# Birthweight and preterm birth: report

Chapter 2.1 of ZIP Code Destiny, by Vishal Singh (NYU Stern). Generated from the chapter's data files; the reading version is at https://vishalsingh.org/zipcode/birthweight-and-preterm-birth. Table n of this report is figure n of the chapter.

## Identification

- Title: Birthweight and preterm birth
- Label: The starting line
- Chapter: 2.1 (Part II, Birth; An American life)
- Question: How do the shares of babies born at low birthweight or preterm change with county deprivation, and how much of the racial gap remains at every level of deprivation?
- Dek: Low birthweight was 7.4% of births in the least deprived tenth of counties and 9.7% in the most deprived, for births in 2018 to 2024 in counties of 100,000 or more residents.
- Grain: counties
- Status: published
- Published: 2026-10-09
- Updated: not revised
- Data sources:
  - CDC WONDER natality. CDC WONDER natality, county by birthweight and gestational age, births in 2018 to 2024; years: 2018-2024; weights: none (full counts of births); county shares weighted by births
  - Area Deprivation Index. Neighborhood Atlas Area Deprivation Index 2023 (v4.0.1), tract national rank, averaged to counties with 2019-2023 tract population; years: 2023 release (version 4.0.1; built on ACS 2019-2023); weights: 2019-2023 tract population
  - County Health Rankings. County Health Rankings 2024, low birthweight (births in 2016 to 2022), overall and for non-Hispanic Black, non-Hispanic white and Hispanic mothers; years: 2024 release; weights: births (recovered from published intervals for race groups)
  - CDC WONDER linked birth and infant death. CDC WONDER linked birth and infant death, mother's single race (any ethnicity), 2018 and 2019; years: 2018-2022; weights: none (crude rates from counts)
- Canonical URL: https://vishalsingh.org/zipcode/birthweight-and-preterm-birth
- Report: https://vishalsingh.org/zipcode/birthweight-and-preterm-birth/report
- Markdown: https://vishalsingh.org/zipcode/birthweight-and-preterm-birth/report.md
- BibTeX key: singh2026birthweightandpretermbirth

```bibtex
@misc{singh2026birthweightandpretermbirth,
  author = {Singh, Vishal},
  title = {Birthweight and preterm birth},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/birthweight-and-preterm-birth},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}
```

## Principal estimates

The facts the dek and the opening prose cite, in order of appearance.

| Estimand | Population | Period | Value | Interval | n | Weights | Source | Fact id |
| --- | --- | --- | ---: | --- | ---: | --- | --- | --- |
| births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the least deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 7.4% | 95% CI 7.2% to 7.7% | 57 counties | births in each county | CDC WONDER natality | lbw_d1 |
| births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the most deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 9.7% | 95% CI 9.2% to 10.3% | 58 counties | births in each county | CDC WONDER natality | lbw_d10 |
| population size at which WONDER names a county (a definition) | counties that CDC WONDER natality names individually | births in 2018 to 2024 | 100,000 |   |   | none (definition) | CDC WONDER natality | wonder_threshold |
| birthweight below which a live birth counts as low birthweight (a definition) | live births | all years (a definition) | 2,500 |   |   | none (definition) | CDC WONDER natality | lbw_cutoff_grams |
| gestational age below which a live birth counts as preterm, obstetric estimate (a definition) | live births | all years (a definition) | 37 |   |   | none (definition) | CDC WONDER natality | preterm_cutoff_weeks |
| births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the least deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 8.8% | 95% CI 8.6% to 9.0% | 57 counties | births in each county | CDC WONDER natality | preterm_lo |
| births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the most deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 12.1% | 95% CI 11.3% to 12.7% | 58 counties | births in each county | CDC WONDER natality | preterm_hi |
| count of counties | counties that WONDER natality names individually and that have births | births in 2018 to 2024 | 578 |   |   | none (full count) | CDC WONDER natality | counties_used |
| share of births in the named counties | live births in the 50 states and DC | births in 2018 to 2024 | 79.5% |   | 25,709,900 births | none (full count) | CDC WONDER natality | births_share_covered |
| pooled share of births, from the published county rates and the births they imply, bootstrap interval over counties | live births to non-Hispanic Black mothers in the named counties where County Health Rankings publishes the rate | births in 2016 to 2022 | 14.0% | 95% CI 13.8% to 14.2% | 549 counties | births to mothers of the group in each county | County Health Rankings | lbw_black |
| pooled share of births, from the published county rates and the births they imply, bootstrap interval over counties | live births to non-Hispanic white mothers in the named counties where County Health Rankings publishes the rate | births in 2016 to 2022 | 6.7% | 95% CI 6.6% to 6.8% | 578 counties | births to mothers of the group in each county | County Health Rankings | lbw_white |
| pooled share of births, bootstrap interval over counties | live births to non-Hispanic Black mothers in the least deprived quartile of the named counties by deprivation rank | births in 2016 to 2022 | 12.4% | 95% CI 11.9% to 12.8% | 133 counties | births to mothers of the group in each county | County Health Rankings | lbw_black_q1 |
| pooled share of births, bootstrap interval over counties | live births to non-Hispanic white mothers in the most deprived quartile of the named counties by deprivation rank | births in 2016 to 2022 | 7.6% | 95% CI 7.5% to 7.7% | 145 counties | births to mothers of the group in each county | County Health Rankings | lbw_white_q4 |

## Findings

Low birthweight, a weight under 2,500 grams, was 7.4% of births in the least deprived tenth of the counties that CDC WONDER names individually and 9.7% in the most deprived, for births in 2018 to 2024. Preterm birth, before 37 weeks, was 8.8% against 12.1%. WONDER names only the larger counties, 578 of them. They hold 79.5% of births in the United States, so the pattern covers most newborns, though not those in the smaller counties that WONDER pools.

In County Health Rankings rates for births in 2016 to 2022, low birthweight was 14.0% of births to non-Hispanic Black mothers and 6.7% of births to non-Hispanic white mothers in these counties. The rate for non-Hispanic Black mothers is higher than the rate for non-Hispanic white mothers in every quartile of county deprivation. In the least deprived quartile of counties it is 12.4%, higher than the rate for non-Hispanic white mothers in the most deprived quartile, 7.6%. The gap between the two groups is therefore not a difference between richer and poorer counties alone.

Everything in this chapter describes counties and groups of births, not individual mothers. A county's deprivation rank says how disadvantaged its neighborhoods are on average, and a rate is the share of that county's births. Infant mortality, the outcome that follows these births, is the subject of Chapter 2.2, Infant mortality (https://vishalsingh.org/zipcode/infant-mortality).

### Why the first two measurements matter

Birthweight and gestational age are recorded on the birth certificate, which makes them the standard early measures of how a pregnancy went. A baby under 2,500 grams counts as low birthweight, and a baby born before 37 weeks counts as preterm. In the counties WONDER names, 8.3% of babies born in 2018 to 2024 were low birthweight and 10.1% were preterm. The two overlap, since a baby born early is usually small, though a baby can also be small at full term. Across the large counties the two county shares move closely together, with a correlation of 0.84.

Economists and epidemiologists study these measures because they predict what comes later. The literature on the fetal origins of adult health links conditions in the womb to test scores, educational attainment and income as well as health in adulthood.[1] A review by Aizer and Currie reports that health at birth predicts education, income and disability. It also reports that a mother's disadvantage is associated with worse health at birth through her health behaviors, her exposure to environmental hazards, her access to medical care and her own health.[2] A gradient in birthweight across places therefore matters beyond the delivery room, although these data cannot show how much of any later outcome a baby's birthweight accounts for.

### Where low birthweight is most common

Low birthweight is not spread evenly across the country. In County Health Rankings rates for births in 2016 to 2022, 89% of the counties in the highest tenth of rates are in the South, which holds 47% of the mapped counties. Of the same highest tenth, 64% are in Alabama, Georgia, Louisiana, Mississippi and South Carolina, which hold 14%. Pooled over births, the rate is 9.1% in the South and 7.8% in the rest of the country.

Table 1. Low birthweight is most common in the South, where 89% of the counties in the highest tenth of shares lie, against 47% of mapped counties

The map draws every county with at least 20 low-birthweight births in the period, 2,900 counties in all. The other 243 counties are gray, and the tooltip gives the reason for each: 98 have no published rate and 145 fall below the floor. County Health Rankings is a second source for this chapter. It takes its birth counts from the same national vital statistics system as WONDER, although the periods overlap only in 2018 to 2022 (WONDER 2018 to 2024, County Health Rankings 2016 to 2022). Where both cover a county they agree. Across the large counties the correlation between the two county shares is 0.984. That agreement lets the chapter use WONDER for the deprivation gradient and County Health Rankings for the map and for rates by mother's race and ethnicity, which the WONDER county files used here do not carry.

