DHI HIV positivity rates during the research period with the average national estimates reported by the CPH of the MoH of Ukraine for 2022–2024 across various groups [ 27 ]. Overall, the positivity rates exceeded the national average by 363%. Key populations showed high HIV positivity rates compared to national estimates, with the most substantial difference being observed among former prisoners, PWID, and MSM. In contrast, PLHIV partners exhibited lower positivity rates (detailed data and calculations in Appendix 7). Cost-Effectiveness As shown in Fig. 8 , despite having twice the higher promotion cost, TESTporuch DHI demonstrates a 40% savings in the total cost per HIV case identified compared to conventional testing methods used from 2022 to 2024. The key efficiency factor driving this advantage is that the DHI showed a higher positivity rate than the national estimates, dramatically reducing the costs of HIV testing services needed per positive case identified (detailed calculations in Appendix 8). Discussion Principal Findings The TESTporuch DHI reached and engaged a diverse audience across Ukraine, with a robust uptake in areas directly affected by the conflict. Notably, the representation of key populations among TESTporuch users substantially exceeded the national average reported for HIV testing interventions in Ukraine in 2024 (3.76%) by 131%, indicating effective targeting and engagement [ 27 ]. This aligns with findings from Daher et al. [ 32 ], who demonstrated that digital interventions for HIV/STIs were both feasible and effective at reaching key populations, with strong positive effects on clinic attendance rates. Temporal analysis of user engagement revealed a significant correlation between active communication campaign periods and improved performance metrics and showed even stronger key population engagement. Communication campaigns increased conversion effectiveness by 24% (first campaign) and 132% (second campaign) compared to non-campaign periods. Both campaigns boosted users' key population engagement (17–48% increase), and the second campaign also increased HIV risk identification among users by 6%. This reinforces findings from our previous ARTporuch study [ 36 ], which similarly demonstrated that strategic information campaigns were crucial for effective user engagement and service uptake during wartime. Notably, the first general-population campaign showed limited conversation effectiveness and decreased HIV risk identification by 8%, while the second key population-targeted campaign achieved substantially better results. The conversion rate from TESTporuch to ARTporuch as a potential HIV positivity rate is substantially higher than the average national estimates, exceeding the overall national HIV testing interventions rate by 363%. Particularly notable was the high HIV positivity rate among key populations, which was 159% higher than the national average, demonstrating the effectiveness of DHI in engaging most at-risk groups in HIV testing services. Similarly, McGuire et al. [ 33 ] found that HIV self-testing with digital supports was acceptable (77–97%), feasible (93–95%), and effective in supporting linkage to care (53–100%). However, while our results show promise compared to average national estimates, it's essential to acknowledge that they remain below the exceptional effectiveness achieved by advanced HIV intervention strategies, which are commonly used by NGOs at a community level [ 31 ]. For example, the Optimized HIV Case Finding (OCF) strategy, implemented in 2016–2017 among PWID, showed up to 20% positivity rates among 40,735 tests, nearly twice as high as the rate demonstrated by TESTporuch [ 28 ]. Simultaneously, Index Case Testing (ICT) demonstrated positivity rates of 19.4% of 5,021 PLHIV partners [ 29 ]. Our cost analysis revealed that implementing the multi-component DHI was highly cost-effective, with $ 879.63 per HIV case identified, representing a 40% reduction compared to national estimates from HIV testing interventions nationwide. This substantial cost advantage underscores the economic value proposition of DHI in resource-constrained settings. These cost-effectiveness findings align with Campbell et al [ 34 ] study of mHealth interventions in Vancouver, which found relatively low costs per participant ( $ 36.72 of health professionals’ time per year for problem responders) and high acceptability among vulnerable PLHIV. Our study achieved comparable efficiency in resource utilization, although the wartime context presented unique challenges and higher implementation costs. Additionally, our cost-effectiveness results align with those of Yun et al. [ 35 ], who demonstrated that mobile phone-based digital interventions for HIV prevention among MSM in China were highly cost-effective at $ 2,599.87 per infection averted. Similar to our findings, they observed that DHIs were particularly cost-effective among high-risk populations with more frequent risk behaviors. The TESTporuch DHI builds upon previous findings by implementing a comprehensive digital approach designed explicitly for a humanitarian crisis context. While most studies primarily focused on stable settings [ 37 , 38 , 39 ], our work demonstrates the adaptability of DHI during active conflict. This supports emerging evidence from previous research regarding DHI as a crisis-response component to reconnect PLHIV to healthcare during wartime [ 26 ]. Strengths and Limitations Our methodological approach of using conversion from TESTporuch to ARTporuch as a proxy for HIV case identification represents both a limitation and a strength. While we could not verify confirmed HIV diagnoses through clinical records due to war-related restrictions on HIV registry access, this approach offered a practical alternative. The ARTporuch chatbot required personal data and mandatory registration procedures designed specifically for PLHIV, making it unsuitable and impractical for non-HIV-infected individuals to register. Furthermore, our case identification numbers likely underestimate the DHI's effectiveness, as many users might have accessed HIV testing services without subsequently using the ARTporuch chatbot, which is not mandatory for those diagnosed with HIV. Several war-related limitations affected this study. Approximately 20% of Ukraine's territory, with about 3 to 3.5 million Ukrainians, remains under temporary occupation [ 51 ], limiting chatbot accessibility in these regions and creating geographical disparities in service coverage. The digital divide, exacerbated by electricity disruptions, internet access limitations, and varying levels of digital literacy across different populations, likely introduced selection bias in our sample [ 40 , 41 ]. These factors constrain the generalizability of our findings to the entire Ukrainian population. The severe disruption of health services complicates direct comparisons between our findings and the data on the effectiveness of HIV testing interventions before the full-scale invasion. Additionally, war-related restrictions prevented assessment of the proportion of previously engaged PLHIV who reconnected to care through our intervention. Financial data for HIV testing interventions also became unavailable after 2021 due to wartime disruptions, necessitating cost modeling based on pre-war estimates for our comparative analysis. Finally, our focus on implementation outcomes rather than clinical endpoints means that the impact on HIV transmission, viral suppression, and mortality could not be assessed within the study timeframe. Implications Our findings have several important implications for HIV service delivery in resource-limited and crisis-affected settings. First, they demonstrate that DHI can effectively bridge gaps in conventional health services when infrastructure is compromised. The DHI provided a lifeline to vulnerable populations who might otherwise have lost access to essential HIV services during conflict. However, as our findings suggest, DHI may best serve as a complementary instrument rather than a replacement for traditional health strategies, enhancing but not substituting existing healthcare delivery systems. The principles applied here could extend beyond HIV to other chronic and high-burden diseases requiring continued engagement in care and prevention [ 42 , 44 ]. Second, the cost-effectiveness evaluation provides compelling evidence for policymakers and donors considering investments in digital health initiatives for health promotion interventions, even in challenging conditions. The substantially lower cost per HIV case identified compared to national estimates suggests that digital approaches could optimize resource allocation in settings with funding constraints. Third, the stratified analysis showing higher effectiveness among key populations indicates that DHI can be particularly valuable for reaching groups traditionally underserved by conventional health systems. However, this success in reaching target audiences largely depends on a well-designed DHI adoption strategy, where not only the presence of communication campaigns but also their strategic planning and precise audience targeting are critical components. Fourth, the DHI implementation results during armed conflict suggest that digital health initiatives should be integrated into humanitarian response planning. However, this integration requires developing specific regulatory frameworks and rapid deployment protocols for DHI during crises. Preparedness for rapid deployment of DHI could become a standard component of emergency response frameworks, ensuring both effectiveness and compliance with security and data protection standards during emergencies. Future Research Directions A critical area for future research is developing a comprehensive DHI lifecycle management framework for resource-limited and conflict-affected settings. Our experience demonstrated that coordinated management throughout the entire lifecycle was essential for successful implementation. However, as noted by Angerer et al. [ 43 ], most digital health literature focuses more on technology and less on management aspects. Similarly, Bashi et al. [ 44 ] identified a lack of evidence regarding applying consistent frameworks for digital health interventions. Future studies should address this gap by establishing standardized DHI planning, implementation, and evaluation approaches that can be adapted to challenging humanitarian contexts. Conclusion The TESTporuch DHI has demonstrated promising outcomes and cost-effectiveness in promoting HIV testing, especially among key populations at the highest risk, in war-affected Ukraine. As national health systems increasingly face disruptions from various crises, findings underscore the value of digital health initiatives as a component of essential healthcare delivery strategies for vulnerable communities, informing future evidence-based decision-making and resource allocation in conflict-affected and resource-limited settings. Declarations Ethics approval and consent to participate The study was conducted in accordance with Ukraine's current legislation on personal data