Time to nursing home admission and death in people with dementia: systematic review and meta-analysis

Table of Contents

Overall Summary

Study Background and Main Findings

This systematic review and meta-analysis synthesized data from 261 studies, encompassing over 5.5 million participants, to provide a comprehensive overview of dementia prognosis. The study found that median survival from diagnosis was significantly influenced by age, ranging from 8.9 years for women at age 60 to 2.2 years for men at age 85. Women generally exhibited shorter survival than men, primarily due to later age at diagnosis. Survival was longer in Asia compared to the US and Europe, and longer for Alzheimer's disease compared to other dementia types. The median time to nursing home admission was 3.3 years, with 13% of individuals admitted within the first year after diagnosis, increasing to 57% at five years. Clinical characteristics and study methodology explained 51% of the heterogeneity in survival and 55% of the heterogeneity in nursing home admission.

Research Impact and Future Directions

The study provides a comprehensive and valuable synthesis of the current evidence on dementia prognosis, clearly demonstrating the significant impact of age, sex, and dementia subtype on survival and nursing home admission. While the study makes a strong case for moving towards individualized prognostic information, it also highlights the limitations of existing research and the need for more rigorous and inclusive studies. The distinction between correlation and causation is appropriately maintained throughout the paper, with the authors acknowledging that observed associations do not necessarily imply causal relationships.

The practical utility of the findings is substantial, offering clinicians and researchers valuable tools for estimating prognosis and informing care planning. The study's findings are well-placed within the existing literature, building upon previous research while also identifying critical gaps and areas for improvement. The age- and sex-specific estimates, in particular, provide a more nuanced understanding of dementia prognosis compared to previous, more generalized estimates.

Clinicians can use the findings to engage in more informed discussions with patients and families about prognosis and long-term care needs, particularly regarding the likelihood of nursing home admission. However, the study also emphasizes the need for caution when applying these estimates, particularly those derived from clinic-based samples, to the broader population. The authors rightly highlight the uncertainties inherent in prognostic estimates and the need for ongoing research to refine these estimates and address the identified limitations.

A critical unanswered question is the extent to which these findings can be generalized to underrepresented populations, particularly those from Africa and Latin America. While the methodological limitations, such as the potential for bias due to missing data and the heterogeneity in study methodologies, are acknowledged, they do not fundamentally undermine the study's conclusions. However, future research should prioritize addressing these limitations to enhance the robustness and generalizability of the findings. Further investigation into the impact of specific comorbidities and the development of more sophisticated statistical models to account for competing risks are also crucial next steps.

Critical Analysis and Recommendations

Comprehensive Data Synthesis (written-content)
The study effectively synthesized a large body of research, encompassing 261 studies and over 5.5 million participants. This provides a robust overview of dementia prognosis, enhancing the reliability and generalizability of the findings.
Section: Abstract
Detailed Analysis of Prognostic Factors (written-content)
The study meticulously examined the impact of various patient and study characteristics on survival and nursing home admission. This provides valuable insights into the heterogeneity of dementia prognosis and highlights the importance of individualized assessment.
Section: Results
Effective Use of Visualizations (graphical-figure)
The inclusion of figures, such as boxplots and bubble plots, enhances the reader's understanding of the data. These visualizations facilitate comparison of outcomes across different subgroups, making complex data more accessible.
Section: Results
Thorough Discussion of Methodological Considerations (written-content)
The Discussion demonstrates a strong understanding of methodological issues, particularly regarding survival analysis and competing risks. This critical appraisal enhances the credibility of the study's interpretations and provides valuable guidance for future research.
Section: Discussion
Clarify Statistical Methods (written-content)
The Abstract lacks specific details on the statistical methods used for the meta-analysis. Including this information would enhance methodological transparency and allow readers to better assess the rigor of the analysis.
Section: Abstract
Expand on Implications for Clinical Practice (written-content)
The Discussion could provide more specific guidance on how the findings can be translated into clinical practice. Elaborating on the use of age- and sex-specific estimates in patient counseling and care planning would enhance the practical relevance of the study.
Section: Discussion
Strengthen the Discussion of Future Research Directions (written-content)
The Discussion could provide a more detailed roadmap for future research. Providing specific recommendations for study design, data collection, and analysis would enhance the study's contribution to the field and guide future investigations.
Section: Discussion
Expand on the Call for Diverse and Inclusive Research (written-content)
The Conclusions section could provide a more explicit and detailed call for diverse and inclusive research. Emphasizing the importance of addressing disparities and ensuring applicability to all populations would strengthen the study's contribution to health equity.
Section: Conclusions

