Factors affecting availability of HIV Viral Load/Early Infant Diagnosis Point of Care testing Commodities and performance of testing platforms in public health facilities in Masaka region, Uganda

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However, challenges persist, especially concerning the HIV Viral Load (VL)/Early Infant Diagnosis (EID) testing coverage and commodity availability, leading to stock-outs and delays in testing processes. This study assessed the factors influencing commodity availability and testing platform performance in public health facilities. Methods A cross-sectional study design was used, incorporating both quantitative and qualitative data collection methods. Primary and secondary data were collected, with a data abstraction form from stock cards/stock books and Point of Care (POC) data systems for HIV VL and EID POC consumption data in the respective facilities. In-depth interviews, guided by an interview guide, were conducted with healthcare workers to capture factors affecting HIV EID/VL POC testing commodities and platform performance. STATA 15.0 was used for quantitative data analysis, while thematic analysis was used for qualitative data. Results Overall, the average stock-out duration per month was 9 days for VL POC cartridges and 8 days for EID POC cartridges. Most of the facilities 59% experienced EID cartridge stock-outs for fewer than 5 days per month, while 27% faced stock-outs exceeding 10 days. Similarly, 59% of facilities had VL POC cartridge shortages for fewer than 5 days, whereas 32% experienced stock-outs for more than 10 days. Stock availability was significantly associated with positivity rates. Furthermore, the mean equipment utilization rate was 47%, with only 18% of facilities achieving optimal utilization. Factors significantly influencing POC platform performance included device type (aOR = 3.3, P = 0.039), positivity rate (aOR = 12, P = 0.017), sample error rate (aOR = 5, P = 0.01), and frequent result uploads to national systems (aOR = 3.8, P = 0.019). Conclusions The findings highlight persistent supply chain inefficiencies, with some facilities experiencing prolonged stock-outs. Low platform utilization was driven by equipment downtime, cartridge shortages, and inadequate staff training. Key challenges included supply chain delays, funding constraints, infrastructure gaps, and staffing shortages. Strengthening forecasting, procurement, distribution, and staff training alongside better coordination and infrastructure investment will be crucial for improving POC testing services and enhancing early HIV diagnosis. Stock availability Early Infant Diagnosis Viral Load Point of Care Human immunodeficiency virus GeneXpert M-pima testing platform and performance Figures Figure 1 Figure 2 Introduction The battle against HIV/AIDS continues to be a major worldwide health challenge, with sub-Saharan Africa bearing a disproportionate weight of the disease. The Joint United Nations Programme on HIV/AIDS through the 95-95-95 target requires that 95% of people living with HIV (PLHIV) know their status, 95% of people who know their status are accessing treatment, and 95% of people on treatment are virally suppressed ( 1 ). As of 2022, there were 1.5 million children (0-14years) living with HIV, 76% had viral suppression and only 72% were on Antiretroviral Therapy (ART) ( 2 , 3 ). It is further estimated that only 60% of newborns who are exposed to HIV receive an HIV test in their first two months of life ( 3 ). Delays in HIV testing among exposed newborns are a major contributing factor to children's inadequate access to ART ( 4 ). Therefore achieving maximum viral suppression and Early Infant Diagnosis (EID) of HIV infection is an important strategy to accelerate the global response to HIV/AIDS and achieve significant progress by 2030 ( 5 ). Achieving these requires uninterrupted supply of required commodities for testing and high performance of the testing platforms. The World Health Organisation (WHO) in an effort to address delays in testing and facilitating earlier initiation of antiretroviral therapy (ART) in infants prequalified two HIV EID and Viral Load Point of Care Testing (VL POC) diagnostic technologies, Xpert® Analyser (Cepheid) and m-PIMA™ (ABBOTT) platform ( 6 ). Studies from South Africa, Malawi, and Zambia have demonstrated that the implementation of these POC technologies significantly enhanced both the timeliness of diagnosis and the uptake of ART among HIV-exposed infants ( 7 ) ( 8 ) ( 9 ). However, a major public health concern in sub-Saharan Africa is the presence of supply chain bottlenecks for necessary medical supplies at the different levels of healthcare facilities ( 10 ). At the heart of these bottlenecks lies the inadequate availability of essential commodities, critical to avoid, improve and cure infectious and preventable diseases ( 11 ). These shortages not only disrupt the workflow within healthcare facilities but also hinder the timely diagnosis and monitoring of HIV patients, resulting in suboptimal testing rates and compromising the overall effectiveness of the testing platforms. The availability of medical supplies is an indicator of how well any existing healthcare service delivery system is performing ( 11 ), and it is impacted by a number of factors such as inventory management practices, efficient transport system, financial constraints, data management practices, costly reagents and consumables for VL testing ( 11 ) ( 12 ) ( 13 ). While existing studies have documented the challenges and impacts of commodity shortages on healthcare delivery in various regions, ( 14 ) ( 15 ), there are notable gaps specifically related to the availability of HIV VL and EID POC testing commodities. In addition, studies on the performance of point-of-care (POC) testing platforms exist from Kenya and South Africa but have primarily focused on diagnostic accuracy, comparing POC testing to traditional laboratory methods ( 7 ) ( 16 ). Therefore, there exists a gap in research on the actual utilization and performance of POC testing platforms. This study assessed the factors affecting availability of HIV Viral Load/Early Infant Diagnosis point of care testing commodities and performance of testing platforms in public health facilities in Masaka region, Uganda. Understanding these factors in low-resource settings can inform targeted interventions to enhance the resilience and effectiveness of HIV testing platforms and ultimately improve outcomes for exposed infants. Methodology Study design and setting. A cross-sectional study design was employed involving collection of both quantitative and qualitative data. Firstly, quantitative data was collected from primary data source tools i.e. stock cards to assess the availability of HIV VL/EID POC testing commodities in public health facilities in the Masaka region, Uganda. Secondly, the VL and EID data that gleaned from three POC data systems: Alis, LabXpert, and Sympheos was compared across these systems, analyzed, juxtaposed against predetermined testing targets within the platforms systems. By scrutinizing these figures, the study aimed to establish device utilization rate from the expected benchmarks, providing invaluable insights into the performance of HIV VL/EID POC testing platforms. Finally, qualitative data collected through interviews with the health facility staff, was used to explore factors affecting the availability of testing commodities and performance of these testing platforms. The study encompassed all HIV VL/EID POC sites in the Masaka region of Uganda. This region has 22 HIV VL/EID POC testing public health facilities representative of different levels of care which include one Regional Referral Hospital, 4 General hospitals, 12 Health Centre IV and 5 Health Centre III. This inclusive approach facilitates a comprehensive examination of the factors and opportunities pertaining to HIV Early Infant Diagnosis (EID) and Viral Load (VL) Point-of-Care (POC) testing within a diverse and dynamic context. The MOH, Uganda has a total of 16 regions which include Arua, Entebbe, Fortportal,Gulu, Hoima, Jinja, Kabale, Kayunga, Lira, Masaka, Mbale, Mbarara, Karamoja, Mubende, Soroti and Yumbe region. The 16 regions have varying HIV prevalence rates from 2.1% in Karamoja region to 8.1% in Masaka region. There are 326 POC testing sites in Uganda. The choice to include Masaka region stems from its heightened prevalence of HIV of 8.1% amongst all MOH regions in the country. This highlights a unique vantage point for the significance of investigating HIV VL/EID POC testing dynamics within this locale. Study population, sample size and sampling method Consumption data and stock out days of HIV VL/EID POC tests for FY 23/24 (July 2023-June 2024) were extracted from the POC data systems and stock cards accordingly in all the 22 facilities. For qualitative data collection, interviews were conducted in all 22 public health facilities, with participants purposively selected. Research Instruments and study variables Data abstraction checklist was utilized to determine availability that is stock out days per month, and number of tests of VL/EID POC done per month per category. Analysis of tests done versus targets per testing platform was used to determine the performance. The health care workers were interviewed using an interview guide providing qualitative insights into the factors affecting availability of HIV VL/EID POC testing commodities and performance of testing platforms. The study variables assessed are summarized in Table 1 . Table 1 Study variables No Objectives Variables Source of data and data collection tool 1. Availability of testing commodities • Stock out days • Stock card 2. Performance of testing platforms • Number of tests done for VL, EID per month • Number of tests required per platform • Sympheos for M-pima • LabXpert for genexpert • African Laboratory Information System (ALIS) 3. Factors affecting testing commodities and platform performance • Health facility factors • Health system related factors • Health Care worker/ interview guide Data Collection Techniques In this study, data abstraction forms served as a primary data collection technique to gather quantitative insights from stock cards/stock books and POC data systems for HIV VL and Early Infant EID POC consumption data in their respective facilities by the researcher. An interview guide was designed to get responses from Health Care workers on factors affecting HIV EID/VL POC testing commodities and platform performance. The KoBoCollect tool was used to facilitate electronic data collection, ensuring efficient and accurate data entry. During data collection, informed consents were obtained from all participants to ensure their voluntary participation and understanding of the study. Permission to carry out this study was obtained from the facility in-charges in the different facilities. Data analysis and management Data was exported to STATA version 15 for cleaning and analysis. Both descriptive and inferential statistics were used. Descriptive statistics included mean, range, median and standard deviation for numerical variables and frequencies for categorical variables. Inferential statistics using multivariate regression analysis was performed to establish the relationship between the dependent variables and independent variables. The relationship was considered statistically significant if the P value was less than 0.05 at 95% confidence interval. The items were considered available if stock-outs occurred for 5 or less days per month, while a commodity stock-out was defined as a stock-out lasting more than 5 days per month. This classification aligns with the WHO recommendation, which considers commodities available if they are in stock for more than 80% of the reporting period (i.e., stock-out days account for less than 20% of the total reporting period)( 17 ). The performance of the M-pima analyzer and GeneXpert machine was defined based on device throughput. The M-pima machine operates with a single module, while GeneXpert machines have varying module counts. On average, each module is expected to process three samples per day. A module processing fewer than three tests per day was considered low-performing, while processing three or more tests was classified as high performance. For qualitative data, thematic analysis was employed to code and interpret the data, focusing on factors influencing HIV VL/EID POC testing commodity availability and testing platform performance. These factors include health facility attributes, and broader health system elements. The findings were systematically