### Low birthweight and preterm birth rise with county deprivation

To place counties on one scale, each census tract carries its Area Deprivation Index national rank, which orders the country's neighborhoods by deprivation using income, education, employment and housing measures from the American Community Survey.[3] A higher rank means more deprived. A county's rank in this chapter is the average of its tracts' ranks, weighting each tract by its population. The counties in the gradient are the 578 that WONDER natality names individually, those with 100,000 or more residents. WONDER pools the births of every other county in a state into one row labeled Unidentified Counties, so smaller counties cannot be separated. The named counties hold 20.5 million births in 2018 to 2024. A tenth of counties here is a tenth of these large counties, so the most deprived tenth is relative to large counties and not to the nation.

Low birthweight rises at every step from the least deprived tenth, 7.4% (95% CI 7.2% to 7.7%), to the most deprived, 9.7% (95% CI 9.2% to 10.3%), a difference of 2.3 percentage points (95% CI 1.7 percentage points to 2.9 percentage points). Preterm birth follows the same path, from 8.8% to 12.1%, a larger difference of 3.2 percentage points (95% CI 2.5 percentage points to 3.9 percentage points) computed from the unrounded shares.

Across all 578 counties the low-birthweight share moves with the deprivation rank. It is higher by 0.38 percentage points (95% CI 0.32 percentage points to 0.44 percentage points) for each ten-point step in the rank, and the correlation is 0.52. The preterm share is higher by 0.49 percentage points for each ten-point step in the rank, with a correlation of 0.69. The intervals are wider in the most deprived tenth than in the least deprived one, because the most deprived tenth holds fewer births and its counties' shares vary more. Averaging the counties without weighting by births gives the same ordering.

Table 2. Low birthweight rises from 7.4% of births in the least deprived tenth of large counties to 9.7% in the most deprived

Restricting the gradient to large counties could change the picture, so County Health Rankings offers a check. It publishes low-birthweight rates for 3,040 counties that also have a deprivation rank. In tenths of all those counties the share is 7.6% in the least deprived tenth and 10.9% in the most deprived. The middle tenths are level, so the climb is steadier across large counties than across the whole country. The most deprived tenth of all counties is made up of small counties: its median county has 770 births in the period, against 12,055 in the least deprived tenth, and its interval is wider.

WONDER withholds any cell with fewer than 10 births, so a county's count below 2,500 grams is not always published whole. A withheld cell is treated as missing: it is left out of both the count and the denominator, which is the births in the published cells. The birthweight cut has 11,250 withheld cells in these counties. Because each holds fewer than 10 births, together they could hold at most 0.5% of births. The gestational-age cut has 8,704 withheld cells, which could hold at most 0.4% of births. A pooled share could therefore move by no more than that amount.

The withheld cells are not spread evenly. Withheld cells under 2,500 grams number 1.6 per county-year in the most deprived tenth against 1.0 in the least deprived. A withheld cell of that kind lowers a published share, because its births leave the count and the denominator together. With fewer than 10 births in each, the withheld birthweight cells could hold at most 1.3% of births in the most deprived tenth and 0.2% in the least deprived. The published difference between the tenths could therefore be too small by up to the first amount, and too large by up to the second.

### The gap by mother's race

The gradient averages over mothers. Splitting births by the mother's race and ethnicity shows a gap larger than the one between the most and least deprived counties. County Health Rankings counts mothers as non-Hispanic Black, non-Hispanic white or Hispanic of any race. In the large counties, 14.0% of births to non-Hispanic Black mothers in 2016 to 2022 were low birthweight, against 7.5% for Hispanic mothers and 6.7% for non-Hispanic white mothers. The rate for non-Hispanic Black mothers is 2.1 (95% CI 2.0 to 2.1) times the rate for non-Hispanic white mothers, a gap of 7.3 percentage points (95% CI 7.0 percentage points to 7.4 percentage points). The difference between the most and least deprived tenths of counties was 2.3 percentage points, for births in 2018 to 2024.

Infant deaths follow the same order. WONDER's linked birth and infant death records report mother's race only for 2018 and 2019, as a single race of any ethnicity. They give 10.3 deaths per 1,000 live births for births to Black mothers against 4.7 for births to white mothers, a group that includes Hispanic mothers.

Table 3. Non-Hispanic Black mothers had low-birthweight births at 14.0% against 6.7% for non-Hispanic white mothers, a ratio of 2.1

These are rates for groups of births in counties and say nothing about why a given mother's baby is born small. One study that bears on the explanation compared infants born in Illinois from 1980 to 1995. Infants of African-born Black mothers were heavier on average than infants of U.S.-born Black mothers and closer in weight to infants of U.S.-born white mothers.[4] That pattern is hard to square with an explanation based on ancestry alone. Women who migrate are a selected group, though, so the comparison cannot rule out other differences between the two groups of Black mothers.

### The gap at every level of deprivation

Counties differ in deprivation, so the race gap could shrink once counties are compared at the same level. In every quartile of county deprivation, the rate for non-Hispanic Black mothers is higher than the rate for non-Hispanic white mothers, for births in 2016 to 2022 (Table 4). The gap is 6.2 percentage points (95% CI 5.8 percentage points to 6.6 percentage points) in the least deprived quartile and 8.0 percentage points (95% CI 7.7 percentage points to 8.2 percentage points) in the most deprived, and the second is larger than the first by 1.8 percentage points (95% CI 1.3 percentage points to 2.3 percentage points). The rate for non-Hispanic Black mothers in the least deprived quartile, 12.4% (95% CI 11.9% to 12.8%), is higher than the rate for non-Hispanic white mothers in the most deprived quartile, 7.6% (95% CI 7.5% to 7.7%), by 4.8 percentage points (95% CI 4.4 percentage points to 5.2 percentage points). The second view of the figure shows the same order for infant mortality, with Black and white mothers counted by single race of any ethnicity, in counties of 250,000 residents or more, for births in 2018 and 2019.

Table 4. Low birthweight is more common for non-Hispanic Black mothers in the least deprived quartile of counties (12.4%) than for non-Hispanic white mothers in the most deprived (7.6%)

 (Note: The weathering hypothesis proposed by Geronimus holds that the health of Black women may begin to deteriorate in early adulthood as a physical consequence of cumulative socioeconomic disadvantage.[5] A gap that persists at every level of county deprivation is consistent with it, but a comparison across counties cannot separate it from other explanations.)

A county's rank averages over neighborhoods that differ a great deal. A Black mother in a county ranked among the least deprived may live in a neighborhood more deprived than the county average. A white mother in a county ranked among the most deprived may live in a less deprived one. The comparison therefore cannot show that mothers under the same neighborhood conditions have different outcomes. What it shows is that sorting counties by deprivation does not remove the gap. A comparison of Black and white children raised in the same tract is in Chapter 4.3, Race, income and mobility (https://vishalsingh.org/zipcode/race-income-and-mobility).

### Births fell between 2018 and 2024

Births in the United States fell from 3.79 million in 2018 to 3.63 million in 2024, a decline of 4.3%. The shares in this chapter are rates, so they do not move with the count, but the pattern describes a smaller number of babies each year. Where the decline is steepest is the subject of Chapter 2.4, Where births are falling (https://vishalsingh.org/zipcode/where-births-are-falling).

### Two studies of income and insurance during pregnancy

The gradient and the gap are descriptions. Two studies of policy changes give evidence on whether income support and insurance coverage during pregnancy change birthweight, and each is reported under its own design.

> Causal evidence: Income support and birth outcomes (Difference-in-differences)
>
> Hoynes, Miller and Simon studied federal expansions of the Earned Income Tax Credit by comparing births to mothers whom the tax reforms gave larger credits with births to mothers who received smaller ones, before and after each reform.[6] The comparison removes changes that affected both groups, so the study attributes the lower rate of low birthweight and the higher mean birthweight among mothers with larger credits to the added income. The estimates are largest for single mothers with little education and for births to Black mothers. The paper's candidate mechanisms are more prenatal care and less smoking, and it finds little role for changes in health insurance.

> Causal evidence: Medicaid eligibility for pregnant women (State and year fixed effects on simulated eligibility)
>
> During the 1980s, states widened Medicaid eligibility for pregnant women at different times and by different amounts. Currie and Gruber built a measure of the share of women eligible under each state's rules in each year by applying the rules to a fixed national sample of women, so the measure moves with the rules and not with a state's own population. They regressed state-by-year birth outcomes on that measure with state and year fixed effects.[7] Where eligibility expanded, infant mortality fell by more than low birthweight did, and changes aimed at specific low-income groups had larger effects than broad expansions to higher incomes, which fewer eligible women took up.

Both sets of estimates apply to the policies and years studied. They do not measure how much of the county gradient or the race gap those policies would close.

