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Timing and tempo of puberty and neurodevelopment following adversity: A registered report.

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  1. [1] § Method › Measures › Adversity exposure ↔ ELAfromABCD_ELAplus.Rmd, lines 255–388 · score 0.67 · sum score, neighborhood, crime, safety, incarceration, CRPBI

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  1. ---
  2. title: "acesinabcd"
  3. author: "FB"
  4. date: "2024-03-11"
  5. output:
  6. word_document: default
  7. html_document: default
  8. ---
  10. ```{r setup, include=FALSE}
  11. knitr::opts_chunk$set(echo = TRUE)
  12. ```
  14. ## ELA+ scores for ABCD data
  16. Running this code will create ELA+ scores for Baseline through Year 3.
  17. Need to run the setup file (ELAfromABCD_setup.Rmd) first to create the necessary RDS and CSV files.
  18. IMPORTANT: Make sure to run the ENTIRE code to get the correct scores.
  20. ```{r load-packages}
  21. library(dplyr)
  22. library(tidyr)
  23. ```
  25. ```{r load-data}
  27. #read RDS file
  28. ela_in_abcd=readRDS("ela_in_abcd.rds")
  29. mh_py_le_bs=read.csv("mh_py_le_bs.csv")
  31. #create baseline dataset
  32. ela_in_abcd_bs = ela_in_abcd[ela_in_abcd$eventname == "baseline_year_1_arm_1", ]
  34. #add in life events "baseline" data
  35. #remove the variable label from main data set and re-add without eventname so the calculated baseline appears inline with other baseline data
  36. ela_in_abcd_bs = ela_in_abcd_bs[c("src_subject_id","eventname","ksads_ptsd_raw_761_p","ksads_ptsd_raw_762_p","ksads_ptsd_raw_763_p", "ksads_ptsd_raw_764_p","ksads_ptsd_raw_765_p","ksads_ptsd_raw_767_p","ksads_ptsd_raw_768_p","ksads_ptsd_raw_769_p","ksads_ptsd_raw_766_p","ksads_ptsd_raw_756_p","ksads_ptsd_raw_757_p","ksads_ptsd_raw_758_p","ksads_ptsd_raw_759_p","ksads_ptsd_raw_760_p","fes_youth_q5","crpbi_y_ss_parent_ace","demo_fam_exp1_v2","demo_fam_exp2_v2","demo_fam_exp3_v2","demo_fam_exp4_v2","demo_fam_exp5_v2","demo_fam_exp6_v2","demo_fam_exp7_v2","asr_scr_totprob_t","asr_q06_p","asr_q06_p_ace","asr_scr_totprob_t_ace","famhx_ss_momdad_ma_p","famhx_ss_momdad_alc_p","famhx_ss_momdad_dg_p","famhx_ss_momdad_dprs_p","famhx_ss_momdad_hspd_p","famhx_ss_momdad_scd_p","famhx_ss_momdad_vs_p","nbh_crime_y_ela","neighborhood2r_p_ela","neighborhood3r_p_ela")]
  38. ela_in_abcd_bs = left_join(ela_in_abcd_bs, mh_py_le_bs, by=c("src_subject_id"))
  40. #create separate dfs for remaining time points
  41. ela_in_abcd_yr1 = ela_in_abcd[ela_in_abcd$eventname == "1_year_follow_up_y_arm_1", ]
  42. ela_in_abcd_yr2 = ela_in_abcd[ela_in_abcd$eventname == "2_year_follow_up_y_arm_1", ]
  43. ela_in_abcd_yr3 = ela_in_abcd[ela_in_abcd$eventname == "3_year_follow_up_y_arm_1", ]
  44. ela_in_abcd_yr4 = ela_in_abcd[ela_in_abcd$eventname == "4_year_follow_up_y_arm_1", ]
  46. #creating function to sum items in each domain where it will sum across the row as long as at least one variable is not NA
  47. sum_score <- function(x){
  49. if (all(is.na(x))) {
  51. suma <- NA
  52. }
  53. else {
  54. suma <- sum(x, na.rm = T)
  55. }
  57. return(suma)
  58. }
  60. ```
  63. ```{r ela-plus-baseline}
  64. #calculating baseline scores for each domain
  65. #for domains with multiple items, if any of the items is endorsed, domain score = 1
  67. #abuse_phy
  68. #KSADS Shot, stabbed, or beaten brutally by a non-family member ksads_ptsd_raw_761_p
  69. #KSADS Shot, stabbed, or beaten brutally by a grown up in the home ksads_ptsd_raw_762_p
  70. #KSADS Beaten to the point of having bruises by a grown up in the home ksads_ptsd_raw_763_p
  71. #KSADS A non-family member threatened to kill your child ksads_ptsd_raw_764_p
  72. #KSADS A family member threatened to kill your child ksads_ptsd_raw_765_p
  74. #create dataframe with phys abuse items
  75. abuse_phy_bs_data = c("ksads_ptsd_raw_761_p" , "ksads_ptsd_raw_762_p", "ksads_ptsd_raw_763_p", "ksads_ptsd_raw_764_p", "ksads_ptsd_raw_765_p")
  77. #sum the items as long as they're not all NA
  78. ela_in_abcd_bs$abuse_phy_sum = apply(ela_in_abcd_bs[abuse_phy_bs_data],1, sum_score)
  80. #binary recode as anything > 0 is 1
  81. ela_in_abcd_bs$abuse_phy_bs = ifelse(ela_in_abcd_bs$abuse_phy_sum == 0, 0, 1)
  84. #abuse_sex
  85. #KSADS A grown up in the home touched your child in their privates, had your child touch their privates, or did other sexual things to your child ksads_ptsd_raw_767_p
  86. #KSADS An adult outside your family touched your child in their privates, had your child touch their privates or did other sexual things to your child ksads_ptsd_raw_768_p
  87. #KSADS A peer forced your child to do something sexually ksads_ptsd_raw_769_p
  89. #create dataframe with sexual abuse items
  90. abuse_sex_bs_data = c("ksads_ptsd_raw_767_p" , "ksads_ptsd_raw_768_p", "ksads_ptsd_raw_769_p")
  92. #sum the items as long as they're not all NA
  93. ela_in_abcd_bs$abuse_sex_sum = apply(ela_in_abcd_bs[abuse_sex_bs_data],1, sum_score)
  95. #binary recode as anything > 0 is 1
  96. ela_in_abcd_bs$abuse_sex_bs = ifelse(ela_in_abcd_bs$abuse_sex_sum == 0, 0, 1)
  99. #abuse_emo
  100. #Family Environment Family members often criticize each other. fes_youth_q5
  101. #utilizing youth report only to ensure youth exposure (e.g., not parents criticizing without kid present)
  103. #yes/no item; no sum or recode necessary
  104. ela_in_abcd_bs$abuse_emo_bs = ela_in_abcd_bs$fes_youth_q5
  107. #neglect_phy
  108. #Demographics Needed food but couldn't afford to buy it or couldn't afford to go out to get it?P demo_fam_exp1_v2_l/demo_fam_exp1_v2
  109. #Demographics Had someone who needed to see a doctor or go to the hospital but didn't go because you could not afford it?P demo_fam_exp6_v2/demo_fam_exp6_v2_l
  110. #Demographics Had someone who needed a dentist but couldn't go because you could not afford it?P demo_fam_exp7_v2/demo_fam_exp7_v2_l
  112. #create dataframe with physical neglect items
  113. neglect_phy_bs_data = c("demo_fam_exp1_v2", "demo_fam_exp6_v2", "demo_fam_exp7_v2")
  115. #sum the items as long as they're not all NA
  116. ela_in_abcd_bs$neglect_phy_sum = apply(ela_in_abcd_bs[neglect_phy_bs_data],1, sum_score)
  118. #binary recode as anything > 0 is 1
  119. ela_in_abcd_bs$neglect_phy_bs = ifelse(ela_in_abcd_bs$neglect_phy_sum == 0, 0, 1)
  122. #neglect_emo
  123. #CRPBI parent *CRPBI Mean Parent (primary caregiver)Y crpbi_y_ss_parent_ace
  125. #all recoding done previously
  126. ela_in_abcd_bs$neglect_emo_bs = ela_in_abcd_bs$crpbi_y_ss_parent_ace
  129. #divorce
  130. #Life Events Parents separated or divorced?Y,P ple_separ_p/ple_separ_y (ple_separ_p_bs/ple_separ_y_bs)
