The socioeconomic and demographic factors

In the literature, some researches examined the difference in suicide rates between males and females, and observed the well-known philosophy of “gender paradox” in suicide—i.e., females typically have higher rates of suicide ideation, but lower rates of suicide mortality compared to males [23, 24]. In addition to gender, other demographic factors can also play a critical role in linking to a higher suicide risk. A meta-analysis was performed to highlight that although demographic factors were found to be statistically significant, they were weak (i.e., no single demographic factor appeared to be particularly strong) in contributing to the overall complex phenomenon of suicidal behaviors [16]. Additionally, the authors suggested that further studies are needed to understand the effects of demographics on suicide mortality rates. Another nationwide study conducted for Iran from 2006 to 2010 concluded that certain demographic factors such as gender, age and education level could influence people in adopting different methods to commit the suicide [25]. The authors found that younger generation was more likely to use a highly violent method such as firearms to complete suicide, while the elderly people often selected hanging and poisoning as means to commit suicide [25]. In addition, it was found that men preferred hanging while women preferred self-burning to end their lives [25]. The authors also concluded that hanging was more prevalent among low educated people while poisoning was more popular among higher educated groups [25].

The suicide mortality rate varies significantly among different racial and ethnic groups. A previous study concluded that African Americans were more likely to select violent methods in committing suicides than Caucasians, when socioeconomic status and other factors were kept constant [26]. Other studies revealed that the adolescent minority groups such as Native Hawaiian/Pacific Islander and American Indian/Alaska Native as well as multi-racial groups were highly vulnerable to committing suicides, compared to their Asian, Black, Hispanic, and White counterparts [27, 28]. The educational attainment can be also linked to suicide risk. For example, people with a college degree or higher, exhibited lowest rates of suicide, whereas those with a high school diploma only were found to be more vulnerable with an increased risk of suicide [29]. Similarly, the results from another study pointed out that men with lower educational attainment had a higher risk of suicide in eight out of ten European countries, while suicide rates among women was found to be low and less consistent across all the countries [30].

Economic condition also plays a critical role in affecting suicide mortality rates. This is expected and it is established that poor socio-economic conditions characterized by higher incidence of poverty, lack of health insurance and higher unemployment rates are critical in affecting the mental health and wellbeing of adults in metropolitan areas [31, 32]. For instance, a study conducted in Taiwan to explore the relationship between unemployment rate and suicide rate, found that a 1% increase in absolute unemployment rate was linked to a 4.9% increase in the relative age-adjusted suicide rate from 1978–2006 [33]. Suicide rate was found to be statistically different across genders—men were found to be more likely to commit suicides compared to women in face of economic turmoil and financial issues [34]. This gender difference in suicide mortality rate during economic crisis is also unwrapped in another study, where researchers found that men with lower per capita income more frequently committed suicides, while such a phenomenon was not observed in the female group [35].

Previous exploratory data analysis on suicide rates in rural and urban counties in the U.S. revealed that the age-adjusted suicide rate for most of the rural counties was 1.8 times higher than most of the urban counties in 2017, and its rate had been rapidly increasing over the past decade. The authors, however, neither attribute a cause to such an increase in the suicide rates, nor did it explain why the difference was observed between rural and urban counties [5].

