Open paghosh opened 5 months ago
[x] 1) infant death,
[x] 2) low birth weight (weight <2500grams)
[ ] 3) We cannot use preterm variable for having too many missing observations, but I think it correlates well with low birth variable
prams_df$MORB_LAB %>% summary Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 1.000 1.000 1.000 1.232 1.000 2.000 28889
[x] Instead of preterm, we have DEFECT: BC: BIRTH DEFECT BABY? tiny fraction as expected answered yes for birth defect (expected!):
Asian == CHINESE, JAPANESE, FILIPINO or OTH ASIAN
Hawaiian race seem to yield unstable curve especially after 2015. I think we may need to drop this race due to its small sample with only 6858 Hawaiian pregnant women surveyed from 2000 to 2019; note that in 2018 and 2019 the share is zero due to only few women being surveyed and non of them happen to be a teenager.
@paghosh @RJaggad (Somehow Dr. June isn't available for tagging!): is it okay to drop Hawaiian race due to the sample limit? The plot though seem to confirm the hypothesis that Indian Americans have higher share of teenage pregnancy.
Note: I used the sample weights to compute the proportions--number of pregnant women below the age of 20 divided by the total number of pregnant women within each race.
@ahmedelfatmaoui @paghosh Dr. June has not yet participated in this repository. I will tell her tomorrow when I meet her. Also, the graph looks really good and is very consistent with the Teen age pregnancy data that CDC has reported for various races.
My doubt is: Will we weight the sample for even distribution amongst races in this data set or consider the same sample size that we have right now? I have very less knowledge about weighting the samples and wish to learn more, so I apologize if I am suggesting something wrong, but can weighting the sample fix the problem of the proportion differences across all races?
I was also figuring out the potential confounders in this study: 1. education was one of them. We can explore the effect of education across the teen age groups by stratifying them, and compare with adult age groups. 2. I also think the regional variation across states will influence this association e.g.: rural vs. urban or conservative vs. liberal. 3. Poverty and socio-economic status will indirectly influence this association because of being underprivileged and having less awareness in regards to healthcare. 4. The effect of having an insurance, and type of insurance: private vs. public which one was associated with higher vaccination rates in the study subjects. I am just putting it in the discussion so that we can think about potential covariates in our regression model.
Thank you.
Hi @RJaggad, The figure without the sampling weights shows similar trends--no qualitative difference actually
@ahmedelfatmaoui I was seeing the Percentage of teen age pregnancy amongst all races actually, I agree that the trend is similar. Does that mean we don't need to weight the sample if the trends are same? Thank you Ahmed.
@RJaggad it just means that using weighted mean (using sample weights) is not different from taking the mean (using standard arithmetic mean). We are using the whole PRAMS sample in either case.
@ahmedelfatmaoui I understood now what you are saying. However, I will read about this to understand more. Thank you so much for being so patient with my doubts Ahmed.
@ahmedelfatmaoui Ahmed: Thank you for your great patience and work! I just returned from travel and have talked with Dr. Ghosh @paghosh and Rashmi @RJaggad here we need your further assistance:
Reproducing the document from Dr. Zhao's email on March 29:
Teenage pregnancy, maternal immunization, and infant mortality among Native Americans and other minorities
Research question:
How does teenage pregnancy impact maternal immunization and infant mortality across Native Americans and other US racial and ethnic minorities?
Variables:
To examine the differential effects of teenage pregnancy versus adult pregnancy on maternal immunization and infant health outcomes. The x variable is a binary variable of the teenage age threshold <=19 years old. The two y variables at the mother level are 1) mother’s Tdap vaccination status, 2) mother’s flu vaccination status. Furthermore, the three y variables at the infant level are 1) infant death, 2) low birth weight, 3) preterm birth (or premature birth).
Study population:
The main study population is Native Americans, variable name “American Indians or Alaska Natives” (NA). Additional populations include all racial and ethnic groups, such as Native Hawaiians, White, Black, Hispanic, Chinese, Filipinos, and Japanese.
Hypotheses:
Within the NA racial group, compared to adult mothers, teenage mothers have lower vaccination rates (including both Tdap, and flu), through which their infants have worse health outcomes.
Between the NA and Native Hawaiian groups, compared to Native Hawaiian teenage mothers, NA teenage mothers have equal vaccination rates (including both Tdap, and flu), through which their infants might have equal health outcomes.
Across all racial groups, NA teenage mothers have the lowest vaccination rates (including both Tdap, and flu), through which their infants have the worst health outcomes.
Motivation:
This study is motivated by four facts: 1) NA teenage pregnancy (measured as teenage birth rate) at 29.2% is the highest among all US racial and ethnic groups (e.g., non-Hispanic Blacks 25.8%, Hispanics 25.3%, non-Hispanic White 11.4%); 2) Low birth weight is the top second leading cause of infant death in the US among all racial and ethnic groups, and is the top third leading cause among the NA group; 3) NAs are a historically racially disadvantaged population in urgent need of policy intervention; 4) Mothers’ acquired immunity through Tdap and flu vaccinations affect unborn fetus and born infant immunity (thus infant health outcomes) through three biological mechanisms (see our upcoming Medicaid paper); 5)
Teenage/Adolescent pregnancy definition: 1.American Pregnancy association: A teenage pregnancy, as defined by the American Pregnancy Association, is a pregnancy that occurs for a woman under the age of 20. Although technically not a teenager, a young woman 12 or under who is pregnant falls into this definition of teenage pregnancy as well.
2.WHO: Teenage pregnancy (%) Percentage of pregnant women aged 19 years or younger in a given period of time.
3.UNICEF: The United Nations Children Fund (UNICEF), defines teenage pregnancy as “a teenage girl, usually within the ages of 13-19, becoming pregnant and refers to girls who have not reached legal adulthood, which varies across the world” Ref: United Nations Children's Fund 2008. The state of the world's children 2009. Maternal and newborn health, unicef.