  132. #create dataframe with parent divorce items
  133. divorce_bs_data = c("ple_separ_p_bs", "ple_separ_y_bs")
  135. #sum the items as long as they're not both NA
  136. ela_in_abcd_bs$divorce_bs_sum = apply(ela_in_abcd_bs[divorce_bs_data],1, sum_score)
  138. #binary recode as anything > 0 is 1
  139. ela_in_abcd_bs$divorce_bs = ifelse(ela_in_abcd_bs$divorce_bs_sum == 0, 0, 1)
  142. #violence in home
  143. #KSADS Witness the grownups in the home push, shove or hit one anotherP ksads_ptsd_raw_766_p
  145. #yes/no item; no sum or recode necessary
  146. ela_in_abcd_bs$violence_bs = ela_in_abcd_bs$ksads_ptsd_raw_766_p
  148. #mental illness
  149. #Life Events Family member had mental/emotional problem?Y,P ple_mh_p/ple_mh_y (ple_mh_p_bs/ple_mh_y_bs)
  150. #Adult Self-Report Total Problems ASR Syndrome Scale (t-score)P asr_scr_totprob_t_ace (1=greater than t-score>63)
  151. #Family History Depression Biological ParentP famhx_ss_momdad_dprs_p
  152. #Family History Suicide Biological ParentP famhx_ss_momdad_scd_p
  153. #Family History Mania Biological ParentP famhx_ss_momdad_ma_p
  154. #Family History Psychosis Biological ParentP famhx_ss_momdad_vs_p
  155. #Family History Either parent hospitalized due to emotional/mental problem famhx_ss_momdad_hspd_p
  157. #create dataframe with mental illness items
  158. mi_sum_bs_data = c("ple_mh_p_bs", "ple_mh_y_bs", "asr_scr_totprob_t_ace", "famhx_ss_momdad_dprs_p", "famhx_ss_momdad_scd_p", "famhx_ss_momdad_ma_p", "famhx_ss_momdad_vs_p", "famhx_ss_momdad_hspd_p")
  160. #sum the items as long as they're not all NA
  161. ela_in_abcd_bs$mi_sum = apply(ela_in_abcd_bs[mi_sum_bs_data],1, sum_score)
  163. #binary recode as anything > 0 is 1
  164. ela_in_abcd_bs$mi_bs = ifelse(ela_in_abcd_bs$mi_sum == 0, 0, 1)
  167. #Incarcerated Relative
  168. #Life Events Someone in the family was arrested?Y,P ple_arrest_p/ple_arrest_y (ple_arrest_p_bs/ple_arrest_y_bs)
  169. #Life Events Parents/caregiver got into trouble with the law?Y,P ple_law_p/ple_law_y (ple_law_p_bs/ple_law_y_bs)
  170. #Life Events One of the parents/caregivers went to jail?Y,P ple_jail_p/ple_jail_y (ple_jail_p_bs/ple_jail_y_bs)
  172. #create dataframe with incarceration items
  173. incar_sum_bs_data = c("ple_arrest_p_bs", "ple_arrest_y_bs", "ple_law_p_bs", "ple_law_y_bs", "ple_jail_p_bs", "ple_jail_y_bs")
  175. #sum the items as long as they're not all NA
  176. ela_in_abcd_bs$incar_sum = apply(ela_in_abcd_bs[incar_sum_bs_data],1, sum_score)
  178. #binary recode as anything > 0 is 1
  179. ela_in_abcd_bs$incar_bs = ifelse(ela_in_abcd_bs$incar_sum == 0, 0, 1)
  182. #Substance Use
  183. #Life Events Family member had drug and/or alcohol problem?Y,P ple_sud_p/ple_sud_y (ple_sud_p_bs/ple_sud_y_bs)
  184. #Adult Self-Report I use drugs (other than alcohol, nicotine) for nonmedical purposesP asr_q06_p_ace
  185. #Family History AUD Biological ParentP famhx_ss_momdad_alc_p
  186. #Family History SUD Biological ParentP famhx_ss_momdad_dg_p
  188. #create dataframe with substance use items
  189. su_sum_bs_data = c("ple_sud_p_bs", "ple_sud_y_bs", "asr_q06_p_ace", "famhx_ss_momdad_alc_p", "famhx_ss_momdad_dg_p")
  191. #sum the items as long as they're not all NA
  192. ela_in_abcd_bs$su_sum = apply(ela_in_abcd_bs[su_sum_bs_data],1, sum_score)
  194. #binary recode as anything > 0 is 1
  195. ela_in_abcd_bs$su_bs = ifelse(ela_in_abcd_bs$su_sum == 0, 0, 1)
  197. #Exposure
  198. #ksads_ptsd_raw_756_p Witnessed or caught in a fire that caused significant property damage or personal injury
  199. #ksads_ptsd_raw_757_p Witnessed or caught in a natural disaster that caused significant property damage or personal injury
  200. #ksads_ptsd_raw_758_p Witnessed or present during an act of terrorism (e.g., Boston marathon bombing)
  201. #ksads_ptsd_raw_759_p Witnessed death or mass destruction in a war zone
  202. #ksads_ptsd_raw_760_p Witnessed someone shot or stabbed in the community
  203. #0 = No; 1 = Yes
  205. #create dataframe with exposure items
  206. exposure_bs_data = c("ksads_ptsd_raw_756_p" , "ksads_ptsd_raw_757_p", "ksads_ptsd_raw_758_p", "ksads_ptsd_raw_759_p", "ksads_ptsd_raw_760_p")
  208. #sum the items as long as they're not all NA
  209. ela_in_abcd_bs$exposure_sum = apply(ela_in_abcd_bs[exposure_bs_data],1, sum_score)
  211. #binary recode as anything > 0 is 1
  212. ela_in_abcd_bs$exposure_bs = ifelse(ela_in_abcd_bs$exposure_sum == 0, 0, 1)
  214. #Safety
  215. #Life Events Was a victim of crime/violence/assault? ple_victim_p/ple_victim_y (ple_victim_p_bs/ple_victim_y_bs)
  216. #*My neighborhood is safe from crime. neighborhood3r_p_ace/nbh_crime_y_ela
  217. #Violence is not a problem in my neighborhood. neighborhood2r_p_ace
  218. #recoded in setup: 4/5=0, 3/2/1=1
  220. #create dataframe with safety items
  221. safety_sum_bs_data = c("neighborhood2r_p_ela","neighborhood3r_p_ela","nbh_crime_y_ela", "ple_victim_p_bs", "ple_victim_y_bs")
  223. #sum the items as long as they're not all NA
  224. ela_in_abcd_bs$safety_sum = apply(ela_in_abcd_bs[safety_sum_bs_data],1, sum_score)
  226. #binary recode as anything > 0 is 1
  227. ela_in_abcd_bs$safety_bs = ifelse(ela_in_abcd_bs$safety_sum == 0, 0, 1)
  230. #Resources
  231. #Were without telephone service because you could not afford it?P demo_fam_exp2_v2
  232. #Didn't pay the full amount of the rent or mortgage because you could not afford it?P demo_fam_exp3_v2
  233. #Were evicted from your home for not paying the rent or mortgage?P demo_fam_exp4_v2
  234. #Had services turned off by the gas or electric company, or the oil company wouldn't deliver oil because payments were not made?P demo_fam_exp5_v2
  236. #create dataframe with resources items
  237. resources_bs_data = c("demo_fam_exp2_v2" , "demo_fam_exp3_v2", "demo_fam_exp4_v2", "demo_fam_exp5_v2")
  239. #sum the items as long as they're not all NA
  240. ela_in_abcd_bs$resources_sum = apply(ela_in_abcd_bs[resources_bs_data],1, sum_score)
  242. #binary recode as anything > 0 is 1
  243. ela_in_abcd_bs$resources_bs = ifelse(ela_in_abcd_bs$resources_sum == 0, 0, 1)
  245. #Family Separation
  246. #not collected until year 3/4
  247. #Placed in foster care? ple_foster_care_p/ple_foster_care_y
  248. #One of the parents/caregivers was deported? ple_deported_p/ple_deported_y
  250. #*SUM SCORE*
  251. ela_in_abcd_bs$ela_plus_bs = ela_in_abcd_bs$abuse_phy_bs + ela_in_abcd_bs$abuse_sex_bs + ela_in_abcd_bs$abuse_emo_bs + ela_in_abcd_bs$neglect_phy_bs + ela_in_abcd_bs$neglect_emo_bs + ela_in_abcd_bs$divorce_bs + ela_in_abcd_bs$violence_bs + ela_in_abcd_bs$incar_bs + ela_in_abcd_bs$su_bs + ela_in_abcd_bs$mi_bs + ela_in_abcd_bs$exposure_bs + ela_in_abcd_bs$safety_bs + ela_in_abcd_bs$resources_bs