The climatic factors

To understand the impact of environmental factors on suicide rates, some researchers investigated the weather-induced higher risk of suicide. A systemic literature review was conducted to highlight that air temperature had a significant influence on suicidal acts, but their correlation could be either positive or negative, depending on the variation of sociological or geographic factors across different populations [36]. In another study [17], the authors established a distributed lag nonlinear model (DLNM) to determine the relationship between suicide rates and air temperature in Toronto, Ontario (Canada), and Jackson, Mississippi (USA). The models from both the locations concluded that warmer than normal temperatures had a positive correlation with the total number of suicides. However, the authors claimed that since the data was only from two cities, it might not be sufficient to establish immediate clinical implications, but can guide further investigations to better understand and quantify the suicide rates associated with temperature changes [17]. A positive correlation between elevated temperatures and suicide rates had also been established in another nationwide study [18], where the authors analyzed decades of historical data (1968–2004 in the U.S. and 1990–2010 in Mexico) and demonstrated that the relationship between temperature and suicide was roughly linear using distributed lag models. It was observed that a 1°C increase in average monthly temperature could contribute to an increase in the monthly suicide rate by 0.68% in the U.S., and 2.1% in Mexico. The study projected that under climate change, suicide mortality rate would increase by 1.4% in the U.S. and 2.3% in Mexico by 2050 [18]. On the contrary, a negative correlation between temperature and suicide rates was observed in the other studies [19–21], suggesting that decreasing temperature was linked to a growing rate of suicide incidents. The cause of this contradiction might be explained as climate variation could have heterogeneous effects across geographic areas [37]. In this view, the further studies are needed to investigate the complex interactions of climate-induced shifts in suicidal behaviors by controlling other factors such as spatiotemporal and socioeconomic backgrounds.

Existing research gaps and research questions

Previous research studies have been investigated the impacts of certain environmental factors in relation to suicide risks. However, to the authors’ best knowledge, some knowledge gaps still exist that are summarized below.

1. Most of the existing studies examined the relationships of socioeconomic, demographic and climate factors with suicide rates using a silo-ed approach, without considering the complex interactions among these factors [16, 21, 27–30].
2. The associations of socio-environmental factors with the suicide rates significantly vary across the various metropolitan regions, but little attention has been paid to compare such relational disparities [4–6].
3. Most of the previous studies applied the traditional linear models and basic statistical analyses (e.g., Pearson correlation coefficient) to characterize the relationships between the potential risk factors and the increased risk of suicide [12, 14, 15, 20]. These traditional approaches, however, fail to adequately capture the nonlinear characteristics in the complex structure of data in modeling suicide risks [28].
4. Moreover, in the face of climate change and growing urbanization, the models’ strong capability of predicting suicide risks is particularly critical. However, a robust predictive approach in modeling such suicide risks has attracted little/no attention in the previous studies [36–38].

In this paper, we aim to address the above-mentioned existing gaps by answering the following research questions:

1. Is there a significant disparity in suicide rates across the large and medium/small metropolitan areas in the US?
2. What are the key socio-environmental factors that contribute to the suicide rates in the large vs. medium/small metropolitan areas, and how are these factors associated with the suicide rates?
3. Do non-parametric nonlinear machine learning models better predict the suicide rates compared to the traditionally-used linear models, while capturing the complex interactions between suicides and socio-environmental factors?

To answer the research questions, we aim to develop a data-driven predictive approach to identify and evaluate the socio-environmental factors in relation to the suicide rates across the large vs. medium/small metropolitan areas in the US during 2000–2017, leveraging a library of advanced statistical learning techniques. A comparative assessment of the key influencing factors is conducted to analyze the disparities in the associations of the socio-environmental factors across the large and medium/small metropolitan areas.

Data collection

Suicide mortality data was collected from the Centers for Disease Control and Prevention (CDC) for the period 2000–2017 monthly using CDC’s WONDER tool [39] based on the variables—County, Month, Year, Intent of injury. “Intent of injury” describes an act of injury caused on purpose by oneself or by another person, with the goal of injuring or killing themselves or others [40]. Here, the intent of injury is specified as “suicide” in this study.

In addition, the county-level socioeconomic and demographic information were collected from the U.S. Department of Agriculture (USDA) Economic Research Service (ERS) [41] for the period of analysis from 2000–2017. And, climate data was obtained from the National Oceanic and Atmospheric Administration’s (NOAA) National Climatic Data Center (NCDC) [42]. The climate data captures several weather variations on a monthly basis from 2000–2017.

