Coffee and tea consumption and the risk of head and neck cancer: An updated pooled analysis in the International Head and Neck Cancer Epidemiology Consortium

Table of Contents

Overall Summary

Study Background and Main Findings

This pooled analysis of 14 case-control studies from the INHANCE consortium, involving 9548 HNC cases and 15,783 controls, found that consuming >4 cups of caffeinated coffee daily was associated with a statistically significant decreased risk of HNC overall (OR: 0.83; 95% CI: 0.69-1.00), oral cavity cancer (OR: 0.70; 95% CI: 0.55-0.89), and oropharyngeal cancer (OR: 0.78; 95% CI: 0.61-0.99). Drinking 3-4 cups daily was inversely associated with hypopharyngeal cancer (OR: 0.59; 95% CI: 0.39-0.91). Decaffeinated coffee consumption showed an inverse association with oral cavity cancer risk (>0 to <1 cup daily: OR: 0.69; 95% CI: 0.55-0.87). Tea consumption of >0 to ≤1 cup daily was inversely associated with overall HNC (OR: 0.85; 95% CI: 0.74-0.97) and hypopharyngeal cancer (OR: 0.71; 95% CI: 0.59-0.87), but >1 cup daily was associated with an increased risk of laryngeal cancer (OR: 1.38; 95% CI: 1.09-1.74).

Research Impact and Future Directions

The study provides evidence for an association between coffee and tea consumption and HNC risk, but the relationship is complex and varies by subsite and consumption level. While higher caffeinated coffee consumption is associated with a reduced risk of HNC overall, oral cavity, and oropharyngeal cancers, the findings for tea are mixed, with a potential increased risk of laryngeal cancer at higher consumption levels. The study clearly distinguishes between correlation and causation, acknowledging that the observed associations do not prove a causal relationship.

The findings have potential practical utility in informing future research and may contribute to developing primary prevention strategies. However, the study's limitations, particularly the predominantly North American and European populations, restrict the generalizability of the results. The findings are placed within the context of existing literature, acknowledging inconsistencies and highlighting the need for further research in diverse populations.

While the study suggests a potential protective effect of coffee, particularly at higher consumption levels, it is premature to make definitive public health recommendations. The increased risk of laryngeal cancer associated with higher tea consumption warrants caution and further investigation. Key uncertainties include the role of different coffee and tea types, preparation methods, and the influence of unmeasured confounders.

Critical unanswered questions remain regarding the specific mechanisms underlying the observed associations and the optimal consumption levels for potential risk reduction. The study's limitations, including potential recall bias and the limited generalizability, do not fundamentally undermine the main conclusions, but they highlight the need for further research. Future studies should focus on diverse populations, explore dose-response relationships in more detail, and investigate the role of specific coffee and tea constituents.

Critical Analysis and Recommendations

Large Sample Size and Pooled Analysis (written-content)
The study utilizes a large dataset from the INHANCE consortium, enhancing statistical power and generalizability of the findings to broader populations.
Section: Abstract
Comprehensive Adjustment for Confounders (written-content)
The analysis adjusts for a wide range of sociodemographic and lifestyle factors, increasing the validity of the findings by minimizing potential confounding effects.
Section: Abstract
Detailed Subsite Analysis (written-content)
The study examines associations for specific HNC subsites, providing a more nuanced understanding of the relationship between coffee/tea consumption and different cancer types.
Section: Abstract
Rigorous Statistical Methods (written-content)
The use of two-stage random-effects logistic regression and heterogeneity testing demonstrates a robust statistical approach appropriate for pooled data, increasing confidence in the results.
Section: Materials and Methods
Clarify Data Harmonization Procedures (written-content)
The Methods section lacks specific details about the data harmonization process across the 14 different studies. Providing more information on how comparability was ensured would significantly enhance the transparency and reproducibility of the study.
Section: Materials and Methods
Provide More Context for Null Findings (written-content)
The Results section reports that caffeinated coffee drinking status was not associated with the risk of HNC overall or its subsites. Adding a brief discussion of potential reasons for the lack of association at lower consumption levels would strengthen the paper by acknowledging the complexity of the relationship.
Section: Results
Discuss Potential Mechanisms for Laryngeal Cancer Finding (written-content)
The Results section reports an increased risk of laryngeal cancer with higher tea consumption but does not offer any potential explanations. Briefly discussing potential mechanisms would provide valuable context for interpreting this result and enhance the paper's depth.
Section: Results
Further Explore Laryngeal Cancer Finding (written-content)
The Discussion briefly mentions a potential mechanism for the increased risk of laryngeal cancer with higher tea consumption. Expanding on this finding, including a discussion of alternative explanations and potential confounding factors, would demonstrate a more in-depth consideration of this unexpected result.
Section: Discussion

