018-medications

Medications

The collection of medication information in the Human Phenotype Project study relies on self-reporting of medication usage via online and smartphone applications, as well as collection by staff members through telephone or face-to-face interviews during follow-up.

Self-reporting occurs when participants disclose their medication use to researchers. Having accurate and complete data on medication usage is essential for understanding the true impact of these medications on individuals’ health.

Data availability:

  • During registration (sign-up) for the study, participants are asked to report their regular medications online, with an autofill feature, selecting from a predefined drop-down list.
  • During follow-up calls or visits, participants update their medication intake.
  • App medication logging.

All three sources of data are stored in 1 parquet file: medications.parquet

from pheno_utils import PhenoLoader
pl = PhenoLoader('medications')
pl
PhenoLoader for medications with
16 fields
2 tables: ['medications', 'age_sex']

Data dictionary

pl.dict
folder_id feature_set field_string relative_location description_string bulk_dictionary data_coding stability field_type units sampling_rate strata sexed array list_of_tags debut transformation pandas_dtype completed bulk_file_extension
tabular_field_name
collection_timestamp 18 medications Collection time medications/medications.parquet Collection time NaN NaN NaN Datetime NaN NaN Collection time Both sexes Single NaN NaN NaN datetime64[ns, Asia/Jerusalem] NaN NaN
collection_date 18 medications Collection date medications/medications.parquet Collection date NaN NaN NaN Date NaN NaN Primary Both sexes Single NaN NaN NaN datetime64[ns] NaN NaN
timezone 18 medications Timezone medications/medications.parquet Timezone NaN NaN NaN Categorical (single) NaN NaN Collection time Both sexes Single NaN NaN NaN category NaN NaN
medication 18 medications Medication name medications/medications.parquet Medication name NaN NaN NaN Categorical (single) NaN NaN Primary Both sexes Single NaN NaN NaN category NaN NaN
api 18 medications Active Pharmaceutical Ingredient medications/medications.parquet Active Pharmaceutical Ingredient NaN NaN NaN Categorical (multiple) NaN NaN Primary Both sexes Multiple NaN NaN NaN object NaN NaN
atc3 18 medications List of atc codes level 3 medications/medications.parquet List of atc codes level 3 NaN NaN NaN Categorical (multiple) NaN NaN Primary Both sexes Multiple NaN NaN NaN object NaN NaN
atc4 18 medications List of atc codes level 4 medications/medications.parquet List of atc codes level 4 NaN NaN NaN Categorical (multiple) NaN NaN Primary Both sexes Multiple NaN NaN NaN object NaN NaN
atc5 18 medications List of atc codes level 5 medications/medications.parquet List of atc codes level 5 NaN NaN NaN Categorical (multiple) NaN NaN Primary Both sexes Multiple NaN NaN NaN object NaN NaN
start_month 18 medications Start month medications/medications.parquet Start month of medication taking NaN 042_03 NaN Categorical (single) NaN NaN Primary Both sexes Single NaN NaN NaN Int64 NaN NaN
start_year 18 medications Start year medications/medications.parquet Start year of medication taking NaN NaN NaN Integer NaN NaN Primary Both sexes Single NaN NaN NaN Int64 NaN NaN
start_date 18 medications Start date combined month + year medications/medications.parquet Start date combined month + year NaN NaN NaN Date NaN NaN Primary Both sexes Single NaN NaN NaN datetime64[ns] NaN NaN
supplements 18 medications Is supplements medications/medications.parquet Is it supplements NaN NaN NaN Categorical (single) NaN NaN Primary Both sexes Single NaN NaN NaN bool NaN NaN
source 18 medications Data Source medications/medications.parquet data source NaN NaN NaN Categorical (single) NaN NaN Primary Both sexes Single NaN NaN NaN category NaN NaN
medication_mapping 18 medications_auxiliary Medication mapping medications/medications.parquet Mapping to ATC codes NaN NaN NaN Mapping table (group) NaN NaN Auxiliary Both sexes Single NaN NaN NaN category NaN csv