organized around the identified themes and presented in a structured format. To enhance the credibility and depth of the analysis, illustrative quotes and examples from the interview transcripts were incorporated, providing rich, contextual evidence to support the interpretation of the findings. Results Socio-demographic and clinical characteristics of the study Majority of the facilities included in the study were HCIVs 12 (55%) and HCIIIs 5 (23%). Additionally, 17 (77%) of the facilities utilize the M-pima platform for POC testing, and 12 (55%) of the facilities serve as non-hubs (Table 2 ). Table 2 Characteristics of the facilities included in the study Characteristics Frequency (%), N = 22 Facility Level HC III 5(23%) HC IV 12(55%) General Hospital 4(18%) Regional Referral Hospital 1(4%) Device type GeneXpert 5(23%) M-pima 17(77%) Hub Category Hub 10(45%) Non-hub 12(55%) The participants interviewed included laboratory technicians 8 (36%), logistics officers 5 (23%), data officers 5 (23%) and clinicians 4 (18%). Availability of the HIV VL/EID POC testing commodities The average stock-out duration for VL POC cartridges was 9 days (SD = 5.4), with a median of 11 days. Similarly, the average stock-out duration for EID POC cartridges was 8 days (SD = 5.7), with a median of 9 days (Table 3 ). The majority (59%) of the facilities experienced EID cartridge stockouts for fewer than 5 days per month, while 27% faced stockouts exceeding 10 days per month. Likewise, 59% of the facilities experienced VL POC cartridge shortages for fewer than 5 days per month, whereas 32% faced stockouts for more than 10 days per month (Fig. 2 ). Table 3 Stock out days of POC commodities Variable No. of stock-out days Mean SD Median IQR Min Max Skewness Kurtosis VL POC 9 8.9 5.4 11 3–13 0 17 -0.36 1.64 EID POC 8 7.9 5.7 8.5 2–13 0 15 -0.09 1.31 SD-Standard Deviation; IQR-Interquartile range Factors associated with commodity stock availability at facilities At the multivariate level, stock availability was significantly associated with the positivity rate of the POC tests conducted (aOR = 7.87, P = 0.04). Facilities with a higher positivity rate are more likely to be stocked with commodities. The findings indicate that general hospitals are three times less likely to experience stockouts of POC commodities compared to HC IIIs. Additionally, facilities with an error rate exceeding 5% are 2.6 times more likely to have commodities in stock than those with lower error rates (Table 4 ). Table 4 Multivariate analysis of factors associated with commodity stock out of POC commodities Characteristics Stock Availability Adjusted OR (95% CI) p-Value Commodity available n (%) Commodity Stocked Out n (%) Facility Level HC III 3(60) 2(40) 1 HCIV 8(66) 4(34) 1.0(0.15–11.49) 0.79 General Hospital 1( 25 ) 3(75) 3.5(0.15–59.89) 0.47 RRH 1(100) 0 1.1(0.06–40.63) 0.81 Device GeneXpert 4(80) 1( 20 ) 1 Mpima 9(53) 8(47) 0.32(0.22–12.80) 0.61 Hub Hub 6(60) 4(40) 1 Non hub 7(58) 5(42) 0.93(0.17–5.15) 0.94 Positivity 1% 8(80) 2( 20 ) 7.87(1.10-56.12) 0.04** Error rate 5% 1( 25 ) 3(75) 2.6(0.75–5.32) 0.29 Data Upload 50%% 9(82) 4( 18 ) 2.22(0.17–28.86) 0.54 Utilisation rate < 100% 2(50) 2(50) 1 ≥ 100% 7(39) 11(61) 1.57(0.18–13.86) 0.41 Commodity available (stock out 5 days per month) Performance of the HIV VL/EID POC testing platforms. The mean utilization rate was 47% (SD = 50%), with a median of 32% and an interquartile range (IQR) of 14%–56% (Table 5 ). Facilities achieving a high equipment utilization rate of over 100% comprised 18% of the total. In contrast, the majority of the facilities 73% operated below 50% machine utilization (Fig. 3 ). Table 5 Descriptive summary of the POC equipment utilization rate Variable Obs Mean SD Median IQR Min Max Skewness Kurtosis Utilization rate 22 47% 50% 32% 14%-56% 2% 197% 1.62 4.93 Obs-Observations; SD-Standard Deviation; IQR-Interquartile range Factors associated with performance of the HIV VL/EID POC testing platforms The factors significantly associated with performance of the POC testing platforms were device type (aOR = 3.3, P value = 0.039), positivity rate (aOR = 12, P value = 0.017), sample error rate (aOR = 5, P value = 0.01) and frequent result upload onto the national dashboard/systems (aOR = 3.8, P value = 0.019). Additionally, results indicate that general hospitals are 2.6 times more likely to have a higher utilization rate compared to HCIIIs, while RRHs are 1.5 times more likely to have a higher utilization rate than HCIIIs. Regarding testing platforms, the mPIMA machine is 33% more likely to achieve a higher utilization rate compared to the GeneXpert machine. Furthermore, non-hub facilities are 70% less likely to fully utilize testing devices compared to hub facilities (Table 6 ). Table 6 Multivariate analysis of factors associated with performance of the POC testing platforms Characteristics Utilization rate Adjusted OR (95% CI) p-Value Low utilization rate Frequency (%) High utilization rate Frequency (%) Facility Level HC III 4( 25 ) 1( 17 ) 1 HCIV 10(63) 2(33) 0.8(0.0-6.13) 0.30 General Hospital 1( 6 ) 3(50) 2.6(0.11–43.17) 0.42 RRH 1( 6 ) 0 1.5(0.06–40.63) 0.81 Device type GeneXpert 4( 25 ) 1( 17 ) 1 M-pima 12(75) 5(83) 3.3(1.06–9.96) 0.039** Hub Hub 10(63) 2(33) 1 Non hub 6(37) 4(67) 0.7(0.40–9.02) 0.220 Positivity 1% 4(29) 6(100) 12(1.56–92.3) 0.017** Error rate 5% 2( 13 ) 2(33) 5(1.45–17.27) 0.01** Data Upload 50% 9(56) 1( 17 ) 3.8(1.24–11.29) 0.019** Stock availability ≤ 5 days 2 7 1 0.684 > 5 days 2 11 0.57(0.18–13.86) Low utilization (< 100%), high utilization (≥ 100%) Factors affecting availability of HIV VL/EID POC testing commodities and performance of testing platforms. The factors highlighted in the interviews regarding the availability of HIV/EID POC testing commodities and the performance of testing machines are summarized in the Table 8 . Table 8 Factors affecting availability HIV/EID POC testing Commodities and the performance of testing platforms No Factor affecting availability of HIV/EID POC testing commodities Frequency 1 Supply chain inefficiencies 45.7% 2 Community sensitization on Maternal Child Health (MCH) services 19.6% 3 Frequent machine breakdown 15.2% 4 Data collection system 10.9% 5 Funding and Budget Constraints 8.7% No Factor affecting performance of testing platforms Frequency 1 Supply chain delays and stockouts 33.9% 2 Sample quality and availability 23.2% 3 Frequent machine breakdown 14.3% 4 Awareness and training gaps 12.5% 5 Human Resource and Workload Constraints 7.1% 6 Power inconsistencies 5.4% 7 Funding and budget limitations 3.6% Factors affecting the availability of HIV/EID POC testing commodities Supply chain inefficiencies Health workers across facilities reported that supply chain inefficiencies significantly affect the availability of POC stock for EID and VL testing. They highlighted that, delays in procurement and distribution, coupled with ineffective ordering and inventory management, further exacerbate the problem, leading to an undersupply of critical testing commodities. Additionally, the short shelf life of some commodities increases the risk of expiries before use, further worsening stock shortages. “ We experience frequent delays in delivery, which affects service continuity. Even when we order on time, there are sometimes national stockouts, leading to inconsistent supply by Central Public Health Laboratory (CPHL).” (KII 1 ) " There is no proper ordering system, making it hard to track and request supplies efficiently. We try to practice good ordering practices but inconsistent supply makes it difficult ." (KII 4) “ As a facility we don’t have an account to order for commodities from CPHL, it is the hub with an account and supposed to order for us. So, it becomes hard to call the hub and tell them to make for us an order because they sometimes be busy ” (KII 3) “ Sometimes we receive commodities that are almost expiring making it difficult to use them before expiration because sometimes there is low patient turn-up ” (KII 1) Data collection system It was observed that some facilities lacked standard data collection tools. This affected the harmony and the quality of the data collected from various facilities, e.g. some facilities lacked or had un-updated stock cards so it is hard to monitor inventory in such cases. “ Despite several trainings in Logistics Management Information System, five out of twenty-two facilities had poor inventory keeping with data entries not done in real time . But it takes so long to correct issues. You will be going through the same issues year in year out. Right now, am collecting data for quantification, and am seeing the same mistakes as we were having last year, stock cards are not filled.” ( KII4) The study further noted that there was a persistent use of old data to quantify and forecast for future needs despite a growing demand. One operator put it succinctly; “I will say ok we did 38 tests, but that is because there was no sensitization, those are people who were just willing. The moment you release the results; people get encouraged to send more tests. So, you find that your 38 tests in the next months automatically just goes up to 78 because you released the results the previous month, but they would have supplied you 38 test cartridges” (KII 3). Frequent machine breakdown Health workers noted that frequent breakdown of the testing devices often disrupted testing services, making it difficult to utilize all the supplied cartridges before they expired. As a result, expired stock led to wastage and reduced the overall efficiency of EID/VL POC testing at healthcare facilities. “The machine they gave us reads a lot of errors and sometimes refuses to go on completely. We called the number on the machine [service provider] and it was taken for repair however, it took a very long time to be returned. All the commodities that were supplied at that time expired because we were not using them” (KII 3) Community sensitization on Maternal Child Health (MCH) services Community sensitization on MCH services has greatly increased awareness and demand for these services, particularly at POC sites. As more people seek MCH services, the demand for essential testing cartridges also rises. This increased demand can sometimes outpace supply, leading to shortages of testing cartridges. “Every Tuesday at a community church, we sensitize communities on the MCH services. So many mothers now know the advantages of testing and having the baby tested, this increases demand leading to use of the cartridges allocated to the facility. Again, when we order for commodities, they take many weeks to be delivered” (KII 4) Coordination related challenges/ Funding Constraints EID/VL POC commodity availability requires effective resource mobilization among major players such as government, donors and implementing partners. (KII 4) stated “ We have a lot of partners that are pushing this POC programme but there is very little that they do in terms of procurement of testing cartridges and I think it’s time they also get engaged in the procurement. ” Factors affecting performance of testing platforms Supply Chain and Stock-out Issues The frequent stock-out of essential supplies such as cartridges, reagents, and other testing consumables hinders the continuous operation of POC equipment. Facilities often face inconsistent supply and shortages, which disrupt service delivery. In some cases, the commodities provided have short expiration dates, leading to wastage and further exacerbating stock limitations. Without a stable supply of testing commodities, healthcare workers are unable to maximize the utilization of POC platforms. “ Most of the time, we have patients who need testing, but the machines are just sitting idle because we don’t have cartridges. It’s frustrating because we are ready to work, but we simply don’t have the supplies. And sometimes when we receive the commodities, they expire in a short time and then we go back to not having.” (KII 4) "The supply is very inconsistent. One time we get enough, then the next time, we don’t get anything at all. This makes planning very difficult and affects our ability to use the POC machines effectively." (KII 3) Power/electricity inconsistencies Unreliable power supply in some facilities affects the utilization of POC testing equipment for EID and HIV VL testing. Facilities that lack backup power sources, such as generators or solar power, are forced to leave machines idle for extended periods during power outages. "Power cuts happen often, and when they do, we have to stop testing. Some samples even go bad because we can't process them in time. We need a stable power