Economic comparison between Hospital at Home and traditional hospitalization using a simulation-based approach

International audienceHospital at Home (HAH) is a concept slowly expanding over time. At first this type of organization was used to accomplish low-technical tasks. The main objective was to increase bed availability in hospitals for new patients. Nowadays, HAH structures are able to undertake more technical complex care such as (but not limited to) end-of-life care, chemotherapy and rehabilitation. The purpose of this paper is to propose a new methodology to make an unbiased economic comparison between HAH structures and traditional hospitalization. This article accomplishes two main objectives: in the first part the authors propose a comprehensive literature review dealing with the comparison between traditional hospital and home care structures from an economic standpoint, showing that results are highly dependent on initial conditions of the study (patient health state, territory settings, bio-medical parameters); in the second part the authors propose an unbiased economic comparison approach between health care provided in traditional hospital and home care network using formal modelling with Petri nets and discrete event simulation. As an example for the comparison a multi-session treatment is proposed. Various scenarios are tested to ensure that results will be maintained even if initial conditions change. Relevant performance indicators used for comparison are economic costs from the point of view of the insurance and economic costs related to the consumption of resources

Calculating the Expected Value of Sample Information in Practice: Considerations from Three Case Studies

Investing efficiently in future research to improve policy decisions is an important goal. Expected Value of Sample Information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Therefore, a number of more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared and therefore their relative advantages and disadvantages are not clear. A consortium of EVSI researchers, including the developers of several approximation methods, compared four EVSI methods using three previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared, and the relative advantages and implementation challenges of the methods were highlighted. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. All the evaluated methods gave estimates similar to those given by traditional Monte Carlo, suggesting that EVSI can now be efficiently computed with confidence in realistic examples.Comment: 11 pages, 3 figure

The Economic Costs of Malaria in Children in three Sub-Saharan Countries: Ghana, Tanzania and Kenya.

Malaria causes significant mortality and morbidity in sub-Saharan Africa (SSA), especially among children less than five years of age (U5 children). Although the economic burden of malaria in this region has been assessed previously, the extent and variation of this burden remains unclear. This study aimed to estimate the economic costs of malaria in U5 children in three countries (Ghana, Tanzania and Kenya). Health system and household costs previously estimated were integrated with costs associated with co-morbidities, complications and productivity losses due to death. Several models were developed to estimate the expected treatment cost per episode per child, across different age groups, by level of severity and with or without controlling for treatment-seeking behaviour. Total annual costs (2009) were calculated by multiplying the treatment cost per episode according to severity by the number of episodes. Annual health system prevention costs were added to this estimate. Household and health system costs per malaria episode ranged from approximately US$5 for non-complicated malaria in Tanzania to US$288 for cerebral malaria with neurological sequelae in Kenya. On average, up to 55% of these costs in Ghana and Tanzania and 70% in Kenya were assumed by the household, and of these costs 46% in Ghana and 85% in Tanzania and Kenya were indirect costs. Expected values of potential future earnings (in thousands) lost due to premature death of children aged 0--1 and 1--4 years were US$11.8 and US$13.8 in Ghana, US$6.9 and US$8.1 in Tanzania, and US$7.6 and US$8.9 in Kenya, respectively. The expected treatment costs per episode per child ranged from a minimum of US$1.29 for children aged 2--11 months in Tanzania to a maximum of US$22.9 for children aged 0--24 months in Kenya. The total annual costs (in millions) were estimated at US$37.8, US$131.9 and US$109.0 nationwide in Ghana, Tanzania and Kenya and included average treatment costs per case of US$11.99, US$6.79 and US$20.54, respectively. This study provides important insight into the economic burden of malaria in SSA that may assist policy makers when designing future malaria control interventions

A derivative-free approach for a simulation-based optimization problem in healthcare

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach

Social Interaction Layers in Complex Networks for the Dynamical Epidemic Modeling of COVID-19 in Brazil

We are currently living in a state of uncertainty due to the pandemic caused by the Sars-CoV-2 virus. There are several factors involved in the epidemic spreading such as the individual characteristics of each city/country. The true shape of the epidemic dynamics is a large, complex system such as most of the social systems. In this context, Complex networks are a great candidate to analyze these systems due to their ability to tackle structural and dynamical properties. Therefore this study presents a new approach to model the COVID-19 epidemic using a multi-layer complex network, where nodes represent people, edges are social contacts, and layers represent different social activities. The model improves the traditional SIR and it is applied to study the Brazilian epidemic by analyzing possible future actions and their consequences. The network is characterized using statistics of infection, death, and hospitalization time. To simulate isolation, social distancing, or precautionary measures we remove layers and/or reduce the intensity of social contacts. Results show that even taking various optimistic assumptions, the current isolation levels in Brazil still may lead to a critical scenario for the healthcare system and a considerable death toll (average of 149,000). If all activities return to normal, the epidemic growth may suffer a steep increase, and the demand for ICU beds may surpass 3 times the country's capacity. This would surely lead to a catastrophic scenario, as our estimation reaches an average of 212,000 deaths even considering that all cases are effectively treated. The increase of isolation (up to a lockdown) shows to be the best option to keep the situation under the healthcare system capacity, aside from ensuring a faster decrease of new case occurrences (months of difference), and a significantly smaller death toll (average of 87,000).Comment: 16 pages, 7 figures, 2 table

Understanding Technology Diffusion and Spatial Accessibility in the Home Healthcare Industry

Home healthcare is becoming an important alternative to institutionalized care. It not only reduces costs but also increases health outcomes and patient satisfaction. However, the availability and efficiency of home healthcare services need to be improved as the aging population increases in the US. Hence, understanding home healthcare utilization and access are the essential steps to develop strategies ensuring effective and sustainable services to patients. This research aims to study two main issues in the US home healthcare system: diffusion and long-term impacts of home telehealth and potential spatial accessibility of home healthcare services. Home telehealth is a promising technology that can increase efficiency and health outcomes. However, the diffusion of this technology has been slow basically due to lack of reimbursement and lack of evidence on its impacts. In the first part of this dissertation, we study the innovation characteristics affecting home telehealth diffusion among agencies and develop a system dynamics model to demonstrate the impacts of home telehealth on healthcare utilization and overall healthcare cost. Next, we study the potential spatial access to home healthcare services. Potential spatial accessibility refers to the availability of a service in a given area based on geographical factors, such as distance and location. In this part of the dissertation, a new measure that simultaneously considers both staffing levels and eligible populations is developed and used in a case study to highlight the spatial disparities in access in Arkansas. To the best of our knowledge, no previous measure has been proposed to quantify the potential spatial accessibility of home healthcare services within a geographic region. Then, we examine the factors that are associated with accessibility across the study region by space-varying coefficient models. The results of this part of the dissertation can inform policies that positively impact access to home healthcare services