Data preprocessing, aggregation and visualization

Based on the publicly available data, we collected the county-level time series data of monthly suicide rates. A county was selected if it satisfied the following two criteria: 1) it is within a state that constantly reports higher rates of suicides and fall within the upper 50^th percentile of all the 50 US states, ranked in terms of witnessing a consistent high suicide rate (number of suicides per 100,000 of population) since 2016; and 2) reported more than ten suicide cases every month during 2000–2017. The first criterion is applied to avoid perturbations in suicide rates caused by short-term factors such us social upheaval or influx of tourists [43, 44]. The second criterion is followed by the CDC’s policy on protecting personally identifiable information by disabling reporting a county where the monthly suicide incident was below the pre-determined “cut-off” value (i.e., death counts of nine or fewer) [45]. Thus, counties reporting less than ten suicides per month do not appear in the publicly available CDC dataset.

To investigate the suicide disparity across the various metropolitan counties, the selected counties are further classified into two groups based on the latest version of 2013 NCHS Urban–Rural Classification Scheme for Counties [46]. The “large metro” group contains the counties having a population of one million or more measured by metropolitan statistical area (MSA), while the “medium/small metro” group refers to those counties with MSA population of at least 50,000, but not more than one million population. This classification criterion is validated by discriminant analysis with settlement density, socio-economic and demographic variables [46]. The selected counties used in our study are summarized in Table 1. The distributions of the normalized suicide mortality rates in the large and medium/small metros are exhibited in Fig 1. From Fig 1, we observe that the distribution of suicide rate in the medium/small metropolitan counties is highly right-skewed, depicting that these counties sporadically witness higher suicide rates. In addition, we performed the statistical t-test showing that there is a significant difference in suicide rates between large and medium/small metropolitan areas (p < 0.05). This validates our hypothesis that the suicide disparity exists across the large vs. medium/small metropolitan areas, and thus we develop two different models for the two types of counties.

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Table 1. Study samples.

https://doi.org/10.1371/journal.pone.0258824.t001

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Fig 1. Violin plot depicting normalized suicide mortality rates between large and medium/small metropolitan areas.

Violin plots are similar to box plots, with a rotated kernel density plot on each side showing the probability density of the data at different values.

https://doi.org/10.1371/journal.pone.0258824.g001

To investigate the key socio-environmental factors that could explain the suicide disparity shown in Fig 1, all the datasets were integrated using year, month and county as the “common keys”. These variables (i.e., year, month and county) are used in the model as proxies to capture the unobserved heterogeneity. Based on exploratory data analysis, we found that there are no significant increasing or decreasing trends in the annual or monthly suicide mortality rates in either of the large and medium/small metropolitan counties. However, more outliers are observed in the post 2000s, indicating suicide rates are increasing in some of the counties (see S1 and S2 Figs in S1 File).

For socio-environmental factors, the variables are removed if they satisfied the following criteria: 1) more than 20% of missing inputs; or 2) highly correlated with other variables based on Pearson correlation coefficient (ρ≥0.9 or ρ≤-0.9). Removing the highly correlated variables can avoid the “masking effect” of certain variables or model overfitting, and will help identification and unbiased assessment of the key factors [47, 48]. Finally, the socio-environmental variables used in this study are displayed in Table 2. After variable selection and data aggregation, we then normalized the suicide mortality counts to suicides mortality rates per 100,000 population, to eliminate the effect of population size in a county. Finally, the final dataset included 2,496 observations and 33 variables including 29 socio-environmental variables in Table 2, three proxy variables (year, month and county) and one response variable (normalized suicide rates).

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Table 2. Description of socio-environmental variables.