protection (Law of Ukraine "On Protection of Personal Data" No. 2297-VI, dated June 1, 2010, with amendments, current version as of January 18, 2025, based on Law 3980-IX) and did not require additional informed consent from participants. Following the definition of consent in the Law, users provided their agreement by accepting the Terms of Use and Privacy Policy before activating the chatbot, which explicitly informed them that anonymized data might be used for research purposes, epidemiological surveillance, and service improvement. The Terms of Use and Privacy Policy informed users about secure data storage and anonymization practices, and, following Articles 8 and 15 of the Law, provided mechanisms for users to withdraw their consent or delete personal information at any time. This research was conducted in compliance with the ethical principles of the Declaration of Helsinki regarding research on human data. Secondary research on stored data was performed after consideration and approval by the Research Ethics Committee of the National University of Kyiv–Mohyla Academy (registration number: FWA00030125) on April 23, 2025 (protocol number 7). The research also adhered to the ethical principles of the Belmont Report and the institutional policies of the National University of Kyiv–Mohyla Academy. The use of secondary depersonalized data and implementation experience for research purposes was officially approved by the Community Action for HIV Control project (see Additional file 6). Consent for publication Not applicable. The manuscript does not contain any individual personal data that would require specific consent for publication. Availability of data and materials This published article and its supplementary information files include all the data needed to evaluate the conclusions. Additional data generated and/or analyzed during the study are available from the corresponding author upon reasonable request. Competing interests Olga Chervak and Kateryna Krasnikova are employed by Pact Ukraine, which is mentioned in the manuscript as an implementing organization of the Community Action for HIV Control project, which may represent a potential conflict of interest. Other authors declare no conflicts of interest. Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions HA conceived and designed the study, developed the methodology, performed software analysis, validated the findings, conducted formal analysis and investigation, curated data, drafted the original manuscript, created visualizations, supervised the project, and administered the project. OC contributed to conceptualization, validated the findings, and reviewed and edited the manuscript. KK performed formal analysis and reviewed and edited the manuscript. MS provided supervision. TY validated the findings. All authors read and approved the final manuscript. Acknowledgements The authors express sincere gratitude to all who contributed to this research. Special thanks to the Community Action for HIV Control project implemented by Pact Inc., in cooperation with FHI360 and the International Charitable Foundation 'AIDS Foundation East–West' (AFEW-Ukraine), and to the American people for their financial support, which made this initiative possible. The authors extend their appreciation to the State Enterprise Center of Public Health of the Ministry of Health of Ukraine for implementing the '#TESTporuch' initiatives that have supported thousands of Ukrainians. The results of this work belong to the Ukrainian people. Unity and resilience during difficult times make the impossible possible. We are immensely grateful to all Ukrainians who have endured and continue to fight for freedom, as well as to our international partners and countries who stand with Ukraine during these challenging times. References World Health Organization. Global strategy on digital health 2020-2025. Geneva: WHO; 2021. Erku D, Khatri R, Endalamaw A, Beks H, Samuel S, Birru EM. Digital health interventions to improve access to and quality of primary health care services: A scoping review. Int J Environ Res Public Health. 2023;20(16):6854. Yi S, Yam ELY, Cheruvettolil K, Adhikari D, Kimera J, Kumpakha B, et al. 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Accessed 27 Apr 2025. Wilkinson T, Wang M, Friedman J, Prestidge M. A framework for the economic evaluation of digital health interventions. Policy Research Working Papers 10407. Washington, DC: World Bank; 2023. Public Health Center of Ukraine. The #TESTporuch website has started working: getting tested for HIV is now even easier. 2023. https://phc.org.ua/news/zapracyuvav-sayt-testporuch-pereviritis-na-vil-teper-sche-prostishe. Accessed 27 Apr 2025. Foundation for Prevention of Chemical Dependencies and AIDS. The TESTPORUCH Telegram chatbot has been launched, which will help assess the risk of HIV infection and find a route for HIV testing. 2022. https://helpme.com.ua/zapracyuvav-chat-bot-v-telegram-testporuch-yakij-dopomozhe-ociniti-rizik-infikuvannya-vil-ta-znajti-marshrut-dlya-testuvannya-na-vil/. Accessed 27 Apr 2025. Ministry of Health of Ukraine. #TESTporuch: A site has been launched that makes it easier to get tested for HIV. 2023. https://moz.gov.ua/uk/testporuch-zapracjuvav-sajt-scho-dozvoljae-pereviritis-na-vil-sche-prostishe-. Accessed 27 Apr 2025. Stiftung Wissenschaft und Politik. Russia in the occupied territories of Ukraine. 2024. https://www.swp-berlin.org/10.18449/2024C38/. Accessed 27 Apr 2025. European Union Drugs Agency. Using the evidence-based model for improving opioid agonist treatment scale-up in Ukraine. 2024. https://www.euda.europa.eu/drugs-library/using-evidence-based-model-improving-opioid-agonist-treatment-scale-ukraine_en. Accessed 27 Apr 2025. Additional Declarations No competing interests reported. Supplementary Files Appendix1.FirstCommunicationCampaignMessagesandCreativesExamples.pdf Additional file 1: Appendix 1. First Communication Campaign Messages and Creatives Examples. Appendix2.SecondCommunicationCampaignMessagesandCreativesExamples.pdf Additional file 2: Appendix 2. Second Communication Campaign Messages and Creatives Examples. Appendix3.TESTporuchMultilandingWebsiteDesignandContent.pdf Additional file 3: Appendix 3. Multi-landing Website Design and Content. Appendix4.TESTporuchChatbotAlgorithmOverview.pdf Additional file 4: Appendix 4. TESTporuch Chatbot Algorithm Overview. Appendix5.IntegratedTESTporuchandARTporuchDataset.xlsx Additional file 5: Appendix 5. Integrated TESTporuch and ARTporuch Dataset. Appendix6.ConfirmationofParticipationandPermissiontoUseData.pdf Additional file 6: Appendix 6. Confirmation of Participation and Permission to Use Data. Appendix7.HIVPositivityRateComparisonDataandCalculations.docx Additional file 7: Appendix 7. HIV Positivity Rate Comparison Data and Calculations. Appendix8.CostDataModelingandCalculations..docx Additional file 8: Appendix 8. Cost Data Modeling and Calculations. 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1","display":"","copyAsset":false,"role":"figure","size":83384,"visible":true,"origin":"","legend":"\u003cp\u003eThe TESTporuch DHI Components and User Flow\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/cbbaa77f51c1afa11d0ba650.png"},{"id":92475516,"identity":"2351347c-3110-4eae-9d61-aaf38490d990","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21964,"visible":true,"origin":"","legend":"\u003cp\u003eDatabase Integration Model Between TESTporuch and ARTporuch Chatbots\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*[user_profile_data] represents the comprehensive set of anonymized user information collected through the TESTporuch chatbot, which was used for subsequent analytical purposes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/a8d2ddbf45a03af4f94c7f72.png"},{"id":92475518,"identity":"eef3a3b0-c3df-4ada-820b-85c28dda348f","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62521,"visible":true,"origin":"","legend":"\u003cp\u003eDHI lifecycle framework for cost analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/0d19dc3ec73e569bcb58e722.png"},{"id":92475519,"identity":"f3c33de4-8094-41ec-b13c-802d7e3da219","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78703,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of TESTporuch Chatbot Users by Oblasts of Ukraine\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Oblasts with temporarily occupied territories (TOT) and active hostilities.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e**The Autonomous Republic of Crimea (ARC) was unilaterally temporarily annexed by Russia in 2014.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/502b0c3dc56b02a6ff66706b.png"},{"id":92475520,"identity":"36689947-b10c-4e5a-a030-a583ff652811","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94733,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Distribution of Users and Conversions, August 2022 - January 2025\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Communication campaign period\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst communication campaign period (April-July 2023)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSecond communication campaign period with key population focus (March-June 2024)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/a60c05b98d89290f8e9dafc6.png"},{"id":92477770,"identity":"0c39a23b-9764-4569-a950-5a75f7a74ca8","added_by":"auto","created_at":"2025-09-30 07:25:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77329,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Conversions from TESTporuch to ARTporuch by Oblasts of Ukraine\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Oblasts with temporarily occupied territories (TOT) and active hostilities.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e**The Autonomous Republic of Crimea (ARC) was unilaterally annexed by Russia in 2014.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/0642812109041f435635d8a4.png"},{"id":92475529,"identity":"71601f0e-d44d-4f38-8627-e334eb0ac17a","added_by":"auto","created_at":"2025-09-30 07:17:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":39704,"visible":true,"origin":"","legend":"\u003cp\u003eTESTporuch DHI HIV Positivity Rate Comparison with National Estimates\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/fd10393b9335540d2fe70818.png"},{"id":92477769,"identity":"287a096b-2691-4470-ac2b-7abd8f6991d7","added_by":"auto","created_at":"2025-09-30 07:25:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":70258,"visible":true,"origin":"","legend":"\u003cp\u003eThe DHI Cost per HIV Case Identified Compared to National Estimates\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/2deb6a5c43bcb61edc1b5f34.png"},{"id":92480379,"identity":"0a28001d-9581-4d7d-a7d2-029e37e56ed9","added_by":"auto","created_at":"2025-09-30 07:41:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1575102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/c07a3735-3cc3-42ad-891d-0fa6fac9095a.pdf"},{"id":92475556,"identity":"6f90c10f-76d6-4794-a801-fabf3b7f2b25","added_by":"auto","created_at":"2025-09-30 07:17:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23548630,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1:\u003c/p\u003e\n\u003cp\u003eAppendix 1. First Communication Campaign Messages and Creatives Examples.