Correction: Prevalence and risk factors for chronic kidney disease of unknown cause in Malawi: a cross-sectional analysis in a rural and urban population

Correction to: BMC Nephrology (2020) 21:387https://doi.org/10.1186/s12882-020-02034-x

Following publication of the original article [1], the authors identified some errors in Abstract, Tables 1, 2 and 3; Fig. 3 and Supplementary files – correcting an error in the calculation of the eGFR in the published version of the paper.

NB: although these corrections mean that the numbers for the eGFR analyses (e.g. eGFR < 60) have changed, the main findings of the paper have not changed.

Also, the authors wanted to have removed the following sentence under the section “urban rural comparison of eGFR”

Due to the small proportion of participants in the eGFR < 90 category in the urban population, we could not conduct logistic regression analyses for Area 25.

Corrected Abstract

Background

An epidemic of chronic kidney disease of unknown cause (CKDu) is occurring in rural communities in tropical regions of low-and middle-income countries in South America and India. Little information is available from Southern African countries which have similar climatic and occupational characteristics to CKDu-endemic countries. We investigated whether CKDu is prevalent in Malawi and identified its potential risk factors in this setting.

Methods

We conducted a cross-sectional study from January–August 2018 collecting bio samples and anthropometric data in two Malawian populations. The sample comprised adults > 18 years (n = 821) without diabetes, hypertension, and proteinuria. Estimates of glomerular filtration rate (eGFR) were calculated using the CKD-EPI2009 equation. Linear and logistic regression models were applied with potential risk factors, to estimate risk of reduced eGFR.

Results

The mean eGFR was 112.3 ± 22.9 ml/min per 1.73m² and the mean participant age was 33.5 ± 12.7 years. The prevalence of eGFR < 60 was 2.1% (95% confidence interval (95% CI) 1.2, 3.2); the prevalence of eGFR < 90 was 16.1% (95% CI = 13.6, 18.7). We observed a higher prevalence of eGFR < 90 in the urban population (21.8% (16.8, 27.5)), versus rural (13.7% (10.9, 16.7)). Age was associated with increased risk of eGFR < 90 [Odds ratio (OR) (95%CI) = 1.79 (1.53, 2.13) per ten-year increment]. Lower risk of eGFR < 90 was observed for rural participants [OR (95%CI) = 0.43 (0.24, 0.79)].

Conclusions

Reduced kidney function consistent with the definition of CKDu is not common in the areas of Malawi sampled, compared to that observed in other tropical or sub-tropical countries in Central America and South Asia. Reduced eGFR < 90 was related to age and was more common in urban areas. These findings are important as they contradict some current hypothesis that CKDu is endemic across tropical and sub-tropical countries. This study has enabled standardized comparisons of impaired kidney function between and within tropical/subtropical regions of the world and will help form the basis for further etiological research, surveillance strategies, and the implementation and evaluation of interventions.

Corrected Tables

Table 1 Sociodemographic and anthropometric characteristics of study participants without diabetes, hypertension, and heavy proteinuria) n = 821

Table 2 Associations between sociodemographic and anthropometric characteristics and eGFR < 90 in participants without diabetes, hypertension, proteinuria, n = 821

Table 3 Associations between sociodemographic and anthropometric characteristics and estimated glomerular filtration rate (eGFR) (fully adjusted) after removal of those with hypertension, diabetes and proteinuria, Area 25 (n = 243) and Bonje (n = 578)

Fig. 3

Histogram of eGFR distribution in the sample population

Supplementary files

Table S1 Sociodemographic and anthropometric characteristics of overall study participants (prior to exclusion of population with diabetes, hypertension and heavy proteinuria)

Table S2 Linear and logistic regression models, showing both minimally and fully adjusted models Bonje (n = 578)

Table S3 Linear regression models, showing both minimally and fully adjusted models, Area 25 (n = 243)