A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line

Abstract

Abstract:The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health services. This study aims to describe and evaluate the use of LS24. Since for LS24 data, the location attribute is an important source of information to describe its use, this study analyses the number of calls received, at a municipal level, under two different spatial econometric approaches. This analysis is important for future development of decision support indicators in a hospital context, based on the economic impact of the use of this health line. Considering the discrete nature of data, the number of calls to LS24 in each municipality is better modelled by a Poisson model, with some possible covariates: demographic, socio-economic information, characteristics of the Portuguese health system and development indicators. In order to explain model spatial variability, the data autocorrelation can be explained in a Bayesian setting through different hierarchical log-Poisson regression models. A different approach uses an autoregressive methodology, also for count data. A log-Poisson model with a spatial lag autocorrelation component is further considered, better framed under a Bayesian paradigm. With this empirical study we find strong evidence for a spatial structure in the data and obtain similar conclusions with both perspectives of the analysis. This supports the view that the addition of a spatial structure to the model improves estimation, even in the case where some relevant covariates have been included.

Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China

“…(2) Spatial Autocorrelation Spatial autocorrelation was used to analyze the spatial statistical data and assess the relativity of the variables [20]. We used the Moran index of spatial autocorrelation as follows:…”

Spatial Statistical Models: An Overview under the Bayesian Approach

“…Bayesian spatial statistics has been embraced in economics to identify the spatial pattern of a household's share of economic distress, to understand the formation of new business, and in studies on consumer and producer behavior. It has been used to identify the impact of economic, social, and demographic factors on the spatial variability of the household share of economic distress [51]; identify the spatial structure of the calls to the Portuguese health line, accounting for the demographics, socio-economic information, and characteristics of the health systems [52]; identify clustering in severe mobility crash risk and diagnosing of active transportation safety issues [53]. Moreover, it has been employed in the analysis of spatial patterns and hotspot detection of violent and property crimes at a small spatial scale in Toronto, Canada [54]; map the main features of fertility, such as timing, pace, and scale, and to detect spatial disparity in fertility transition in Brazil [55].…”

“…The class of others accommodates statistical models outside the above-listed classes. For instance, Reference [91] adopted a generalized hierarchical mixed model to determine the risk factors of the radon-222 noble gas; Reference [90] used a generalized hierarchical mixed model to determine the impact of carbon (IV) oxide on the prevalence of malnutrition; Reference [100] adopted a survival statistical model to map the prevalence of hospitalization due to Dengue in Wahidin Hospital in Makassar, Indonesia; Reference [52] adopted the spatial econometrics model (lag-model) to estimate the global spatial correlation of the calls to the Portuguese national health line.…”

Bayesian Spatial Process Models for Activation Patterns in Transcranial Magnetic Stimulation Mapping

“…Bayesian spatial statistics has been embraced in Economics to identify the spatial pattern of a household's share of economic distress, to understand the formation of new business, and in studies of consumer and producer behavior. It has been used to identify the impact of economic, social, and demographic factors on spatial variability of household share of economic distress (BENASSI; NACCARATO, 2017); identify the spatial structure of the calls to Portuguese health line, accounting for demographic, socio-economic information, and characteristics of the health systems (SIMÕES et al, 2017); identify clustering in severe mobility crash risk, diagnosing of active transportation safety issues (OSAMA; SAYED, 2019). Moreover, it has been employed in the analysis of spatial patterns and hotspot detection of violent and property crimes at a small spatial scale in Toronto, Canada (LAW; QUICK; JADAVJI, 2020); map main features of fertility, such as timing, pace, and scale, and to detect spatial disparity in fertility transition in Brazil (POTTER et al, 2010).…”