Factors associated with medical consumable availability in level 1 facilities in Malawi: a secondary analysis of a facility census.

BACKGROUND: Medical consumable stock-outs negatively affect health outcomes not only by impeding or delaying the effective delivery of services but also by discouraging patients from seeking care. Consequently, supply chain strengthening is being adopted as a key component of national health strategies. However, evidence on the factors associated with increased consumable availability is limited. METHODS: In this study, we used the 2018-19 Harmonised Health Facility Assessment data from Malawi to identify the factors associated with the availability of consumables in level 1 facilities, ie, rural hospitals or health centres with a small number of beds and a sparsely equipped operating room for minor procedures. We estimate a multilevel logistic regression model with a binary outcome variable representing consumable availability (of 130 consumables across 940 facilities) and explanatory variables chosen based on current evidence. Further subgroup analyses are carried out to assess the presence of effect modification by level of care, facility ownership, and a categorisation of consumables by public health or disease programme, Malawi's Essential Medicine List classification, whether the consumable is a drug or not, and level of average national availability. FINDINGS: Our results suggest that the following characteristics had a positive association with consumable availability-level 1b facilities or community hospitals had 64% (odds ratio [OR] 1·64, 95% CI 1·37-1·97) higher odds of consumable availability than level 1a facilities or health centres, Christian Health Association of Malawi and private-for-profit ownership had 63% (1·63, 1·40-1·89) and 49% (1·49, 1·24-1·80) higher odds respectively than government-owned facilities, the availability of a computer had 46% (1·46, 1·32-1·62) higher odds than in its absence, pharmacists managing drug orders had 85% (1·85, 1·40-2·44) higher odds than a drug store clerk, proximity to the corresponding regional administrative office (facilities greater than 75 km away had 21% lower odds [0·79, 0·63-0·98] than facilities within 10 km of the district health office), and having three drug order fulfilments in the 3 months before the survey had 14% (1·14, 1·02-1·27) higher odds than one fulfilment in 3 months. Further, consumables categorised as vital in Malawi's Essential Medicine List performed considerably better with 235% (OR 3·35, 95% CI 1·60-7·05) higher odds than other essential or non-essential consumables and drugs performed worse with 79% (0·21, 0·08-0·51) lower odds than other medical consumables in terms of availability across facilities. INTERPRETATION: Our results provide evidence on the areas of intervention with potential to improve consumable availability. Further exploration of the health and resource consequences of the strategies discussed will be useful in guiding investments into supply chain strengthening. FUNDING: UK Research and Innovation as part of the Global Challenges Research Fund (Thanzi La Onse; reference MR/P028004/1), the Wellcome Trust (Thanzi La Mawa; reference 223120/Z/21/Z), the UK Medical Research Council, the UK Department for International Development, and the EU (reference MR/R015600/1).

Analysis of Geographic Accessibility of Breast, Lung, and Colorectal Cancer Screening Centers Among American Indian and Alaskan Native Tribes.

PURPOSE: To evaluate geographic accessibility of ACR mammographic screening (MS), lung cancer screening (LCS), and CT colorectal cancer screening (CTCS) centers among US federally recognized American Indian and Alaskan Native (AI/AN) tribes. METHODS: Distances from AI/AN tribes' ZIP codes to their closest ACR-accredited LCS and CTCS centers were recorded using tools from the ACR website. The FDA's database was used for MS. Persistent adult poverty (PPC-A), persistent child poverty (PPC-C), and rurality indexes (rural-urban continuum codes) were from the US Department of Agriculture. Logistic and linear regression analyses were used to assess distances to screening centers and relationships among rurality, PPC-A, and PPC-C. RESULTS: Five hundred ninety-four federally recognized AI/AN tribes met the inclusion criteria. Among all closest MS, LCS, or CTCS center to AI/AN tribes, 77.8% (1,387 of 1,782) were located within 200 miles, with a mean distance of 53.6 ± 53.0 miles. Most tribes (93.6% [557 of 594]) had MS centers within 200 miles, 76.4% (454 of 594) had LCS centers within 200 miles, and 63.5% (376 of 594) had CTCS centers within 200 miles. Counties with PPC-A (odds ratio [OR], 0.47; P < .001) and PPC-C (OR, 0.19; P < .001) were significantly associated with decreased odds of having a cancer screening center within 200 miles. PPC-C was associated with decreased likelihood of having an LCS center (OR, 0.24; P < .001) and an CTCS center (OR, 0.52; P < .001) within the same state as the tribe's location. No significant association was found between PPC-A and PPC-C and MS centers. CONCLUSIONS: AI/AN tribes experience distance barriers to ACR-accredited screening centers, resulting in cancer screening deserts. Programs are needed to increase equity in screening access among AI/AN tribes.

