The challenge is centered around developing better methods for prediction of Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) as early as possible. Phase 2: Algorithms and Approaches Social Determinants Track — is focused on building innovative models for early detection of AD/ADRD using social determinants of health.
Current methods of screening for AD/ADRD are time intensive and difficult to perform. Models that can flag individuals with a high likelihood of cognitive decline early based on social determinants have the potential to catch and treat cognitive decline earlier, and to reduce disparities in care for marginalized groups.
Prizes, with the exception of community code, will be awarded based on a combination of leaderboard score and model report. For details on the timeline and requirements, see the home page. No prizes will be awarded based on leaderboard score alone. All winners will be required to submit their modeling code. DrivenData will rerun the full model training and inference pipeline to confirm all winners' leaderboard scores.
The data for this competition comes from a national longitudinal study of adults 50 years and older in Mexico, the Mexican Health and Aging Study (MHAS). The study includes information about demographics, economic circumstances, migration, physical limitations, self-reported health, and lifestyle behaviors.
Overview of the data files provided for this competition:
├── submission_format.csv
├── test_features.csv
├── train_features.csv
└── train_labels.csvAll data files are hosted by the Mexican Health and Aging Study (MHAS), and can be downloaded via the link on the data download page.
The feature data for this competition includes survey responses from both 2003 and 2012. Some individuals in the dataset responded to the 2003 survey only, some responded to the 2012 survey only, and some responded to both. Missing values are present where the information either was not collected or where the question does not apply.
Data comes from in-person interviews. Individuals for the survey were selected to be a nationally representative sample of Mexicans aged 50 or older.
External data usage: Per the competition rules, external data is not allowed in this competition. However, participants can use pre-trained computer vision models as long as they were (1) available freely and openly in that form at the start of the competition and (2) not trained on any data associated with the ground truth data for this challenge.
Many features are available for both 2003 and 2012. Columns collected in 2003 end with _03, and columns collected in 2012 end with _12. Columns that are year-agnostic do not contain either year.
train_features.csv and test_features.csv include the following columns:
uid(str): Unique identifier for the individual. Each row is one individual.age_03/age_12(str): Binned age groupurban_03/urban_12(str): Locality size. Either 0. <100,000 (rural) or 1. 100,000+ (urban)married_03/married_12(str): Marital statusn_mar_03/n_mar_12(float): Number of marriagesedu_gru_03/edu_gru_12(str): Binned education leveln_living_child_03/n_living_child_12(str): Binned number of living childrenmigration_03/migration_12(float, 0 or 1): Has lived or worked in the U.S.glob_hlth_03/glob_hlth_12(str): Self-reported global healthadl_dress_03/adl_dress_12(float, 0 or 1): Has difficulty getting dressedadl_walk_03/adl_walk_12(float, 0 or 1): Has difficulty walking from one side of the room to the otheradl_bath_03/adl_bath_12(float, 0 or 1): Has difficulty bathing themselves in a tub or showeradl_eat_03/adl_eat_12(float, 0 or 1): Has difficulty eatingadl_bed_03/adl_bed_12(float, 0 or 1): Has difficulty getting in and out of bedadl_toilet_03/adl_toilet_12(float, 0 or 1): Has difficulty using the toiletn_adl_03/n_adl_12(float): Number of activities of daily living (ADL) limitations (0-5)iadl_money_03/iadl_money_12(float, 0 or 1): Has difficulty managing moneyiadl_meds_03/iadl_meds_12(float, 0 or 1): Has difficulty taking medicationsiadl_shop_03/iadl_shop_12(float, 0 or 1): Has difficulty shopping for groceriesiadl_meals_03/iadl_meals_12(float, 0 or 1): Has difficulty preparing a hot mealn_iadl_03/n_iadl_12(float): Number of instrumental activities of daily living (IADL) limitations (0-4)depressed_03/depressed_12(float, 0 or 1): Most of the past week, felt depressedhard_03/hard_12(float, 0 or 1): Most of the past week, felt that everything was an effortrestless_03/restless_12(float, 0 or 1): Most of the past week, felt that their sleep was restlesshappy_03/happy_12(float, 0 or 1): Most of the past week, felt happylonely_03/lonely_12(float, 0 or 1): Most of the past week, felt lonelyenjoy_03/enjoy_12(float, 0 or 1): Most of the past week, felt that they enjoyed lifesad_03/sad_12(float, 0 or 