supply or reliable power backup like solar power. Without that, we are always struggling to keep our testing services running." (KII 3) Frequent machine breakdown Many facilities struggle with old or poorly maintained equipment, leading to frequent breakdown that disrupt operations. In some cases, delays in repairs and a lack of readily available technical support worsen the situation, leaving machines out of service for extended periods. Additionally, a shortage of spare parts further prolongs downtime, forcing healthcare workers to either refer patients to other facilities or postpone testing. As a result, machine underutilization remains a persistent issue. “ The machine keeps breaking down, and every time we report it, repairs take too long. Patients keep coming, but we can't test them. Sometimes the issue is minor, but because we don’t have a technician on-site, we have to wait for someone to come from CPHL, which can take weeks." (KII 3) “Our machine has been down for months, and there’s no clear timeline on when it will be fixed. We have no choice but to refer samples to CPHL." (KII 4) Human Resource and Workload Constraints The shortage of trained laboratory personnel increases workload pressure on available staff, leading to delays in testing and result processing. In some facilities, staff members are required to multitask, balancing POC testing with other laboratory responsibilities. This contributes to burnout and inefficiencies, limiting the overall utilization of POC equipment. “ The few of us handling testing are constantly overworked. More staff and regular capacity-building trainings would really help improve testing” (KII 3) Awareness and Training Gaps A lack of clinician sensitization regarding the availability and purpose of POC testing platforms affects utilization. Many healthcare providers are either unaware of the equipment or fail to fully integrate it into their patient management workflows. Without adequate training, clinicians may underutilize POC platforms. “ There needs to be more training on the purpose and use of these machines. Without it, they just sit idle” (KII 3) Funding and Budget Limitations Most testing commodities and equipment maintenance depend on donor funding, with minimal government support. Limited financial resources constrain the procurement of supplies and hinder efforts to expand POC testing capacity. Inadequate funding also affects timely servicing and replacement of faulty equipment, further reducing the efficiency of POC testing. "The lack of consistent financial support from our implementing partner makes it difficult to expand POC testing. We’re stuck with old equipment that often breaks down, and we were told that there’s no money to replace it” (KII 4) Sample Quality and Availability Issues The availability of high-risk (eligible client) mothers and babies directly influences testing volumes at facilities. Poor sample collection and handling practices can lead to errors and test failures, affecting overall utilization. Additionally, fluctuating sample volumes due to inconsistent patient flow means that some facilities may experience periods of underutilization of POC platforms, while others are overwhelmed with demand. “There are times when we don’t have enough patients to meet testing targets, so the machines sit idle. But then when there’s a rush, we are overwhelmed and can’t process all the samples in time." (KII 3) Discussion This study highlights the availability and performance of POC HIV VL and EID testing, crucial for strengthening diagnostic capacity and efficiency in developing countries. These findings can guide interventions to improve timely, accurate testing, enhancing patient care and HIV treatment programs. From the current study, the average stock-out duration for VL POC cartridges was 9 days (SD = 5.4), while for EID POC cartridges, it was 8 days (SD = 5.7). Stock-out patterns revealed that 59% of the facilities in the study experienced shortages for fewer than 5 days per month, indicating stock availability in alignment with WHO guidelines ( 17 ). The long average stockout durations however, underscores the challenges in maintaining a steady supply of POC testing commodities. This aligns with findings from a study in Wakiso District, Uganda, which highlighted challenges in maintaining a steady supply of HIV tracer commodities and also a 2020 UNICEF report about HIV EID and VL POC diagnostics market and supply update ( 18 ) ( 7 ). In line with the Wakiso study and UNICEF report, supply chain delays, funding constraints, and reliance on external resources were identified as key contributors to stockouts. Inconsistent access to testing commodities and functional devices hinders service delivery negatively impacting on the attainment of HIV testing and treatment targets. Uganda still lags behind in attainment of 95-95-95 HIV targets. As of 2023, the country had made significant progress, reaching 92-90-94 ( 19 ). However, gaps remain in diagnosis, treatment, and viral suppression, underscoring the need for intensified efforts to bridge these gaps. Ensuring a reliable supply chain that assures continued access to testing commodities is crucial for timely HIV diagnosis and treatment, as demonstrated by studies in Uganda ( 20 ) and Baltimore ( 21 ). The study also found that facilities with higher positivity rates were more likely to maintain essential stock, possibly due to increased demand and proactive stock management. General hospitals were better stocked than smaller facilities, benefiting from more stable resources and supply chains similarly to findings from a study in Nigeria that suggested that higher-level facilities may optimize testing usage more effectively due to better resources and infrastructure ( 22 ). In addition, the difference in the results could be due to higher volume of expected patients at higher level facilities with improved resource allocation compared to lower-level facilities. The findings of the current study showed low utilization of POC testing platforms, with only 18% of facilities achieving a utilization rate of 100% and above, and a mean rate of 47% (SD = 50%). It was found out that many facilities struggled to maximize their testing capacity due to operational challenges, including supply chain and stock-out issues, power inconsistences and frequent equipment breakdowns. Delays in maintenance further contributed to prolonged downtimes, limiting efficiency. The study is in agreement with previous study in Dodoma, Tanzania that highlighted that the continuous lack of medications and commodities in public health facilities affects healthcare utilization and individual decisions to see medical professionals ( 15 ). The study suggests that improving supply chain management to ensure a consistent supply of cartridges and establishing a proactive maintenance framework with regular checks and repairs will help reduce equipment breakdowns and downtime, ultimately improving machine utilization. Additionally, the quantitative analysis identified several factors with a significant statistical relationship to POC testing performance. These factors included device type (aOR = 3.3, P = 0.039), positivity rate (aOR = 12, P = 0.017), error rate (aOR = 12, P = 0.01), and test result uploads (aOR = 3.8, P = 0.019). General hospitals and regional referral hospitals were more likely to optimize testing usage compared to smaller facilities, which reflected differences in available resources and infrastructure. A possible explanation could be the higher volume of samples collected at higher-level facilities compared to lower-level ones, similar to a study that assessed GeneXpert MTB/RIF performance by facility type and level in Nigeria( 20 ). Additionally, these findings align with a previous study that highlighted inconsistencies in machine utilization and the availability of diagnostic commodities across different healthcare facilities. This suggests that localized factors, such as facility-level management practices and regional supply chain issues, play a significant role in exacerbating these challenges( 23 ) ( 24 ). The GeneXpert machine outperformed the mPIMA device, likely due to its higher sample throughput, while hub facilities exhibited greater efficiency than non-hub sites similar to findings from a study in Northern Uganda that highlighted the role of centralized hubs in optimizing diagnostic testing through improved resource allocation and infrastructure ( 25 ). Regular training for staff on the efficient use of testing platforms and troubleshooting common issues is crucial for minimizing error rates and improving overall performance. Equipping healthcare workers with the necessary skills and knowledge not only enhances the accuracy and reliability of diagnostic processes but also ensures the optimal utilization of available resources. Continuous capacity-building initiatives help address common technical challenges, reduce downtime due to equipment malfunctions, and streamline workflow efficiency within healthcare facilities. Additionally, high sample error rates contribute to increased operational costs by wasting both time and diagnostic commodities. Frequent errors necessitate repeated testing, leading to additional expenditures on reagents, cartridges, and other essential materials. This inefficiency places a significant strain on already limited healthcare budgets and can delay timely patient diagnosis and treatment. By addressing the underlying causes of high error rates through targeted training and quality control measures, healthcare facilities can improve productivity, reduce unnecessary costs, and ultimately enhance patient outcomes. Study Limitations The study had limitations such as time-specific focus which meant that it possibly did not fully reflect changes in commodity availability and testing platform performance over time due to factors such as shifting funding, policies, or disease prevalence. Nonetheless, the data provided meaningful insights into the operational challenges of POC testing during the study period. The study’s generalizability is limited, as it was conducted only in public hospitals within one region, excluding private and other regional settings. Still, the inclusion of various healthcare levels ensured representativeness within the targeted population, making the findings adaptable to similar contexts. Conclusion The findings indicate that while most facilities experienced stock-outs for fewer than five days per month, a substantial proportion faced prolonged shortages, highlighting persistent supply chain inefficiencies. Additionally, platform utilization varied, with only a small fraction of facilities achieving optimal usage due to factors such as equipment downtime, inconsistent supply of cartridges, and inadequate staff training. Key challenges affecting both availability and performance included supply chain delays, funding constraints, infrastructural limitations, and human resource shortages. Recommendations There is a need to strengthen supply chain management by improving forecasting, procurement, and coordination with regional and national stakeholders using real-time data. The healthcare facilities should also maintain accurate stock records and conduct routine audits to support data-driven decision-making and accountability. To improve testing platform performance, equipment issues should be addressed through preventive maintenance, reliable power backup, and timely technical support, while also facilitating regular training for healthcare workers. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Makerere University School of Health Sciences Research and Ethics Committee (MAKSHSREC) under approval number MAKSHSREC-2024-765. Participants were informed about the purpose of the research, and confidentiality maintained throughout the study. All collected data was anonymized to protect the identities of the participants. Competing interests The authors declare that they have no competing interests Consent for publication Not applicable Funding This study did not receive any specific funding. Author Contribution NE conceived and designed the study, coordinated data collection, and led the analysis and interpretation. KR contributed to study conceptualization, methodology, and data validation. NW supported data analysis and interpretation. BV, THO, FA and AJ contributed to validation, critical review, and interpretation of findings. NE drafted the initial manuscript, and all authors contributed to manuscript review and editing. All authors read and approved the final version of the manuscript. Data Availability The datasets used and/or analyzed during this study are available and can be shared upon request. References Ministry of Health, Uganda. Consolidated Guidelines For The Prevention And Treatment Of Hiv And Aids In Uganda. 