{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What this file does" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examines the relative contribution of various the four main SVI themes — socioeconomic status, household composition & disability, minority status & language, housing type and transportation — to vaccination rates. \n", "\n", "Background reading: https://www.cdc.gov/mmwr/volumes/70/wr/mm7012e1.htm?s_cid=mm7012e1_w#contribAff \"State and local jurisdictions should also consider analyzing SVI metrics at the level of the census tract.\"\n", "Main findings: \n", " 1) Socioeconomic factors have strongest effect on vaccine uptake \n", " 2) Education was associated with the greatest disparity \n", " \n", "My findings are consistent with this, though my methodology differs slightly. Don't think I cant really go this far with the analysis. \n", "\n", "Steps:\n", "- Join SVI with vaxx rate\n", "- Run correlations with overall SVI, themes\n", "- Run regression with four themes separately\n", "- Run multivariate regression with all four " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tools" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#analysis tools\n", "import pandas as pd\n", "import plotly.express as px\n", "from sklearn.linear_model import LinearRegression\n", "import matplotlib.pyplot as plt\n", "\n", "#regression tools\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import ols\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read in data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#read in SVI data \n", "df_ct_svi = pd.read_csv('Connecticut.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#isolate SVI theme variables\n", "df_ct_svi_themes = df_ct_svi[['FIPS','RPL_THEME1','RPL_THEME2','RPL_THEME3','RPL_THEME4','RPL_THEMES']]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5/26/2021 831\n", "Name: DateUpdate, dtype: int64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#when\n", "df_vax_ct['DateUpdate'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#CT vaccination data \n", "#df_vax_ct = pd.read_csv('COVID-19_Vaccinations_by_Census_Tract (5).csv')\n", "df_vax_ct = pd.read_csv('COVP Coverage by Census Tract_ Ages 16 and Up (5).csv')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(830, 831)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#looks like we're missing a Census tract in the overall\n", "len(df_ct_svi_themes),len(df_vax_ct)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "830" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#prepped data after merge\n", "df_final = df_vax_ct.merge(df_ct_svi_themes, left_on='GEOID10', right_on='FIPS')\n", "len(df_final)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "827" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#removing values for which the overall SVI index is suppressed\n", "df_final_svi_not_suppressed = df_final[df_final['RPL_THEMES']!=-999]\n", "len(df_final_svi_not_suppressed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Descriptive statistics " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['OBJECTID', 'GEOID10', 'CTTractID', 'DateUpdate', 'TractTown',\n", " 'Cov_16Plus', 'Cov_16_44', 'Cov_45_64', 'Cov_65Plus', 'needvac_16_plus',\n", " 'needvac_16_44', 'needvac_45_64', 'needvac_65_plus'],\n", " dtype='object')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_vax_ct.columns" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "65.98694943" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4VmZ+NzN/CNxH62eg+ngfMd/4dpR1gxzuQ3OLg+Y88+3AM5n56bZF24ENzfQG4P7l7lsvZeYnMvOszByjNb7/mZnXAI8AH2pWq1j3i8B3IuLcpuli4GmKjzet0zHrIuLdzc/8kbpLj3eb+cZ3O3Btc9XMOuBw2+mb+WXmwD6Ay4D/Br4B/Gm/+9PDOn+F1lu0J4EnmsdltM4/7wCeA/4DOKPffe3hv8EE8EAz/XPAV4C9wD8Bp/S7fz2o9wJgZzPm/wKsHIbxBv4M+DqwB/gH4JSK4w3cTevvCj+k9U7t+vnGFwhaVwZ+A9hN62qiBffh7QckqaBBPi0jSZqH4S5JBRnuklSQ4S5JBRnuklSQ4S5JBRnuklTQ/wHSxxa3QmYumgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#50% of Census tracts under the statewide