# Analyzing Georgia data This article puts two datasets side by side: infant mortality from the Georgia Department of Public Health and the poverty rate from the U.S. Census. Infant mortality, the number of babies who die before their first birthday per 1,000 live births, is one of the most widely used measures of a community's health. The article shows the steps any such analysis needs. ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib.ticker import PercentFormatter import georgiacivicdata as gcd ``` ## Pick the datasets ```python gcd.search("infant mortality", n=3)[["name", "title", "year_min", "year_max"]] ``` ```text name title year_min year_max 0 infant_deaths Infant Deaths and Infant Mortality Rates 2000 2025 1 fetal_deaths Fetal Deaths and Fetal Mortality Rates 2000 2025 2 birth_outcomes Birth Outcomes (Low Birthweight and Prete... 2000 2025 ``` ```python gcd.search("poverty rate", level="counties", n=3)[["name", "title", "year_min", "year_max"]] ``` ```text name title year_min year_max 0 poverty Poverty (ACS) 2020 2024 1 poverty_by_nativity Poverty by Nativity and Citizenship (ACS) 2020 2024 2 low_income_low_access_tracts Low-Income, Low-Access Tracts (USDA 2025 ... 2025 2025 ``` `infant_deaths` holds infant deaths and live births from the state's OASIS system, by county of residence; `poverty` is the American Community Survey (ACS) 5-year estimate of the share of people below the federal poverty line. Before joining them, check that they share a detail level and a key, and which years they cover: ```python infant_info = gcd.describe("infant_deaths") poverty_info = gcd.describe("poverty") print( "Shared detail levels:", sorted(set(infant_info.detail_levels) & set(poverty_info.detail_levels)), ) print("Shared grain:", sorted(set(infant_info.grain) & set(poverty_info.grain))) print("Infant deaths years:", min(infant_info.years), "-", max(infant_info.years)) print("Poverty years:", min(poverty_info.years), "-", max(poverty_info.years)) ``` ```text Shared detail levels: ['counties', 'states'] Shared grain: ['county_fips', 'tract_geoid', 'year'] Infant deaths years: 2000 - 2025 Poverty years: 2020 - 2024 ``` Both publish county rows keyed by `county_fips`. The ACS figures are 5-year estimates named by their end year, so the 2024 vintage pools survey responses from 2020 to 2024. We match it with the infant deaths of 2020 to 2024. ## Read the notes first The dataset's notes say which columns can't be added up. Causes and ages at death nest inside each other (`all` contains every cause, and `neonatal` contains `early_neonatal`), so summing across them counts the same deaths more than once: ```python infant_info.notes["non_additive"]["column"].tolist() ``` ```text ['cause', 'age_at_death'] ``` Pinning both to `all` gives one row per county, year and group. The notes also set a floor on any figure we compute ourselves: ```python print(infant_info.notes["pooled_min_count"]["reason"].iloc[0]) ``` ```text OASIS withholds rates based on 1-4 deaths (shown as * on OASIS), so no figure combined from fewer than 5 deaths is given. ``` ## Pool infant mortality from counts `gcd.columns()` names the numerator and denominator of the rate: ```python cols = gcd.columns("infant_deaths") cols.loc[cols["metric_component"].notna(), ["column", "metric_component"]] ``` ```text column metric_component 11 num_deaths numerator 12 num_births denominator ``` ```python infant = gcd.rows( "infant_deaths", detail="counties", demographic="all", cause="all", age_at_death="all", year=range(2020, 2025), quiet=True, ) pd.DataFrame( { "county_years": [len(infant)], "with_a_rate": [infant["death_rate_per_1k_births"].notna().sum()], "median_births": [infant["num_births"].median()], } ) ``` ```text county_years with_a_rate median_births 0 795 385 258.0 ``` Infant deaths are rare and most of Georgia's 159 counties are small. In a typical county-year a few hundred babies are born and a single death moves the rate by several points. That is why about half of the county-years have no published rate at all. A county's rate over several years is the sum of its deaths over the sum of its births, not the average of its yearly rates: ```python pooled = ( infant.groupby(["county_fips", "county_name"], as_index=False) .agg(deaths=("num_deaths", "sum"), births=("num_births", "sum")) .assign(infant_mortality=lambda d: 1000 * d["deaths"] / d["births"]) ) pooled.sort_values("births", ascending=False).head() ``` ```text county_fips county_name deaths births infant_mortality 66 13135 Gwinnett 323 56843 5.682318 59 13121 Fulton 389 56491 6.886053 43 13089 DeKalb 299 49609 6.027132 32 13067 Cobb 229 43244 5.295532 30 13063 Clayton 177 19212 9.212992 ``` ```python few = int((pooled["deaths"] < 5).sum()) print(f"Counties with fewer than five infant deaths in 2020-2024: {few}") ``` ```text Counties with fewer than five infant deaths in 2020-2024: 41 ``` Even pooled over five years, those