2022; Available from: https://guluhospital.net/wp-content/uploads/2023/05/Consolidated-Hiv-Aids-Guidelines-2022.pdf Han WM, Law MG, Egger M, Wools-Kaloustian K, Moore R, McGowan C, et al. Global estimates of viral suppression in children and adolescents and adults on antiretroviral therapy adjusted for missing viral load measurements: a multiregional, retrospective cohort study in 31 countries. Lancet HIV. 2021;8(12):e766–75. World Health Organisation. Updated Recommendations on HIV Prevention, Infant Diagnosis, Antiretroviral Initiation And Monitoring. 2021. Chinguwo F, Nyondo-Mipando AL. Integration of Early Infant Diagnosis of HIV Services Into Village Health Clinics in Ntcheu, Malawi: An Exploratory Qualitative Study. J Int Assoc Provid AIDS Care. 2021;20:2325958220981256. UNAIDS. The path that ends AIDS: UNAIDS Global AIDS Update 2023. 2023. UNICEF. HIV Early Infant Diagnosis and Viral Load Point of Care Diagnostics: Market and Supply Update. 2020. Kufa T, Mazanderani AH, Sherman GG, Mukendi A, Murray T, Moyo F, et al. Point-of‐care HIV maternal viral load and early infant diagnosis testing around time of delivery at tertiary obstetric units in South Africa: a prospective study of coverage, results return and turn‐around times. J Int AIDS Soc. 2020;23(4):e25487. Ganesh P, Heller T, Chione B, Gumulira J, Gugsa S, Khan S, et al. Near Point-of-Care HIV Viral Load: Targeted Testing at Large Facilities. JAIDS J Acquir Immune Defic Syndr. 2021;86(2):258–63. Chibwesha CJ, Mollan KR, Ford CE, Shibemba A, Saha PT, Lusaka M, et al. A Randomized Trial of Point-of-Care Early Infant Human Immunodeficiency Virus (HIV) Diagnosis in Zambia. Clin Infect Dis. 2022;75(2):260–8. Mathias S, Isangula K, Kahwa A, Kimaro G, Ngadaya E, Mwenda L, et al. Factors Affecting the Availability of Essential Health Commodities in Tanzania with a Special Focus on the Tracer Commodities. Tanzan J Hlth Res. 2024;25(2):838–49. Atiga O, Walters J, Pisa N. Challenges of medical commodity availability in public and private health care facilities in the Upper East Region of Ghana: a patient-centered perspective. BMC Health Serv Res. 2023;23(1):719. Ministry of Health, Uganda. FY 2024/2025 Integrated Quantification for Essential Medicines and Health Supplies. 2024. Lubega P, Nalugya SJ, Kimuli AN, Twinokusiima M, Khasalamwa M, Kyomugisa R, et al. Adherence to viral load testing guidelines, barriers, and associated factors among persons living with HIV on ART in Southwestern Uganda: a mixed-methods study. BMC Public Health. 2022;22(1):1268. Adzimah ED, Awuah-Gyawu M, Aikins I. Prince Agyemang Duah. An Assessment Of Health Commodities Management Practices In Health Care Delivery; A Supply Chain Perspective. The Case of Selected Hospitals In Ashanti Region-Ghana. Kuwawenaruwa A, Wyss K, Wiedenmayer K, Metta E, Tediosi F. The effects of medicines availability and stock-outs on household’s utilization of healthcare services in Dodoma region, Tanzania. Health Policy Plann. 2020;35(3):323–33. Salvatore PP, De Broucker G, Vojnov L, Moss WJ, Dowdy DW, Sutcliffe CG. Modeling the cost-effectiveness of point-of-care platforms for infant diagnosis of HIV in sub-Saharan African countries. AIDS. 2021;35(2):287–97. Ewen M, Zweekhorst M, Regeer B, Laing R. Baseline assessment of WHO’s target for both availability and affordability of essential medicines to treat non-communicable diseases. Podobnik B, editor. PLoS ONE. 2017;12(2):e0171284. Lule F, Rajab K, Banzimana S, Asingizwe D. Assessing determinants of the availability of HIV tracer commodities in health facilities in Wakiso District, Uganda. J Pharm Policy Pract. 2024;17(1):2306846. UNAIDS. UNAIDS: Results and Transparency Portal. Available from: https://open.unaids.org/countries/uganda Muyingo S, Etoori D, Lotay P, Malamba S, Olweny J, Keesler K, et al. The procurement and supply chain strengthening project: improving public health supply chains for better access to HIV medicines, Uganda 2011–2016. J Pharm Policy Pract. 2022;15(1):72. Bayan MH, Smalls T, Boudreau A, Mirza AW, Pasco C, Demko ZO, et al. Evaluating the impact of point-of-care HIV viral load assessment on linkage to care in Baltimore, MD: a randomized controlled trial. BMC Infect Dis. 2023;23(1):570. Gidado M, Nwokoye N, Ogbudebe C, Nsa B, Nwadike P, Ajiboye P, et al. Assessment of GeneXpert MTB/RIF performance by type and level of health-care facilities in Nigeria. Niger Med J. 2019;60(1):33. Lugada E, Komakech H, Ochola I, Mwebaze S, Olowo Oteba M, Okidi Ladwar D. Health supply chain system in Uganda: current issues, structure, performance, and implications for systems strengthening. J Pharm Policy Pract. 2022;15(1):14. Lugada E, Ochola I, Kirunda A, Sembatya M, Mwebaze S, Olowo M, et al. Health supply chain system in Uganda: assessment of status and of performance of health facilities. J Pharm Policy Pract. 2022;15(1):58. Karamagi E, Nturo J, Donggo P, Kyobutungi I, Aloyo J, Sensalire S, et al. Using quality improvement to improve the utilisation of GeneXpert testing at five lab hubs in Northern Uganda. BMJ Open Qual. 2017;6(2):e000201. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":289100,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Proportion of facilities experiencing stock out of POC cartridges\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8058672/v1/534f73183c35175a2c959987.jpeg"},{"id":98780122,"identity":"1612d53b-32ce-4c07-bfd5-a3f972b90981","added_by":"auto","created_at":"2025-12-22 12:31:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82888,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: The utilization rate of the POC testing platforms\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8058672/v1/a5bded9d5f6fb3ac8874794d.jpg"},{"id":106344244,"identity":"fd3ca457-bc60-4b42-b280-e6a3a84fbfab","added_by":"auto","created_at":"2026-04-07 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2108834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8058672/v1/dc701205-bbe6-4ff6-a17e-8d39509a265c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors affecting availability of HIV Viral Load/Early Infant Diagnosis Point of Care testing Commodities and performance of testing platforms in public health facilities in Masaka region, Uganda","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe battle against HIV/AIDS continues to be a major worldwide health challenge, with sub-Saharan Africa bearing a disproportionate weight of the disease. The Joint United Nations Programme on HIV/AIDS through the 95-95-95 target requires that 95% of people living with HIV (PLHIV) know their status, 95% of people who know their status are accessing treatment, and 95% of people on treatment are virally suppressed (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As of 2022, there were 1.5\u0026nbsp;million children (0-14years) living with HIV, 76% had viral suppression and only 72% were on Antiretroviral Therapy (ART) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is further estimated that only 60% of newborns who are exposed to HIV receive an HIV test in their first two months of life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Delays in HIV testing among exposed newborns are a major contributing factor to children's inadequate access to ART (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore achieving maximum viral suppression and Early Infant Diagnosis (EID) of HIV infection is an important strategy to accelerate the global response to HIV/AIDS and achieve significant progress by 2030 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Achieving these requires uninterrupted supply of required commodities for testing and high performance of the testing platforms.\u003c/p\u003e \u003cp\u003eThe World Health Organisation (WHO) in an effort to address delays in testing and facilitating earlier initiation of antiretroviral therapy (ART) in infants prequalified two HIV EID and Viral Load Point of Care Testing (VL POC) diagnostic technologies, Xpert\u0026reg; Analyser (Cepheid) and m-PIMA\u0026trade; (ABBOTT) platform (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies from South Africa, Malawi, and Zambia have demonstrated that the implementation of these POC technologies significantly enhanced both the timeliness of diagnosis and the uptake of ART among HIV-exposed infants (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, a major public health concern in sub-Saharan Africa is the presence of supply chain bottlenecks for necessary medical supplies at the different levels of healthcare facilities (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). At the heart of these bottlenecks lies the inadequate availability of essential commodities, critical to avoid, improve and cure infectious and preventable diseases (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These shortages not only disrupt the workflow within healthcare facilities but also hinder the timely diagnosis and monitoring of HIV patients, resulting in suboptimal testing rates and compromising the overall effectiveness of the testing platforms.\u003c/p\u003e \u003cp\u003eThe availability of medical supplies is an indicator of how well any existing healthcare service delivery system is performing (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and it is impacted by a number of factors such as inventory management practices, efficient transport system, financial constraints, data management practices, costly reagents and consumables for VL testing (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). While existing studies have documented the challenges and impacts of commodity shortages on healthcare delivery in various regions, (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), there are notable gaps specifically related to the availability of HIV VL and EID POC testing commodities. In addition, studies on the performance of point-of-care (POC) testing platforms exist from Kenya and South Africa but have primarily focused on diagnostic accuracy, comparing POC testing to traditional laboratory methods (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Therefore, there exists a gap in research on the actual utilization and performance of POC testing platforms.\u003c/p\u003e \u003cp\u003eThis study assessed the factors affecting availability of HIV Viral Load/Early Infant Diagnosis point of care testing commodities and performance of testing platforms in public health facilities in Masaka region, Uganda. Understanding these factors in low-resource settings can inform targeted interventions to enhance the resilience and effectiveness of HIV testing platforms and ultimately improve outcomes for exposed infants.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e \u003cb\u003eStudy design and setting.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA cross-sectional study design was employed involving collection of both quantitative and qualitative data. Firstly, quantitative data was collected from primary data source tools i.e. stock cards to assess the availability of HIV VL/EID POC testing commodities in public health facilities in the Masaka region, Uganda. Secondly, the VL and EID data that gleaned from three POC data systems: Alis, LabXpert, and Sympheos was compared across these systems, analyzed, juxtaposed against predetermined testing targets within the platforms systems. By scrutinizing these figures, the study aimed to establish device utilization rate from the expected benchmarks, providing invaluable insights into the performance of HIV VL/EID POC testing platforms.\u003c/p\u003e \u003cp\u003eFinally, qualitative data collected through interviews with the health facility staff, was used to explore factors affecting the availability of testing commodities and performance of these testing platforms.\u003c/p\u003e \u003cp\u003eThe study encompassed all HIV VL/EID POC sites in the Masaka region of Uganda. This region has 22 HIV VL/EID POC testing public health facilities representative of different levels of care which include one Regional Referral Hospital, 4 General hospitals, 12 Health Centre IV and 5 Health Centre III. This inclusive approach facilitates a comprehensive examination of the factors and opportunities pertaining to HIV Early Infant Diagnosis (EID) and Viral Load (VL) Point-of-Care (POC) testing within a diverse and dynamic context. The MOH, Uganda has a total of 16 regions which include Arua, Entebbe, Fortportal,Gulu, Hoima, Jinja, Kabale, Kayunga, Lira, Masaka, Mbale, Mbarara, Karamoja, Mubende, Soroti and Yumbe region. The 16 regions have varying HIV prevalence rates from 2.1% in Karamoja region to 8.1% in Masaka region. There are 326 POC testing sites in Uganda.