coverage rate\n", "# df_vax_ct['Sixteen_plus'].hist()\n", "# df_vax_ct['Sixteen_plus'].median()\n", "\n", "df_vax_ct['Cov_16Plus'].hist()\n", "df_vax_ct['Cov_16Plus'].median()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "86.4142539" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#much higher covg overall with just 65+\n", "df_vax_ct['Cov_65Plus'].hist()\n", "df_vax_ct['Cov_65Plus'].median()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#SVI index value distribution\n", "df_final_svi_not_suppressed['RPL_THEMES'].hist()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#socioeconomic factors\n", "df_final_svi_not_suppressed['RPL_THEME1'].hist()\n", "df_final_svi_not_suppressed['RPL_THEME1'].median()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4994" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#minority and english language ability \n", "df_final_svi_not_suppressed['RPL_THEME3'].hist()\n", "df_final_svi_not_suppressed['RPL_THEME3'].median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation analyses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Overall SVI" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Cov_16PlusRPL_THEMES
Cov_16Plus1.000000-0.598524
RPL_THEMES-0.5985241.000000
\n", "
" ], "text/plain": [ " Cov_16Plus RPL_THEMES\n", "Cov_16Plus 1.000000 -0.598524\n", "RPL_THEMES -0.598524 1.000000" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#strong -ve correlation with overall; more vulnerable, lower vaxx rate\n", "# df_final_svi_not_suppressed[df_final_svi_not_suppressed['RPL_THEMES']!=-999][['Sixteen_plus','RPL_THEMES']].corr()\n", "df_final_svi_not_suppressed[df_final_svi_not_suppressed['RPL_THEMES']!=-999][['Cov_16Plus','RPL_THEMES']].corr()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "RPL_THEMES=%{x}
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"white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "RPL_THEME3" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Cov_16Plus" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot to confirm; tighter tail\n", "fig = px.scatter(df_final_svi_not_suppressed_3, x=\"RPL_THEME3\", y=\"Cov_16Plus\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "d) Housing situation and transportation" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "827" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#remove suppressed\n", "df_final_svi_not_suppressed_4 = df_final[df_final['RPL_THEME4']!=-999]\n", "len(df_final_svi_not_suppressed_4)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Cov_16PlusRPL_THEME4
Cov_16Plus1.000000-0.425789
RPL_THEME4-0.4257891.000000
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" ], "text/plain": [ " Cov_16Plus RPL_THEME4\n", "Cov_16Plus 1.000000 -0.425789\n", "RPL_THEME4 -0.425789 1.000000" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#weak negative\n", "df_final_svi_not_suppressed_4[['Cov_16Plus','RPL_THEME4']].corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Variable isolation for regression analysis" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#overall vaccination rate, SVI themes, overall SVI\n", "#df_final_final = df_final[['Sixteen_plus','RPL_THEMES','RPL_THEME1','RPL_THEME2','RPL_THEME3','RPL_THEME4']]\n", "df_final_final = df_final[['Cov_16Plus','RPL_THEMES','RPL_THEME1','RPL_THEME2','RPL_THEME3','RPL_THEME4']]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Cov_16Plus float64\n", "RPL_THEMES float64\n", "RPL_THEME1 float64\n", "RPL_THEME2 float64\n", "RPL_THEME3 float64\n", "RPL_THEME4 float64\n", "dtype: object" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#checking that all datatypes are numbers\n", "df_final_final.dtypes" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "827" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#removing all suppressed values\n", "df_final_for_regression = df_final_final[~df_final_final.eq(-999).any(1)]\n", "len(df_final_for_regression)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "df_final_for_regression.to_clipboard()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Cov_16PlusRPL_THEME3RPL_THEME1
