Health Economics Review最新文献

Background: China started a pilot public hospital reform in 2012 to improve governance and efficiency in healthcare services delivery among county-level hospitals. This study aims to investigate the impact of the pilot reform on hospital efficiency and productivity by using a unique dataset of county hospitals in East China during 2009-2015.

Methods: A three-stage approach is used. First, this study uses the output-oriented data envelopment analysis (DEA) to estimate hospital efficiency with variable returns to scale. Second, propensity score matching is used to address potential biases associated with the selection of counties for the pilot program. In the third stage, we assess the impact of the pilot reform on efficiency by using a Tobit Difference-in-Differences approach.

Results: The average level of hospital efficiency for the whole sample experienced a rapid drop in 2013, then returned to a peak in 2014. Except in the reform year (2012), the overall hospital efficiency for the post-reform period is higher than that for the pre-reform period. The baseline model results show that the pilot reform is associated with a 3% decline in pure technical efficiency and a 2.3% increase in hospital scale efficiency, respectively. Our findings are robust when we apply bootstrapped DEA efficiency scores and use different specifications.

Conclusion: The findings of this study suggest no improvements in overall hospital efficiency associated with the pilot reform, possibly due to the combined effects of inefficient governance and hospital scale expansion. This study suggests that further efforts are needed to increase county hospital performance by strengthening management and optimizing resource utiliziation.

Background: Out-of-pocket payment remains one of the ways to finance health care in Ethiopia accounting 31%. These out-of-pocket health expense leads citizens' face catastrophic and impoverishing expenditure. The most recent survey-based study of catastrophic and impoverishing health expenditure was done from the 2015/16 consumption and expenditure survey with finding of 2.1% and 1% respectively.

Objective: To assess catastrophic and impoverishing out-of-pocket health expenditure and the determinant factors of catastrophic health expenditure in Ethiopia, 2023 from the 2018/19 socioeconomic survey.

Methodology: A secondary data from Ethiopian socioeconomic survey 2018/19 conducted by Ethiopia's Central Statistical Agency and World Bank was used to assess the catastrophic and impoverishing health expenditure at the national and subnational level by the Wagstaff and Van Doorslaer and Xu et al. methodology. Then binary logistic regression was computed by the STATA (ver.12) software to assess the determinant factors of catastrophic health expenditure.

Result: From 6770 households 1.49% and 0.89% of them in Ethiopia faced catastrophic and impoverishing health expenditure respectively at 10% threshold level and households having a member with more facility visit had increased likelihood of facing catastrophic health expenditure (AOR = 2.45, 95%CI; 1.6-3.8) and also having member being hospitalized in the household had increased odds of facing catastrophic health expenditure (Adjusted odds ratio, AOR = 1.9, 95% confidence interval, CI; 1.19- 3.16). On the contrary, there is a decreased likelihood of facing catastrophic health expenditure among those who were insured for health (AOR = 0.58, 95%CI; 0.35- 0.97) and was in the richest consumption quintile group (AOR = 0.6, 95%CI; 0.47- 0.65).

Conclusion and recommendation: The finding indicates that there are still notable households facing catastrophic and impoverishing out-of-pocket health expenditure in Ethiopia especially in the lower consumption quintiles indicating inequity. In addition it is found that those with health insurance coverage, lower hospitalization and health service utilization had lower chance of facing catastrophic health payment. So it is suggested that activities that reduce hospitalization rate, increase insurance coverage and addressing the poor must be in place so that the catastrophic health cost incurred can be lowered at national level.

Background: Cancelled operations can potentially impact both health and patient experience through their effect on waiting times. However, identifying causal relationships is challenging. One possible solution is to consider 'exogenous shocks' to the system as a type of natural experiment to quantify impacts. In this study, we investigate the 2017/18 national cancellation policy in the English National Health Service (NHS), introduced to alleviate winter pressures due to influenza related admissions. Our aim is to see whether this policy can be used to isolate the impact of changes in the supply of care on waiting times and so inform system recovery from major exogenous shocks, such as the coronavirus pandemic.

Methods: To assess the impact of cancellations on hospital activity and waiting times, we use aggregate quarterly hospital-level data on planned admissions and last-minute planned operations (2013/14 to 2019/20); and individual-level data on waiting times for planned care (2015/16 to 2018/19). We analyse trends in volume of activity and waiting times, and examine waiting times distributions for patients who were admitted for planned surgery from the waiting list before and after the 2017/18 cancellation policy.

