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

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

• Objectives: The study aimed to determine how the day of the week and national holidays influence short-term changes in suicide risk across different countries. This involves looking at daily patterns and specific holiday periods to see if there are noticeable increases or decreases in suicide rates.
• Design: The study used a multicountry, two-stage, time series design. A time series design analyzes data collected over time to identify trends and patterns. The "two-stage" aspect likely refers to a statistical method involving analyzing data within each location first, and then combining these results across locations.
• Setting: The study used data from the Multi-city Multi-country Collaborative Research Network database, covering 740 locations in 26 countries and territories between 1971 and 2019. This broad geographical and temporal scope allows for comparisons across diverse cultural contexts and time periods.
• Participants: The study included all registered suicides within the specified locations during the study period, totaling 1,701,286 cases. This large sample size provides substantial statistical power to detect even small effects.
• Main Outcome Measures: The primary outcome measured was daily suicide mortality. This refers to the number of deaths by suicide occurring each day.
• Results: Mondays showed the highest suicide risk among weekdays. Weekends had lower risk in many countries but higher risk in some. New Year's Day was linked to increased risk, while other holidays showed varied results depending on the region. Relative risk, a measure of how much more likely an event is in one group compared to another, was used to quantify these associations.
• Conclusions: The study concluded that suicide risk varies with the day of the week and holidays, with Mondays and New Year's Day being high-risk periods. The findings highlight the need for targeted suicide prevention strategies that consider these temporal patterns.

Strengths

• Comprehensive Data

  The study uses a large dataset spanning numerous countries and a long time period, making the findings more generalizable and robust.

  "Data from 740 locations in 26 countries and territories, with overlapping periods between 1971 and 2019" (Page 1)
• Clear Methodology

  The abstract clearly states the study design (multicountry, two-stage, time series) and the outcome measure (daily suicide mortality), allowing readers to quickly understand the study's approach.

  "Multicountry, two stage, time series design." (Page 1)

Suggestions for Improvement

• Quantify "Most Countries"

  Explanation: The abstract frequently uses the phrase "most countries" without specifying the exact number or proportion. This lacks precision and makes it difficult to assess the true scope of the findings. because Precise quantification strengthens the scientific rigor of the claims.

  Implementation: Replace "most countries" with specific numbers or percentages. For example, instead of "Mondays had peak suicide risk during weekdays across all countries", state "Mondays had peak suicide risk during weekdays in X out of Y countries studied (Z%)."

• Elaborate on Two-Stage Design

  Explanation: While the abstract mentions a "two-stage" time series design, it doesn't explain what the two stages are. This makes it difficult for readers to fully grasp the methodological approach. because Providing more detail on the two stages would enhance the transparency and reproducibility of the study.

  Implementation: Briefly describe the two stages of the analysis. For example, indicate whether the first stage involved analyzing data within each location separately and the second stage combined these results.

• Clarify "Mixed Results"

  Explanation: The abstract mentions "mixed results" for holiday effects but doesn't elaborate on the nature of these mixed results. because Specifying the inconsistencies observed in previous research provides valuable context for the current study's findings.

  Implementation: Briefly describe the nature of the mixed results. For example, explain whether previous studies found conflicting associations between holidays and suicide risk (e.g., some showing increased risk, others showing decreased risk, and some showing no association).

• Expand on Heterogeneity

  Explanation: The abstract notes heterogeneity in weekend and holiday effects but doesn't provide details on the patterns of this heterogeneity. because Describing the observed patterns of heterogeneity across countries or regions would enrich the findings and provide insights for targeted interventions.

  Implementation: Provide a concise description of the heterogeneity. For example, mention specific regions or country groupings that exhibited different patterns of suicide risk on weekends and holidays.

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

• Global Public Health Concern: Suicide is a major global health problem, causing over 700,000 deaths in 2019, exceeding mortality from diseases like malaria, HIV/AIDS, and breast cancer. It's a leading cause of premature death, especially among young people (15-29 years old).
• Sociological Context: Since Durkheim's work, sociological theories have linked suicide to social factors and individual characteristics. These factors can contribute to both short-term and long-term suicidal thoughts and behaviors.
• Time-Varying Factors: Suicide rates are influenced by time-related factors. Historically, suicides peak in spring and early summer. Shorter-term variations exist related to the day of the week and holidays.
• Previous Research: Studies have shown a peak in suicide risk on Mondays and a decrease during weekends. However, the impact of holidays on suicide risk has yielded mixed results in prior research.

Strengths

• Contextual Background

  The introduction effectively establishes the context of the study by presenting suicide as a major public health issue and highlighting its sociological dimensions. This provides a solid foundation for the research question.

