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Background: Type 1 diabetes (T1D) management exerts a considerable financial burden on patients, caregivers, and developing nations at large. In Ghana, a key governments effort to attenuate the financial burden of T1D on patients was to fashion safety-net mechanisms through financial risk pooling/sharing known as the National Health Insurance Scheme (NHIS). However, there is limited research on patients and caregivers' experiences with the cost of managing T1D within the NHIS in Ghana. Objective: This study explored the cost of T1D management, and the impact of the NHIS policy on mitigating costs of care. Methods: A semi-structured interview guide was developed to collect qualitative data from 28 young people living with T1D (PLWD), 12 caregivers, 6 healthcare providers, and other stakeholders in Western, Central and the Greater Accra regions. Multiple data collection techniques including mystery client and in-depth interviews were used to collect data. Thematic content analysis was performed with QSR NVivo 14. Results: Five key domains/themes which are: cost of T1D management supplies; cost of clinical care; cost of transportation; cost of diet; and NHIS were identified. The daily cost of blood glucose testing and insulin injection per day was between GHC 5–7 (US$ 0.6 to 1.0). The NHIS did not cover supplies such as strips, glucometers, HbA1c tests, and periodic medical tests. Even for those cost which were covered by the NHIS (mainly pre-mixed insulin), marked government delays in funds reimbursement to accredited NHIS facilities compelled providers to push the financial obligation onto patients and caregivers. Such cost obligations were fulfilled through out-of-pocket top-up or full payment of insulin of about GHC 15–25 (US$ 2–4), and GHC 25–50 (US$4–8) depending on the region and place of residence. Conclusion: The cost of managing T1D was a burden for patients and their caregivers. There was a commodification of life-saving insulin on the Ghanaian market, and the NHIS did not function well to ease the cost-burden of T1D management on patients and caregivers. The findings call for the need to scale up NHIS services to include simple supplies, particularly test strips, and always ensure the availability of life-saving insulin in healthcare facilities. [ABSTRACT FROM AUTHOR]

Abstract Background Type 1 diabetes (T1D) management exerts a considerable financial burden on patients, caregivers, and developing nations at large. In Ghana, a key governments effort to attenuate the financial burden of T1D on patients was to fashion safety-net mechanisms through financial risk pooling/sharing known as the National Health Insurance Scheme (NHIS). However, there is limited research on patients and caregivers’ experiences with the cost of managing T1D within the NHIS in Ghana. Objective This study explored the cost of T1D management, and the impact of the NHIS policy on mitigating costs of care. Methods A semi-structured interview guide was developed to collect qualitative data from 28 young people living with T1D (PLWD), 12 caregivers, 6 healthcare providers, and other stakeholders in Western, Central and the Greater Accra regions. Multiple data collection techniques including mystery client and in-depth interviews were used to collect data. Thematic content analysis was performed with QSR NVivo 14. Results Five key domains/themes which are: cost of T1D management supplies; cost of clinical care; cost of transportation; cost of diet; and NHIS were identified. The daily cost of blood glucose testing and insulin injection per day was between GHC 5–7 (US$ 0.6 to 1.0). The NHIS did not cover supplies such as strips, glucometers, HbA1c tests, and periodic medical tests. Even for those cost which were covered by the NHIS (mainly pre-mixed insulin), marked government delays in funds reimbursement to accredited NHIS facilities compelled providers to push the financial obligation onto patients and caregivers. Such cost obligations were fulfilled through out-of-pocket top-up or full payment of insulin of about GHC 15–25 (US$ 2–4), and GHC 25–50 (US$4–8) depending on the region and place of residence. Conclusion The cost of managing T1D was a burden for patients and their caregivers. There was a commodification of life-saving insulin on the Ghanaian market, and the NHIS did not function well to ease the cost-burden of T1D management on patients and caregivers. The findings call for the need to scale up NHIS services to include simple supplies, particularly test strips, and always ensure the availability of life-saving insulin in healthcare facilities.