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Materials and Methods

Key Aspects

Strengths

Suggestions for Improvement

Statistical analysis

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

TABLE 1 Characteristics of head and neck cancer cases and controls of select...
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TABLE 1 Characteristics of head and neck cancer cases and controls of select INHANCE consortium studies.

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TABLE 1 Characteristics of head and neck cancer cases and controls of select INHANCE consortium studies.
First Reference in Text
Of the cases, 92.9% were non-Hispanic White individuals and 79.3% were males (Table 1).
Description
  • Purpose of the table: This table summarizes the demographic and lifestyle characteristics of two groups of people: those who have head and neck cancer (called "cases") and those who do not have it (called "controls"). The purpose is to show the differences and similarities between these two groups to help understand what factors might be associated with the development of head and neck cancer. INHANCE consortium here refers to a specific international research group focused on head and neck cancer epidemiology.
  • Structure of the table: The table is organized with characteristics listed on the left-hand side, such as age, sex, and smoking habits. Each characteristic is further broken down into categories. For instance, "age" is split into different age ranges. For each category, the table shows the number of individuals and their percentage within the "cases" group and the "controls" group. For instance, under the characteristic "sex", there are categories for "Female" and "Male". If there were 100 people in the "cases" group and 70 were male, it would show "70" as the number and "70%" as the percentage for males in the cases group.
  • Types of data presented: The table presents both categorical and continuous data. Categorical data are divided into groups or categories. Examples include "sex" which has categories "Male" and "Female", or "Education level" which might have categories like "No education" and "Less than junior high school". Continuous data, on the other hand, are numerical and represent measurements. For example, "Duration of cigarette smoking (years)" is a continuous variable and is summarized by the mean, which is the average value, and the standard deviation, which is a measure of how spread out the values are around the mean. For instance, if the mean duration of cigarette smoking is 30 years with a standard deviation of 5 years, it means most people in that group smoked for around 25 to 35 years.
  • Statistical significance: At the bottom of the table, it is noted that a p-value was calculated for all characteristics using a two-sided chi-squared test. A p-value is a statistical measure that helps determine if the observed differences between the "cases" and "controls" groups are likely due to chance or if they represent a real association. In this context, a p-value less than 0.05 is considered statistically significant. This means that if the p-value for a particular characteristic is less than 0.05, the difference in that characteristic between the two groups is unlikely to be due to random chance and could be a meaningful finding.
Scientific Validity
  • Relevance of variables: The variables chosen for inclusion in Table 1, such as age, sex, race/ethnicity, education, BMI, smoking status, alcohol consumption, and fruit/vegetable intake, are well-established risk factors or potential confounders for head and neck cancer. Their inclusion aligns with standard epidemiological practice for investigating cancer etiology.
  • Appropriateness of statistical test: The use of the chi-squared test for categorical variables and t-tests for continuous variables is appropriate for comparing characteristics between the case and control groups. The note indicating a significance level of p < .05 is standard for determining statistical significance.
  • Data source and pooling: The data is derived from the INHANCE consortium, which combines data from multiple studies. This pooling approach can increase statistical power and generalizability. However, potential heterogeneity between studies should be considered and addressed in the analysis, as is done in the methods section.
  • Limitations in the table: The table provides a good overview of the characteristics, but it does not present any measure of association between each characteristic and head and neck cancer risk, like an odds ratio. This is not a limitation as such, because it is not the purpose of the table. It is also unclear whether the characteristics are adjusted for each other, although this is mentioned to be done in the methods section.
Communication
  • Clarity of presentation: The table is well-organized and easy to read. The use of bold text for subheadings and the clear labeling of columns and rows enhance readability. The presentation of data as both counts and percentages allows for easy interpretation.
  • Use of abbreviations: Abbreviations like BMI and INHANCE are defined in the footnote, which is helpful for readers who may not be familiar with these terms. However, providing a brief definition of the chi-squared test and its purpose would be good, but might be considered too general by the authors.
  • Completeness of information: The table provides a comprehensive overview of the demographic and lifestyle characteristics of the study population. However, it might be beneficial to include a brief description of the study population size in the caption or the table itself, although it is mentioned in the text.
  • Potential improvements: While the table is generally well-presented, adding a column showing the p-values for each characteristic would make it even more informative. This would allow readers to quickly assess the statistical significance of the differences between the case and control groups for each variable.
TABLE 2 The association with HNC by anatomical subsite for coffee drinking...
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TABLE 2 The association with HNC by anatomical subsite for coffee drinking status and daily coffee consumption among HNC cases and controls from select INHANCE consortium studies.