  "Suicide is an important global public health concern." (Page 1)
• Rationale for Research

  The introduction clearly articulates the rationale for the study by summarizing existing research on temporal variations in suicide risk and pointing out the inconsistencies in findings related to holiday effects. This justifies the need for further investigation.

  "However, previous results for holiday effects regarding suicide were mixed." (Page 1)

Suggestions for Improvement

• Expand on Social Factors

  While the introduction mentions social factors, it would benefit from elaborating on the specific social factors relevant to the study. because This would provide a more nuanced understanding of the social context of suicide and its potential link to temporal variations.

  Implementation: Include examples of social factors, such as social integration, economic conditions, or cultural norms, that might influence suicide risk and how these factors might interact with daily or weekly cycles.

• Clarify "Mixed Results"

  The introduction mentions "mixed results" regarding holiday effects but doesn't elaborate on the nature of these discrepancies. because Providing more detail on the inconsistencies observed in previous research would strengthen the rationale for the current study.

  Implementation: Briefly describe the nature of the mixed results. For example, explain whether previous studies found conflicting associations between holidays and suicide risk (e.g., some showing increased risk, others showing decreased risk, and some showing no association).

• Strengthen Connection to Abstract

  The introduction could better connect to the abstract by explicitly stating the study's objectives. because This would create a smoother transition and reinforce the research questions being addressed.

  Implementation: Include a concise statement of the study's objectives, mirroring the objectives outlined in the abstract. This will ensure alignment between the two sections and provide a clear focus for the reader.

• Elaborate on Durkheim's Work

  The introduction references Durkheim's work but doesn't explain its specific relevance to the study. because Briefly outlining Durkheim's key ideas about social integration and suicide would enhance the sociological context of the research.

  Implementation: Briefly explain how Durkheim's concepts, such as social integration and anomie, relate to the study's focus on temporal variations in suicide risk. This could involve discussing how social rhythms and routines might influence suicide risk.

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

• Data Collection: Suicide data, including daily counts and mean temperatures, were collected for 740 locations across 26 countries from the Multi-country Multi-city Collaborative Research Network database. Holiday information was gathered from a calendar website. National holidays were categorized as Christmas, New Year's Day, or other national holidays. Daily suicide counts were aggregated by country, and temperature data was weighted by suicide counts to account for population density.
• Statistical Analysis - Stage 1: Quasi-Poisson regression models were used to analyze the association between daily suicide counts and day of the week/holiday indicators. The models included lagged variables for a five-day window around holidays to capture potential anticipatory or delayed effects. Seasonal trends, long-term trends, and temperature were controlled for using natural cubic splines and cross-basis functions, respectively. A natural cubic spline is a smooth curve used to model non-linear relationships, while a cross-basis function models the interaction between two variables.
• Statistical Analysis - Stage 2: Meta-regression with a random intercept was used to combine the country-specific estimates from the first stage. Regional indicators were included as meta-predictors to account for heterogeneity between countries. A random intercept allows each country to have its own baseline level of suicide risk.
• Subgroup and Sensitivity Analyses: Subpopulation analyses were conducted by sex and age group (0-64 and 65+). A simulation study was performed to assess potential bias from misclassification of suicide registrations on weekends. A sensitivity analysis was conducted using more recent US mortality data (1999-2019).

Strengths

• Detailed Data Description

  The methods section provides a comprehensive description of the data sources, including the specific database used and how holidays were categorized. This enhances the transparency and reproducibility of the study.

  "We collected suicide data for 740 locations in 26 countries from the database of the Multi-country Multi-city Collaborative Research Network (https://mccstudy.lshtm.ac.uk/)." (Page 2)
• Robust Statistical Approach

  The two-stage analysis, using quasi-Poisson regression and meta-regression, is a statistically sound approach for analyzing this type of data. The inclusion of lagged variables and adjustment for confounders strengthens the analysis.

  "We performed a two stage analysis. In the first stage, we fitted a quasi-Poisson regression model..." (Page 2)

Suggestions for Improvement

• Clarify Holiday Selection Rationale

  The rationale for selecting only Christmas and New Year's Day as universal holidays, while other holidays were categorized differently, could be further clarified. because The current explanation regarding heterogeneity is somewhat vague and doesn't fully justify the chosen approach. A more detailed justification, perhaps referencing cultural significance or data availability, would strengthen the methodological rigor.

  Implementation: Expand on the rationale for focusing on Christmas and New Year's Day. Discuss the criteria used to determine which holidays were considered "universal" and provide a more detailed explanation of the challenges posed by heterogeneous holidays across countries. Consider discussing the potential impact of including or excluding specific holidays on the results.