Section Analysis

Abstract

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 1 | Study characteristics. Values are number (percentage) unless stated...
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Table 1 | Study characteristics. Values are number (percentage) unless stated otherwise

Figure/Table Image (Page 3)
Table 1 | Study characteristics. Values are number (percentage) unless stated otherwise
First Reference in Text
Table 1 shows the characteristics of the included studies.
Description
  • Purpose of the Table: This table summarizes the key features of all the studies that the authors examined in their research. It's like a detailed overview that helps readers understand what kind of studies were included and what their main attributes were. Imagine it as a spreadsheet that lists all the important details of each study, allowing for a quick comparison and understanding of the data collected.
  • Organization of the Table: The table is organized into columns and rows. Each row represents a different characteristic of the studies, such as the location where the study was conducted, the type of dementia that was studied, or the setting of the study (like a clinic or a community). Each column provides information about these characteristics, either for all studies combined or for specific groups of studies, such as those focused on mortality or nursing home admissions.
  • Types of Data Presented: The table mainly presents two types of data: numbers and percentages. For example, it might show that '243 (55)' studies were conducted in Europe, meaning 243 studies, which make up 55% of the total, were from Europe. Some rows show a 'median' and 'interquartile range (IQR)'. The median is the middle value in a dataset - think of it like the value that separates the higher half from the lower half. The IQR shows the range within which the middle 50% of the values fall. It gives you an idea of how spread out the data is around the median. For example, if the median age is 78.8 and the IQR is 75.4-82.0, it means that half of the studies had an average participant age between 75.4 and 82.0 years.
  • Specific Characteristics Covered: The table covers a wide range of study characteristics. This includes demographic information like the average age and percentage of women in the studies, the geographical location of the studies, the types of dementia examined (such as Alzheimer's disease or vascular dementia), the setting of the study (clinic, community, or nursing home), the method used to confirm the dementia diagnosis (like clinical examination or medical records), and the period when the study was conducted. It also includes details about the study's design, such as whether it was observational or interventional. Observational studies simply observe participants without intervening, while interventional studies involve some kind of treatment or intervention to see its effect.
Scientific Validity
  • Relevance of Included Characteristics: The characteristics included in Table 1 are highly relevant to the study's objectives. They cover demographic, methodological, and clinical variables that could potentially influence the outcomes of survival and nursing home admission in dementia patients. The inclusion of variables like age, sex, dementia type, study setting, and geographical location allows for an assessment of how these factors might contribute to heterogeneity in outcomes.
  • Completeness of Data: The table provides a comprehensive overview of the included studies, but there is some missing data, as acknowledged in the footnote. For instance, data on study start year, maximum follow-up time, sex, and age are missing for a small percentage of studies. While this is a limitation, the authors have been transparent about it. The impact of this missing data on the overall analysis should be further discussed in the limitations section.
  • Methodological Rigor: The table reflects a rigorous methodological approach in terms of the breadth of characteristics assessed. By including details on case ascertainment, time of inclusion, and study enrolment period, the authors provide a clear picture of the methodological diversity of the included studies. This is crucial for a systematic review and meta-analysis, as it allows readers to understand the potential sources of bias and variability in the results.
  • Potential for Bias: The characteristics presented in the table can help identify potential sources of bias. For example, the table shows that a significant proportion of studies were conducted in clinic-based settings, which might introduce selection bias as clinic populations may differ from the general population of people with dementia. Additionally, the varying methods of case ascertainment (clinical examination, medical records, registry) could introduce different types of bias.
Communication
  • Clarity of Presentation: The table is generally well-organized and easy to understand. The use of bold text for categories and the clear labeling of columns and rows enhance readability. The caption is concise and accurately describes the table's content.
  • Use of Abbreviations: The table uses abbreviations such as 'IQR' and 'NA'. While 'IQR' (Interquartile Range) is defined in the footnote, 'NA' (Not Applicable) is not explicitly defined within the table's context, which could be clarified. However, most readers familiar with this type of research will likely understand these abbreviations.
  • Footnote for Clarification: The footnote provides important information about the data presented, such as the handling of study populations and the presence of missing data. This adds to the transparency and helps readers interpret the table accurately.
  • Visual Appeal: The table is visually uncluttered, which aids in comprehension. The use of horizontal lines to separate different sections of the table is effective. However, the table is quite dense, and some readers might find it overwhelming. Consider if splitting the table into smaller, more focused tables could improve readability without sacrificing comprehensiveness.
Fig 1 | Bubble plots of median survival according to age at dementia diagnosis,...
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Fig 1 | Bubble plots of median survival according to age at dementia diagnosis, stratified by time of inclusion and study setting, and of median time to nursing home admission, according to age at diagnosis