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TABLE 2 The association with HNC by anatomical subsite for coffee drinking status and daily coffee consumption among HNC cases and controls from select INHANCE consortium studies.
First Reference in Text
Caffeinated coffee drinking status was not associated with the risk of HNC, and its subsites compared to non-coffee drinkers (Table 2).
Description
  • Overall purpose: This table explores whether there's a link between drinking coffee and the risk of getting head and neck cancer (HNC). It looks at this connection in detail by considering different types of coffee (caffeinated and decaffeinated) and different locations of cancer within the head and neck region (e.g., oral cavity, oropharynx).
  • Structure and organization: The table is divided into sections based on the type of coffee (caffeinated or decaffeinated) and the specific location of the cancer. For each section, it compares people who drink coffee ("cases") to those who don't drink coffee ("controls"). It then presents the results for different levels of coffee consumption, such as "non-coffee drinker," ">0 to <3 cups," "3-4 cups," and ">4 cups" for caffeinated coffee. It's like sorting people into groups based on how much coffee they drink and then checking if one group has more cancer cases than the others.
  • Data presentation and key metrics: For each level of coffee consumption, the table shows the number of controls (people without cancer) and cases (people with cancer). It also provides a number called "OR" (Odds Ratio) along with a range called "95% CI" (95% Confidence Interval). The OR is a measure of association that tells us if drinking coffee is linked to a higher or lower risk of cancer. An OR of 1 means there's no association. An OR greater than 1 suggests an increased risk, while an OR less than 1 suggests a decreased risk. The 95% CI is a range of values that likely contains the true OR. Think of it like a margin of error – the wider the range, the less certain we are about the true OR. For instance, an OR of 0.83 with a 95% CI of 0.69 to 1.00 means we are 95% confident that the true OR is between 0.69 and 1.00.
  • Statistical measures: The table also includes "p for heterogeneity" and "p for trend." "P for heterogeneity" helps us understand if the results are consistent across different studies within the INHANCE consortium. A low p-value (less than 0.05) suggests that the results vary significantly between studies. "P for trend" tests if there's a trend between the level of coffee consumption and the risk of cancer. For example, it checks if drinking more cups of coffee is consistently associated with a higher or lower risk. A low p-value (less than 0.05) for the trend test suggests a statistically significant trend.
  • Footnotes: The table has extensive footnotes that explain which studies were included in each analysis, the adjustments made in the statistical models, and the abbreviations used. These footnotes provide important details about the methodology and are essential for understanding the nuances of the results. For example, footnote 'a' indicates that the analysis for decaffeinated coffee drinking status included specific studies, while footnote 'b' lists the studies included in the analysis for caffeinated coffee consumption.
Scientific Validity
  • Adjustment for confounders: The analysis adjusts for a comprehensive set of potential confounders, including age, sex, study center, race/ethnicity, education, BMI, smoking, alcohol consumption, and fruit and vegetable intake. This adjustment is crucial for isolating the specific effect of coffee consumption on HNC risk.
  • Stratification by anatomical subsite: The stratification by anatomical subsite (oral cavity, oropharynx, hypopharynx, larynx) is a strength, as it allows for a more nuanced understanding of the association between coffee consumption and HNC risk. This approach acknowledges that HNC is not a homogeneous disease and that risk factors may vary by subsite.
  • Assessment of dose-response relationship: The table examines different levels of coffee consumption, allowing for an assessment of a potential dose-response relationship. The inclusion of the p-value for trend provides a statistical test for this relationship, which is a standard approach in epidemiological studies.
  • Heterogeneity testing: The assessment of heterogeneity is important, given that the data are pooled from multiple studies. The use of random-effects models when heterogeneity is present is appropriate.
  • Limitations of data presentation: The table is comprehensive, but it might have been helpful to have an overall summary estimate for the effect of any coffee consumption vs. non-coffee consumption, in addition to the subsite-specific analyses. The numerous footnotes, while necessary, make the table somewhat complex to navigate.
Communication
  • Clarity of structure: The table is well-structured, with clear headings and subheadings that facilitate navigation. The organization by coffee type and anatomical subsite makes logical sense.
  • Use of technical terms: The table uses appropriate technical terms, such as "Odds Ratio" and "Confidence Interval," which are standard in epidemiological reporting. However, some readers might find the extensive use of abbreviations and the detailed statistical information challenging to interpret. The use of superscripts for footnotes is effective but could be overwhelming due to their quantity.
  • Complexity of information: The table presents a large amount of information, which can be difficult to digest at a glance. The detailed breakdown by subsite and consumption level is valuable but adds to the complexity. The extensive footnotes, while informative, further contribute to the density of the information.
  • Potential improvements: To improve readability, the authors could consider using a more visually distinct way to separate the sections on caffeinated and decaffeinated coffee. Additionally, providing a brief explanation of the "p for trend" in the footnote could be helpful for readers less familiar with this statistical concept. A graphical representation of the main findings, such as a forest plot, could also be a valuable addition to the paper, complementing the detailed information in the table.
TABLE 3 The association with HNC by anatomical subsite for tea drinking status...
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TABLE 3 The association with HNC by anatomical subsite for tea drinking status and daily tea consumption among HNC cases and controls from select INHANCE consortium studies.