\u003c/p\u003e \u003cp\u003eThe choice to include Masaka region stems from its heightened prevalence of HIV of 8.1% amongst all MOH regions in the country. This highlights a unique vantage point for the significance of investigating HIV VL/EID POC testing dynamics within this locale.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population, sample size and sampling method\u003c/h2\u003e \u003cp\u003eConsumption data and stock out days of HIV VL/EID POC tests for FY 23/24 (July 2023-June 2024) were extracted from the POC data systems and stock cards accordingly in all the 22 facilities. For qualitative data collection, interviews were conducted in all 22 public health facilities, with participants purposively selected.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Instruments and study variables\u003c/h3\u003e\n\u003cp\u003eData abstraction checklist was utilized to determine availability that is stock out days per month, and number of tests of VL/EID POC done per month per category. Analysis of tests done versus targets per testing platform was used to determine the performance. The health care workers were interviewed using an interview guide providing qualitative insights into the factors affecting availability of HIV VL/EID POC testing commodities and performance of testing platforms.\u003c/p\u003e \u003cp\u003eThe study variables assessed are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObjectives\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource of data and data collection tool\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of testing commodities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Stock out days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Stock card\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerformance of testing platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Number of tests done for VL, EID per month\u003c/p\u003e \u003cp\u003e\u0026bull; Number of tests required per platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Sympheos for M-pima\u003c/p\u003e \u003cp\u003e\u0026bull; LabXpert for genexpert\u003c/p\u003e \u003cp\u003e\u0026bull; African Laboratory Information System (ALIS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors affecting testing commodities and platform performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026bull; Health facility factors\u003c/p\u003e \u003cp\u003e\u0026bull; Health system related factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Health Care worker/ interview guide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection Techniques\u003c/h3\u003e\n\u003cp\u003eIn this study, data abstraction forms served as a primary data collection technique to gather quantitative insights from stock cards/stock books and POC data systems for HIV VL and Early Infant EID POC consumption data in their respective facilities by the researcher. An interview guide was designed to get responses from Health Care workers on factors affecting HIV EID/VL POC testing commodities and platform performance. The KoBoCollect tool was used to facilitate electronic data collection, ensuring efficient and accurate data entry.\u003c/p\u003e \u003cp\u003e During data collection, informed consents were obtained from all participants to ensure their voluntary participation and understanding of the study. Permission to carry out this study was obtained from the facility in-charges in the different facilities.\u003c/p\u003e\n\u003ch3\u003eData analysis and management\u003c/h3\u003e\n\u003cp\u003eData was exported to STATA version 15 for cleaning and analysis. Both descriptive and inferential statistics were used. Descriptive statistics included mean, range, median and standard deviation for numerical variables and frequencies for categorical variables. Inferential statistics using multivariate regression analysis was performed to establish the relationship between the dependent variables and independent variables. The relationship was considered statistically significant if the P value was less than 0.05 at 95% confidence interval.\u003c/p\u003e \u003cp\u003eThe items were considered available if stock-outs occurred for 5 or less days per month, while a commodity stock-out was defined as a stock-out lasting more than 5 days per month. This classification aligns with the WHO recommendation, which considers commodities available if they are in stock for more than 80% of the reporting period (i.e., stock-out days account for less than 20% of the total reporting period)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe performance of the M-pima analyzer and GeneXpert machine was defined based on device throughput. The M-pima machine operates with a single module, while GeneXpert machines have varying module counts. On average, each module is expected to process three samples per day. A module processing fewer than three tests per day was considered low-performing, while processing three or more tests was classified as high performance.\u003c/p\u003e \u003cp\u003eFor qualitative data, thematic analysis was employed to code and interpret the data, focusing on factors influencing HIV VL/EID POC testing commodity availability and testing platform performance. These factors include health facility attributes, and broader health system elements. The findings were systematically organized around the identified themes and presented in a structured format. To enhance the credibility and depth of the analysis, illustrative quotes and examples from the interview transcripts were incorporated, providing rich, contextual evidence to support the interpretation of the findings.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic and clinical characteristics of the study\u003c/h2\u003e \u003cp\u003eMajority of the facilities included in the study were HCIVs 12 (55%) and HCIIIs 5 (23%). Additionally, 17 (77%) of the facilities utilize the M-pima platform for POC testing, and 12 (55%) of the facilities serve as non-hubs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the facilities included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (%), N\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(55%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional Referral Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDevice type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneXpert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-pima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHub Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-hub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(55%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe participants interviewed included laboratory technicians 8 (36%), logistics officers 5 (23%), data officers 5 (23%) and clinicians 4 (18%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAvailability of the HIV VL/EID POC testing commodities\u003c/h3\u003e\n\u003cp\u003eThe average stock-out duration for VL POC cartridges was 9 days (SD\u0026thinsp;=\u0026thinsp;5.4), with a median of 11 days. Similarly, the average stock-out duration for EID POC cartridges was 8 days (SD\u0026thinsp;=\u0026thinsp;5.7), with a median of 9 days (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe majority (59%) of the facilities experienced EID cartridge stockouts for fewer than 5 days per month, while 27% faced stockouts exceeding 10 days per month. Likewise, 59% of the facilities experienced VL POC cartridge shortages for fewer than 5 days per month, whereas 32% faced stockouts for more than 10 days per month (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStock out days of POC commodities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of stock-out days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVL POC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEID POC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSD-Standard Deviation; IQR-Interquartile range\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with commodity stock availability at facilities\u003c/h2\u003e \u003cp\u003eAt the multivariate level, stock availability was significantly associated with the positivity rate of the POC tests conducted (aOR\u0026thinsp;=\u0026thinsp;7.87, P\u0026thinsp;=\u0026thinsp;0.04). Facilities with a higher positivity rate are more likely to be stocked with commodities. The findings indicate that general hospitals are three times less likely to experience stockouts of POC commodities compared to HC IIIs. Additionally, facilities with an error rate exceeding 5% are 2.6 times more likely to have commodities in stock than those with lower error rates (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of factors associated with commodity stock out of POC commodities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStock Availability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommodity available n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommodity Stocked Out n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0(0.15\u0026ndash;11.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5(0.15\u0026ndash;59.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1(0.06\u0026ndash;40.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDevice\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneXpert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMpima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32(0.22\u0026ndash;12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHub\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon hub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.17\u0026ndash;5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.87(1.10-56.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.04**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eError rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6(0.75\u0026ndash;5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Upload\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50%%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22(0.17\u0026ndash;28.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUtilisation rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57(0.18\u0026ndash;13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCommodity available (stock out \u0026lt;\u0026thinsp;5 days per month), commodity stockout (stockout\u0026thinsp;\u0026gt;\u0026thinsp;5 days per month)\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePerformance of the HIV VL/EID POC testing platforms.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mean utilization rate was 47% (SD\u0026thinsp;=\u0026thinsp;50%), with a median of 32% and an interquartile range (IQR) of 14%\u0026ndash;56% (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFacilities achieving a high equipment utilization rate of over 100% comprised 18% of the total. In contrast, the majority of the facilities 73% operated below 50% machine utilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive summary of the POC equipment utilization rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%-56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e197%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eObs-Observations; SD-Standard Deviation; IQR-Interquartile range\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with performance of the HIV VL/EID POC testing platforms\u003c/h2\u003e \u003cp\u003eThe factors significantly associated with performance of the POC testing platforms were device type (aOR\u0026thinsp;=\u0026thinsp;3.3, P value\u0026thinsp;=\u0026thinsp;0.039), positivity rate (aOR\u0026thinsp;=\u0026thinsp;12, P value\u0026thinsp;=\u0026thinsp;0.017), sample error rate (aOR\u0026thinsp;=\u0026thinsp;5, P value\u0026thinsp;=\u0026thinsp;0.01) and frequent result upload onto the national dashboard/systems (aOR\u0026thinsp;=\u0026thinsp;3.8, P value\u0026thinsp;=\u0026thinsp;0.019). Additionally, results indicate that general hospitals are 2.6 times more likely to have a higher utilization rate compared to HCIIIs, while RRHs are 1.5 times more likely to have a higher utilization rate than HCIIIs.