Determinants of Rulindo tea farmers’ perception towards organic farming: a MASS::stepAIC() approach

Muhamadi, Shakiru; Boz, İsmet

doi:10.1590/0103-8478cr20200695

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Normalized z scores and counts for all topics were made available to a backward selection algorithm in order to enable higher prediction and also to illustrate the generation of models using an automated approach. The function step Akaike information criterion (AIC) in the MASS library on R was used to select at each step the model that minimizes the prediction criterion AIC [ 29 ]. The adjusted R 2 and AIC values for each of the final models were calculated.…”

Section: Methodsmentioning

confidence: 99%

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Cuomo¹,

Purushothaman²,

Calac³

et al. 2023

JMIR Form Res

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Background There were an estimated 100,306 drug overdose deaths between April 2020 and April 2021, a three-quarter increase from the prior 12-month period. There is an approximate 6-month reporting lag for provisional counts of drug overdose deaths from the National Vital Statistics System, and the highest level of geospatial resolution is at the state level. By contrast, public social media data are available close to real-time and are often accessible with precise coordinates. Objective The purpose of this study is to assess whether county-level overdose mortality burden could be estimated using opioid-related Twitter data. Methods International Classification of Diseases (ICD) codes for poisoning or exposure to overdose at the county level were obtained from CDC WONDER. Demographics were collected from the American Community Survey. The Twitter Application Programming Interface was used to obtain tweets that contained any of the 36 terms with drug names. An unsupervised classification approach was used for clustering tweets. Population-normalized variables and polynomial population-normalized variables were produced. Furthermore, z scores of the Getis Ord Gi clustering statistic were produced, and both these scores and their polynomial counterparts were explored in regression modeling of county-level overdose mortality burden. A series of linear regression models were used for predictive modeling to explore the interpretability of the analytical output. Results Modeling overdose mortality with normalized demographic variables alone explained only 7.4% of the variability in county-level overdose mortality, whereas this was approximately doubled by the use of specific demographic and Twitter data covariates based on a backward selection approach. The highest adjusted R2 and lowest AIC (Akaike Info Criterion) were obtained for the model with normalized demographic variables, normalized z scores from geospatial analyses, and normalized topic counts (adjusted R2=0.133, AIC=8546.8). The z scores of the Getis Ord Gi statistic appeared to have improved utility over population-normalization alone. In this model, median age, female population, and tweets about web-based drug sales were positively associated with opioid mortality. Asian race and Hispanic ethnicity were significantly negatively associated with county-level burdens of overdose mortality. Conclusions Social media data, when transformed using certain statistical approaches, may add utility to the goal of producing closer to real-time county-level estimates of overdose mortality. Prediction of opioid-related outcomes can be advanced to inform prevention and treatment decisions. This interdisciplinary approach can facilitate evidence-based funding decisions for various substance use disorder prevention and treatment programs.

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Section: Methodsmentioning

confidence: 99%