\u003c/p\u003e","description":"","filename":"Appendix1.FirstCommunicationCampaignMessagesandCreativesExamples.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/518e01f906364fe70350725c.pdf"},{"id":92477787,"identity":"f386ecf9-0605-4acb-9519-d2b8f42fa6ce","added_by":"auto","created_at":"2025-09-30 07:25:06","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18571224,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2:\u003c/p\u003e\n\u003cp\u003eAppendix 2. Second Communication Campaign Messages and Creatives Examples.\u003c/p\u003e","description":"","filename":"Appendix2.SecondCommunicationCampaignMessagesandCreativesExamples.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/82bcebd5b5278457687046e9.pdf"},{"id":92475558,"identity":"857758f7-76b1-4d37-b3a6-1574a36f9a50","added_by":"auto","created_at":"2025-09-30 07:17:06","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":24121371,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3:\u003c/p\u003e\n\u003cp\u003eAppendix 3. Multi-landing Website Design and Content.\u003c/p\u003e","description":"","filename":"Appendix3.TESTporuchMultilandingWebsiteDesignandContent.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/d93338913638e8af52e66c80.pdf"},{"id":92475523,"identity":"b5dcd77f-35ee-4958-b08e-9352cdee8eee","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":619807,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4:\u003c/p\u003e\n\u003cp\u003eAppendix 4. TESTporuch Chatbot Algorithm Overview.\u003c/p\u003e","description":"","filename":"Appendix4.TESTporuchChatbotAlgorithmOverview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/32a8d60e55b9b8b152e69849.pdf"},{"id":92477771,"identity":"24a954a0-368b-48b1-b3e9-69977d8b9450","added_by":"auto","created_at":"2025-09-30 07:25:05","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":408689,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 5:\u003c/p\u003e\n\u003cp\u003eAppendix 5. Integrated TESTporuch and ARTporuch Dataset.\u003c/p\u003e","description":"","filename":"Appendix5.IntegratedTESTporuchandARTporuchDataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/2aba18ea9d55f0b8d64f180b.xlsx"},{"id":92475525,"identity":"d80dbcd9-d31d-4586-9277-f2d9a97931ba","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":427234,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 6:\u003c/p\u003e\n\u003cp\u003eAppendix 6. Confirmation of Participation and Permission to Use Data.\u003c/p\u003e","description":"","filename":"Appendix6.ConfirmationofParticipationandPermissiontoUseData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/e8bad01a80e07d55372c63a9.pdf"},{"id":92475522,"identity":"fd40364b-04ba-4890-8b62-19a45818b0c2","added_by":"auto","created_at":"2025-09-30 07:17:04","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":25891,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 7:\u003c/p\u003e\n\u003cp\u003eAppendix 7. HIV Positivity Rate Comparison Data and Calculations.\u003c/p\u003e","description":"","filename":"Appendix7.HIVPositivityRateComparisonDataandCalculations.docx","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/ddc93aa81ed3bbe028dbbbb4.docx"},{"id":92475542,"identity":"3575bdb6-48d2-4f75-ba6d-4fdcf80b1555","added_by":"auto","created_at":"2025-09-30 07:17:05","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":25464,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 8:\u003c/p\u003e\n\u003cp\u003eAppendix 8. Cost Data Modeling and Calculations.\u003c/p\u003e","description":"","filename":"Appendix8.CostDataModelingandCalculations..docx","url":"https://assets-eu.researchsquare.com/files/rs-6640828/v1/6c066eca50ff16b55c4705e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Health Initiative for HIV Testing Promotion in War-Affected Ukraine: Effectiveness Evaluation","fulltext":[{"header":"Background","content":"\u003cp\u003eDigital health has emerged as a powerful approach to making health systems more efficient and sustainable, enabling the delivery of high-quality, affordable, and equitable care to individuals and communities [1] [2]. The digital health landscape has rapidly evolved in recent years, with an increasing recognition of its potential to transform healthcare delivery models, particularly in low- and middle-income countries (LMICs) [3] [4] [5]. The COVID-19 pandemic further accelerated the adoption of digital health technologies globally, as health systems were forced to adapt to lockdowns and social distancing measures [6]. For Ukraine, this transformation was compounded by the onset of the full-scale invasion by Russia in February 2022, leading to a humanitarian crisis, creating unprecedented challenges for the health system and particularly affecting vulnerable populations, including people living with human immunodeficiency virus (PLHIV) [7,8]. Internal displacement has affected approximately 5.1 million people and forced more than 6 million to seek refuge abroad, disrupting the healthcare infrastructure, with over 1,000 health facilities damaged by shelling, and more than 200 destroyed since the war began [13] [12]. These circumstances have created significant challenges for continuous HIV care, as thousands of PLHIV have been disconnected from their regular healthcare providers and treatment [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUkraine has been facing significant challenges in HIV care, with alarming clinical indicators compared to other European nations. In 2021, Ukraine had the second-highest rate of newly diagnosed HIV infections in the WHO European Region, with 37.1 cases per 100,000 population [10]. Vulnerable communities, such as key populations, are disproportionately affected by HIV and continue to face stigma and discrimination, which additionally contributes to their being underserved by conventional health systems [11]. Non-governmental organizations (NGOs) play a critical role in Ukraine's HIV response, particularly in reaching key populations at higher risk of HIV with testing and prevention services at the community level. A recent cost-effectiveness analysis demonstrated that NGOs’ community-based interventions targeting key populations in Ukraine have prevented considerable new HIV infections and are highly cost-effective [31]. However, of the approximately 245,000 PLHIV in Ukraine (as of early 2022) [10], about one-third are still unaware of their HIV status [9] [26]. This represents a significant public health challenge, especially in the context of war-related disruptions to health services.\u003c/p\u003e\n\u003cp\u003eDigital health can serve as a critical bridge to maintaining continuity of care in these challenging circumstances [15,16]. Research indicates that digital health interventions (DHIs) can improve HIV prevention, testing uptake, linkage to care, and treatment adherence among vulnerable populations [17]. These interventions are especially valuable in contexts where traditional healthcare delivery is compromised, such as conflict-affected regions [18]. Still, information about implementing DHI in war-affected settings is minimal [45].\u003c/p\u003e\n\u003cp\u003eDespite the potential benefits, implementing DHIs in resource-limited and conflict settings faces numerous challenges, including digital literacy barriers, limited internet connectivity, and concerns about data privacy and security [19,20]. In Ukraine specifically, the war has created additional obstacles, such as electricity outages, destruction of digital infrastructure, and disruption of mobile networks [21].\u003c/p\u003e\n\u003cp\u003eEvaluating DHI in resource-limited and conflict settings is essential for understanding its effectiveness and potential to strengthen fragile health systems [22, 23]. Assessing both the outcomes and cost-effectiveness of these interventions is crucial, especially in resource-constrained environments where optimizing limited resources is imperative [24, 25]. Such evaluations offer valuable insights for evidence-informed decision-making in health policy and programs aiming to implement effective digital health initiatives in emergency and resource-limited settings.\u003c/p\u003e\n\u003cp\u003eThis study aims to evaluate the effectiveness of a digital health intervention to promote HIV testing among war-affected Ukrainians.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003cp\u003eThis study evaluates the outcomes and costs of the DHI named TESTporuch (\u0026quot;TEST nearby\u0026quot;), designed to promote HIV testing in wartime Ukraine between August 2022 and January 2025. The World Bank\u0026apos;s Framework for the Economic Evaluation of Digital Health Interventions informed the study design [47].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDHI Description\u003c/h2\u003e\n\u003cp\u003eThe DHI was developed by the State Enterprise Center of Public Health of the Ministry of Health of Ukraine (the CPH of the MoH of Ukraine) with the Community Action for HIV Control Project support [48]. Following digital marketing strategy principles, the DHI integrated three synergistic elements, each serving a distinct role in the HIV testing promotion funnel (Figure 1): Communication campaigns to provide outreach through targeted multi-channel digital marketing; Multi-landing website to engage visitors with dynamic, population-specific interfaces tailored to different key populations; and Telegram-based chatbot to facilitate linkage to services through personalized HIV risk assessment and connections to HIV testing service delivery points. This comprehensive approach created a seamless user journey specifically designed to promote HIV testing uptake among hard-to-reach populations in Ukraine during wartime conditions.\u003c/p\u003e\n\u003ch3\u003eCommunication Campaign\u003c/h3\u003e\n\u003cp\u003eA communication campaign was implemented as a component of the intervention. In addition, information about the chatbot and multi-landing launch was disseminated through communication materials through official government and community stakeholder channels [49, 50].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo sequential information campaigns were implemented during 2023-2024 within the framework of a national initiative to enhance demand and access to HIV services across Ukraine (excluding temporarily occupied territories), coordinated by the CPH of the MoH of Ukraine. Both campaigns utilized a combination of digital (\u0026ge;70%) and offline (\u0026le;30%) channels. The present study analyzes only the digital component of these campaigns designed to facilitate user engagement with the multi-landing website. Campaign messaging consistently emphasized accessibility, privacy, and cost-free aspects of HIV testing services through communication strategies tailored to specific audience segments (see Supplementary Materials 1 and 2 for campaign messages and creative examples).