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TABLE 3 The association with HNC by anatomical subsite for tea drinking status and daily tea consumption among HNC cases and controls from select INHANCE consortium studies.
First Reference in Text
Tea drinkers had a reduced risk of hypopharyngeal cancer (OR, 0.71; 95% CI, 0.59-0.87) compared with non-tea drinkers (Table 3).
Description
  • Purpose of the table: This table investigates the relationship between tea consumption and the risk of head and neck cancer (HNC). Like a detective comparing clues, it examines whether drinking tea is associated with a higher, lower, or similar risk of developing HNC compared to not drinking tea. It breaks down the investigation by looking at specific locations of cancer within the head and neck, referred to as anatomical subsites.
  • Structure and organization: The table is organized into rows and columns. The rows represent different categories of tea consumption, such as "non-tea drinker," ">0 to ≤1 cup," and ">1 cup." The columns represent different anatomical subsites of HNC, including "Oral cavity," "Oropharynx," "Hypopharynx," and "Larynx." Each cell at the intersection of a row and column presents data comparing tea drinkers and non-tea drinkers for that specific cancer subsite. It is as if we are dividing a large group of people into smaller groups based on their tea drinking habits and the location of their cancer and then comparing the groups to see if there are any noticeable differences.
  • Data presentation and key metrics: For each category of tea consumption and cancer subsite, the table shows the number of "controls" (people without cancer) and "cases" (people with cancer). It also presents the "OR" (Odds Ratio) and "95% CI" (95% Confidence Interval). The OR is a measure of association, like a clue that suggests whether tea drinking is linked to a higher or lower risk of cancer. An OR of 1 suggests no association, while an OR greater than 1 suggests an increased risk and an OR less than 1 suggests a decreased risk. The 95% CI provides a range of values that likely contains the true OR. It's similar to a range of guesses – the narrower the range, the more confident we are about the OR. For example, an OR of 0.71 with a 95% CI of 0.59 to 0.87 means that we are 95% confident that the true OR is between 0.59 and 0.87, suggesting a reduced risk.
  • Statistical measures: The table includes "p for heterogeneity" and "p for trend." "P for heterogeneity" checks if the results are consistent across different studies within the INHANCE consortium. A low p-value (less than 0.05) indicates that the results vary significantly between studies. "P for trend" tests if there's a consistent trend between the amount of tea consumed and the risk of cancer. For instance, it checks if drinking more tea is consistently associated with a higher or lower risk. A low p-value (less than 0.05) for the trend test suggests a statistically significant trend, meaning the observed trend is unlikely due to random chance.
  • Footnotes and adjustments: The table has footnotes that provide important details about the methodology, such as which studies were included in each analysis and the factors that were adjusted for in the statistical models. These adjustments are like controlling for other variables that could influence the results, such as age, sex, and smoking habits. By adjusting for these factors, we can isolate the specific effect of tea consumption on HNC risk. Footnote 'a', for instance, indicates that the results were adjusted for study center, age, sex, race/ethnicity, education, body mass index, daily cigarette consumption, duration of cigarette consumption, duration of cigar usage, duration of pipe usage, daily alcohol consumption, fruit consumption, and vegetable consumption.
