Abstract
Health-related parameters are critical as indicators of development, and as a result, governments allocate a sizable portion of their budgets to the health sector. The most fundamental variable considered an indicator of health development is the infant mortality rate, which was used as the dependent variable in this study. The data utilized in the study were compiled from the TURKSTAT web page and the TR Ministry of Health's health annuals, with the year 2019 serving as the reference point for access to all data. In the study, econometric analyses were performed while keeping the notion of contiguity in mind to reveal the factors healthily affecting the infant mortality rate at the NUTS 3 level, which encompasses all provinces in the TURKSTAT regional categorization. The distribution of infant mortality rates by provinces in Turkey was analyzed in this context, and it was discovered that there was a high degree of clustering between provinces. This clustering structure indicated the presence of a spatial relationship between provinces, and it was from this point of view that spatial econometric analysis of health services in Turkey was conducted. Analyzes were carried out using STATA and GeoDa package programs. The diagnostic tests revealed the presence of spatial autocorrelation, necessitating the employment of the spatial autoregressive model (SAR Model) to explain the relationship between the variables. As a result, it was concluded that both the variables included in the study and the infant mortality rate in nearby locations have an effect on the infant mortality rate for each province.
Highlights
In the constitution of the World Health Organization, health is defined as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO Constitution 07.04.1948)
With the assumption that the state of neighbouring locations has an effect on health-related indicators, spatial econometric techniques were preferred in this study
The spatial autoregressive model (Fischer and Wang, 2011:33) or another saying spatial lag model (Elhorst, 2010:13), one of the most frequently used models in practice (SAR model) describes the spatial correlation in the dependent variable. This model specification is based on theoretical reasoning, such as emphasizing the neighbourhood effect or spatial externality that exists across spatial units and is evident in the dependent variable
Summary
In the constitution of the World Health Organization, health is defined as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO Constitution 07.04.1948). The infant mortality rate is commonly used to compare the progress of the social health level of countries over time, the status of various regions within countries, and the change within these regions through time, as well as to compare countries in terms of health levels (Hamzaoğlu, 2017: 288). Factors such as advancements in the field of health, innovative treatment and follow-up procedures, the provision of health services, and access to these services all contribute to the increase and decrease in infant mortality rates. A high rate indicates the presence of unfavourable socio-economic indicators, such as insufficient preventative health care, a low level of education, unequal distribution of income, environmental pollution, and noise (Barlas et al, 2014)
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