Abstract Historically, the NHS did not routinely collect cost data, unlike many countries with private insurance markets. In 1998, for the first time the government mandated NHS trusts to submit estimates of their costs of service, known as reference costs. These have informed a wide range of health economic evaluations and important functions in the health service, such as setting prices. Reference costs are collected by progressively disaggregating budgets top-down into disease and treatment groups. Despite ongoing improvements to methods and guidance, these submissions continued to suffer a lack of accuracy and comparability, fundamentally undermining their credibility for critical functions. To overcome these issues, there was a long-held ambition to collect “patient-level” cost data. Patient-level costs are estimated with a combination of disaggregating budgets but also capturing the patient-level “causality of costs” bottom-up in the allocation of resources to patient episodes. These not only aim to capture more of the drivers of costs, but also improve consistency of reporting between providers. The change in methods may confer improvements to data quality, though judgement is still required and achieving consistency between trusts will take further work. Estimated costs may also change in important ways that may take many years to fully understand. We end on a cautionary note that patient-level cost methods may unlock potential, they alone contribute little to our understanding of the complexities involved with service quality or need, while that potential will require substantial investment to realise. Many healthcare resources cannot be attributed to individual patients so the very notion of “patient-level” costs may be misplaced. High hopes have been put in these new data, though much more work is now necessary to understand their quality, what they show and how their use will impact the system.

Abstract Background Expanding fiscal space for health can be defined as providing additional budgetary resources for health, which is highly important during biological crises. This study aimed to provide a model for financing the treatment costs during biological crises using the development of the fiscal space approach. Methods This study employed a descriptive mixed-method design, consisting of three stages. In the first stage, a systematic review of relevant literature was conducted using multiple databases, including Scopus, PubMed, and Google Scholar. A total of 45 studies that met the inclusion criteria were selected. In the second stage, a panel of 14 experts identified five primary and 32 secondary strategies using an open questionnaire. Any additional strategies not identified during the literature review were added if a consensus was reached by experts. In the final stage, the Best Worst Method (BWM) was used to prioritize the identified strategies and sub-strategies based on their feasibility, effectiveness, quick yield, and fairness. Results Five strategies and fifty sub-strategies were identified. The most important strategies were the increase in health sector-specific resources (0.3889), increase in efficiency of health expenditures (0.2778), structural reforms (0.1111), health sector-specific grants and foreign aid (0.1667), and conducive macroeconomic conditions (0.05556). The most important sub-strategies were establishing and increasing earmarked taxes for the health sector (0.0140), expanding Universal Health Coverage (UHC) plans (0.0103), attracting the participation of non-governmental organizations (NGOs) and charitable organizations in the health sector (0.0096), integrating basic social insurance funds (0.0934), and tax exemptions for economic activists in the health sector (0.009303) during the crisis. Conclusion This study identified five main strategies and 50 sub-strategies for financing the treatment costs during biological crises. The most important strategies were increasing health sector-specific resources, improving efficiency of health expenditures, and implementing structural reforms. To finance health expenditures, harmful and luxury goods taxes can be increased and allocated to the health sector during crises. UHC plans should be improved and expanded, and the capacity of NGOs and charitable organizations should be better utilized during crises.

Historically, the NHS did not routinely collect cost data, unlike many countries with private insurance markets. In 1998, for the first time the government mandated NHS trusts to submit estimates of their costs of service, known as reference costs. These have informed a wide range of health economic evaluations and important functions in the health service, such as setting prices. Reference costs are collected by progressively disaggregating budgets top-down into disease and treatment groups. Despite ongoing improvements to methods and guidance, these submissions continued to suffer a lack of accuracy and comparability, fundamentally undermining their credibility for critical functions. To overcome these issues, there was a long-held ambition to collect "patient-level" cost data. Patient-level costs are estimated with a combination of disaggregating budgets but also capturing the patient-level "causality of costs" bottom-up in the allocation of resources to patient episodes. These not only aim to capture more of the drivers of costs, but also improve consistency of reporting between providers. The change in methods may confer improvements to data quality, though judgement is still required and achieving consistency between trusts will take further work. Estimated costs may also change in important ways that may take many years to fully understand. We end on a cautionary note that patient-level cost methods may unlock potential, they alone contribute little to our understanding of the complexities involved with service quality or need, while that potential will require substantial investment to realise. Many healthcare resources cannot be attributed to individual patients so the very notion of "patient-level" costs may be misplaced. High hopes have been put in these new data, though much more work is now necessary to understand their quality, what they show and how their use will impact the system. [ABSTRACT FROM AUTHOR]

