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. 2025 Mar 3;14:15. doi: 10.1186/s40249-025-01283-y

Table 7.

Regression analysis for the prevalence of TB per 100,000 population in the fiscal years 2020–2023

Factors 2020–2021 2021–2022 2022–2023
OLS SLM SEM OLS SLM SEM OLS SLM SEM
LSTN 2.23*** 1.09*** 1.534*** 3.046*** 1.206** 1.607* 3.653*** 1.750*** 1.835**
Urban 2.13** 1.84*** 2.605 *** 3.264** 2.624*** 3.425*** 3.456*** 2.643*** 3.168***
Precipitation −0.366** 0.167 0.012
Constant 49.24 12.52 56.362 62.618 9.143 77.142 96.256 33.135 71.413
ρ 0.64 0.728 0.619
λ (varies between 0.1 and 1.0) 0.682 0.760 0.701
F-stat (minimum value = 0) 26.36 29.221 26.196
R-Squared (coefficient of determination) 0.400 0.640 0.655 0.426 0.721 0.712 0.499 0.696 0.689
Log likelihood −360.064 −344.816 344.745 −382.381 361.505 −363.482 −371.041 357.225 −359.462
AIC 726.128 697.632 695.49 770.762 731.009 732.964 750.082 724.45 726.924
BIC 733.16 707.007 702.522 777.793 740.384 739.995 759.457 736.169 736.299

Bold values indicate the best regression model to predict the spatial association

ρ Rho, λ Lumda, OLS Oridnary least square, SEM Spatial error model, SLM Spatial lag model, AIC Akaike information crierion, BIC Bayesian information criterion, F-stat F-statistic

Coefficient significant at *0.05, **0.01, ***0.005