Cov_16Plus1.000000-0.464355-0.705686
RPL_THEME3-0.4643551.0000000.740349
RPL_THEME1-0.7056860.7403491.000000
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" ], "text/plain": [ " Cov_16Plus RPL_THEME3 RPL_THEME1\n", "Cov_16Plus 1.000000 -0.464355 -0.705686\n", "RPL_THEME3 -0.464355 1.000000 0.740349\n", "RPL_THEME1 -0.705686 0.740349 1.000000" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#looking at theme 3 and theme 1\n", "df_final_for_regression[['Cov_16Plus','RPL_THEME3', 'RPL_THEME1']].corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression coefficients" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-44.57808844 3.83435884 7.03035487 0.39472198]\n" ] } ], "source": [ "#quick look at coeeficients\n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEME1','RPL_THEME2','RPL_THEME3','RPL_THEME4']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-31.57712397]\n" ] } ], "source": [ "#overall\n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEMES']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-37.2306182]\n" ] } ], "source": [ "#Socioeconomic alone\n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEME1']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-15.7425461]\n" ] } ], "source": [ "#Household composition and disability \n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEME2']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-24.49159313]\n" ] } ], "source": [ "#Minority status and language\n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEME3']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-22.46239408]\n" ] } ], "source": [ "#Housing type and transportation\n", "regression_model = LinearRegression()\n", "training_data = df_final_for_regression[['RPL_THEME4']]\n", "target_values = df_final_for_regression[\"Cov_16Plus\"]\n", "regression_model.fit(training_data, target_values)\n", "estimates = regression_model.coef_\n", "print(estimates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression summary" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Cov_16Plus R-squared: 0.358
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 460.5
Date: Wed, 02 Jun 2021 Prob (F-statistic): 1.62e-81
Time: 13:46:14 Log-Likelihood: -3243.2
No. Observations: 827 AIC: 6490.
Df Residuals: 825 BIC: 6500.
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 80.5096 0.850 94.739 0.000 78.842 82.178
RPL_THEMES -31.5771 1.471 -21.459 0.000 -34.465 -28.689
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 252.561 Durbin-Watson: 1.380
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1568.565
Skew: -1.234 Prob(JB): 0.00
Kurtosis: 9.279 Cond. No. 4.39


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Cov_16Plus R-squared: 0.358\n", "Model: OLS Adj. R-squared: 0.357\n", "Method: Least Squares F-statistic: 460.5\n", "Date: Wed, 02 Jun 2021 Prob (F-statistic): 1.62e-81\n", "Time: 13:46:14 Log-Likelihood: -3243.2\n", "No. Observations: 827 AIC: 6490.\n", "Df Residuals: 825 BIC: 6500.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 80.5096 0.850 94.739 0.000 78.842 82.178\n", "RPL_THEMES -31.5771 1.471 -21.459 0.000 -34.465 -28.689\n", "==============================================================================\n", "Omnibus: 252.561 Durbin-Watson: 1.380\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1568.565\n", "Skew: -1.234 Prob(JB): 0.00\n", "Kurtosis: 9.279 Cond. No. 4.39\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with overall SVI \n", "vaccination_model = ols(\"\"\"Cov_16Plus ~ RPL_THEMES\"\"\", data=df_final_for_regression).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Cov_16Plus R-squared: 0.510
Model: OLS Adj. R-squared: 0.507
Method: Least Squares F-statistic: 213.5
Date: Wed, 02 Jun 2021 Prob (F-statistic): 1.49e-125
Time: 13:46:17 Log-Likelihood: -3132.1
No. Observations: 827 AIC: 6274.
Df Residuals: 822 BIC: 6298.
