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Background: Universal Health Coverage (UHC) aims for equitable access to healthcare, minimizing the financial burden (FB) on households. This dissertation examines the effectiveness of standard indicators like impoverishment and catastrophic health expenditure (CHE) in reflecting FB and its inequality in Cambodia. Recent publications have highlighted Cambodia’s progress in reducing CHE, impoverishment, and inequality. However, this dissertation's core contention is that by using the standard metrics of FB, one masks its impact among the lowest-income groups and inequality, misrepresenting trends and policy achievements.
Objectives: This dissertation evaluates FB using standard and alternative measures to understand FB trends and inequalities across Cambodia's population, and appropriately reflect the burden on lowest-income households, thus better informing policy responses for achieving UHC.
Methods: Employing data from the Cambodian Socio-Economic Surveys (CSES) from 2004 to 2019/20, this dissertation comprises three studies. The first assesses standard FB indicators and resulting inequality across socioeconomic and regional groups. The second examines impoverishment sensitivity when building various consumption aggregates and setting poverty lines. The third introduces an alternative FB measure, Excessive Financial Burden (EFB), comparing it against standard CHE metrics to evaluate financial hardship and associated inequality.
Results: Initial findings showed a 27% decrease in standard CHE incidence from 2004 to 2009, with significant reductions in FB for households seeking medical care. However, from 2009 onwards, CHE reductions stagnated. The sensitivity analysis revealed that methodological choices highly influence impoverishment estimates, making it a poor choice to assess FB trends. The introduction of EFB highlighted worsening FB from 2009 to 2019, particularly among the lowest-income households, with EFB inequality deepening significantly. EFB with wealth index rankings appears more effective at capturing economic shocks due to healthcare spending, and its decomposition provided valuable insights to mitigate inequality in the medium term.
Conclusions: The study underscores the limitations of standard CHE and impoverishment measures in capturing the full extent of FB and suggests that alternative measures like EFB, combined with wealth indices, provide a more comprehensive view of economic shocks due to healthcare spending. The findings advocate for policy measures to extend social health protection and improve targeting to address FB and inequality effectively. The methodology and insights from this study offer a framework for other low and middle-income countries striving to achieve UHC.
This paper empirically analyzes the Todaro Paradox for eight developing countries for the period from 1992 to 2019. Having different data characteristics, we apply three different panel approaches (Fixed Effect, Random Effect, and Full Modified Ordinary Least Square) by using distinct models. Our findings from different models depict that the Todaro Paradox is valid for the sample economies. Specifically, we observe a negative relationship between the price level ratio of purchasing power parity conversion factor (GDP) to market exchange rate and urban population contrary to the price level ratio of the purchasing power parity conversion factor (GDP) to the market exchange rate – rural population nexus. Thanks to obtaining these links, we apply the third empirical model to verify the Todaro Paradox. The analysis of the price level ratio of the purchasing power parity conversion factor (GDP) to the market exchange rate and total unemployment in the urban population provides strong evidence for the validity of this paradox. Deviated from the previous literature, this paper applies the price level ratio of the purchasing power parity conversion factor (GDP) to the market exchange rate since the higher the purchasing power parity of a country, the lower the rate of rural-urban migration is expected. By using one extra variable (unemployment), we test the Todaro Paradox. This combination of variables as well as different panel techniques (Fixed Effect, Random Effect, and Full Modified Ordinary Least Square) allow us to draw more robust conclusions. To address the challenges posed by rural-urban migration, policies should be designed to promote sustainable development in both urban and rural areas. This can include measures to create employment opportunities and improve the quality of life in both areas, as well as policies to regulate migration and manage the pressures caused by rapid urbanization.