  253. ```
  255. ```{r ela-plus-yr1}
  256. #yr 1
  258. #abuse_phy
  259. #cannot be calculated at Year 1 due to no KSADS
  260. #domain score will be equal to that at Baseline
  262. #abuse_sex
  263. #cannot be calculated at Year 1 due to no KSADS
  264. #domain score will be equal to that at Baseline
  266. #abuse_emo
  267. #Family Environment Family members often criticize each other. fes_youth_q5
  268. #utilizing youth only to ensure youth exposure
  269. ela_in_abcd_yr1$abuse_emo_yr1 = ela_in_abcd_yr1$fes_youth_q5
  272. #neglect_phy
  273. #Demographics Needed food but couldn't afford to buy it or couldn't afford to go out to get it?P demo_fam_exp1_v2_l/demo_fam_exp1_v2
  274. #Demographics Had someone who needed to see a doctor or go to the hospital but didn't go because you could not afford it?P demo_fam_exp6_v2/demo_fam_exp6_v2_l
  275. #Demographics Had someone who needed a dentist but couldn't go because you could not afford it?P demo_fam_exp7_v2/demo_fam_exp7_v2_l
  277. #create dataframe with physical neglect items
  278. neglect_phy_sum_yr1_data = c("demo_fam_exp1_v2_l", "demo_fam_exp6_v2_l", "demo_fam_exp7_v2_l")
  280. #sum the items as long as they're not all NA
  281. ela_in_abcd_yr1$neglect_phy_sum = apply(ela_in_abcd_yr1[neglect_phy_sum_yr1_data],1, sum_score)
  283. #binary recode as anything > 0 is 1
  284. ela_in_abcd_yr1$neglect_phy_yr1 = ifelse(ela_in_abcd_yr1$neglect_phy_sum == 0, 0, 1)
  287. #neglect_emo
  288. #CRPBI parent *CRPBI Mean Parent (primary caregiver)Y crpbi_y_ss_parent_ace
  289. ela_in_abcd_yr1$neglect_emo_yr1 = ela_in_abcd_yr1$crpbi_y_ss_parent_ace
  291. #divorce
  292. #create dataframe with divorce items
  293. divorce_yr1_data = c("ple_separ_p" , "ple_separ_y")
  295. #sum the items as long as they're not all NA
  296. ela_in_abcd_yr1$divorce_sum = apply(ela_in_abcd_yr1[divorce_yr1_data],1, sum_score)
  298. #binary recode as anything > 0 is 1
  299. ela_in_abcd_yr1$divorce_yr1 = ifelse(ela_in_abcd_yr1$divorce_sum == 0, 0, 1)
  302. #violence in home (violence)
  303. #cannot be calculated at Year 1 due to no KSADS
  304. #domain score will be equal to that at Baseline
  306. #mental illness (mi)
  307. #Life Events Family member had mental/emotional problem?Y,P ple_mh_p/ple_mh_y (ple_mh_p_yr1/ple_mh_y_yr1)
  308. #No ASR or Fam Hx update at Y1
  310. #create dataframe with mi items
  311. mi_sum_yr1_data = c("ple_mh_p", "ple_mh_y")
  313. #sum the items as long as they're not all NA
  314. ela_in_abcd_yr1$mi_sum = apply(ela_in_abcd_yr1[mi_sum_yr1_data],1, sum_score)
  316. #binary recode as anything > 0 is 1
  317. ela_in_abcd_yr1$mi_yr1 = ifelse(ela_in_abcd_yr1$mi_sum == 0, 0, 1)
  320. #Incarcerated Relative
  321. #Life Events Someone in the family was arrested?Y,P ple_arrest_p/ple_arrest_y (ple_arrest_p_yr1/ple_arrest_y_yr1)
  322. #Life Events Parents/caregiver got into trouble with the law?Y,P ple_law_p/ple_law_y (ple_law_p_yr1/ple_law_y_yr1)
  323. #Life Events One of the parents/caregivers went to jail?Y,P ple_jail_p/ple_jail_y (ple_jail_p_yr1/ple_jail_y_yr1)
  325. #create dataframe with incarceration items
  326. incar_sum_yr1_data = c("ple_arrest_p" , "ple_arrest_y" , "ple_law_p" , "ple_law_y" , "ple_jail_y" , "ple_jail_p")
  328. #sum the items as long as they're not all NA
  329. ela_in_abcd_yr1$incar_sum = apply(ela_in_abcd_yr1[incar_sum_yr1_data],1, sum_score)
  331. #binary recode as anything > 0 is 1
  332. ela_in_abcd_yr1$incar_yr1 = ifelse(ela_in_abcd_yr1$incar_sum == 0, 0, 1)
  335. #Substance Use
  336. #Life Events Family member had drug and/or alcohol problem?Y,P ple_sud_p/ple_sud_y (ple_sud_p_yr1/ple_sud_y_yr1)
  337. #No ASR or Fam Hx update at Y1
  338. ela_in_abcd_yr1$ple_sud_y_yr1 = ela_in_abcd_yr1$ple_sud_y
  340. #create dataframe with substance use items
  341. su_sum_yr1_data = c("ple_sud_p","ple_sud_y")
  343. #sum the items as long as they're not all NA
  344. ela_in_abcd_yr1$su_sum = apply(ela_in_abcd_yr1[su_sum_yr1_data],1, sum_score)
  346. #binary recode as anything > 0 is 1
  347. ela_in_abcd_yr1$su_yr1 = ifelse(ela_in_abcd_yr1$su_sum == 0, 0, 1)
  349. #Exposure
  350. #cannot be calculated at Year 1 due to no KSADS
  351. #domain score will be equal to that at Baseline
  353. #Safety
  354. #Life Events Was a victim of crime/violence/assault? ple_victim_p/ple_victim_y (ple_victim_p_bs/ple_victim_y_bs)
  355. #*My neighborhood is safe from crime. neighborhood3r_p_ace/nbh_crime_y_ela
  356. #Violence is not a problem in my neighborhood. neighborhood2r_p_ace
  357. #recoded in setup: 4/5=0, 3/2/1=1
  359. #create dataframe with safety items
  360. safety_sum_yr1_data = c("neighborhood2r_p_ela","neighborhood3r_p_ela","nbh_crime_y_ela", "ple_victim_p", "ple_victim_y")
  362. #sum the items as long as they're not all NA
  363. ela_in_abcd_yr1$safety_sum = apply(ela_in_abcd_yr1[safety_sum_yr1_data],1, sum_score)
  365. #binary recode as anything > 0 is 1
  366. ela_in_abcd_yr1$safety_yr1 = ifelse(ela_in_abcd_yr1$safety_sum == 0, 0, 1)
  368. #Resources
  369. #Were without telephone service because you could not afford it?P demo_fam_exp2_v2
  370. #Didn't pay the full amount of the rent or mortgage because you could not afford it?P demo_fam_exp3_v2
  371. #Were evicted from your home for not paying the rent or mortgage?P demo_fam_exp4_v2
  372. #Had services turned off by the gas or electric company, or the oil company wouldn't deliver oil because payments were not made?P demo_fam_exp5_v2
  374. #create dataframe with resources items
  375. resources_yr1_data = c("demo_fam_exp2_v2_l" , "demo_fam_exp3_v2_l", "demo_fam_exp4_v2_l", "demo_fam_exp5_v2_l")
  377. #sum the items as long as they're not all NA
  378. ela_in_abcd_yr1$resources_sum = apply(ela_in_abcd_yr1[resources_yr1_data],1, sum_score)
  380. #binary recode as anything > 0 is 1
  381. ela_in_abcd_yr1$resources_yr1 = ifelse(ela_in_abcd_yr1$resources_sum == 0, 0, 1)
  383. #Family Separation
  384. #not collected until year 3/4
  385. #Placed in foster care? ple_foster_care_p/ple_foster_care_y
  386. #One of the parents/caregivers was deported? ple_deported_p/ple_deported_y
  388. ```
  390. ```{r ela-plus-yr2}
  391. #yr 2
  393. #abuse_phy
  394. #KSADS Shot, stabbed, or beaten brutally by a non-family member ksads_ptsd_raw_761_p
  395. #KSADS Shot, stabbed, or beaten brutally by a grown up in the home ksads_ptsd_raw_762_p
  396. #KSADS Beaten to the point of having bruises by a grown up in the home ksads_ptsd_raw_763_p
