May 24, 2021
When looking at disparities for COVID-19 deaths between different race/ethnicity groups, please keep in mind that some states and counties have data completeness issues, as discussed in the death data completeness analysis.
All the data below uses the CDC Case Surveillance data up to April 15 rather than the NCHS data.
#@title
import pandas as pd
import altair as alt
from vega_datasets import data
from google.colab import auth
auth.authenticate_user()
# Turn off the three-dot menu for Altair/Vega charts.
alt.renderers.set_embed_options(actions=False)
#@title
CASES = 'Cases'
DEATHS = 'Deaths'
DATASET = 'cdc'
metric = DEATHS
project_id = 'msm-secure-data-1b'
cdc_table = '`%s.ndunlap_secure.cdc_restricted_access_20210430`' % project_id
date = 'DATE(2021, 4, 15)'
date_display_name = 'Apr 15'
# Chart settings.
total_cases_scale_max = 2000000
scatter_height = 350
scatter_width = 350
map_height = 350
map_width = 500
us_states = alt.topo_feature(data.us_10m.url, 'states')
us_counties = alt.topo_feature(data.us_10m.url+"#", 'counties')
territories = ('PR', 'GU', 'VI', 'MP', 'AS')
#@title
cdc_states_query = ('''
SELECT
res_state,
COUNT(*) as cdc_cases
FROM
%s
WHERE
death_yn = 'Yes'
GROUP BY
res_state
''' % cdc_table)
cdc_counties_query = ('''
SELECT
res_state,
res_county,
race_ethnicity_combined,
COUNT(*) as cases
FROM
%s
WHERE
death_yn = 'Yes'
GROUP BY
res_county,
res_state,
race_ethnicity_combined
''' % cdc_table)
cdc_overall_query = ('''
SELECT
race_ethnicity_combined,
COUNT(*) as cases
FROM
%s
WHERE
death_yn = 'Yes'
GROUP BY
1
''' % cdc_table)
cdc_age_query = ('''
SELECT
race_ethnicity_combined,
age_group,
COUNT(*) as cases
FROM
%s
WHERE
death_yn = 'Yes'
GROUP BY
1, 2
''' % cdc_table)
#@title
df = pd.io.gbq.read_gbq(cdc_counties_query, project_id=project_id)
for territory in territories:
df = df[df.res_state != territory]
project_id = 'msm-secure-data-1b'
df_county_fips_map = pd.io.gbq.read_gbq(f'''
SELECT
*
FROM
`msm-secure-data-1b.ndunlap_secure.county_fips_mapping`
''', project_id=project_id)
df_county_fips_map.cdc_county = df_county_fips_map.cdc_county.str.lower()
df_county_fips_map['state_county'] = df_county_fips_map.state + '-' + df_county_fips_map.cdc_county
df_county_fips_map['state_county'] = df_county_fips_map.state_county.astype('string').str.strip()
df_county_fips_map.set_index('state_county', inplace=True)
#@title
# Concatenate the state and county names because county names are not unique across states.
df.res_county = df.res_county.str.lower()
df['state_county'] = df.res_state + '-' + df.res_county
df['state_county'] = df.state_county.astype('string').str.strip()
df.set_index('state_county', inplace=True)
df['race_ethnicity_combined'] = df.race_ethnicity_combined.astype('string').str.strip()
race_ethnicity_combined_map = {
'Asian, Non-Hispanic': 'asian_cases',
'Black, Non-Hispanic': 'black_cases',
'White, Non-Hispanic': 'white_cases',
'American Indian/Alaska Native, Non-Hispanic': 'aian_cases',
'Hispanic/Latino': 'hispanic_cases',
'Multiple/Other, Non-Hispanic': 'other_cases',
'Native Hawaiian/Other Pacific Islander, Non-Hispanic': 'nhpi_cases',
'Missing': 'unknown_cases',
'Unknown': 'unknown_cases',
'NA': 'na_cases',
}
df = df.replace(to_replace={'race_ethnicity_combined': race_ethnicity_combined_map})
#@title
merged_df = df.join(df_county_fips_map, on="state_county", how='inner', lsuffix='_left', rsuffix='_right')