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Fig 1 | Bubble plots of median survival according to age at dementia diagnosis, stratified by time of inclusion and study setting, and of median time to nursing home admission, according to age at diagnosis
First Reference in Text
Median survival was 4.8 years from incident diagnosis onwards (IQR 4.0-6.0; 66 studies), in line with an overall five year survival probability of 51%. In 53 studies among people with prevalent dementia, survival was a median 3.1 years (IQR 2.4-5.6) (fig 1).
Description
  • Purpose of the Figure: This figure uses bubble plots to show the relationship between the age at which someone is diagnosed with dementia and how long they are expected to live afterward (median survival), or how long it takes before they are admitted to a nursing home. Think of it like a visual way to see how age impacts these two outcomes. It also breaks down the survival data based on when the dementia was included in the study (at diagnosis or later) and where the study was conducted (like a clinic or a nursing home).
  • Structure of the Bubble Plots: A bubble plot is a type of graph where each point (or 'bubble') represents data from one study. The position of the bubble along the horizontal axis (x-axis) shows the average age of dementia diagnosis in that study. The position along the vertical axis (y-axis) shows either the median survival time or the median time to nursing home admission. The size of each bubble indicates the number of participants in that study – a bigger bubble means more participants. Imagine bigger bubbles as representing studies with more weight or more certainty because they include more people.
  • Stratification in the Plots: The survival data is 'stratified,' which means it's divided into subgroups based on two factors: 'time of inclusion' and 'study setting.' 'Time of inclusion' refers to whether the study participants were included right when they were diagnosed ('incident') or at some point after diagnosis ('prevalent'). 'Study setting' refers to the type of place where the study was conducted, such as a clinic, a community, or a nursing home. Each of these subgroups is represented by a different line and set of bubbles on the plot, making it easy to compare them. It's like having separate mini-graphs within the main graph for each subgroup.
  • Median Time to Nursing Home Admission: In addition to survival time, the figure also includes a separate plot showing the median time to nursing home admission. This is simply the average time it took for people in each study to be admitted to a nursing home after their dementia diagnosis. This plot is also organized by age at diagnosis, allowing you to see how age influences the time to nursing home admission.
Scientific Validity
  • Appropriateness of Visualization Technique: Bubble plots are an appropriate choice for visualizing the relationship between three variables: age at diagnosis, median survival/time to nursing home admission, and study size. The use of different lines for different strata (time of inclusion and study setting) allows for a clear comparison between these groups. However, it's important to note that the plots are based on aggregated study-level data, not individual patient data. This could potentially mask within-study variations.
  • Clarity of Stratification Variables: The choice of stratification variables (time of inclusion and study setting) is justified and relevant to the research question. These factors are known to potentially influence survival and nursing home admission in dementia. The clear labeling of these strata in the figure enhances the interpretability of the results.
  • Representation of Study Size: The use of bubble size to represent study size is a strength, as it visually conveys the weight that each study contributes to the overall picture. Larger studies, which presumably provide more precise estimates, are given more visual prominence. However, the exact scaling of the bubble sizes should be clearly defined in the figure legend or methods section.
  • Limitations of Aggregated Data: As mentioned earlier, the figure is based on aggregated study-level data. This means that the results represent averages across studies and may not accurately reflect the experiences of individual patients. Additionally, the figure does not account for potential confounding variables that might influence the relationship between age at diagnosis and survival/time to nursing home admission. A more detailed analysis of these factors is presented in subsequent sections of the paper.
Communication
  • Clarity of Axis Labels: The axes are clearly labeled with appropriate units (years). The use of different y-axis labels for the survival and nursing home admission plots is also clear and easy to understand.
  • Legend and Key: The figure includes a legend that explains the different colors and shapes used to represent the different strata. This is essential for interpreting the figure correctly. However, the legend could be improved by explicitly stating what the size of the bubbles represents (i.e., the number of participants).
  • Overall Visual Appeal: The figure is visually appealing and uncluttered. The use of different colors and shapes for the different strata makes it easy to distinguish between them. The trend lines also help to visualize the overall relationship between age at diagnosis and survival/time to nursing home admission.
  • Potential for Misinterpretation: While the figure is generally well-designed, there is a potential for misinterpretation if readers are not familiar with bubble plots or the concept of stratification. The caption could be improved by briefly explaining these concepts. Additionally, the figure does not include any indication of the uncertainty around the estimates (e.g., confidence intervals). This could lead readers to overinterpret the precision of the results.
Fig 2 | Boxplots of yearly probabilities for survival and nursing home...
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Fig 2 | Boxplots of yearly probabilities for survival and nursing home admission, with boxes indicating 25th to 75th centiles (IQR) and whiskers depicting 1.5 times the interquartile range (capped off at most extreme observations within this range)