  397. #KSADS A non-family member threatened to kill your child ksads_ptsd_raw_764_p
  398. #KSADS A family member threatened to kill your child ksads_ptsd_raw_765_p
  400. #create dataframe with physical abuse items
  401. abuse_phy_yr2_data = c("ksads_ptsd_raw_761_p" , "ksads_ptsd_raw_762_p", "ksads_ptsd_raw_763_p", "ksads_ptsd_raw_764_p", "ksads_ptsd_raw_765_p")
  403. #sum the items as long as they're not all NA
  404. ela_in_abcd_yr2$abuse_phy_sum = apply(ela_in_abcd_yr2[abuse_phy_yr2_data],1, sum_score)
  406. #binary recode as anything > 0 is 1
  407. ela_in_abcd_yr2$abuse_phy_yr2 = ifelse(ela_in_abcd_yr2$abuse_phy_sum == 0, 0, 1)
  410. #abuse_sex
  411. #KSADS A grown up in the home touched your child in their privates, had your child touch their privates, or did other sexual things to your childP ksads_ptsd_raw_767_p
  412. #KSADS An adult outside your family touched your child in their privates, had your child touch their privates or did other sexual things to your childP ksads_ptsd_raw_768_p
  413. #KSADS A peer forced your child to do something sexuallyP ksads_ptsd_raw_769_p
  415. #create dataframe with sexual abuse items
  416. abuse_sex_yr2_data = c("ksads_ptsd_raw_767_p" , "ksads_ptsd_raw_768_p", "ksads_ptsd_raw_769_p")
  418. #sum the items as long as they're not all NA
  419. ela_in_abcd_yr2$abuse_sex_sum = apply(ela_in_abcd_yr2[abuse_sex_yr2_data],1, sum_score)
  421. #binary recode as anything > 0 is 1
  422. ela_in_abcd_yr2$abuse_sex_yr2 = ifelse(ela_in_abcd_yr2$abuse_sex_sum == 0, 0, 1)
  425. #abuse_emo
  426. #Family Environment Family members often criticize each other. fes_youth_q5
  427. #utilizing youth only to ensure youth exposure
  429. #no recode necessary (binary yes/no variable)
  430. ela_in_abcd_yr2$abuse_emo_yr2 = ela_in_abcd_yr2$fes_youth_q5
  433. #neglect_phy
  434. #Demographics Needed food but couldn't afford to buy it or couldn't afford to go out to get it?P demo_fam_exp1_v2_l/demo_fam_exp1_v2
  435. #Demographics Had someone who needed to see a doctor or go to the hospital but didn't go because you could not afford it?P demo_fam_exp6_v2/demo_fam_exp6_v2_l
  436. #Demographics Had someone who needed a dentist but couldn't go because you could not afford it?P demo_fam_exp7_v2/demo_fam_exp7_v2_l
  438. #create dataframe with physical neglect items
  439. neglect_phy_yr2_data = c("demo_fam_exp1_v2_l" , "demo_fam_exp6_v2_l", "demo_fam_exp7_v2_l")
  441. #sum the items as long as they're not all NA
  442. ela_in_abcd_yr2$neglect_phy_sum = apply(ela_in_abcd_yr2[neglect_phy_yr2_data],1, sum_score)
  444. #binary recode as anything > 0 is 1
  445. ela_in_abcd_yr2$neglect_phy_yr2 = ifelse(ela_in_abcd_yr2$neglect_phy_sum == 0, 0, 1)
  448. #neglect_emo
  449. #CRPBI parent *CRPBI Mean Parent (primary caregiver)Y crpbi_y_ss_parent_ace
  450. #only ~5000 with scores, so without further information, omitting for Year 2
  451. #domain score will stay same as Year 1
  454. #divorce
  455. #Life Events Parents separated or divorced?Y,P ple_separ_p/ple_separ_y (ple_separ_p_yr2/ple_separ_y_yr2)
  457. #create dataframe with divorce items
  458. divorce_yr2_data = c("ple_separ_p", "ple_separ_y")
  460. #sum the items as long as they're not all NA
  461. ela_in_abcd_yr2$divorce_sum = apply(ela_in_abcd_yr2[divorce_yr2_data],1, sum_score)
  463. #binary recode as anything > 0 is 1
  464. ela_in_abcd_yr2$divorce_yr2 = ifelse(ela_in_abcd_yr2$divorce_sum == 0, 0, 1)
  467. #violence in home (violence)
  468. #KSADS Witness the grownups in the home push, shove or hit one anotherP ksads_ptsd_raw_766_p
  470. #no recode necessary (binary yes/no variable)
  471. ela_in_abcd_yr2$violence_yr2 = ela_in_abcd_yr2$ksads_ptsd_raw_766_p
  474. #mental illness (mi)
  475. #Life Events Family member had mental/emotional problem?Y,P ple_mh_p/ple_mh_y (ple_mh_p_yr2/ple_mh_y_yr2)
  476. #Adult Self-Report Total Problems ASR Syndrome Scale (t-score)P asr_scr_totprob_t_ace (1=greater than t-score>=64)
  477. #No Fam Hx at Y2
  479. #create dataframe with mental illness items
  480. mi_yr2_data = c("ple_mh_p", "ple_mh_y", "asr_scr_totprob_t_ace")
  482. #sum the items as long as they're not all NA
  483. ela_in_abcd_yr2$mi_sum = apply(ela_in_abcd_yr2[mi_yr2_data],1, sum_score)
  485. #binary recode as anything > 0 is 1
  486. ela_in_abcd_yr2$mi_yr2 = ifelse(ela_in_abcd_yr2$mi_sum == 0, 0, 1)
  489. #Incarcerated Relative
  490. #Life Events Someone in the family was arrested?Y,P ple_arrest_p/ple_arrest_y (ple_arrest_p_yr2/ple_arrest_y_yr2)
  491. #Life Events Parents/caregiver got into trouble with the law?Y,P ple_law_p/ple_law_y (ple_law_p_yr2/ple_law_y_yr2)
  492. #Life Events One of the parents/caregivers went to jail?Y,P ple_jail_p/ple_jail_y (ple_jail_p_yr2/ple_jail_y_yr2)
  494. #create dataframe with incarceration items
  495. incar_yr2_data = c("ple_arrest_p" , "ple_arrest_y" , "ple_law_p", "ple_law_y", "ple_jail_p", "ple_jail_y")
  497. #sum the items as long as they're not all NA
  498. ela_in_abcd_yr2$incar_sum = apply(ela_in_abcd_yr2[incar_yr2_data],1, sum_score)
  500. #binary recode as anything > 0 is 1
  501. ela_in_abcd_yr2$incar_yr2 = ifelse(ela_in_abcd_yr2$incar_sum == 0, 0, 1)
  504. #Substance Use
  505. #Life Events Family member had drug and/or alcohol problem?Y,P ple_sud_p/ple_sud_y (ple_sud_p_yr2/ple_sud_y_yr2)
  506. #Adult Self-Report I use drugs (other than alcohol, nicotine) for nonmedical purposesP asr_q06_p_ace
  507. #No Fam Hx update at Y2
  509. #create dataframe with substance use items
  510. su_yr2_data = c("ple_sud_p","ple_sud_y","asr_q06_p_ace")
  512. #sum the items as long as they're not all NA
  513. ela_in_abcd_yr2$su_sum = apply(ela_in_abcd_yr2[su_yr2_data],1, sum_score)
  515. #binary recode as anything > 0 is 1
  516. ela_in_abcd_yr2$su_yr2 = ifelse(ela_in_abcd_yr2$su_sum == 0, 0, 1)
  518. #Exposure
  519. #Witnessed or caught in a fire that caused significant property damage or personal injuryP ksads_ptsd_raw_756_p
  520. #Witnessed or caught in a natural disaster that caused significant property damage or personal injuryP ksads_ptsd_raw_757_p
  521. #Witnessed or present during an act of terrorism (e.g., Boston marathon bombing)P ksads_ptsd_raw_758_p
  522. #Witnessed death or mass destruction in a war zoneP ksads_ptsd_raw_759_p
  523. #Witnessed someone shot or stabbed in the community ksads_ptsd_raw_760_p
  524. #0 = No; 1 = Yes
  526. #create dataframe with exposure items
  527. exposure_yr2_data = c("ksads_ptsd_raw_756_p" , "ksads_ptsd_raw_757_p", "ksads_ptsd_raw_758_p", "ksads_ptsd_raw_759_p", "ksads_ptsd_raw_760_p")