https://doi.org/10.1371/journal.pone.0258824.t002

Research framework

The schematic of our proposed research framework is exhibited in Fig 2. The research framework consists of three major steps: (i) data processing; (ii) model training and testing; and (iii) model inferencing. In Step (i), county-level suicide mortality information and multifaceted socio-environmental variables at different spatiotemporal scales were processed by a series of procedures ranging from data collection, cleaning, normalization, aggregation and visualization. Final aggregated dataset was divided into two independent subsets based on urbanization level—(1) large metros, and (2) medium/small metros. More details in Step (i) can be found in the section of Data collection, preprocessing and visualization. Then in Step (ii), a library of regression models were trained and tested separately on each subset. More specifically, we performed the model training and testing by leveraging a 30-fold 80–20 randomized holdout technique. This technique can be described as follows: in each subset, 20% of the data is randomly held-out as test set to evaluate the model’s out-of-sample predictive accuracy, while the remaining 80% of the data is used as training set for training the models. This process is repeated 30 times to ensure that all the data is used at least once to produce generalized errors in training and testing the models [49]. The average model performance across all iterations is then calculated in terms of three commonly-used statistical metrics—R², RMSE (root mean square error) and MAE (mean absolute error). Finally, the model that outperforms other models in terms of out-of-sample predictive accuracy as well as a comparative better goodness-of-fit is selected as the final model in this paper. Finally in Step (iii), leveraging the selected model, we analyzed the suicide disparity across large and medium/small metropolitan areas, in relation to socio-environmental factors, using the variable importance ranking and partial dependence plots.

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Fig 2. Schematic of the proposed data-driven research framework.

https://doi.org/10.1371/journal.pone.0258824.g002

Supervised learning

Supervised learning method is applied to estimate a regression function capable of predicting the response variable Y conditioned on a set of inputs X, such that the loss function for measuring errors is minimized. The generalized form can be mathematically written as Y = f(X)+ϵ, where ϵ is the irreducible error follows [49, 50]. The loss function , representing the deviation of observed values from the estimated values of Y, typically can be calculated through the absolute error (L1 norm) or squared error (L2 norm). That is, where N is the total number of data points.

Note that supervised statistical learning models can be parametric, semi-parametric or non-parametric. Parametric models generally assume a particular functional form that relates the input variables to the response. The assumed functional form helps with the ease of estimation and model interpretability, but comes at the cost of predictive accuracy since the assumptions (such as normality and linearity) often do not hold for real cases. On the other hand, non-parametric models that make no assumption about the distribution of the response variable or the shape of the function relating the response to the predictors, are free to learn any functional form of the response from the training data. By utilizing data in novel ways to estimate the dependencies, the non-parametric models often have a superior predictive power than parametric models. However, the non-parametric methods are data-intensive and highly dependent on the quality of the data.

In this study, the response variable Y is represented by the normalized suicide rates, and rest of the variables in the dataset are denoted as the predictor variables X. The function f is construct through a library predictive models including generalized linear models (GLM) [51], ridge and lasso regression [52], generalized additive models (GAM) [53], multi-adaptive regression splines (MARS) [54], and ensemble tree based models including random forest (RF) [55] and Bayesian additive regression trees (BART) [56]. By implementing a series of experiments, RF outperforms all the models in terms of both goodness-of-fit and predictive accuracy and thus, we select RF as our final model to assess socio-environmental affects on suicide mortality rates in the metropolitan counties. Details of random forest algorithm and model selection techniques are provided in the following subsections.

Comparative assessment of model performance and final model selection

A summary of the models’ performances, developed for both the large metropolitan and the medium/small metropolitan counties, are provided in Tables 3 and 4 respectively. Performance of the models, in terms of in-sample model fit and out-of-sample predictive accuracy, is evaluated using three statistical metrics (i.e. R², RMSE, MAE) that averaged across the 30 iterations. Model’s in-sample fit indicates it’s ability to capture the underlying structure of the data and explains response as a function of the predictors, while the predictive accuracy measures the model’s ability to make future predictions.

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Table 3. Large metropolitan counties: Model performance comparison.

https://doi.org/10.1371/journal.pone.0258824.t003

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Table 4. Medium/Small metropolitan counties: Model performance comparison.

https://doi.org/10.1371/journal.pone.0258824.t004

From Table 3 that presents the performances of the models developed for the large metropolitan counties, we observe that random forest and gradient boosting method are the two most competitive algorithms that outperform all the other models in terms of goodness-of-fit. However, in terms of predictive accuracy, random forest outperforms the gradient boosting method. Thus, we selected random forest model to capture and predict suicide mortality rate in the large metropolitan counties. Similar patterns can be found in Table 4 that exhibit the performances of the models developed for the medium/small metropolitan counties. Gradient boosting method tops the list in terms of goodness-of-fit followed by random forest, however random forest outperforms the gradient boosting method with regard to predictive accuracy. This phenomenon indicates gradient boosting method is overfitting the training data. Note that, BART model has a slightly higher predictive accuracy than random forest model, but it also demonstrates much higher loss in fitting training data to the model. Therefore, we selected random forest as our final model to make further inferences of key socio-environmental impacts on suicide disparities in metropolitan areas.