National inventory of authorized diagnostic imaging equipment in Ghana: data as of September 2020.

Introduction: to address the challenge of inadequate and non-equitable distribution of diagnostic imaging equipment, countries are encouraged to evaluate the distribution of installed systems and undertake adequate monitoring to ensure equitability. Ghana´s medical imaging resources have been analyzed in this study and evaluated against the status in other countries. Methods: data on registered medical imaging equipment were retrieved from the database of the Nuclear Regulatory Authority and analyzed. The equipment/population ratio was mapped out graphically for the 16 regions of Ghana. Comparison of the equipment/population ratio was made with the situation in other countries. Results: six hundred and seventy-four diagnostic imaging equipment units from 266 medical imaging facilities (2.5 units/facility), comprising computed tomography (CT), general X-ray, dental X-ray, single-photon emission computed tomography (SPECT) gamma camera, fluoroscopy, mammography and magnetic resonance imaging (MRI) were surveyed nationally. None of the imaging systems measured above the Organization for Economic Co-operation and Development (OECD) average imaging units per million populations (u/mp). The overall equipment/population ratio estimated nationally was 21.4 u/mp. Majority of the imaging systems were general X-ray, installed in the Greater Accra and Ashanti regions. The regional estimates of equipment/population ratios were Greater Accra (49.6 u/mp), Ashanti (22.4 u/mp), Western (21.4 u/mp), Eastern (20.6 u/mp), Bono East (20.0 u/mp), Bono (19.2 u/mp), Volta (17.9 u/mp), Upper West (16.7 u/mp), Oti (12.5 u/mp), Central (11.9 u/mp), Northern (8.9 u/mp), Ahafo (8.9 u/mp), Upper East (6.9 u/mp), Western North (6.7 u/mp), Savannah (5.5 u/mp) and North-East (1.7 u/mp). Conclusion: medical imaging equipment shortfall exist across all imaging modalities in Ghana. A wide inter-regional disparity in the distribution of medical imaging equipment exists contrary to WHO´s recommendation for equitable distribution. A concerted national plan will be needed to address the disparity.

Guiding placement of health facilities using multiple malaria criteria and an interactive tool.

BACKGROUND: Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities. METHODS: Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria. RESULTS: Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria. CONCLUSIONS: This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.

Singapore's health-care system: key features, challenges, and shifts.

Since Singapore became an independent nation in 1965, the development of its health-care system has been underpinned by an emphasis on personal responsibility for health, and active government intervention to ensure access and affordability through targeted subsidies and to reduce unnecessary costs. Singapore is achieving good health outcomes, with a total health expenditure of 4·47% of gross domestic product in 2016. However, the health-care system is contending with increased stress, as reflected in so-called pain points that have led to public concern, including shortages in acute hospital beds and intermediate and long-term care (ILTC) services, and high out-of-pocket payments. The main drivers of these challenges are the rising prevalence of non-communicable diseases and rapid population ageing, limitations in the delivery and organisation of primary care and ILTC, and financial incentives that might inadvertently impede care integration. To address these challenges, Singapore's Ministry of Health implemented a comprehensive set of reforms in 2012 under its Healthcare 2020 Masterplan. These reforms substantially increased the capacity of public hospital beds and ILTC services in the community, expanded subsidies for primary care and long-term care, and introduced a series of financing health-care reforms to strengthen financial protection and coverage. However, it became clear that these measures alone would not address the underlying drivers of system stress in the long term. Instead, the system requires, and is making, much more fundamental changes to its approach. In 2016, the Ministry of Health encapsulated the required shifts in terms of the so-called Three Beyonds-namely, beyond health care to health, beyond hospital to community, and beyond quality to value.