This study estimates the prevalence of health insurance coverage and associated socioeconomic factors in the Democratic Republic of Congo (DRC). Using the nationally representative household survey of the 2017/2018 DRC Multiple Indicator Cluster Survey (MICS), we applied weighted logistic regression models to identify regions and subgroups with low health insurance coverage. The study's findings revealed a low insurance coverage of less than 5%, with significant disparities across provinces and socioeconomic status. Additionally, three factors were strongly associated with the low health insurance coverage rates: education, wealth, and financial inclusion proxied by bank account ownership. Consequently, we recommend that the government, the private sector, and donors prioritize programs targeting provinces with less coverage and individuals without formal education to increase health insurance coverage. This study encourages the government to establish national programs to improve financial inclusion, which could positively impact poverty reduction and health insurance coverage. We also propose that the government initiates pilot projects for premium exemptions and through subsidies for vulnerable populations in the short term, and ensure formal employment for the majority of the population in the long term to facilitate the proper collection of premiums for individuals. Overall, this study contributes to the literature on health insurance in the DRC and sub‐Saharan Africa by identifying the socioeconomic factors that explain the prevalence of health insurance coverage. The findings of this study have important policy implications for the government, the private sector, and donors to promote health insurance coverage and achieve universal health coverage.
The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain ® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis.
The article considers supply chain networks of process industries within several days or months. Continuous-time production, distribution, and recycling of goods and their subsequent reintegration into closed loops is analyzed. The aim of the study is to quantify trade-offs resulting from decisions on the usage of recyclable quantities. A mixed-integer linear programming model based on a new network structure with third-party contractors is developed. A numerical study consists of thirty scenarios with randomly generated data. The model allows for generating compromise solutions in acceptable computation times, if limiting the transfer of recyclable goods to disposal results in decreasing network profits.
Die vorliegende Arbeit untersucht und vergleicht die Wachstumsraten dreier Szenarien, in denen Umweltverschmutzung zunehmend internalisiert wird, wobei Umweltverschmutzung als Externalität des Vermögens angenommen wird. Die Darstellung erfolgt anhand eines endogenen Wachstumsmodell, unter der Annahme einer positiven Kreuzelastizität des Nutzens in Bezug auf Konsum und Umweltverschmutzung, welche als Kompensationseffekt bezeichnet wird. In dem ersten Szenario findet keine Internalisierung statt, die Haushalte gehen von einer konstanten Umweltqualität aus, auf die sie keinen Einfluss haben. Im zweiten Szenario berücksichtigen die Haushalte den Anstieg der Umweltverschmutzung, ignorieren jedoch dessen Ursache. Das dritte Szenario bildet das soziale Optimum ab, die Haushalte internalisieren sowohl den Anstieg der Umweltverschmutzung als auch den Zusammenhang zwischen ihrem Vermögen und der Umweltverschmutzung. Untersucht wird dies für die drei Fälle, dass die Umweltverschmutzung proportional, unter- und überproportional mit zunehmenden vermögen steigt. Der Vergleich der drei Szenarien zeigt, dass mit dem Kompensationseffekt als treibende Kraft das Wachstum im zweiten Szenario am höchsten ausfällt, da die Haushalte auf die Umweltverschmutzung mit mehr Konsum reagieren, um den Disnutzen der Umweltverschmutzung abzumildern. Im dritten Szenario fällt das Wachstum hingegen am geringsten aus. Über den Kompensationseffekt haben die Haushalte zwar eine gesteigerte Präferenz für Konsum, sie berücksichtigen aber gleichfalls, dass der Vermögensaufbau für den Konsum eine steigende Umweltverschmutzung zur Folge hat. Dies führt insgesamt dazu, dass die Haushalte zur Schonung der Umwelt auf Wachstum verzichten. Positives Wachstum ist jedoch auch im sozialen Optimum unter der Voraussetzung möglich, dass die mit dem Vermögen einhergehende Umweltverschmutzung hinreichend gering ist. Dies gilt selbst für den Fall, dass die Umweltverschmutzung überproportional mit zunehmenden Vermögen ansteigt. Neben der Erkenntnis, dass Umweltverschmutzung zu einer Reduktion, aber nicht zwangsläufig zu einer Abkehr von Wachstum führen muss, zeigt die Arbeit, dass Anpassung an die Folgen von Umweltverschmutzung und Ursachenbekämpfung zusammen betrieben werden müssen. Eine Fokussierung auf Anpassung führt zu suboptimal hohen Wachstum.