# Create a crosstab table with rows = counties, columns = race_ethnicity_combined.
crosstab_df = pd.crosstab(merged_df['county_fips'], merged_df.race_ethnicity_combined, values=merged_df.cases, aggfunc=sum,
margins=True,
margins_name='total_cases'
)
# Have to reset_index() to go from pandas multi-index to single index.
crosstab_df = crosstab_df.reset_index()
crosstab_df.drop(axis=0, index=len(crosstab_df) - 1, inplace=True)
crosstab_df['county_fips'] = crosstab_df.county_fips.astype(int)
crosstab_df['total_known_cases'] = crosstab_df['total_cases'] - crosstab_df.na_cases.fillna(0) - crosstab_df.unknown_cases.fillna(0)
#@title
df_acs_name_lookup = pd.io.gbq.read_gbq(f'''
SELECT
*
FROM
`msm-internal-data.ipums_acs.acs_2019_5year_county`
''', project_id=project_id)
df_acs_name_lookup['state_county'] = df_acs_name_lookup.county.astype('string').str.strip() + ', ' + df_acs_name_lookup.state.astype('string').str.strip()
df_acs_name_lookup.drop(columns=['state', 'county'], inplace=True)
df_acs_name_lookup.set_index('county_fips', inplace=True)
county_chart_df = crosstab_df.join(df_acs_name_lookup, on="county_fips", how='inner', lsuffix='_left', rsuffix='_right')
county_chart_df.county_fips = county_chart_df.county_fips.astype(int)
#@title
chart_df = county_chart_df.copy(deep=True)
chart_df.reset_index(inplace=True)
chart_df.county_fips = chart_df.county_fips.astype(int)
chart_df['percent_known_cases'] = round(chart_df.total_known_cases / chart_df.total_cases, 2)
chart_df['total_known_or_na_cases'] = chart_df.total_known_cases + chart_df.na_cases
chart_df['percent_known_or_na_cases'] = round(chart_df.total_known_or_na_cases / chart_df.total_cases, 2)
#@title
race_ethnicity_groups = ['black', 'hispanic', 'aian', 'nhpi', 'asian', 'white', 'other']
def GenerateColNames(group):
cases_col = group + '_cases'
pop_col = group + '_pop'
pop_percent_col = group + '_percent'
cases_percent_col = group + '_cases_percent'
cases_percent_with_unknown_col = group + '_cases_percent_with_unknown'
cases_per_100_col = group + '_cases_per_100'
cases_to_pop_col= group + '_cases_to_pop'
cases_to_pop_with_unknown_col= group + '_cases_to_pop_with_unknown'
return {'cases': cases_col,
'pop': pop_col,
'pop_percent': pop_percent_col,
'cases_per_100': cases_per_100_col,
'cases_percent': cases_percent_col,
'cases_percent_with_unknown': cases_percent_with_unknown_col,
'cases_to_pop': cases_to_pop_col,
'cases_to_pop_with_unknown': cases_to_pop_with_unknown_col,
}
group_names = {}
for group in race_ethnicity_groups:
group_names[group] = GenerateColNames(group)
for group in race_ethnicity_groups:
chart_df[group_names[group]['cases_per_100']] = round(chart_df[group_names[group]['cases']] / chart_df[group_names[group]['pop']], 4)
chart_df[group_names[group]['cases_percent']] = round(chart_df[group_names[group]['cases']] / chart_df.total_known_cases, 2)
chart_df[group_names[group]['cases_percent_with_unknown']] = round(chart_df[group_names[group]['cases']] / chart_df.total_cases, 2)
chart_df[group_names[group]['cases_to_pop']] = round(
chart_df[group_names[group]['cases_percent']] / chart_df[group_names[group]['pop_percent']], 2)
chart_df[group_names[group]['cases_to_pop_with_unknown']] = round(
chart_df[group_names[group]['cases_percent_with_unknown']] / chart_df[group_names[group]['pop_percent']], 2)
chart_df.reset_index(inplace=True)
#@title
filter_data = False
#MIN_POP_PERCENT = 0.001
MIN_POP = 100
MIN_PERCENT_KNOWN = 0.5
MIN_CASES = 5
group_to_display_name = {
'black': 'Black',
'white': 'White',
'hispanic': 'Hispanic/Latino',
'asian': 'Asian',
'nhpi': 'Native Hawaiian/Pacific Islander',
'aian': 'American Indian/Alaska Native',
'other': 'Other or multiple race/ethnicity',
