Pulling Data

vivarium_inputs provides an interface to pull data from GBD + auxiliary data. Use this interface to examine data that you want to use in a model to ensure it passes all validations and looks as you expect. You have 2 choices for pulling data:

vivarium_inputs.interface.get_measure() will pull data through the same process as simulations do, which will perform the same validations, transformations, and standardizations as occur for data that is used in a simulation.

vivarium_inputs.interface.get_raw_data() will pull raw data from GBD, skipping all validations in order to explore and investigate.

Both of the above methods can retrieve entity-measure data (e.g., prevalence data for a cause or exposure for a risk factor), population structure, and life expectancy. Functions to retrieve data about the extents of certain demographic variables – vivarium_inputs.interface.get_age_bins() and vivarium_inputs.interface.get_demographic_dimensions() are somewhat orthogonal and imply the same data modifications inherent in calling vivarium_inputs.interface.get_measure().

Which should I use… get_measure() versus get_raw_data()

Prefer vivarium_inputs.interface.get_measure() over vivarium_inputs.interface.get_raw_data(). vivarium_inputs.interface.get_measure() will produce simulation-prepped data. If vivarium_inputs.interface.get_measure() fails, or the data it returns doesn’t match your expectations, then vivarium_inputs.interface.get_raw_data() might provide some insight into what is happening.

Pulling Simulation-Prepped Data

For simulation-prepped data, the interface provides separate methods to pull entity-measure data and population structure and life expectancy data. Additionally, methods to pull age bin data and demographic dimensions are provided. Simulation- prepped data has had GBD IDs replaced with meaningful values or ranges and expansion over all demographic dimensions has been performed. We’ll walk through how to pull data using each of these functions.

Entity-Measure Data

The interface provides vivarium_inputs.interface.get_measure() for pulling specific measure data for an entity for a single location. entity should be a gbd_mapping.base_template.ModelableEntity (e.g., a cause from gbd_mapping), while measure should be a string describing the measure for which you want to retrieve data (e.g., ‘prevalence’ or ‘relative_risk’). A list of possible measures for each entity kind is included in the table below. Finally, location should be the string location for which you want to pull data (e.g., ‘Ethiopia’), in the form used by GBD (e.g., ‘United States’ instead of ‘USA’).

To pull simulation-prepped entity-measure data, you must have plenty of available memory. Please have at least 50GB on a qlogin.

For example, to pull prevalence data for diarrheal diseases in Kenya, we would do the following:

from gbd_mapping import causes
from vivarium_inputs import get_measure

prev = get_measure(causes.diarrheal_diseases, 'prevalence', 'Kenya')
print(prev.head())

  draw location     sex        age_start        age_end  year_start  year_end     value
  0    Kenya  Female              0.0       0.019178        1990      1991  0.032557
  0    Kenya  Female              0.0       0.019178        1991      1992  0.031751
  0    Kenya  Female              0.0       0.019178        1992      1993  0.031039
  0    Kenya  Female              0.0       0.019178        1993      1994  0.030458
  0    Kenya  Female              0.0       0.019178        1994      1995  0.030039

The following table lists the measures available for each entity kind:

Available Entity-Measure Pairs
Entity Kind	Measures
sequela	incidence prevalence birth_prevalence disability_weight
cause	incidence prevalence birth_prevalence disability_weight remission cause_specific_mortality excess_mortality
risk_factor	exposure exposure_standard_deviation exposure_distribution_weights relative_risk population_attributable_fraction mediation_factors
alternative_risk_factor	exposure exposure_standard_deviation exposure_distribution_weights
etiology	population_attributable_fraction
covariate	estimate
healthcare_entity	cost utilization
health_technology	cost

Population Structure Data

To pull population data for a specific location, vivarium_inputs.interface provides vivarium_inputs.interface.get_population_structure(), which returns population data in the input format expected by a simulation.

For example, to pull population data for Kenya, we would do the following:

from vivarium_inputs import get_population_structure

pop = get_population_structure('Kenya')
print(pop.head())

  location     sex        age_start        age_end  year_start  year_end        value
  Kenya  Female              0.0       0.019178        1990      1991  9251.406428
  Kenya  Female              0.0       0.019178        1991      1992  9371.524292
  Kenya  Female              0.0       0.019178        1992      1993  9488.631659
  Kenya  Female              0.0       0.019178        1993      1994  9592.689862
  Kenya  Female              0.0       0.019178        1994      1995  9701.918801

Life Expectancy Data

To pull life expectancy data, vivarium_inputs.interface provides vivarium_inputs.interface.get_theoretical_minimum_risk_life_expectancy(), which returns life expectancy data in the input format expected by a simulation. Because life expectancy is not location specific, the function takes no arguments.