Background: Expanding fiscal space for health can be defined as providing additional budgetary resources for health, which is highly important during biological crises. This study aimed to provide a model for financing the treatment costs during biological crises using the development of the fiscal space approach. Methods: This study employed a descriptive mixed-method design, consisting of three stages. In the first stage, a systematic review of relevant literature was conducted using multiple databases, including Scopus, PubMed, and Google Scholar. A total of 45 studies that met the inclusion criteria were selected. In the second stage, a panel of 14 experts identified five primary and 32 secondary strategies using an open questionnaire. Any additional strategies not identified during the literature review were added if a consensus was reached by experts. In the final stage, the Best Worst Method (BWM) was used to prioritize the identified strategies and sub-strategies based on their feasibility, effectiveness, quick yield, and fairness. Results: Five strategies and fifty sub-strategies were identified. The most important strategies were the increase in health sector-specific resources (0.3889), increase in efficiency of health expenditures (0.2778), structural reforms (0.1111), health sector-specific grants and foreign aid (0.1667), and conducive macroeconomic conditions (0.05556). The most important sub-strategies were establishing and increasing earmarked taxes for the health sector (0.0140), expanding Universal Health Coverage (UHC) plans (0.0103), attracting the participation of non-governmental organizations (NGOs) and charitable organizations in the health sector (0.0096), integrating basic social insurance funds (0.0934), and tax exemptions for economic activists in the health sector (0.009303) during the crisis. Conclusion: This study identified five main strategies and 50 sub-strategies for financing the treatment costs during biological crises. The most important strategies were increasing health sector-specific resources, improving efficiency of health expenditures, and implementing structural reforms. To finance health expenditures, harmful and luxury goods taxes can be increased and allocated to the health sector during crises. UHC plans should be improved and expanded, and the capacity of NGOs and charitable organizations should be better utilized during crises. [ABSTRACT FROM AUTHOR]

Abstract Background Health insurance is among the healthcare financing reforms proposed to increase the available healthcare resources and to decrease the risk of household financial crisis. Recently, Ethiopia has been implementing community-based health insurance which mainly targets the very large rural agricultural sector and small and informal sector in urban settings. Therefore, this study was aimed to assess the coverage of health insurance and its determinants in Ethiopia. Methods Data were extracted from the 2019 mini Ethiopian Demographic and Health Survey (EDHS) to assess determinants of health insurance coverage in Ethiopia. The analysis included a weighted sample of 8663 respondents. Multivariable logistic regression analysis was conducted and the results were presented as adjusted odds ratio (AOR) at 95% confidence interval (CI), statistical significance was declared at a p-value

Abstract Background Public providers in Jordan are facing increasing health demands due to human crises. This study aimed to benchmark the unit costs of hospital services in public providers in Jordan to provide insights into the outlook for public health care costs. Methods The unit costs of hospital services per admission, inpatient days, outpatient visits, emergency visits and surgical operations were estimated using the standard average costing method (top-down) for the fiscal year 2018–2019. The unit costs per inpatient day were estimated for nine specialities and staff in Jordanian dinars (exchange rate JOD 1 = USD 1.41). Results The average unit cost per admission in Jordan was JOD 782.300 (USD 1101.80), the per inpatient day cost was JOD 236.600 (USD 333.20), the per bed day cost was JOD 172.900 (USD 244.90), the per outpatient visit cost was JOD 58.400 (USD 82.30), the per operation cost was JOD 449.600 (USD 633.20) and the per emergency room visit cost was JOD 31.800 (USD 44.80). The specialities of ICU/CCU and OB/GYN presented the highest unit costs per inpatient day across providers: JOD 377.800 (USD 532.90) and JOD 362.600 (USD 510.70), respectively. The average salaried unit cost of staff depended mainly on year of employment. Nonetheless, the unit costs varied depending on the service utilization, type of service and organizational outlet. Conclusions Knowledge of how unit costs vary across public providers in Jordan is essential to outline cost control strategies and inform future research. Institutionalization of the cost information system and high-level governmental support are necessary to generate a routine practice of collecting and sharing cost information.