### Low birthweight keeps its gradient under the standardized index and flattens under the degree share

Table 5 ranks the 578 large counties that have all four rankings in four ways. Ranked by the ADI on this common sample, the low-birthweight share of births is 7.4% (95% CI 7.2% to 7.7%) in the least deprived tenth and 9.6% (95% CI 9.1% to 10.2%) in the most deprived, and the rank correlation between tenth and share is +0.99. Under the standardized index the two ends are 7.2% (95% CI 7.0% to 7.3%) and 9.7% (95% CI 9.0% to 10.4%), with a correlation of +0.99, and under median household income they are 7.2% (95% CI 7.0% to 7.3%) and 10.0% (95% CI 9.4% to 10.6%), with a correlation of +0.96, so neither counts as a change under the book's rule (Chapter 9.1, How far one ranking goes (https://vishalsingh.org/zipcode/how-far-one-ranking-goes)). Ranked by the bachelor's degree share, the ends are 7.6% (95% CI 7.2% to 7.9%) and 8.2% (95% CI 7.8% to 8.6%) and their intervals overlap, which the rule counts as a change, although income and degree share are checks that cannot switch the book's ranking on their own.

Table 5. Low birthweight is steadily worse toward the more deprived end under the ADI; the two ends do not differ detectably under the degree-share ranking

### What these data cannot show

The county figures describe places, and a county rate is an average over the births in it. A county ranked as least deprived can hold deprived neighborhoods, and the Area Deprivation Index understates deprivation in high-cost cities (Chapter 1.1, What the deprivation index measures (https://vishalsingh.org/zipcode/anatomy-of-deprivation)). A pattern across counties does not describe the babies in any one of them.

The WONDER figures cover only counties of 100,000 or more residents, which hold 79.5% of births. Births in smaller counties are pooled by WONDER and cannot be placed on the deprivation scale, and the check against County Health Rankings shows that the pattern is less regular across all counties than across large ones.

The sources differ in period and definition. The deprivation index draws on the American Community Survey for 2019 to 2023, while the births span 2018 to 2024. Rates by mother's race come from County Health Rankings for births in 2016 to 2022, for non-Hispanic Black, non-Hispanic white and Hispanic mothers, and they are pooled over the counties where a rate is published. Infant mortality by mother's race comes from 2018 and 2019 only, for single races of any ethnicity, so its white group includes Hispanic mothers and is not the group in the low-birthweight rates. The infant mortality county view covers only larger counties and leaves out county-years in which WONDER withholds the deaths, which are 13.6% of Black births and 5.2% of white births there. The intervals leave out those differences in source, period and group.

Finally, none of these comparisons identifies a mechanism. Deprivation and race are tied to smoking, prenatal care, chronic stress, pollution, maternal age and education, and to the quality of the nearest hospital. Birthweight is associated with all of them. The two policy studies above are the only evidence in this chapter on what changes the numbers, and they speak to income and coverage, not to the rest. A county map cannot settle the mechanism behind any one county's rate or behind the gap between groups.

## Tables

### Table 1. Low birthweight is most common in the South, where 89% of the counties in the highest tenth of shares lie, against 47% of mapped counties

Share of live births under 2,500 grams by county, County Health Rankings 2024, births in 2016 to 2022

No inline rows: the values of Table 1 are in https://vishalsingh.org/zipcode/data/birthweight-and-preterm-birth/lbw_map.json.

Note: Counties with fewer than 10 low-birthweight births in the period have no rate in County Health Rankings, and counties with fewer than 20 are drawn as no data here, the same floor the book uses for death maps. 243 counties are not mapped.

Note: The South is the Census South region (16 states and DC). County Health Rankings rates come from the same NCHS birth records as WONDER; across the large counties the two sources correlate at 0.984.

Note: Connecticut is shown as its eight legacy counties, the coding of the source. Alaska is drawn. Puerto Rico is not drawn.

Source: County Health Rankings 2024, births in 2016-2022 (NCHS natality counts); county rates, population-weighted by births

Data: https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/lbw_map.json

### Table 2. Low birthweight rises from 7.4% of births in the least deprived tenth of large counties to 9.7% in the most deprived

Share of live births under 2,500 grams (and before 37 weeks in the second tab) by county deprivation, 2018 to 2024

#### Table 2.1. Low birthweight, large counties

Across 578 large counties the low-birthweight share moves with the deprivation rank, a correlation of 0.52

Share of live births under 2,500 grams against the county deprivation rank, one dot per county, 2018 to 2024, with births-weighted decile means

