Alzheimer’s disease mortality among taxi and ambulance drivers: population based cross sectional study

• Purpose of the table: This table shows the demographic and socioeconomic characteristics of people who have died and whose occupations were related to navigation, such as taxi drivers, ambulance drivers, and pilots. It breaks down the data by different job types to compare these groups. Essentially, it sets the stage to understand if the people in these jobs are similar or different in terms of age, sex, race, and education.
• Structure of the table: The table is organized with different navigational occupations listed in columns, such as "Ambulance drivers," "Taxi drivers and chauffeurs," "Aircraft pilots," and so on. Each column represents a specific occupational group. There is also a column for "All other occupations" which serves as a comparison group. The rows of the table list various characteristics like age, sex, race and ethnicity, and educational attainment. The values in each cell represent either the average value (for age) or the number and percentage of individuals (for categorical variables like sex or education level) within each occupational group that share a particular characteristic.
• Definition of key variables: The table includes several key variables. "Age, mean, years (SD)" refers to the average age at death for each group, with SD standing for standard deviation, which is a measure of how spread out the ages are around the average. "Sex, no. (%)" indicates the number and percentage of males and females in each group. "Race and ethnicity, no. (%)" breaks down each group by racial and ethnic categories, such as Asian or Pacific Islander, Black, Hispanic, and others, showing the number and percentage in each category. "Educational attainment, no. (%)" shows the number and percentage of individuals in each group who have achieved different levels of education, from less than high school to a graduate degree. It also includes a category "Unknown" for cases where the educational level is not reported.
• Technical terms specific to this table: The term "decedents" is used to refer to the people who have died, and their data is being analyzed. "Navigational occupation" refers to jobs that involve navigating or directing a vehicle as a primary part of the work, like driving a taxi or piloting an aircraft. The phrase "reported navigational occupation" implies that the occupation was recorded on official documents, likely death certificates. "GED" stands for General Educational Development, which is a set of tests that, when passed, certify that the test taker has met high-school level academic skills.

• Data Representation: The table appropriately represents demographic and socioeconomic data for different occupational groups. However, the validity of comparisons across groups depends on the accuracy and completeness of the underlying data from the National Vital Statistics System. Potential biases in reporting or coding occupations could affect the validity of these comparisons.
• Variable Selection: The selected variables (age, sex, race and ethnicity, educational attainment) are standard demographic descriptors and are relevant for controlling for potential confounding factors in the subsequent analyses. The inclusion of 'Unknown' categories for some variables is a good practice as it acknowledges missing data, but the impact of these missing data on the overall analysis should be considered.
• Statistical Descriptors: The use of mean and standard deviation for age is appropriate for summarizing continuous data. The use of counts and percentages for categorical variables is standard and suitable for this type of data. The table provides a clear descriptive summary of each group, which is crucial for understanding the composition of each occupational group before proceeding to further statistical analysis.

• Clarity of Column Headers: The column headers are clear and self-explanatory, such as 'Ambulance drivers,' 'Taxi drivers and chauffeurs,' etc. The use of 'n=' to indicate the sample size for each group is standard and well-understood in scientific literature.
• Readability of Row Labels: The row labels for demographic characteristics are straightforward. However, the term 'decedents' might not be immediately clear to all readers, although it is technically correct.
• Use of Abbreviations: Abbreviations like 'SD' for standard deviation and 'GED' for general educational development are used. While 'SD' is commonly understood, 'GED' is explained in a footnote, which is helpful. However, the footnote is placed far below the table which could hinder immediate understanding.
• Overall Table Layout: The table is well-organized, with a clear distinction between different occupational groups and demographic categories. However, the table is quite wide, which might make it difficult to view on smaller screens or when printed.
• Footnote Placement and Clarity: The footnote explaining 'GED' is appropriate but could be more effective if placed closer to the relevant row or if 'GED' was spelled out in the row label itself. Additionally, the table could benefit from a brief introductory sentence or note explaining the source of the data (National Vital Statistics System) and the years covered.

