Exercise 1 Solution (A)
We will use the following article:
Palis, Marchand & Oviedo-Joekes. (2020). The relationship between sense of community belonging and self-rated mental health among Canadians with mental or substance use disorders. Journal of Mental Health, 29(2): 168-175. DOI: 10.1080/09638237.2018.1437602 (available in the “Library Online Course Reserves”: open link.
- Download the CCHS MH topical index
- Download the CCHS MH Data Dictionary
Question 1: [60% grade]
1(a) Importing dataset
1(b) Subsetting according to eligibility
Subset the dataset according to the eligibility criteria / restriction specified in the paper
- Identify the variable needed for eligibility criteria
Hint
- Read the first paragraph of Analytic sample (page 2) for the eligibility criteria
- Eligibility criteria was determined based on only one variable. Only work with ‘YES’ category.
1(c) Retaining necessary variables
In the dataset, retain only the variables associated with outcome measure, explanatory variable, potential confounders and survey weight. There should be eight variables (one outcome, one exposure, five confounders, and one survey weight).
Here are the steps:
Identify the outcome variable
Identify the explanatory variable
Identify the potential confounders
Identify the survey weight variable
-
Hint
- Read
- first and second paragraphs of Study variables for the outcome, explanatory and confounding variables
- third paragraph of the Statistical analyses for the survey weights variable.
- There were five potential confounders.
- Potentially useful functions for this exercise:
dat <- with(dat, data.frame(srmh = SCR_082, # Outcome - SMRH
community = GEN_10, # explanatory - community belonging
sex = DHH_SEX, # sex
age = DHHGAGE, # age
race = SDCGCGT, # respondent's racial identity
income = INCG7, # main source of income
help = PNC_01A, # received help for problems
weight = WTS_M)) # sampling weight1(e) Creating analytic dataset
Outcome variable has a category ‘NOT STATED’, but for our analysis, we will omit anyone associated with this category. Similarly, for explanatory variable, we have categories such as DON’T KNOW, REFUSAL and NOT STATED. We will omit anyone with these categories.
- Assign missing values for categories such as DON’T KNOW, REFUSAL and NOT STATED.
- Recode the variables as shown in Table 1 in the article. You can use any function/package of your choice. Here is an example (but feel free to use other functions. In R there are many other ways to do this same task.
## your code here
# levels(your.data.frame$your.age.variable) <-
# list("15 to 24 years" = c("15 TO 19 YEARS", "20 TO 24 YEARS"),
# "25 to 34 years" = c("25 TO 29 YEARS", "30 TO 34 YEARS"),
# "35 to 44 years" = c("35 TO 39 YEARS", "40 TO 44 YEARS"),
# "45 to 54 years" = c("45 TO 49 YEARS", "50 TO 54 YEARS"),
# "55 to 64 years" = c("55 TO 59 YEARS", "60 TO 64 YEARS"),
# "65 years or older" = c("65 TO 69 YEARS", "70 TO 74 YEARS",
# "75 TO 79 YEARS", "80 YEARS OR MORE"))
# Outcome variable: Self-rated Mental Health
#table(dat$srmh, useNA = "always")
dat$srmh <- car::recode(dat$srmh, " c('FAIR','POOR') = 'Poor or Fair';
'GOOD' = 'Good'; c('EXCELLENT', 'VERY GOOD') =
'Very good or excellent'; else = NA ")
dat$srmh <- factor(dat$srmh, levels=c("Poor or Fair", "Good", "Very good or excellent"))
# Explanatory variable: Community belonging
#table(dat$community, useNA = "always")
dat$community <- recode(dat$community, recodes = " 'VERY STRONG' = 'Very strong';
'SOMEWHAT STRONG' = 'Somewhat strong'; 'SOMEWHAT WEAK' =
'Somewhat weak'; 'VERY WEAK' = 'Very weak'; else = NA ")
dat$community <- factor(dat$community, levels = c("Very weak", "Somewhat weak",
"Somewhat strong", "Very strong"))
# Sex
#table(dat$sex, useNA = "always")
dat$sex <- recode(dat$sex, recodes = "'MALE' = 'Males'; 'FEMALE' = 'Females';
else = NA")
# Age group
#table(dat$age, useNA = "always")
levels(dat$age) <- list("15 to 24 years" = c("15 TO 19 YEARS", "20 TO 24 YEARS"),
"25 to 34 years" = c("25 TO 29 YEARS", "30 TO 34 YEARS"),
"35 to 44 years" = c("35 TO 39 YEARS", "40 TO 44 YEARS"),
"45 to 54 years" = c("45 TO 49 YEARS", "50 TO 54 YEARS"),
"55 to 64 years" = c("55 TO 59 YEARS", "60 TO 64 YEARS"),
"65 years or older" = c("65 TO 69 YEARS", "70 TO 74 YEARS",
"75 TO 79 YEARS", "80 YEARS OR MORE"))
# Race/Ethnicity
#table(dat$race, useNA = "always")
dat$race <- recode(dat$race, " 'WHITE'='White'; 'NON-WHITE'='Non-white'; else=NA ")
# Income
#table(dat$income, useNA = "always")
levels(dat$income) <- list("Employment Income" = "EMPLOYMENT INC.",
"Worker's Compensation" = "EI/WORKER'S COMP",
"Senior Benefits" = "SENIOR BENEFITS",
"Other" = "OTHER",
"Not applicable" = "NOT APPLICABLE")1(f) Number of columns and variable names
Report the number of columns in your analytic dataset, and the variable names.