\u003c/p\u003e \u003cp\u003eRegarding testing platforms, the mPIMA machine is 33% more likely to achieve a higher utilization rate compared to the GeneXpert machine. Furthermore, non-hub facilities are 70% less likely to fully utilize testing devices compared to hub facilities (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of factors associated with performance of the POC testing platforms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUtilization rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow utilization rate\u003c/p\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHigh utilization rate\u003c/p\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacility Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8(0.0-6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6(0.11\u0026ndash;43.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5(0.06\u0026ndash;40.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDevice type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneXpert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-pima\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3(1.06\u0026ndash;9.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.039**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHub\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon hub\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7(0.40\u0026ndash;9.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePositivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(1.56\u0026ndash;92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eError rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(1.45\u0026ndash;17.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Upload\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8(1.24\u0026ndash;11.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStock availability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026nbsp;5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57(0.18\u0026ndash;13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLow utilization (\u0026lt;\u0026thinsp;100%), high utilization (\u0026ge;\u0026thinsp;100%)\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFactors affecting availability of HIV VL/EID POC testing commodities and performance of testing platforms.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe factors highlighted in the interviews regarding the availability of HIV/EID POC testing commodities and the performance of testing machines are summarized in the Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors affecting availability HIV/EID POC testing Commodities and the performance of testing platforms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor affecting availability of HIV/EID POC testing commodities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupply chain inefficiencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity sensitization on Maternal Child Health (MCH) services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequent machine breakdown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData collection system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunding and Budget Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFactor affecting performance of testing platforms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFrequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupply chain delays and stockouts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample quality and availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequent machine breakdown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwareness and training gaps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Resource and Workload Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePower inconsistencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunding and budget limitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFactors affecting the availability of HIV/EID POC testing commodities\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eSupply chain inefficiencies\u003c/h2\u003e \u003cp\u003eHealth workers across facilities reported that supply chain inefficiencies significantly affect the availability of POC stock for EID and VL testing. They highlighted that, delays in procurement and distribution, coupled with ineffective ordering and inventory management, further exacerbate the problem, leading to an undersupply of critical testing commodities. Additionally, the short shelf life of some commodities increases the risk of expiries before use, further worsening stock shortages.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eWe experience frequent delays in delivery, which affects service continuity. Even when we order on time, there are sometimes national stockouts, leading to inconsistent supply by Central Public Health Laboratory (CPHL).\u0026rdquo; (KII 1\u003c/em\u003e)\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eThere is no proper ordering system, making it hard to track and request supplies efficiently. We try to practice good ordering practices but inconsistent supply makes it difficult\u003c/em\u003e.\" (KII 4)\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eAs a facility we don\u0026rsquo;t have an account to order for commodities from CPHL, it is the hub with an account and supposed to order for us. So, it becomes hard to call the hub and tell them to make for us an order because they sometimes be busy\u003c/em\u003e\u0026rdquo; (KII 3)\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eSometimes we receive commodities that are almost expiring making it difficult to use them before expiration because sometimes there is low patient turn-up\u003c/em\u003e\u0026rdquo; (KII 1)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData collection system\u003c/h2\u003e \u003cp\u003eIt was observed that some facilities lacked standard data collection tools. This affected the harmony and the quality of the data collected from various facilities, e.g. some facilities lacked or had un-updated stock cards so it is hard to monitor inventory in such cases.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eDespite several trainings in Logistics Management Information System, five out of twenty-two facilities had poor inventory keeping with data entries not done in real time\u003c/em\u003e. \u003cem\u003eBut it takes so long to correct issues. You will be going through the same issues year in year out. Right now, am collecting data for quantification, and am seeing the same mistakes as we were having last year, stock cards are not filled.\u0026rdquo;\u003c/em\u003e (\u003cem\u003eKII4)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe study further noted that there was a persistent use of old data to quantify and forecast for future needs despite a growing demand. One operator put it succinctly; \u003cem\u003e\u0026ldquo;I will say ok we did 38 tests, but that is because there was no sensitization, those are people who were just willing. The moment you release the results; people get encouraged to send more tests. So, you find that your 38 tests in the next months automatically just goes up to 78 because you released the results the previous month, but they would have supplied you 38 test cartridges\u0026rdquo;\u003c/em\u003e (KII 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFrequent machine breakdown\u003c/h2\u003e \u003cp\u003eHealth workers noted that frequent breakdown of the testing devices often disrupted testing services, making it difficult to utilize all the supplied cartridges before they expired. As a result, expired stock led to wastage and reduced the overall efficiency of EID/VL POC testing at healthcare facilities.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The machine they gave us reads a lot of errors and sometimes refuses to go on completely. We called the number on the machine [service provider] and it was taken for repair however, it took a very long time to be returned. All the commodities that were supplied at that time expired because we were not using them\u0026rdquo;\u003c/em\u003e (KII 3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCommunity sensitization on Maternal Child Health (MCH) services\u003c/h2\u003e \u003cp\u003eCommunity sensitization on MCH services has greatly increased awareness and demand for these services, particularly at POC sites. As more people seek MCH services, the demand for essential testing cartridges also rises. This increased demand can sometimes outpace supply, leading to shortages of testing cartridges.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Every Tuesday at a community church, we sensitize communities on the MCH services. So many mothers now know the advantages of testing and having the baby tested, this increases demand leading to use of the cartridges allocated to the facility. Again, when we order for commodities, they take many weeks to be delivered\u0026rdquo; (KII 4)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCoordination related challenges/ Funding Constraints\u003c/h2\u003e \u003cp\u003eEID/VL POC commodity availability requires effective resource mobilization among major players such as government, donors and implementing partners. (KII 4) stated \u0026ldquo;\u003cem\u003eWe have a lot of partners that are pushing this POC programme but there is very little that they do in terms of procurement of testing cartridges and I think it\u0026rsquo;s time they also get engaged in the procurement.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFactors affecting performance of testing platforms\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSupply Chain and Stock-out Issues\u003c/h2\u003e \u003cp\u003eThe frequent stock-out of essential supplies such as cartridges, reagents, and other testing consumables hinders the continuous operation of POC equipment. Facilities often face inconsistent supply and shortages, which disrupt service delivery. In some cases, the commodities provided have short expiration dates, leading to wastage and further exacerbating stock limitations. Without a stable supply of testing commodities, healthcare workers are unable to maximize the utilization of POC platforms.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eMost of the time, we have patients who need testing, but the machines are just sitting idle because we don\u0026rsquo;t have cartridges. It\u0026rsquo;s frustrating because we are ready to work, but we simply don\u0026rsquo;t have the supplies. And sometimes when we receive the commodities, they expire in a short time and then we go back to not having.\u0026rdquo; (KII 4)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"The supply is very inconsistent. One time we get enough, then the next time, we don\u0026rsquo;t get anything at all. This makes planning very difficult and affects our ability to use the POC machines effectively.\" (KII 3)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePower/electricity inconsistencies\u003c/h2\u003e \u003cp\u003eUnreliable power supply in some facilities affects the utilization of POC testing equipment for EID and HIV VL testing. Facilities that lack backup power sources, such as generators or solar power, are forced to leave machines idle for extended periods during power outages.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Power cuts happen often, and when they do, we have to stop testing. Some samples even go bad because we can't process them in time. We need a stable power supply or reliable power backup like solar power. Without that, we are always struggling to keep our testing services running.\" (KII 3)\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eFrequent machine breakdown\u003c/h2\u003e \u003cp\u003eMany facilities struggle with old or poorly maintained equipment, leading to frequent breakdown that disrupt operations. In some cases, delays in repairs and a lack of readily available technical support worsen the situation, leaving machines out of service for extended periods. Additionally, a shortage of spare parts further prolongs downtime, forcing healthcare workers to either refer patients to other facilities or postpone testing. As a result, machine underutilization remains a persistent issue.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eThe machine keeps breaking down, and every time we report it, repairs take too long. Patients keep coming, but we can't test them. Sometimes the issue is minor, but because we don\u0026rsquo;t have a technician on-site, we have to wait for someone to come from CPHL, which can take weeks.\" (KII 3)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Our machine has been down for months, and there\u0026rsquo;s no clear timeline on when it will be fixed. We have no choice but to refer samples to CPHL.