'total': 'Total'
}
group_to_short_name = {
'black': 'Black',
'white': 'White',
'hispanic': 'Hispanic',
'asian': 'Asian',
'nhpi': 'NHPI',
'aian': 'AIAN',
'other': 'Other',
'total': 'Total'
}
chart_col_to_color_scheme = {
'cases_per_100': 'yelloworangebrown',
'cases_to_pop': 'blueorange',
'cases_to_pop_with_unknown': 'blueorange',
'percent_known_cases': 'redyellowblue',
'percent_known_or_na_cases': 'redyellowblue',
}
chart_col_to_legend_format = {
'cases_per_100': '.2%',
'cases_to_pop': '.1f',
'cases_to_pop_with_unknown': '.1f',
'percent_known_cases': '.0%',
'percent_known_or_na_cases': '.0%',
}
def GenerateCountyMap(chart_df, chart_col, group, group_names, metric, date):
group_chart_col = chart_col
if group:
group_chart_col = group_names[group][chart_col]
group_display_name = ''
if group:
group_display_name = group_to_short_name[group]
group_short_name = group_to_short_name[group]
chart_col_to_range = {
'cases_per_100': [0, .005],
'cases_to_pop': [0, 2],
'cases_to_pop_with_unknown': [0, 2],
'percent_known_cases': [0, 1],
'percent_known_or_na_cases': [0, 1],
}
if metric == CASES:
prevalence_text = 'who had COVID-19'
else:
prevalence_text = 'who died of COVID-19'
col_to_title = {
'total_cases': group_display_name + ' ' + metric + ' up to ' + date,
'cases_per_100': 'Percent of ' + group_display_name + ' Population ' + prevalence_text + ' up to ' + date,
'cases_to_pop': 'Ratio of ' + group_display_name + ' ' + metric + ' Share to Population Share' + ' up to ' + date,
'cases_to_pop_with_unknown': 'Ratio of ' + group_display_name + ' ' + metric + ' Share to Population Share' + ' up to ' + date,
'percent_known_cases': 'Percent of CDC ' + metric + ' with Known Race/Ethnicity' + ' up to ' + date,
'percent_known_or_na_cases': 'Percent of CDC ' + metric + ' with Known or Suppressed Race/Ethnicity' + ' up to ' + date,
}
filtered_chart_df = chart_df
if group and filter_data:
#filtered_chart_df = filtered_chart_df[filtered_chart_df[group_names[group]['pop_percent']] > MIN_POP_PERCENT]
filtered_chart_df = filtered_chart_df[filtered_chart_df[group_names[group]['pop']] > MIN_POP]
filtered_chart_df = filtered_chart_df[filtered_chart_df['percent_known_cases'] > MIN_PERCENT_KNOWN]
filtered_chart_df = filtered_chart_df[filtered_chart_df[group_names[group]['cases']] > MIN_CASES]
highlight = alt.selection_single(on='mouseover', fields=['id', 'county_fips'], empty='none')
data_cols = ['state_county',
'percent_known_cases',
'percent_known_or_na_cases',
'total_cases']
if group:
data_cols.extend([
group_names[group]['cases'],
group_names[group]['pop'],
group_names[group]['pop_percent'],
group_names[group]['cases_per_100'],
group_names[group]['cases_percent'],
group_names[group]['cases_percent_with_unknown'],
group_names[group]['cases_to_pop'],
group_names[group]['cases_to_pop_with_unknown'],
])
tooltips = [alt.Tooltip('state_county:N', title='County'),
alt.Tooltip('percent_known_cases:Q', format='.0%', title=metric + ' with race/ethnicity')
]
if chart_col in ('percent_known_cases', 'percent_known_or_na_cases'):
tooltips.extend([
alt.Tooltip('total_cases:Q', format=',.0f', title=metric)
])
if chart_col == 'percent_known_or_na_cases':
tooltips.extend([
alt.Tooltip('percent_known_or_na_cases:Q', format='.0%',
title=metric + ' with known or suppressed race/ethnicity')
])
if group:
tooltips.extend([
alt.Tooltip(group_names[group]['cases'] + ':Q', format=',',
title=group_short_name + ' ' + metric.lower()),
])
if chart_col == 'cases_per_100':
tooltips.extend([
alt.Tooltip(group_names[group]['pop'] + ':Q', format=',',
title=group_short_name + ' population'),
alt.Tooltip(group_names[group]['cases_per_100'] + ':Q', format='.2%',