Question 2: Table 1 [20% grade]
Reproduce Table 1 presented in the article (or see below). Omit the ‘Main source of income’ variable from the table. The table you produce should report numbers as follows, with all columns as shown in the table. In other words, the numbers should match.
| Self-rated Mental Health Variable | Total n(%) | Poor or Fair n(%) | Good n(%) | Very good or excellent n(%) |
|---|---|---|---|---|
| Study sample | 2628 (100) | 1002 (38.1) | 885 (33.7) | 741 (28.2) |
| Community belonging | ||||
| - Very weak | 480 (18.3) | 282 (28.1) | 118 (13.3)a | 80 (10.8)a |
| - Somewhat weak | 857 (32.6) | 358 (35.7) | 309 (34.9) | 190 (25.6) |
| - Somewhat strong | 1005 (38.2) | 288 (28.7) | 362 (40.9) | 355 (47.9) |
| - Very strong | 286 (10.9) | 74 (7.4)a | 96 (10.8)a | 116 (15.7)a |
| Sex | ||||
| - Females | 1407 (53.5) | 616 (61.5) | 487 (55.0) | 304 (41.0) |
| - Males | 1221 (46.5) | 386 (38.5) | 398 (45.0) | 437 (59.0) |
| Age group | ||||
| - 15 to 24 years | 740 (28.2) | 191 (19.1) | 264 (29.8) | 285 (38.5) |
| - 25 to 34 years | 475 (18.1) | 141 (14.1) | 167 (18.9) | 167 (22.5) |
| - 35 to 44 years | 393 (15.0) | 185 (18.5) | 119 (13.4)a | 89 (12.0)a |
| - 45 to 54 years | 438 (16.6) | 220 (22.0) | 139 (15.7) | 79 (10.7)a |
| - 55 to 64 years | 379 (14.4) | 198 (19.7) | 113 (12.8)a | 68 (9.2)a |
| - 65 years or older | 203 (7.7) | 67 (6.6)a | 83 (8.4)a | 53 (7.1)b |
| Race/Ethnicity | ||||
| - Non-white | 458 (17.4) | 184 (18.4) | 140 (15.8) | 134 (18.1) |
| - White | 2170 (82.6) | 818 (81.6) | 745 (84.2) | 607 (81.9) |
| Main source of income | ||||
| - Employment Income^d | 1054 (40.1) | 289 (28.8) | 386 (43.6) | 379 (51.1) |
| - Worker’s Compensation^e | 160 (6.1) | 91 (9.1)a | 44 (5.0)b | 25 (3.4)c |
| - Senior Benefits^f | 134 (5.1) | 57 (5.7)a | 42 (4.7)b | 35 (4.7) |
| - Other^g | 184 (7.0) | 82 (8.2)a | 60 (6.8)a | 42 (5.7)b |
| - Not applicable^h | 851 (32.4) | 402 (40.1) | 263 (29.7) | 186 (25.1) |
| - Not Stated^i | 245 (9.3) | 81 (8.1)a | 90 (10.2)a | 74 (10.0) |
\(^a\) Coefficient of variation between 16.6 and 25.0%. \(^b\) Coefficient of variation between 25.1 and 33.3%. \(^c\) Coefficient of variation > 33.3%. \(^d\) Employment Income: Wages/salaries or self-employment. \(^e\) Worker’s compensation: Employment insurance or worker’s compensation or social assistance/welfare. \(^f\) Senior Benefits: Benefits from Canada or Quebec Pension Plan or job related retirement pensions, superannuation and annuities or RRSP/RRIF of Old Age Security and Guaranteed Income Supplement. \(^g\) Other: Dividends/interest or child tax benefit or child support or alimony or other or no income. \(^h\) Not applicable: Respondents who live in a household with only one person. The income variable “main source of personal income” is applicable only to those that live in a household of more than one person. \(^i\) Not Stated: Question was not answered (don’t know, refusal, not stated).