| adi | births | county | lbw |
| ---: | ---: | --- | ---: |
| 27.27 | 35153 | Ada County, ID | 6.35 |
| 31.50 | 43865 | Adams County, CO | 9.34 |
| 53.02 | 6346 | Adams County, PA | 5.23 |
| 67.21 | 13354 | Aiken County, SC | 9.03 |
| 57.27 | 18344 | Alachua County, FL | 10.40 |
| 65.57 | 13473 | Alamance County, NC | 9.47 |
| 7.82 | 119533 | Alameda County, CA | 7.49 |
| 52.09 | 19652 | Albany County, NY | 8.64 |
| 18.67 | 16409 | Alexandria city, VA | 6.56 |
| 57.15 | 8760 | Allegan County, MI | 5.82 |
| 60.06 | 85018 | Allegheny County, PA | 8.94 |
| 67.85 | 36535 | Allen County, IN | 9.01 |
| 77.54 | 8561 | Allen County, OH | 8.92 |
| 37.23 | 25918 | Anchorage Borough, AK | 6.89 |
| 66.51 | 15986 | Anderson County, SC | 9.33 |
| 61.55 | 8066 | Androscoggin County, ME | 8.12 |
| 26.31 | 47568 | Anne Arundel County, MD | 7.57 |
| 41.11 | 28547 | Anoka County, MN | 6.93 |
| 25.25 | 52722 | Arapahoe County, CO | 10.10 |
| 11.96 | 17788 | Arlington County, VA | 6.18 |
| 49.71 | 11589 | Ascension Parish, LA | 9.58 |
| 80.50 | 7231 | Ashtabula County, OH | 6.37 |
| 54.06 | 19373 | Atlantic County, NJ | 7.89 |
| 50.20 | 17176 | Baldwin County, AL | 7.93 |
| 42.28 | 65464 | Baltimore County, MD | 9.32 |
| 64.94 | 50993 | Baltimore city, MD | 12.02 |
| 20.78 | 10379 | Barnstable County, MA | 5.43 |
| 54.23 | 9308 | Bartow County, GA | 6.77 |
| 55.85 | 14687 | Bay County, FL | 8.26 |
| 80.21 | 6392 | Bay County, MI | 7.58 |
| 37.13 | 13141 | Beaufort County, SC | 8.11 |
| 69.45 | 10623 | Beaver County, PA | 6.79 |
| 64.57 | 42199 | Bell County, TX | 9.12 |
| 48.22 | 26503 | Benton County, AR | 7.35 |
| 40.98 | 17730 | Benton County, WA | 6.27 |
| 19.42 | 63171 | Bergen County, NJ | 7.44 |
| 50.80 | 20807 | Berkeley County, SC | 9.31 |
| 54.82 | 10636 | Berkeley County, WV | 8.17 |
| 60.09 | 31836 | Berks County, PA | 7.92 |
| 49.99 | 6398 | Berkshire County, MA | 7.68 |
| 54.44 | 46209 | Bernalillo County, NM | 9.73 |
| 66.53 | 10686 | Berrien County, MI | 8 |
| 59.17 | 183350 | Bexar County, TX | 8.83 |
| 75.80 | 14382 | Bibb County, GA | 13.54 |
| 69.55 | 11194 | Black Hawk County, IA | 8.05 |
| 76.45 | 8120 | Blair County, PA | 6.09 |
| 53.45 | 9120 | Blount County, TN | 7.61 |
| 46 | 12633 | Bonneville County, ID | 7.81 |
| 49.97 | 11867 | Boone County, KY | 6.60 |
| 58.43 | 13758 | Boone County, MO | 8.40 |
| 66.36 | 11338 | Bossier Parish, LA | 11.42 |
| 15.94 | 17248 | Boulder County, CO | 7.77 |
| 49.43 | 32862 | Brazoria County, TX | 8.32 |
| 55.97 | 18815 | Brazos County, TX | 8.09 |
| 48.90 | 36470 | Brevard County, FL | 8.41 |
| 34.02 | 39240 | Bristol County, MA | 8.48 |
| 31.09 | 124256 | Bronx County, NY | 10.45 |
| 78.39 | 12584 | Broome County, NY | 7.98 |
| 40.97 | 145472 | Broward County, FL | 9.63 |
| 56.90 | 21131 | Brown County, WI | 7.06 |
| 48.22 | 7781 | Brunswick County, NC | 7.76 |
| 29.71 | 39164 | Bucks County, PA | 6.99 |
| 42.50 | 16802 | Buncombe County, NC | 8.30 |
| 40.10 | 31808 | Burlington County, NJ | 7.72 |
| 58.33 | 30299 | Butler County, OH | 8 |
| 50.28 | 12033 | Butler County, PA | 5.92 |
| 38.13 | 14612 | Butte County, CA | 6.20 |
| 46.20 | 18406 | Cabarrus County, NC | 8.82 |
| 37.55 | 14438 | Cache County, UT | 5.83 |
| 76.41 | 20103 | Caddo Parish, LA | 14.52 |
| 66.05 | 18828 | Calcasieu Parish, LA | 10.55 |
| 80.93 | 9096 | Calhoun County, AL | 8.77 |
| 77.35 | 10306 | Calhoun County, MI | 8.76 |
| 84.10 | 8283 | Cambria County, PA | 8.21 |
| 54.47 | 42450 | Camden County, NJ | 9.05 |
| 84.05 | 41975 | Cameron County, TX | 8.51 |
| 57.52 | 13874 | Canadian County, OK | 6.76 |
| 42.39 | 22809 | Canyon County, ID | 6.65 |
| 60.26 | 11132 | Carroll County, GA | 8.14 |
| 29.66 | 11977 | Carroll County, MD | 5.58 |
| 49.09 | 16894 | Cass County, ND | 6.99 |
| 64.85 | 11691 | Catawba County, NC | 8.15 |
| 44.54 | 8004 | Cecil County, MD | 7.08 |
| 46.22 | 7675 | Centre County, PA | 4.55 |
| 66.50 | 14484 | Champaign County, IL | 8.81 |
| 30 | 12676 | Charles County, MD | 9.27 |
| 35.04 | 34737 | Charleston County, SC | 9.36 |
| 51.12 | 7422 | Charlotte County, FL | 7.89 |
| 56.20 | 25303 | Chatham County, GA | 11.15 |
| 83.81 | 8651 | Chautauqua County, NY | 7.72 |
| 33.22 | 19655 | Cherokee County, GA | 6.77 |
| 39.92 | 19793 | Chesapeake city, VA | 9.07 |
| 26.68 | 38149 | Chester County, PA | 6.19 |
| 40.91 | 28155 | Chesterfield County, VA | 7.96 |
| 31.50 | 9595 | Chittenden County, VT | 5.44 |
| 66.62 | 7253 | Citrus County, FL | 7.34 |
| 21.10 | 26316 | Clackamas County, OR | 5.97 |
| 64.21 | 10055 | Clark County, IN | 7.84 |
| 39.24 | 176293 | Clark County, NV | 9.37 |
| 77.65 | 11089 | Clark County, OH | 8.89 |
| 26.64 | 38736 | Clark County, WA | 6.67 |
| 53.22 | 9286 | Clarke County, GA | 9.88 |
| 49.93 | 15614 | Clay County, FL | 8.42 |
| 52.12 | 20890 | Clay County, MO | 7.43 |
| 71.44 | 24539 | Clayton County, GA | 13.52 |
| 55.43 | 15577 | Clermont County, OH | 7.38 |
| 59.97 | 19200 | Cleveland County, OK | 7.63 |
| 37.38 | 61759 | Cobb County, GA | 8.82 |
| 71.06 | 8859 | Cochise County, AZ | 7.32 |
| 40.84 | 9348 | Coconino County, AZ | 6.95 |
| 30.31 | 23129 | Collier County, FL | 7.14 |
| 26.55 | 85845 | Collin County, TX | 7.47 |
| 48.03 | 11898 | Columbia County, GA | 7.21 |
| 80.83 | 6778 | Columbiana County, OH | 7.32 |
| 35.12 | 12791 | Comal County, TX | 6.84 |
| 77.51 | 11401 | Comanche County, OK | 7.63 |
| 12.71 | 81039 | Contra Costa County, CA | 7.34 |
| 47.08 | 384972 | Cook County, IL | 8.94 |
| 43.78 | 11498 | Coweta County, GA | 9.35 |
| 41.54 | 8603 | Cowlitz County, WA | 5.87 |
| 66.65 | 9031 | Craven County, NC | 8.23 |
| 30.73 | 19393 | Cumberland County, ME | 6.76 |
| 72.53 | 36672 | Cumberland County, NC | 10.35 |
| 68.20 | 12811 | Cumberland County, NJ | 9.47 |
| 53.15 | 18447 | Cumberland County, PA | 6.79 |
| 69.33 | 92117 | Cuyahoga County, OH | 10.62 |
| 35.84 | 34751 | Dakota County, MN | 6.78 |
| 51.84 | 258092 | Dallas County, TX | 8.49 |
| 37.44 | 39430 | Dane County, WI | 7.16 |
| 62.88 | 23065 | Dauphin County, PA | 9.31 |
| 68.39 | 12395 | Davidson County, NC | 9.54 |
| 38 | 70746 | Davidson County, TN | 9.12 |
| 26.64 | 34072 | Davis County, UT | 7.41 |
| 44.32 | 70179 | DeKalb County, GA | 10.70 |
| 60.71 | 7341 | DeKalb County, IL | 7.53 |
| 56.53 | 15346 | DeSoto County, MS | 9.96 |
| 83.14 | 7493 | Delaware County, IN | 7.69 |
| 29.76 | 14891 | Delaware County, OH | 6.53 |
| 47.79 | 44118 | Delaware County, PA | 8.56 |
| 31.79 | 75980 | Denton County, TX | 7.44 |
| 21.31 | 64002 | Denver County, CO | 9.97 |
| 21.34 | 13013 | Deschutes County, OR | 6.50 |
| 16.41 | 59412 | District of Columbia, DC | 10.07 |
| 70.08 | 17975 | Dona Ana County, NM | 8.19 |
| 50.03 | 13688 | Dorchester County, SC | 9.18 |
| 13.04 | 25912 | Douglas County, CO | 8.76 |
| 52.95 | 11757 | Douglas County, GA | 10.92 |
| 51.04 | 7191 | Douglas County, KS | 6.60 |
| 55.92 | 56047 | Douglas County, NE | 8.94 |
| 53.80 | 6999 | Douglas County, OR | 6.39 |
| 34.30 | 68211 | DuPage County, IL | 6.87 |
| 41.88 | 28912 | Durham County, NC | 8.83 |
| 38.30 | 18277 | Dutchess County, NY | 7.42 |
| 55.54 | 89270 | Duval County, FL | 10.39 |
| 61.83 | 38895 | East Baton Rouge Parish, LA | 12.33 |
| 63.59 | 7301 | Eaton County, MI | 7.86 |
| 66.99 | 20874 | Ector County, TX | 9.26 |
| 17.71 | 10781 | El Dorado County, CA | 6.05 |
| 32.73 | 63679 | El Paso County, CO | 9.65 |
| 74.50 | 78465 | El Paso County, TX | 9.30 |
| 67.65 | 20605 | Elkhart County, IN | 6.82 |
| 45.56 | 18052 | Ellis County, TX | 7.30 |
| 62.29 | 65100 | Erie County, NY | 8.98 |
| 74.57 | 18441 | Erie County, PA | 9.13 |
| 62.76 | 26111 | Escambia County, FL | 10.78 |
| 21.97 | 59384 | Essex County, MA | 7.26 |
| 33.03 | 69651 | Essex County, NJ | 9.26 |