Background: Health insurance is among the healthcare financing reforms proposed to increase the available healthcare resources and to decrease the risk of household financial crisis. Recently, Ethiopia has been implementing community-based health insurance which mainly targets the very large rural agricultural sector and small and informal sector in urban settings. Therefore, this study was aimed to assess the coverage of health insurance and its determinants in Ethiopia. Methods: Data were extracted from the 2019 mini Ethiopian Demographic and Health Survey (EDHS) to assess determinants of health insurance coverage in Ethiopia. The analysis included a weighted sample of 8663 respondents. Multivariable logistic regression analysis was conducted and the results were presented as adjusted odds ratio (AOR) at 95% confidence interval (CI), statistical significance was declared at a p-value < 0.05 in all analyses. Results: The health insurance coverage in Ethiopia was 28.1% (95%CI: 27.2%, 29%). Administration regions (Tigray: AOR = 16.9, 95%CI: 5.53, 51.59, Amhara: AOR = 25.8, 95%CI: 8.52, 78.02, Oromia, AOR = 4.27, 95%CI: 1.41, 12.92, Southern Nations, Nationalities and Peoples region, AOR = 4.06, 95%CI: 1.34, 12.32, Addis Ababa, AOR = 4.65, 95%CI: 1.46, 14.78), place of residence (rural, AOR = 1.38, 95%CI: 1.17, 1.63), sex of household head (male; AOR = 1.23, 95%CI: 1.07, 1.41), wealth index (middle, AOR = 1.75, 95%CI: 1.46, 2.09, richer, AOR = 1.86, 95%CI: 1.55, 2.24), family size (≥ 5 members, AOR = 1.17, 95%CI: 1.03, 1.33), having under-five children (AOR = 1.22, 95%CI: 1.076, 1.38), and age of household head (31–40 years, AOR = 1.71, 95%CI: 1.45, 2.01, 41–64 years, AOR = 2.49, 95%CI: 2.12, 2.92, 65 + years, AOR = 2.43, 95%CI: 2.01, 2.93) were factors associated with health insurance coverage. Conclusions: Less than one-third of Ethiopians were covered by health insurance. Socio-economic factors and demographic factors were found to associate with health insurance coverage in Ethiopia. Therefore, enhancing health insurance coverage through contextualized implementation strategies would be emphasized. [ABSTRACT FROM AUTHOR]

Background: Health insurance is among the healthcare financing reforms proposed to increase the available healthcare resources and to decrease the risk of household financial crisis. Recently, Ethiopia has been implementing community-based health insurance which mainly targets the very large rural agricultural sector and small and informal sector in urban settings. Therefore, this study was aimed to assess the coverage of health insurance and its determinants in Ethiopia. Methods: Data were extracted from the 2019 mini Ethiopian Demographic and Health Survey (EDHS) to assess determinants of health insurance coverage in Ethiopia. The analysis included a weighted sample of 8663 respondents. Multivariable logistic regression analysis was conducted and the results were presented as adjusted odds ratio (AOR) at 95% confidence interval (CI), statistical significance was declared at a p-value < 0.05 in all analyses. Results: The health insurance coverage in Ethiopia was 28.1% (95%CI: 27.2%, 29%). Administration regions (Tigray: AOR = 16.9, 95%CI: 5.53, 51.59, Amhara: AOR = 25.8, 95%CI: 8.52, 78.02, Oromia, AOR = 4.27, 95%CI: 1.41, 12.92, Southern Nations, Nationalities and Peoples region, AOR = 4.06, 95%CI: 1.34, 12.32, Addis Ababa, AOR = 4.65, 95%CI: 1.46, 14.78), place of residence (rural, AOR = 1.38, 95%CI: 1.17, 1.63), sex of household head (male; AOR = 1.23, 95%CI: 1.07, 1.41), wealth index (middle, AOR = 1.75, 95%CI: 1.46, 2.09, richer, AOR = 1.86, 95%CI: 1.55, 2.24), family size (≥ 5 members, AOR = 1.17, 95%CI: 1.03, 1.33), having under-five children (AOR = 1.22, 95%CI: 1.076, 1.38), and age of household head (31–40 years, AOR = 1.71, 95%CI: 1.45, 2.01, 41–64 years, AOR = 2.49, 95%CI: 2.12, 2.92, 65 + years, AOR = 2.43, 95%CI: 2.01, 2.93) were factors associated with health insurance coverage. Conclusions: Less than one-third of Ethiopians were covered by health insurance. Socio-economic factors and demographic factors were found to associate with health insurance coverage in Ethiopia. Therefore, enhancing health insurance coverage through contextualized implementation strategies would be emphasized. [ABSTRACT FROM AUTHOR]