Scientific Validity
  • Comprehensive adjustment for confounders: The study adjusts for a wide range of potential confounders, including demographic factors, smoking and alcohol consumption, and dietary factors. This thorough adjustment strengthens the validity of the findings by minimizing the possibility that the observed associations are due to factors other than tea consumption.
  • Stratification by anatomical subsite: Similar to Table 2, stratifying the analysis by anatomical subsite is a methodological strength. This allows for a more precise examination of the relationship between tea consumption and HNC risk, acknowledging the heterogeneity of HNC.
  • Assessment of dose-response relationship: The inclusion of different categories of tea consumption (">0 to ≤1 cup," ">1 cup") and the calculation of the p-value for trend allow for an evaluation of a potential dose-response relationship. This is important for understanding whether the amount of tea consumed influences the risk of HNC.
  • Heterogeneity assessment: The consideration of heterogeneity across studies is crucial for pooled analyses. The use of random-effects models when heterogeneity is present is appropriate and enhances the robustness of the findings.
  • Limitations of the analysis: The table does not specify the type of tea consumed (e.g., black, green, oolong). Different types of tea have varying levels of bioactive compounds, which could differentially affect HNC risk. This lack of detail limits the interpretability of the findings. Also, the table only presents the results for two categories of daily tea consumption, which might be insufficient to fully capture the dose-response relationship. There is no category for less than daily consumption.
Communication
  • Clarity and organization: The table is well-organized and clearly structured, with distinct sections for tea drinking status and daily tea consumption. The use of bold font for headings and the clear labeling of rows and columns facilitate readability.
  • Use of technical terms: The table employs standard epidemiological terms like "Odds Ratio" and "95% Confidence Interval." While these terms are appropriate for a scientific audience, they might be challenging for a lay reader to understand. The footnotes provide some explanation, but a more explicit definition of these terms within the table or caption could be beneficial.
  • Density of information: The table presents a substantial amount of data, which can be overwhelming for the reader. The detailed breakdown by anatomical subsite is valuable but contributes to the complexity of the information presented. The numerous footnotes, while necessary, add to the density of the table.
  • Potential improvements: To enhance clarity, the authors could consider visually separating the results for different anatomical subsites more distinctly. Additionally, providing a brief explanation of "p for trend" and "p for heterogeneity" in the footnotes would be helpful for readers less familiar with these statistical concepts. A graphical representation of the key findings, such as a forest plot, could complement the detailed information in the table and make the results more accessible to a wider audience.
TABLE 4 ORs for oral cavity and oropharyngeal cancer risk for drinking >4 cups...
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TABLE 4 ORs for oral cavity and oropharyngeal cancer risk for drinking >4 cups of caffeinated coffee daily versus non-coffee drinkers across strata of selected factors from select INHANCE consortium studies.