• Purpose and content of the table: This table presents data on the number of deaths attributed to Alzheimer's disease across different occupations. It aims to compare the prevalence of Alzheimer's-related deaths among various professions, particularly those involving navigation. The table lists the total number of deaths within each occupation, the number of those deaths caused by Alzheimer's, the raw percentage of Alzheimer's deaths, and an adjusted percentage that accounts for factors like age, sex, race, and education. It also includes an adjusted odds ratio, which is a statistical measure that quantifies the strength of association between Alzheimer's disease and each occupation compared to a reference group (chief executives in this case).
• Explanation of key columns: "Deaths, no." represents the total number of deceased individuals within each listed occupation. "Alzheimer's deaths, no." is the count of those deaths specifically attributed to Alzheimer's disease. "Alzheimer's deaths, %" is a simple calculation of the percentage of deaths due to Alzheimer's within each group. "Alzheimer's deaths, adjusted % (95% CI)" is a more complex measure. The 'adjusted %' accounts for differences in age, sex, race, ethnic group, and education levels across the occupations, providing a fairer comparison. The "95% CI" stands for the 95% confidence interval, which is a range of values that likely contains the true adjusted percentage 95% of the time if we were to repeat the study many times. "Adjusted odds ratio (95% CI)" compares the odds of dying from Alzheimer's in each occupation to the odds in a reference group (chief executives), after adjusting for demographic factors. An odds ratio of 1 means no difference in odds, less than 1 means lower odds, and greater than 1 means higher odds. The 95% CI for the odds ratio similarly gives a range within which the true odds ratio likely falls.
• Occupations compared: The table focuses on several occupations: ambulance drivers, taxi drivers and chauffeurs, bus drivers, chief executives, ship captains, and aircraft pilots. These occupations are chosen to explore the hypothesis that jobs requiring frequent spatial and navigational processing, like those of taxi and ambulance drivers, might be associated with a lower risk of Alzheimer's disease. Chief executives are used as a comparison group because they represent a different type of occupation, presumably with different cognitive demands. The adjusted odds ratios are calculated using chief executives as a reference, which allows for a comparison of each occupation's Alzheimer's mortality risk relative to this group.

• Appropriateness of Adjustments: The adjustment for age, sex, race, ethnic group, and educational attainment is crucial for a valid comparison of Alzheimer's disease mortality across occupations. These factors are known to be associated with both Alzheimer's risk and occupational choice. The use of logistic regression to perform these adjustments is a standard and appropriate statistical method.
• Choice of Reference Group: Using chief executives as the reference group for calculating adjusted odds ratios is an arbitrary but acceptable choice. However, it's important to note that the interpretation of the odds ratios depends on this choice. A different reference group could yield different odds ratios, although the overall pattern of results would likely be similar.
• Statistical Significance: The inclusion of p-values helps to assess the statistical significance of the observed differences in Alzheimer's mortality. However, the reliance on a strict p-value threshold (e.g., p < 0.05) for determining significance can be misleading, especially when multiple comparisons are made. The 95% confidence intervals provide a more informative measure of the precision of the estimates.
• Limitations of Observational Data: The study is based on observational data, which limits the ability to draw causal inferences. While the adjustments attempt to control for confounding factors, there may be unmeasured confounders that influence both occupational choice and Alzheimer's risk. The observed associations, therefore, do not necessarily imply a causal relationship.

• Clarity of Column Headers: The column headers are mostly clear, but "Alzheimer's deaths, adjusted % (95% CI)" could be more descriptive. For instance, it could be expanded to "Adjusted Percentage of Deaths from Alzheimer's Disease (95% Confidence Interval)," although this would make the header quite long.
• Use of Abbreviations and Technical Terms: The table uses abbreviations like "no." for number and "CI" for confidence interval, which are standard in scientific tables. However, the meaning of "adjusted" might not be immediately obvious to all readers. A brief explanation in the table footnote would be helpful.
• Presentation of Odds Ratios: The presentation of adjusted odds ratios with 95% confidence intervals is appropriate and allows for a clear comparison of Alzheimer's mortality risk across occupations. The use of chief executives as a reference group is explicitly stated in the table footnote, which is good practice.
• Overall Table Structure: The table is well-organized and easy to follow. The occupations are listed in a logical order, and the different measures of Alzheimer's mortality are clearly presented. However, the table could benefit from a more descriptive title that highlights the main finding or the purpose of the comparison.
• Accessibility to Non-Expert Readers: While the table is technically sound, it might be challenging for non-expert readers to fully understand the meaning of adjusted percentages and odds ratios. A brief explanation of these concepts in the table footnote or in the main text would improve the table's accessibility.