Empanelment of health care facilities under Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB PM-JAY) in India.

INTRODUCTION: India's Pradhan Mantri Jan Arogya Yojana (PM-JAY) is the world's largest health assurance scheme providing health cover of 500,000 INR (about USD 6,800) per family per year. It provides financial support for secondary and tertiary care hospitalization expenses to about 500 million of India's poorest households through various insurance models with care delivered by public and private empanelled providers. This study undertook to describe the provider empanelment of PM-JAY, a key element of its functioning and determinant of its impact. METHODS: We carried out secondary analysis of cross-sectional administrative program data publicly available in PM-JAY portal for 30 Indian states and 06 UTs. We analysed the state wise distribution, type and sector of empanelled hospitals and services offered through PM-JAY scheme across all the states and UTs. RESULTS: We found that out of the total facilities empanelled (N = 20,257) under the scheme in 2020, more than half (N = 11,367, 56%) were in the public sector, while 8,157 (40%) facilities were private for profit, and 733 (4%) were private not for profit entities. State wise distribution of hospitals showed that five states (Karnataka (N = 2,996, 14.9%), Gujarat (N = 2,672, 13.3%), Uttar Pradesh (N = 2,627, 13%), Tamil Nadu (N = 2315, 11.5%) and Rajasthan (N = 2,093 facilities, 10.4%) contributed to more than 60% of empanelled PMJAY facilities: We also observed that 40% of facilities were offering between two and five specialties while 14% of empanelled hospitals provided 21-24 specialties. CONCLUSION: A majority of the hospital empanelled under the scheme are in states with previous experience of implementing publicly funded health insurance schemes, with the exception of Uttar Pradesh. Reasons underlying these patterns of empanelment as well as the impact of empanelment on service access, utilisation, population health and financial risk protection warrant further study. While the inclusion and regulation of the private sector is a goal that may be served by empanelment, the role of public sector remains critical, particularly in underserved areas of India.

Factors influencing healthy role models in medical school to conduct healthy behavior: a qualitative study.

OBJECTIVES: This study aimed to identify the factors that support or inhibit medical teachers as healthy role models in medical school to conduct healthy behavior. METHODS: This qualitative study involved semi-structured in-depth interviews with medical teachers categorized as healthy role models in a medical school from a previous survey. Ten medical teachers were selected using purposive sampling. Three medical teachers were interviewed by direct meetings, and the remaining were phone interviewed, with one interview facilitated by chat using WhatsApp. Transcribed interviews were coded openly. Themes were finalized through discussion and debate to reach a consensus. RESULTS: Two themes were identified: perceived facilitators and perceived barriers, which were classified into four categories and 13 subcategories: intrinsic facilitators (motivation, conscious awareness, having physical limitations, knowledge, and economic reasons); extrinsic facilitators (the impact on doing a particular job, feedback, time, and environment); intrinsic barriers (the lack of self-motivation and having physical limitations); and extrinsic barriers (the burden of responsibilities for being medical teachers and environment). CONCLUSIONS: Factors that support and inhibit medical teachers as healthy role models in medical school are influenced by intrinsic and extrinsic factors. This result could be used by medical schools to design appropriate interventions to help medical teachers as healthy role models in conducting healthy behavior. More studies are needed to explore other factors that influence medical teachers to conduct healthy behavior. During the COVID-19 pandemic, healthy role models in medical schools are vitally important and significantly contribute to the overall health of a nation.

Spatial analysis of Brazil's COVID-19 response capacity: a proposal for a Healthcare Infrastructure Index. / Análise espacial das condições de enfrentamento à COVID-19: uma proposta de Índice da Infraestrutura da Saúde do Brasil.