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TABLE 4 ORs for oral cavity and oropharyngeal cancer risk for drinking >4 cups of caffeinated coffee daily versus non-coffee drinkers across strata of selected factors from select INHANCE consortium studies.
First Reference in Text
To assess potential effect modifiers for drinking >4 cups of caffeinated coffee daily and oral cavity and oropharyngeal cancers, we conducted a stratified analysis (Table 4).
Description
  • Purpose of the table: This table explores whether the relationship between drinking a lot of caffeinated coffee (more than 4 cups a day) and the risk of getting oral cavity and oropharyngeal cancers is different across various groups of people. It's like investigating if a medicine works the same way for men and women, or for younger and older people. In this case, the 'medicine' is drinking more than 4 cups of caffeinated coffee daily, and the 'effect' is the risk of these cancers. The groups are defined by factors like age, sex, smoking habits, and so on. This is referred to as assessing for effect modification.
  • Structure and organization: The table is organized with different factors listed on the left-hand side, such as age, sex, tobacco smoking, alcohol intake, fruit intake, vegetable intake, study region, study period, and type of controls. Each factor is divided into categories, or "strata". For example, "age" is split into "<55" and "≥55". For each stratum, the table compares people who drink more than 4 cups of caffeinated coffee a day to those who don't drink coffee. It then shows the "OR" (Odds Ratio) and "95% CI" (95% Confidence Interval) for each group. Each stratum essentially gets its own mini-analysis to see if the coffee-cancer relationship holds true within that specific group.
  • Data presentation and key metrics: For each stratum, the table shows the number of cases (people with cancer) and controls (people without cancer) for both coffee drinkers (">4 cups") and non-coffee drinkers. The "OR" (Odds Ratio) is the key metric here. It tells us if drinking a lot of coffee is linked to a higher, lower, or similar risk of cancer within that specific group. An OR of 1 means no difference in risk. An OR greater than 1 suggests an increased risk, while an OR less than 1 suggests a decreased risk. The "95% CI" (95% Confidence Interval) gives us a range of values that likely contains the true OR. Think of it like a net – the wider the net, the less certain we are about where the true OR lies. For example, in the "Age (years)" category, for people aged less than 55, an OR of 0.74 with a 95% CI of 0.59 to 0.93 suggests that drinking more than 4 cups of coffee is associated with a reduced risk of these cancers in this age group, and we are 95% confident the true OR lies within that range.
  • Footnotes and adjustments: The table has footnotes that provide important details. Footnote 'a' explains that the analyses were adjusted for several factors, including study center, age, sex, race/ethnicity, education, body mass index, and so on. These adjustments are crucial because they help isolate the specific effect of coffee consumption on cancer risk, separate from other potential influences. It's like making sure we're comparing apples to apples and not apples to oranges. Footnote 'b' indicates that the analysis does not include the Saarland study due to sparse stratum data. Footnote 'c' indicates that the sum of cases and controls may not add up to the total due to missing values. These footnotes are important for understanding the limitations and nuances of the analysis.