Fig 1 | Mortality from Alzheimer's disease among ambulance drivers, taxi drivers, and other occupations. Risk adjusted percentages and mortality odds ratios were adjusted for age at death, sex, race, ethnic group, and educational attainment using logistic regression. In the bottom graph, a logarithmic scale was used for the y axis to allow for accurate visual comparison of effect sizes between occupations, as the logarithmic scale equalizes the distances between ratios and their reciprocals. Adjusted odds ratios were calculated using chief executives (US Census Bureau occupation code 0010) as an arbitrary reference group

• Overall purpose of the figure: This figure visually compares the death rates from Alzheimer's disease among different occupations, focusing on ambulance drivers and taxi drivers. It uses graphs to show how these occupations relate to Alzheimer's mortality after taking into account various factors like age, sex, race, ethnicity, and education level. The main idea is to see if people in jobs that involve a lot of navigation, like taxi and ambulance drivers, have different rates of Alzheimer's deaths compared to other jobs.
• Description of the top graph: The top graph plots the percentage of deaths due to Alzheimer's disease on the vertical axis (y-axis) against the average age at death for each occupation on the horizontal axis (x-axis). Each point on the graph represents a different occupation. The percentage of Alzheimer's deaths has been 'risk-adjusted,' meaning that the researchers have used a statistical method called logistic regression to account for differences in age, sex, race, ethnic group, and education across the occupations. This adjustment allows for a fairer comparison of Alzheimer's mortality rates. Occupations are color-coded: blue dots represent ambulance drivers, orange dots represent taxi drivers, and gray dots represent all other occupations. This graph helps to visualize whether occupations with similar average ages at death have different rates of Alzheimer's deaths.
• Description of the bottom graph: The bottom graph shows the 'risk-adjusted Alzheimer's mortality odds ratio' on the y-axis, again plotted against different occupations. The odds ratio is a way of comparing the odds of dying from Alzheimer's in one group to the odds in another group. Here, the odds ratios are adjusted for the same factors as in the top graph (age, sex, race, ethnicity, and education). The y-axis uses a logarithmic scale, which means that equal distances on the axis represent equal ratios, making it easier to compare the relative differences in odds ratios between occupations. The graph also uses color-coding to differentiate between ambulance drivers (blue), taxi drivers (orange), and other occupations (gray). The adjusted odds ratios are calculated relative to a reference group, which in this case is chief executives. An odds ratio of 1 would mean that the odds of dying from Alzheimer's are the same as for chief executives, while an odds ratio less than 1 means the odds are lower, and greater than 1 means the odds are higher.
• Explanation of logistic regression and its use here: Logistic regression is a statistical method used to analyze the relationship between a dependent variable (in this case, whether someone died from Alzheimer's disease) and one or more independent variables (like age, sex, occupation, etc.). It's particularly useful when the dependent variable is binary, meaning it can only take two values (e.g., yes or no, died from Alzheimer's or not). In this study, logistic regression is used to adjust the percentages and odds ratios for the effects of age, sex, race, ethnic group, and education. This is important because these factors might influence both the likelihood of dying from Alzheimer's and the type of occupation someone has. By adjusting for these factors, the researchers can get a clearer picture of the relationship between occupation and Alzheimer's mortality.
• Explanation of logarithmic scale in the bottom graph: A logarithmic scale is a way of displaying numerical data over a very wide range of values in a compact way. In this graph, a logarithmic scale is used on the y-axis to represent the odds ratios. This means that instead of each unit on the y-axis representing an increase of 1, each unit represents a multiplication by a constant factor (e.g., each unit could represent a doubling or tripling of the odds ratio). The reason for using a logarithmic scale here is that it makes it easier to visually compare ratios that are much smaller than 1 with ratios that are much larger than 1. If a regular linear scale were used, the differences between very small odds ratios would be hard to see. The caption explains that the logarithmic scale 'equalizes the distances between ratios and their reciprocals,' which means that, for example, an odds ratio of 2 (double the odds) is the same distance from 1 as an odds ratio of 0.5 (half the odds) on the logarithmic scale.

