Table 4.

Associated factors and types of analysis.

Authors	Analysis for Association	Associated Factors
ChannaJayasumana et al. [20]	Unadjusted odds ratio, Mann–Whitney test	Farming, use of herbicide during farming, Well
VanDervort DR. [21]	Geographically weighted regression	People residing near sugarcane agriculture field areas based on the global information system (GIS) technique
SA. Hamilton et al. [22]	Linear regression, logistic regression	Regular meat eater, Age per 10-year increase, Gender
M Gonzalez et al. [23]	Compared with rapid decline, probability-weighted logistic regression	Current or former employees of banana plantations, sugarcane farming, cane cutters, seeders, duration of farming, Use of Herbicide during farming, substance abuse, availability of shade during working hours, people who frequently work in a hot environment, Consuming pipe water supply
E Siriwardhana et al. [24]	Linear logistic model analysis	Farming, paddy farming, Herbicide, substance abuse, Working under the sun for more than 6 h per day and consuming less than 3 litres of water per day, consumption of NSAID drugs, history of malaria
N Jayatilake et al. [16]	Logistic regression	Farming, Substance abuse, Smoking, Age more than 39 years, Gender
S Nanayakkara [25]	Univariate and multiple logistic analyses, Student-Newman-Keuls (SNK) multiple range test, student t-test	History of Malaria
L Lopez et al. [26]	F test	Sugarcane workers, tobacco use
T Rango et al. [27]	Logistic regression	Presence of trace elements such as cadmium (Cd), arsenic (As), lead (Pb), and uranium (U) in the available water sources
K Gobalarajah et al. [28]	Regression analysis between creatinine of CKDu and explanatory variables	Dissolved solids and Arsenic, Phosphate content
S Mascarenhas et al. [29]	Descriptive statistic was performed (Differences at p < 0.05 were considered to be significant.)	Blood lead level in affected individuals, pH of groundwater of endemic areas and its seasonal variation
JM jayaseka ra et al. [30]	GIS distribution mapping	Gender, age group over 40 years, farmers. The source of drinking water (shallow wells, tube wells and water reservoirs), patients who consumed boiled water, Clustering of the disease
E Siriwardhana et al. [31]	Fisher’s exact test, chi-square test v	Urine B2M level, Food habits, Consumption of foods which are locally produced, Surface water used for consumption by the local community
MA Jayasumana [32]	Logistic regression and proportion	Arsenic level, Chronic Arsenic Toxicity
R Osorio et al. [33]	Linear correlation analysis with Pearson and coefficient of determination	Age more than 50 years.
X Xing et al. [34]	Multiple logistic regression.	Age more than 60 years, Nephrotoxic drugs, Alcohol consumption
R Chandrajith et al. [35]	T-test	Heavy metals in water bodies (Cadmium, fluoride level)
M Selvarajah et al. [36]	Chi-square test and descriptive statistics	Age, Gender, Family history of CKD
Nanayakkara et al. [37]	Spearman’s rank correlation, Welch’s t-test	N-acetyl-B-D-glucosaminidase (NAG) and alpha1-microglobulin (A1M) excretion
N Athuraliya et al. [38]	Logistic regression	a1-microglobulin (A1M) excretion
de Silva MW et al. [39]	Correlation	Gender (wage labourers)
S Wijetunge et al. [40]	Corellation test	Consuming water from abandoned water sources and well water, the presence of heavy metals in abandoned wells [such as Calcium (Ca), Magnesium (Mg), Barium (Ba), Strontium (Sr), Iron (Fe), Titanium (Ti), and Vanadium (V)], family history of CKD
Siddarth et al. [41]	Chi square test, multinomial logistic regression	Genetic factor
B Guttierrez et al. [42]	χ2 test or Fisher’s exact test, binary logistic regression	Genetic factors
S Sayanthooran et al. [43]	Logistic regression	Genetic factors