Scientific Validity
  • Appropriateness of stratified analysis: Conducting a stratified analysis is a valid and important approach to assess effect modification. By examining the association between coffee consumption and cancer risk within different subgroups, the researchers can identify potential variations in the effect that might be masked in an overall analysis.
  • Selection of stratification factors: The factors chosen for stratification (age, sex, tobacco smoking, alcohol intake, etc.) are relevant and commonly investigated as potential effect modifiers in cancer epidemiology. These factors are known to be associated with HNC risk and could plausibly interact with coffee consumption.
  • Adjustment for confounders: The adjustment for multiple confounders in each stratum-specific analysis is crucial for controlling for potential biases and isolating the specific effect of coffee consumption. The comprehensive list of adjusted factors strengthens the validity of the findings.
  • Limitations of the analysis: The table only examines the effect modification for the highest category of coffee consumption (">4 cups"). It would also be informative to investigate effect modification for other levels of consumption. Additionally, the exclusion of the Saarland study due to sparse data might limit the generalizability of the findings. The statistical power to detect effect modification might be limited in some strata with small numbers of cases and controls.
Communication
  • Clarity of presentation: The table is generally well-organized and easy to understand. The clear labeling of rows and columns, and the use of bold font for subheadings, enhance readability. The presentation of data for each stratum allows for a straightforward comparison of ORs across different groups.
  • Use of technical terms: The table uses appropriate technical terms like "Odds Ratio" and "95% Confidence Interval." While these terms are standard in epidemiological reporting, they might be challenging for a lay reader. The caption provides some context, but a more explicit definition of these terms within the table or caption could be beneficial.
  • Density of information: The table presents a large amount of data, which can be somewhat overwhelming. However, the clear organization and the focus on a single exposure-outcome relationship (coffee consumption and oral/oropharyngeal cancer risk) make the information manageable.
  • Potential improvements: To enhance clarity, the authors could consider providing a brief explanation of "effect modification" in the caption or footnotes. Additionally, a graphical representation of the stratum-specific ORs, such as a forest plot, could complement the table and make the results more accessible. Including the p-values for interaction would also provide a more formal statistical assessment of effect modification.
FIGURE 1 Study-specific odds ratios for >4 cups of caffeinated coffee daily...
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FIGURE 1 Study-specific odds ratios for >4 cups of caffeinated coffee daily versus non-coffee drinkers for oral cavity and oropharyngeal cancers.

