A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile

Abstract

Background: There is a strong spatial correlation between demographics and chronic diseases in urban areas. Thus, most of the public policies aimed at improving prevention plans and optimizing the allocation of resources in health networks should be designed specifically for the socioeconomic reality of the population. One way to tackle this challenge is by exploring within a small geographical area the spatial patterns that link the sociodemographic attributes that characterize a community, its risk of suffering chronic diseases, and the accessibility of health treatment. Due to the inherent complexity of cities, soft clustering methods are recommended to find fuzzy spatial patterns. Our main motivation is to provide health planners with valuable spatial information to support decision-making. For the case study, we chose to investigate diabetes in Santiago, Chile. Methods: To deal with spatiality, we combine two statistical techniques: spatial microsimulation and a self-organizing map (SOM). Spatial microsimulation allows spatial disaggregation of health indicators data to a small area level. In turn, SOM, unlike classical clustering methods, incorporates a learning component through neural networks, which makes it more appropriate to model complex adaptive systems, such as cities. Thus, while spatial microsimulation generates the data for the analysis, the SOM method finds the relevant socioeconomic clusters. We selected age, sex, income, prevalence of diabetes, distance to public health services, and type of health insurance as input variables. We used public surveys as input data. Results: We found four significant spatial clusters representing 75 percent of the whole population in Santiago. Two clusters correspond to people with low educational levels, low income, high accessibility to public health services, and a high prevalence of diabetes. However, one presents a significantly higher level of diabetes than the other. The second pair of clusters is made up of people with high educational levels, high income, and low prevalence of diabetes. What differentiates both clusters is accessibility to health centers. The average distance to the health centers of one group almost doubles that of the other. Conclusions: In this study, we combined two statistical techniques: spatial microsimulation and selforganising maps to explore the relationship between diabetes and socio-demographics in Santiago, Chile. The results have allowed us to corroborate the importance of the spatial factor in the analysis of chronic diseases as a way of suggesting differentiated solutions to spatially explicit problems. SOM turned out to be a good choice to deal with fuzzy health and

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

“…Other health behaviours or conditions identified in the review included problem gambling [18], mental health and alcohol consumption [19] and combinations of health outcomes [20][21][22][23]. Dental health [24], diabetes [25] and mortality [26] are more recent topics that were modelled. Spatial microsimulation for resource allocation, with the example of maternity services in England [27], or estimating the impacts of policies on health [28] were more unusual applications of this method for public health interests.…”

Addressing the socioeconomic divide in computational modeling for infectious diseases

“…Efforts in this direction have started by building remotely sensed socioeconomic maps in several countries combining multiple data sources like mobility, satellite, night-light emission, or online social media, but still much research is needed 49 – 52 . Both traditional statistical approaches such as Iterative Proportional Fitting, Monte Carlo sampling, and machine learning techniques such as Self-Organized Maps or Generative Adversarial networks are appropriate candidates to generate synthetic behavioral data from novel data sources 53 – 55 . The synthetic and inferred data could then be used to represent mobility or contact patterns of subpopulations that will feed epidemic models, in a similar way to what is customarily done for age-dependent contact matrices 56 , 57 .…”

COVID-19 and Dispositions of the Chilean Healthcare System: Sociomedical Networks in Care Decisions of Chronic Illnesses

“…These initiatives have not changed the fact that inequality and privatization are still the hallmarks of the Chilean health care system ( Rotarou and Sakellariou, 2017a ). Offering an example of this, the study conducted by Crespo et al (2020) showed that there is a strong spatial correlation between demographics and chronic diseases in Chilean urban areas. High-income groups with high educational levels had a significantly lower prevalence of diabetes compared to a low-income and low educational level group.…”

“…High-income groups with high educational levels had a significantly lower prevalence of diabetes compared to a low-income and low educational level group. What explained the difference of prevalence in both groups was the average distance to their health centers: the low-income group’s distance to their health provider was almost double the distance of the high-income group ( Crespo et al, 2020 ).…”