The number of obstetric departments in German hospitals has declined in the last decades. In particular, rural hospitals are challenged to sustain their delivery services. In this paper, we analyse the role of variation and overheads of obstetric departments from the perspective of current and future German hospital financing. For this purpose, we develop a Monte Carlo simulation model that analyses the workload of the labour room and the obstetric ward. The results show that a hospital with less than 640 deliveries per year cannot break even. In order to offer services 24 h per day, 365 days per year, five nurses, five midwives, and five gynaecologists are needed. This results in high fixed costs. At the same time, the variation coefficient of the labour room and the obstetric ward declines with an increasing number of deliveries. Consequently, small hospitals have a higher risk of over- and under-utilization in the course of the year. This paper acknowledges that economics is not the only decision dimension. The quality of the institution and the transport to the hospital have to be considered, as well as the population’s wish for nearby services. However, the simulations clearly demonstrate that unless the hospital financing system is changed fundamentally, the decline in the number of hospitals offering delivery services will continue.
The Flipped Break-Even: Re-Balancing Demand- and Supply-Side Financing of Health Centers in Cambodia
(2023)
Supply-side healthcare financing still dominates healthcare financing in many countries where the government provides line-item budgets for health facilities irrespective of the quantity or quality of services rendered. There is a risk that this approach will reduce the efficiency of services and the value of money for patients. This paper analyzes the situation of public health centers in Cambodia to determine the relevance of supply- and demand-side financing as well as lump sum and performance-based financing. Based on a sample of the provinces of Kampong Thom and Kampot in the year 2019, we determined the income and expenditure of each facility and computed the unit cost with comprehensive step-down costing. Furthermore, the National Quality Enhancement Monitoring Tool (NQEMT) provided us with a quality score for each facility. Finally, we calculated the efficiency as the quotient of quality and cost per service unit as well as correlations between the variables. The results show that the largest share of income was received from supply-side financing, i.e., the government supports the health centers with line-item budgets irrespective of the number of patients and the quality of care. This paper demonstrates that the efficiency of public health centers increases if the relevance of performance-based financing increases. Thus, the authors recommend increasing performance-based financing in Cambodia to improve value-based healthcare. There are several alternatives available to re-balance demand- and supply-side financing, and all of them must be thoroughly analyzed before they are implemented.
Die COVID-19-Krise hatte erhebliche Auswirkungen auf die Grenzregion Polen — Deutschland. Der Beitrag untersucht, ob die Pandemie tatsächlich den Abstand zwischen der Grenzregion als strukturschwacher Region und dem Rest von Deutschland erhöhen wird, oder ob die Krise sogar eine Chance bietet. Hierzu wurden neben einer Online-Befragung ebenfalls Experteninterviews durchgeführt und ausgewertet. Die Zielregion ist vergleichsweise gut durch die Krise gekommen, jedoch sollte die Region die Chance nutzen und bereits begonnene Veränderungen wie Digitalisierung und Flexibilisierung weiter vorantreiben.
This study investigates a closed-loop supply chain network design and planning problem, integrating environmental goals and resilience strategies using a novel columnwise robust optimization approach combined with fuzzy and stochastic programming methods. The closed-loop supply chain network encompasses all processes and activities, including supply, production, recycling, and transportation, emphasizing environmentally friendly criteria at both the customer and supply chain levels. The research presents a quantifiable green scale value using the customer greenness tendency index and the supply chain greenness level to optimize environmental objectives while minimizing the overall costs within the supply chain network.
The study adopts five resilience strategies to mitigate supply and production disruptions: backup supplier contracting, raw material prepositioning, and multiple sourcing for supply disruptions. Final product prepositioning and backup production facility strategies are utilized for production-related disruptions. To address disruption and uncertainties, robust optimization approaches are used through a hybrid robust-fuzzy-stochastic programming method, incorporating two-stage scenario-based stochastic programming, fuzzy chance-constrained programming, and a novel robust columnwise uncertainty approach. The robust column-wise approach shifts from the constraint-wise perspectives to a columnwise approach, addressing budget uncertainty independently for each constraint. The augmented epsilon constraint method is used as a solution approach for the bi-objective mathematical model, with implementation examples solved using the GAMS solver.
Practical applications of this approach demonstrate its effectiveness in improving supply chain robustness and adaptability through parameter-tuning exercises and scenario analyses. This research contributes to green supply chain management by addressing gaps such as customer behavior, supply chain greenness performance, energy resource modes, hybrid formulations for uncertainties, resilience strategies, and demand uncertainty management. These insights guide supply chain practitioners toward more environmentally responsible and resilient practices, emphasizing the importance of incorporating customer preferences and highlighting the economic implications of prioritizing customer satisfaction.