\u003c/p\u003e\n\u003cp\u003eThe first campaign, conducted from April to July 2023, targeted \u0026quot;infantile\u0026quot; (the general population who do not test for HIV despite risky behaviors). This campaign aimed to increase awareness about community-based HIV testing services provided by NGOs and to drive demand for these services. Digital channels included dating sites, adult websites, gossip and lifestyle news portals, and marketplaces. Promotion tactics employed Facebook and Instagram advertising, YouTube video campaigns, Google Display Network banner advertising, Vpoint programmatic advertising, and contextual targeting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second campaign, implemented from March to May 2024, focused on targeting specific key populations: female sex workers (FSW), men who have sex with men (MSM), and persons who inject drugs (PWID). Digital channels included platforms similar to the first campaign, with refined targeting. Promotion technologies utilized included Google Display Network, Facebook and Instagram advertising, Performance Max automated campaigns, DemandGen (demand generation) tactics, and Vpoint programmatic targeting. Additional promotion methods included blogger engagement, mobile application advertising, and contextual advertising on popular regional websites. Geotargeting strategies were employed to reach PWID in specific territories.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMulti-landing Website\u003c/h3\u003e\n\u003cp\u003eA multi-landing website was launched in April 2023 following the chatbot deployment. The website was designed for specific key populations, including PWID, MSM, and FSW, as well as the general population at risk of HIV infection (see Appendix 3 for the multi-landing website design and content).\u003c/p\u003e\n\u003cp\u003eThe multi-landing platform featured four distinct user interfaces, each offering population-specific content and messaging while maintaining a unified technical infrastructure. Each interface presented targeted information about HIV, testing procedures, an open interactive map with HIV testing service delivery points, and direct integration with the chatbot. The website architecture employed responsive design principles to ensure functionality across various devices, with particular optimization for mobile access.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe website\u0026apos;s launch was postponed due to a government prohibition on providing open information about the locations of health facilities, citing the threat of an attack on them.\u003c/p\u003e\n\u003ch3\u003eChatbot\u003c/h3\u003e\n\u003cp\u003eLaunched in August 2022, the TESTporuch chatbot, beginning with privacy assurances, offers personalized HIV risk evaluation through a structured algorithm of initial screening and in-depth risk assessment questions with privacy assurances. It also provides location-based referrals to HIV testing service delivery points across Ukraine with personalized recommendations based on user profiles (see Appendix 4 for algorithm specifications). Additional functionalities include connections to the National HIV/AIDS Hotline, information on pre- and post-exposure prophylaxis, free oral test ordering for HIV self-testing, a personal user account with stored search history, and reminders for HIV testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe system was adopted to operate within Ukraine\u0026apos;s wartime constraints, incorporating security features to protect the location information of HIV services delivery points from potential targeting. Development occurred between June and August 2022, using a low-code platform to minimize costs while ensuring compliance with the national security requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe chatbot\u0026rsquo;s messenger selection criteria included widespread adoption among target populations and perceived privacy features, making it an accessible channel for reaching key populations in Ukraine. The chatbot development platform was selected based on formal registration in Ukraine and compliance with the General Data Protection Regulation (GDPR).\u003c/p\u003e\n\u003ch3\u003eDHI Classification\u003c/h3\u003e\n\u003cp\u003eAccording to the WHO Classification of Digital Interventions, Services and Applications in Health (CDISAH) [46], the TESTporuch DHI addresses multiple health system challenges: communication roadblocks (1.4), lack of access to information (1.5), poor experience of persons (3.1), low demand for services (5.1), loss to follow-up (5.4), insufficient person engagement (8.1), and inadequate representation (9.2). The targeted primary users for this intervention are persons at risk of HIV infection, for whom it transmits targeted health information based on health status or demographics (1.1.2), targeted alerts and reminders (1.1.3), look-up of information on health and health services (1.6.1), and simulated human-like conversations (1.6.2). These capabilities are delivered through communication systems (A1), decision support systems (A3), and telehealth systems (A9).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Collection and Preparation\u003c/h2\u003e\n\u003cp\u003eThe study utilized multiple data sources for a comprehensive evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, anonymized user interaction data were extracted from two interconnected Telegram-based chatbots: TESTporuch, which focused on HIV testing promotion, and ARTporuch, which aims to reconnect war-affected Ukrainian PLHIV to antiretroviral therapy, as well as other medical and social services in Ukraine and abroad. A left join operation was performed between the TESTporuch and ARTporuch users\u0026rsquo; data tables in databases, utilizing unique Telegram identifiers (UID) as the primary key for database integration and ensuring the availability of registration timestamps within the study period (Figure 2).\u003c/p\u003e\n\u003cp\u003eAs a next step, specific inclusion criteria were established to identify TESTporuch users who subsequently registered in ARTporuch as a proxy for HIV positive cases. Users were included in the further statistical analysis if they: (1) registered in both TESTporuch and ARTporuch platforms; (2) completed TESTporuch initial screening; and (3) had a TESTporuch registration timestamp preceding or coinciding with ARTporuch registration. Data preprocessing and integration were conducted using Microsoft Power BI with standardized validation procedures to ensure data integrity and compatibility between the chatbot\u0026rsquo;s databases. Data cleaning procedures included removing duplicate entries, managing missing data, and verifying data completeness. Timestamps were converted to a uniform date format to enable proper chronological analysis of user registration and activity patterns. The resulting integrated dataset, with all personal identifiers removed, is available in Appendix 5.\u003c/p\u003e\n\u003cp\u003eSecond, project documentation, including technical specifications, implementation reports, and financial records, was systematically reviewed to characterize the TESTporuch DHI features and determine cost structures. Implementation reports from two sequential digital communication campaigns (2023-2024) were analyzed to present the target population\u0026apos;s reach.\u003c/p\u003e\n\u003cp\u003eAlso, Google Analytics data for the multi-landing website provided supplementary insights on user engagement patterns between April 2023 and January 2025.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eData were analyzed using the R statistical software package (version 4.1.2). Descriptive statistics were applied to analyze user data across all three intervention components (chatbot, multi-landing website, and communication campaign), characterizing the user population through frequencies and proportions for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the TESTporuch chatbot, user interaction data were analyzed based on predefined categories, with detailed descriptions of all metrics provided in Table 1. For the analysis of conversion from TESTporuch to ARTporuch, we calculated the conversion rate defined as the percentage of potential HIV-positive cases relative to the total number of users who completed initial screening. Pearson\u0026apos;s chi-square test with Yates\u0026apos; continuity correction was used to assess the statistical significance of differences in conversion rates between user groups. Fisher\u0026apos;s exact test was used to calculate odds ratios (OR) with 95% confidence intervals. Relative risk (RR) was calculated using the epitools package in R. A p-value \u0026lt; 0.05 was considered statistically significant for all analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Key Metrics and Definitions for TESTporuch Chatbot Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric/User Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTotal users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eNumber of unique users who accessed the chatbot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eInitial screening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who completed the preliminary sorting stage of the chatbot algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eReferred for HIV testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who utilized the chatbot\u0026apos;s HIV testing service delivery point search function\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eIdentified HIV risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eNumber of users with any identified HIV risk factors based on responses to screening questions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRisky behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported behaviors associated with increased HIV transmission risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRecent potential HIV exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported recent (within 48 hours) potential HIV exposure requiring urgent intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eKey population member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who identified themselves as belonging to at least one key population group at higher risk for HIV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eUnprotected sex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported sexual contact with a partner whose HIV status was unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eChemsex experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported using psychoactive substances during sexual activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSTI history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported history of sexually transmitted infections (STIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePWID partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported sexual contact with PWID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOST (Opioid Substitution Therapy) experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who reported participation in OST\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMSM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as MSM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePLHIV partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as sexual partners of PLHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFSW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as FSW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePWID\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as PWID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTransgender people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as transgender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFormer prisoners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who self-identified as former prisoners\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eConversion to ARTporuch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 413px;\"\u003e\n \u003cp\u003eUsers who met criteria for potential HIV positive case\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003eCost Analysis Methodology\u003c/h2\u003e\n\u003cp\u003eCost analysis was conducted employing the author\u0026apos;s DHI lifecycle framework that categorized expenditures across two main stages: Creation and Maintenance. The Creation stage encompassed three phases: Preparation, Development, and Launch. The Maintenance stage included the Operation and Update phases. This model allowed for a structured assessment of both capital expenditures (CAPEX) and operational expenses (OPEX) throughout the DHI lifecycle (see Figure 3). For each phase, costs were categorized by type (Human Resources, Software, Services) and unit of measurement (number of hours or items). All costs were documented in US dollars based on the exchange rate at the time of the transaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEffectiveness Evaluation Methodology\u003c/p\u003e\n\u003cp\u003eA structured approach was established to evaluate the DHI\u0026apos;s two key effectiveness metrics, HIV positivity rates and cost-effectiveness, compared to national estimates that represent the overall effectiveness of HIV testing interventions nationwide. The HIV positivity rate was defined as the conversion rate derived from the statistical analysis. Comparative data for national estimates were obtained from official statistical reports published by the CPH of the MoH of Ukraine for 2022-2024. Averages from years\u0026rsquo; reports spanning this timeframe were calculated to ensure appropriate comparison with our intervention period (August 2022 through January 2025). For the cost-effectiveness evaluation, we calculated the cost per HIV case identified by dividing the total DHI cost by the number of potential HIV positive cases. These figures were then compared with modeled national estimates for 2022-2024, projected based on publicly available data from 2021. Since the DHI does not include the physical testing component, the cost of HIV testing services is added to ensure a fair comparison with the national estimates.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDHI User Data Analysis\u003c/p\u003e\n\u003cp\u003eCommunication Campaign\u003c/p\u003e\n\u003cp\u003eCombining both campaigns, 32,982,883 impressions were delivered, generating 192,511 clicks across all target audiences. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, key populations were reached, particularly emphasizing MSM and FSW. MSM was the most responsive audience segment in the first campaign, while the FSW segment represented about 40% of the campaign\u0026apos;s reach, and the infantile audience showed less responsiveness. The second campaign showed more balanced results for FSW and MSM, while PWID with geotargeting showed the lowest reach with equal clicks.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDigital Communication Campaign Performance Metrics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCampaign\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAudience\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpressions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClicks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCampaign 1 (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,689,456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116,406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eCampaign 2 (2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,736,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,514,834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,041,966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,293,427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75,105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,982,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191,511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMulti-landing Website Performance\u003c/p\u003e\n\u003cp\u003eGoogle Analytics data for the multi-landing website presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e showed 195,496 users from April 2023 to January 2025. In total, key population-specific interfaces attracted 63.7% of total website traffic, with the MSM interface accounting for the most significant proportion. Available age data revealed that key populations (MSM and FSW) had higher engagement among the 55\u0026ndash;64 and 35\u0026ndash;44 age groups, while the general population interface showed stronger representation across all age groups, with no age data available for PWID users.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMulti-landing Website Users by Key Population Interface and Age Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterface\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActive Users\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eMSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e81,302\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eFSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42,475\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e728\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eGeneral population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e70991\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e195,496\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eChatbot\u003c/p\u003e\n\u003cp\u003eBetween August 2022 and January 2025, the TESTporuch chatbot was accessed by 4,968 unique users. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the key usage metrics, indicating that nearly all completed the initial screening process, half of the users were identified as having an HIV infection risk, and over two-thirds proceeded to utilize the HIV testing service delivery point search functionality. The most frequently reported specific risk factor was unprotected sex with a partner whose HIV status was unknown, and MSM represented the largest group among key populations.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKey Usage Metrics of the Chatbot\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOverall usage:\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial screening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferred for HIV testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV risk status\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdentified HIV risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisky behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecent potential HIV exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKey population member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey population groups\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLHIV partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransgender people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer prisoners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnprotected sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemsex experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTI history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOST experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe geographical distribution of users revealed notable variations across Ukraine, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Usage patterns concentrated in central and western regions, with Kyiv city showing the highest engagement, followed by Lviv, Kyiv Oblast, and Dnipropetrovsk Oblast. In contrast, southern and eastern oblasts showed substantially lower usage, particularly those with the most temporarily occupied territories like Donetsk, Luhansk, and Kherson. This reveals a pattern of higher engagement in central and western regions compared to southern and eastern oblasts of Ukraine.\u003c/p\u003e\n\u003cp\u003eAnalysis of the transition from TESTporuch chatbot revealed that 3.15% (146 out of 4,640) of users who completed the initial screening converted to the ARTporuch chatbot, as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. This conversion rate varied by HIV risk status, with key population members exhibiting significantly higher conversion rates (7.91%), along with specific groups: former prisoners (14.29%), PWID (10.81%), MSM (9.62%), and PLHIV partners (7.69%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of Conversion from TESTporuch to ARTporuch by User Categories\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUser Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Users\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConverted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial screening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIV risk status:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith identified HIV risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.94\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout identified HIV risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59 (0.41\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith recent potential HIV exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.80\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout recent potential HIV exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.51\u0026ndash;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith risky behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.82\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout risky behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.59\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKey population members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64 (1.74\u0026ndash;3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-key population members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (0.21\u0026ndash;0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eKey population groups:\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28 (1.97\u0026ndash;5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLHIV partners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56 (1.06\u0026ndash;5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87 (0.49\u0026ndash;5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.73 (0.95\u0026ndash;10.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransgender people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68 (0.30\u0026ndash;11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer prisoners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13 (0.96\u0026ndash;17.