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

Association between the home-to-healthcare center distance and hearing aid abandonment among older adults

Background

Access to audiology services for older adults residing in sparsely populated regions is often limited compared to those in central urban areas. The geographic accessibility to follow-up care, particularly the influence of distance, may contribute to an increased risk of hearing aid abandonment.

Objective

To assess the association between the home-to-healthcare-calibration-center distance and hearing aid abandonment among older adults fitted in the Chilean public health system.

Methods

455 patients who received hearing aids from two public hospitals in two regions were considered. Univariate and multivariate Poisson regression models with robust variance estimation were used to analyze the association between the geographical distance and hearing aid abandonment, accounting for confounding effects.

Results

Approximately 18% of the sample abandoned the hearing aid, and around 50% reported using the hearing aid every day. A twofold increase in distance between home and the hearing center yielded a 35% (RR = 1.35; 95% CI: 1.04-1.74; p = 0.022) increased risk of hearing aid abandonment. Also, those in the second quintile had a 2.17 times the risk of abandoning the hearing aid compared to the first quintile (up to 2.3 km). Under the assumption that patients reside within the first quintile of distance, a potential reduction of 45% in the incidence of hearing aid abandonment would be observed. The observed risk remained consistent across different statistical models to assess sensitivity.

Conclusion

A higher distance between the residence and the healthcare center increases hearing aid abandonment risk. The association may be explained by barriers in purchasing supplies required to maintain the device (batteries, cleaning elements, potential repairs, or maintenance).

Oxidative potential of metal-polluted urban dust as a potential environmental stressor for chronic diseases

Oxidative stress (OS) associated with metals in urban dust has become a public health concern. Chronic diseases linked to general inflammation are particularly affected by OS. This research analyzes the spatial distribution of metals associated with OS, the urban dust´s oxidative potential (OP), and the occurrence of diseases whose treatments are affected by OS. We collected 70 urban dust samples during pre- and post-monsoon seasons to achieve this. We analyzed particle size distribution and morphology by scanning electron microscopy, as well as metal(loid)s by portable X-ray fluorescence, and OP of dust in artificial lysosomal fluid by using an ascorbic acid depletion assay. Our results show that the mean concentration of Fe, Pb, As, Cr, Cu, and V in pre-monsoon was 83,984.6, 98.4, 23.5, 165.8, 301.3, and 141.9 mg kg^-1, while during post-monsoon was 50,638.8, 73.9, 16.7, 124.3, 178.9, and 133.5 mg kg^-1, respectively. Impoverished areas with the highest presence of cardiovascular, cancer, diabetes, and respiratory diseases coincide with contaminated areas where young adults live. We identified significant differences in the OP between seasons. OP increases during the pre-monsoon (from 7.8 to 237.5 nmol AA min^-1) compared to the post-monsoon season (from 1.6 to 163.2 nmol AA min^-1). OP values are much higher than measured standards corresponding to contaminated soil and urban particulate matter, which means that additional sources beside metals cause the elevated OP. The results show no risk from chronic exposure to metals; however, our results highlight the importance of studying dust as an environmental factor that may potentially increase oxidative stress.

Spatial clusters of diabetes: individual and neighborhood characteristics in the ELSA-Brasil cohort study

