Antiretroviral therapy (ART) is needed across the lifetime to maintain viral suppression for people living with HIV. In South Africa, obstacles to reliable access to ART persist and are magnified in rural areas, where HIV services are also typically costlier to deliver. A recent pilot randomized study (the Deliver Health Study) found that home-delivered ART refills, provided at a low user fee, effectively overcame logistical barriers to access and improved clinical outcomes in rural South Africa. In the present costing study using the payer perspective, we conducted retrospective activity-based micro-costing of home-delivered ART within the Deliver Health Study and when provided at-scale (in a rural setting), and compared to facility-based costs using provincial expenditure data (covering both rural and urban settings). Within the context of the pilot Deliver Health Study which had an average of three deliveries per day for three days a week, home-delivered ART cost (in 2022 USD) $794 in the first year and $714 for subsequent years per client after subtracting client fees, compared with $167 per client in provincial clinic-based care. We estimated that home-delivered ART can reasonably be scaled up to 12 home deliveries per day for five days per week in the rural setting. When delivered at scale, home-delivered ART cost $267 in the first year and $183 for subsequent years per client. Average costs of home delivery further decreased when increasing the duration of refills from three-months to six- and 12-month scripts (from $183 to $177 and $135 per client, respectively). Personnel costs were the largest cost for home-delivered refills while ART drug costs were the largest cost of clinic-based refills. When provided at scale, home-delivered ART in a rural setting not only offers clinical benefits for a hard-to-reach population but is also comparable in cost to the provincial standard of care.

BACKGROUND: Eswatini faces persistent challenges in providing care for diabetes and hypertension, exacerbated by a shortage of healthcare workers. The implementation of WHO-PEN interventions aimed to address these issues, yet their effects on healthcare worker time requirements and associated costs remain unclear.
METHODS: This study employed a time-and-motion analysis and a bottom-up cost assessment to quantify the human and financial resources required for scaling up WHO-PEN interventions nationally in Eswatini for all estimated diabetic and hypertensive patients.
RESULTS: Findings reveal that healthcare workers in intervention-arm clinics reported longer workday durations compared to those in control-arm clinics, yet spent less time per patient while seeing more patients. The implementation of WHO-PEN interventions increased the workload on healthcare workers but also led to a notable increase in patient care utilization. Furthermore, a morning peak in patient visits was identified, suggesting potential opportunities for optimizing patient flow. Notably, scaling up care provision nationally with WHO-PEN interventions proved to be more cost saving than expanding standard-of-care treatment.
CONCLUSIONS: WHO-PEN interventions hold promise in improving access to diabetes and hypertension care in Eswatini while offering an efficient solution. However, addressing challenges in healthcare workforce creation and retention is crucial for sustained effectiveness. Policy makers must consider all aspects of the WHO-PEN intervention for informed decision-making. Trial registration US Clinical Trials Registry. NCT04183413. Trial registration date: December 3, 2019. https://ichgcp.net/clinical-trials-registry/NCT04183413.

BACKGROUND: The electronic National Immunization Information System (NIIS) was introduced nationwide in Vietnam in 2017. Health workers were expected to use the NIIS alongside the legacy paper-based system. Starting in 2018, Hanoi and Son La provinces transitioned to paperless reporting. Interventions to support this transition included data guidelines and training, internet-based data review meetings, and additional supportive supervision visits.
OBJECTIVE: This study aims to assess (1) changes in NIIS data quality and use, (2) changes in immunization program outcomes, and (3) the economic costs of using the NIIS versus the traditional paper system.
METHODS: This mixed methods study took place in Hanoi and Son La provinces. It aimed to analyses pre- and postintervention data from various sources including the NIIS; household and health facility surveys; and interviews to measure NIIS data quality, data use, and immunization program outcomes. Financial data were collected at the national, provincial, district, and health facility levels through record review and interviews. An activity-based costing approach was conducted from a health system perspective.
RESULTS: NIIS data timeliness significantly improved from pre- to postintervention in both provinces. For example, the mean number of days from birth date to NIIS registration before and after intervention dropped from 18.6 (SD 65.5) to 5.7 (SD 31.4) days in Hanoi (P<.001) and from 36.1 (SD 94.2) to 11.7 (40.1) days in Son La (P<.001). Data from Son La showed that the completeness and accuracy improved, while Hanoi exhibited mixed results, possibly influenced by the COVID-19 pandemic. Data use improved; at postintervention, 100% (667/667) of facilities in both provinces used NIIS data for activities beyond monthly reporting compared with 34.8% (202/580) in Hanoi and 29.4% (55/187) in Son La at preintervention. Across nearly all antigens, the percentage of children who received the vaccine on time was higher in the postintervention cohort compared with the preintervention cohort. Up-front costs associated with developing and deploying the NIIS were estimated at US $0.48 per child in the study provinces. The commune health center level showed cost savings from changing from the paper system to the NIIS, mainly driven by human resource time savings. At the administrative level, incremental costs resulted from changing from the paper system to the NIIS, as some costs increased, such as labor costs for supportive supervision and additional capital costs for equipment associated with the NIIS.
CONCLUSIONS: The Hanoi and Son La provinces successfully transitioned to paperless reporting while maintaining or improving NIIS data quality and data use. However, improvements in data quality were not associated with improvements in the immunization program outcomes in both provinces. The COVID-19 pandemic likely had a negative influence on immunization program outcomes, particularly in Hanoi. These improvements entail up-front financial costs.

