THE BURDEN OF TRAVELLING DISTANCE TO THE DIALYSIS CENTRE ON CKD PATIENTS IN INDIA

Introduction:

Hemodialysis (HD) forms the mainstay renal replacement therapy for approximately 94% of end-stage renal disease (ESRD) patients in India. It was estimated that in 2018, there were approximately 1,75,000 patients on maintenance hemodialysis in India. To this, nearly 220,000 new patients are being added every year. These patients have to travel to one of the approximately 5000 existing dialysis centres distributed across the country. Traveling to and from hemodialysis is a burden on time, cost and employment challenges to patients.

Methods:

All patients taking hemodialysis at the 220 centers over 12 states in India, operated and managed by Apex Kidney Care (AKC), over the past 15 years, were included in this study. A total of 16252 patients whose addresses were available and who survived beyond the first 90 days after initiation of hemodialysis were included. There were 7987 patients from public centres and 8265 patients from private centres. The study period was from August 15, 2008, to December 31, 2023. The road distance between the patient's residence and the hemodialysis center he travelled to was calculated. The pin codes were used as a proxy for the exact address. These coordinates were then fed to a Bing maps application programming interface [free version] to calculate the one-way road distance travelled by every patient to his hemodialysis centre. These distances were then divided into groups of <10kms, 10-20kms, 20-30kms, 30-40kms, 40-50kms and >50kms. These distances travelled were compared for each tier of city, and the impact of this distance on the patients dialysis frequency was analysed. Analysis was done on Jupiter notebook: 6.4.12 using the “Kopri: 10.119.2.17, IEOR, IIT Bombay” server.

Results:

The median one way distance travelled by the entire population of patients in the country was 19 km [IQR, 8-47]. The median distance travelled by patients to the public centers across the country was 23kms [ IQR, 10-53] as against 16kms [IQR 7-36] for the private centres [p value <0.001]. The travel distance also varied by the tier of city. Patients in tier 1 cities had the least median travel distance of 12kms [IQR, 7-25], followed by tier 2 cities at 17kms [IQR, 7-16], and was maximum for tier 3 cities at 38kms [IQR, 17-76] (p-value <0.001). The distribution of patients in each of the six groups of travel distance were as follows: 5244 patients (<10kms), 3698 patients (10-20kms), 1538 patients (20-30kms), 1370 patients (30-40kms), 551 patients (40-50kms), and 3851 patients (>50kms). There was a statistically significant inverse correlation between travel distance and hemodialysis frequency, with a median travel distance of 20kms for the ≤2.5/week frequency patients and a median of 13kms for ≥2.5/week sessions, (p <0.001). There was a twofold higher rate of dialysis discontinuation in the >50km group (1.5%) as compared to the <10kms group (0.74%).

Conclusions:

This comprehensive study has evaluated distances travelled by hemodialysis patients in India using geospatial analysis. Traveling to a dialysis centre remains a challenge for patients and affects compliance to treatment. This data provides important information to policymakers when planning deployment of new centres across the country.

I have no potential conflict of interest to disclose.

I did not use generative AI and AI-assisted technologies in the writing process.