Results: The final quarter of 2017/18 had the highest number of cancelled planned operations since 2013/14 and the lowest number of planned admissions since 2015/16. However, the trend in mean and median waiting times was similar across the study period. Therefore, the 2017/18 national postponement policy had no identifiable impact on waiting times trends.

Conclusions: Despite the high numbers of cancelled planned operations in 2017/18, we could not identify an impact on waiting times. A plausible explanation is that hospital managers routinely anticipate winter pressures and reduce planned activity to manage bed occupancy. Therefore, the 2017/18 national postponement policy merely reinforced existing local decisions. The lack of a suitable counterfactual from which to infer what would have happened in 2017/18 in the absence of a postponement policy makes it impossible to isolate the impact on waiting times. This means that previous NHS cancellation policies are of limited use for informing system recovery from major exogenous shocks, such as the coronavirus pandemic.

Different types of mathematical models can be used to forecast the development of diseases as well as associated costs and analyse the cost-effectiveness of interventions. The set of models available to assess these parameters, reach from simple independent equations to highly complex agent-based simulations. For many diseases, it is simple to distinguish between infectious diseases and chronic-degenerative diseases. For infectious diseases, dynamic models are most appropriate because they allow for feedback from the number of infected to the number of new infections, while for the latter Markov models are more appropriate since this feedback is not required. However, for some diseases, the aforementioned distinction is not as clear. Cervical cancer, for instance, is caused by a sexually transmitted virus, and therefore falls under the definition of an infectious disease. However, once infected, the condition can progress to a chronic disease. Consequently, cervical cancer could be considered an infectious or a chronic-degenerative disease, depending on the stage of infection. In this paper, we will analyse the applicability of different mathematical models for epidemiological and economic processes focusing on cervical cancer. For this purpose, we will present the basic structure of different models. We will then conduct a literature analysis of the mathematical models used to predict the spread of cervical cancer. Based on these findings we will draw conclusions about which models can be used for which purpose and which disease. We conclude that each type of model has its advantages and disadvantages, but the choice of model type often seems arbitrary. In the case of cervical cancer, homogenous Markov models seem appropriate if a cohort of newly infected is followed for a shorter period, for instance, to assess the impact of screening programs. For long-term consequences, such as the impact of a vaccination program, a feedback loop from former infections to the future likelihood of infections is required. This can be done using system dynamics or inhomogeneous Markov models. Discrete event or agent-based simulations can be used in the case of cervical cancer when small cohorts or specific characteristics of individuals are required. However, these models require more effort than Markov or System Dynamics models.

Background: Foodborne disease is a great concern to low- and middle-income countries. To prevent illness and death, intervention strategies need to be implemented across the food safety system and should include promoting the adoption of safe food handling practices. The positive association between education and health has been well-established, and one possible mechanism is that education may improve health by encouraging individuals to adopt more appropriate protective practices. Decisions regarding adoption of these practices may also be influenced by the food safety risks individuals face, the trade-offs they make to maximize utility, or behavior biases which may be correlated with education. This study aims to estimate the heterogeneous association between education and the adoption of safe food handling practices among people facing different levels of food safety risk.

Methods: Models were constructed based on the Grossman health model and risk as well as behavior bias theories. Multivariate logistic regression models were estimated to explore the heterogeneous associations using data from a community survey conducted in Ethiopia. Agricultural household status and livestock presence were used as proxies to represent varying risk levels. Average marginal effects were estimated to provide a more accessible interpretation of the results.

Results: Results showed that the association between education and certain safe food handling practices was positive among individuals in households assumed to face higher food safety risks, while the association was less pronounced (or even negative) for those facing lower levels of risk. We observed that secondary education attainment was associated with a 20 percentage points increase (p < 0.01) in the probability of washing hands compared to the reference group (illiterate) in agricultural households. However, for non-agricultural households, secondary education was associated with a 10 percentage points decrease (p < 0.05) in probability. Similar patterns were found for washing surface.

Conclusions: Education is associated with increased adoption of safe food handling practices among individuals facing higher food safety risks. This has important implications for developing targeted policies focused on individuals most susceptible to foodborne diseases. Future policies aimed at increasing the adoption of safe food handling practices should also integrate individuals' decision-making processes and behavior biases in the context of varying risk levels.

Introduction: The annual increase in emergency department (ED) visits in Taiwan has led to overcrowding in major hospitals and extended patient stays in the ED. International studies suggest that prolonged ED stays may influence healthcare costs and clinical outcomes for hospitalized patients. However, such investigations are scarce in Taiwan. This study aims to explore the effects of ED stay duration on inpatient medical utilization and mortality risk.