# Complete case data
dat <- as.data.frame(na.omit(dat))
vars <- c("community", "sex", "age", "race")
# Summary table
tab1 <- CreateTableOne(vars = vars, data = dat, strata ="srmh", includeNA = F, test = F,
addOverall = T)
#print(tab1, showAllLevels = T)
kableone <- function(x, ...) {
capture.output(x <- print(x, showAllLevels= TRUE, padColnames = TRUE,
insertLevel = TRUE))
knitr::kable(x, ...)
}
kableone(tab1, printToggle = FALSE)| level | Overall | Poor or Fair | Good | Very good or excellent | |
|---|---|---|---|---|---|
| n | 2628 | 1002 | 885 | 741 | |
| community (%) | Very weak | 480 (18.3) | 282 (28.1) | 118 (13.3) | 80 (10.8) |
| Somewhat weak | 857 (32.6) | 358 (35.7) | 309 (34.9) | 190 (25.6) | |
| Somewhat strong | 1005 (38.2) | 288 (28.7) | 362 (40.9) | 355 (47.9) | |
| Very strong | 286 (10.9) | 74 ( 7.4) | 96 (10.8) | 116 (15.7) | |
| sex (%) | Females | 1407 (53.5) | 616 (61.5) | 487 (55.0) | 304 (41.0) |
| Males | 1221 (46.5) | 386 (38.5) | 398 (45.0) | 437 (59.0) | |
| age (%) | 15 to 24 years | 740 (28.2) | 191 (19.1) | 264 (29.8) | 285 (38.5) |
| 25 to 34 years | 475 (18.1) | 141 (14.1) | 167 (18.9) | 167 (22.5) | |
| 35 to 44 years | 393 (15.0) | 185 (18.5) | 119 (13.4) | 89 (12.0) | |
| 45 to 54 years | 438 (16.7) | 220 (22.0) | 139 (15.7) | 79 (10.7) | |
| 55 to 64 years | 379 (14.4) | 198 (19.8) | 113 (12.8) | 68 ( 9.2) | |
| 65 years or older | 203 ( 7.7) | 67 ( 6.7) | 83 ( 9.4) | 53 ( 7.2) | |
| race (%) | Non-white | 458 (17.4) | 184 (18.4) | 140 (15.8) | 134 (18.1) |
| White | 2170 (82.6) | 818 (81.6) | 745 (84.2) | 607 (81.9) |
Question 3: [20% grade]
3(a) Subset
Subset the dataset excluding ‘Very good or excellent’ responses from the self-rated mental health variable
3(b) Recode
Recode self-rated mental health variable and make it a binary variable: ‘Good’ vs. ‘Poor’ (simplifying category labels only). Convert that variable to a factor variable with ‘Poor’ being the reference level.
3(c) Regression
Run a logistic regression model for finding the relationship between community belonging (Reference: Very weak) and self-rated mental health (Reference: Poor) among respondents with mental or substance use disorders. Adjust the model for three confounders: sex, age, and race/ethnicity. Do not need to report summary of the model.
3(d) Reporting odds ratio
Report the odds ratios and associated confidence intervals. Publish or jtools package could be useful to report the odds ratios with confidence intervals.
require(Publish)
publish(fit)
#> Variable Units OddsRatio CI.95 p-value
#> community Very weak Ref
#> Somewhat weak 1.93 [1.48;2.53] < 1e-04
#> Somewhat strong 2.90 [2.22;3.80] < 1e-04
#> Very strong 3.32 [2.27;4.85] < 1e-04
#> sex Females Ref
#> Males 1.32 [1.09;1.60] 0.003993
#> age 15 to 24 years Ref
#> 25 to 34 years 0.85 [0.63;1.15] 0.292243
#> 35 to 44 years 0.45 [0.33;0.61] < 1e-04
#> 45 to 54 years 0.45 [0.34;0.61] < 1e-04
#> 55 to 64 years 0.41 [0.30;0.56] < 1e-04
#> 65 years or older 0.87 [0.59;1.27] 0.468623
#> race Non-white Ref
#> White 1.32 [1.03;1.71] 0.030025Knit your file
Please knit your file once you finished and submit the knitted PDF or doc file. Please also fill-up the following table:
Group name: ** xyz **
| Student initial | % contribution |
|---|---|
| Student 1 initial | x% |
| Student 2 initial | x% |
| Student 3 initial | x% |