• Appropriateness of Risk Adjustment: Adjusting for age, sex, race, ethnic group, and educational attainment is crucial for a valid comparison of Alzheimer's mortality across occupations. These factors are known confounders, as they are associated with both Alzheimer's risk and occupational choice. The use of logistic regression for this adjustment is a standard and appropriate statistical method.
• Use of Logarithmic Scale: The use of a logarithmic scale for the odds ratio is appropriate and justified. It allows for a more accurate visual comparison of effect sizes, especially when dealing with ratios that span several orders of magnitude. The explanation in the caption is accurate and helpful.
• Choice of Reference Group for Odds Ratios: Using chief executives as the reference group is an arbitrary but acceptable choice. It's important to keep in mind that the interpretation of the odds ratios depends on this choice. However, the overall pattern of results would likely be similar regardless of the reference group used.
• Limitations of Visualizations: While the graphs are informative, they do not provide precise numerical estimates of the adjusted percentages or odds ratios. Readers need to refer to Table 2 for these values. Additionally, the graphs do not show the confidence intervals around the estimates, which are important for assessing the statistical significance of the observed differences.

• Clarity of Axis Labels and Legends: The axis labels are generally clear, but the y-axis label in the bottom graph ("Risk-adjusted Alzheimer's mortality odds ratio") could be more descriptive. For instance, it could be expanded to "Risk-Adjusted Odds Ratio of Death from Alzheimer's Disease (Relative to Chief Executives)." The use of color-coded dots to represent different occupations is effective, and the legend is clear.
• Effectiveness of Logarithmic Scale Explanation: The explanation of the logarithmic scale in the caption is accurate and relatively easy to understand. However, the concept of a logarithmic scale might still be challenging for some readers. A brief explanation in the main text or a visual aid in the figure itself could further improve understanding.
• Overall Clarity of the Figure: The figure is well-designed and effectively conveys the main findings of the study. The use of two complementary graphs provides a comprehensive picture of the relationship between occupation and Alzheimer's mortality. However, the figure might be challenging for readers who are not familiar with the concepts of risk adjustment, odds ratios, and logarithmic scales.
• Accessibility to Non-Expert Readers: The figure is somewhat accessible to non-expert readers, especially the top graph. However, the bottom graph and the concepts of odds ratios and logarithmic scales might be difficult to grasp without some background knowledge in statistics. Providing a more detailed explanation of these concepts in the main text or in a supplementary material could improve the figure's accessibility.
• Caption Clarity and Completeness: The caption is detailed and provides most of the necessary information to interpret the figure. However, it could explicitly state that each dot in the graphs represents an occupation. Also, while it mentions the adjustment variables, it could briefly reiterate why these adjustments are necessary.

Fig 2 | Mortality from Alzheimer's disease as underlying or contributing cause of death by occupation. Risk adjusted mortality odds ratios were adjusted for age at death, sex, race, ethnic group, and educational attainment using logistic regression A logarithmic scale was used for the y axis to allow for accurate visual comparison of effect sizes between occupations, as the logarithmic scale equalizes the distances between ratios and their reciprocals. Adjusted odds ratios were calculated using chief executives (US Census Bureau occupation code 0010) as an arbitrary reference group

