| Literature DB >> 33225936 |
Boshen Wang1,2, Lei Han2, Jinbo Wen3, Juan Zhang4, Baoli Zhu5,6.
Abstract
BACKGROUND: With an estimated > 800,000 suicide-related deaths and potentially several attempts for each death in the world. The purpose of this study was to determine the epidemiological characteristics of self-poisoning with pesticides within the Jiangsu province in China.Entities:
Keywords: Cross-sectional study; Pesticide; Self-poisoning
Year: 2020 PMID: 33225936 PMCID: PMC7681979 DOI: 10.1186/s12888-020-02882-9
Source DB: PubMed Journal: BMC Psychiatry ISSN: 1471-244X Impact factor: 3.630
Fig. 6a Trends in rate of pesticide suicides and socioeconomic and agriculture-related factors in Jiangsu from 2006 to 2018. b Correlation plot showing the heatmap with the Pearson’s correlation coefficient values for Self-poisoning with pesticides, % population in farming, Pesticide sold (10,000 ton), % Engel coefficient, Unemployment rate, Divorce rate, GDP per capita (US$). Color key of the heatmap is shown at the right of the plot. For B data is presented as Pearson’s correlation coefficient values, *p-value< 0.05, **p-value< 0.01
Fig. 1a Distribution of cases and deaths based on pesticide poisoning by year. b Fatality of pesticide self-poisoning and proportion of cases per 100,000 population by year
Fig. 2a Distribution of pesticide poisoning cases and deaths by sex. b Fatality and proportion of pesticide self-poisoning cases by sex per 100,000 population (***P < 0.001 relative to pesticide self-poisoning cases among males). c-d Distribution of pesticide self-poisoning cases and deaths in Jiangsu Province based on city polygons (2006–2018). (The map depicted in Fig. 2 was designed by our own team)
Fig. 3a Distribution of pesticide poisoning cases and deaths by sex. b Fatality and proportion of pesticide self-poisoning cases by sex per 100,000 population. (*** P < 0.001 relative to the 0–14 years age group; ### P < 0.001 relative to the 15–64 year age group). c-d Heatmap based on distribution of pesticide poisoning cases and deaths by age
Fig. 4a-b Distribution of pesticide self-poisoning cases and deaths by month. c Fatality of pesticide self-poisoning cases by month
Fig. 5a-b Cases of self-poisoning with different types of pesticides
Binary logistic regression analysis of pesticide-related deaths and different types of pesticides adjusted for age, sex and type of pesticide (OR: odds ratio; CI: confidence interval)
| Self-poisoning with pesticides | |||
|---|---|---|---|
| Death (vs No Death) | Age | 1.033 (1.031–1.036) | < 0.001 |
| Sex | |||
| Male | |||
| Female | 0.862 (0.784–0.948) | 0.002 | |
| Different types of pesticidesa | |||
| T60.0X2 | |||
| T60.1X2 | 0.657 (0.553–0.779) | < 0.001 | |
| T60.2X2 | 0.684 (0.569–0.823) | < 0.001 | |
| T60.3X2 | 0.499 (0.340–0.731) | < 0.001 | |
| T60.4X2 | 0.622 (0.332–1.165) | 0.138 | |
| T60.8X2 | 0.937 (0.739–1.189) | 0.592 | |
| T60.92 | |||
a T60.0X2: Toxic effect of organophosphate and carbamate insecticides, intentional self-harm
T60.1X2: Toxic effect of halogenated insecticides, intentional self-harm
T60.2X2: Toxic effect of other insecticides, intentional self-harm
T60.3X2: Toxic effect of herbicides and fungicides, intentional self-harm
T60.4X2: Toxic effect of rodenticides, intentional self-harm
T60.8X2: Toxic effect of other pesticides, intentional self-harm
T60.92: Toxic effect of unspecified pesticide, intentional self-harm