Stata software 13

Data statistical analysis was performed with Stata software13. A probabilistic nonlinear model was performed with a dichotomous response (people ≥ 65 years having or not having 25(OH)D deficiency), and a regressor that refers to a probit model. Average marginal effects were calculated for each interaction, and likelihoods were predicted. The model specification is: $$y_i=F+\left(\begin{array}{c}{\beta }_0+{\beta }_{1}Sex{o}_i+{\beta }_{2}Descendenciapueblos{O}_i+{\beta }_{3}Zon{a}_i+{\beta }_{4}LuzSola{r}_i\\ +\sum _{05}^{19}\text{Re}gio{}^{\prime }{n}_i\\ \end{array}\right)+{\epsilon }_i$$
where F(.) corresponds to the cumulative distribution function assumed to be probit, which is a link between the determinants and the probability of presenting 25(OH)D deficiency; thus, the probit model to be estimated corresponds to: $$\text{Probit}(p)=\sqrt{2}er{f}^{-1}(2p-1)$$

Data from the same sentinel site location district and village and date of sample collection were combined for each species across all bioassays. Percent mortality and 95% confidence intervals for the WHO susceptibility tests were calculated by the binomial exact method using STATA software 13 (Stata Corp LP, College Station, TX, USA). Trend analyses were performed for all tested insecticides. To estimate insecticide resistance trends from 2004 to 2020, aggregated mosquito mortalities from each sentinel site and time were performed using regression analysis of mortality versus the Julian dates of bioassays using STATA software. Only insecticides that were tested across multiple survey years have been included in the analysis. A linear regression model was fitted for species composition as the dependent variable against time (years).
The association between resistance markers and phenotypic resistance was analysed using Chi-square tests on a subsample of An. gambiae and An. arabiensis. The subsample was weighted by the inverse of the sampling fraction i.e., subsample/total collected to represent the relative proportion in the total population. The kdr genotype frequencies among dead and live An. gambiae s.l. in susceptibility test across different survey years and clusters were compared using the Genepop software, version 4.0.

The latent class growth model, which is a model used to describe the development feature of samples in a certain period and classify the development feature, was used to illustrate and classify the developmental trajectories of urban and rural depression symptoms in this study. Small values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and adjusted Bayesian Information Criterion (aBIC) are preferred in selecting the number of latent classes [37 (link)]. After determining the trajectory groups, Chi-square analysis was used to test the differences in respondents’ characteristics among depression trajectories groups within urban and rural areas. Then, multinomial logistic regression was used to explore the association between trajectories of depression symptoms and baseline multimorbidity, physical disability and other independent variables in urban and rural areas.
Mplus 8.0 was used to apply the latent class growth model, and STATA software 13 was used to execute the multinomial logistic regression and Chi-square analysis, with statistical significance at p < 0.05.