• Purpose of the figure: This figure explores how often Alzheimer's disease is listed as a cause of death, either the main cause or a contributing factor, across different jobs. It specifically looks at whether being an ambulance driver or a taxi driver is associated with a different likelihood of having Alzheimer's mentioned on one's death certificate compared to other occupations.
• What the graph shows: The graph plots occupations along the horizontal axis (x-axis) and a measure called the "risk-adjusted mortality odds ratio" on the vertical axis (y-axis). This odds ratio is a way of comparing the odds of having Alzheimer's listed on the death certificate for people in different jobs, after adjusting for factors like age, sex, race, ethnicity, and education level. The adjustment is done using a statistical technique called logistic regression, which helps to ensure that the comparisons are fair. A logarithmic scale is used on the y-axis. This type of scale is useful for displaying data that covers a wide range of values. On a logarithmic scale, equal distances represent equal *ratios* rather than equal *differences*. This allows for easier visual comparison of the odds ratios, especially when some are very small and others are very large. The graph uses different colors to mark different types of occupations: taxi drivers are shown in orange, ambulance drivers in blue, and all other occupations in gray.
• Explanation of "underlying or contributing cause of death": When someone dies, there can be multiple factors contributing to their death. The "underlying cause" is the main disease or condition that initiated the sequence of events leading to death. A "contributing cause" is any other disease or condition that also played a role but was not the primary cause. In this study, the researchers are looking at death certificates where Alzheimer's disease is listed as either the underlying cause or a contributing cause. This means they are considering cases where Alzheimer's was a significant factor in the person's death, even if it wasn't the sole cause.
• Explanation of "risk-adjusted mortality odds ratio": An odds ratio is a way to compare the odds of an event happening in one group to the odds of it happening in another group. In this case, the event is having Alzheimer's disease listed on the death certificate. The "mortality odds ratio" compares the odds of this event for people in different occupations. "Risk-adjusted" means that the odds ratios have been adjusted to account for differences in factors like age, sex, race, ethnicity, and education level between the groups. This adjustment is important because these factors can influence both a person's risk of developing Alzheimer's and the type of job they have. By adjusting for these factors, the researchers can get a clearer picture of the relationship between occupation and Alzheimer's mortality. The adjusted odds ratios are calculated relative to a reference group, which in this case is chief executives. An odds ratio of 1 means that the odds of having Alzheimer's on the death certificate are the same as for chief executives. An odds ratio less than 1 means the odds are lower, and an odds ratio greater than 1 means the odds are higher.
• Use of logarithmic scale: A logarithmic scale is used on the y-axis to make it easier to compare odds ratios that are very different in size. On a logarithmic scale, the distance between 1 and 2 is the same as the distance between 10 and 20, or between 100 and 200. This is because each step on the scale represents a multiplication by a constant factor (e.g., doubling). The caption explains that the logarithmic scale "equalizes the distances between ratios and their reciprocals." This means that an odds ratio of 2 (double the odds) is the same distance from 1 as an odds ratio of 0.5 (half the odds) on the logarithmic scale.

• Appropriateness of Adjustment: Adjusting for age, sex, race, ethnic group, and educational attainment is essential for a valid comparison of Alzheimer's mortality across occupations. These are well-established confounders in epidemiological studies, influencing both Alzheimer's risk and occupational choices. The use of logistic regression for this adjustment is statistically sound.
• Inclusion of Underlying or Contributing Cause: Considering Alzheimer's as both an underlying and contributing cause of death broadens the scope of the analysis and provides a more comprehensive picture of the disease's impact. This approach is justified given the complex and multifactorial nature of Alzheimer's disease and its contribution to mortality.
• Use of Logarithmic Scale: The application of a logarithmic scale for the y-axis is appropriate for visualizing odds ratios, especially when they vary widely. This allows for a more accurate visual comparison of effect sizes across different occupations. The explanation provided in the caption regarding the equalization of distances between ratios and their reciprocals is accurate.
• Choice of Reference Group: The use of chief executives as a reference group for calculating adjusted odds ratios is arbitrary but does not invalidate the findings. The interpretation of the odds ratios is contingent on this choice, but the relative comparisons between other occupations remain informative.

• Clarity of Axis Labels: The y-axis label, "Risk-adjusted mortality odds ratio," could be more descriptive. While technically accurate, it might not be immediately clear to all readers. An alternative could be "Adjusted Odds Ratio of Alzheimer's Disease on Death Certificate (Relative to Chief Executives)." The x-axis is implicitly understood to represent different occupations, but this could be stated explicitly.
• Effectiveness of Color Coding: The use of different colors to represent taxi drivers, ambulance drivers, and other occupations is effective for visual differentiation. The legend, although not shown in the provided text, is presumably clear and easy to understand.
• Explanation of Logarithmic Scale: The explanation of the logarithmic scale in the caption is accurate but could be challenging for readers unfamiliar with this concept. A more intuitive explanation or a visual aid within the figure could enhance understanding.
• Overall Clarity and Accessibility: The figure effectively communicates the main findings of the sensitivity analysis, showing that the pattern observed in the primary analysis holds when considering Alzheimer's as an underlying or contributing cause of death. However, the figure's reliance on the concept of odds ratios and the use of a logarithmic scale might make it less accessible to readers without a strong statistical background.
• Caption Completeness: The caption provides a comprehensive description of the figure's components and the methods used. However, it could benefit from a brief introductory sentence summarizing the figure's main message or purpose. Additionally, while it mentions the adjustment variables, it could briefly reiterate why these adjustments are crucial for the analysis.

