Productivity growth in outpatient child and adolescent mental health services: The impact of case-mix adjustment

Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression

Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.

Adding a Social Risk Adjustment Into the Estimation of Efficiency: The Case of Chilean Hospitals

BACKGROUND AND OBJECTIVES

There is much interest in adding social variables to hospital performance assessments. Many of the existing analyses, however, already include patients' diagnosis data, and it is not clear that adding a social adjustment variable would improve the quality of the results: the growing literature on this issue provides mixed results. The purpose in this study was to add evidence from a developing country into this discussion.

METHODS

We estimate the efficiency of hospitals controlling for casemix, with and without adjusting the hospital's casemix for the patients' sociodemographic variables. The magnitude of the adjustment is based on the observed impact of age, sex, and income on length of stay, conditional on the diagnosis related group (DRG). We use a data envelopment analysis (DEA) to assess the efficiency of 50 Chilean hospitals' discharges, including 780 DRGs and covering about 60% of total discharges in Chile from 2013 to 2015.

RESULTS

We found that the sociodemographic adjustment introduces very small changes in the DEA estimation of efficiency. The underlying reason is the relatively low influence of sociodemographics on hospital costs, conditional on DRG, and the changing pattern of sociodemographics across DRGs for any given hospital.

CONCLUSION

We conclude that the casemix-adjusted estimation of hospital efficiency is robust to the heterogeneity of patients' sociodemographic heterogeneity across hospitals. These results confirm, in a developing country, what has been observed in developed countries. For management purposes, then, the processing costs of adding social variables into hospitals' performance assessments might not be justified.

Health system productivity in China: a comparison of pre- and post-2009 healthcare reform

In 2009, China launched an ambitious health system reform that combined extending social health insurance scheme with improving efficiency, access and quality of care in the country. To assess the impact of the policy on efficiency and productivity change, we investigated the country's health system performance at provincial levels during pre- and post-reform period. Outputs were measured using multiple health outcomes (namely, non-communicable diseases free healthy life years and infant and maternal survival rates), while health expenditure, number of medical personnel and hospital beds per 1000 residents were used as proxy measures for health inputs. Changes in productivity were quantified using a bootstrap Malmquist productivity index (MPI). The analysis focused on the period between 2004 and 2015. This was to capture pre- and post-policy implementation experience and to ensure that enough time was allowed for the policy to work through. Finally, a bootstrap Tobit regression model for panel data was applied to examine the potential effects of contextual factors on productivity change. The result showed that the reform has had negative effects on productivity. Only scale efficiency had improved steadily, but the decline in the scale of technological change observed during the same period meant that the progress in scale efficiency had been masked. Better economic performance (as measured by per capita Gross Domestic Product (GDP)) and higher human resource to capital investment ratio (as measured by density of medical staff per hospital beds) tended to boost productivity growth, while population aging, low educational attainment and higher percentage of out-of-pocket (OOP) payments had adverse effects. Improving health system productivity in China requires improving financial risk protection and maintaining proper balance between human and capital investment in the country.

Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments

Background: Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS).Aim: To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation.Method: We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression.Results: The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix.Conclusion: The conceptually-derived classification merits further testing and development in the context of collaborative decision making.

Service-level variation, patient-level factors, and treatment outcome in those seen by child mental health services

Service comparison is a policy priority but is not without controversy. This paper aims to investigate the amount of service-level variation in outcomes in child mental health, whether it differed when examining outcomes unadjusted vs. adjusted for expected change over time, and which patient-level characteristics were associated with the difference observed between services. Multilevel regressions were used on N = 3256 young people (53% male, mean age 11.33 years) from 13 child mental health services. Outcome was measured using the parent-reported Strengths and Difficulties Questionnaire. The results showed there was 4-5% service-level variation in outcomes. Findings were broadly consistent across unadjusted vs. adjusted outcomes. Young people with autism or infrequent case characteristics (e.g., substance misuse) had greater risk of poor outcomes. Comparison of services with high proportions of young people with autism or infrequent case characteristics requiring specialist input needs particular caution as these young people may be at greater risk of poor outcomes.

On evaluating health centers groups in Lisbon and Tagus Valley: efficiency, equity and quality

Background

Bearing in mind the increasing health expenses and their weight in the Portuguese gross domestic product, it is of the utmost importance to evaluate the performance of Primary Health Care providers taking into account both efficiency, quality and equity. This paper aims to contribute to a better understanding of the performance of Primary Health Care by measuring it in a Portuguese region (Lisbon and Tagus Valley) and identifying best practices. It also intends to evaluate the quality and equity provided.

Methods

For the purpose of measuring the efficiency of the health care centers (ACES) the non-parametric full frontier technique of data envelopment analysis (DEA) was adopted. The recent partial frontier method of order-m was also used to estimate the influence of exogenous variables on the efficiency of the ACES. The horizontal equity was investigated by applying the non-parametric Kruskal-Wallis test with multiple comparisons. Moreover, the quality of service was analyzed by using the ratio between the complaints and the total activity of the ACES.

Results

On the whole, a significant level of inefficiency was observed, although there was a general improvement in efficiency between 2009 and 2010. It was found that nursing was the service with the lowest scores. Concerning the horizontal equity, the analysis showed that there is no evidence of relevant disparities between the different subregions(NUTS III). Concerning the exogenous variables, the purchasing power, the percentage of patients aged 65 years old or older and the population size affect the efficiency negatively.

Conclusions

This research shows that better usage of the available resources and the creation of a learning network and dissemination of best practices will contribute to improvements in the efficiency of the ACES while maintaining or even improving quality and equity. It was also proved that the market structure does matter when efficiency measurement is addressed.

Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data Envelopment Analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency (Hollingsworth, 2008), but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized (Gajewski, Lee, Bott, Piamjariyakul and Taunton, 2009; Ruggiero, 2004). We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators® (NDNQI®) to estimate nursing units' efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.

Technical efficiency of primary health units in Kailahun and Kenema districts of Sierra Leone

BACKGROUND

The objectives of the study reported in this paper were to (i) estimate the technical efficiency of samples of community health centres (CHCs), community health posts (CHPs) and maternal and child health posts (MCHPs) in Kailahun and Kenema districts of Sierra Leone, (ii) estimate the output increases needed to make inefficient MCHPs, CHCs and CHPs efficient, and (iii) explore strategies for increasing technical efficiency of these institutions.

METHODS

This study applies the data envelopment analysis (DEA) approach to analyse technical efficiency of random samples of 36 MCHPs, 22 CHCs and 21 CHPs using input and output data for 2008.

RESULTS

The findings indicate that 77.8% of the MCHPs, 59.1% of the CHCs and 66.7% of the CHPs were variable returns to scale technically inefficient. The average variable returns to scale technical efficiency was 68.2% (SD = 27.2) among the MCHPs, 69.2% (SD = 33.2) among the CHCs and 59% (SD = 34.7) among the CHPs.

CONCLUSION

This study reveals significant technical inefficiencies in the use of health system resources among peripheral health units in Kailahun and Kenema districts of Sierra Leone. There is need to strengthen national and district health information systems to routinely track the quantities and prices of resources injected into the health care systems and health service outcomes (indicators of coverage, quality and health status) to facilitate regular efficiency analyses.