counties had too few infant deaths for a rate under the dataset's own rule. We keep their counts for the group sums below but leave them out of any county-level figure. Pooling and averaging give different answers whenever counties differ in size. Here is the comparison with the published state rows: ```python state = gcd.rows( "infant_deaths", detail="states", demographic="all", cause="all", age_at_death="all", year=range(2020, 2025), quiet=True, ) pd.DataFrame( { "infant_mortality": [ pooled["infant_mortality"].mean(), 1000 * pooled["deaths"].sum() / pooled["births"].sum(), 1000 * state["num_deaths"].sum() / state["num_births"].sum(), ], "births": pd.array( [None, pooled["births"].sum(), state["num_births"].sum()], dtype="Int64" ), }, index=["mean of county rates", "pooled from county counts", "state rows"], ) ``` ```text infant_mortality births mean of county rates 6.939697 pooled from county counts 6.651060 623660 state rows 6.651060 623660 ``` The counties add up to the state row exactly, so the rate pooled from county counts matches the state's. The plain mean of county rates is further off, because it weighs a county with a hundred births like one with fifty thousand. ## Join the poverty rate ```python poverty = gcd.rows( "poverty", detail="counties", year=2024, race_iteration="all", quiet=True, )[["county_fips", "poverty_rate", "poverty_rate_moe"]] joined = pooled.merge(poverty, on="county_fips", how="inner") len(joined) ``` ```text 159 ``` The ACS reports a 90% margin of error beside each estimate; for small counties it can be a sizable share of the estimate: ```python moe_share = joined["poverty_rate_moe"] / joined["poverty_rate"] pd.Series({"median_moe_share": moe_share.median(), "max_moe_share": moe_share.max()}) ``` ```text median_moe_share 0.190001 max_moe_share 0.507069 dtype: float64 ``` ```python rated = joined[joined["deaths"] >= 5] x = rated["poverty_rate"].to_numpy(dtype=float) y = rated["infant_mortality"].to_numpy(dtype=float) w = rated["births"].to_numpy(dtype=float) slope, intercept = np.polyfit(x, y, 1, w=np.sqrt(w)) fig, ax = plt.subplots(figsize=(7, 4.5)) ax.scatter(x, y, s=w / w.max() * 400, alpha=0.5, color="#b91c1c", edgecolor="none") grid = np.linspace(x.min(), x.max(), 50) ax.plot(grid, intercept + slope * grid, color="0.3") ax.xaxis.set_major_formatter(PercentFormatter(1)) ax.set_xlabel("Share of people below the poverty line, 2020-2024") ax.set_ylabel("Infant deaths per 1,000 live births, 2020-2024") ax.set_title("Georgia counties: infant mortality and poverty") ax.spines[["top", "right"]].set_visible(False) plt.show() ``` ![image](../_images/524694862e7415905a2bd439fe9a332f57cddcd8bcd5573939212ca060909359.png) Each point is a county with at least five infant deaths in 2020-2024, sized by its births; the line is a straight-line fit weighted by births. ```python # Spearman's rank correlation: the correlation of the ranks round(float(rated["poverty_rate"].rank().corr(rated["infant_mortality"].rank())), 2) ``` ```text 0.37 ``` County by county the association is modest, partly because even five-year rates in small counties carry a lot of chance variation. Grouping counties by their poverty rate and pooling each group's counts gives steadier rates. Every county counts here, including those with too few deaths for a rate of their own: ```python thirds = joined.assign( poverty_group=pd.qcut( joined["poverty_rate"], 3, labels=["Lowest third", "Middle third", "Highest third"] ) ) ( thirds.groupby("poverty_group", observed=True) .agg(counties=("county_fips", "size"), deaths=("deaths", "sum"), births=("births", "sum")) .assign(infant_mortality=lambda d: 1000 * d["deaths"] / d["births"]) ) ``` ```text counties deaths births infant_mortality poverty_group Lowest third 53 2345 395522 5.928874 Middle third 53 1229 155432 7.906995 Highest third 53 574 72706 7.894809 ``` Babies born in the middle and highest-poverty thirds of counties die before their first birthday at a rate about a third higher than babies born in the lowest-poverty third. The lowest-poverty third holds most of the state's births, so it sets the state's rate. ## What these figures can't tell - **Association, not causation.** Poverty is one of many ways counties differ; nothing here says a county's poverty rate causes infant deaths. - **Counties are not families.** A county's poverty rate describes all of its residents, not the families of the babies born there. - **Estimates carry error.** The ACS poverty rate is a survey estimate with a margin of error, larger in small counties; a county near a group boundary could sit in either. - **Small counts swing.** A county's rate on a few deaths says little on its own; group and pool before comparing places. - **Infant mortality has many causes.** The dataset's `cause` and `age_at_death` filters split deaths into prematurity, birth defects, sudden infant death and others, which this article does not look at.