One of the concerns linked to the COVID-19 pandemic is the capacity of health systems to respond to the demand for care for people with the disease. The objective of this study was to create a COVID-19 response Healthcare Infrastructure Index (HII), calculate the index for each state in Brazil, and determine its spatial distribution within and across regions. The HII was constructed using principal component factor analysis. The adequacy of the statistical model was tested using the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity. The spatial distribution of the HII was analyzed using exploratory spatial data analysis. The data were obtained from DATASUS, the Federal Nursing Council, Ministry of Health, Government Procurement Portal, and the Transparency Portal. The nine states in the country's North and Northeast regions showed the lowest indices, while the five states from the Southeast and South regions showed the highest indices. Low-low clusters were observed in Amazonas and Pará and high-high clusters were found in Minas Gerais, Rio de Janeiro, São Paulo, and Paraná.

Uma das preocupações ligadas à pandemia da COVID-19 se refere à capacidade da estrutura do sistema de saúde suportar a demanda por atendimento e tratamento de pessoas acometidas por esta doença. Diante disso, o objetivo deste estudo consiste em criar e mapear o Índice de Infraestrutura de Saúde (IIS) das Unidades da Federação (UFs) brasileiras, bem como verificar a sua distribuição espacial. Para isso, foi aplicada a metodologia de Análise Fatorial por Componentes Principais. Aplicou-se os testes de Bartlett e Kaiser-Meyer-Olkin para verificação da sua adequabilidade. Em seguida procedeu-se a Análise Exploratória de Dados Espaciais. Os dados foram coletados no DATASUS, COFEN, Ministério da Saúde, Portal de Compras do Governo e Portal da Transparência. Quanto aos resultados, o índice revelou que nove estados do Norte e Nordeste registraram os menores índices e cinco estados do Sudeste e Sul apresentaram os maiores índices. Foi registrado um cluster baixo-baixo nos estados do Amazonas e Pará e um Cluster alto-alto em Minas Gerais, Rio de Janeiro, São Paulo e Paraná.

Análise espacial das condições de enfrentamento à COVID-19: uma proposta de Índice da Infraestrutura da Saúde do Brasil / Spatial analysis of Brazil's COVID-19 response capacity: a proposal for a Healthcare Infrastructure Index

Resumo Uma das preocupações ligadas à pandemia da COVID-19 se refere à capacidade da estrutura do sistema de saúde suportar a demanda por atendimento e tratamento de pessoas acometidas por esta doença. Diante disso, o objetivo deste estudo consiste em criar e mapear o Índice de Infraestrutura de Saúde (IIS) das Unidades da Federação (UFs) brasileiras, bem como verificar a sua distribuição espacial. Para isso, foi aplicada a metodologia de Análise Fatorial por Componentes Principais. Aplicou-se os testes de Bartlett e Kaiser-Meyer-Olkin para verificação da sua adequabilidade. Em seguida procedeu-se a Análise Exploratória de Dados Espaciais. Os dados foram coletados no DATASUS, COFEN, Ministério da Saúde, Portal de Compras do Governo e Portal da Transparência. Quanto aos resultados, o índice revelou que nove estados do Norte e Nordeste registraram os menores índices e cinco estados do Sudeste e Sul apresentaram os maiores índices. Foi registrado um cluster baixo-baixo nos estados do Amazonas e Pará e um Cluster alto-alto em Minas Gerais, Rio de Janeiro, São Paulo e Paraná.

Abstract One of the concerns linked to the COVID-19 pandemic is the capacity of health systems to respond to the demand for care for people with the disease. The objective of this study was to create a COVID-19 response Healthcare Infrastructure Index (HII), calculate the index for each state in Brazil, and determine its spatial distribution within and across regions. The HII was constructed using principal component factor analysis. The adequacy of the statistical model was tested using the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity. The spatial distribution of the HII was analyzed using exploratory spatial data analysis. The data were obtained from DATASUS, the Federal Nursing Council, Ministry of Health, Government Procurement Portal, and the Transparency Portal. The nine states in the country's North and Northeast regions showed the lowest indices, while the five states from the Southeast and South regions showed the highest indices. Low-low clusters were observed in Amazonas and Pará and high-high clusters were found in Minas Gerais, Rio de Janeiro, São Paulo, and Paraná.

A qualitative study of the barriers to utilizing healthcare services among the tribal population in Assam.