Compared to the “null model” (a.k.a. “mean-only” model), which is often used as a benchmark model in statistical analyses, the random forest algorithm offered an improvement of 66.3% on in-sample RMSE and 66.9% on in-sample MAE, while for the predictive accuracy it offered an improvement of 25.2% on out-of-sample RMSE and 25.7% on out-of-sample MAE for the large metropolitan counties dataset. On the other hand, for the medium/small metropolitan counties dataset, the random forest algorithm provided an improvement of 74.5% on in-sample RMSE and 73.2% on in-sample MAE, while from the predictive accuracy perspective, it offered an improvement of 42% on out-of-sample RMSE and 38.9% on out-of-sample MAE.

Models’ diagnostics.

To validate our finally selected random forest in capturing the suicide variations between both the large and medium/small metropolitan counties, we analyzed the Q-Q plots of the model residuals as depicted in Figs 3(A) and 4(A). A residual Q-Q plot is a graph that plots quantiles of the models’ residuals versus quantiles of the standard normal distribution. From Figs 3(A) and 4(A), we observe that the residuals mostly fall along the 45 line of the normal quantile plot, with slight deviations at the tails. The deviated tails at the extremes indicate that there are unobserved heterogeneities, most likely associated with non socio-environmental factors (e.g., victim-level information on pre-existing clinical conditions, health behaviors, family issues, etc.) which could not be captured in our model. The higher R² values of the models—e.g., R² = 0.886 and R² = 0.934 for large metro and medium/small metro counties models respectively, the higher values of Pearson correlation coefficients (ρ = 0.950 in large metro; ρ = 0.972 in medium/small metro) between the actual and the fitted values, and the residuals Q-Q plots indicate that the selected random forest model can adequately capture the variation in the data and model the suicide mortality rates as a function of the various socio-environmental factors.

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Fig 3. Large metropolitan counties: Model diagnostics of final random forest model.

(A) Residuals QQ plot (the blue dashed lines represent 95% confidence intervals); (B) Predicted versus actual suicide counts, normalized per 100,000 of population.

https://doi.org/10.1371/journal.pone.0258824.g003

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Fig 4. Medium/Small metropolitan counties: Model diagnostics of final random forest model.

(A) Residuals QQ plot (the blue dashed lines represent 95% confidence intervals); (B) Predicted versus actual suicide counts, normalized per 100,000 of population.

https://doi.org/10.1371/journal.pone.0258824.g004

Key predictors identification and ranking

Variable importance is calculated based on “variable inclusion proportion”, which is the fraction of times a given predictor was used in growing a regression tree (see more details in the description of random forest algorithm 1). In this paper, the variable importance ranking can be used to indicate the main influencing factors by their relevance of suicide mortality rates. For the sake of brevity, we selected top 15 variables in predicting suicide rates in the large and medium/small metropolitan counties. Table 5 exhibits those 15 variables and their sign of the correlation coefficients with the response variable.

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Table 5. Summary of top 15 variables in large and medium/small areas.

https://doi.org/10.1371/journal.pone.0258824.t005

As we observe from Fig 5, socio-demographic factors (race, gender, age, and education) have a different yet significant influence on the suicide mortality rate in both large and medium/small metropolitan areas. Economic factors (i.e., unemployment rate and the median household income) have more impacts on suicide rates in the median/small metropolitan regions than in the large metropolitan regions. In addition, suicide rates in the large metros are found to be more sensitive to specific climatic variables (DX90—number of days higher than 90 F, DX70—number of days higher than 70 F, HDSD—season-to-date heating degree days, and EMXP—extreme maximum precipitation in a month); while suicides in the medium/small metros are more sensitive to season-to-date cooling degree days (CDSD). The rational behind our findings is explained in the following subsections.