\" (KII 4)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eHuman Resource and Workload Constraints\u003c/h2\u003e \u003cp\u003eThe shortage of trained laboratory personnel increases workload pressure on available staff, leading to delays in testing and result processing. In some facilities, staff members are required to multitask, balancing POC testing with other laboratory responsibilities. This contributes to burnout and inefficiencies, limiting the overall utilization of POC equipment.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eThe few of us handling testing are constantly overworked. More staff and regular capacity-building trainings would really help improve testing\u0026rdquo; (KII 3)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eAwareness and Training Gaps\u003c/h2\u003e \u003cp\u003eA lack of clinician sensitization regarding the availability and purpose of POC testing platforms affects utilization. Many healthcare providers are either unaware of the equipment or fail to fully integrate it into their patient management workflows. Without adequate training, clinicians may underutilize POC platforms.\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eThere needs to be more training on the purpose and use of these machines. Without it, they just sit idle\u0026rdquo;\u003c/em\u003e (KII 3)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding and Budget Limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMost testing commodities and equipment maintenance depend on donor funding, with minimal government support. Limited financial resources constrain the procurement of supplies and hinder efforts to expand POC testing capacity. Inadequate funding also affects timely servicing and replacement of faulty equipment, further reducing the efficiency of POC testing.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"The lack of consistent financial support from our implementing partner makes it difficult to expand POC testing. We\u0026rsquo;re stuck with old equipment that often breaks down, and we were told that there\u0026rsquo;s no money to replace it\u0026rdquo; (KII 4)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eSample Quality and Availability Issues\u003c/h2\u003e \u003cp\u003eThe availability of high-risk (eligible client) mothers and babies directly influences testing volumes at facilities. Poor sample collection and handling practices can lead to errors and test failures, affecting overall utilization. Additionally, fluctuating sample volumes due to inconsistent patient flow means that some facilities may experience periods of underutilization of POC platforms, while others are overwhelmed with demand.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;There are times when we don\u0026rsquo;t have enough patients to meet testing targets, so the machines sit idle. But then when there\u0026rsquo;s a rush, we are overwhelmed and can\u0026rsquo;t process all the samples in time.\" (KII 3)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the availability and performance of POC HIV VL and EID testing, crucial for strengthening diagnostic capacity and efficiency in developing countries. These findings can guide interventions to improve timely, accurate testing, enhancing patient care and HIV treatment programs.\u003c/p\u003e \u003cp\u003eFrom the current study, the average stock-out duration for VL POC cartridges was 9 days (SD\u0026thinsp;=\u0026thinsp;5.4), while for EID POC cartridges, it was 8 days (SD\u0026thinsp;=\u0026thinsp;5.7). Stock-out patterns revealed that 59% of the facilities in the study experienced shortages for fewer than 5 days per month, indicating stock availability in alignment with WHO guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The long average stockout durations however, underscores the challenges in maintaining a steady supply of POC testing commodities. This aligns with findings from a study in Wakiso District, Uganda, which highlighted challenges in maintaining a steady supply of HIV tracer commodities and also a 2020 UNICEF report about HIV EID and VL POC diagnostics market and supply update (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In line with the Wakiso study and UNICEF report, supply chain delays, funding constraints, and reliance on external resources were identified as key contributors to stockouts. Inconsistent access to testing commodities and functional devices hinders service delivery negatively impacting on the attainment of HIV testing and treatment targets. Uganda still lags behind in attainment of 95-95-95 HIV targets. As of 2023, the country had made significant progress, reaching 92-90-94 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, gaps remain in diagnosis, treatment, and viral suppression, underscoring the need for intensified efforts to bridge these gaps. Ensuring a reliable supply chain that assures continued access to testing commodities is crucial for timely HIV diagnosis and treatment, as demonstrated by studies in Uganda (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and Baltimore (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study also found that facilities with higher positivity rates were more likely to maintain essential stock, possibly due to increased demand and proactive stock management. General hospitals were better stocked than smaller facilities, benefiting from more stable resources and supply chains similarly to findings from a study in Nigeria that suggested that higher-level facilities may optimize testing usage more effectively due to better resources and infrastructure (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In addition, the difference in the results could be due to higher volume of expected patients at higher level facilities with improved resource allocation compared to lower-level facilities.\u003c/p\u003e \u003cp\u003eThe findings of the current study showed low utilization of POC testing platforms, with only 18% of facilities achieving a utilization rate of 100% and above, and a mean rate of 47% (SD\u0026thinsp;=\u0026thinsp;50%). It was found out that many facilities struggled to maximize their testing capacity due to operational challenges, including supply chain and stock-out issues, power inconsistences and frequent equipment breakdowns. Delays in maintenance further contributed to prolonged downtimes, limiting efficiency. The study is in agreement with previous study in Dodoma, Tanzania that highlighted that the continuous lack of medications and commodities in public health facilities affects healthcare utilization and individual decisions to see medical professionals (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study suggests that improving supply chain management to ensure a consistent supply of cartridges and establishing a proactive maintenance framework with regular checks and repairs will help reduce equipment breakdowns and downtime, ultimately improving machine utilization. Additionally, the quantitative analysis identified several factors with a significant statistical relationship to POC testing performance. These factors included device type (aOR\u0026thinsp;=\u0026thinsp;3.3, P\u0026thinsp;=\u0026thinsp;0.039), positivity rate (aOR\u0026thinsp;=\u0026thinsp;12, P\u0026thinsp;=\u0026thinsp;0.017), error rate (aOR\u0026thinsp;=\u0026thinsp;12, P\u0026thinsp;=\u0026thinsp;0.01), and test result uploads (aOR\u0026thinsp;=\u0026thinsp;3.8, P\u0026thinsp;=\u0026thinsp;0.019). General hospitals and regional referral hospitals were more likely to optimize testing usage compared to smaller facilities, which reflected differences in available resources and infrastructure. A possible explanation could be the higher volume of samples collected at higher-level facilities compared to lower-level ones, similar to a study that assessed GeneXpert MTB/RIF performance by facility type and level in Nigeria(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Additionally, these findings align with a previous study that highlighted inconsistencies in machine utilization and the availability of diagnostic commodities across different healthcare facilities. This suggests that localized factors, such as facility-level management practices and regional supply chain issues, play a significant role in exacerbating these challenges(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe GeneXpert machine outperformed the mPIMA device, likely due to its higher sample throughput, while hub facilities exhibited greater efficiency than non-hub sites similar to findings from a study in Northern Uganda that highlighted the role of centralized hubs in optimizing diagnostic testing through improved resource allocation and infrastructure (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Regular training for staff on the efficient use of testing platforms and troubleshooting common issues is crucial for minimizing error rates and improving overall performance. Equipping healthcare workers with the necessary skills and knowledge not only enhances the accuracy and reliability of diagnostic processes but also ensures the optimal utilization of available resources. Continuous capacity-building initiatives help address common technical challenges, reduce downtime due to equipment malfunctions, and streamline workflow efficiency within healthcare facilities. Additionally, high sample error rates contribute to increased operational costs by wasting both time and diagnostic commodities. Frequent errors necessitate repeated testing, leading to additional expenditures on reagents, cartridges, and other essential materials. This inefficiency places a significant strain on already limited healthcare budgets and can delay timely patient diagnosis and treatment. By addressing the underlying causes of high error rates through targeted training and quality control measures, healthcare facilities can improve productivity, reduce unnecessary costs, and ultimately enhance patient outcomes.\u003c/p\u003e\n\u003ch3\u003eStudy Limitations\u003c/h3\u003e\n\u003cp\u003eThe study had limitations such as time-specific focus which meant that it possibly did not fully reflect changes in commodity availability and testing platform performance over time due to factors such as shifting funding, policies, or disease prevalence. Nonetheless, the data provided meaningful insights into the operational challenges of POC testing during the study period. The study\u0026rsquo;s generalizability is limited, as it was conducted only in public hospitals within one region, excluding private and other regional settings. Still, the inclusion of various healthcare levels ensured representativeness within the targeted population, making the findings adaptable to similar contexts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings indicate that while most facilities experienced stock-outs for fewer than five days per month, a substantial proportion faced prolonged shortages, highlighting persistent supply chain inefficiencies. Additionally, platform utilization varied, with only a small fraction of facilities achieving optimal usage due to factors such as equipment downtime, inconsistent supply of cartridges, and inadequate staff training. Key challenges affecting both availability and performance included supply chain delays, funding constraints, infrastructural limitations, and human resource shortages.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eThere is a need to strengthen supply chain management by improving forecasting, procurement, and coordination with regional and national stakeholders using real-time data. The healthcare facilities should also maintain accurate stock records and conduct routine audits to support data-driven decision-making and accountability. To improve testing platform performance, equipment issues should be addressed through preventive maintenance, reliable power backup, and timely technical support, while also facilitating regular training for healthcare workers.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval was obtained from the Makerere University School of Health Sciences Research and Ethics Committee (MAKSHSREC) under approval number MAKSHSREC-2024-765. Participants were informed about the purpose of the research, and confidentiality maintained throughout the study. All collected data was anonymized to protect the identities of the participants.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study did not receive any specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNE conceived and designed the study, coordinated data collection, and led the analysis and interpretation. KR contributed to study conceptualization, methodology, and data validation. NW supported data analysis and interpretation. BV, THO, FA and AJ contributed to validation, critical review, and interpretation of findings. NE drafted the initial manuscript, and all authors contributed to manuscript review and editing. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during this study are available and can be shared upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMinistry of Health, Uganda. Consolidated Guidelines For The Prevention And Treatment Of Hiv And Aids In Uganda. 2022; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guluhospital.net/wp-content/uploads/2023/05/Consolidated-Hiv-Aids-Guidelines-2022.pdf\u003c/span\u003e\u003cspan address=\"https://guluhospital.net/wp-content/uploads/2023/05/Consolidated-Hiv-Aids-Guidelines-2022.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan WM, Law MG, Egger M, Wools-Kaloustian K, Moore R, McGowan C, et al. Global estimates of viral suppression in children and adolescents and adults on antiretroviral therapy adjusted for missing viral load measurements: a multiregional, retrospective cohort study in 31 countries. Lancet HIV. 2021;8(12):e766\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organisation. Updated Recommendations on HIV Prevention, Infant Diagnosis, Antiretroviral Initiation And Monitoring. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinguwo F, Nyondo-Mipando AL. Integration of Early Infant Diagnosis of HIV Services Into Village Health Clinics in Ntcheu, Malawi: An Exploratory Qualitative Study. J Int Assoc Provid AIDS Care. 2021;20:2325958220981256.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS. The path that ends AIDS: UNAIDS Global AIDS Update 2023. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF. HIV Early Infant Diagnosis and Viral Load Point of Care Diagnostics: Market and Supply Update. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKufa T, Mazanderani AH, Sherman GG, Mukendi A, Murray T, Moyo F, et al. Point-of‐care HIV maternal viral load and early infant diagnosis testing around time of delivery at tertiary obstetric units in South Africa: a prospective study of coverage, results return and turn‐around times. J Int AIDS Soc. 2020;23(4):e25487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanesh P, Heller T, Chione B, Gumulira J, Gugsa S, Khan S, et al. Near Point-of-Care HIV Viral Load: Targeted Testing at Large Facilities. JAIDS J Acquir Immune Defic Syndr. 2021;86(2):258\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChibwesha CJ, Mollan KR, Ford CE, Shibemba A, Saha PT, Lusaka M, et al. A Randomized Trial of Point-of-Care Early Infant Human Immunodeficiency Virus (HIV) Diagnosis in Zambia. Clin Infect Dis. 2022;75(2):260\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathias S, Isangula K, Kahwa A, Kimaro G, Ngadaya E, Mwenda L, et al. Factors Affecting the Availability of Essential Health Commodities in Tanzania with a Special Focus on the Tracer Commodities. Tanzan J Hlth Res. 2024;25(2):838\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtiga O, Walters J, Pisa N. Challenges of medical commodity availability in public and private health care facilities in the Upper East Region of Ghana: a patient-centered perspective. BMC Health Serv Res. 2023;23(1):719.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health, Uganda. FY 2024/2025 Integrated Quantification for Essential Medicines and Health Supplies. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubega P, Nalugya SJ, Kimuli AN, Twinokusiima M, Khasalamwa M, Kyomugisa R, et al. Adherence to viral load testing guidelines, barriers, and associated factors among persons living with HIV on ART in Southwestern Uganda: a mixed-methods study. BMC Public Health. 2022;22(1):1268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdzimah ED, Awuah-Gyawu M, Aikins I. Prince Agyemang Duah. An Assessment Of Health Commodities Management Practices In Health Care Delivery; A Supply Chain Perspective. The Case of Selected Hospitals In Ashanti Region-Ghana.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuwawenaruwa A, Wyss K, Wiedenmayer K, Metta E, Tediosi F. The effects of medicines availability and stock-outs on household\u0026rsquo;s utilization of healthcare services in Dodoma region, Tanzania. Health Policy Plann. 2020;35(3):323\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvatore PP, De Broucker G, Vojnov L, Moss WJ, Dowdy DW, Sutcliffe CG. Modeling the cost-effectiveness of point-of-care platforms for infant diagnosis of HIV in sub-Saharan African countries. AIDS. 2021;35(2):287\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwen M, Zweekhorst M, Regeer B, Laing R. Baseline assessment of WHO\u0026rsquo;s target for both availability and affordability of essential medicines to treat non-communicable diseases. Podobnik B, editor. PLoS ONE. 2017;12(2):e0171284.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLule F, Rajab K, Banzimana S, Asingizwe D. Assessing determinants of the availability of HIV tracer commodities in health facilities in Wakiso District, Uganda. J Pharm Policy Pract. 2024;17(1):2306846.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS. UNAIDS: Results and Transparency Portal. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://open.unaids.org/countries/uganda\u003c/span\u003e\u003cspan address=\"https://open.unaids.org/countries/uganda\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuyingo S, Etoori D, Lotay P, Malamba S, Olweny J, Keesler K, et al. The procurement and supply chain strengthening project: improving public health supply chains for better access to HIV medicines, Uganda 2011\u0026ndash;2016. J Pharm Policy Pract. 2022;15(1):72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayan MH, Smalls T, Boudreau A, Mirza AW, Pasco C, Demko ZO, et al. Evaluating the impact of point-of-care HIV viral load assessment on linkage to care in Baltimore, MD: a randomized controlled trial. BMC Infect Dis. 2023;23(1):570.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGidado M, Nwokoye N, Ogbudebe C, Nsa B, Nwadike P, Ajiboye P, et al. Assessment of GeneXpert MTB/RIF performance by type and level of health-care facilities in Nigeria. Niger Med J. 2019;60(1):33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugada E, Komakech H, Ochola I, Mwebaze S, Olowo Oteba M, Okidi Ladwar D. Health supply chain system in Uganda: current issues, structure, performance, and implications for systems strengthening. J Pharm Policy Pract. 2022;15(1):14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugada E, Ochola I, Kirunda A, Sembatya M, Mwebaze S, Olowo M, et al. Health supply chain system in Uganda: assessment of status and of performance of health facilities. J Pharm Policy Pract. 2022;15(1):58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaramagi E, Nturo J, Donggo P, Kyobutungi I, Aloyo J, Sensalire S, et al. Using quality improvement to improve the utilisation of GeneXpert testing at five lab hubs in Northern Uganda. BMJ Open Qual. 2017;6(2):e000201.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stock availability, Early Infant Diagnosis, Viral Load, Point of Care, Human immunodeficiency virus, GeneXpert, M-pima, testing platform and performance","lastPublishedDoi":"10.21203/rs.3.rs-8058672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8058672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Joint United Nations Programme on HIV/AIDS has set ambitious targets to improve diagnosis and treatment rates, aiming for 95% of people living with HIV to know their status and receive treatment. However, challenges persist, especially concerning the HIV Viral Load (VL)/Early Infant Diagnosis (EID) testing coverage and commodity availability, leading to stock-outs and delays in testing processes. This study assessed the factors influencing commodity availability and testing platform performance in public health facilities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study design was used, incorporating both quantitative and qualitative data collection methods. Primary and secondary data were collected, with a data abstraction form from stock cards/stock books and Point of Care (POC) data systems for HIV VL and EID POC consumption data in the respective facilities. In-depth interviews, guided by an interview guide, were conducted with healthcare workers to capture factors affecting HIV EID/VL POC testing commodities and platform performance. STATA 15.0 was used for quantitative data analysis, while thematic analysis was used for qualitative data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, the average stock-out duration per month was 9 days for VL POC cartridges and 8 days for EID POC cartridges. Most of the facilities 59% experienced EID cartridge stock-outs for fewer than 5 days per month, while 27% faced stock-outs exceeding 10 days. Similarly, 59% of facilities had VL POC cartridge shortages for fewer than 5 days, whereas 32% experienced stock-outs for more than 10 days. Stock availability was significantly associated with positivity rates. Furthermore, the mean equipment utilization rate was 47%, with only 18% of facilities achieving optimal utilization. Factors significantly influencing POC platform performance included device type (aOR\u0026thinsp;=\u0026thinsp;3.3, P\u0026thinsp;=\u0026thinsp;0.039), positivity rate (aOR\u0026thinsp;=\u0026thinsp;12, P\u0026thinsp;=\u0026thinsp;0.017), sample error rate (aOR\u0026thinsp;=\u0026thinsp;5, P\u0026thinsp;=\u0026thinsp;0.01), and frequent result uploads to national systems (aOR\u0026thinsp;=\u0026thinsp;3.8, P\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings highlight persistent supply chain inefficiencies, with some facilities experiencing prolonged stock-outs. Low platform utilization was driven by equipment downtime, cartridge shortages, and inadequate staff training. Key challenges included supply chain delays, funding constraints, infrastructure gaps, and staffing shortages. Strengthening forecasting, procurement, distribution, and staff training alongside better coordination and infrastructure investment will be crucial for improving POC testing services and enhancing early HIV diagnosis.\u003c/p\u003e","manuscriptTitle":"Factors affecting availability of HIV Viral Load/Early Infant Diagnosis Point of Care testing Commodities and performance of testing platforms in public health facilities in Masaka region, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:07:12","doi":"10.21203/rs.3.rs-8058672/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-01T09:07:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T13:04:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T06:45:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T22:34:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T15:54:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T11:45:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T09:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182410898475447922036915793978181084932","date":"2025-12-16T13:38:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185825680542675834360999667442870678830","date":"2025-12-15T20:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99297755410269207758363267557152154165","date":"2025-12-15T06:39:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164964829644593798927224534631834739818","date":"2025-12-14T17:12:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245026665831731410472618867116563638743","date":"2025-12-12T13:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177070627974121748318424867835886424101","date":"2025-12-12T11:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315353809415935151152694225850609101592","date":"2025-12-12T10:59:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T08:58:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-17T09:52:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-13T12:20:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T12:19:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-11-07T15:51:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b126cbdd-7ebd-4f67-8ed9-22ae6fb21055","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:07:43+00:00","versionOfRecord":{"articleIdentity":"rs-8058672","link":"https://doi.org/10.1186/s12879-026-13130-y","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-03-31 15:58:56","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2025-12-22 09:07:12","video":"","vorDoi":"10.1186/s12879-026-13130-y","vorDoiUrl":"https://doi.org/10.1186/s12879-026-13130-y","workflowStages":[]},"version":"v1","identity":"rs-8058672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8058672","identity":"rs-8058672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}