• Justify Model Specifications

  The justification for specific model choices, such as the degrees of freedom for the natural cubic spline and the knots placement for the cross-basis function, could be strengthened. because Providing a more detailed rationale for these choices, perhaps referencing prior literature or statistical tests, would enhance the transparency and reproducibility of the analysis.

  Implementation: Provide a more detailed explanation for the chosen degrees of freedom for the time spline and the placement of knots for the temperature cross-basis function. Reference any statistical tests or prior research that informed these decisions. Consider including a sensitivity analysis to assess the impact of different model specifications on the results.

• Elaborate on Misclassification Bias Assessment

  While the methods section mentions a simulation study to address potential weekend misclassification bias, it lacks details about the simulation's design and implementation. because Providing more information about the simulation parameters and assumptions would enhance the transparency and credibility of the bias assessment.

  Implementation: Describe the simulation study in more detail, including the specific scenarios considered, the assumptions made about misclassification rates, and the methods used to generate simulated data. Report the results of the simulation study and discuss its implications for the interpretation of the main findings.

• Clarify Meta-Regression Random Effects

  The methods section mentions using a random intercept in the meta-regression but doesn't specify whether any other random effects were included. because Clearly stating whether only the intercept was random or if other coefficients (e.g., for day of the week) were also treated as random effects would improve the clarity of the statistical model.

  Implementation: Explicitly state whether only the intercept was modeled as a random effect in the meta-regression or if any slope coefficients were also treated as random. Justify the choice of random effects structure and discuss its implications for the interpretation of the results.

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

• Day of the Week Effect: Mondays consistently showed the highest suicide risk among weekdays across the studied countries. This effect was quantified using relative risks, comparing Monday's risk to Wednesday (the reference day).
• Weekend Effect: Suicide risk on weekends varied geographically. Some regions, like South/Central America, South Africa, and Finland, experienced higher weekend risks, while others, like North America, Europe, and Asia, showed lower risks.
• Christmas Effect: The impact of Christmas on suicide risk was marginal and inconsistent across regions. Some areas showed a slight increase in risk around Christmas, while others showed a decrease.
• New Year's Day Effect: New Year's Day was associated with a peak in suicide risk in most countries. This effect was more pronounced in men.
• Other National Holidays Effect: Other national holidays generally showed a pattern of decreased suicide risk before the holiday and increased risk afterward. This pattern was more evident in men and in European and Asian countries.
• Meta-Regression Results: Meta-regression analysis, a technique for analyzing combined results from multiple studies, revealed that regional differences largely explained the heterogeneity (variability) observed in the effects of days of the week and holidays on suicide risk. The I2 statistic, a measure of heterogeneity, decreased substantially after accounting for regional differences.
• Misclassification Bias Assessment: A simulation study, a method for artificially generating data to test the impact of different scenarios, was conducted to assess the potential bias introduced by misclassification of suicide registrations on weekends. The results suggest that the main findings are robust to misclassification rates of up to 10%.

Strengths

• Comprehensive Presentation of Results

  The results section provides a thorough overview of the findings, covering the effects of days of the week, various holidays, and regional variations. The use of relative risks and confidence intervals allows for clear interpretation of the magnitude and precision of the effects.

  "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)." (Page 4)
• Effective Use of Visualizations

  The figures effectively illustrate the key findings, particularly the variations in suicide risk across days of the week and holidays. Figure 2, for example, clearly shows the elevated risk on Mondays and the varying weekend patterns across different locations.

  "Fig 2 | Risks of suicide by the day of the week with the corresponding 95% confidence intervals (vertical lines)." (Page 5)

Suggestions for Improvement

• Clarify Heterogeneity Discussion

  While the results mention heterogeneity and its explanation by regional differences, the discussion could be more specific. because Simply stating that regional differences explain heterogeneity without elaborating on the specific patterns of variation across regions leaves the reader with an incomplete understanding of the findings.

  Implementation: Provide more detail on the patterns of heterogeneity. For example, describe which regions showed higher or lower effects for specific days or holidays. Consider grouping countries with similar patterns and discussing potential cultural or societal factors contributing to these differences.

• Elaborate on Simulation Study Results

  The results of the simulation study regarding misclassification bias are mentioned briefly but lack detail. because Providing more information about the simulation's parameters, assumptions, and specific findings would strengthen the robustness of the conclusions.

  Implementation: Expand on the simulation study results. Describe the range of misclassification rates tested, the specific impact on the Monday effect, and the implications for the overall interpretation of the findings. Consider including a table or figure summarizing the simulation results.