title='Percent ' + prevalence_text)
])
elif chart_col in ('cases_to_pop', 'cases_to_pop_with_unknown'):
tooltips.extend([
alt.Tooltip(group_names[group]['cases_percent_with_unknown'] + ':Q', format='.1%',
title='Percent of total ' + metric.lower()),
alt.Tooltip(group_names[group]['cases_percent'] + ':Q', format='.1%',
title='Percent of known race/ethnicity ' + metric.lower()),
alt.Tooltip(group_names[group]['pop_percent'] + ':Q', format='.1%',
title=group_short_name + ' percent of population'),
alt.Tooltip(group_names[group]['cases_to_pop'] + ':Q', format='.2f',
title='Ratio of percent of known race/ethnicity ' + metric.lower() + ' to percent of population'),
alt.Tooltip(group_names[group]['cases_to_pop_with_unknown'] + ':Q', format='.2f',
title='Ratio of percent of total ' + metric.lower() + ' to percent of population'),
])
reverse_scale = False
if chart_col == 'cases_to_pop':
reverse_scale = False
plot = alt.Chart(us_counties).mark_geoshape(
stroke='white',
strokeOpacity=.2,
strokeWidth=1
).project(
type='albersUsa'
).transform_lookup(
lookup='id',
from_=alt.LookupData(filtered_chart_df, 'county_fips', data_cols)
).encode(
alt.Color(group_chart_col,
type='quantitative',
legend=alt.Legend(format=chart_col_to_legend_format[chart_col]),
scale=alt.Scale(scheme=chart_col_to_color_scheme[chart_col],
reverse=reverse_scale,
domain=chart_col_to_range[chart_col],
clamp=True,
),
title=''),
tooltip=tooltips
).add_selection(
highlight,
)
states_outline = alt.Chart(us_states).mark_geoshape(stroke='white', strokeWidth=1.5, fillOpacity=0, fill='white').project(
type='albersUsa'
)
states_fill = alt.Chart(us_states).mark_geoshape(
fill='silver',
stroke='white'
).project('albersUsa')
layered_map = alt.layer(states_fill, plot, states_outline).properties(
title=col_to_title[chart_col],
)
return layered_map
#@title
group_charts = {'cases_per_100': {}, 'cases_to_pop': {}, 'cases_to_pop_with_unknown': {}}
for group in race_ethnicity_groups:
for value in ('cases_per_100', 'cases_to_pop', 'cases_to_pop_with_unknown'):
group_charts[value][group] = GenerateCountyMap(
chart_df, value, group, group_names, metric, date_display_name)
#@title
overall_df = pd.io.gbq.read_gbq(cdc_overall_query, project_id=project_id)
overall_df['race_ethnicity_combined'] = overall_df.race_ethnicity_combined.astype('string').str.strip()
overall_df = overall_df.replace(to_replace={'race_ethnicity_combined': race_ethnicity_combined_map})
overall_df = overall_df.set_index('race_ethnicity_combined')
chart_denominator = 1000
cases_list = [overall_df.cases['hispanic_cases'] / chart_denominator,
overall_df.cases['black_cases'] / chart_denominator,
overall_df.cases['white_cases'] / chart_denominator,
overall_df.cases['asian_cases'] / chart_denominator,
overall_df.cases['nhpi_cases'] / chart_denominator,
overall_df.cases['aian_cases'] / chart_denominator,
overall_df.cases.sum() / chart_denominator,
]
# Population data from https://api.census.gov/data/2019/acs/acs1/profile?get=NAME,DP05_0071E,DP05_0078E,DP05_0077E,DP05_0080E,DP05_0081E,DP05_0079E,DP05_0070E&for=us:1
pop_list = [
60481746 / chart_denominator,
40596040 / chart_denominator,
196789401 / chart_denominator,
18427914 / chart_denominator,
565473 / chart_denominator,
2236348 / chart_denominator,
328239523 / chart_denominator,
]
percent_list = []
for i in range(len(cases_list)):
percent_list.append(cases_list[i] / pop_list[i])
prevalence = pd.DataFrame.from_dict({'group': [
'Hispanic/Latino',
'Black',
'White',
'Asian',
'Native Hawaiian/Pacific Islander',
'American Indian/Alaska Native',
'*Total Including Unknowns*',
], 'percent': percent_list,
'cases': cases_list,
'population': pop_list,
})
bars = alt.Chart(prevalence).mark_bar().encode(
