Temporal Variations in Suicide Risk: The Influence of Day of the Week and National Holidays Across Multiple Countries

Table of Contents

Overall Summary

Overview

The study investigates the relationship between suicide risk and temporal factors, including days of the week and national holidays, across 26 countries. Using a multicountry, two-stage, time series design, the researchers analyzed data from 740 locations, including 1.7 million suicide cases, from 1971 to 2019. The aim was to explore patterns and trends, identifying days and periods with heightened suicide risk to inform targeted prevention strategies. The study highlighted Mondays as having the highest suicide risk among weekdays, with New Year's Day also associated with increased risk. The findings suggest cultural variations in weekend effects, underscoring the need for culturally sensitive interventions.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure

Description: Fig. 1. Visualizes the geographical distribution of Monday suicides across 26 countries using a color-coded world map.

Relevance: Highlights the observed Monday effect at a global level but lacks regional granularity and statistical significance markers.

Table

Description: Table 1. Lists study locations, periods, and data on suicide events and temperature by country.

Relevance: Provides a foundational overview of the dataset's scope but includes missing data and period variations that could affect analysis.

Conclusion

The study illuminates significant temporal patterns in suicide risk, with Mondays and New Year's Day identified as high-risk periods across multiple countries. These findings underscore the necessity for targeted suicide prevention strategies that consider temporal and cultural variations. By highlighting the influence of the 'broken-promise effect' and regional societal factors, the research contributes valuable insights for public health interventions. Future studies should delve deeper into regional heterogeneity and consider the COVID-19 pandemic's impact on suicide patterns. Policymakers and practitioners can utilize these insights to allocate resources effectively and develop culturally sensitive prevention strategies tailored to specific temporal patterns.

Section Analysis

Abstract

Overview

This abstract summarizes a study investigating the link between suicide risk, days of the week, and national holidays across multiple countries. Using a time series design, which analyzes data over time to identify trends, the study analyzed data from 740 locations in 26 countries and territories. The study found Mondays had the highest suicide risk during weekdays, while Saturdays or Sundays had the lowest risk in many North American, Asian, and European countries. However, this weekend trend was reversed in some South and Central American countries, Finland, and South Africa. New Year's Day was associated with increased suicide risk in most countries, while the association with Christmas was less clear. Other national holidays showed a weak association with decreased suicide risk, except in Central and South American countries, where risk increased after the holidays.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

This introduction sets the stage for a study on suicide risk and its temporal variations. It establishes suicide as a significant global public health issue, highlighting its impact through statistics from the World Health Organization. The introduction then delves into the sociological context of suicide, referencing Durkheim's work and the influence of social and individual factors. It also introduces the concept of time-varying factors affecting suicide rates, such as seasonal patterns and shorter-term variations related to days of the week and holidays. The introduction concludes by summarizing previous research on these shorter-term variations, noting the established peak on Mondays and the mixed findings regarding holiday effects, setting the context for the current study's investigation.

Key Aspects

Strengths

Suggestions for Improvement

Methods

Overview

This section details the methodology used to investigate the relationship between suicide risk, days of the week, and holidays across 26 countries. The study uses suicide data from the Multi-country Multi-city Collaborative Research Network, spanning 1971-2019. A two-stage analysis was employed. The first stage involved quasi-Poisson regression models to estimate the associations between suicide counts and day of the week/holiday indicators, adjusting for seasonality, long-term trends, and temperature. Quasi-Poisson regression is suitable for count data where the variance might be greater than the mean. The second stage used meta-regression with a random intercept to pool the country-specific estimates from the first stage and account for regional differences. Meta-regression analyzes results from multiple studies to identify overall trends and factors influencing variation. The study also included subpopulation analyses by sex and age group and addressed potential biases due to weekend misclassification of suicide registrations.

Key Aspects

Strengths

Suggestions for Improvement

Results

Overview

This section presents the findings of the study investigating the relationship between suicide risk and temporal factors. The study analyzed data from over 1.7 million suicides across 26 countries. The results show that Mondays had the highest suicide counts compared to other weekdays, with relative risks (a measure of how much more likely an event is in one group compared to another) ranging from 1.02 to 1.17. Weekend suicide risk varied by region, being higher in South/Central America, South Africa, and Finland, but lower in North America, Europe, and Asia. A slight increase in suicide risk was observed around Christmas in some regions, but the effect was heterogeneous. New Year's Day showed a peak in suicide risk across most countries, with relative risks ranging from 0.93 to 1.93. Other national holidays showed a general trend of decreased risk before the holiday and increased risk afterward. These patterns were generally consistent across sex and age groups (0-64 years). Meta-regression analysis revealed that regional differences largely explained the heterogeneity in the results.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Fig. 1. Geographical locations of the 740 sites in 26 countries included in the...
Full Caption

Fig. 1. Geographical locations of the 740 sites in 26 countries included in the study and the corresponding percentage of suicide counts on Monday during the study period.