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Fig 2 | Boxplots of yearly probabilities for survival and nursing home admission, with boxes indicating 25th to 75th centiles (IQR) and whiskers depicting 1.5 times the interquartile range (capped off at most extreme observations within this range)
First Reference in Text
Yearly probabilities of survival ranged from 90% at one year after diagnosis to 69% at three years’ follow-up, 51% at five years, and 21% at 10 years (fig 2).
Description
  • Purpose of the Figure: This figure uses boxplots to show the yearly probabilities of survival and of being admitted to a nursing home for people with dementia. Imagine you're tracking a group of people with dementia over time. Each year, you check how many are still alive and how many have been admitted to a nursing home. This figure summarizes those yearly percentages for multiple studies.
  • Structure of Boxplots: A boxplot is a way to visualize the distribution of data. In this figure, each boxplot represents one year after diagnosis. The 'box' part of the boxplot shows the range where the middle 50% of the data falls (the interquartile range or IQR). Think of it like this: if you lined up all the studies' yearly survival rates from lowest to highest, the box would cover the rates from the 25th percentile (the value at the 25% mark) to the 75th percentile (the value at the 75% mark). The line inside the box is the median – the middle value. The 'whiskers' extending from the box usually show the range of the data, excluding outliers. Here, they represent 1.5 times the IQR, which is a common way to show the spread of the data while identifying potential extreme values. Any data points beyond the whiskers are considered potential outliers.
  • Yearly Probabilities for Survival: The top part of the figure shows the yearly probabilities of survival. Each boxplot represents a different year after diagnosis, from year 1 to year 10. For each year, the boxplot summarizes the survival probabilities reported by different studies. For example, the boxplot for year 1 might show that across all studies, the median survival probability was 90%, with the middle 50% of studies reporting survival probabilities between, say, 85% and 95%.
  • Yearly Probabilities for Nursing Home Admission: The bottom part of the figure shows the yearly probabilities of nursing home admission. This is similar to the survival plot, but instead of showing the percentage of people still alive, it shows the percentage of people admitted to a nursing home each year after diagnosis. The boxplots here represent years 1 through 5. For each year, the boxplot summarizes the nursing home admission probabilities reported by different studies.
  • Number of Studies per Year: The numbers at the bottom of each plot (e.g., 'Studies 244 240 219...') indicate how many studies provided data for each year. For example, 244 studies provided data for survival at year 1, while only 87 studies provided data for survival at year 10. This is important because it shows that the estimates for later years are based on fewer studies and might, therefore, be less reliable.
Scientific Validity
  • Appropriateness of Boxplots: Boxplots are an appropriate choice for visualizing the distribution of yearly probabilities across multiple studies. They effectively summarize the central tendency (median) and spread (IQR) of the data and allow for easy comparison of probabilities across different years. However, it's important to note that boxplots do not show the individual data points (i.e., the yearly probabilities reported by each study). This could potentially mask important variations between studies.
  • Handling of Outliers: The use of whiskers to represent 1.5 times the IQR is a standard method for identifying potential outliers. However, the figure does not explicitly show these outliers. While this is not necessarily a major limitation, it would be helpful to indicate the number of outliers and their values, as this could provide further insights into the variability of the data.
  • Limitations of Yearly Probabilities: The yearly probabilities presented in the figure are based on aggregated study-level data and may not accurately reflect the experiences of individual patients. Additionally, the figure does not account for potential confounding variables that might influence survival and nursing home admission. It's also important to note that the number of studies contributing to each year's estimate decreases over time, particularly for the later years. This could affect the precision and reliability of the estimates for those years.
  • Comparison of Survival and Nursing Home Admission: The figure effectively compares the yearly probabilities of survival and nursing home admission by presenting them in separate but adjacent plots. This allows for a direct visual comparison of these two outcomes over time. However, it's important to note that the two plots have different time scales (10 years for survival vs. 5 years for nursing home admission). This should be made clear in the caption or figure legend.
Communication
  • Clarity of Axis Labels: The axes are clearly labeled, with the y-axis representing the probability (from 0 to 1.0) and the x-axis representing the time since diagnosis (in years). The use of separate plots for survival and nursing home admission is also clear and easy to understand.