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eRisk factors:\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnprotected sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59 (0.90\u0026ndash;2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemsex experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54 (0.40\u0026ndash;4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTI history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWID partners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.11\u0026ndash;3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOST experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62 (0.40-15.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eHIV testing referral:\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.79\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot referred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.60\u0026ndash;1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the seasonal distribution of users and conversion rate, which revealed notable temporal patterns throughout the study period. Following initial engagement during the launch period (Summer 2022), user numbers showed cyclical variation with distinct seasonal patterns. Spring and summer periods consistently demonstrated higher metrics, with Spring 2024 recording the highest user acquisition (1,025 new users) and Summer 2023 achieving the peak conversion rate (4.7%).\u003c/p\u003e\n\u003cp\u003eCommunication campaign periods were associated with increased conversion rates. The first campaign period (April-July 2023) coincided with increasing conversion rates that reached 4.7% in Summer 2023, representing a 24% improvement from the pre-campaign Winter 2022\u0026ndash;2023 period. The second campaign (March-June 2024) corresponded with even more substantial improvements in Spring-Summer 2024 (3.3\u0026ndash;4.4% conversion rates), showing a 132% increase compared to Winter 2023\u0026ndash;2024. Additionally, the campaigns substantially boosted key population engagement (17\u0026ndash;48% increase) and, during the second campaign, increased HIV risk identification among users by 6%.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003cp\u003eThe geographical distribution of conversions from TESTporuch to ARTporuch showed notable variations across Ukraine, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Kyiv city demonstrated the highest absolute number of conversions with users, though its conversion rate was relatively low at 2.2%. Several eastern and southern oblasts of Ukraine showed higher conversion rates despite lower absolute numbers: Donetsk Oblast achieved a 10.5% rate, and Mykolaiv Oblast\u0026mdash;9.3%. Cherkasy Oblast recorded substantial numbers and the highest rate among major regions (11.7%). Almost fully temporarily occupied, Luhansk Oblast reached a 100% conversion rate with only one HIV case. Western oblasts of Ukraine, like Lviv, had higher user numbers but moderate conversion rates (3.0%). These patterns indicate that while usage was concentrated in safer western and central regions, effectiveness in converting users to care varied substantially across the country.\u003c/p\u003e\n \u003cp\u003eDHI Cost Analysis\u003c/p\u003e\n \u003cp\u003eThe total cost of implementing the TESTporuch DHI amounted to \u003cspan\u003e$\u003c/span\u003e94,962 over the observation period. As detailed in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, capital expenditures accounted for \u003cspan\u003e$\u003c/span\u003e10,682 (11.2%), while operational expenditures represented the majority at \u003cspan\u003e$\u003c/span\u003e84,280 (88.8%).\u003c/p\u003e\n \u003cp\u003eCost distribution analysis showed that most expenses occurred during the Maintenance stage, primarily within the operation phase (\u003cspan\u003e$\u003c/span\u003e83,444, 87.9% of total costs). Communication campaigns represented the largest expense category (\u003cspan\u003e$\u003c/span\u003e67,000, 70.6%), followed by project management activities (\u003cspan\u003e$\u003c/span\u003e8,704, 9.2%). During the Creation stage, website development (\u003cspan\u003e$\u003c/span\u003e4,000) and project management activities across all phases (\u003cspan\u003e$\u003c/span\u003e4,352) constituted the major expenses. By resource type, technical services represented the largest expenditure (75.1%, \u003cspan\u003e$\u003c/span\u003e71,300), followed by human resources (24.3%, \u003cspan\u003e$\u003c/span\u003e23,112), with software subscriptions accounting for the smallest portion (0.6%, \u003cspan\u003e$\u003c/span\u003e550).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCost Structure of the DHI TESTporuch by Lifecycle Stages and Phases\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCost per Unit, \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuantity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Cost, \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e1.Creation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e1.Preparation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject initiation, stakeholder and team coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital Specialist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArchitecture planning, consultative support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIV/AIDS Consultants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsultative support for intervention planning and methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2.Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo-code/Low-code Developer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot creation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot Development Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly subscription to the service for chatbot development and hosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStakeholder and development team coordination, creating technical specifications for website\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eData Analyst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreating HIV testing points database, data collection tools development, data collection support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite Development (multi-landing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThird-party contractors for website development according to technical specifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e3.Launch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePromotion organization, search and selection of promotion service providers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo-code/Low-code Developer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBug fixes and functionality monitoring during launch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot Development Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly subscription to chatbot development and hosting service during launch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal CAPEX\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10682\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e2.Maintenance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e4.Operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePromotion (digital marketing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThird-party contractors for communication campaign organization (2023\u0026ndash;2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot Development Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly subscription to the service for chatbot hosting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoftware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot Administrator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChatbot operation support, minor bug fixes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite Administrator (multi-landing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite operation support, minor bug fixes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProject maintenance coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e5.Update\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo-code/Low-code Developer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixing significant chatbot issues, system simplification to reduce costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWebsite Update Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixing significant website issues, UX/UI improvements implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal OPEX\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e84280\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e94962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDHI Effectiveness\u003c/p\u003e\n \u003cp\u003eHIV Positivity Rates Comparison\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e compares the TESTporuch DHI HIV positivity rates during the research period with the average national estimates reported by the CPH of the MoH of Ukraine for 2022\u0026ndash;2024 across various groups [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Overall, the positivity rates exceeded the national average by 363%. Key populations showed high HIV positivity rates compared to national estimates, with the most substantial difference being observed among former prisoners, PWID, and MSM. In contrast, PLHIV partners exhibited lower positivity rates (detailed data and calculations in Appendix 7).\u003c/p\u003e\n \u003cp\u003eCost-Effectiveness\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, despite having twice the higher promotion cost, TESTporuch DHI demonstrates a 40% savings in the total cost per HIV case identified compared to conventional testing methods used from 2022 to 2024. The key efficiency factor driving this advantage is that the DHI showed a higher positivity rate than the national estimates, dramatically reducing the costs of HIV testing services needed per positive case identified (detailed calculations in Appendix 8).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrincipal Findings\u003c/p\u003e\u003cp\u003eThe TESTporuch DHI reached and engaged a diverse audience across Ukraine, with a robust uptake in areas directly affected by the conflict. Notably, the representation of key populations among TESTporuch users substantially exceeded the national average reported for HIV testing interventions in Ukraine in 2024 (3.76%) by 131%, indicating effective targeting and engagement [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This aligns with findings from Daher et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], who demonstrated that digital interventions for HIV/STIs were both feasible and effective at reaching key populations, with strong positive effects on clinic attendance rates.