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Fig 5. Variable importance ranking of top 15 predictors.

Top 15 socio-environmental factors selected from random forest in relation to suicide rates are shown in (a) and (b) with respect to large metros and medium/small metros.

https://doi.org/10.1371/journal.pone.0258824.g005

Model inference: Comparative assessment across large and medium/small metropolitan counties

The disparities of suicide mortality rate across large and medium/small metropolitan areas are examined based on the key factors identified in Table 5. The relative influences of those key socio-demographic, climatic and economic factors on the suicide mortality are illustrated using partial dependence plots (PDPs) (see the Eq 2), where in each plot the y-axis represents the averaged suicide mortality rate influenced only by the predictor variable in the x-axis, considering all the other predictor variables to be constant [58].

Socio-demographic factors.

A detailed insight on socio-demographic impacts on suicide rates across metropolitan areas is provided here. Specific racial groups such as Asian (AA), Black (BA), American Indians and Alaska natives (IA), native Hawaiian and Pacific Islander (NA) and Non-Hispanic (NH) are all ranked as top 15 predictors, but with different impacts on suicide rates across large and medium/small metropolitan areas.

The trend between the proportion of Asian population (AA) and suicide rate can be observed in Fig 6. This graph demonstrates that, as a growing AA, the averaged suicide rate first drops quickly and then stabilizes at a certain point. Specifically, suicide rate stabilizes at 1.28 per 100,000 population as the AA reaches above 11% in the large metros; while in the medium/small metros, suicide rates stabilizes at 1.9 per 100,000 population as the AA reaches above 6%. This can infer that a community with higher Asian population (under certain threshold) could have a lower suicide rate. In general, Asian population is less likely to commit suicide. Previous study also indicated Asians were at low risk for suicide mortality compared to other racial groups such as White non-Hispanics and Black non-Hispanics [59]. The association between the proportion of Black population (BA) and the suicide rates is demonstrated in Fig 6. In large metropolitan counties, we observed that suicide mortality rate increases as the BA grows, and declines as the BA exceeds 40%. On the contrary, this relationship is different in the medium/small metropolitan areas, where a higher BA is related to a lower suicide rates—the average suicide rate goes down from 2.5 to 1.9 counts per 100,000 population of the county as the BA grows over 15%. This opposite relation of suicide rates and Black population across different urbanized regions could be explained by the previous studies implying that Black population living in urban areas might feel more stressed and strained of the urban life due to unaccustomed social isolation or difficulty acculturating to middle-class suburban living [6, 60].

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Fig 6. Suicide mortality rate and race: (A) Large metro areas; (B) Medium/small metro areas.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g006

From Fig 6, we observe that higher proportion of Non-Hispanic population (NH) is also associated with increasing suicide mortality rate, and this relationship is consistent in both the large and medium/small metropolitan counties. Note that, with a higher NH, the average suicide rate also increases. From the x-axis of Fig 6, the NH is a major group in the population and can account for the overall suicide rates, which is lined up with the existing research [2]. Similarly, the relationship of suicide rates and the proportions of American Indian and Alaska natives (IA) can be observed from Fig 6. For the large metros, the relationship depicts a step-function. More specifically, with IA ranging between 0.0–1.5% and greater than 2%, the suicide mortality rate shows an increasing trend, with an exception of a decreasing trend in the range of 1.5–2.0Ḟor the medium/small counties, we observe an increasing trend where the IA ranges between 0.0–1.0%, but after that the trend is slightly decreasing. Fig 6 demonstrates the relationship of suicide rates and the proportions of Native Hawaiian or Pacific Islander (NA) population. More specifically, the suicide mortality rate shows an increasing trend with an increasing NA in the large metropolitan counties, whereas, in contrast it shows a decreasing trend with an increasing NA in the medium/small metropolitan counties.