| 79.30 | 8297 | Etowah County, AL | 8.03 |
| 14.29 | 89085 | Fairfax County, VA | 7.05 |
| 26.85 | 69289 | Fairfield County, CT | 7.22 |
| 53.08 | 11758 | Fairfield County, OH | 7.21 |
| 64.89 | 10394 | Faulkner County, AR | 7.65 |
| 31.83 | 6286 | Fayette County, GA | 9.46 |
| 55.11 | 26238 | Fayette County, KY | 9.23 |
| 82.19 | 8340 | Fayette County, PA | 8.56 |
| 77.03 | 11123 | Florence County, SC | 13.09 |
| 64.43 | 7111 | Fond du Lac County, WI | 6.31 |
| 22.24 | 16062 | Forsyth County, GA | 6.84 |
| 62.43 | 30894 | Forsyth County, NC | 10.40 |
| 37.63 | 64832 | Fort Bend County, TX | 8.77 |
| 62.33 | 8030 | Franklin County, MO | 7.42 |
| 55.13 | 123800 | Franklin County, OH | 9.29 |
| 58.84 | 11941 | Franklin County, PA | 6.90 |
| 27.94 | 21568 | Frederick County, MD | 6.61 |
| 46.34 | 96600 | Fresno County, CA | 7.76 |
| 36.01 | 86524 | Fulton County, GA | 11.37 |
| 50.28 | 27382 | Galveston County, TX | 8.83 |
| 62.53 | 18847 | Gaston County, NC | 10.28 |
| 73.71 | 30357 | Genesee County, MI | 10.49 |
| 45 | 20365 | Gloucester County, NJ | 7.79 |
| 61.48 | 11943 | Grayson County, TX | 7.44 |
| 68.78 | 23058 | Greene County, MO | 8.16 |
| 56.86 | 11888 | Greene County, OH | 6.86 |
| 52.19 | 45163 | Greenville County, SC | 8.48 |
| 71.55 | 11625 | Gregg County, TX | 8.94 |
| 49.21 | 13972 | Guadalupe County, TX | 7.62 |
| 62.39 | 41153 | Guilford County, NC | 10.02 |
| 41.21 | 76833 | Gwinnett County, GA | 9.25 |
| 49.03 | 18786 | Hall County, GA | 6.80 |
| 33.43 | 26854 | Hamilton County, IN | 6.49 |
| 59.80 | 72644 | Hamilton County, OH | 9.90 |
| 51.80 | 30828 | Hamilton County, TN | 8.71 |
| 54.48 | 33272 | Hampden County, MA | 8.96 |
| 37.36 | 6428 | Hampshire County, MA | 6.40 |
| 60.22 | 11058 | Hampton city, VA | 10.17 |
| 64.32 | 9474 | Hardin County, KY | 7.58 |
| 36.71 | 18296 | Harford County, MD | 7.55 |
| 63.61 | 13138 | Harnett County, NC | 8.33 |
| 55.59 | 455394 | Harris County, TX | 9.17 |
| 69.85 | 17487 | Harrison County, MS | 10.87 |
| 47.60 | 62452 | Hartford County, CT | 8.64 |
| 29.53 | 14568 | Hawaii County, HI | 8.70 |
| 40.66 | 21150 | Hays County, TX | 6.97 |
| 49.59 | 7168 | Henderson County, NC | 7.53 |
| 45.88 | 12504 | Hendricks County, IN | 6.43 |
| 36.21 | 103352 | Hennepin County, MN | 7.74 |
| 42.82 | 25990 | Henrico County, VA | 8.35 |
| 49.74 | 19461 | Henry County, GA | 12.03 |
| 60.04 | 11629 | Hernando County, FL | 8.33 |
| 83.04 | 96697 | Hidalgo County, TX | 9.29 |
| 45.98 | 119285 | Hillsborough County, FL | 9.03 |
| 35.48 | 29024 | Hillsborough County, NH | 6.91 |
| 80.51 | 19288 | Hinds County, MS | 15.39 |
| 11.87 | 78677 | Honolulu County, HI | 8.52 |
| 57.80 | 22858 | Horry County, SC | 8.36 |
| 74.54 | 9155 | Houston County, AL | 10.05 |
| 64.74 | 13805 | Houston County, GA | 9.57 |
| 19.35 | 22481 | Howard County, MD | 8.32 |
| 24.79 | 65616 | Hudson County, NJ | 8.28 |
| 36.22 | 8646 | Humboldt County, CA | 5.54 |
| 22.68 | 7266 | Hunterdon County, NJ | 4.94 |
| 57.41 | 16878 | Imperial County, CA | 6.24 |
| 49.19 | 8699 | Indian River County, FL | 7.33 |
| 68 | 20257 | Ingham County, MI | 8.71 |
| 50.75 | 13891 | Iredell County, NC | 8.29 |
| 72.38 | 11398 | Jackson County, MI | 9.02 |
| 64.74 | 60827 | Jackson County, MO | 9.83 |
| 71.13 | 10872 | Jackson County, MS | 9.68 |
| 36.24 | 14713 | Jackson County, OR | 6.98 |
| 77.71 | 10937 | Jasper County, MO | 7.44 |
| 62.60 | 56397 | Jefferson County, AL | 11.36 |
| 19.12 | 38651 | Jefferson County, CO | 9.02 |
| 60.18 | 67103 | Jefferson County, KY | 9.39 |
| 59.66 | 16932 | Jefferson County, MO | 8.20 |
| 70.75 | 12213 | Jefferson County, NY | 7.56 |
| 75.11 | 22535 | Jefferson County, TX | 10.73 |
| 60.17 | 37531 | Jefferson Parish, LA | 10.22 |
| 48.25 | 11748 | Johnson County, IA | 6.42 |
| 52.49 | 13231 | Johnson County, IN | 7.34 |
| 35.27 | 48504 | Johnson County, KS | 6.53 |
| 55.51 | 16420 | Johnson County, TX | 6.66 |
| 53.69 | 18770 | Johnston County, NC | 8.05 |
| 60.96 | 19313 | Kalamazoo County, MI | 7.94 |
| 80.09 | 11993 | Kanawha County, WV | 10.63 |
| 45.72 | 40039 | Kane County, IL | 7.43 |
| 69.71 | 8127 | Kankakee County, IL | 8.33 |
| 50.78 | 16592 | Kaufman County, TX | 7.73 |
| 42.23 | 10514 | Kendall County, IL | 6.35 |
| 64.60 | 7573 | Kennebec County, ME | 6.75 |
| 54.96 | 12018 | Kenosha County, WI | 7.20 |
| 51.03 | 14752 | Kent County, DE | 9.39 |
| 51.75 | 57233 | Kent County, MI | 8.79 |
| 39.65 | 10222 | Kent County, RI | 6.38 |
| 58.65 | 15280 | Kenton County, KY | 7.69 |
| 52.63 | 86859 | Kern County, CA | 7.65 |
| 12.92 | 163987 | King County, WA | 6.98 |
| 51.62 | 15013 | Kings County, CA | 6.52 |
| 9.96 | 243582 | Kings County, NY | 7.87 |
| 24.37 | 19887 | Kitsap County, WA | 6.59 |
| 52.49 | 37089 | Knox County, TN | 8.13 |
| 30.06 | 13287 | Kootenai County, ID | 5.04 |
| 58.76 | 7890 | La Crosse County, WI | 5.81 |
| 70.57 | 7995 | La Porte County, IN | 8.12 |
| 73.64 | 7663 | LaSalle County, IL | 6.66 |
| 70.82 | 14150 | Lackawanna County, PA | 8.38 |
| 60.48 | 23128 | Lafayette Parish, LA | 9.70 |
| 52.55 | 25103 | Lake County, FL | 8.56 |
| 42.15 | 47218 | Lake County, IL | 7.64 |
| 65.67 | 37650 | Lake County, IN | 8.95 |
| 62.50 | 14729 | Lake County, OH | 7.18 |
| 55.73 | 26011 | Lancaster County, NE | 7.04 |
| 49.56 | 48591 | Lancaster County, PA | 6.29 |
| 37.21 | 21449 | Lane County, OR | 6.59 |
| 24.18 | 21477 | Larimer County, CO | 8.72 |
| 60.56 | 10966 | Lebanon County, PA | 7.23 |
| 59.15 | 13390 | Lee County, AL | 9.61 |
| 47.61 | 51118 | Lee County, FL | 8.64 |
| 54.75 | 28087 | Lehigh County, PA | 8.38 |
| 57.97 | 19801 | Leon County, FL | 11.05 |
| 62.02 | 22719 | Lexington County, SC | 8.84 |
| 55.12 | 13283 | Licking County, OH | 7.21 |
| 63.31 | 18307 | Linn County, IA | 6.80 |
| 43.32 | 10146 | Linn County, OR | 6 |
| 43.13 | 9953 | Litchfield County, CT | 5.58 |
| 40.20 | 11994 | Livingston County, MI | 6.22 |
| 61.02 | 12929 | Livingston Parish, LA | 8.21 |
| 64.59 | 22154 | Lorain County, OH | 8.77 |
| 14.23 | 688901 | Los Angeles County, CA | 7.54 |
| 11.94 | 33757 | Loudoun County, VA | 6.65 |
| 73.96 | 11240 | Lowndes County, GA | 11.63 |
| 67.26 | 28358 | Lubbock County, TX | 8.81 |
| 74.43 | 34713 | Lucas County, OH | 9.97 |
| 75.48 | 22473 | Luzerne County, PA | 8.39 |
| 69.88 | 7892 | Lycoming County, PA | 7.39 |
| 58.72 | 61638 | Macomb County, MI | 9.18 |
| 81.61 | 8213 | Macon County, IL | 11.12 |
| 43.77 | 14714 | Madera County, CA | 6 |
| 53.80 | 30893 | Madison County, AL | 10.12 |
| 68.92 | 18745 | Madison County, IL | 8.81 |
| 78.21 | 9983 | Madison County, IN | 8.79 |
| 78.54 | 16277 | Mahoning County, OH | 10.81 |
| 46.74 | 25916 | Manatee County, FL | 8.09 |
| 64.36 | 10200 | Marathon County, WI | 6.25 |
| 36.11 | 354643 | Maricopa County, AZ | 7.64 |
| 4.89 | 15052 | Marin County, CA | 5.01 |
| 66.59 | 25152 | Marion County, FL | 8.72 |
| 67.02 | 98934 | Marion County, IN | 10 |
| 38.99 | 27904 | Marion County, OR | 6.49 |
| 40.77 | 8868 | Martin County, FL | 6.15 |
| 11.59 | 11878 | Maui County, HI | 7.34 |
| 45.48 | 21327 | McHenry County, IL | 6.44 |
| 63.42 | 11786 | McLean County, IL | 7.26 |
| 66.20 | 23344 | McLennan County, TX | 8.66 |
| 40.70 | 104482 | Mecklenburg County, NC | 9.65 |
| 49.06 | 11554 | Medina County, OH | 5.68 |
| 42.31 | 26538 | Merced County, CA | 6.40 |
| 42.50 | 28605 | Mercer County, NJ | 7.89 |
| 77.08 | 7124 | Mercer County, PA | 7.78 |
| 41.73 | 8984 | Merrimack County, NH | 5.77 |
| 42.80 | 10649 | Mesa County, CO | 8.12 |
| 63.70 | 8098 | Miami County, OH | 5.29 |
| 37.24 | 204515 | Miami-Dade County, FL | 8.08 |
| 39.08 | 9099 | Middlesex County, CT | 6.57 |
| 14.53 | 112506 | Middlesex County, MA | 7.06 |
| 29.18 | 59673 | Middlesex County, NJ | 8.11 |