Df Model: 4
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 81.3825 0.911 89.320 0.000 79.594 83.171
RPL_THEME1 -44.5781 2.220 -20.080 0.000 -48.936 -40.221
RPL_THEME2 3.8344 1.487 2.579 0.010 0.916 6.753
RPL_THEME3 7.0304 1.959 3.589 0.000 3.185 10.876
RPL_THEME4 0.3947 1.695 0.233 0.816 -2.932 3.722
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 287.175 Durbin-Watson: 1.434
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2523.478
Skew: -1.318 Prob(JB): 0.00
Kurtosis: 11.142 Cond. No. 10.6


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Cov_16Plus R-squared: 0.510\n", "Model: OLS Adj. R-squared: 0.507\n", "Method: Least Squares F-statistic: 213.5\n", "Date: Wed, 02 Jun 2021 Prob (F-statistic): 1.49e-125\n", "Time: 13:46:17 Log-Likelihood: -3132.1\n", "No. Observations: 827 AIC: 6274.\n", "Df Residuals: 822 BIC: 6298.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 81.3825 0.911 89.320 0.000 79.594 83.171\n", "RPL_THEME1 -44.5781 2.220 -20.080 0.000 -48.936 -40.221\n", "RPL_THEME2 3.8344 1.487 2.579 0.010 0.916 6.753\n", "RPL_THEME3 7.0304 1.959 3.589 0.000 3.185 10.876\n", "RPL_THEME4 0.3947 1.695 0.233 0.816 -2.932 3.722\n", "==============================================================================\n", "Omnibus: 287.175 Durbin-Watson: 1.434\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 2523.478\n", "Skew: -1.318 Prob(JB): 0.00\n", "Kurtosis: 11.142 Cond. No. 10.6\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with the themes\n", "vaccination_model = ols(\"\"\"Cov_16Plus ~ RPL_THEME1\n", " + RPL_THEME2\n", " + RPL_THEME3\n", " + RPL_THEME4\"\"\", data=df_final_for_regression).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Questions:\n", "\n", " 1) Can I say that socioeconomic factors are the strongest predictor of vaccination rate within factors considered by the SVI? \n", " 2) Can I say that when socioeconomic factors are controlled for, tracts with more minorities are associated with higher vaccine uptake? \n", " 3) Can I do a further analysis with the socioeconomic indicators? \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Effect modification" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sixteen_plus R-squared: 0.383
Model: OLS Adj. R-squared: 0.377
Method: Least Squares F-statistic: 63.25
Date: Tue, 25 May 2021 Prob (F-statistic): 1.42e-41
Time: 13:07:15 Log-Likelihood: -1554.9
No. Observations: 413 AIC: 3120.
Df Residuals: 408 BIC: 3140.
Df Model: 4
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 95.3612 2.833 33.658 0.000 89.792 100.931
RPL_THEME1 -74.4783 5.696 -13.076 0.000 -85.675 -63.281
RPL_THEME2 5.9810 2.055 2.911 0.004 1.942 10.020
RPL_THEME3 10.0209 2.851 3.515 0.000 4.417 15.625
RPL_THEME4 3.0670 2.412 1.271 0.204 -1.675 7.809
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 78.961 Durbin-Watson: 1.914
Prob(Omnibus): 0.000 Jarque-Bera (JB): 522.618
Skew: -0.609 Prob(JB): 3.27e-114
Kurtosis: 8.375 Cond. No. 21.2


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sixteen_plus R-squared: 0.383\n", "Model: OLS Adj. R-squared: 0.377\n", "Method: Least Squares F-statistic: 63.25\n", "Date: Tue, 25 May 2021 Prob (F-statistic): 1.42e-41\n", "Time: 13:07:15 Log-Likelihood: -1554.9\n", "No. Observations: 413 AIC: 3120.\n", "Df Residuals: 408 BIC: 3140.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 95.3612 2.833 33.658 0.000 89.792 100.931\n", "RPL_THEME1 -74.4783 5.696 -13.076 0.000 -85.675 -63.281\n", "RPL_THEME2 5.9810 2.055 2.911 0.004 1.942 10.020\n", "RPL_THEME3 10.0209 2.851 3.515 0.000 4.417 15.625\n", "RPL_THEME4 3.0670 2.412 1.271 0.204 -1.675 7.809\n", "==============================================================================\n", "Omnibus: 78.961 Durbin-Watson: 1.914\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 522.618\n", "Skew: -0.609 Prob(JB): 3.27e-114\n", "Kurtosis: 8.375 Cond. No. 21.2\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with the themes\n", "vaccination_model = ols(\"\"\"Sixteen_plus ~ RPL_THEME1\n", " + RPL_THEME2\n", " + RPL_THEME3\n", " + RPL_THEME4\"\"\", data=df_final_for_regression[df_final_for_regression['RPL_THEME1']>0.5]).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sixteen_plus R-squared: 0.221
Model: OLS Adj. R-squared: 0.213
Method: Least Squares F-statistic: 28.90
Date: Tue, 25 May 2021 Prob (F-statistic): 3.63e-21
Time: 13:07:19 Log-Likelihood: -1523.2
No. Observations: 413 AIC: 3056.