Fig 3 | Mortality from Alzheimer's disease among bus drivers, aircraft pilots, ship captains, and other occupations. Risk-adjusted percentages and mortality odds ratios were adjusted for age at death, sex, race, ethnicity, and educational attainment using logistic regression. In the bottom graph, a logarithmic scale was used for the y axis to allow for accurate visual comparison of effect sizes between occupations, as the logarithmic scale equalizes the distances between ratios and their reciprocals. Adjusted odds ratios were calculated using chief executives (US Census Bureau occupation code 0010) as an arbitrary reference group

• Purpose of the figure: This figure examines the death rates from Alzheimer's disease in occupations that involve transportation but have less demand for on-the-spot navigation compared to taxi or ambulance drivers. These occupations include bus drivers, aircraft pilots, and ship captains. The researchers are trying to see if the lower Alzheimer's mortality seen in taxi and ambulance drivers is also found in these other transportation jobs, or if it's unique to jobs with high navigational demands.
• Structure of the graphs: Similar to previous figures, this figure contains two graphs. The top graph plots the risk-adjusted percentage of deaths from Alzheimer's disease against the mean age at death for each occupation. The bottom graph shows the risk-adjusted mortality odds ratio for each occupation. In both graphs, different occupations are represented by different colored dots: bus drivers are shown in blue, aircraft pilots in orange, ship captains in purple, and other occupations in gray. The odds ratios are plotted on a logarithmic scale on the y-axis, which means that equal distances on the axis represent equal ratios. This makes it easier to compare the relative differences in odds ratios between occupations, especially when some odds ratios are very small and others are very large.
• Explanation of risk adjustment: The term "risk-adjusted" means that the percentages and odds ratios have been adjusted to account for differences in age, sex, race, ethnicity, and education level across the different occupations. This is important because these factors can affect both a person's risk of developing Alzheimer's disease and the type of job they have. For example, if one occupation has a much older workforce than another, we might expect to see more Alzheimer's deaths in that occupation simply due to age, even if the job itself has no effect on Alzheimer's risk. By adjusting for these factors using a statistical technique called logistic regression, the researchers can get a clearer picture of the relationship between occupation and Alzheimer's mortality, separate from the influence of these other factors.
• Explanation of odds ratios and reference group: An odds ratio is a way to compare the odds of an event happening in one group to the odds of it happening in another group. In this case, the event is dying from Alzheimer's disease. The "mortality odds ratio" compares the odds of this event for people in different occupations. The adjusted odds ratios are calculated relative to a reference group, which in this case is chief executives. This means that the odds of dying from Alzheimer's for each occupation are compared to the odds for chief executives. An odds ratio of 1 means that the odds are the same as for chief executives. An odds ratio less than 1 means the odds are lower, and an odds ratio greater than 1 means the odds are higher. For example, an odds ratio of 0.5 would mean that people in a particular occupation are half as likely to die from Alzheimer's as chief executives, while an odds ratio of 2 would mean they are twice as likely.
• Use of logarithmic scale: The bottom graph uses a logarithmic scale on the y-axis. This type of scale is useful for displaying data that covers a wide range of values. On a logarithmic scale, equal distances represent equal *ratios* rather than equal *differences*. For example, the distance between an odds ratio of 1 and 2 is the same as the distance between 2 and 4, or between 0.5 and 1. This is because each step on the scale represents a multiplication by a constant factor (e.g., doubling). The caption explains that the logarithmic scale "equalizes the distances between ratios and their reciprocals." This means that an odds ratio of 2 (double the odds) is the same distance from 1 as an odds ratio of 0.5 (half the odds) on the logarithmic scale. Using a logarithmic scale makes it easier to visually compare odds ratios that are very different in size.