UNASSIGNED: Kenya included oral PrEP in the national guidelines as part of combination HIV prevention, and subsequently began providing PrEP to individuals who are at elevated risk of HIV infection in 2017. However, as scale-up continued, there was a recognized gap in knowledge on the cost of delivering oral PrEP. This gap limited the ability of the Government of Kenya to budget for its PrEP scale-up and to evaluate PrEP relative to other HIV prevention strategies. The following study calculated the actual costs of oral PrEP scale-up as it was being delivered in ten counties in Kenya. This costing also allowed for a comparison of various models of service delivery in different geographic regions from the perspective of service providers in Kenya. In addition, the analysis was also conducted to understand factors that indicate why some individuals place a greater value on PrEP than others, using a contingent valuation technique.
UNASSIGNED: Data collection was completed between November 2017 and September 2018. Costing data was collected from 44 Kenyan health facilities, consisting of 23 public facilities, 5 private facilities and 16 drop-in centers (DICEs) through a cross-sectional survey in ten counties. Financial and programmatic data were collected from financial and asset records and through interviewer administered questionnaires. The costs associated with PrEP provision were calculated using an ingredients-based costing approach which involved identification and costing of all the economic inputs (both direct and indirect) used in PrEP service delivery. In addition, a contingent valuation study was conducted at the same 44 facilities to understand factors that reveal why some individuals place a greater value on PrEP than others. Interviews were conducted with 2,258 individuals (1,940 current PrEP clients and 318 non-PrEP clients). A contingent valuation method using a \"payment card approach\" was used to determine the maximum willingness to pay (WTP) of respondents regarding obtaining access to oral PrEP services.
UNASSIGNED: The weighted cost of providing PrEP was $253 per person year, ranging from $217 at health centers to $283 at dispensaries. Drop-in centers (DICEs), which served about two-thirds of the client volume at surveyed facilities, had a unit cost of $276. The unit cost was highest for facilities targeting MSM ($355), while it was lowest for those targeting FSW ($248). The unit cost for facilities targeting AGYW was $323 per person year. The largest percentage of costs were attributable to personnel (58.5%), followed by the cost of drugs, which represented 25% of all costs. The median WTP for PrEP was $2 per month (mean was $4.07 per month). This covers only one-third of the monthly cost of the medication (approximately $6 per month) and less than 10% of the full cost of delivering PrEP ($21 per month). A sizable proportion of current clients (27%) were unwilling to pay anything for PrEP. Certain populations put a higher value on PrEP services, including: FSW and MSM, Muslims, individuals with higher education, persons between the ages of 20 and 35, and households with a higher income and expenditures.
UNASSIGNED: This is the most recent and comprehensive study on the cost of PrEP delivery in Kenya. These results will be used in determining resource requirements and for resource mobilization to facilitate sustainable PrEP scale-up in Kenya and beyond. This contingent valuation study does have important implications for Kenya\'s PrEP program. First, it indicates that some populations are more motivated to adopt oral PrEP, as indicated by their higher WTP for the service. MSM and FSW, for example, placed a higher value on PrEP than AGYW. Higher educated individuals, in turn, put a much higher value on PrEP than those with less education (which may also reflect the higher \"ability to pay\" among those with more education). This suggests that any attempt to increase demand or improve PrEP continuation should consider these differences in client populations. Cost recovery from existing PrEP clients would have potentially negative consequences for uptake and continuation.