| 48.70 | 21004 | Midland County, TX | 7.75 |
| 66.79 | 84871 | Milwaukee County, WI | 11.06 |
| 52.58 | 19552 | Minnehaha County, SD | 7.16 |
| 33.02 | 7492 | Missoula County, MT | 7.06 |
| 72.95 | 36155 | Mobile County, AL | 12.23 |
| 60.90 | 12258 | Mohave County, AZ | 7.01 |
| 20.24 | 42807 | Monmouth County, NJ | 6.92 |
| 53.66 | 7584 | Monroe County, IN | 7.39 |
| 62.39 | 10396 | Monroe County, MI | 7.02 |
| 65.30 | 52483 | Monroe County, NY | 9.38 |
| 55.79 | 9662 | Monroe County, PA | 8.58 |
| 16.90 | 38951 | Monterey County, CA | 6.57 |
| 74.34 | 21006 | Montgomery County, AL | 13.32 |
| 18.93 | 81398 | Montgomery County, MD | 7.43 |
| 72.95 | 43206 | Montgomery County, OH | 9.19 |
| 31.41 | 59114 | Montgomery County, PA | 7.42 |
| 56.86 | 25284 | Montgomery County, TN | 8.84 |
| 43.03 | 56753 | Montgomery County, TX | 7.44 |
| 71.62 | 10187 | Morgan County, AL | 8.41 |
| 19.35 | 33488 | Morris County, NJ | 6.06 |
| 24.43 | 52583 | Multnomah County, OR | 7.18 |
| 73.55 | 18953 | Muscogee County, GA | 12.06 |
| 73.29 | 13260 | Muskegon County, MI | 9.79 |
| 12.88 | 8402 | Napa County, CA | 5.36 |
| 13.81 | 96140 | Nassau County, NY | 8.10 |
| 74.73 | 8909 | Navajo County, AZ | 7.77 |
| 43.82 | 42669 | New Castle County, DE | 9.35 |
| 43.45 | 15175 | New Hanover County, NC | 8.74 |
| 48.10 | 59132 | New Haven County, CT | 8.37 |
| 46.99 | 17374 | New London County, CT | 7.54 |
| 8.92 | 103804 | New York County, NY | 8.22 |
| 62.74 | 17861 | Newport News city, VA | 10.06 |
| 71 | 13750 | Niagara County, NY | 8.62 |
| 14.99 | 49215 | Norfolk County, MA | 6.83 |
| 53.51 | 22047 | Norfolk city, VA | 11.02 |
| 50.03 | 19439 | Northampton County, PA | 7.77 |
| 68.22 | 30358 | Nueces County, TX | 8.96 |
| 43.65 | 88000 | Oakland County, MI | 8.11 |
| 35.14 | 69158 | Ocean County, NJ | 5.77 |
| 45.21 | 18428 | Okaloosa County, FL | 8.23 |
| 66.32 | 77198 | Oklahoma County, OK | 9.12 |
| 45.24 | 13733 | Olmsted County, MN | 6.69 |
| 72.81 | 16435 | Oneida County, NY | 8.61 |
| 67.47 | 33511 | Onondaga County, NY | 8.65 |
| 63.46 | 26615 | Onslow County, NC | 7.05 |
| 62.35 | 6746 | Ontario County, NY | 6.24 |
| 11.18 | 224357 | Orange County, CA | 6.80 |
| 44.12 | 114737 | Orange County, FL | 9.12 |
| 31.23 | 8057 | Orange County, NC | 7.90 |
| 36.70 | 37934 | Orange County, NY | 6.93 |
| 51.55 | 29500 | Orleans Parish, LA | 12.43 |
| 47.66 | 33136 | Osceola County, FL | 8.54 |
| 78.33 | 8126 | Oswego County, NY | 7.29 |
| 47.32 | 21833 | Ottawa County, MI | 6.22 |
| 74.81 | 13939 | Ouachita Parish, LA | 13.14 |
| 55.44 | 14935 | Outagamie County, WI | 6.17 |
| 38.83 | 103939 | Palm Beach County, FL | 8.85 |
| 40 | 12572 | Parker County, TX | 6.12 |
| 55.44 | 38238 | Pasco County, FL | 8.06 |
| 34.54 | 44279 | Passaic County, NJ | 9.01 |
| 46.14 | 12685 | Paulding County, GA | 8.90 |
| 53.49 | 9998 | Pennington County, SD | 6.77 |
| 68.96 | 8987 | Penobscot County, ME | 6.09 |
| 74.69 | 15375 | Peoria County, IL | 9.90 |
| 62.40 | 136032 | Philadelphia County, PA | 11.07 |
| 65.16 | 8567 | Pickens County, SC | 7.57 |
| 26.23 | 76617 | Pierce County, WA | 6.99 |
| 53.40 | 70658 | Pima County, AZ | 8.29 |
| 50.23 | 34632 | Pinal County, AZ | 7.09 |
| 48.07 | 52352 | Pinellas County, FL | 8.31 |
| 71.07 | 14425 | Pitt County, NC | 11.62 |
| 15.87 | 26154 | Placer County, CA | 5.53 |
| 23.13 | 36711 | Plymouth County, MA | 7.07 |
| 62.59 | 60308 | Polk County, FL | 9.18 |
| 55.05 | 45272 | Polk County, IA | 7.60 |
| 63.47 | 9502 | Portage County, OH | 7.17 |
| 52.82 | 11257 | Porter County, IN | 6.91 |
| 81.09 | 11015 | Potter County, TX | 9.43 |
| 33.05 | 78003 | Prince George's County, MD | 9.51 |
| 23.33 | 43996 | Prince William County, VA | 7.55 |
| 42.93 | 48401 | Providence County, RI | 8.25 |
| 58.04 | 12052 | Pueblo County, CO | 10.64 |
| 68.10 | 34780 | Pulaski County, AR | 11.78 |
| 16.43 | 175615 | Queens County, NY | 9.07 |
| 59.85 | 15175 | Racine County, WI | 8.77 |
| 46.60 | 47539 | Ramsey County, MN | 8.41 |
| 58.16 | 11721 | Randall County, TX | 7.58 |
| 75.29 | 10893 | Randolph County, NC | 8.40 |
| 61.45 | 12159 | Rankin County, MS | 10.38 |
| 74.64 | 11353 | Rapides Parish, LA | 11.97 |
| 56.87 | 10764 | Rensselaer County, NY | 7.52 |
| 78.32 | 9503 | Richland County, OH | 6.94 |
| 64.44 | 32209 | Richland County, SC | 11.42 |
| 76.90 | 19804 | Richmond County, GA | 12.49 |
| 15.94 | 35393 | Richmond County, NY | 8.02 |
| 49.79 | 20920 | Richmond city, VA | 10.97 |
| 28.37 | 192414 | Riverside County, CA | 7.22 |
| 93.12 | 12742 | Robeson County, NC | 11.58 |
| 64.47 | 12737 | Rock County, WI | 8.23 |
| 76.81 | 11018 | Rock Island County, IL | 8.57 |
| 25.76 | 19034 | Rockingham County, NH | 6.17 |
| 21.10 | 43343 | Rockland County, NY | 5.69 |
| 66.20 | 11123 | Rowan County, NC | 9.62 |
| 41.48 | 30611 | Rutherford County, TN | 8.60 |
| 27.87 | 127643 | Sacramento County, CA | 7.21 |
| 78.74 | 13891 | Saginaw County, MI | 9.77 |
| 62.29 | 9615 | Saline County, AR | 7.85 |
| 27.27 | 105826 | Salt Lake County, UT | 7.73 |
| 31.95 | 187306 | San Bernardino County, CA | 8.13 |
| 13.98 | 262290 | San Diego County, CA | 6.94 |
| 3.15 | 53480 | San Francisco County, CA | 7.21 |
| 28.64 | 68585 | San Joaquin County, CA | 7.71 |
| 75.94 | 9185 | San Juan County, NM | 7.20 |
| 12.69 | 16917 | San Luis Obispo County, CA | 5.98 |
| 3.18 | 53632 | San Mateo County, CA | 6.67 |
| 50.90 | 9297 | Sandoval County, NM | 8.48 |
| 69.76 | 14439 | Sangamon County, IL | 11.58 |
| 17.02 | 38130 | Santa Barbara County, CA | 6.56 |
| 5.05 | 137356 | Santa Clara County, CA | 7.29 |
| 8.58 | 15846 | Santa Cruz County, CA | 5.74 |
| 38.81 | 7907 | Santa Fe County, NM | 10.29 |
| 48.30 | 13795 | Santa Rosa County, FL | 7.79 |
| 38.87 | 20475 | Sarasota County, FL | 7.49 |
| 40.87 | 14113 | Saratoga County, NY | 6.19 |
| 45.82 | 16792 | Sarpy County, NE | 7.14 |
| 62.46 | 12448 | Schenectady County, NY | 9.05 |
| 79.37 | 8894 | Schuylkill County, PA | 7.65 |
| 61.41 | 13720 | Scott County, IA | 7.73 |
| 31.07 | 11747 | Scott County, MN | 6.25 |
| 77.36 | 11210 | Sebastian County, AR | 8.01 |
| 68.53 | 46179 | Sedgwick County, KS | 8.35 |
| 40.38 | 31709 | Seminole County, FL | 7.92 |
| 44.05 | 12870 | Shasta County, CA | 6.40 |
| 72.58 | 13889 | Shawnee County, KS | 8.03 |
| 62.28 | 8041 | Sheboygan County, WI | 6.60 |
| 45.87 | 16085 | Shelby County, AL | 8.56 |
| 66.30 | 86868 | Shelby County, TN | 11.97 |
| 28.34 | 9624 | Skagit County, WA | 5.18 |
| 63.08 | 21093 | Smith County, TX | 7.76 |
| 17.55 | 68121 | Snohomish County, WA | 6.71 |
| 20.56 | 34136 | Solano County, CA | 7.36 |
| 21.63 | 22160 | Somerset County, NJ | 7.02 |
| 14.25 | 30993 | Sonoma County, CA | 5.89 |
| 66.48 | 28675 | Spartanburg County, SC | 8.78 |
| 40.64 | 40755 | Spokane County, WA | 6.89 |
| 33.95 | 11116 | Spotsylvania County, VA | 7.35 |
| 44.15 | 29232 | St. Charles County, MO | 7.29 |
| 70.57 | 19164 | St. Clair County, IL | 11.12 |
| 64.39 | 10308 | St. Clair County, MI | 6.64 |
| 29.10 | 16651 | St. Johns County, FL | 6.88 |
| 71.71 | 22927 | St. Joseph County, IN | 8.93 |
| 83.17 | 6920 | St. Lawrence County, NY | 6.09 |
| 63.29 | 12515 | St. Louis County, MN | 6.79 |
| 53.57 | 73959 | St. Louis County, MO | 10.13 |
| 70.84 | 25311 | St. Louis city, MO | 12.45 |
| 50.80 | 23974 | St. Lucie County, FL | 9.01 |
| 31.67 | 9557 | St. Mary's County, MD | 6.37 |
| 50.75 | 20203 | St. Tammany Parish, LA | 8.56 |
| 24.63 | 12630 | Stafford County, VA | 7.75 |
| 33.77 | 49716 | Stanislaus County, CA | 6.54 |
| 71.61 | 27695 | Stark County, OH | 8.52 |
| 54.03 | 14064 | Stearns County, MN | 6.36 |
| 40.55 | 8060 | Strafford County, NH | 6.23 |
| 16.54 | 59170 | Suffolk County, MA | 8.20 |
| 19.74 | 108298 | Suffolk County, NY | 8.06 |