1): Most of the past week, felt sadtired_03/tired_12(float, 0 or 1): Most of the past week, felt tiredenergetic_03/energetic_12(float, 0 or 1): Most of the past week, felt they had a lot of energyn_depr_03/n_depr_12(float): Number of CES-D depressive symptoms (0-9)cesd_depressed_03/cesd_depressed_12(float, 0 or 1): Has 5+ CES-D depressive symptomshypertension_03/hypertension_12(float, 0 or 1): Has been diagnosed with hypertensiondiabetes_03/diabetes_12(float, 0 or 1): Has been diagnosed with diabetesresp_ill_03/resp_ill_12(float, 0 or 1): Has been diagnosed with respiratory illnessarthritis_03/arthritis_12(float, 0 or 1): Has been diagnosed with arthritis/rheumatismhrt_attack_03/hrt_attack_12(float, 0 or 1): Has been told they had a heart attackstroke_03/stroke_12(float, 0 or 1): Has been told they had a strokecancer_03/cancer_12(float, 0 or 1): Has been diagnosed with cancern_illnesses_03/n_illnesses_12(float): Number of illnesses (0-7)bmi_03/bmi_12(str): Binned body mass indexexer_3xwk_03/exer_3xwk_12(float, 0 or 1): Exercises 3+ times per weekalcohol_03/alcohol_12(float, 0 or 1): Currently drinks alcoholtobacco_03/tobacco_12(float, 0 or 1): Currently smokes tobaccotest_chol_03/test_chol_12(float, 0 or 1): Has had a cholesterol blood testtest_tuber_03/test_tuber_12(float, 0 or 1): Has been tested for tuberculosistest_diab_03/test_diab_12(float, 0 or 1): Has been tested for diabetestest_pres_03/test_pres_12(float, 0 or 1): Has been tested for high blood pressurehosp_03/hosp_12(float, 0 or 1): Has been hospitalized at least one night in the last yearvisit_med_03/visit_med_12(float, 0 or 1): Has visited a doctor at least once in the last yearout_proc_03/out_proc_12(float, 0 or 1): Has had at least one outpatient procedure in the last yearvisit_dental_03/visit_dental_12(float, 0 or 1): Has visited a dentist at least once in the last yearimss_03/imss_12(float, 0 or 1): Has health coverage with IMSSissste_03/issste_12(float, 0 or 1): Has health coverage with ISSSTE/ISSSTE Estatalpem_def_mar_03/pem_def_mar_12(float, 0 or 1): Has health coverage with PEMEX, Defensa, or Marinainsur_private_03/insur_private_12(float, 0 or 1): Has health coverage with private health insuranceinsur_other_03/insur_other_12(float, 0 or 1): Has health coverage with other health insuranceseg_pop_12(float, 0 or 1): Has health coverage with Seguro Popularinsured_03/insured_12(float, 0 or 1): Has health insurancedecis_famil_03/decis_famil_12(str): Weight in family decisionsdecis_personal_03/decis_personal_12(str): Weight over personal decisionsemployment_03/employment_12(str): Employment statusvax_flu_12(float, 0 or 1): Has been vaccinated against fluvax_pneu_12(float, 0 or 1): Has been vaccinated against pneumoniacare_adult_12(float, 0 or 1): Uses time to look after a sick or disabled adultcare_child_12(float, 0 or 1): Uses time to look after children under 12volunteer_12(float, 0 or 1): Uses time to volunteer for a non-profitattends_class_12(float, 0 or 1): Uses time to attend training course, lecture, or classattends_club_12(float, 0 or 1): Uses time to attend sports or social clubreads_12(float, 0 or 1): Uses time to read books, magazines, newspapersgames_12(float, 0 or 1): Uses time to do crosswords, jigsaw puzzles, number gamestable_games_12(float, 0 or 1): Uses time to play tabletop games. E.g., cards, dominoes, chesscomms_tel_comp_12(float, 0 or 1): Uses time to talk on the phone or send message/use the web on a computeract_mant_12(float, 0 or 1): Uses time to maintain a house, do repairs, garden, etc.tv_12(float, 0 or 1): Uses time to watch televisionsewing_12(float, 0 or 1): Uses time to sew, emboider, knit, make craftssatis_ideal_12(str): How much they agree with the statement that their life is close to idealsatis_excel_12(str): How much they agree with the statement that life is excellentsatis_fine_12(str): How much they agree with the statement that they are satisfied with their lifecosas_imp_12(str): How much they agree with the statement that they have achieved the things in life that are important to themwouldnt_change_12(str): How much they agree with the statement that they would change almost nothing about their lifememory_12(str): Self-reported memoryragender(str): Genderrameduc_m(str): Mother's education levelrafeduc_m(str): Father's education levelsgender_03/sgender_12(str): Spouse's genderrjob_hrswk_03/rjob_hrswk_12(float): Hours per week that they worked at their main jobrjlocc_m_03/rjlocc_m_12(str): Category of their longest occuptationrjob_end_03/rjob_end_12(float): Year that their last job endedrjobend_reason_03/rjobend_reason_12(str): Reason that their last job endedrearnings_03/rearnings_12(float): Earnings from employmentsearnings_03/searnings_12(float): Spouse's earnings from employmenthincome_03/hincome_12(float): Household