First Reference in Text
Monday accounted for approximately 15-18% of total suicides (fig 1).
Description
  • Map Visualization: Figure 1 presents a world map highlighting the 26 countries included in the study. Each country's geographical location is marked, and a color scale represents the percentage of suicides occurring on Mondays within that country during the study period. The color scale ranges from darker shades (higher percentage) to lighter shades (lower percentage), allowing for a visual comparison of Monday's suicide proportion across different regions.
  • Data Aggregation: The map displays aggregated data. The percentage shown for each country represents the proportion of suicides specifically on Mondays relative to the total number of suicides across all days of the week for that country. This provides a summarized view of the day-of-the-week effect on suicide incidence for each included nation.
Scientific Validity
  • Data Granularity: While the map effectively visualizes the overall distribution of Monday suicides, the aggregation at the country level obscures potential within-country variations. For larger countries like the USA or Canada, regional differences might be substantial and are lost in this visualization. Presenting data at a finer granularity (e.g., regional or city level) could reveal more nuanced patterns.
  • Statistical Significance: The figure lacks any indication of statistical significance. It's crucial to clarify whether the observed differences in Monday suicide percentages across countries are statistically significant or could be due to random variation. Adding confidence intervals or p-values to the visualization or accompanying text would strengthen the scientific validity.
Communication
  • Color Scale Clarity: The color scale, while providing a visual representation of the data, could be improved. A clear legend with numerical values corresponding to the color shades is essential for accurate interpretation. Additionally, ensuring sufficient color contrast for accessibility is crucial.
  • Contextualization: The figure caption and reference text could benefit from more context. Explicitly stating the study period and the total number of suicides across all countries would provide a clearer frame of reference for the reader. Furthermore, briefly mentioning the overall range of Monday suicide percentages observed across the countries would enhance the figure's impact.
Fig. 2. Risks of suicide by the day of the week with the corresponding 95%...
Full Caption

Fig. 2. Risks of suicide by the day of the week with the corresponding 95% confidence intervals (vertical lines).

First Reference in Text
The risks of suicide were higher on Mondays compared with Wednesdays (reference) and other weekdays in the total population, with relative risks ranging from 1.02 (95% CI 0.95 to 1.10) in Costa Rica to 1.17 (1.09 to 1.25) in Chile (fig 2).
Description
  • Relative Risk Visualization: The figure displays the relative risk of suicide for each day of the week, using Wednesday as the baseline (reference) day. Relative risk is a measure of how much more likely an event (in this case, suicide) is on a given day compared to the reference day. A relative risk of 1 means the risk is the same as the reference day, a value greater than 1 indicates a higher risk, and a value less than 1 indicates a lower risk.
  • Confidence Intervals: The 95% confidence intervals, represented by vertical lines, provide a measure of uncertainty around the estimated relative risks. They indicate the range within which the true relative risk is likely to fall 95% of the time. If the confidence interval includes 1, it suggests that the observed difference in risk may not be statistically significant.
  • Stratification: The figure presents data stratified by region (North America, Central America, etc.), sex (male, female), and overall total population. This allows for comparisons of the day-of-the-week effect across different demographic groups and geographical areas.
Scientific Validity
  • Choice of Reference Day: The choice of Wednesday as the reference day should be justified. Is there a specific rationale for this selection, or is it arbitrary? Exploring other reference days (e.g., Sunday or Monday) and discussing the sensitivity of the results to this choice would strengthen the analysis.
  • Control for Confounders: The analysis should clearly state which confounders were controlled for in the calculation of relative risks. Factors like seasonality, holidays, and socioeconomic variables can influence suicide rates and should be accounted for to avoid spurious associations.
  • Heterogeneity: Given the substantial variation in relative risks across countries, exploring the sources of this heterogeneity is crucial. Factors like cultural differences, healthcare systems, and data quality could contribute to these variations and should be investigated.
Communication
  • Visual Clarity: The figure is somewhat cluttered, particularly with the large number of overlapping confidence intervals. Using different colors or symbols for different regions or demographic groups could improve readability. Additionally, separating the graphs for total, male, and female populations into distinct panels might enhance clarity.
  • Axis Labels and Legend: Clear and concise axis labels and a comprehensive legend are essential. The y-axis should be labeled "Relative Risk (Reference: Wednesday)" to explicitly indicate the reference day. The legend should clearly identify the colors or symbols used for each region and demographic group.
Fig. 3. Risks of suicide around Christmas with the corresponding 95% confidence...
Full Caption