To use:

from vivarium_inputs import get_theoretical_minimum_risk_life_expectancy

life_exp = get_theoretical_minimum_risk_life_expectancy()
print(life_exp.head())

         age_start        age_end      value
           0.00           0.01  87.885872
           0.01           0.02  87.877086
           0.02           0.03  87.868299
           0.03           0.04  87.859513
           0.04           0.05  87.850727

Age Bin Data

To see what age bins GBD uses that are used in age-specific data, vivarium_inputs provides vivarium_inputs.interface.get_age_bins(), which returns the start, end, and name of each GBD age bin expected to appear in age-specific data (with the exception of life expectancy, which uses its own age ranges).

from vivarium_inputs import get_age_bins

age_bins = get_age_bins()
print(age_bins.head())

         age_start        age_end  age_group_name
       0.000000       0.019178  Early Neonatal
       0.019178       0.076712   Late Neonatal
       0.076712       1.000000   Post Neonatal
       1.000000       5.000000          1 to 4
       5.000000      10.000000          5 to 9

Demographic Dimensions Data

Finally, to view the full extent of all demographic dimensions that is expected in input data to the simulation, vivarium_inputs provides vivarium_inputs.interface.get_demographic_dimensions(), which expects a location argument to fill the location dimension.

from vivarium_inputs import get_demographic_dimensions

dem_dims = get_demographic_dimensions('Kenya')
print(dem_dims.head())

  location     sex        age_start        age_end  year_start  year_end
  Kenya  Female              0.0       0.019178        1990      1991
  Kenya  Female              0.0       0.019178        1991      1992
  Kenya  Female              0.0       0.019178        1992      1993
  Kenya  Female              0.0       0.019178        1993      1994
  Kenya  Female              0.0       0.019178        1994      1995

Pulling Raw GBD Data

The interface provides vivarium_inputs.interface.get_raw_data(), which can be used to pull entity-measure data as well as population structure and life expectancy. Raw validation checks are not performed to return data that can be investigated for oddities. The only filtering that occurs is by applicable measure id, metric id, or to most detailed causes where relevant. No formatting or reshaping of the data is done. The following sections detail how to pull each type of data.

Entity-Measure Data

The interface provides vivarium_inputs.interface.get_raw_data() for pulling specific raw measure data for an entity for a single location from GBD, without the prep work that occurs on data for a simulation.

entity should be a gbd_mapping.base_template.ModelableEntity (e.g., a cause from gbd_mapping), while measure should be a string describing the measure for which you want to retrieve data (e.g., ‘prevalence’ or ‘relative_risk’). A list of possible measures for each entity kind is included in the table below. Finally, location should be the string location for which you want to pull data (e.g., ‘Ethiopia’), in the form used by GBD (e.g., ‘United States’ instead of ‘USA’).

For example, to pull raw prevalence data for diarrheal diseases in Kenya, we would do the following:

from gbd_mapping import causes
from vivarium_inputs import get_raw_data

prev = get_raw_data(causes.diarrheal_diseases, 'prevalence', 'Kenya')
print(prev.head())

      year_id  age_group_id  sex_id  measure_id  cause_id    draw_0    ...      draw_999  location_id  metric_id
   1990             2       1           5       302  0.030940    ...      0.029214          180          3
   1990             3       1           5       302  0.063305    ...      0.059538          180          3
   1990             4       1           5       302  0.056916    ...      0.058788          180          3
   1990             5       1           5       302  0.026376    ...      0.035843          180          3
   1990             6       1           5       302  0.011728    ...      0.011231          180          3

The following table lists the measures available for each entity kind for pulling raw data:

Available Entity-Measure Pairs
Entity Kind	Measures
sequela	incidence prevalence birth_prevalence disability_weight
cause	incidence prevalence birth_prevalence disability_weight remission deaths
risk_factor	exposure exposure_standard_deviation exposure_distribution_weights relative_risk population_attributable_fraction mediation_factors
alternative_risk_factor	exposure exposure_standard_deviation exposure_distribution_weights
etiology	population_attributable_fraction
covariate	estimate
healthcare_entity	cost utilization
health_technology	cost

Population Structure Data

To pull raw population data for a specific location, we will actually use the same vivarium_inputs.interface.get_raw_data() function we used for pulling entity-measure data, with a special Population entity.

For example, to pull population data for Kenya, we would do the following:

from vivarium_inputs import get_raw_data
from vivarium_inputs.globals import Population

pop = get_raw_data(Population(), 'structure', 'Kenya')
print(pop.head())

   age_group_id  location_id  year_id  sex_id   population  run_id
           2          180     1950       1  2747.467163     117
           2          180     1950       2  2484.512754     117
           2          180     1950       3  5231.979917     117
           2          180     1951       1  3146.320799     117
           2          180     1951       2  3038.538221     117

Life Expectancy Data

Similarly to pull life expectancy data, we will use the same vivarium_inputs.interface.get_raw_data() function with the special Population entity. Life expectancy data is not location-specific, so we’ll just use the ‘Global’ location.

To use:

from vivarium_inputs import get_raw_data
from vivarium_inputs.globals import Population

life_exp = get_raw_data(Population(), 'theoretical_minimum_risk_life_expectancy', 'Global')
print(life_exp.head())

    age  life_expectancy
0.00        87.885872
0.01        87.877086
0.02        87.868299
0.03        87.859513
0.04        87.850727