Methods: This study analyzed data from 42,139 patients at a central Taiwan medical center, using generalized estimating equations (GEE) to evaluate hospital stay duration and costs. Logistic regression assessed mortality risks after hospitalization.

Results: GEE analysis showed longer ED stays led to increased hospital stays: patients with 24-48 h in the ED had an additional 2.27 days (P < 0.001), and those with ≥ 48 h had an additional 3.22 days (P < 0.001). Logistic regression indicated higher mortality risks for patients with 24-48 h (OR = 1.73, P < 0.001) and ≥ 48 h (OR = 2.23, P < 0.001) in the ED compared to those with ≤ 2 h. Conversely, longer ED stays were associated with lower hospitalization costs; patients with ≥ 48 h in the ED incurred $1,211 less in costs compared to those with ≤ 2 h (P < 0.001). Logistic regression revealed that longer ED stays were linked to higher mortality risks, with patients staying 24-48 h in the ED showing an OR of 1.726 (P < 0.001) and those with ≥ 48 h an OR of 2.225 (P < 0.001).

Conclusion: Prolonged ED stays are associated with longer hospital stays, higher mortality risks, and lower hospitalization costs due to resource consumption in the ED. These findings highlight the need for strategies to reduce ED stay durations to improve patient outcomes and optimize resource use.

Patient moral hazard is a significant issue in healthcare system reform and a prominent factor affecting the efficiency of healthcare services in China. Based on the consolidation of urban and rural resident health insurance, this paper employs a staggered DID model to analyze the impact of patient moral hazard on the healthcare service utilization. The findings are as follows. First, the healthcare service utilization significantly increases after the consolidation of urban and rural resident health insurance. This conclusion remains robust when subjected to the placebo test, the mitigation of non-random policy effects, and the exclusion of other insurance type interference. Second, after considering the effects of patient healthcare demand release, supplier-induced demand, and collusion between doctors and patients, we find the evidence of patient moral hazard leading to increased healthcare service utilization. Third, patient moral hazard is mainly manifested in the heightened utilization of patients with general illnesses and middle income, indicating the phenomena of "over-treatment for minor ailments". The study is limited by its focus on expanded reimbursement in urban and rural insurance consolidation, excluding details like fund management changes. Future research should incorporate more policy details and longer time horizons.

Background: Medicare Advantage (MA) penetration rates have shown an increase in rural areas in the past decade, increasing the bargaining power of MA plans relative to rural hospitals. We study the effect that this increase has had in the revenue of rural hospitals through reductions in the number of inpatient days paid by the plans, which has been reported to be part of the financial bargaining between the two parties.

Methods: We use 2014-2020 hospital level data from the American Hospital Association's annual survey and county-level MA penetration rates. We estimate the correlation between MA penetration rates and Medicare and non-Medicare inpatient days using multivariate regressions with hospital and year fixed effects. We use results for urban areas where competition among multiple MA sponsors reduces their individual bargaining power as a falsification test.

Results: We find that a 10 percentage points increase in the county-level MA penetration rate is associated with a decrease of 0.87% inpatient days paid to rural hospitals, which unveils a new main factor affecting the fragile finances of rural hospitals. Consistent with our hypothesis, urban hospitals do not exhibit similar effects, underscoring the role of MA plans in rural areas.

Conclusions: As MA plans increase their penetration in rural areas, their bargaining power increases relative to rural hospitals. MA plans use this increased bargaining power to reduce the number of paid inpatient days, which creates adverse financial conditions for rural hospitals. Policymakers can safeguard rural hospitals by modifying the fee-for-service prices received by rural hospitals or strengthening the network adequacy criteria of MA plans for rural areas.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) prevalence rates are around 5-10% of school-aged children. We test whether medication use for ADHD decreases the likelihood of risky behaviour (sexual behaviour, alcohol, tobacco, and drug consumption) and injuries amongst children aged 6-18.

Methods: We use a large administrative dataset for the whole population of Catalan children in Spain who were born between 1998 and 2012. We apply a scale that contains alternative definitions of ADHD so that over-diagnosis is also identified and estimate a count data model to explain the number of visits whilst accounting for confounding. Our identification strategy relies on instrumenting medication using an average indicator of the probability of prescribing medication for each most visited healthcare centre provider.

Results: Our results suggest that medication use significantly reduced the number of visits of children diagnosed with ADHD for injuries but not risky behaviour. This finding is robust irrespective of the considered span or the grace period after including ADHD-related comorbidities as controls.

Conclusion: In line with previous literature, medication use amongst children with ADHD reduces the prevalence of injuries but not risky behaviours.