x=alt.X('percent', axis=alt.Axis(format='.2%'), title=''),
y=alt.Y('group', sort='-x', title=''),
color=alt.Color('group',
scale=alt.Scale(scheme='tableau20'),
title='',
legend=None),
tooltip=[
alt.Tooltip('group:N', title='Race/Ethnicity Group'),
alt.Tooltip('percent:Q', format='.2%', title='Percent who died'),
alt.Tooltip('cases:Q', format=',.2f', title='Deaths in group (thousands)'),
alt.Tooltip('population:Q', format=',.0f', title='Population of group (thousands)'),
]
).properties(
title='Percent of Race/Ethnicity Group who died from COVID-19 based on Incomplete CDC Data up to %s' % date_display_name
)
bars.display()
#alt.concat(bars).properties(
# title=alt.TitleParams(
# ['Source: U.S. Census Bureau\'s American Community Survey 2019 5-year estimates for population data.'],
# baseline='bottom',
# dy=20,
# orient='bottom',
# fontWeight='normal',
# fontSize=11
# )
#).display()
#@title
# The age population numbers come from the ACS 2019 1-year via IPUMS microdata
# which allows us to calculate age buckets not available in the ACS API.
# We had to combine Asian and NHPI due to IPUMS reporting categories.
# https://usa.ipums.org/usa-action/variables/RACE#codes_section
# The age_dict was pre-calculated in another colab because it take a while to run.
# https://colab.research.google.com/drive/1b2U0SvZq4oxRXDujc8oWg5LbQDJKRawe#scrollTo=GC1CVtok_GwL
age_dict = {'aian': {'0-9': 288641.0,
'10-19': 341153.0,
'20-29': 319143.0,
'30-39': 302075.0,
'40-49': 270994.0,
'50-59': 284191.0,
'60-69': 236906.0,
'70-79': 122475.0,
'80+': 46877.0,
'total': 2212455.0},
'api': {'0-9': 1857491.0,
'10-19': 2136643.0,
'20-29': 2788000.0,
'30-39': 3235052.0,
'40-49': 2909062.0,
'50-59': 2361661.0,
'60-69': 1893205.0,
'70-79': 1084520.0,
'80+': 560923.0,
'total': 18826557.0},
'black': {'0-9': 5144709.0,
'10-19': 5869466.0,
'20-29': 6302817.0,
'30-39': 5703178.0,
'40-49': 5152576.0,
'50-59': 5060647.0,
'60-69': 4240796.0,
'70-79': 2154455.0,
'80+': 1022325.0,
'total': 40650969.0},
'hispanic': {'0-9': 10021829.0,
'10-19': 10673378.0,
'20-29': 9714731.0,
'30-39': 9112371.0,
'40-49': 7989372.0,
'50-59': 6076318.0,
'60-69': 3913694.0,
'70-79': 1991765.0,
'80+': 991319.0,
'total': 60484777.0},
'other': {'0-9': 2339910.0,
'10-19': 2027458.0,
'20-29': 1513225.0,
'30-39': 1169024.0,
'40-49': 822915.0,
'50-59': 626001.0,
'60-69': 453421.0,
'70-79': 222395.0,
'80+': 95165.0,
'total': 9269514.0},
'total': {'0-9': 39028311.0,
'10-19': 42736680.0,
'20-29': 44726365.0,
'30-39': 44148037.0,
'40-49': 40644111.0,
'50-59': 41899718.0,
'60-69': 38395555.0,
'70-79': 23971046.0,
'80+': 12689700.0,
'total': 328239523.0},
'white': {'0-9': 19375731.0,
'10-19': 21688582.0,
'20-29': 24088449.0,
'30-39': 24626337.0,
'40-49': 23499192.0,
'50-59': 27490900.0,
'60-69': 27657533.0,
'70-79': 18395436.0,
'80+': 9973091.0,
'total': 196795251.0}
}
age_pop_df = pd.DataFrame.from_dict(age_dict)
age_pop_df = age_pop_df.drop('total')
#@title
age_df = pd.io.gbq.read_gbq(cdc_age_query, project_id=project_id)
age_df['race_ethnicity_combined'] = age_df.race_ethnicity_combined.astype('string').str.strip()
age_df = age_df.replace(to_replace={'race_ethnicity_combined': race_ethnicity_combined_map})
age_crosstab_df = pd.crosstab(age_df['age_group'], age_df.race_ethnicity_combined, values=age_df.cases, aggfunc=sum,
margins=True,
margins_name='total_cases'
)
age_crosstab_df = age_crosstab_df.drop('total_cases')
#age_crosstab_df = age_crosstab_df.drop('Unknown')
age_crosstab_df = age_crosstab_df.drop('Missing')
age_crosstab_df = age_crosstab_df.drop('NA')
The CDC data allows us to see the percentage of people within each age and race/ethnicity group who died from COVID-19.
