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Our analysis set pulls 38 variables of interest from the larger PRAMS dataset. I selected these variables through a review of the methodological guidance provided for statistical analysis of the PRAMS dataset (e.g., survey, codebook, past analyses) and the conceptual framework provided by the SUPERNOVA Lab.
Outcome Variable(s) - Maternal Depression
Variable
Survey Item
Description
MH_PPDR
Q48. Since your new baby was born, how often have you felt down, depressed, or hopeless?
Used to categorize respondents who report PPD [target].
MH_PPINT
Q49. How often have you had little interest or little pleasure in doing things you usually enjoyed?
Used to categorize respondents who report symptoms of PPD.
Direct Effect Variables
Variable(s)
Variable Label
Model Variable
Survey Item
Description
INCOME8
MAT_ED
Income Education
Socioeconomic Status
Q50. During the 12 months before you new baby was born, what was your yearly total household income before taxes (from all sources)?
N/A
Used to categorize individuals by income range.
PRAMS collected education information but the question is not in the survey.
PRAMS collected race/ethnicity information but the question is not in the survey.
Who do we want to be the reference group for the intersectionality analysis?
MAT_AGE_PU
Age
Age
Q3. What is your date of birth?
Used to categorize individuals by age range.
Indirect Effect Variables
Variable(s)
Variable Label
Model Variable
Survey Item(s)
Description
PAD6HUS; PADXHUS; PAD_FAM; PAD_OTH
Experiences with Interpersonal Violence (IPV)
Discrimination
Q28. In the 12 months before you got pregnant with your new baby, did any of the following people push, hit, slap, choke, or physically hurt you in other way?
Q29. During your most recent pregnancy, did any of the following people push, hit, slap, kick, or physically hurt you in any other way?
Used to identify individuals who may have experienced physical violence before or during their pregnancy.
Q9. During the month before you got pregnant with your new baby, what kind of health insurance did you have?
Used to examine whether or not the individual had access to insurance before becoming pregnant.
Could potentially be divided to private, public, and other insurance categories.
MAT_WIC
WIC Availability
Social Support
N/A
PRAMS collected WIC information but the question is not in the survey.
SMK2YRS SMK63B_A DRK2YRS DRK83B_A
Smoking History (Past 2 Years) Smoking History (3 Months Pre-Pregnancy); Drinking History (Past 2 Years); Drinking History (3 Months Pre-Pregnancy)
Substance Use
Q19. Have you smoked any cigarettes in the past 2 years?
Q20. In the 3 months before you got pregnant, how many cigarettes did you smoke on an average day?
Q26. Have you had any alcoholic drinks in the past 2 years?
Q27. During the 3 months before you got pregnant, how many alcoholic drinks did you have in an average week?
Used to identify individuals with a history of smoking or drinking. There are additional questions related frequency of substance and types of products.
PRE_MHDP
Pre-Pregnancy Depression
Pre-Pregnancy Depression
Q4. During the 3 months before you got pregnant with your new baby, did you have any of the following health conditions?
Used to identify individuals with a history of depression before pregnancy.
Key Takeaways - with transformed Variables
There are more White and Black respondents than all other groups, but distributions are similar across groups.
37% of indivduals report experiencing PPD (n = 82,446), with around 3 out of every 4 (~73%) reporting that they sometimes experience PPD.
A similar amount of individuals (37%) report experiencing symptoms of PPD (n = 83,453), with around 66% reporting that they sometimes PPD symptoms.
So of the ~83% of live births represented in this survey, PRAMS estimates that around 1/3 women have experienced prenatal and post-partum depression, which is higher than comparable studies that indicate a prevalence of about ~10% using the NSDUH survey, another nationally representative sample of maternal health factors.
Distributions seem consistent across variables and I currently don’t see start differences in distributions that would require subgroup analyses unless we are interested.
There is an item about desire to be pregnant that could be interesting to look into.
Otherwise, the next step is to complete feature engineering, bivariate analyses (e.g., chi-square and correspondence analysis), and multivariate modeling (e.g., logistic regression and random forests)
Frequency Analysis
F1. How many survey responses indicate that a mother was living with PPD at the time of the PRAMS18 survey?
66075 (30%) of individuals report a survey response that indicates PPD based on our criteria based on their answer of Sometimes, Often/Almost Always, and Always experiencing PPD at the time of the survey.
66634 (30$) also reported symptoms in the PRAMS8 survey.
t1 <-table(prams.transformed$MH_PPDPR2)t1
BLANK/DK Does Not Indicate PPD Indicates PPD
4845 150461 66075
prams.transformed %>%count(MH_PPDPR2, sort = T)
MH_PPDPR2 n
1 Does Not Indicate PPD 150461
2 Indicates PPD 66075
3 BLANK/DK 4845
MH_PPINT2 n
1 PPD Symptoms Not Present 149993
2 PPD Symptoms Present 66634
3 BLANK/DK 4754
F2. What is the proportion of PPD in our dataset and across racial/ethnic categories?
