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Created: Sep 20, 2019
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Depression Scores in Older Adults
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Introduction

Depression is a diagnosable condition that is classified as a mood disorder. Depression can foster long-term symptoms such as overwhelming sadness, low energy, loss of appetite and lack of interest in things that once brought someone happiness. Depression affects nearly 16.2 million adults in the United States, however depression is less prevalent among older adults than it is younger adults. (Amy Fiske, 2009) Depression in older adults is associated with increased risk of death and disability. Cognitive impairment, increased risk of dementia and increased risk of suicide are all symptoms of depression that are suffered more by older adults than younger adults. (Joanne Rodda, 2011) Depression can be caused by a vast number of things, some of those being seasonal depression, if a person has any comorbidities, or where they live. A long-term study was done on individuals 65 years of age or older to investigate the relationship between geographical location, health status, and depression.

Methods

Researchers at Wentworth Medical Center in upstate New York gathered information from 20 healthy individuals in three different geographical locations: Florida, New York and North Carolina. Each person was then given a standardized test to measure depression where higher scores indicate higher levels of depression. Similarly, information was gathered from 20 individuals with one or more comorbidities, arthritis, hypertension, and/or heart ailment, for each of the three geographical locations. Each of these individuals were also given the standardized test to measure depression.

Analysis

A QQ-Plot for residuals was used to check normality and a scatter plot of residuals vs. fits was used to check constant variability. Nearly all points in the QQ-Plot are generally close to the diagonal line, which indicates no evidence of deviation from normality. The scatter plot shows some grouping between 5 to 8 and 14 to 16, however the vertical variability doesn’t show that any are substantially larger or smaller compared to the rest, so we are comfortable assuming constant variability.

Result 1: QQ Plot   [Info]
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Result 2: Scatter Plot   [Info]
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A two-way ANOVA design with interaction was used to compare the geographical location and comorbidities on depression scores. We find that with a (p-value = 0.262), there is no evidence of an interaction in this case. Analyzing our main effects separately, we see that Location has a (p-value = 0.0516) while Health has a (p-value <0.0001), which shows us that there is not a difference in group means among location, but there is a difference in group means among health. We will look at Tukey comparisons to examine our main effects for differences in more detail.

Result 3: Two Way ANOVA   [Info]

Two Way Analysis of Variance results:


Responses: Score
Row factor: Location
Column factor: Health

ANOVA table


SourceDFSSMSF-StatP-value
Location254.01666727.0083333.04364370.0516
Health11778.71778.7200.44662<0.0001
Interaction224.0512.0251.35513050.262
Error1141011.68.8736842
Total1192868.3667

Means table


CMBHealthy
Florida14.55.5510.025
New York15.25811.625
North Carolina13.957.0510.5
14.5666676.866666710.716667

Tukey HSD results (95% level) for Location:


Florida subtracted from
DifferenceLowerUpperP-value
New York1.60.0182111423.18178890.0468
North Carolina0.475-1.10678892.05678890.7563
New York subtracted from
DifferenceLowerUpperP-value
North Carolina-1.125-2.70678890.456788860.2138

Tukey HSD results (95% level) for Health:


CMB subtracted from
DifferenceLowerUpperP-value
Healthy-7.7-8.777393-6.622607<0.0001

Tukey HSD results (95% level) for Location*Health:


Florida,CMB subtracted from
DifferenceLowerUpperP-value
Florida,Healthy-8.95-11.680654-6.2193455<0.0001
New York,CMB0.75-1.98065453.48065450.9676
New York,Healthy-6.5-9.2306545-3.7693455<0.0001
North Carolina,CMB-0.55-3.28065452.18065450.9919
North Carolina,Healthy-7.45-10.180654-4.7193455<0.0001
Florida,Healthy subtracted from
DifferenceLowerUpperP-value
New York,CMB9.76.969345512.430654<0.0001
New York,Healthy

Since the p-value for location is close to our significance level of 0.05, I’d still like to add it in our discussion of differences, for the sake of understanding.