incomehinc_business_03/hinc_business_12(float): Household income from businesshinc_rent_03/hinc_rent_12(float): Household income from renthinc_assets_03/hinc_assets_12(float): Household income from financial assetshinc_cap_03/hinc_cap_12(float): Household capital incomerinc_pension_03/rinc_pension_12(float): Income from pensionssinc_pension_03/sinc_pension_12(float): Spouse's income from pensionsrrelgimp_03/rrelgimp_12(str): Importance of religionrrfcntx_m_12(str): How often they see friends and relativesrsocact_m_12(str): How often they have social activitiesrrelgwk_12(str): Participates in weekly religious servicesa16a_12(float, 0 or 1): Year when respondent first left for the U.S., if they ever lived in the U.S.a21_12(float): Total years lived or worked in the U.S.a22_12(str): Main job type during longest stay in the U.S.a33b_12(str): U.S. residency statusa34_12(str): Speaks Englishj11_12(str): Floor material of residence
The target variable in this competition is a composite score reflecting cognitive function across seven different domains. Composite scores are calculated based on in-depth cognitive assessments that were administered in person as part of the MHAS Cognitive Aging Ancillary Study (Mex-Cog). A higher score is better, and the maximum possible score is 384.
The target data includes scores from two survey years: 2016 and 2021. Some individuals are in 2016 only, some are in 2021 only, and some are in both. train_labels.csv indicates for which years an individual has available scores.
train_labels.csv includes the following columns. Each row is a unique combination of uid and year.
uid(str): Unique identifier for the individualyear(int): Year the individual received the scorecomposite_score(int): Composite score across the seven domains listed below
Labelled training data example
The first few rows in train_labels.csv are:
| uid | year | composite_score |
|---|---|---|
| aace | 2021 | 175 |
| aanz | 2021 | 206 |
| aape | 2016 | 161 |
| aape | 2021 | 144 |
In 2021, individual aace got a score of 175. Individuals aace and aanz only have scores for 2021, while individual aape has scores for both 2016 and 2021.
The feature data only includes information through 2012, while cognitive scores are based on surveys conducted in 2016 and 2021. Participants will be predicting composite score 4 and 9 years in the future from the perspective of the feature data.
Composite score is the number of points that an individual received across seven domains:
| Domain | Example task or question | Possible score |
|---|---|---|
| Orientation | Where are we now? | 9 |
| Immediate memory | Word repetition tests | 95 |
| Delayed memory | Delayed recall of a short story | 106 |
| Attention | Ability to count backwards | 65 |
| Language | Write a sentence | 14 |
| Constructional praxis | Physically copy a drawn figure | 12 |
| Executive function | Simple math question | 83 |
Cognitive assessment scores are useful to determine an individual's risk of AD/ADRD and their need for treatment, but are time-intensive and complex to perform. Predicting an individual's likely cognitive ability in the future based on various social determinants of health could save time for clinicians and improve availability of cognitive screening.
The format for submission is a .csv with the same columns as train_labels.csv:
Required format: .csv file with the following columns:
uid(str): Unique identifier for the individualyear(int): Year the individual received the scorecomposite_score(int): Predicted composite score for the individual in the given year
To create a submission, download submission_format.csv and replace the placeholder value of 0 with your predictions. Not every individual has a score for every year. The uid plus year combinations in submission_format.csv indicate which person-years to generate predictions for.
For example, if the first few rows of your predictions are:
| uid | year | composite_score |
|---|---|---|
| abxu | 2016 | 150 |
| aeol | 2016 | 275 |
| aeol | 2021 | 200 |
That means you are predicting individual abxu will get a score of 150 in 2016, and individual aeol will get a score of 275 in 2016 and 200 in 2021.
Leaderboard performance is evaluated using root-mean squared error (RMSE). RMSE is the square root of the mean of squared differences between estimated and observed values. This is an error metric, so a lower value is better. RMSE is implemented in scikit-learn, with the squared parameter set to False.
Note that final prizes will be awarded based on a combination of leaderboard score and model reports. No prize depends on leaderboard score alone. Winners will be required to submit their modeling code to verify their leaderboard score and adherence to the competition rules. Competition arenas