Use of Spatial Panel-data Models to Investigate Factors Related to Incidence of End-stage Renal Disease: a Nationwide Longitudinal Study in Taiwan

Background: The assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD).

Methods: A longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient's residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors.

Results: In total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most "social environmental factors," except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate.

Conclusion: Future comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.