\u003c/p\u003e\u003cp\u003eTemporal analysis of user engagement revealed a significant correlation between active communication campaign periods and improved performance metrics and showed even stronger key population engagement. Communication campaigns increased conversion effectiveness by 24% (first campaign) and 132% (second campaign) compared to non-campaign periods. Both campaigns boosted users' key population engagement (17\u0026ndash;48% increase), and the second campaign also increased HIV risk identification among users by 6%. This reinforces findings from our previous ARTporuch study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which similarly demonstrated that strategic information campaigns were crucial for effective user engagement and service uptake during wartime. Notably, the first general-population campaign showed limited conversation effectiveness and decreased HIV risk identification by 8%, while the second key population-targeted campaign achieved substantially better results.\u003c/p\u003e\u003cp\u003eThe conversion rate from TESTporuch to ARTporuch as a potential HIV positivity rate is substantially higher than the average national estimates, exceeding the overall national HIV testing interventions rate by 363%. Particularly notable was the high HIV positivity rate among key populations, which was 159% higher than the national average, demonstrating the effectiveness of DHI in engaging most at-risk groups in HIV testing services. Similarly, McGuire et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] found that HIV self-testing with digital supports was acceptable (77\u0026ndash;97%), feasible (93\u0026ndash;95%), and effective in supporting linkage to care (53\u0026ndash;100%). However, while our results show promise compared to average national estimates, it's essential to acknowledge that they remain below the exceptional effectiveness achieved by advanced HIV intervention strategies, which are commonly used by NGOs at a community level [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For example, the Optimized HIV Case Finding (OCF) strategy, implemented in 2016\u0026ndash;2017 among PWID, showed up to 20% positivity rates among 40,735 tests, nearly twice as high as the rate demonstrated by TESTporuch [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Simultaneously, Index Case Testing (ICT) demonstrated positivity rates of 19.4% of 5,021 PLHIV partners [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur cost analysis revealed that implementing the multi-component DHI was highly cost-effective, with \u003cspan\u003e$\u003c/span\u003e879.63 per HIV case identified, representing a 40% reduction compared to national estimates from HIV testing interventions nationwide. This substantial cost advantage underscores the economic value proposition of DHI in resource-constrained settings. These cost-effectiveness findings align with Campbell et al [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] study of mHealth interventions in Vancouver, which found relatively low costs per participant (\u003cspan\u003e$\u003c/span\u003e36.72 of health professionals\u0026rsquo; time per year for problem responders) and high acceptability among vulnerable PLHIV. Our study achieved comparable efficiency in resource utilization, although the wartime context presented unique challenges and higher implementation costs. Additionally, our cost-effectiveness results align with those of Yun et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], who demonstrated that mobile phone-based digital interventions for HIV prevention among MSM in China were highly cost-effective at \u003cspan\u003e$\u003c/span\u003e2,599.87 per infection averted. Similar to our findings, they observed that DHIs were particularly cost-effective among high-risk populations with more frequent risk behaviors.\u003c/p\u003e\u003cp\u003eThe TESTporuch DHI builds upon previous findings by implementing a comprehensive digital approach designed explicitly for a humanitarian crisis context. While most studies primarily focused on stable settings [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], our work demonstrates the adaptability of DHI during active conflict. This supports emerging evidence from previous research regarding DHI as a crisis-response component to reconnect PLHIV to healthcare during wartime [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStrengths and Limitations\u003c/p\u003e\u003cp\u003eOur methodological approach of using conversion from TESTporuch to ARTporuch as a proxy for HIV case identification represents both a limitation and a strength. While we could not verify confirmed HIV diagnoses through clinical records due to war-related restrictions on HIV registry access, this approach offered a practical alternative. The ARTporuch chatbot required personal data and mandatory registration procedures designed specifically for PLHIV, making it unsuitable and impractical for non-HIV-infected individuals to register. Furthermore, our case identification numbers likely underestimate the DHI's effectiveness, as many users might have accessed HIV testing services without subsequently using the ARTporuch chatbot, which is not mandatory for those diagnosed with HIV.\u003c/p\u003e\u003cp\u003eSeveral war-related limitations affected this study. Approximately 20% of Ukraine's territory, with about 3 to 3.5\u0026nbsp;million Ukrainians, remains under temporary occupation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], limiting chatbot accessibility in these regions and creating geographical disparities in service coverage. The digital divide, exacerbated by electricity disruptions, internet access limitations, and varying levels of digital literacy across different populations, likely introduced selection bias in our sample [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These factors constrain the generalizability of our findings to the entire Ukrainian population.\u003c/p\u003e\u003cp\u003eThe severe disruption of health services complicates direct comparisons between our findings and the data on the effectiveness of HIV testing interventions before the full-scale invasion. Additionally, war-related restrictions prevented assessment of the proportion of previously engaged PLHIV who reconnected to care through our intervention. Financial data for HIV testing interventions also became unavailable after 2021 due to wartime disruptions, necessitating cost modeling based on pre-war estimates for our comparative analysis. Finally, our focus on implementation outcomes rather than clinical endpoints means that the impact on HIV transmission, viral suppression, and mortality could not be assessed within the study timeframe.\u003c/p\u003e\u003cp\u003eImplications\u003c/p\u003e\u003cp\u003eOur findings have several important implications for HIV service delivery in resource-limited and crisis-affected settings.\u003c/p\u003e\u003cp\u003eFirst, they demonstrate that DHI can effectively bridge gaps in conventional health services when infrastructure is compromised. The DHI provided a lifeline to vulnerable populations who might otherwise have lost access to essential HIV services during conflict. However, as our findings suggest, DHI may best serve as a complementary instrument rather than a replacement for traditional health strategies, enhancing but not substituting existing healthcare delivery systems. The principles applied here could extend beyond HIV to other chronic and high-burden diseases requiring continued engagement in care and prevention [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, the cost-effectiveness evaluation provides compelling evidence for policymakers and donors considering investments in digital health initiatives for health promotion interventions, even in challenging conditions. The substantially lower cost per HIV case identified compared to national estimates suggests that digital approaches could optimize resource allocation in settings with funding constraints.\u003c/p\u003e\u003cp\u003eThird, the stratified analysis showing higher effectiveness among key populations indicates that DHI can be particularly valuable for reaching groups traditionally underserved by conventional health systems. However, this success in reaching target audiences largely depends on a well-designed DHI adoption strategy, where not only the presence of communication campaigns but also their strategic planning and precise audience targeting are critical components.\u003c/p\u003e\u003cp\u003eFourth, the DHI implementation results during armed conflict suggest that digital health initiatives should be integrated into humanitarian response planning. However, this integration requires developing specific regulatory frameworks and rapid deployment protocols for DHI during crises. Preparedness for rapid deployment of DHI could become a standard component of emergency response frameworks, ensuring both effectiveness and compliance with security and data protection standards during emergencies.\u003c/p\u003e\u003cp\u003eFuture Research Directions\u003c/p\u003e\u003cp\u003eA critical area for future research is developing a comprehensive DHI lifecycle management framework for resource-limited and conflict-affected settings. Our experience demonstrated that coordinated management throughout the entire lifecycle was essential for successful implementation. However, as noted by Angerer et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], most digital health literature focuses more on technology and less on management aspects. Similarly, Bashi et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] identified a lack of evidence regarding applying consistent frameworks for digital health interventions. Future studies should address this gap by establishing standardized DHI planning, implementation, and evaluation approaches that can be adapted to challenging humanitarian contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe TESTporuch DHI has demonstrated promising outcomes and cost-effectiveness in promoting HIV testing, especially among key populations at the highest risk, in war-affected Ukraine. As national health systems increasingly face disruptions from various crises, findings underscore the value of digital health initiatives as a component of essential healthcare delivery strategies for vulnerable communities, informing future evidence-based decision-making and resource allocation in conflict-affected and resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with Ukraine\u0026apos;s current legislation on personal data protection (Law of Ukraine \u0026quot;On Protection of Personal Data\u0026quot; No. 2297-VI, dated June 1, 2010, with amendments, current version as of January 18, 2025, based on Law 3980-IX) and did not require additional informed consent from participants. Following the definition of consent in the Law, users provided their agreement by accepting the Terms of Use and Privacy Policy before activating the chatbot, which explicitly informed them that anonymized data might be used for research purposes, epidemiological surveillance, and service improvement. The Terms of Use and Privacy Policy informed users about secure data storage and anonymization practices, and, following Articles 8 and 15 of the Law, provided mechanisms for users to withdraw their consent or delete personal information at any time.