  • Legend and Caption: The figure includes a caption that explains the components of the boxplots (boxes indicating the IQR and whiskers representing 1.5 times the IQR). This is essential for interpreting the figure correctly. However, the caption could be improved by briefly explaining the concept of yearly probabilities and by explicitly stating that the two plots have different time scales.
  • Overall Visual Appeal: The figure is visually appealing and uncluttered. The use of blue color for the boxplots is easy on the eyes. The horizontal lines within the boxes (representing the median) are clearly visible. The inclusion of the number of studies for each year at the bottom of each plot is helpful.
  • Potential for Misinterpretation: While the figure is generally well-designed, there is a potential for misinterpretation if readers are not familiar with boxplots or the concept of yearly probabilities. The caption could be improved by providing a more detailed explanation of these concepts. Additionally, the figure does not include any indication of the uncertainty around the estimates (e.g., confidence intervals). This could lead readers to overinterpret the precision of the results.
Table 2 | Effect of clinical and study characteristics on survival
Figure/Table Image (Page 6)
Table 2 | Effect of clinical and study characteristics on survival
First Reference in Text
Consequently, in contemporary studies the remaining life expectancy from diagnosis onwards varied from 6.5 years at mean age 60 years to 2.2 years at mean age 85 years for men, and from 8.9 years to 4.5 years at the same ages for women (see supplementary table S8).
Description
  • Purpose of the Table: This table shows how different factors, related to the patients and the studies themselves, are linked to how long people with dementia survive after diagnosis. It's like trying to understand what influences survival time - is it the person's age, the type of dementia they have, where the study was conducted, or something else? The table breaks down these factors and shows their individual and combined effects on survival time.
  • Organization of the Table: The table is organized into rows and columns. Each row represents a specific characteristic, like age, sex, type of dementia, or the setting of the study. The columns show the results of statistical analyses that were performed to see how each characteristic is related to survival time. These analyses are presented as 'models,' which are basically different ways of looking at the data.
  • Statistical Models: The table presents three different statistical models: Model 1 (univariable), Model 2 (age and sex adjusted), and Model 3 (full model). A univariable analysis looks at the effect of each characteristic on survival time, one at a time. For example, how does age alone relate to survival? An age and sex adjusted analysis, as in Model 2, looks at the effect of each characteristic while taking into account the effects of age and sex. This is important because age and sex are known to strongly influence survival. Model 3 is the 'full model' - it looks at the effects of all characteristics simultaneously. It's like putting all the pieces of the puzzle together to see the big picture. A common type of univariable analysis is a linear regression, which finds the best-fitting line through a scatter plot of data points to show the relationship between two variables. Multivariate analysis is an extension of this, but with multiple variables.
  • Beta Coefficients and P-values: For each characteristic in each model, the table shows a 'beta coefficient' (β) and a 'P-value'. The beta coefficient is a number that indicates the strength and direction of the relationship between the characteristic and survival time. For example, a negative beta coefficient for age means that older age is associated with shorter survival. The 95% CI is the likely range of the true value, with 95% certainty. The P-value is a measure of the statistical significance of the relationship. A P-value less than 0.05 generally means that the relationship is statistically significant, which means it's unlikely to be due to chance. If you were to repeat the study many times, a p-value below 0.05 would suggest that you'd find a similar relationship in at least 95% of those repetitions. It's like saying there's strong evidence that the characteristic is truly associated with survival time.
  • Reference Categories: For some characteristics that have different categories (like dementia type or study setting), one category is chosen as the 'reference' category. This is the baseline against which the other categories are compared. For example, in the 'Dementia type' row, 'All cause' is the reference category. The beta coefficients for the other categories (Alzheimer's disease and Other) show how survival time in those categories differs from survival time in the 'All cause' category. In this case, the positive values for Alzheimer's and Other indicate that those with these diagnoses have a longer median survival time than those with 'all cause' dementia.
Scientific Validity