• Provide More Context for Figure 1

  Figure 1 presents the geographical locations and Monday suicide percentages, but its purpose and connection to the overall analysis are not explicitly stated. because Clarifying the figure's role in the study would improve its interpretability and relevance.

  Implementation: Add a more descriptive caption to Figure 1, explaining its purpose and how it relates to the subsequent analyses. For example, explain whether the Monday percentages are descriptive statistics for each location or if they were used in the statistical models. Discuss any notable patterns observed in the geographical distribution of Monday suicide percentages.

• Discuss Limitations of Subpopulation Analysis

  The results mention the inability to assess holiday associations in older individuals due to insufficient data. because Acknowledging this limitation explicitly and discussing its potential impact on the generalizability of the findings is crucial for transparency.

  Implementation: Add a paragraph discussing the limitations of the subpopulation analysis, specifically the inability to assess holiday effects in older adults. Explain the implications of this limitation and suggest potential future research directions to address this gap.

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

• Interpretation of Monday and New Year's Day Effects: The higher suicide risk observed on Mondays and New Year's Day is consistent with the "broken-promise effect theory," which posits that the start of the week or year can lead to increased stress and unmet expectations, potentially triggering suicidal behavior. Increased alcohol consumption around New Year's Day is also suggested as a contributing factor.
• Explanation of Mixed Weekend Effects: The study found varying weekend effects on suicide risk across different countries. This variation is attributed to potential cultural differences in alcohol consumption patterns, working conditions, and other factors like religion. Countries with higher weekend alcohol consumption tend to show increased suicide risk during weekends.
• Discussion of Sex Differences: The study observed that men are more vulnerable to the temporal variations in suicide risk than women. This difference is potentially linked to disparities in social capital, with men being more susceptible to isolation and stress, and to higher economic activity among men, making them more vulnerable to the pressures of the work week and holidays.
• Limitations and Strengths: The discussion acknowledges limitations such as the use of aggregated data, preventing analysis at the location level, limited data availability for some countries, potential underreporting of suicides in less industrialized nations, and the inability to address the impact of different types of holidays. The strengths highlighted include the large sample size, robust statistical methods, and the inclusion of multiple countries, allowing for the identification of regional variations in suicide patterns.
• Implications for Suicide Prevention: The findings highlight the importance of developing targeted suicide prevention strategies that consider the temporal variations in risk. The study suggests prioritizing interventions around Mondays, New Year's Day, and other high-risk periods, and tailoring these interventions to specific countries and populations based on their unique cultural and social contexts.

Strengths

• Comprehensive Discussion of Findings

  The discussion effectively integrates the study's findings with existing literature and theories, providing a nuanced interpretation of the observed patterns. The discussion of the broken-promise effect and the influence of alcohol consumption adds depth to the analysis.

  "Our findings on Mondays and New Year’s Day were broadly consistent with previous studies that could be explained by the “broken-promise effect theory”." (Page 7)
• Thorough Acknowledgment of Limitations

  The discussion transparently addresses the study's limitations, including data aggregation issues, limited data availability, and potential underreporting. This strengthens the credibility of the research by acknowledging potential biases and areas for future investigation.

  "This study has several limitations. Firstly, we were unable to use data at the location level because of a problem with model convergence." (Page 9)

Suggestions for Improvement

• Deepen Discussion of Regional Heterogeneity

  While the discussion mentions regional differences, it could benefit from a more in-depth exploration of these variations. because Simply attributing differences to "cultural factors" is too general and doesn't provide actionable insights. A more nuanced discussion of specific regional patterns and potential contributing factors would enhance the practical implications of the study.

  Implementation: Expand on the discussion of regional heterogeneity by providing specific examples of how suicide risk patterns vary across different regions. Discuss potential cultural, societal, or economic factors that might contribute to these variations. Consider incorporating additional literature or data to support these explanations.

• Strengthen Connection to Prior Research on Holidays

  The discussion could be strengthened by more explicitly connecting the findings on holiday effects to the mixed results reported in the introduction. because This would create a stronger narrative flow and demonstrate how the study addresses the existing gaps in knowledge. The current discussion mentions the heterogeneity of holiday effects but doesn't directly link it back to the prior inconsistencies.

  Implementation: Reiterate the mixed findings on holiday effects from the introduction and explicitly state how the current study's findings contribute to resolving these inconsistencies. Discuss whether the observed heterogeneity aligns with or contradicts previous research and explain any discrepancies.

