Family History (FAMHX) summary scores

Introduction

The Family History (FAMHX) survey is a parent survey used to collect information about endorsement of substance use and mental health history of the participant’s family members.

❗Data requirements

Where to get data?

The FAMHX item-level data is available to authorized users of the NBDC Data Hub as part of the tabulated data resource. Please refer to the Get started page for general information on how to assemble and download a dataset.

For the computation of the FAMHX summary scores, one needs to have access to the mh_p_famhx table.

FAMHX summary score functions

We compute two types of FAMHX summary scores (format: mh_p_famhx_xxxxx):

1. Summarize the endorsements from one family member (input field)
   
   We developed one basic function, compute_famhx_endorsement() that can be used to compute all FAMHX summary scores by changing the value of the vars_matches parameter, which sets the input variable used to compute the summary score, the name of which is set by changing the value of the name parameter.
   
   Note: The single input field maps to multiple columns / fields in the release data because each option is provided as its own column.
   
   
2. Summarize the endorsements from multiple family members (input fields)
   
   For this set of scores, we first use the compute_famhx_endorsement() to compute the endorsement indicators for each input variable and then summarize over the multiple endorsements to compute a score. These FAMHX scores are listed below.

One family member (input field)

The following table gives an overview of the FAMHX summary scores that are computed from one family member (input field), the associated name of the input field and the function call that produces the score:

The primary computation in the compute_famhx_endorsement() is the following, where,

data = either the entire mh_p_famhx table or a subset containing all the necessary fields as shown above.

combine_checkboxes() = function that combines the multiple columns for an input field, each representing one option, into a single column

data_ss <- data |>
  combine_checkboxes(
    var_basename = var_matches,
    name = "tmp_values"
  ) |>
  transmute(
    !!name := case_when(
      purrr::map_int(tmp_values, length) == 0 ~ NA,
      purrr::map_lgl(tmp_values, ~ 777 %in% .x) ~ NA,
      purrr::map_lgl(tmp_values, ~ 888 %in% .x) ~ NA,
      purrr::map_lgl(tmp_values, ~ 999 %in% .x) ~ NA,
      purrr::map_lgl(tmp_values, ~ 0 %in% .x) ~ 0,
      .default = 1
    ) |>
      as.integer()
  )

The classification logic applied to the combined, single field can be read as:

• If no response was provided, or the responses included 777, 888, or 999, the indicator is set to NA
• If the responses included the value 0, the indicator is set to 0
• For all other cases, the indicator is set to 1

Multiple family members (input fields)

For some of the FAMHX scores (shown below), multiple family members (input fields) were summarized to create one endorsement summary score.

Example

For example, here are the primary computation steps in the compute_mh_p_famhx__drg__moth__fath_indicator() function that computes the mh_p_famhx__drg__moth__fath_indicator score, which aggregates over two individual indicators, mh_p_famhx__drg__fath_indicator and mh_p_famhx__drg__moth_indicator.

# Subset the famhx_config file to obtain the
# metadata corresponding to the relevant input fields
config_sub <- ABCDscores::famhx_config |>
  filter(
    input_vars %in% c(
      "mh_p_famhx__drg__fath_001",
      "mh_p_famhx__drg__moth_001"
    )
  )

# Compute the `mh_p_famhx__drg__moth__fath_indicator` score
data_ss <- purrr::map_dfc(
  ## Step 1: compute indicators corresponding to each input field
  config_sub$call,
  ~ eval(parse(text = .x))
) |>
  ## Step 2: aggregate the individual indicators
  transmute(
    !!name := case_when(
      mh_p_famhx__drg__fath_indicator == 1 |
        mh_p_famhx__drg__moth_indicator == 1 ~ 1,
      mh_p_famhx__drg__fath_indicator == 0 &
        mh_p_famhx__drg__moth_indicator == 0 ~ 0,
      .default = NA
    ) |>
      as.integer()
  )

The aggregation logic applied above can be read as:

• If either mh_p_famhx__drg__fath_indicator == 1 or mh_p_famhx__drg__moth_indicator == 1, set indicator to 1.
• If both mh_p_famhx__drg__fath_indicator and mh_p_famhx__drg__moth_indicator are 0, set indicator to 0.
• For all other cases, the indicator is set to NA