#@title
race_list = ['Hispanic/Latino'] * 9
race_list.extend(['Black'] * 9)
race_list.extend(['White'] * 9)
race_list.extend(['Asian/NHPI'] * 9)
race_list.extend(['AIAN'] * 9)
race_list.extend(['*Total Including Unknowns*'] * 9)
cases_list = list(age_crosstab_df.hispanic_cases.fillna(0).values / chart_denominator)
cases_list.extend(list(age_crosstab_df.black_cases.fillna(0).values / chart_denominator))
cases_list.extend(list(age_crosstab_df.white_cases.fillna(0).values / chart_denominator))
cases_list.extend(list((age_crosstab_df.asian_cases.fillna(0).values + age_crosstab_df.fillna(0).nhpi_cases.values) / chart_denominator))
cases_list.extend(list(age_crosstab_df.aian_cases.fillna(0).values / chart_denominator))
cases_list.extend(list(age_crosstab_df.total_cases.fillna(0).values / chart_denominator))
pop_list = list(age_pop_df.hispanic.values / chart_denominator)
pop_list.extend(list(age_pop_df.black.values / chart_denominator))
pop_list.extend(list(age_pop_df.white.values / chart_denominator))
pop_list.extend(list(age_pop_df.api.values / chart_denominator))
pop_list.extend(list(age_pop_df.aian.values / chart_denominator))
pop_list.extend(list(age_pop_df.total.values / chart_denominator))
percent_list = list(age_crosstab_df.hispanic_cases.fillna(0).values / age_pop_df.hispanic.values)
percent_list.extend(list(age_crosstab_df.black_cases.fillna(0).values / age_pop_df.black.values))
percent_list.extend(list(age_crosstab_df.white_cases.fillna(0).values / age_pop_df.white.values))
percent_list.extend(list((age_crosstab_df.asian_cases.fillna(0).values + list(age_crosstab_df.nhpi_cases.values)) / age_pop_df.api.values))
percent_list.extend(list(age_crosstab_df.aian_cases.fillna(0).values / age_pop_df.aian.values))
percent_list.extend(list(age_crosstab_df.total_cases.fillna(0).values / age_pop_df.total.values))
age_chart_df = pd.DataFrame.from_dict({'group': race_list,
'age': ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80+'] * 6,
'percent': percent_list,
'cases': cases_list,
'population': pop_list
})
alt.Chart(age_chart_df).mark_bar().encode(
x=alt.X('percent', sort='y', axis=alt.Axis(format='.1%'), title=''),
y=alt.Y('age', title='Age'),
column=alt.Column('group',
title='Percent of Age and Race/Ethnicity Group who died from COVID-19 based on Incomplete CDC Data up to %s' % date_display_name,
header=alt.Header(titleFontSize=13)