This study identified spatial clusters of type 2 diabetes mellitus among participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) residing in two cities and verified individual and neighborhood socioeconomic environmental characteristics associated with the spatial clusters. A cross-sectional study was conducted with 4,335 participants. Type 2 diabetes mellitus was defined as fasting blood glucose ≥ 126mg/dL (7.0mmol/L), oral glucose tolerance test ≥ 200mg/dL (11.1mmol/L), or glycated hemoglobin ≥ 6.5% (48mmol/L); by antidiabetic drug use; or by the self-reported medical diagnosis of type 2 diabetes mellitus. Neighborhood socioeconomic characteristics were obtained from the 2011 Brazilian census. A spatial data analysis was conducted with the SaTScan method to detect spatial clusters. Logistic regression models were fitted to estimate the magnitude of associations. In total, 336 and 343 participants had type 2 diabetes mellitus in Belo Horizonte, Minas Gerais State (13.5%) and Salvador, Bahia State (18.5%), respectively. Two cluster areas showing a high chance of type 2 diabetes mellitus were identified in Belo Horizonte and Salvador. In both cities, participants living in the high type 2 diabetes mellitus cluster area were more likely to be mixed-race or black and have a low schooling level and manual work; these were also considered low-income areas. On the other hand, participants in the low type 2 diabetes mellitus cluster area of Salvador were less likely to be black and have low schooling level (university degree) and live in a low-income area. More vulnerable individual and neighborhood socioeconomic characteristics were associated with living in clusters of higher type 2 diabetes mellitus occurrence , whereas better contextual profiles were associated with clusters of lower prevalence.

Systems Thinking and Complexity Science Methods and the Policy Process in Non-communicable Disease Prevention: A Systematic Scoping Review

BACKGROUND

Given the complex determinants of non-communicable diseases (NCDs), and the dynamic policy landscape, researchers and policymakers are exploring the use of systems thinking and complexity science (STCS) in developing effective policies. The aim of this review is to systematically identify and analyse existing applications of STCS-informed methods in NCD prevention policy.

METHODS

Systematic scoping review: We searched academic databases (Medline, Scopus, Web of Science, EMBASE) for all publications indexed by 13 October 2020, screening titles, abstracts and full texts and extracting data according to published guidelines. We summarised key data from each study, mapping applications of methods informed by STCS to policy process domains. We conducted a thematic analysis to identify advantages, limitations, barriers and facilitators to using STCS.

RESULTS

4681 papers were screened and 112 papers were included in this review. The most common policy areas were tobacco control, obesity prevention and physical activity promotion. Methods applied included system dynamics modelling, agent-based modelling and concept mapping. Advantages included supporting evidence-informed decision-making; modelling complex systems and addressing multi-sectoral problems. Limitations included the abstraction of reality by STCS methods, despite aims of encompassing greater complexity. Challenges included resource-intensiveness; lack of stakeholder trust in models; and results that were too complex to be comprehensible to stakeholders. Ensuring stakeholder ownership and presenting findings in a user-friendly way facilitated STCS use.

CONCLUSION

This review maps the proliferating applications of STCS methods in NCD prevention policy. STCS methods have the potential to generate tailored and dynamic evidence, adding robustness to evidence-informed policymaking, but must be accessible to policy stakeholders and have strong stakeholder ownership to build consensus and change stakeholder perspectives. Evaluations of whether, and under what circumstances, STCS methods lead to more effective policies compared to conventional methods are lacking, and would enable more targeted and constructive use of these methods.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

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

This article is an empirical work on decision-making processes in the case of persons with chronic illnesses in the COVID19 pandemic context, regarding their medical care and self-care. Medical decisions are processes that guide the production of a health diagnosis or treatment, using the available information, where the patients' preferences are often incorporated. This article tackles the impact of the pandemic on chronically ill patients' medical decisions when the care system has been significantly altered by it. Considering that health decisions are importantly embedded in social and economic conditions, the pandemic affects a precarious care system and constrains individual possibilities. Chile has a weak support infrastructure for caregivers and a health care system that promotes private health and a low-quality public health system. Hence, the pandemic is an adverse context for chronically ill patients and it alters the conditions in which medical decisions are taken. We performed 10 interviews with chronically ill patients who took responsibility for their own health decisions: five patients diagnosed with common chronic diseases and five patients diagnosed with rare chronic diseases. After Reflexive Thematic Analysis, the results show that the Chilean health system is extremely precarious and that not many alternatives are or have been offered to chronically ill patients in the COVID19 context.

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

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.