Fig. 3. Risks of suicide around Christmas with the corresponding 95% confidence intervals (vertical lines).

First Reference in Text
Suicide risk marginally increased on Christmas day and for two days after in the total and male populations, but not in the female group (fig 3).
Description
  • Relative Risk and Time Period: This figure displays the relative risk of suicide in the days surrounding Christmas, specifically from two days before Christmas (Day -2) to two days after Christmas (Day +2). Christmas Day itself is labeled as Day 0. Like in Figure 2, relative risk quantifies the change in suicide risk compared to a reference period – here, it's non-holiday days that are not New Year's Day, Christmas, or other national holidays included in the study. A relative risk greater than 1 indicates an increased risk compared to the reference period, while a relative risk less than 1 indicates a decreased risk.
  • Confidence Intervals: The vertical lines represent the 95% confidence intervals for each relative risk estimate. These intervals give a range of plausible values for the true relative risk. If a confidence interval crosses the line at relative risk 1, it suggests the difference might not be statistically significant.
  • Stratification: The figure stratifies the data by total population, male population, and female population, allowing for comparisons of how suicide risk fluctuates around Christmas differently for each group. It also presents data separately for different regions and countries, similar to Figure 2.
Scientific Validity
  • Reference Period Selection: The choice of reference period (non-holiday days excluding New Year's Day and other national holidays) requires careful justification. The rationale for excluding these specific days should be clearly explained. How might the results change if a different reference period were used? A sensitivity analysis exploring alternative reference periods would strengthen the robustness of the findings.
  • "Marginal" Increase: The term "marginally increased" in the reference text is vague and needs clarification. Quantifying the increase with specific relative risk values and discussing the statistical significance of this increase is essential. If the observed increase is not statistically significant, the term "marginal" might be misleading.
  • Consideration of Confounders: The analysis should explicitly address potential confounding factors. Variables like seasonal affective disorder, changes in alcohol consumption patterns during the holiday season, and access to mental health services could influence suicide risk around Christmas. Controlling for these confounders is crucial for accurate interpretation of the results.
Communication
  • Visual Clutter: Similar to Figure 2, the figure suffers from visual clutter due to overlapping confidence intervals and multiple data points. Simplifying the presentation by focusing on key comparisons (e.g., Christmas Day vs. the reference period) or using different visual cues (colors, symbols) for different groups could improve readability.
  • Y-axis Scale: The y-axis scale should be consistent across all panels of the figure to facilitate direct comparisons between total, male, and female populations. Using different y-axis ranges can visually exaggerate or downplay differences between groups.
  • Legend Clarity: The legend should clearly define all symbols and colors used in the figure. Providing a brief explanation of the reference period within the legend would also enhance understanding.
Fig. 4. Risks of suicide around New Year's Day with the corresponding 95%...
Full Caption

Fig. 4. Risks of suicide around New Year's Day with the corresponding 95% confidence intervals (vertical lines).