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FIGURE 1 Study-specific odds ratios for >4 cups of caffeinated coffee daily versus non-coffee drinkers for oral cavity and oropharyngeal cancers.
First Reference in Text
Figure 1 shows study-specific OR for drinking >4 cups of caffeinated coffee daily compared to non-coffee drinkers for oral cavity and oropharyngeal cancers combined.
Description
  • Purpose of the figure: This figure is a visual representation of how drinking more than 4 cups of caffeinated coffee per day is associated with the risk of oral cavity and oropharyngeal cancers, compared to not drinking coffee. It shows the results from different individual studies that are part of the larger INHANCE consortium. You can think of it like a summary of different polls on the same topic – each poll gives its own result, and the figure shows all these results together.
  • Structure and components: The figure is a type of graph called a forest plot. On the left side, it lists the names of the individual studies, along with the years they were conducted. Each study gets its own horizontal line. On each line, there's a square that represents the study's result, called the "Odds Ratio" (OR). The size of the square reflects the weight of the study in the overall analysis, which often relates to the study's sample size. Bigger squares mean the study has a larger influence on the overall result. There are also horizontal lines extending from each square, which represent the "95% Confidence Interval" (CI) – a range of values that likely contains the true OR. It's like saying, "We're pretty sure the real answer is somewhere between this lower value and this higher value." The position of the square and the length of the horizontal lines are important. If the square is to the left of the vertical line at 1, it suggests that coffee drinking is associated with a reduced risk of cancer in that study. If it's to the right, it suggests an increased risk. If the horizontal line crosses the vertical line at 1, it means the result is not statistically significant.
  • Key metrics: Odds Ratio (OR) and 95% Confidence Interval (CI): The "Odds Ratio" (OR) is a measure of association between an exposure (drinking coffee) and an outcome (cancer). An OR of 1 means there's no association – coffee drinking doesn't change the risk. An OR less than 1 suggests that coffee drinking is associated with a reduced risk of cancer, while an OR greater than 1 suggests an increased risk. The "95% Confidence Interval" (CI) gives us a range of values that likely contains the true OR. For example, if a study has an OR of 0.5 with a 95% CI of 0.3 to 0.8, it means that in that study, coffee drinkers had half the risk of cancer compared to non-coffee drinkers, and we're 95% confident that the true OR is between 0.3 and 0.8. If the CI includes 1, as in a range of 0.8 to 1.2, it means the result is not statistically significant – we can't be sure if coffee increases or decreases the risk.
  • Overall summary and heterogeneity: At the bottom of the figure, there's a diamond shape that represents the overall result when all the studies are combined. This is called a "pooled" or "summary" OR. The width of the diamond represents the 95% CI for the overall result. The figure also provides information about "heterogeneity," which is a measure of how different the results of the individual studies are. It's like checking if the polls agree with each other. The I² value tells us the percentage of variation across studies that is due to heterogeneity rather than chance. A higher I² value (closer to 100%) means more heterogeneity. The p-value for heterogeneity tells us if the differences between the studies are statistically significant. A low p-value (less than 0.05) suggests significant heterogeneity.
Scientific Validity
  • Appropriateness of using a forest plot: A forest plot is an appropriate and standard way to present the results of multiple studies in a meta-analysis. It allows for a visual comparison of study-specific ORs and their CIs, as well as an assessment of heterogeneity.
  • Selection of studies: The figure includes studies from the INHANCE consortium, which is a large and well-established collaboration. The selection of studies appears to be appropriate, given the research question.
  • Calculation of the overall OR: The overall OR is calculated using an inverse-variance weighted model, which is a standard method for combining study-specific ORs in a meta-analysis. This method gives more weight to studies with larger sample sizes and more precise estimates.
  • Assessment of heterogeneity: The assessment of heterogeneity using the I² statistic and the p-value is important for understanding the consistency of the findings across studies. The reported I² of 0.0% suggests low heterogeneity, indicating that the study results are quite consistent with one another.
  • Limitations: While the forest plot provides a good overview of the study-specific ORs, it does not provide information on the potential sources of heterogeneity (if any). Further analyses, such as subgroup analyses or meta-regression, might be needed to explore potential effect modifiers.
Communication
  • Clarity of presentation: The forest plot is generally clear and well-organized. The use of squares to represent ORs and horizontal lines for CIs is standard and easy to understand. The labeling of the studies and the inclusion of the overall OR and heterogeneity statistics are helpful.
  • Use of technical terms: The figure uses technical terms like "Odds Ratio" and "95% Confidence Interval," which are appropriate for a scientific audience. However, a brief explanation of these terms in the caption or the figure legend would be beneficial for readers who are not familiar with meta-analysis.
  • Visual appeal and readability: The figure is visually appealing and easy to read. The use of different sizes of squares to represent study weights is effective. The alignment of the squares and the horizontal lines makes it easy to compare the results across studies.
  • Potential improvements: To enhance clarity, the authors could consider adding a vertical line at the null value (OR=1) to make it easier to see which studies show a statistically significant association. Additionally, providing a brief explanation of the I² statistic and the p-value for heterogeneity in the figure legend would be helpful. It might also be useful to include the number of cases and controls for each study in the figure or a separate table.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

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