  529. #sum the items as long as they're not all NA
  530. ela_in_abcd_yr2$exposure_sum = apply(ela_in_abcd_yr2[exposure_yr2_data],1, sum_score)
  532. #binary recode as anything > 0 is 1
  533. ela_in_abcd_yr2$exposure_yr2 = ifelse(ela_in_abcd_yr2$exposure_sum == 0, 0, 1)
  535. #Safety
  536. #Life Events Was a victim of crime/violence/assault? ple_victim_p/ple_victim_y (ple_victim_p_bs/ple_victim_y_bs)
  537. #*My neighborhood is safe from crime. neighborhood3r_p_ace/nbh_crime_y_ela
  538. #Violence is not a problem in my neighborhood. neighborhood2r_p_ace
  539. #recoded in setup: 4/5=0, 3/2/1=1
  541. #create dataframe with safety items
  542. safety_sum_yr2_data = c("neighborhood2r_p_ela","neighborhood3r_p_ela","nbh_crime_y_ela", "ple_victim_p", "ple_victim_y")
  544. #sum the items as long as they're not all NA
  545. ela_in_abcd_yr2$safety_sum = apply(ela_in_abcd_yr2[safety_sum_yr2_data],1, sum_score)
  547. #binary recode as anything > 0 is 1
  548. ela_in_abcd_yr2$safety_yr2 = ifelse(ela_in_abcd_yr2$safety_sum == 0, 0, 1)
  550. #Resources
  551. #Were without telephone service because you could not afford it? demo_fam_exp2_v2
  552. #Didn't pay the full amount of the rent or mortgage because you could not afford it? demo_fam_exp3_v2
  553. #Were evicted from your home for not paying the rent or mortgage? demo_fam_exp4_v2
  554. #Had services turned off by the gas or electric company, or the oil company wouldn't deliver oil because payments were not made? demo_fam_exp5_v2
  556. #create dataframe with resources items
  557. resources_yr2_data = c("demo_fam_exp2_v2_l" , "demo_fam_exp3_v2_l", "demo_fam_exp4_v2_l", "demo_fam_exp5_v2_l")
  559. #sum the items as long as they're not all NA
  560. ela_in_abcd_yr2$resources_sum = apply(ela_in_abcd_yr2[resources_yr2_data],1, sum_score)
  562. #binary recode as anything > 0 is 1
  563. ela_in_abcd_yr2$resources_yr2 = ifelse(ela_in_abcd_yr2$resources_sum == 0, 0, 1)
  565. #Separation
  566. #not collected until year 3/4
  567. #Placed in foster care? ple_foster_care_p/ple_foster_care_y
  568. #One of the parents/caregivers was deported? ple_deported_p/ple_deported_y
  570. ```
  572. ```{r ela-plus-yr3}
  573. #yr 3
  575. #abuse_phy
  576. #cannot be calculated at Year 1 due to no KSADS
  577. #domain score will be equal to that at Baseline
  579. #abuse_sex
  580. #cannot be calculated at Year 1 due to no KSADS
  581. #domain score will be equal to that at Baseline
  583. #abuse_emo
  584. #Family Environment Family members often criticize each other. fes_youth_q5
  585. #utilizing youth only to ensure youth exposure
  586. ela_in_abcd_yr3$abuse_emo_yr3 = ela_in_abcd_yr3$fes_youth_q5
  589. #neglect_phy
  590. #Demographics Needed food but couldn't afford to buy it or couldn't afford to go out to get it? demo_fam_exp1_v2_l/demo_fam_exp1_v2
  591. #Demographics Had someone who needed to see a doctor or go to the hospital but didn't go because you could not afford it? demo_fam_exp6_v2/demo_fam_exp6_v2_l
  592. #Demographics Had someone who needed a dentist but couldn't go because you could not afford it? demo_fam_exp7_v2/demo_fam_exp7_v2_l
  594. #create dataframe with physical neglect items
  595. neglect_phy_sum_yr3_data = c("demo_fam_exp1_v2_l", "demo_fam_exp6_v2_l", "demo_fam_exp7_v2_l")
  597. #sum the items as long as they're not all NA
  598. ela_in_abcd_yr3$neglect_phy_sum = apply(ela_in_abcd_yr3[neglect_phy_sum_yr3_data],1, sum_score)
  600. #binary recode as anything > 0 is 1
  601. ela_in_abcd_yr3$neglect_phy_yr3 = ifelse(ela_in_abcd_yr3$neglect_phy_sum == 0, 0, 1)
  604. #neglect_emo
  605. #CRPBI parent *CRPBI Mean Parent (primary caregiver)Y crpbi_y_ss_parent_ace
  607. ela_in_abcd_yr3$neglect_emo_yr3 = ela_in_abcd_yr3$crpbi_y_ss_parent_ace
  609. #divorce
  610. #create dataframe with divorce items
  611. divorce_yr3_data = c("ple_separ_p" , "ple_separ_y")
  613. #sum the items as long as they're not all NA
  614. ela_in_abcd_yr3$divorce_sum = apply(ela_in_abcd_yr3[divorce_yr3_data],1, sum_score)
  616. #binary recode as anything > 0 is 1
  617. ela_in_abcd_yr3$divorce_yr3 = ifelse(ela_in_abcd_yr3$divorce_sum == 0, 0, 1)
  620. #violence in home (violence)
  621. #cannot be calculated at Year 3 due to no KSADS
  622. #domain score will be equal to that at Year 2
  624. #mental illness (mi)
  625. #Life Events Family member had mental/emotional problem? ple_mh_p/ple_mh_y
  627. #create dataframe with mi items
  628. mi_sum_yr3_data = c("ple_mh_p", "ple_mh_y")
  630. #sum the items as long as they're not all NA
  631. ela_in_abcd_yr3$mi_sum = apply(ela_in_abcd_yr3[mi_sum_yr3_data],1, sum_score)
  633. #binary recode as anything > 0 is 1
  634. ela_in_abcd_yr3$mi_yr3 = ifelse(ela_in_abcd_yr3$mi_sum == 0, 0, 1)
  637. #Incarcerated Relative
  638. #Life Events Someone in the family was arrested?Y,P ple_arrest_p/ple_arrest_y
  639. #Life Events Parents/caregiver got into trouble with the law?Y,P ple_law_p/ple_law_y
  640. #Life Events One of the parents/caregivers went to jail?Y,P ple_jail_p/ple_jail_y
  642. #create dataframe with incarceration items
  643. incar_sum_yr3_data = c("ple_arrest_p" , "ple_arrest_y" , "ple_law_p" , "ple_law_y" , "ple_jail_y" , "ple_jail_p")
  645. #sum the items as long as they're not all NA
  646. ela_in_abcd_yr3$incar_sum = apply(ela_in_abcd_yr3[incar_sum_yr3_data],1, sum_score)
  648. #binary recode as anything > 0 is 1
  649. ela_in_abcd_yr3$incar_yr3 = ifelse(ela_in_abcd_yr3$incar_sum == 0, 0, 1)
  652. #Substance Use
  653. #Life Events Family member had drug and/or alcohol problem? ple_sud_p/ple_sud_y
  654. #No ASR or Fam Hx at Y3
  656. #create dataframe with incarceration items
  657. su_sum_yr3_data = c("ple_sud_p","ple_sud_y")
  659. #create dataframe with substance use items
  660. ela_in_abcd_yr3$su_sum = apply(ela_in_abcd_yr3[su_sum_yr3_data],1, sum_score)
  662. #binary recode as anything > 0 is 1
  663. ela_in_abcd_yr3$su_yr3 = ifelse(ela_in_abcd_yr3$su_sum == 0, 0, 1)
  665. #Exposure
  666. #cannot be calculated at Year 3 due to no KSADS
  667. #domain score will be equal to that at Year 2
  669. #Safety
  670. #Life Events Was a victim of crime/violence/assault? ple_victim_p/ple_victim_y
  671. #*My neighborhood is safe from crime. neighborhood3r_p_ace/nbh_crime_y_ela
  672. #Violence is not a problem in my neighborhood. neighborhood2r_p_ace
  673. #recoded in setup: 4/5=0, 3/2/1=1
  675. #create dataframe with safety items
  676. safety_sum_yr3_data = c("neighborhood2r_p_ela","neighborhood3r_p_ela","nbh_crime_y_ela", "ple_victim_p", "ple_victim_y")
  678. #sum the items as long as they're not all NA