• Relevance of the Comparison Group: Comparing occupations with high navigational demands (taxi and ambulance drivers) to those with lower navigational demands within the transportation sector (bus drivers, aircraft pilots, ship captains) is a scientifically valid approach to test the hypothesis that navigational demands are associated with Alzheimer's disease mortality. This comparison helps to isolate the effect of navigational demands from other factors related to transportation work.
• Consistency of Methodology: The figure employs the same methodology as previous figures, using risk-adjusted percentages and odds ratios, adjusted for the same set of confounders (age, sex, race, ethnicity, and educational attainment) using logistic regression. This consistency strengthens the internal validity of the study and allows for meaningful comparisons across figures.
• Appropriateness of Statistical Adjustments: The adjustments for age, sex, race, ethnicity, and educational attainment are crucial for a valid comparison of Alzheimer's mortality across occupations. These factors are known to be associated with both Alzheimer's risk and occupational choice. The use of logistic regression for this adjustment is statistically sound.
• Justification for Logarithmic Scale: The use of a logarithmic scale for the odds ratio is appropriate and well-justified, particularly when dealing with ratios that span several orders of magnitude. It facilitates a more accurate visual comparison of effect sizes across occupations. The explanation in the caption is accurate and helpful.

• Clarity of Axis Labels: The axis labels are generally clear, but, as in previous figures, the y-axis label in the bottom graph ("Risk-adjusted Alzheimer's mortality odds ratio") could be more descriptive. It could be expanded to something like "Adjusted Odds Ratio of Death from Alzheimer's Disease (Relative to Chief Executives)."
• Effectiveness of Color Coding: The use of different colors to represent different occupations is effective for visual differentiation. The legend, although not shown in the provided text, is presumably clear and easy to understand.
• Explanation of Logarithmic Scale: The explanation of the logarithmic scale in the caption is accurate but might be difficult for readers unfamiliar with this concept. A more intuitive explanation or a visual aid within the figure itself could improve understanding.
• Overall Clarity and Accessibility: The figure effectively communicates the finding that the pattern of lower Alzheimer's mortality observed in taxi and ambulance drivers is not seen in other transportation-related occupations with lower navigational demands. However, the figure's reliance on odds ratios and the use of a logarithmic scale might make it less accessible to readers without a strong statistical background.
• Caption Completeness: The caption provides a thorough description of the figure's components and the methods used. However, it could benefit from a brief introductory sentence that summarizes the figure's main message or purpose. Additionally, while it mentions the adjustment variables, it could briefly reiterate why these adjustments are necessary for the analysis.

Fig 4 | Mortality from other forms of dementia among ambulance drivers, taxi drivers, and other occupations. Risk-adjusted mortality odds ratios were adjusted for age at death, sex, race, ethnicity, and educational attainment using logistic regression A logarithmic scale was used for the y axis to allow for accurate visual comparison of effect sizes between occupations, as the logarithmic scale equalizes the distances between ratios and their reciprocals. Adjusted odds ratios were calculated using chief executives (US Census Bureau occupation code 0010) as an arbitrary reference group