Background: Direct and time costs of accessing and using health care may limit health care access, affect welfare loss, and lead to catastrophic spending especially among poorest households. To date, limited attention has been given to time and transport costs and how these costs are distributed across patients, facility and service types especially in poor settings. We aimed to fill this knowledge gap. Methods: We used data from 1407 patients in 150 facilities in Tanzania. Data were collected in January 2012 through patient exit-interviews. All costs were disaggregated across patients, facility and service types. Data were analysed descriptively by using means, medians and equity measures like equity gap, ratio and concentration index. Results: 71% of patients, especially the poorest and rural patients, accessed care on foot. The average travel time and cost were 30 minutes and 0.41USD respectively. The average waiting time and consultation time were 47 min and 13 min respectively. The average medical cost was 0.23 USD but only18% of patients paid for health care. The poorest and rural patients faced substantial time burden to access health care (travel and waiting) but incurred less transport and medical costs compared to their counterparts. The consultation time was similar across patients. Patients spent more time travelling to public facilities and dispensaries while incurring less transport cost than accessing other facility types, but waiting and consultation time was similar across facility types. Patients paid less amount in public than in private facilities. Postnatal care and vaccination clients spent less waiting and consultation time and paid less medical cost than antenatal care clients. Conclusions: Our findings reinforce the need for a greater investment in primary health care to reduce access barriers and cost burdens especially among the worse-offs. Facility's construction and renovation and increased supply of healthcare workers and medical commodities are potential initiatives to consider. Other initiatives may need a multi-sectoral collaboration. [ABSTRACT FROM AUTHOR]

Background: Public providers in Jordan are facing increasing health demands due to human crises. This study aimed to benchmark the unit costs of hospital services in public providers in Jordan to provide insights into the outlook for public health care costs. Methods: The unit costs of hospital services per admission, inpatient days, outpatient visits, emergency visits and surgical operations were estimated using the standard average costing method (top-down) for the fiscal year 2018–2019. The unit costs per inpatient day were estimated for nine specialities and staff in Jordanian dinars (exchange rate JOD 1 = USD 1.41). Results: The average unit cost per admission in Jordan was JOD 782.300 (USD 1101.80), the per inpatient day cost was JOD 236.600 (USD 333.20), the per bed day cost was JOD 172.900 (USD 244.90), the per outpatient visit cost was JOD 58.400 (USD 82.30), the per operation cost was JOD 449.600 (USD 633.20) and the per emergency room visit cost was JOD 31.800 (USD 44.80). The specialities of ICU/CCU and OB/GYN presented the highest unit costs per inpatient day across providers: JOD 377.800 (USD 532.90) and JOD 362.600 (USD 510.70), respectively. The average salaried unit cost of staff depended mainly on year of employment. Nonetheless, the unit costs varied depending on the service utilization, type of service and organizational outlet. Conclusions: Knowledge of how unit costs vary across public providers in Jordan is essential to outline cost control strategies and inform future research. Institutionalization of the cost information system and high-level governmental support are necessary to generate a routine practice of collecting and sharing cost information. [ABSTRACT FROM AUTHOR]

Abstract In order to define strategies to curb the continuing increase in healthcare costs, we describe the cost breakdown of open tibial fractures. Twenty-seven clinical and process variables were recorded retrospectively, and five main hospital related cost categories were defined. Three multivariate linear models were fitted to the data. Total healthcare costs of open tibial fractures were almost twice as high compared to closed fractures and mainly existed of hospitalization costs. Length-of-stay (LOS) was found to be the most important variable driving the healthcare costs of open tibial fractures. Deep infection lead to a 6-fold increase of LOS and 5-fold increase in total healthcare costs of open tibial fractures. Therefore, appropriate international consensus guidelines are required to improve not only the patient outcome (infection prevention) but also reduce overall healthcare cost by focusing on reducing the LOS.

In order to define strategies to curb the continuing increase in healthcare costs, we describe the cost breakdown of open tibial fractures. Twenty-seven clinical and process variables were recorded retrospectively, and five main hospital related cost categories were defined. Three multivariate linear models were fitted to the data. Total healthcare costs of open tibial fractures were almost twice as high compared to closed fractures and mainly existed of hospitalization costs. Length-of-stay (LOS) was found to be the most important variable driving the healthcare costs of open tibial fractures. Deep infection lead to a 6-fold increase of LOS and 5-fold increase in total healthcare costs of open tibial fractures. Therefore, appropriate international consensus guidelines are required to improve not only the patient outcome (infection prevention) but also reduce overall healthcare cost by focusing on reducing the LOS. [ABSTRACT FROM AUTHOR]