Df Residuals: 408 BIC: 3076.
Df Model: 4
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 75.6316 1.327 56.976 0.000 73.022 78.241
RPL_THEME1 -36.7823 3.694 -9.957 0.000 -44.044 -29.521
RPL_THEME2 6.7094 2.062 3.254 0.001 2.656 10.763
RPL_THEME3 8.5137 2.670 3.188 0.002 3.264 13.763
RPL_THEME4 -1.8146 2.153 -0.843 0.400 -6.047 2.418
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 233.315 Durbin-Watson: 1.595
Prob(Omnibus): 0.000 Jarque-Bera (JB): 3479.822
Skew: -2.068 Prob(JB): 0.00
Kurtosis: 16.605 Cond. No. 9.70


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sixteen_plus R-squared: 0.221\n", "Model: OLS Adj. R-squared: 0.213\n", "Method: Least Squares F-statistic: 28.90\n", "Date: Tue, 25 May 2021 Prob (F-statistic): 3.63e-21\n", "Time: 13:07:19 Log-Likelihood: -1523.2\n", "No. Observations: 413 AIC: 3056.\n", "Df Residuals: 408 BIC: 3076.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 75.6316 1.327 56.976 0.000 73.022 78.241\n", "RPL_THEME1 -36.7823 3.694 -9.957 0.000 -44.044 -29.521\n", "RPL_THEME2 6.7094 2.062 3.254 0.001 2.656 10.763\n", "RPL_THEME3 8.5137 2.670 3.188 0.002 3.264 13.763\n", "RPL_THEME4 -1.8146 2.153 -0.843 0.400 -6.047 2.418\n", "==============================================================================\n", "Omnibus: 233.315 Durbin-Watson: 1.595\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 3479.822\n", "Skew: -2.068 Prob(JB): 0.00\n", "Kurtosis: 16.605 Cond. No. 9.70\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with the themes\n", "vaccination_model = ols(\"\"\"Sixteen_plus ~ RPL_THEME1\n", " + RPL_THEME2\n", " + RPL_THEME3\n", " + RPL_THEME4\"\"\", data=df_final_for_regression[df_final_for_regression['RPL_THEME1']<0.5]).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sixteen_plus R-squared: 0.530
Model: OLS Adj. R-squared: 0.525
Method: Least Squares F-statistic: 117.1
Date: Tue, 25 May 2021 Prob (F-statistic): 8.27e-67
Time: 06:13:38 Log-Likelihood: -1577.0
No. Observations: 421 AIC: 3164.
Df Residuals: 416 BIC: 3184.