Gender plays a crucial role in understanding the variations in suicide mortality rates across the large and medium/small metropolitan regions. From Fig 7, we found that in the large metros, as the proportion of females increases, the suicide rate increases up until a certain point (around 1.45 per 100,000) and then it starts to drop sharply. In the medium/small metros, the suicide rate decreases monotonically as the growing of female populations. On average, counties having a higher proportion of females typically witness a lower suicide mortality rate. Previous studies stated that females have higher rates of suicidal ideations and attempts whereas males are more successful in completing a suicide, which is also known as the well-established concept of “gender paradox in suicides” [23, 24].

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Fig 7. Suicide mortality rate and gender: (A) Large metro areas; (B) Medium/small metro areas.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g007

Our studies also found that different age groups have certain impacts on suicide rates across the large and medium/small metropolitan counties. In Fig 8, the suicide trend can be roughly represented in the form of step-function (decreasing first, reaches a plateau, and then increasing), with the increasing of the proportion of children and teenagers (aged under 14). And this trend can be observed in the both large and medium/small metros. It is not surprising that prepubescent children are at risk of conducting suicidal behaviors, as previous studies suggested that by the age of eight or nine children have already formed a thorough understanding of suicide and do have intent to cause self-injury to possibly avoid their emotional pain such as break-ups [61]. Fig 8 relates to the proportion of adolescents aged between 15 to 29 years. In the large metros, the suicide rate has a tipping point when adolescent population is around 22.2% of the population. This indicates that suicide rate have a sharp increase from 1.4 to 1.5 per 100,000 population at the tipping point. Suicides among adolescents are growing in the last decades, and higher proportion of adolescents in the community could be linked to a higher suicide risks. Intriguingly, the suicide rate in the medium/small metros exhibits a downward trend with the increases as adolescent population grows. Note that, suicide rates eventually stabilize at 1.5 and 2.0 per 100,000 population for the large and medium/small metros respectively, indicating the suicide disparities still need to be explained by other factors.

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Fig 8. Suicide mortality rate and age: (A) Large metro areas; (B) Medium/small metro areas.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g008

Our analysis also suggests that elderly people belonging to the age group of over 75 years and living in the medium/small metropolitan areas are vulnerable to committing suicides (see Fig 8). We observe that as the proportion of elderly population in a county increases beyond 7%, the suicide mortality rate steadily increases. On the contrary, this factor does not appear to be significant (not ranked among the top 15 factors) for the suicide mortality rates in the large metropolitan areas. Thus, the elderly population living in medium/small metropolitan settings has a higher risk of suicide, mostly due to the unavailability of sufficient mental health services or accessibility to the healthcare system [62].

The educational attainment of the population is an another key factor that can explain the suicide disparities across different urbanized regions. In the medium/small metropolitan counties, suicide rate shows an overall increasing trend with the growing proportion of people with a high school diploma only or lower (see Fig 9B1 and 9B2), but exhibits an decreasing trend with growing proportion of people having college associate degree (see Fig 9B3); this indicates that people with a lower educational attainment living in the medium/small counties are more vulnerable to suicide risks. However in the large counties, the partial effect of educational attainment on suicide rates is more fluctuating (see Fig 9A1 and 9A2). This can be mostly attributed to the fact that only few large metropolitan counties contain higher percentages of population with lower educational attainment, thus the trend cannot be generalized. We also observe that the suicide rate has a steady downward trend as the percentage of people with college or associate degree increases in the large counties (see Fig 9A3).

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Fig 9. Suicide mortality rate and education: (A) Large metro areas; (B) Medium/small metro areas.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g009

Based on those findings about educational attainment, our analysis demonstrates that people with low levels of education are more likely to be linked to higher suicide rates. This education gradient in the suicide mortality rate, in both the large and medium/small metropolitan areas, can reflect the importance of education in changing the risk perception and health-related behaviors of a population which in turn could improve the overall mental health condition and emotional well-being of a community. This finding is consistent with a cross-national research report showing that the suicide rate is relatively high among group with only a high school degree, and relatively low among people having at least a college degree [29]. To some extent, education is more than enriching knowledge, providing a platform/resource for individuals to improve their coping skills and maintain their physical, mental and social well-being.