First Reference in Text
Risk of suicide peaked on New Year's Day across all countries: ranging in relative risk from 0.93 (95% CI 0.75 to 1.14) in Japan to 1.93 (1.31 to 2.85) in Chile.
Description
  • Relative Risk and Time Period: Similar to Figure 3, this figure presents the relative risk of suicide around New Year's Day, spanning from two days before (Day -2) to two days after (Day +2), with New Year's Day designated as Day 0. Relative risk is calculated in comparison to a reference period of non-holiday days, excluding Christmas and other national holidays addressed in the study. A relative risk above 1 indicates a higher suicide risk compared to the reference period, while a value below 1 suggests a lower risk.
  • Confidence Intervals: The vertical lines associated with each data point represent the 95% confidence intervals. These intervals provide a range of plausible values for the true relative risk. If a confidence interval includes 1, it indicates that the observed difference in risk compared to the reference period may not be statistically significant.
  • Stratification: The data are stratified by total population, male population, and female population, allowing for comparisons of the New Year's Day effect across these groups. The figure also presents data separately for different regions and countries, facilitating cross-regional and cross-country comparisons.
Scientific Validity
  • Reference Period Definition: The reference period used for calculating relative risks (non-holiday days excluding Christmas and other national holidays) needs further clarification. The rationale for excluding these specific days should be explicitly stated. How might the results change if a different reference period were used? A sensitivity analysis exploring alternative reference periods would strengthen the analysis.
  • "Peaked on New Year's Day": The reference text states that "Risk of suicide peaked on New Year's Day across all countries." However, the figure shows that for some regions (e.g., Asia), the peak appears to occur one or two days after New Year's Day. This discrepancy between the text and the figure needs clarification. A more nuanced interpretation of the results is necessary, acknowledging the variations in peak timing across different regions.
  • Potential Confounders: The analysis should account for potential confounding factors that could influence suicide risk around New Year's Day. Factors like increased alcohol consumption, social isolation, and access to mental health services during the holiday period should be considered and controlled for to ensure accurate interpretation of the results.
Communication
  • Visual Clarity: The figure, like the previous ones, is visually cluttered due to overlapping confidence intervals and multiple data points. Using distinct visual cues (colors, symbols) for different regions or demographic groups, or separating the graphs into different panels, would improve readability.
  • Y-axis Scale: Maintaining a consistent y-axis scale across all panels of the figure is crucial for facilitating direct comparisons between different groups. Using different y-axis ranges can visually distort the magnitude of the effects.
  • Legend and Annotations: A clear and comprehensive legend is essential for understanding the figure. The legend should define all symbols, colors, and abbreviations used. Additionally, annotating the figure to highlight key findings (e.g., the timing of the peak risk in different regions) would enhance communication.
Table 1. Study locations, periods, and information on suicide events and...
Full Caption

Table 1. Study locations, periods, and information on suicide events and temperature

First Reference in Text
During the study period, the suicide rate was highest in South Korea and Japan, South Africa, and Estonia, and lowest in the Philippines, Brazil, Mexico, and Paraguay (table 1).
Description
  • Study Locations and Periods: The table provides a breakdown of the study locations, including the number of locations within each country/region and the specific time period covered by the data for that location. This information is crucial for understanding the scope and limitations of the study's geographical and temporal coverage.
  • Suicide Event Data: The table presents data on suicide counts, including the total number of suicides, the proportion of suicides among men, and the proportion of suicides among individuals aged 0-64. This breakdown allows for preliminary comparisons of suicide patterns across different demographic groups.
  • Temperature Data: The table includes information on average temperature for each location. This is likely included as temperature can be a confounding factor influencing suicide rates, and the authors likely controlled for it in their analysis. The table specifies that the average temperature is a "suicide count-weighted average," meaning it's adjusted based on the number of suicides in different locations within a country.
  • Suicide Rate: The table provides age-standardized suicide mortality rates per 100,000 people, sourced from the WHO Global Health Estimates. This standardized rate allows for comparisons between countries/regions with differing age structures.
Scientific Validity
  • Missing Data: The table contains several "NA" values, indicating missing data, particularly for certain demographic breakdowns (e.g., proportion of male suicides in some countries). The authors should explain the reasons for these missing data points and discuss any potential implications for the analysis. If possible, imputing missing values or using alternative data sources should be considered.
  • Study Period Variations: The study periods vary considerably across different locations, ranging from a few years to several decades. This variation in time coverage could introduce bias and affect the comparability of results across locations. The authors should address this limitation and discuss how they handled these temporal variations in their analysis.
  • Justification for Variables: The rationale for including specific variables in the table (e.g., proportion of male suicides, proportion of suicides aged 0-64) should be clearly stated. How do these variables contribute to the research questions addressed in the study?
Communication
  • Table Organization and Clarity: The table could be organized more clearly. Grouping countries by region (as done in Figures 2-5) would improve readability and facilitate comparisons. Additionally, using clearer headings and subheadings would enhance the table's overall structure.
  • Explanation of Abbreviations: The table uses abbreviations (e.g., NA, WHO) without providing explanations. A footnote or a separate section defining these abbreviations is essential for clarity and accessibility.
  • Contextualization of Suicide Rates: While the table provides suicide rates, it lacks context. Including information on the overall global average suicide rate or the rates for specific age groups would provide a better frame of reference for interpreting the country-specific rates.