OBJECTIVE: We aim to explore the barriers to accessing modern healthcare services in two tribal populations in Assam. METHODS: In March 2018, we conducted qualitative research through 60 in-depth interviews with men and women aged 15 to 50 from Bodo and Rabha tribes in Udalguri and Baksa districts of Assam. We interviewed a group of health-service providers from public health facilities to understand the demand-supply balance in those facilities. FINDINGS: On the demand side, direct and indirect financial obstacles, distance to health facilities, poor public transportation, perceived negative behavior of hospital staff, and lack of infrastructure were the main barriers to utilizing healthcare facilities. On the supply side, doctors and nurses in government health facilities were overburdened by demand due to a lack of human resources. CONCLUSIONS: Our study highlights the barriers to utilizing health facilities; these are not always driven by factors linked to the patient's socio-economic status but also depend significantly on the quality of the health services and other contextual factors. Although the government has made efforts to improve the rural healthcare system through national-level programs, our qualitative study shows that these programs have not been successful in enhancing the rural healthcare system in the study area.

Healthcare facilities in low- and middle-income countries affected by COVID-19: Time to upgrade basic infection control and prevention practices.

COVID-19 as a pandemic has spanned across all continents. With the increasing numbers in cases worldwide, even the countries with the best of healthcare facilities are reeling under the burden of the disease. Therefore, in countries with limited access to resources and poor healthcare infrastructure, the low and middle-income countries (LMICs), limiting spread becomes even more challenging. Low- and middle-income countries (LMICs) are severely hit by any outbreak and pandemics and face the lack of infrastructure and problem of overcrowding. Health facilities are compromised and almost exhausted at the time of emergency. There is disruption of normal supply chain, and consumables are not in sufficient quantity. In the current situation, rationalized use of available supplies is important. This paper presents the perspective on the basis of current literature on gaps in various infection prevention and control (IPC) strategies that are being followed currently in LMICs and suggestions for bridging these gaps.

Decentralizing evidence-based decision-making in resource limited setting: A case of SNNP region, Ethiopia.

BACKGROUND: Access to and the use of accurate, valid, reliable, timely, relevant, legible and complete information is vital for safe and reliable healthcare. Though the study area has been implementing standardized Health Management Information System (HMIS), there was a need for information on how well structures were utilizing information and this study was designed to assess HMIS data utilization. METHODS: Facility based retrospective study was conducted in Southern Nations Nationalities and People's Region (SNNPR) in April, 2017. We included data from 163 sample facilities. Data use was evaluated by reviewing eight items from performance monitoring system that included activities from problem identification to monitoring of proposed action plans. Each item reviewed was recoded to yes or no and summed to judge overall performance. RESULTS: About half (52%) of woredas, 26.2% health centers (HCs), 25% hospitals and 6.2% health posts (HPs) reviewed their performance monthly but only 20% woredas, 6.2% HCs, 1.5% HPs and no hospital prepared action plans after reviewing performance. Summary of 8 items assessed showed that majority of facilities (87.5% hospitals, 81.5% HPs and 70.8% HCs) were poor in data utilization. CONCLUSIONS: Only about half of woredas and below one-fifth of health facilities were utilizing HMIS data and a lot to move to catch-up country's information revolution plan. Lower health care systems should be supported in evidence-based decision-making and progress should be monitored routinely quantitatively and/or qualitatively.

Concerns for low-resource countries, with under-prepared intensive care units, facing the COVID-19 pandemic.

BACKGROUND: Low-resource countries with fragile healthcare systems lack trained healthcare professionals and specialized resources for COVID-19 patient hospitalization, including mechanical ventilators. Additional socio-economic complications such as civil war and financial crisis in Libya and other low-resource countries further complicate healthcare delivery. METHODS: A cross-sectional survey evaluating hospital and intensive care unit's capacity and readiness was performed from 16 leading Libyan hospitals in March 2020. In addition, a survey was conducted among 400 doctors who worked in these hospitals to evaluate the status of personal protective equipment. RESULTS: Out of 16 hospitals, the highest hospital capacity was 1000 in-patient beds, while the lowest was 25 beds with a median of 200 (IQR 52-417, range 25-1000) hospital beds. However, a median of only eight (IQR 6-14, range 3-37) available functioning ICU beds were reported in these hospitals. Only 9 (IQR 4.5-14, range 2-20) mechanical ventilators were reported and none of the hospitals had a reverse transcription-polymerase chain reaction machine for COVID-19 testing. Moreover, they relied on one of two central laboratories located in major cities. Our PPE survey revealed that 56.7% hospitals lacked PPE and 53% of healthcare workers reported that they did not receive proper PPE training. In addition, 70% reported that they were buying the PPE themselves as hospitals did not provide them. CONCLUSION: This study provides an alarming overview of the unpreparedness of Libyan hospitals for detecting and treating patients with COVID-19 and limiting the spread of the pandemic.