),
color=alt.Color('group',
# Set the 6 colors manually to match the 7 groups in the chart above.
scale=alt.Scale(range=['#4E79A7', '#A0CBE8', '#F28E2B', '#FFBE7D', '#59A14F', '#B6992D']), title='Race/Ethnicity', legend=None),
tooltip=[
alt.Tooltip('group:N', title='Race/Ethnicity group'),
alt.Tooltip('age:N', title='Age'),
alt.Tooltip('percent:Q', format='.2%', title='Percent who died'),
alt.Tooltip('cases:Q', format=',.2f', title='Deaths in group (thousands)'),
alt.Tooltip('population:Q', format=',.2f', title='Population of group (thousands)'),
]
).properties(
width=110,
).display()
Note:
Percentage of each population group who died from COVID-19 based on incomplete data:
#@title
black = group_charts['cases_per_100']['black'].properties(width=450, height=325)
hispanic = group_charts['cases_per_100']['hispanic'].properties(width=450, height=325)
white = group_charts['cases_per_100']['white'].properties(width=450, height=325)
asian = group_charts['cases_per_100']['asian'].properties(width=450, height=325)
aian = group_charts['cases_per_100']['aian'].properties(width=450, height=325)
nhpi = group_charts['cases_per_100']['nhpi'].properties(width=450, height=325)
((black | hispanic) &
(white | asian) &
(aian | nhpi)).configure_legend(
orient='top',
gradientLength=400,
titleLimit=0,
).configure_view(
strokeWidth=0,
).display()
We can also view disparities by comparing the percentage of deaths with known race/ethnicity that a race/ethnicity group accounts for in a county (the deaths share) vs. the percentage of the total population that a race/ethnicity accounts for in a county (the population share). There is no disparity when the deaths share is equal to the population share for all race/ethnicity groups in a county (ratio = 1.0). When the ratio of deaths share to population share is above 1.0, then a group has a disproportionate number of deaths relative to its share of the population.
#@title
black = group_charts['cases_to_pop']['black'].properties(width=450, height=325)
hispanic = group_charts['cases_to_pop']['hispanic'].properties(width=450, height=325)
white = group_charts['cases_to_pop']['white'].properties(width=450, height=325)
asian = group_charts['cases_to_pop']['asian'].properties(width=450, height=325)
aian = group_charts['cases_to_pop']['aian'].properties(width=450, height=325)
nhpi = group_charts['cases_to_pop']['nhpi'].properties(width=450, height=325)
((black | hispanic) &
(white | asian) &
(aian | nhpi)).configure_legend(
orient='top',
gradientLength=400,
titleLimit=0,
).configure_view(
strokeWidth=0,
).display()
We can also view disparities by comparing the percentage of total deaths -- with or without known race/ethnicity -- that a race/ethnicity group accounts for (the deaths share) vs. the percentage of the total population that a race/ethnicity accounts for in a county (the population share). Counties with more complete data will change less vs. the charts above, and counties with less complete data will change more.
#@title
black = group_charts['cases_to_pop_with_unknown']['black'].properties(width=450, height=325)
hispanic = group_charts['cases_to_pop_with_unknown']['hispanic'].properties(width=450, height=325)
white = group_charts['cases_to_pop_with_unknown']['white'].properties(width=450, height=325)
asian = group_charts['cases_to_pop_with_unknown']['asian'].properties(width=450, height=325)
aian = group_charts['cases_to_pop_with_unknown']['aian'].properties(width=450, height=325)
nhpi = group_charts['cases_to_pop_with_unknown']['nhpi'].properties(width=450, height=325)
((black | hispanic) &
(white | asian) &
(aian | nhpi)).configure_legend(
orient='top',
gradientLength=400,
titleLimit=0,
).configure_view(
strokeWidth=0,
).display()
# Large county-level disparity maps
#@title
#for group in ['black', 'hispanic', 'white', 'asian', 'aian', 'nhpi']:
# (group_charts['cases_per_100'][group]).properties(
# width=900,
# height=650,
# ).configure_legend(
# orient='top-right',
# gradientLength=400,
# titleLimit=0,
# padding=0
# ).configure_view(
# strokeWidth=0,
# ).display()
#@title
#for group in ['black', 'hispanic', 'white', 'asian', 'aian', 'nhpi']:
# (group_charts['cases_to_pop'][group]).properties(
# width=900,
# height=650,
# ).configure_legend(
# orient='top-right',
# gradientLength=400,
# titleLimit=0,
# padding=0
# ).configure_view(
# strokeWidth=0,
# ).display()
Please email us at shli-covid-data-analysis@googlegroups.com with questions or comments.
#%%shell
#jupyter nbconvert --to html 'cdc_death_disparities.ipynb'