Discussion

Overview

This discussion section interprets the results of the study on suicide risk and its temporal variations, connecting them to existing theories and literature. The study found that Mondays had the highest suicide risk during weekdays, a finding consistent with the "broken-promise effect theory," which suggests that the start of the week can lead to distress and unmet expectations. The increased risk on New Year's Day is also discussed in relation to this theory and potential increased alcohol consumption. The study's findings on weekend suicide risk were mixed, with some countries showing increased risk and others decreased risk, potentially related to cultural factors like alcohol consumption patterns and working conditions. The discussion also addresses the observed sex differences, with men being more susceptible to temporal variations, possibly due to differences in social capital and economic activity. The limitations of the study, such as the use of aggregated data and potential underreporting in some countries, are acknowledged, along with the strengths, including the large sample size and robust statistical methods. The discussion concludes by emphasizing the implications of the findings for suicide prevention strategies.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Fig. 5. Risks of suicide around other national holidays (except Christmas and...
Full Caption

Fig. 5. Risks of suicide around other national holidays (except Christmas and New Year's Day) and neighbouring days with the corresponding 95% confidence intervals (vertical lines).

First Reference in Text
We noted that suicide risks decreased on Christmas and other national holidays among men in North American and European countries, although the statistical significance was weak.
Description
  • Relative Risk and Time Period: This figure, similar in structure to Figures 3 and 4, shows the relative risk of suicide around other national holidays (excluding Christmas and New Year's Day). The time period spans from two days before the holiday (Day -2) to two days after (Day +2), with the holiday itself marked as Day 0. Relative risk quantifies the change in suicide risk compared to a reference period of non-holiday days. A relative risk greater than 1 signifies an increased risk compared to the reference period, while a value less than 1 indicates a decreased risk.
  • Confidence Intervals: The vertical lines represent 95% confidence intervals for each relative risk estimate. These intervals provide a range of plausible values for the true relative risk. If a confidence interval crosses 1, it suggests that the observed change in risk might not be statistically significant.
  • Stratification and Grouping: The figure stratifies the data by total population, male population, and female population, allowing for comparisons across these groups. Similar to previous figures, data are presented separately for different regions and countries. However, unlike the focus on single holidays in Figures 3 and 4, this figure aggregates the effects of multiple national holidays, excluding Christmas and New Year's Day.
Scientific Validity
  • Aggregation of Holidays: Aggregating multiple holidays into a single analysis can obscure important variations in the effects of individual holidays. While it provides a broad overview, it might mask opposing trends associated with specific holidays. A more granular analysis examining individual holidays would be more informative and scientifically robust.
  • Reference Period Definition: The definition of the reference period (non-holiday days) needs further clarification. Are weekends included in the reference period? How might the inclusion or exclusion of weekends affect the results? A clear and explicit definition of the reference period is essential for accurate interpretation.
  • Justification for Regional Grouping: The rationale for grouping countries into specific regions (North America, Europe, etc.) should be justified. Are these groupings based on cultural similarities, geographical proximity, or other factors? Exploring alternative groupings or analyzing countries individually could reveal more nuanced patterns.
Communication
  • Visual Clarity and Overlapping Data: The figure suffers from visual clutter due to overlapping confidence intervals and multiple data points, making it difficult to discern clear patterns. Using different visual cues (colors, symbols) for different regions or demographic groups, or separating the graphs into distinct panels, would significantly improve readability.
  • Y-axis Scale Consistency: Maintaining a consistent y-axis scale across all panels of the figure is crucial for facilitating comparisons between different groups. Using different y-axis ranges can visually distort the magnitude of the effects and hinder accurate interpretation.
  • Caption and Reference Text Alignment: The reference text discusses decreased suicide risk on Christmas, while the figure caption and the figure itself focus on other national holidays. This misalignment creates confusion. The reference text should be revised to accurately reflect the content of the figure, or a separate figure should be included to address the Christmas effect specifically.

Conclusion

Overview

This conclusion summarizes the study's findings on the association between suicide risk, day of the week, and national holidays. Using data on 1.7 million suicide cases from multiple countries, the study found that Mondays had the highest suicide risk among weekdays. The impact of weekends on suicide risk varied across countries. New Year's Day and the following days were consistently associated with increased suicide risk. The conclusion highlights the contribution of these findings to national and global suicide prevention strategies, particularly regarding resource allocation and mobilization.

Key Aspects

Strengths

Suggestions for Improvement

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