  • Appropriateness of Statistical Models: The use of three different statistical models (univariable, age and sex adjusted, and full model) is a strength of the study. This allows for a comprehensive assessment of the effects of different characteristics on survival, both individually and in combination. The choice of a full model including all variables is particularly important, as it allows for the control of potential confounding factors. The specific regression method used is appropriate for this type of analysis.
  • Selection of Variables: The variables included in the table are relevant to the research question and are based on existing literature on prognostic factors in dementia. The inclusion of demographic variables (age, sex), clinical variables (dementia type), and study characteristics (setting, geographical location, enrolment period, case ascertainment, time of inclusion) provides a comprehensive overview of potential predictors of survival.
  • Interpretation of Beta Coefficients: The interpretation of the beta coefficients is generally accurate and consistent with the principles of regression analysis. The table correctly identifies the direction and magnitude of the associations between different characteristics and survival. However, it's important to note that the beta coefficients represent the average effect of each characteristic across all studies included in the meta-analysis. These effects may vary between individual studies.
  • Consideration of Confounding: The use of a full model (Model 3) allows for the control of potential confounding factors. This is important because the relationship between one characteristic and survival might be influenced by other characteristics. For example, the effect of study setting on survival might be confounded by age, as studies conducted in nursing homes are likely to include older patients. By including all variables in the full model, the authors have attempted to isolate the independent effect of each characteristic.
  • Limitations of Meta-Regression: While meta-regression is a powerful tool for exploring heterogeneity in meta-analysis, it has limitations. The results are based on aggregated study-level data, not individual patient data. This means that the findings represent average effects across studies and may not accurately reflect the relationships between variables within individual studies. Additionally, the quality of the meta-regression depends on the quality of the included studies and the accuracy of the reported data.
Communication
  • Clarity of Column Headings: The column headings are clear and informative. They accurately describe the content of each column, including the type of model (univariable, age and sex adjusted, full model), the beta coefficients, the 95% confidence intervals, and the P-values.
  • Use of Abbreviations: The table uses abbreviations such as 'β' for beta coefficient and 'CI' for confidence interval. These abbreviations are standard in statistical reporting and are likely to be understood by the target audience (researchers and clinicians in the field of dementia). The abbreviation 'CI' is defined in the footnote.
  • Footnote for Clarification: The footnote provides important information about the interpretation of the table, including the definition of the reference categories for categorical variables and the explanation of the beta coefficients for continuous variables. It also clarifies that the P-value for years of study enrolment represents the trend across categories and mentions the R² value of the full model. This adds to the transparency and helps readers interpret the table accurately.
  • Overall Readability: The table is relatively complex due to the large number of variables and the presentation of three different statistical models. However, the clear organization, the use of bold text for variable names, and the consistent formatting contribute to its overall readability. The table could potentially be improved by adding a brief explanation of the concept of meta-regression and the interpretation of beta coefficients in the table caption or methods section.
Fig 3 | Median survival by type of dementia
Figure/Table Image (Page 7)
Fig 3 | Median survival by type of dementia
First Reference in Text
For dementia subtypes, a higher share of included patients with Alzheimer’s disease was associated with longer survival than all cause dementia (fig 3 and table 2).
Description
  • Purpose of the Figure: This figure uses histograms to show the median survival times for different types of dementia. It's like comparing how long people live after being diagnosed with different forms of dementia, such as Alzheimer's disease, vascular dementia, or frontotemporal dementia. The median survival time is the middle value – half of the people lived longer than this time, and half lived shorter.
  • Structure of Histograms: A histogram is a type of bar graph that shows the distribution of a numerical variable. In this case, the numerical variable is the median survival time. Each bar in the histogram represents a range of survival times (e.g., 0-2 years, 2-4 years, etc.). The height of each bar indicates the number of studies that reported a median survival time within that range. For example, a taller bar for the 4-6 year range would mean that many studies found a median survival time between 4 and 6 years for that type of dementia.