  679. ela_in_abcd_yr3$safety_sum = apply(ela_in_abcd_yr3[safety_sum_yr3_data],1, sum_score)
  681. #binary recode as anything > 0 is 1
  682. ela_in_abcd_yr3$safety_yr3 = ifelse(ela_in_abcd_yr3$safety_sum == 0, 0, 1)
  684. #Resources
  685. #Were without telephone service because you could not afford it? demo_fam_exp2_v2
  686. #Didn't pay the full amount of the rent or mortgage because you could not afford it? demo_fam_exp3_v2
  687. #Were evicted from your home for not paying the rent or mortgage? demo_fam_exp4_v2
  688. #Had services turned off by the gas or electric company, or the oil company wouldn't deliver oil because payments were not made? demo_fam_exp5_v2
  690. #create dataframe with resources items
  691. resources_yr3_data = c("demo_fam_exp2_v2_l" , "demo_fam_exp3_v2_l", "demo_fam_exp4_v2_l", "demo_fam_exp5_v2_l")
  693. #sum the items as long as they're not all NA
  694. ela_in_abcd_yr3$resources_sum = apply(ela_in_abcd_yr3[resources_yr3_data],1, sum_score)
  696. #binary recode as anything > 0 is 1
  697. ela_in_abcd_yr3$resources_yr3 = ifelse(ela_in_abcd_yr3$resources_sum == 0, 0, 1)
  699. ##New domain for Y3 - parents deported/foster care
  701. #create dateframe with separation items
  702. sep_yr3_data = c("ple_foster_care_p","ple_foster_care_y","ple_deported_p","ple_deported_y")
  704. #sum the items as long as they're not all NA
  705. ela_in_abcd_yr3$sep_sum = apply(ela_in_abcd_yr3[sep_yr3_data],1, sum_score)
  707. #binary recode as anything > 0 is 1
  708. ela_in_abcd_yr3$sep_yr3 = ifelse(ela_in_abcd_yr3$sep_sum == 0, 0, 1)
  710. ```
  712. ```{r ela-longitudinal}
  713. ##LONGITUDINAL SCORE CREATION
  715. #join annual scores to Baseline
  716. #pulling just scores, no eventnames (wide format)
  718. bs_scores = ela_in_abcd_bs[c("src_subject_id", "ela_plus_bs", "abuse_phy_bs", "abuse_sex_bs", "abuse_emo_bs", "neglect_phy_bs", "neglect_emo_bs", "divorce_bs", "violence_bs", "mi_bs", "incar_bs", "su_bs","exposure_bs", "safety_bs", "resources_bs")]
  719. yr1_scores = ela_in_abcd_yr1[c("src_subject_id", "abuse_emo_yr1", "neglect_phy_yr1", "neglect_emo_yr1", "divorce_yr1", "mi_yr1", "incar_yr1", "su_yr1","safety_yr1","resources_yr1")]
  720. yr2_scores = ela_in_abcd_yr2[c("src_subject_id", "abuse_phy_yr2", "abuse_sex_yr2", "abuse_emo_yr2", "neglect_phy_yr2", "divorce_yr2", "violence_yr2", "mi_yr2", "incar_yr2", "su_yr2","exposure_yr2", "safety_yr2", "resources_yr2")]
  721. yr3_scores = ela_in_abcd_yr3[c("src_subject_id", "abuse_emo_yr3", "neglect_phy_yr3", "neglect_emo_yr3", "divorce_yr3", "mi_yr3", "incar_yr3", "su_yr3","safety_yr3","resources_yr3","sep_yr3")]
  723. step1 = left_join(bs_scores,yr1_scores, by=c("src_subject_id"))
  724. step2 = left_join(step1,yr2_scores, by=c("src_subject_id"))
  725. ela_final = left_join(step2, yr3_scores, by=c("src_subject_id"))
  727. #save bs data
  728. #prep for return to long format
  729. bs_scores[,'fam_sep'] = NA
  730. bs_scores$eventname="baseline_year_1_arm_1"
  731. lookup = c(ela_plus="ela_plus_bs", abuse_phy = "abuse_phy_bs", abuse_sex = "abuse_sex_bs", abuse_emo = "abuse_emo_bs", neglect_phy = "neglect_phy_bs", neglect_emo = "neglect_emo_bs", divorce = "divorce_bs", violence = "violence_bs", mental = "mi_bs", incar = "incar_bs", sud = "su_bs", exposure = "exposure_bs", safety = "safety_bs", resources = "resources_bs")
  732. bs_scores=rename(bs_scores, all_of(lookup))
  734. #building score - adding items by timepoint
  735. #for each domain, if current and any/all previous timepoints 0, will be 0; otherwise, 1
  737. #yr1 by domain
  738. ela_final$abuse_phy_yr1_build = ifelse(ela_final$abuse_phy_bs == 0, 0, 1)
  739. ela_final$abuse_sex_yr1_build = ifelse(ela_final$abuse_sex_bs == 0, 0, 1)
  740. ela_final$abuse_emo_yr1_build = ifelse(ela_final$abuse_emo_bs == 0 & ela_final$abuse_emo_yr1 == 0, 0, 1)
  741. ela_final$neglect_phy_yr1_build = ifelse(ela_final$neglect_phy_bs == 0 & ela_final$neglect_phy_yr1 == 0, 0, 1)
  742. ela_final$neglect_emo_yr1_build = ifelse(ela_final$neglect_emo_bs == 0 & ela_final$neglect_emo_yr1 == 0, 0, 1)
  743. ela_final$divorce_yr1_build = ifelse(ela_final$divorce_bs == 0 & ela_final$divorce_yr1 == 0, 0, 1)
  744. ela_final$violence_yr1_build = ifelse(ela_final$violence_bs == 0, 0, 1)
  745. ela_final$mi_yr1_build = ifelse(ela_final$mi_bs == 0 & ela_final$mi_yr1 == 0, 0, 1)
  746. ela_final$incar_yr1_build = ifelse(ela_final$incar_bs == 0 & ela_final$incar_yr1 == 0, 0, 1)
  747. ela_final$su_yr1_build = ifelse(ela_final$su_bs == 0 & ela_final$su_yr1 == 0, 0, 1)
  748. ela_final$safety_yr1_build = ifelse(ela_final$safety_bs == 0 & ela_final$safety_yr1 == 0, 0, 1)
  749. ela_final$exposure_yr1_build = ifelse(ela_final$exposure_bs == 0, 0, 1)
  750. ela_final$resources_yr1_build = ifelse(ela_final$resources_bs == 0 & ela_final$resources_yr1 == 0, 0, 1)
  752. #yr1 sum score
  753. ela_final$ela_plus_yr1 = ela_final$abuse_phy_yr1_build + ela_final$abuse_sex_yr1_build + ela_final$abuse_emo_yr1_build + ela_final$neglect_phy_yr1_build + ela_final$neglect_emo_yr1_build + ela_final$divorce_yr1_build + ela_final$violence_yr1_build + ela_final$mi_yr1_build + ela_final$incar_yr1_build + ela_final$su_yr1_build + ela_final$safety_yr1_build + ela_final$exposure_yr1_build + ela_final$resources_yr1_build
  755. #save yr1 data
  756. yr1_scores_final = ela_final[c("src_subject_id", "ela_plus_yr1", "abuse_phy_yr1_build", "abuse_sex_yr1_build", "abuse_emo_yr1_build", "neglect_phy_yr1_build", "neglect_emo_yr1_build", "divorce_yr1_build", "violence_yr1_build", "mi_yr1_build", "incar_yr1_build", "su_yr1_build", "exposure_yr1_build", "safety_yr1_build", "resources_yr1_build")]
  758. #prep for return to long format
  759. #empty column for family separation
  760. yr1_scores_final[,'fam_sep'] = NA
  761. yr1_scores_final$eventname="1_year_follow_up_y_arm_1"
  762. lookup = c(ela_plus = "ela_plus_yr1", abuse_phy = "abuse_phy_yr1_build", abuse_sex = "abuse_sex_yr1_build", abuse_emo = "abuse_emo_yr1_build", neglect_phy = "neglect_phy_yr1_build", neglect_emo = "neglect_emo_yr1_build", divorce = "divorce_yr1_build", violence = "violence_yr1_build", mental = "mi_yr1_build", incar = "incar_yr1_build", sud = "su_yr1_build", exposure = "exposure_yr1_build", safety = "safety_yr1_build", resources = "resources_yr1_build")
  763. yr1_scores_final=rename(yr1_scores_final, all_of(lookup))
  765. #yr2 by domain
  766. ela_final$abuse_phy_yr2_build = ifelse(ela_final$abuse_phy_bs == 0 & ela_final$abuse_phy_yr2 == 0, 0, 1)
  767. ela_final$abuse_sex_yr2_build = ifelse(ela_final$abuse_sex_bs == 0 & ela_final$abuse_sex_yr2 == 0, 0, 1)
