To be honest, I think the paper itself has issues that could make the models and conclusions invalid.
1. The dependent variable is the province’s suicide rate. Bangkok has an estimated population of 8 million, while Samut Songkhram has <200,000 people (Wikipedia.org). It is not surprising that Bangkok has the lowest suicide rate as the denominator(population) is enormous compared to other provinces. Adding an odd suicide case to the total suicide number of Samut Songkhram could result in a huge bias as the denominator is much smaller compared to adding one suicide case to Bangkok. The author did not mention any data transformation. I think the untransformed suicide rate already creates bias.

2. The author made a number of claims without reference. For example, the divorce rate, the author claimed that more educated people were likely to have a higher divorce rate. As for the consumption of alcohol, the author claimed that the rates were lower as people are more educated. The paper did not include a reference on the educational background of the provinces.

3. The author described that the prevalence of alcohol consumption rate was the rate of adults in the province consumed alcohol within one year of the questionnaire, which is meaningless. In my opinion, excessive drinking (alcoholic) and casual drinking habit could have a different impact on mental health and perhaps suicide rate. I think if the “alcohol” variable is derived from the “individuals who had consumed alcohol within one year” question, it is most likely be a confounder.

4. The residual plot (homoscedasticity) indicated that the linear regression assumption is invalid. Thus, linear regression is not suitable for the dataset.

5. The conclusion is also not justified based on the model outcome. The underage drinking variable is not significant (0.05<p<0.1) in the model2. However, the author recommended that the government should impose stricter law enforcement to prevent underage drinking, why?