Analysing outpatient care access for planning purposes: The Basilicata Region experience

The delivery of healthcare services at regional level should be modeled on population needs, stemming from patients' consumption pattern and trying to include unexpressed needs and exclude excesses of demand due to both moral hazard behaviors and inducements from the supply side. We propose a model able to estimate the frequency of access in outpatient care (OC) based on the characteristics of the population. According to empirical evidence, among the determinants of outpatient access we include variables addressing health, socioeconomic status and place of residence, plus variables related to the supply of services. We run generalized linear models for counting data of the Poisson family with the aim of both identifying the determinants of OC utilization and quantifying the related effects. We use the regional administrative database of Basilicata region, year 2019. Results are consistent with literature findings and provide new insights into the analysis of OC, suggesting that our model could easily be implemented by regional policymakers to plan the supply of ambulatory services on population needs.

A non-linear decomposition analysis of children's dietary diversity scores: explaining rural-urban inequality

Sufficient and nourishing foods during the early years of a child's life are essential for optimal growth and healthy life. The existing disparities among rural-urban populations also affect the dietary pattern as well. Therefore, this study aims to identify the factors that contribute towards the rural-urban disparity in children's dietary diversity (CDD) and quantify their importance for the reduction of rural-urban disparities in achieving a minimum dietary diversity level. Using Pakistan Demographic and Health Survey (PDHS) data, version 2017-18, a non-linear decomposition analysis was performed. Eighty-one (81%) of the gap in CDD between rural-urban areas is attributed to the differences in the observed factors (endowments) and of these most of the difference is explained by three factors i.e. number of antenatal care visits (45%), maternal education (18%) and type of toilet facility (15%). There is a need to explore maternal education-related interventions to decrease the rural-urban gap regarding CDD as maternal education may affect CDD through different dimensions. Moreover, such programs should be initiated that may be helpful to enhance women's role in society, such as skilled education, well-paid job opportunities and better health facilities.

Prediction of rear-end conflict frequency using multiple-location traffic parameters

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating -for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.