• Elaborate on Implications for Targeted Interventions

  The discussion mentions targeted interventions but could benefit from more specific recommendations. because General statements about prioritizing interventions are not as impactful as concrete suggestions for specific strategies. Providing examples of targeted interventions would enhance the practical value of the study for policymakers and practitioners.

  Implementation: Provide specific examples of targeted interventions that could be implemented to address the identified high-risk periods. These could include public awareness campaigns, increased mental health service availability, or specific outreach programs targeting vulnerable populations. Tailor these suggestions to the different temporal patterns observed, such as increased staffing or support services around Mondays and holidays.

• Discuss the Potential Impact of COVID-19

  Given the study's timeframe (up to 2019), the potential impact of the COVID-19 pandemic on suicide rates should be addressed. because The pandemic has significantly altered social and economic conditions, which could influence suicide risk and its temporal patterns. Failing to acknowledge this potential confounding factor weakens the generalizability of the findings to the present day.

  Implementation: Add a paragraph discussing the potential impact of the COVID-19 pandemic on suicide rates and their temporal variations. Acknowledge that the study's data predates the pandemic and that future research is needed to assess its influence. Suggest potential research directions to investigate the pandemic's impact on the relationship between suicide risk and temporal factors.

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

• Monday Peak: Across all countries studied, suicide risk was highest on Mondays compared to other weekdays. This aligns with previous research and suggests a consistent weekly pattern in suicide risk.
• Weekend Variation: The effect of weekends on suicide risk was not uniform across all countries. Some countries showed a decrease in risk on weekends, while others showed an increase or no significant change. This suggests that cultural or societal factors may influence weekend suicide risk.
• New Year's Risk: New Year's Day and the days immediately following were associated with an elevated suicide risk across most countries. This points to a specific high-risk period during the year that warrants attention in suicide prevention efforts.
• Implications for Prevention: The study's findings have implications for suicide prevention strategies at both national and global levels. Understanding the temporal patterns of suicide risk can inform resource allocation and the development of more targeted interventions.

Strengths

• Concise Summary

  The conclusion effectively summarizes the key findings of the study in a clear and concise manner, highlighting the most important results regarding day of the week and holiday effects.

  "In conclusion, this study provides evidence regarding the association of the day of the week and national holidays with suicide risk using multicountry data with 1.7 million suicide cases." (Page 9)

Suggestions for Improvement

• Expand on Heterogeneity

  The conclusion briefly mentions heterogeneity in weekend effects but could benefit from a more detailed discussion of this variation. because Understanding the specific patterns of heterogeneity across countries is crucial for developing tailored prevention strategies. Simply noting the variation without further elaboration limits the practical implications of the findings.

  Implementation: Provide more specific examples of how weekend effects differed across countries or regions. Discuss potential factors contributing to this heterogeneity, such as cultural norms, working conditions, or access to mental health services. Consider referencing relevant literature or data to support these explanations.

• Strengthen Connection to Discussion

  The conclusion could be strengthened by more explicitly connecting the findings to the interpretations and implications discussed in the previous section. because This would create a stronger sense of closure and reinforce the study's contribution to the field. Currently, the conclusion summarizes the results but doesn't fully integrate them with the broader discussion of their significance.

  Implementation: Briefly reiterate the key interpretations from the discussion section, such as the broken-promise effect for Mondays and New Year's Day, and link them directly to the summarized findings in the conclusion. This will create a more cohesive narrative and emphasize the study's contribution to understanding the underlying mechanisms of temporal variations in suicide risk.

• Elaborate on Future Research Directions

  The conclusion could benefit from a brief mention of future research directions. because Identifying areas for further investigation strengthens the study's contribution to the field and provides a roadmap for future research efforts. The current conclusion focuses solely on the implications of the findings but doesn't suggest avenues for future studies.

  Implementation: Add a sentence or two suggesting potential future research directions, such as investigating the impact of specific holidays in more detail, exploring the underlying mechanisms of weekend heterogeneity, or examining the effectiveness of targeted interventions during high-risk periods. This will enhance the conclusion's impact and encourage further research in this important area.

• Reiterate the Importance of the Research

  While the conclusion mentions implications for suicide prevention, it could be strengthened by reiterating the overall importance of the research. because Emphasizing the study's contribution to a critical public health issue enhances its impact and underscores the need for continued research and action. The current conclusion focuses primarily on summarizing the findings but could benefit from a stronger concluding statement.

  Implementation: Add a concluding sentence that emphasizes the importance of the research in addressing the global burden of suicide. This could involve highlighting the study's contribution to understanding temporal patterns of suicide risk and its implications for developing more effective prevention strategies. This will leave the reader with a stronger sense of the study's significance and its potential to save lives.