Is distance associated with tuberculosis treatment outcomes? A retrospective cohort study in Kampala, Uganda.

BACKGROUND: Challenges accessing nearby health facilities may be a barrier to initiating and completing tuberculosis (TB) treatment. We aimed to evaluate whether distance from residence to health facility chosen for treatment is associated with TB treatment outcomes. METHODS: We conducted a retrospective cohort study of all patients initiating TB treatment at six health facilities in Kampala from 2014 to 2016. We investigated associations between distance to treating facility and unfavorable TB treatment outcomes (death, loss to follow up, or treatment failure) using multivariable Poisson regression. RESULTS: Unfavorable treatment outcomes occurred in 20% (339/1691) of TB patients. The adjusted relative risk (aRR) for unfavorable treatment outcomes (compared to treatment success) was 0.87 (95% confidence interval [CI] 0.70, 1.07) for patients living ≥2 km from the facility compared to those living closer. When we separately compared each type of unfavorable treatment outcome to favorable outcomes, those living ≥2 km from the facility had increased risk of death (aRR 1.42 [95%CI 0.99, 2.03]) but decreased risk for loss to follow-up (aRR 0.57 [95%CI 0.41, 0.78]) than those living within 2 km. CONCLUSIONS: Distance from home residence to TB treatment facility is associated with increased risk of death but decreased risk of loss to follow up. Those who seek care further from home may have advanced disease, but once enrolled may be more likely to remain in treatment.

Surface urban heat islands in the mega city of Karachi, their spatial distribution and health emergency response infrastructure.

The 2015 heat wave resulted in an estimated over 1200 deaths during the month of June. However, there were no records on the spatial distribution of the effects of this heat wave. An analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) daily data was conducted to identify regions that experienced above normal temperatures in 2015. An analysis of the monthly averages showed that in general April and May were the warmer months in Karachi, unlike the case in 2015. In addition, the general warm trends were common in the highly industrialised Sindh Industrial Trading Estate (SITE) and Liaquatabad towns, while Gadap, with its mostly barren land, and New Karachi also experience higher temperatures. Coastal towns were naturally cooler and more habitable in the given scenario. A count of the spatial presence of health facilities for the city was also extracted where Gadap and Korangi were poorly served while the more affluent towns of Defence Housing Authority (DHA) and Gulshan-e-Iqbal appeared to be better served.

Process Evaluation of Skilled Delivery Service in Hadiya Zone in Southern Nations, Nationalities, and Peoples Region, Ethiopia.

Pregnancy-related death is a cause for maternal and newborn mortality and morbidity as well as an obstacle for economic growth. Three-quarters of mothers' lives can be saved if women have access to a skilled health worker at delivery and emergency obstetric care. This evaluation was conducted to assess skilled delivery service implementation level by using three dimensions (availability, compliance, and acceptability) and identify major contributing issues for underutilization of the service. The evaluation design is cross-sectional. The study included 846 mothers who gave birth in Hadiya zone within one year prior to study period, using one year delivery records. Epi Info 3.5.3 and SPSS version 16 were employed for data analysis. Based on selected indicators, resource availability was inadequate for health facilities, human resource medical equipment, and rooms. On the compliance dimension, skilled delivery service coverage (34.8%), active management of third stage labor (32.7%), and health information at discharge and in postnatal care (PNC) visit (7.1%) critically complied with or poorly agreed to the guidelines and targets. Regarding skilled delivery service acceptability, welcoming, privacy keeping, reassurance during labor pain, follow-up, baby care, comfortability (rooms, beds, and clothing), cost of service, and episiotomy (without local anesthesia) were not acceptable.