\u003c/p\u003e\n\u003cp\u003eThis research was conducted in compliance with the ethical principles of the Declaration of Helsinki regarding research on human data. Secondary research on stored data was performed after consideration and approval by the Research Ethics Committee of the National University of Kyiv\u0026ndash;Mohyla Academy (registration number: FWA00030125) on April 23, 2025 (protocol number 7). The research also adhered to the ethical principles of the Belmont Report and the institutional policies of the National University of Kyiv\u0026ndash;Mohyla Academy.\u003c/p\u003e\n\u003cp\u003eThe use of secondary depersonalized data and implementation experience for research purposes was officially approved by the Community Action for HIV Control project (see Additional file 6).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain any individual personal data that would require specific consent for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThis published article and its supplementary information files include all the data needed to evaluate the conclusions. Additional data generated and/or analyzed during the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eOlga Chervak and Kateryna Krasnikova are employed by Pact Ukraine, which is mentioned in the manuscript as an implementing organization of the Community Action for HIV Control project, which may represent a potential conflict of interest. Other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eHA conceived and designed the study, developed the methodology, performed software analysis, validated the findings, conducted formal analysis and investigation, curated data, drafted the original manuscript, created visualizations, supervised the project, and administered the project. OC contributed to conceptualization, validated the findings, and reviewed and edited the manuscript. KK performed formal analysis and reviewed and edited the manuscript. MS provided supervision. TY validated the findings. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors express sincere gratitude to all who contributed to this research. Special thanks to the Community Action for HIV Control project implemented by Pact Inc., in cooperation with FHI360 and the International Charitable Foundation \u0026apos;AIDS Foundation East\u0026ndash;West\u0026apos; (AFEW-Ukraine), and to the American people for their financial support, which made this initiative possible. The authors extend their appreciation to the State Enterprise Center of Public Health of the Ministry of Health of Ukraine for implementing the \u0026apos;#TESTporuch\u0026apos; initiatives that have supported thousands of Ukrainians. The results of this work belong to the Ukrainian people. Unity and resilience during difficult times make the impossible possible. We are immensely grateful to all Ukrainians who have endured and continue to fight for freedom, as well as to our international partners and countries who stand with Ukraine during these challenging times. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global strategy on digital health 2020-2025. Geneva: WHO; 2021.\u003c/li\u003e\n\u003cli\u003eErku D, Khatri R, Endalamaw A, Beks H, Samuel S, Birru EM. Digital health interventions to improve access to and quality of primary health care services: A scoping review. Int J Environ Res Public Health. 2023;20(16):6854.\u003c/li\u003e\n\u003cli\u003eYi S, Yam ELY, Cheruvettolil K, Adhikari D, Kimera J, Kumpakha B, et al. Perspectives of digital health innovations in low- and middle-income health care systems from South and Southeast Asia. J Med Internet Res. 2024;26.\u003c/li\u003e\n\u003cli\u003eBashi N, Karunanithi M, Fatehi F, Ding H, Walters D. 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BMC Infect Dis. 2023;23:291.\u003c/li\u003e\n\u003cli\u003eAlistar SS, Owens DK, Brandeau ML. Effectiveness and cost effectiveness of expanding harm reduction and antiretroviral therapy in a mixed HIV epidemic: A modeling analysis for Ukraine. PLoS Med. 2011;8(3).\u003c/li\u003e\n\u003cli\u003eTrickey A, Walker JG, Bivegete S, Semchuk N, Saliuk T, Varetska O, et al. Impact and cost-effectiveness of non-governmental organizations on the HIV epidemic in Ukraine among men who have sex with men. AIDS. 2022;36(14):2025-34.\u003c/li\u003e\n\u003cli\u003eDaher J, Vijh R, Linthwaite B, Dave S, Kim J, Dheda K, et al. Do digital innovations for HIV and sexually transmitted infections work? Results from a systematic review (1996-2017). BMJ Open. 2017;7(11).\u003c/li\u003e\n\u003cli\u003eMcGuire M, de Waal A, Karellis A, Janssen R, Engel N, Sampath R, et al. HIV self-testing with digital supports as the new paradigm: A systematic review of global evidence (2010-2021). 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The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations. Int J Med Inform. 2018;114:114-20.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Classification of digital interventions, services and applications in health: a shared language to describe the uses of digital technology for health, 2nd ed. 2023. https://www.who.int/publications/i/item/9789240081949. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003cli\u003eWilkinson T, Wang M, Friedman J, Prestidge M. A framework for the economic evaluation of digital health interventions. Policy Research Working Papers 10407. Washington, DC: World Bank; 2023.\u003c/li\u003e\n\u003cli\u003ePublic Health Center of Ukraine. The #TESTporuch website has started working: getting tested for HIV is now even easier. 2023. https://phc.org.ua/news/zapracyuvav-sayt-testporuch-pereviritis-na-vil-teper-sche-prostishe. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003cli\u003eFoundation for Prevention of Chemical Dependencies and AIDS. The TESTPORUCH Telegram chatbot has been launched, which will help assess the risk of HIV infection and find a route for HIV testing. 2022. https://helpme.com.ua/zapracyuvav-chat-bot-v-telegram-testporuch-yakij-dopomozhe-ociniti-rizik-infikuvannya-vil-ta-znajti-marshrut-dlya-testuvannya-na-vil/. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003cli\u003eMinistry of Health of Ukraine. #TESTporuch: A site has been launched that makes it easier to get tested for HIV. 2023. https://moz.gov.ua/uk/testporuch-zapracjuvav-sajt-scho-dozvoljae-pereviritis-na-vil-sche-prostishe-. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003cli\u003eStiftung Wissenschaft und Politik. Russia in the occupied territories of Ukraine. 2024. https://www.swp-berlin.org/10.18449/2024C38/. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003cli\u003eEuropean Union Drugs Agency. Using the evidence-based model for improving opioid agonist treatment scale-up in Ukraine. 2024. https://www.euda.europa.eu/drugs-library/using-evidence-based-model-improving-opioid-agonist-treatment-scale-ukraine_en. Accessed 27 Apr 2025.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"digital health, HIV testing, health promotion, chatbot, cost-effectiveness, resource-limited settings, conflict-affected settings, humanitarian crisis, health system resilience","lastPublishedDoi":"10.21203/rs.3.rs-6640828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6640828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: With approximately one-third of Ukrainians living with human immunodeficiency virus (HIV) unaware of their status, the ongoing military conflict has severely disrupted healthcare delivery, creating significant barriers for HIV testing and care engagement. Digital health initiatives have emerged in response to humanitarian crises, but their effectiveness still requires evaluation. This evaluates the effectiveness of a digital health intervention to promote HIV testing among war-affected Ukrainians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study examined the outcomes and costs of TESTporuch ('TEST nearby'), a digital health intervention that integrates a messenger-based chatbot, a multi-landing website, and digital communication campaigns to evaluate its effectiveness. Data on user interactions with the chatbot, website visitors, and communication campaign metrics from August 2022 to January 2025 were analyzed along with project documentation. The conversion from TESTporuch to ARTporuch (a specialized chatbot that connects people living with HIV to antiretroviral therapy and related services) served as a proxy for HIV-positive cases, enabling the calculation of HIV positivity rate and cost-effectiveness for comparison with national estimates reflecting the overall effectiveness of HIV testing interventions nationwide.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The digital communication campaigns generated 32,982,883 impressions, while the multi-landing website engaged 179,072 visitors. Among 4,968 unique chatbot users, 93.4% completed the initial screening phase, HIV infection risk was identified in 50.8% of users, and 8.7% self-identified as key populations. Overall, the digital health intervention demonstrated a HIV positivity rate of 3.15%, exceeding the national average by 363%. The interventions surpassed the national average twice in engaging key populations, which also demonstrated conversion rates of 7.91%, surpassing the national average by 159%. The digital health intervention has a total cost of $879.63 per HIV case identified, 40% lower than the overall national estimates from HIV testing interventions. Digital communication campaigns increased conversion effectiveness up to 132% and boosted key population engagement compared to non-campaign periods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The TESTporuch digital health intervention demonstrated promising outcomes and cost-effectiveness in promoting HIV testing among war-affected Ukrainians, particularly engaging key populations at high risk. The findings suggest that similar digital health initiatives may constitute an effective component of healthcare delivery strategies in conflict-affected and resource-limited settings for vulnerable communities.\u003c/p\u003e","manuscriptTitle":"Digital Health Initiative for HIV Testing Promotion in War-Affected Ukraine: Effectiveness Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:16:59","doi":"10.21203/rs.3.rs-6640828/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"282330045269899277007489924911268118095","date":"2025-09-18T13:55:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T08:40:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-18T10:15:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T15:08:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-18T14:16:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2025-05-18T14:14:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c87ad1bd-44a4-47d4-8bf8-7f9bcb58268e","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T07:17:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:16:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6640828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6640828","identity":"rs-6640828","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}