Two-thirds of the 2.4 million newborn deaths that occurred in 2020 within the first 28 days of life might have been avoided by implementing existing low-cost evidence-based interventions for all sick and small newborns. An open-source digital quality improvement tool (Neotree) combining data capture with education and clinical decision support is a promising solution for this implementation gap.
We present results from a cost analysis of a pilot implementation of Neotree in 3 hospitals in Malawi and Zimbabwe.
We combined activity-based costing and expenditure approaches to estimate the development and implementation cost of a Neotree pilot in 1 hospital in Malawi, Kamuzu Central Hospital (KCH), and 2 hospitals in Zimbabwe, Sally Mugabe Central Hospital (SMCH) and Chinhoyi Provincial Hospital (CPH). We estimated the costs from a provider perspective over 12 months. Data were collected through expenditure reports, monthly staff time-use surveys, and project staff interviews. Sensitivity and scenario analyses were conducted to assess the impact of uncertainties on the results or estimate potential costs at scale. A pilot time-motion survey was conducted at KCH and a comparable hospital where Neotree was not implemented.
Total cost of pilot implementation of Neotree at KCH, SMCH, and CPH was US $37,748, US $52,331, and US $41,764, respectively. Average monthly cost per admitted child was US $15, US $15, and US $58, respectively. Staff costs were the main cost component (average 73% of total costs, ranging from 63% to 79%). The results from the sensitivity analysis showed that uncertainty around the number of admissions had a significant impact on the costs in all hospitals. In Malawi, replacing monthly web hosting with a server also had a significant impact on the costs. Under routine (nonresearch) conditions and at scale, total costs are estimated to fall substantially, up to 76%, reducing cost per admitted child to as low as US $5 in KCH, US $4 in SMCH, and US $14 in CPH. Median time to admit a baby was 27 (IQR 20-40) minutes using Neotree (n=250) compared to 26 (IQR 21-30) minutes using paper-based systems (n=34), and the median time to discharge a baby was 9 (IQR 7-13) minutes for Neotree (n=246) compared to 3 (IQR 2-4) minutes for paper-based systems (n=50).
Neotree is a time- and cost-efficient tool, comparable with the results from limited similar mHealth decision-support tools in low- and middle-income countries. Implementation costs of Neotree varied substantially between the hospitals, mainly due to hospital size. The implementation costs could be substantially reduced at scale due to economies of scale because of integration to the health systems and reductions in cost items such as staff and overhead. More studies assessing the impact and cost-effectiveness of large-scale mHealth decision-support tools are needed.

The \'Cost of Health Services in India (CHSI)\' is the first large scale multi-site facility costing study to incorporate evidence from a national sample of both private and public sectors at different levels of the health system in India. This paper provides an overview of the extent of heterogeneity in costs caused by various supply-side factors.A total of 38 public (11 tertiary care and 27 secondary care) and 16 private hospitals were sampled from 11 states of India. From the sampled facilities, a total of 327 specialties were included, with 48, 79 and 200 specialties covered in tertiary, private and district hospitals respectively. A mixed methodology consisting of both bottom-up and top-down costing was used for data collection. Unit costs per service output were calculated at the cost centre level (outpatient, inpatient, operating theatre, and ICU) and compared across provider type and geographical location.The unadjusted cost per admission was highest for tertiary facilities (₹ 5690, 75 USD) followed by private facilities (₹ 4839, 64 USD) and district hospitals (₹ 3447, 45 USD). Differences in unit costs were found across types of providers, resulting from both variations in capacity utilisation, length of stay and the scale of activity. In addition, significant differences in costs were found associated with geographical location (city classification).The reliance on cost information from single sites or small samples ignores the issue of heterogeneity driven by both demand and supply-side factors. The CHSI cost data set provides a unique insight into cost variability across different types of providers in India. The present analysis shows that both geographical location and the scale of activity are important determinants for deriving the cost of a health service and should be accounted for in healthcare decision making from budgeting to economic evaluation and price-setting.