  768. ela_final$abuse_emo_yr2_build = ifelse(ela_final$abuse_emo_bs == 0 & ela_final$abuse_emo_yr1 == 0 & ela_final$abuse_emo_yr2 == 0, 0, 1)
  769. ela_final$neglect_phy_yr2_build = ifelse(ela_final$neglect_phy_bs == 0 & ela_final$neglect_phy_yr1 == 0 & ela_final$neglect_phy_yr2 == 0, 0, 1)
  770. ela_final$neglect_emo_yr2_build = ela_final$neglect_emo_yr1_build
  771. ela_final$divorce_yr2_build = ifelse(ela_final$divorce_bs == 0 & ela_final$divorce_yr1 == 0 & ela_final$divorce_yr2 == 0, 0, 1)
  772. ela_final$violence_yr2_build = ifelse(ela_final$violence_bs == 0 & ela_final$violence_yr2 == 0, 0, 1)
  773. ela_final$mi_yr2_build = ifelse(ela_final$mi_bs == 0 & ela_final$mi_yr1 == 0 & ela_final$mi_yr2 == 0, 0, 1)
  774. ela_final$incar_yr2_build = ifelse(ela_final$incar_bs == 0 & ela_final$incar_yr1 == 0 & ela_final$incar_yr2 == 0, 0, 1)
  775. ela_final$su_yr2_build = ifelse(ela_final$su_bs == 0 & ela_final$su_yr1 == 0 & ela_final$su_yr2 == 0, 0, 1)
  776. ela_final$exposure_yr2_build = ifelse(ela_final$exposure_bs == 0 & ela_final$exposure_yr2 == 0, 0, 1)
  777. ela_final$safety_yr2_build = ifelse(ela_final$safety_bs == 0 & ela_final$safety_yr1 ==0 & ela_final$safety_yr2 == 0, 0, 1)
  778. ela_final$resources_yr2_build = ifelse(ela_final$resources_bs == 0 & ela_final$resources_yr1 ==0 & ela_final$resources_yr2 == 0, 0, 1)
  780. #yr2 sum score
  781. ela_final$ela_plus_yr2 = ela_final$abuse_phy_yr2_build + ela_final$abuse_sex_yr2_build + ela_final$abuse_emo_yr2_build + ela_final$neglect_phy_yr2_build + ela_final$neglect_emo_yr2_build + ela_final$divorce_yr2_build + ela_final$violence_yr2_build + ela_final$mi_yr2_build + ela_final$incar_yr2_build + ela_final$su_yr2_build + ela_final$exposure_yr2_build + ela_final$safety_yr2_build + ela_final$resources_yr2_build
  783. #save yr2 data
  784. yr2_scores_final = ela_final[c("src_subject_id", "ela_plus_yr2", "abuse_phy_yr2_build", "abuse_sex_yr2_build", "abuse_emo_yr2_build", "neglect_phy_yr2_build", "neglect_emo_yr2_build", "divorce_yr2_build", "violence_yr2_build", "mi_yr2_build", "incar_yr2_build", "su_yr2_build", "exposure_yr2_build", "safety_yr2_build", "resources_yr2_build")]
  786. #prep for return to long format
  787. #empty column for family separation
  788. yr2_scores_final[,'fam_sep'] = NA
  789. yr2_scores_final$eventname="2_year_follow_up_y_arm_1"
  790. lookup = c(ela_plus = "ela_plus_yr2", abuse_phy = "abuse_phy_yr2_build", abuse_sex = "abuse_sex_yr2_build", abuse_emo = "abuse_emo_yr2_build", neglect_phy = "neglect_phy_yr2_build", neglect_emo = "neglect_emo_yr2_build", divorce = "divorce_yr2_build", violence = "violence_yr2_build", mental = "mi_yr2_build", incar = "incar_yr2_build", sud = "su_yr2_build", exposure = "exposure_yr2_build", safety = "safety_yr2_build", resources = "resources_yr2_build")
  791. yr2_scores_final=rename(yr2_scores_final, all_of(lookup))
  793. #yr3 by domain
  794. ela_final$abuse_phy_yr3_build = ifelse(ela_final$abuse_phy_bs == 0 & ela_final$abuse_phy_yr2 == 0, 0, 1)
  795. ela_final$abuse_sex_yr3_build = ifelse(ela_final$abuse_sex_bs == 0 & ela_final$abuse_sex_yr2 == 0, 0, 1)
  796. ela_final$abuse_emo_yr3_build = ifelse(ela_final$abuse_emo_bs == 0 & ela_final$abuse_emo_yr1 == 0 & ela_final$abuse_emo_yr2 == 0 & ela_final$abuse_emo_yr3 == 0, 0, 1)
  797. ela_final$neglect_phy_yr3_build = ifelse(ela_final$neglect_phy_bs == 0 & ela_final$neglect_phy_yr1 == 0 & ela_final$neglect_phy_yr2 == 0 & ela_final$neglect_phy_yr3 == 0, 0, 1)
  798. ela_final$neglect_emo_yr3_build = ifelse(ela_final$neglect_emo_bs == 0 & ela_final$neglect_emo_yr1 == 0 & ela_final$neglect_emo_yr3 == 0, 0, 1)
  799. ela_final$divorce_yr3_build = ifelse(ela_final$divorce_bs == 0 & ela_final$divorce_yr1 == 0 & ela_final$divorce_yr2 == 0 & ela_final$divorce_yr3 == 0, 0, 1)
  800. ela_final$mi_yr3_build = ifelse(ela_final$mi_bs == 0 & ela_final$mi_yr1 == 0 & ela_final$mi_yr2 == 0 & ela_final$mi_yr3 == 0, 0, 1)
  801. ela_final$incar_yr3_build = ifelse(ela_final$incar_bs == 0 & ela_final$incar_yr1 == 0 & ela_final$incar_yr2 == 0 & ela_final$incar_yr3 == 0, 0, 1)
  802. ela_final$su_yr3_build = ifelse(ela_final$su_bs == 0 & ela_final$su_yr1 == 0 & ela_final$su_yr2 == 0 & ela_final$su_yr3 == 0, 0, 1)
  803. ela_final$violence_yr3_build = ifelse(ela_final$violence_bs == 0 & ela_final$violence_yr2 == 0, 0, 1)
  804. ela_final$exposure_yr3_build = ifelse(ela_final$exposure_bs == 0 & ela_final$exposure_yr2 == 0, 0, 1)
  805. ela_final$safety_yr3_build = ifelse(ela_final$safety_bs == 0 & ela_final$safety_yr1 == 0 & ela_final$safety_yr2 == 0 & ela_final$safety_yr3 == 0, 0, 1)
  806. ela_final$resources_yr3_build = ifelse(ela_final$resources_bs == 0 & ela_final$resources_yr1 == 0 & ela_final$resources_yr2 == 0 & ela_final$resources_yr3 == 0, 0, 1)
  808. #yr3 sum score
  809. ela_final$ela_plus_yr3 = ela_final$abuse_phy_yr3_build + ela_final$abuse_sex_yr3_build + ela_final$abuse_emo_yr3_build + ela_final$neglect_phy_yr3_build + ela_final$neglect_emo_yr3_build + ela_final$divorce_yr3_build + ela_final$violence_yr3_build + ela_final$mi_yr3_build + ela_final$incar_yr3_build + ela_final$su_yr3_build + ela_final$exposure_yr3_build + ela_final$safety_yr3_build + ela_final$resources_yr3_build + ela_final$sep_yr3
  811. #save yr3 data
  812. yr3_scores_final = ela_final[c("src_subject_id", "ela_plus_yr3", "abuse_phy_yr3_build", "abuse_sex_yr3_build", "abuse_emo_yr3_build", "neglect_phy_yr3_build", "neglect_emo_yr3_build", "divorce_yr3_build", "violence_yr3_build", "mi_yr3_build", "incar_yr3_build", "su_yr3_build", "exposure_yr3_build", "safety_yr3_build", "resources_yr3_build","sep_yr3")]
  814. #prep for return to long format
  815. #empty column for family separation
  816. yr3_scores_final$eventname="3_year_follow_up_y_arm_1"
  817. lookup = c(ela_plus = "ela_plus_yr3", abuse_phy = "abuse_phy_yr3_build", abuse_sex = "abuse_sex_yr3_build", abuse_emo = "abuse_emo_yr3_build", neglect_phy = "neglect_phy_yr3_build", neglect_emo = "neglect_emo_yr3_build", divorce = "divorce_yr3_build", violence = "violence_yr3_build", mental = "mi_yr3_build", incar = "incar_yr3_build", sud = "su_yr3_build", exposure = "exposure_yr3_build", safety = "safety_yr3_build", resources = "resources_yr3_build", fam_sep = "sep_yr3")
  818. yr3_scores_final=rename(yr3_scores_final, all_of(lookup))
  820. #combine datasets
  821. step1 = rbind(bs_scores,yr1_scores_final)
  822. step2 = rbind(step1,yr2_scores_final)
  823. ela_plus_scores = rbind(step2,yr3_scores_final)
  825. #save with eventnames
  826. write.csv(ela_plus_scores, "ela_plus_abcd.csv")
  828. ```