The first 500 of 578 rows; every row is in https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/lbw_by_deprivation.json.

#### Table 2.2. Preterm and low birthweight

Preterm birth is more common than low birthweight in every decile and rises more, from 8.8% to 12.1%

Births-weighted share of live births before 37 weeks and under 2,500 grams by decile of county deprivation, 2018 to 2024

| conf_high | conf_low | decile | estimate | measure | n |
| ---: | ---: | ---: | ---: | --- | ---: |
| 9.01 | 8.59 | 1 | 8.82 | Preterm birth | 4,151,203 |
| 7.68 | 7.18 | 1 | 7.43 | Low birthweight | 4,150,007 |
| 9.48 | 9.08 | 2 | 9.28 | Preterm birth | 2,647,866 |
| 8.00 | 7.32 | 2 | 7.63 | Low birthweight | 2,646,210 |
| 10.12 | 9.47 | 3 | 9.82 | Preterm birth | 2,523,259 |
| 8.36 | 7.55 | 3 | 7.96 | Low birthweight | 2,522,181 |
| 10.29 | 9.80 | 4 | 10.05 | Preterm birth | 1,825,582 |
| 8.67 | 7.84 | 4 | 8.29 | Low birthweight | 1,824,679 |
| 10.53 | 9.98 | 5 | 10.27 | Preterm birth | 1,923,159 |
| 8.65 | 7.97 | 5 | 8.37 | Low birthweight | 1,922,260 |
| 11.28 | 10.26 | 6 | 10.80 | Preterm birth | 2,196,492 |
| 8.98 | 8.29 | 6 | 8.69 | Low birthweight | 2,195,346 |
| 11.22 | 10.56 | 7 | 10.92 | Preterm birth | 1,520,420 |
| 9.50 | 8.57 | 7 | 9.05 | Low birthweight | 1,519,471 |
| 11.59 | 10.92 | 8 | 11.27 | Preterm birth | 1,469,109 |
| 9.85 | 8.89 | 8 | 9.40 | Low birthweight | 1,468,025 |
| 11.82 | 11.01 | 9 | 11.42 | Preterm birth | 1,269,566 |
| 10.18 | 9.00 | 9 | 9.63 | Low birthweight | 1,268,228 |
| 12.66 | 11.32 | 10 | 12.05 | Preterm birth | 891,594 |
| 10.26 | 9.20 | 10 | 9.68 | Low birthweight | 890,018 |

#### Table 2.3. All counties

Across all counties with a published rate the share is 7.6% in the least deprived tenth and 10.9% in the most deprived, and levels off in between

Births-weighted low-birthweight share by decile of county deprivation rank, 3,040 counties, County Health Rankings, births in 2016 to 2022

| conf_high | conf_low | decile | estimate | n |
| ---: | ---: | ---: | ---: | ---: |
| 7.77 | 7.41 | 1 | 7.58 | 10,018,514 |
| 8.44 | 8.00 | 2 | 8.24 | 4,922,587 |
| 8.81 | 8.36 | 3 | 8.62 | 3,381,491 |
| 9.50 | 8.74 | 4 | 9.14 | 2,597,848 |
| 9.65 | 8.70 | 5 | 9.19 | 1,741,523 |
| 9.56 | 8.80 | 6 | 9.17 | 1,216,083 |
| 9.29 | 8.56 | 7 | 8.89 | 846,209 |
| 9.43 | 8.95 | 8 | 9.17 | 752,806 |
| 10.23 | 9.53 | 9 | 9.88 | 447,237 |
| 11.30 | 10.56 | 10 | 10.93 | 327,359 |

Note: The counties are the 578 that WONDER natality names individually (at least 100,000 residents), which hold 79.5% of births. WONDER pools the births of every other county in each state, so those counties are not in this figure.

Note: A county's deprivation rank is the population-weighted mean of the Area Deprivation Index national ranks of its census tracts (1 is least deprived, 100 most), weighted by 2019-2023 tract population. Deciles hold equal numbers of these counties, so the most deprived tenth is relative to large counties and not to the nation.

Note: WONDER withholds cells of 1 to 9 births. A withheld cell is missing: it is left out of both the count and the denominator. The birthweight cut has 11,250 withheld cells in these counties; at 9 births each they could hold at most 0.5% of births, so a pooled share could move by at most that many percentage points.