The World Health Organization (WHO) has developed a costing tool, the Cervical Cancer Prevention and Control Costing (C4P) tool, to estimate the comprehensive cost of cervical cancer primary, secondary and tertiary prevention in low- and middle-income countries. The tool was piloted in the United Republic of Tanzania, a country with a high incidence of cervical cancer with 62.5 cases per 100,000 women in 2020. This paper presents the costing tool methods as well as the results from the pilot in Tanzania.
The C4P tool estimates the incremental costs of cervical cancer prevention and control programmes. It estimates the financial (monetary costs to the government) and economic costs (opportunity costs). For the pilot, the study team collected data on costs and programme assumptions for human papillomavirus (HPV) vaccination of 14-year-old girls and scaling up of cervical cancer screening (visual inspection with acetic acid and HPV-DNA testing) and treatment for women for 2020-2024. Assumptions were made on how vaccination coverage would increase over the 5 years as well as developing additional screening and treatment capacity through health personnel training and infrastructure strengthening.
The total financial and economic costs of the comprehensive programme during 2020-2024 are projected to be US$68 million and US$124 million, respectively. The financial and economic costs of a fully immunized girl with HPV vaccine are estimated to be US$6.68 and US$17.31, respectively, while the costs per woman screened for cervical cancer are, on average, US$4.02 and US$5.83, respectively; US$6.44 and US$9.37 for pre-cancer treatment, respectively; and US$101 and US$107 for diagnosis of invasive cancer, respectively. The cost of treating and managing invasive cancer range from US$7.05 and US$7.83 for outpatient palliative care to US$800.21 and US$893.80 for radiotherapy, respectively.
The C4P costing tool can assist national cervical cancer programmes to estimate monetary resources needed as well as opportunity costs of reducing national cervical cancer incidence through primary, secondary and tertiary prevention.

India accounts for almost a third of the global burden of oral cancer, a situation worsened by the inability to afford care. When available, aid is often insufficient, and costing is based on informal estimations. This study objectively determines direct healthcare costs of oral cancer in India. The study was performed from a healthcare provider\'s perspective using a validated bottom-up method. Care pathways were determined by prospectively observing the natural management of 100 oral cancer patients treated between October 2019 and March 2020. Specific costing categories were built across services, and apportioned values for each interaction was averaged. Costs of treatment and service utilisation were obtained using probabilistic sensitivity analyses. The unit cost of treating advanced stages (United States Dollar (USD) 2,717) was found to be 42% greater than early stages (USD1,568). There was an 11% reduction in unit costs with increases in socioeconomic status. Medical equipment accounted for 97.8% of capital costs, with the highest contributor being imaging services. Variable costs for surgery in advanced stages were 1.4 times higher than early stages. Compared to surgery alone, the average cost of treatment increased by 44.6% with adjuvant therapy. These results show that over the next decade, India will incur an economic burden of USD 3 billion towards the direct healthcare of oral cancer. Early detection and prevention strategies leading to 20% reduction in advanced stage disease could save USD 30 million annually. These results are critical to deliver a disease-driven and objective reform for oral cancer care.

Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.

Information on the costs of routine immunization programs is needed for budgeting, planning, and domestic resource mobilization. This information is particularly important for countries such as Tanzania that are preparing to transition out of support from Gavi, the Vaccine Alliance. This study aimed to estimate the total and unit costs for of child immunization in Tanzania from July 2016 to June 2017 and make this evidence available to key stakeholders.
We used an ingredients-based approach to collect routine immunization cost data from the facility, district, regional, and national levels. We collected data on the cost of vaccines as well as non-vaccine delivery costs. We estimated total and unit costs from a provider perspective for each level and overall, and examined how costs varied by delivery strategy, geographic area, and facility-level service delivery volume. An evidence-to-policy plan identified key opportunities and stakeholders to target to facilitate the use of results.
The total annual economic cost of the immunization program, inclusive of vaccines, was estimated to be US$138 million (95% CI: 133, 144), or $4.32 ($3.72, $4.98) per dose. The delivery costs made up $45 million (38, 52), or $1.38 (1.06, 1.70) per dose. The costs of facility-based delivery were similar in urban and rural areas, but the costs of outreach delivery were higher in rural areas than in urban areas. The facility-level delivery cost per dose decreased with the facility service delivery volume.
We estimated the costs of the routine immunization program in Tanzania, where no immunization costing study had been conducted for five years. These estimates can inform the program\'s budgeting and planning as Tanzania prepares to transition out of Gavi support. Next steps for evidence-to-policy translation have been identified, including technical support requirements for policy advocacy and planning.