Economic factors.

This study also reveals the association of economic factors and suicide mortality rates across the large and medium/small metropolitan regions. We note that two economic factors (unemployment rate and median household income) that ranked as top 15 factors are of significant importance in influencing the suicide rates in the medium/small metropolitan areas. However, those two economic factors were not found to be important in the large metropolitan areas. As presented in Fig 10, the suicide mortality rate has an increasing trend with the growing of unemployment rate in the medium/small metros. This finding is lined up with one previous research that examined an increase in the relative risk of suicide was linked to the unemployment status [63]. From Fig 10, we also found that the suicide mortality rate can be represented as a step-function of the median household income in the medium/small counties. The suicide mortality rate shows an decreasing trend as the median household income grows within the range from 40,000 to 80,000 USD annually. However, as the median household income increases above 80,000 USD annually, we observe an increasing trend in the suicide mortality rates. Note that, since only few observations fall in a range above 80,000 USD annually, we could consider that for the most cases, the relation between median household income and suicide rates are negative correlated if the median household income is below 80,000 USD annually.

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Fig 10. Suicide mortality rate in economics for (B) Medium/small metro areas only.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g010

From the analysis, suicide rate in the medium/small metros is more sensitive to unemployment rate and median household income compared with suicide rate in the large metros. Living in the medium/small metropolitan counties, people with less than average income or under unemployment may encounter more physical and mental stress, which could act as a trigger to underlying mental illness or chronic depression that can lead to committing suicide. While in the large metros, there are more jobs opportunities. Moreover, some researchers found that people were being unemployment in a society may be considered as lack of social cohesion, which in turns is associated with the higher chance of committing suicide [64, 65].

Climate factors.

Our findings show that suicide rate in the large metropolitan counties are more sensitive to four climate factors—DX90, DX70, HDSD and EMXP, that ranked among the top 15 factors. The suicide rate in the medium/small metropolitan counties, on the other hand, is associated with the seasonal cooling degree days (CDSD). Variable descriptions and partial dependencies are provided in Table 5 and Fig 11, respectively. In the large metropolitan areas, we observe that the suicide mortality rate has an increasing trend with higher extreme temperatures (i.e., DX90 and DX70) (see Fig 11(A1) and 11(A2)). In the medium/small metros, the suicide rate has an upward trend with the increasing seasonal cooling degree days (CDSD), which again clearly reflects that higher temperatures are associated with higher suicide rates. Thus, there is not much disparity in the associations of suicide rates with the climate when large and medium/small metros are compared. These findings are consistent with prior research studies, claiming a strong association between warmer temperatures and suicide rates [17, 18]. In addition, our study demonstrates the nonlinear association between the suicide rate in large metropolitan areas and number of days above 90°F a year (i.e., DX90), which is similar to the inverted J-curve observed between higher temperature and suicides in the previous studies [66]. The linkage between warmer temperature and higher suicides could possibly explained as side effects of thermoregulation or other neurological responses to temperature that alter brain perfusion [67]. A recent study, analyzing around 600 million posts in Twitter, also demonstrated that hotter months were linked to a higher chance of using depressive languages during conversations, which was found to be the underlying cause of suicide [18]. Additionally, our study found that the seasonal heating degree days (HDSD) and the extreme daily maximum precipitation (EMXP) have positive associations with the suicide rates in the large metropolitan areas. To the best of our knowledge, there is no existing research that has examined the associations between HDSD/EMXP and the suicide risks in large metropolitan areas. However, one existing research has demonstrated that higher precipitation is linked to increasing mental health issues [68]. Now, since it is well-recognized that mental health issues can contribute to suicidal behaviors, it is not surprising to observe EMXP having a positive correlation with the suicide rate.

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Fig 11. Suicide mortality rate and climate: (A) Large metro areas; (B) Medium/small metro areas.

Rug lines on the x axis indicate prevalence of data points; black curve is the average marginal effect of the predictor variable; red lines indicate the 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0258824.g011