With 95% confidence we estimate that average scores for depression are 0.018 to 3.18 higher for individuals living in New York than for individuals living in Florida. However, the average scores for individuals living in New York or North Carolina may be up to 2.71 points different but the study results did not show evidence of differences.

With 95% confidence we estimate that average scores for depression are 6.62 to 8.78 higher for individuals with comorbidities than for individuals that are healthy.

 Discussion

While our p-value showed no evidence of interaction between comorbidities and geographical location on depression scores in older adults, we were able to find differences in location and health status separately based on Tukey pairwise comparisons. Individuals with comorbidities are on average more depressed than those individuals who are healthy, while individuals living in Florida are on average less depressed than those individuals living in New York or North Carolina.

Conclusion

The purpose of this study was to investigate the relationship between geographical location, health status and depression. We’ve found no evidence that depression relates to location. However, we might suspect that individuals living in Florida are less depressed than individuals living elsewhere because of the sunshine and beaches within Florida, but geographical location is ultimately a personal choice that people most likely have the ability to change if they want. We did find that individuals with comorbidities are more likely to have more severe depression than individuals that are healthy, which is reasonable to think that older adults tend to be more depressed when they have one or multiple severe health conditions that could potentially shorten their life span.

References

Amy Fiske, J. L. (2009). Depression in Older Adults. Annual Review of Clinical Psychology, 363-389.

 

Joanne Rodda, Z. W. (2011). Depression in Older Adults. The BMJ, 343.

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=90658">Depression Scores in Older Adults</A>

Comments
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By nku.dr.nolan
Sep 23, 2019

Hi Kelsey - glancing through this looks pretty good. Final focus - do you think it's more important to compare comorbidities, or states? (in terms of things we can impact/use?) Another question to think about - how would these study results apply in Kentucky?
By karlystone123
Sep 21, 2019

Hi Kelsey,

Great report! I do think our analysis is very similar, but I did not do a scatter plot with the QQ. I realize this is an opportunity to compare the groups and means for multiple factors and comparisons. I agree that showing the geographical location effect was important even though it was not necessary.
By karlystone123
Sep 21, 2019

Hi Kelsey,

Great report! I do think our analysis is very similar, but I did not do a scatter plot with the QQ. I realize this is an opportunity to compare the groups and means for multiple factors and comparisons. I agree that showing the geographical location effect was important even though it was not necessary.
By teagin.woodrum11
Sep 20, 2019

Hey, great report! And I know you clarified that location wasn't really of significance but you may make rough inferences based on the individual CI's. However I do disagree with one statement in your report. In the discussion you said this:

"...while individuals living in Florida are on average less depressed than those individuals living in New York or North Carolina."

However the CI when comparing Florida to North Carolina contains zero and the p-value is 0.756 so I don't think your statement was accurate. I think it would have been best to just compare Florida to New York.
By dannellyd2
Sep 20, 2019

Kelsey,
I did the same one you did. I like how you still looked into the relationship of location even though the p-value was large enough to not have to. It would make sense in my head as Laura mentioned below that someone in Florida might have a lower depression score rather than someone in New York but the results say otherwise.
By dannellyd2
Sep 20, 2019

Kelsey,
I did the same one you did. I like how you still looked into the relationship of location even though the p-value was large enough to not have to. It would make sense in my head as Laura mentioned below that someone in Florida might have a lower depression score rather than someone in New York but the results say otherwise.
By hoskinsk3
Sep 20, 2019

Kelsey, great report. The report I created show interaction so it was interesting to review your report which did not have interaction. Your conclusion was spot on and your statistical analysis was very good also.
By laura.boettcher24
Sep 20, 2019

Kelsey,
I must agree with your conclusions that you have come up with from this report. It makes sense that individuals with comorbidities are more likely to be depressed. And yes, you did mention geography is a personal choice, but I think we all must admit...sunshine and beaches tend to make a lot of us happier. Because of that, it would make sense that Florida had people that were less depressed.
By cari.hollenkamp13
Sep 20, 2019

Kelsey,
Your report this week was very well put together. It was interesting to read about the possibility of location and it's effect on depression. Good report!

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