  • Types of Dementia Compared: The figure compares the median survival times for six different categories: 'All cause' dementia (meaning all types combined), Alzheimer's disease, vascular dementia, frontotemporal dementia, Lewy body dementia, and Parkinson's disease dementia. There is also a category called 'Other dementia types'. Each category has its own histogram, making it easy to compare the distribution of survival times across different types of dementia.
  • Median Survival Times: The median survival time for each type of dementia is indicated by a number next to the name of the dementia type (e.g., '4.0' for 'All cause'). This number represents the middle value of the median survival times reported by all studies that looked at that particular type of dementia. It's like the average of the averages. For example, if the median survival time for Alzheimer's disease is 5.7, it means that across all studies that examined Alzheimer's disease, the middle value of the reported median survival times was 5.7 years.
Scientific Validity
  • Appropriateness of Histograms: Histograms are an appropriate choice for visualizing the distribution of median survival times across different studies for each type of dementia. They effectively show the range and frequency of reported survival times, allowing for a visual comparison of survival across different dementia types. However, it's important to note that histograms do not show the individual data points (i.e., the median survival time reported by each study). This could potentially mask important variations between studies.
  • Selection of Dementia Types: The selection of dementia types is relevant to the research question and reflects the major subtypes of dementia. The inclusion of an 'All cause' category allows for an overall comparison, while the separate categories for specific dementia types provide a more detailed picture of survival differences. However, the 'Other dementia types' category is quite broad and may include a heterogeneous group of dementias. It would be helpful to provide a more detailed breakdown of this category, if possible.
  • Limitations of Median Survival Times: The use of median survival times provides a useful summary measure of central tendency, but it has limitations. The median does not capture the full distribution of survival times and may not accurately reflect the experiences of individual patients. Additionally, the median survival times are based on aggregated study-level data and may not account for potential confounding variables that might influence survival. The figure should ideally be interpreted in conjunction with other analyses, such as those presented in Table 2, which examine the effects of different characteristics on survival.
  • Comparability of Studies: The scientific validity of the comparison across different dementia types depends on the comparability of the studies included in each category. If the studies differ significantly in terms of patient populations, study settings, or methods of diagnosis, the comparison of median survival times may be misleading. The paper should provide further details on the characteristics of the studies included in each category to allow readers to assess their comparability.
Communication
  • Clarity of Axis Labels: The x-axis is clearly labeled as 'Median survival times (years),' and the y-axis is labeled as 'No of studies.' These labels are appropriate and easy to understand. The use of separate histograms for each dementia type is also clear and facilitates comparison.
  • Legend and Caption: The figure includes a caption that briefly explains the purpose of the figure ('Median survival by type of dementia'). However, the caption could be improved by providing a more detailed explanation of what a histogram is and how to interpret the bars. Additionally, the caption should explicitly state that the numbers next to each dementia type represent the median of the median survival times reported by all studies for that type of dementia.
  • Overall Visual Appeal: The figure is visually appealing and uncluttered. The use of different colors for each histogram could enhance readability, although the current gray scale is sufficient. The horizontal lines within each histogram (representing the median) are not present in this figure, which makes it difficult to visually estimate the median survival time for each dementia type.
  • Potential for Misinterpretation: While the figure is generally well-designed, there is a potential for misinterpretation if readers are not familiar with histograms or the concept of median survival times. The caption could be improved by providing a more detailed explanation of these concepts. Additionally, the figure does not include any indication of the uncertainty around the estimates (e.g., confidence intervals). This could lead readers to overinterpret the precision of the results. It is also not immediately clear that the number near each histogram's name is meant to be a numerical representation of the median survival time for that category, and not a label for the histogram itself.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Key Aspects

Strengths

Suggestions for Improvement

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