• Purpose of the figure: This figure looks at death rates from types of dementia *other* than Alzheimer's disease across different occupations. It specifically focuses on ambulance drivers and taxi drivers, comparing them to other occupations. The goal is to see if the lower rate of Alzheimer's deaths seen in taxi and ambulance drivers is also found with other types of dementia. If it's not, this might suggest that something specific to these jobs affects Alzheimer's risk, rather than dementia risk in general.
• Structure of the graphs: The figure likely contains two graphs (although only one is shown in the provided image). The top graph shows the percentage of deaths due to vascular dementia (a type of dementia caused by reduced blood flow to the brain) for different occupations. The bottom graph shows the percentage of deaths due to unspecified dementia (cases where the specific type of dementia is not recorded). In both graphs, occupations are plotted on the horizontal axis (x-axis), and the risk-adjusted mortality odds ratio is plotted on the vertical axis (y-axis). Different occupations are represented by different colored dots: taxi drivers are shown in orange, ambulance drivers in blue, and all other occupations in gray. The y-axis uses a logarithmic scale. This means that equal distances on the axis represent equal *ratios* rather than equal *differences*. For example, the distance between an odds ratio of 1 and 2 is the same as the distance between 2 and 4. This makes it easier to compare odds ratios that are very different in size.
• Explanation of "other forms of dementia": Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Alzheimer's disease is the most common type of dementia, but there are other forms as well. This figure focuses on two other types: vascular dementia and unspecified dementia. Vascular dementia is caused by problems with blood supply to the brain, often due to a series of small strokes. Unspecified dementia refers to cases where a person has dementia, but the specific type is not known or not recorded on the death certificate.
• Explanation of "risk-adjusted mortality odds ratio": An odds ratio is a way to compare the odds of an event happening in one group to the odds of it happening in another group. In this case, the event is dying from a specific type of dementia (either vascular or unspecified). The "mortality odds ratio" compares the odds of this event for people in different occupations. "Risk-adjusted" means that the odds ratios have been adjusted to account for differences in factors like age, sex, race, ethnicity, and education level between the groups. This adjustment is important because these factors can influence both a person's risk of developing dementia and the type of job they have. By adjusting for these factors using a statistical technique called logistic regression, the researchers can get a clearer picture of the relationship between occupation and dementia mortality. The adjusted odds ratios are calculated relative to a reference group, which in this case is chief executives. An odds ratio of 1 means that the odds of dying from that type of dementia are the same as for chief executives. An odds ratio less than 1 means the odds are lower, and an odds ratio greater than 1 means the odds are higher.
• Use of logarithmic scale: The y-axis uses a logarithmic scale, which is useful for displaying data that covers a wide range of values. On a logarithmic scale, equal distances represent equal *ratios* rather than equal *differences*. For example, the distance between an odds ratio of 1 and 2 is the same as the distance between 10 and 20. This is because each step on the scale represents a multiplication by a constant factor (e.g., doubling). The caption explains that the logarithmic scale "equalizes the distances between ratios and their reciprocals." This means that an odds ratio of 2 (double the odds) is the same distance from 1 as an odds ratio of 0.5 (half the odds) on the logarithmic scale. Using a logarithmic scale makes it easier to visually compare odds ratios that are very different in size.

• Validity of Comparing Different Types of Dementia: Comparing mortality from Alzheimer's disease to mortality from other forms of dementia is a valid approach to investigate whether the observed association between occupation and Alzheimer's mortality is specific to Alzheimer's or reflects a general association with dementia risk. This comparison helps to strengthen the argument that the findings are not due to confounding factors that affect all types of dementia.
• Consistency of Methodology: The figure employs the same methodology as previous figures, using risk-adjusted odds ratios, adjusted for the same set of confounders (age, sex, race, ethnicity, and educational attainment) using logistic regression. This consistency strengthens the internal validity of the study and allows for meaningful comparisons across figures.
• Appropriateness of Statistical Adjustments: The adjustments for age, sex, race, ethnicity, and educational attainment are crucial for a valid comparison of dementia mortality across occupations. These factors are known to be associated with both dementia risk and occupational choice. The use of logistic regression for this adjustment is statistically sound.
• Justification for Logarithmic Scale: The use of a logarithmic scale for the odds ratio is appropriate and well-justified, particularly when dealing with ratios that span several orders of magnitude. It facilitates a more accurate visual comparison of effect sizes across occupations. The explanation in the caption is accurate and helpful.

• Clarity of Axis Labels: The axis labels are generally clear. However, similar to previous figures, the y-axis label ("Risk-adjusted mortality odds ratio") could be more descriptive. It could be expanded to something like "Adjusted Odds Ratio of Death from Other Forms of Dementia (Relative to Chief Executives)." The x-axis is implicitly understood to represent different occupations, but this could be stated explicitly.
• Effectiveness of Color Coding: The use of different colors to represent different occupations is effective for visual differentiation. The legend, although not shown in the provided text, is presumably clear and easy to understand.
• Explanation of Logarithmic Scale: The explanation of the logarithmic scale in the caption is accurate but might be difficult for readers unfamiliar with this concept. A more intuitive explanation or a visual aid within the figure itself could improve understanding.
• Overall Clarity and Accessibility: The figure effectively communicates the finding that the pattern of lower mortality observed in taxi and ambulance drivers for Alzheimer's disease is not seen for other forms of dementia. However, the figure's reliance on odds ratios and the use of a logarithmic scale might make it less accessible to readers without a strong statistical background.
• Caption Completeness: The caption provides a thorough description of the figure's components and the methods used. However, it could benefit from a brief introductory sentence that summarizes the figure's main message or purpose. Additionally, while it mentions the adjustment variables, it could briefly reiterate why these adjustments are necessary for the analysis.