ELAfromABCD_ELAplus.Rmd at commit dc8616b, under MIT · at the source

Overview

Authors: Alexis Brieant1, Natasha Chaku2, Dani Beck3, Niamh MacSweeney3,4, Divyangana Rakesh5, Christian K. Tamnes3,4
ORCID iDs: Alexis Brieant
  1. Department of Psychological Science, University of Vermont, Burlington, VT, USA
  2. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
  3. PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
  4. Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
  5. Department of Neuroimaging, Institute of Psychology, Psychiatry and Neuroscience, King’s College London, London, UK
Institutions: University of Vermont (United States); Indiana University Bloomington (United States); Indiana University (United States); University of Oslo (Norway); Diakonhjemmet Hospital (Norway); King's College London (United Kingdom)
Journal: Developmental cognitive neuroscience, volume 79, article 101738
Dates: received 1 July 2025; accepted 4 May 2026; published online 7 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.dcn.2026.101738 · PMID 42127658 · PMCID PMC13196395 · OpenAlex W4411977089
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.2139/ssrn.5336164
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics
Keywords: Adolescence, Adversity, Working memory, Puberty, Longitudinal, Neurodevelopment
MeSH: Adolescent Development*, Brain*, Memory, Short-Term*, Neurodevelopment*, Puberty*, Stress, Psychological*, Adolescent, Female, Gray Matter, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male (* major topic)
Journal subjects: Registered Report
Topic: Pregnancy-related medical research (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Funding: Brain & Behaviour Research Foundation (32908); New Investigator Research Grant; UKRI Medical Research Council (MR/Z506667/1); Norges Forskningsråd (288083, 323951); Helse Sør‐Øst RHF (2021070, 2023012, 500189); Livsvitenskap, Universitetet i Oslo
Citations: cited by 1 paper (Europe PMC); 113 references in the paper

Abstract

Stress exposure may influence the pace of pubertal and brain development; however, empirical findings are mixed, with evidence for both acceleration and deceleration. We used seven waves of data from the Adolescent Brain Cognitive Development (ABCD) Study (n = 5041) to examine associations between adversity and pubertal and structural brain development, as well as working memory. We hypothesized that higher adversity would predict more advanced puberty and brain structure at baseline, which would in turn be associated with better working memory performance. Pubertal timing and tempo was modeled using logistic growth curves, while subcortical and cortical gray matter volume (GMV) intercepts and slopes were estimated using latent growth curve models. We estimated effects separately for males and females. Earlier pubertal timing was associated with accelerated brain maturation, and adversity was associated with earlier pubertal timing (girls only), faster tempo, and differences in brain structure at baseline; adversity was also associated with lower working memory performance. There were significant indirect effects of higher adversity on lower working memory performance via lower initial GMV and slower pubertal tempo. These results demonstrate how adversity is linked to variation in the timing of brain development across multiple structural metrics.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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FERNLabUVM/pubertybrain

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 708dd4ac062c1416ad39fd421f9d683e28f481f2, 16 June 2026
Languages: Stata (7), R (6), SAS (4)
Size: 140 files, 17 scripts
Software Heritage: not archived
Found in: the text, “Method”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggplot2 (3 files), nlme (2 files), patchwork (2 files), cowplot (1 file), ggpubr (1 file), lme4 (1 file), psych (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
18 files, not copied: shown from their source

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At the source: github.com/FERNLabUVM/pubertybrain

karalk07/abcd-ela

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: dc8616b9d26bddbe4266e66dae8bd351afef5fac, 22 May 2025
Languages: R (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Adversity exposure”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/karalk07/abcd-ela

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 21 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data are available to investigators with an active data use agreement for the Adolescent Brain Cognitive Development (ABCD) Study

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 6 keywords, 13 MeSH terms, 6 funders, 109 references.

Cite

This paper

Brieant, A., Chaku, N., Beck, D., MacSweeney, N., Rakesh, D., & Tamnes, C. K. (2026). Timing and tempo of puberty and neurodevelopment following adversity: A registered report. Developmental cognitive neuroscience, 79, 101738. https://doi.org/10.1016/j.dcn.2026.101738

BibTeX

@article{brieant2026timing,
author = {Brieant, Alexis and Chaku, Natasha and Beck, Dani and MacSweeney, Niamh and Rakesh, Divyangana and Tamnes, Christian K.},
title = {{Timing and tempo of puberty and neurodevelopment following adversity: A registered report}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = may,
volume = {79},
pages = {101738},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101738},
url = {https://doi.org/10.1016/j.dcn.2026.101738},
pmid = {42127658},
pmcid = {PMC13196395}
}

RIS

TY - JOUR
AU - Brieant, Alexis
AU - Chaku, Natasha
AU - Beck, Dani
AU - MacSweeney, Niamh
AU - Rakesh, Divyangana
AU - Tamnes, Christian K.
TI - Timing and tempo of puberty and neurodevelopment following adversity: A registered report
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/05/07
VL - 79
SP - 101738
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101738
UR - https://doi.org/10.1016/j.dcn.2026.101738
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.dcn.2026.101738",
"type": "article-journal",
"title": "Timing and tempo of puberty and neurodevelopment following adversity: A registered report",
"container-title": "Developmental cognitive neuroscience",
"author": [
{
"family": "Brieant",
"given": "Alexis"
},
{
"family": "Chaku",
"given": "Natasha"
},
{
"family": "Beck",
"given": "Dani"
},
{
"family": "MacSweeney",
"given": "Niamh"
},
{
"family": "Rakesh",
"given": "Divyangana"
},
{
"family": "Tamnes",
"given": "Christian K."
}
],
"container-title-short": "Dev Cogn Neurosci",
"volume": "79",
"page": "101738",
"DOI": "10.1016/j.dcn.2026.101738",
"PMID": "42127658",
"PMCID": "PMC13196395",
"ISSN": "1878-9293",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.dcn.2026.101738",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
7
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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