White individuals represent over half of the data collected in PRAMS18 (62%) but report lower prevalence of PPD (30.6%) than American Indians (35%) 35% and similar prevalence to Blacks with 30.1%.
Native Americans/Indigenous groups report a disproportionate frequency of PPD despite representing only 6% of respondents to the PRAMS18 survey.
While the number of respondents who identify as Black or Hispanic are similar, Blacks have a higher proportion of individuals indicated for PPD compared to Hispanics.
Multiracial individuals, Native Hawaiian’s and Pacific Islanders, and Asians report similar prevalence of PPD although Asian and Multiracial individuals represent 8-10 times more individuals in PRAMS18 than Native Hawaiian/Pacific Islanders.
Full Proportions
30.6% of Whites (n = 42034/137405)
30.1% of Blacks (n = 13097/43441),
25.3% of Hispanics (n = 10555/41714)
29% of Multiracial (n = 6567/22376)
29% of Native Hawaiian/Pacific Islander (n = 863/2939)
F3. What is the proportion of individuals in our dataset who reported PPD symptoms and were indicated for PPD?
34% (n = 23006/66075) of the individuals whose responses indicated PPD did not report symptoms at the time of PRAMS of respondents were indicated for PPD whose responses did not detect symptoms of PPD in the survey.
15% (n = 23367/150461) of the individuals expressed experiencing symptoms of PPD even though their responses did not indicate PPD.
DK/BLANK HIGH INCOME LOW INCOME MIDDLE INCOME
MIDWEST 5316 19937 24418 10937
NORTHEAST 3536 19151 13757 7452
SOUTH 5053 14391 21736 7517
WEST 4191 19967 19947 11199
What is YC 1895 2970 6419 1592
F6. What is the breakdown of insurance status and PPDR?
The top three insurance sources are 41% insurance through work (n = 98113, 41%), Medicaid (n 83912, =35%, or are uninsured (n = 17793, 7%) and the remaining through a mix of health exchange coverage, parents’ insurance, military, and other public programs.
After recoding, almost half of respondents have private insurance (n = 10,7411, 48%), followed by public insurance (n = 98207, 44%), with the rest being uninsured or having some other type of insurance (e.g., parental insurance or other)
# A tibble: 4 × 2
insurancetype n
<chr> <int>
1 Private Insurance Coverage 107411
2 Public Insurance Coverage 98207
3 Uninsured 17793
4 Other Insurance Coverage 17145
F7. What is the breakdown of education?
Most respondents have Bachelor’s, Master’s, or Doctorate level degree (n = 77906, 35%), some college (n = 61969, 28%) or high school education/GED (n = 53253, 24%). some high school, middle school and below, or unknown.
prams.transformed %>%count(MAT_ED, sort = T)
MAT_ED n
1 BACHELORS/MASTERS/DOCTORATE/PROF 77906
2 SOME COLLEGE,NO DEG/ASSOCIATE DEG 61969
3 HIGH SCHOOL GRAD/GED 53253
4 9-12 GRADE,NO DIPLOMA 19873
5 <= 8TH GRADE 6334
6 UNKNOWN 2046
F8. What is the breakdown of age?
About 3 out of 4 respondents (n = 170437, 78%) are under the age of 35, meaning most respondents are not in the age range that would designate a higher-risk pregnancy (age 35 or older)
prams.transformed %>%count(age.label, sort = T)
age.label n
1 Under 35 170437
2 35 or older 40392
3 <NA> 10543
4 Unknown 9
Over half of respondents (n = 138188, 62%) are not receiving WIC benefits.
prams.transformed %>%count(MAT_WIC, sort = T)
MAT_WIC n
1 NO 138188
2 YES 79907
3 UNKNOWN 3286
DataExplorer::plot_bar(prams.transformed$MAT_WIC)
F10. How many responded that they experienced symptoms of pre-pregnancy depression or symptoms of depression during pregnancy?
A bit more than 1 out of 3 individuals (n = 84409, 38%) experienced pre-pregnancy depression while a little over 1 out of 10 (n = 34725, 15%) experienced depression symptoms during pregnancy)
prams.transformed %>%count(BPG_DEPRS82, sort = T)
BPG_DEPRS82 n
1 No History of Depression During Pregnancy 184549
2 History of Depression During Pregnancy 34725
3 DK/BLANK 2107
prams.transformed %>%count(PRE_MHDP2, sort = T)
PRE_MHDP2 n
1 History of pre-pregnancy depression symptoms 84409
2 SKIP 72004
3 No depression symptoms pre-pregnancy 60811
4 DK/BLANK 4157