Df Model: 4
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 91.1832 2.702 33.747 0.000 85.872 96.495
RPL_THEME1 -42.4294 3.651 -11.620 0.000 -49.607 -35.252
RPL_THEME2 0.4686 1.954 0.240 0.811 -3.373 4.310
RPL_THEME3 -13.9189 5.162 -2.697 0.007 -24.065 -3.773
RPL_THEME4 4.7185 2.341 2.015 0.044 0.117 9.321
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 84.411 Durbin-Watson: 1.735
Prob(Omnibus): 0.000 Jarque-Bera (JB): 602.551
Skew: -0.630 Prob(JB): 1.44e-131
Kurtosis: 8.724 Cond. No. 20.6


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sixteen_plus R-squared: 0.530\n", "Model: OLS Adj. R-squared: 0.525\n", "Method: Least Squares F-statistic: 117.1\n", "Date: Tue, 25 May 2021 Prob (F-statistic): 8.27e-67\n", "Time: 06:13:38 Log-Likelihood: -1577.0\n", "No. Observations: 421 AIC: 3164.\n", "Df Residuals: 416 BIC: 3184.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 91.1832 2.702 33.747 0.000 85.872 96.495\n", "RPL_THEME1 -42.4294 3.651 -11.620 0.000 -49.607 -35.252\n", "RPL_THEME2 0.4686 1.954 0.240 0.811 -3.373 4.310\n", "RPL_THEME3 -13.9189 5.162 -2.697 0.007 -24.065 -3.773\n", "RPL_THEME4 4.7185 2.341 2.015 0.044 0.117 9.321\n", "==============================================================================\n", "Omnibus: 84.411 Durbin-Watson: 1.735\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 602.551\n", "Skew: -0.630 Prob(JB): 1.44e-131\n", "Kurtosis: 8.724 Cond. No. 20.6\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with the themes\n", "vaccination_model = ols(\"\"\"Sixteen_plus ~ RPL_THEME1\n", " + RPL_THEME2\n", " + RPL_THEME3\n", " + RPL_THEME4\"\"\", data=df_final_for_regression[df_final_for_regression['RPL_THEME3']>0.49]).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: Sixteen_plus R-squared: 0.416
Model: OLS Adj. R-squared: 0.410
Method: Least Squares F-statistic: 71.43
Date: Tue, 25 May 2021 Prob (F-statistic): 1.21e-45
Time: 06:13:46 Log-Likelihood: -1508.2
No. Observations: 406 AIC: 3026.
Df Residuals: 401 BIC: 3046.
Df Model: 4
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
Intercept 75.1372 1.348 55.728 0.000 72.487 77.788
RPL_THEME1 -40.1335 2.817 -14.246 0.000 -45.672 -34.595
RPL_THEME2 9.4401 2.117 4.459 0.000 5.278 13.602
RPL_THEME3 13.4421 3.568 3.767 0.000 6.428 20.456
RPL_THEME4 -4.8846 2.253 -2.168 0.031 -9.314 -0.456
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 193.984 Durbin-Watson: 1.793
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1994.632
Skew: -1.767 Prob(JB): 0.00
Kurtosis: 13.268 Cond. No. 9.03


Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Sixteen_plus R-squared: 0.416\n", "Model: OLS Adj. R-squared: 0.410\n", "Method: Least Squares F-statistic: 71.43\n", "Date: Tue, 25 May 2021 Prob (F-statistic): 1.21e-45\n", "Time: 06:13:46 Log-Likelihood: -1508.2\n", "No. Observations: 406 AIC: 3026.\n", "Df Residuals: 401 BIC: 3046.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 75.1372 1.348 55.728 0.000 72.487 77.788\n", "RPL_THEME1 -40.1335 2.817 -14.246 0.000 -45.672 -34.595\n", "RPL_THEME2 9.4401 2.117 4.459 0.000 5.278 13.602\n", "RPL_THEME3 13.4421 3.568 3.767 0.000 6.428 20.456\n", "RPL_THEME4 -4.8846 2.253 -2.168 0.031 -9.314 -0.456\n", "==============================================================================\n", "Omnibus: 193.984 Durbin-Watson: 1.793\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1994.632\n", "Skew: -1.767 Prob(JB): 0.00\n", "Kurtosis: 13.268 Cond. No. 9.03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sixteen plus with the themes\n", "vaccination_model = ols(\"\"\"Sixteen_plus ~ RPL_THEME1\n", " + RPL_THEME2\n", " + RPL_THEME3\n", " + RPL_THEME4\"\"\", data=df_final_for_regression[df_final_for_regression['RPL_THEME3']<0.49]).fit()\n", "# summarize our model\n", "vaccination_model.summary()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 }