Note: Decile means are births-weighted. Intervals are 95% intervals from a bootstrap over counties, described in the methods note. The rates pool births in 2018 to 2024.

Note: The third tab uses County Health Rankings rates for every county with a published rate, not the WONDER counts, and deciles of all counties, so its most deprived tenth is made up of small counties.

Source: CDC WONDER natality, births in 2018-2024, and Area Deprivation Index 2023; full counts of births, county shares weighted by births (population-weighted); third tab: County Health Rankings 2024, births in 2016-2022, births-weighted (population-weighted)

Data: https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/lbw_by_deprivation.json

### Table 3. Non-Hispanic Black mothers had low-birthweight births at 14.0% against 6.7% for non-Hispanic white mothers, a ratio of 2.1

First panel: share of live births under 2,500 grams in the large counties, by mother's race and ethnicity, County Health Rankings, births in 2016 to 2022. Second panel: infant deaths per 1,000 live births in the United States by mother's single race, any ethnicity, 2018 and 2019

| conf_high | conf_low | label | n | panel | race | value |
| ---: | ---: | --- | ---: | --- | --- | ---: |
| 14.19 | 13.75 | Black (non-Hispanic) | 3,249,387 | Low birthweight (% of live births) | Black | 13.98 |
| 7.70 | 7.39 | Hispanic (any race) | 5,632,060 | Low birthweight (% of live births) | Hispanic | 7.54 |
| 6.82 | 6.64 | White (non-Hispanic) | 9,614,256 | Low birthweight (% of live births) | White | 6.73 |
| 10.49 | 10.14 | Black (any ethnicity) | 1,255,629 | Infant mortality (deaths per 1,000 live births) | Black | 10.32 |
| 4.78 | 4.66 | White (any ethnicity) | 5,617,093 | Infant mortality (deaths per 1,000 live births) | White | 4.72 |

Note: Low birthweight comes from County Health Rankings county rates for the 578 large counties. County Health Rankings counts mothers as non-Hispanic Black, non-Hispanic white or Hispanic of any race. It publishes each rate with its interval but not the births behind it; the births are recovered from the interval, and each group's rate is pooled over the counties where the rate is published.

Note: Infant mortality is pooled over states, so it covers all counties, not only the large ones. WONDER's linked file counts mothers by single race of any ethnicity, so its white group includes Hispanic mothers and is not the same group as in the first panel. WONDER withholds a state's deaths for a race with 1 to 9 deaths; those cells leave both the deaths and the births.

Note: Low-birthweight intervals come from a bootstrap that resamples whole counties. Infant mortality intervals are gamma or normal intervals on the count, which treat births as independent and leave out differences between states.

Source: County Health Rankings 2024, births in 2016-2022 (rates pooled by births, population-weighted); CDC WONDER linked birth and infant death, 2018-2019, mother's race (crude rates)

Data: https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/birth_outcomes_by_race.json

### Table 4. Low birthweight is more common for non-Hispanic Black mothers in the least deprived quartile of counties (12.4%) than for non-Hispanic white mothers in the most deprived (7.6%)

Low birthweight (share of live births under 2,500 grams, births in 2016 to 2022) or infant mortality (deaths per 1,000 live births, 2018 and 2019) by quartile of county deprivation rank and mother's race

| conf_high | conf_low | counties | measure | n | quartile | race | rate |
| ---: | ---: | ---: | --- | ---: | --- | --- | ---: |
| 12.76 | 11.95 | 133 | lbw | 830,778 | Least deprived quartile | Black (non-Hispanic) | 12.37 |
| 6.34 | 6.05 | 144 | lbw | 3,597,717 | Least deprived quartile | White (non-Hispanic) | 6.18 |
| 14.02 | 13.46 | 137 | lbw | 862,783 | Second quartile | Black (non-Hispanic) | 13.76 |
| 6.75 | 6.53 | 145 | lbw | 2,307,987 | Second quartile | White (non-Hispanic) | 6.64 |
| 14.78 | 14.14 | 139 | lbw | 823,241 | Third quartile | Black (non-Hispanic) | 14.46 |
| 7.23 | 7.05 | 144 | lbw | 2,156,968 | Third quartile | White (non-Hispanic) | 7.14 |
| 15.80 | 15.25 | 140 | lbw | 732,585 | Most deprived quartile | Black (non-Hispanic) | 15.53 |
| 7.70 | 7.45 | 145 | lbw | 1,551,584 | Most deprived quartile | White (non-Hispanic) | 7.58 |
| 9.29 | 7.87 | 65 | imr | 229,878 | Least deprived quartile | Black (any ethnicity) | 8.55 |
| 4 | 3.54 | 95 | imr | 1,555,633 | Least deprived quartile | White (any ethnicity) | 3.76 |
| 10.73 | 9.51 | 43 | imr | 214,646 | Second quartile | Black (any ethnicity) | 10.15 |
| 4.74 | 4.19 | 58 | imr | 728,879 | Second quartile | White (any ethnicity) | 4.47 |
| 12.07 | 10.68 | 39 | imr | 200,671 | Third quartile | Black (any ethnicity) | 11.33 |
| 5.42 | 4.87 | 50 | imr | 602,598 | Third quartile | White (any ethnicity) | 5.12 |
| 13.24 | 11.54 | 33 | imr | 148,514 | Most deprived quartile | Black (any ethnicity) | 12.45 |
| 5.67 | 4.93 | 39 | imr | 338,122 | Most deprived quartile | White (any ethnicity) | 5.29 |

Views: Low birthweight (measure = lbw); Infant mortality (measure = imr).

Note: Counties are the 578 large counties, ranked by deprivation as in the gradient figure. The low-birthweight view pools County Health Rankings rates over the counties where each group's rate is published, for non-Hispanic Black and non-Hispanic white mothers; the counts of counties are in the data table.

Note: The infant mortality view covers counties of 250,000 residents or more, where WONDER lists deaths by mother's race, for births in 2018 and 2019, with mothers counted by single race of any ethnicity (the white group includes Hispanic mothers). WONDER withholds a county-year with 1 to 9 deaths. Those county-years are left out of both deaths and births, which removes 13.6% of Black births and 5.2% of white births in these counties, so the bars describe the county-years with at least 10 infant deaths.

Note: Intervals are 95% intervals from a bootstrap that resamples whole counties, described in the methods note, with the same draws for both groups so that gaps have their own interval.

Source: County Health Rankings 2024, births in 2016-2022, and CDC WONDER linked birth and infant death, 2018-2019; rates pooled by births (population-weighted)

Data: https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/weathering.json

### Table 5. Low birthweight is steadily worse toward the more deprived end under the ADI; the two ends do not differ detectably under the degree-share ranking

Births under 2,500 grams (% of live births) by tenth of counties ranked four ways, over the 578 counties that have all four rankings

| bin | conf_high | conf_low | estimate | n_units | ranking |
| ---: | ---: | ---: | ---: | ---: | --- |
| 1 | 7.68 | 7.18 | 7.43 | 57 | Area Deprivation Index |
| 2 | 8.00 | 7.32 | 7.63 | 58 | Area Deprivation Index |
| 3 | 8.47 | 7.63 | 8.07 | 58 | Area Deprivation Index |
| 4 | 8.52 | 7.74 | 8.15 | 58 | Area Deprivation Index |
| 5 | 8.63 | 7.99 | 8.36 | 58 | Area Deprivation Index |
| 6 | 9.01 | 8.29 | 8.73 | 57 | Area Deprivation Index |
| 7 | 9.50 | 8.59 | 9.04 | 58 | Area Deprivation Index |
| 8 | 9.58 | 8.68 | 9.15 | 58 | Area Deprivation Index |
| 9 | 10.36 | 9.28 | 9.86 | 58 | Area Deprivation Index |
| 10 | 10.17 | 9.14 | 9.63 | 58 | Area Deprivation Index |
| 1 | 7.32 | 6.96 | 7.16 | 57 | Standardized index |
| 2 | 7.46 | 7.03 | 7.24 | 58 | Standardized index |
| 3 | 8.42 | 7.41 | 7.92 | 58 | Standardized index |
| 4 | 8.62 | 7.91 | 8.24 | 58 | Standardized index |
| 5 | 8.54 | 7.75 | 8.17 | 58 | Standardized index |
| 6 | 8.85 | 7.89 | 8.28 | 57 | Standardized index |
| 7 | 9.15 | 8.54 | 8.86 | 58 | Standardized index |
| 8 | 9.22 | 8.64 | 8.93 | 58 | Standardized index |
| 9 | 10.15 | 8.78 | 9.42 | 58 | Standardized index |
| 10 | 10.37 | 8.98 | 9.73 | 58 | Standardized index |
| 1 | 7.32 | 6.99 | 7.16 | 57 | Median household income |
| 2 | 7.64 | 7.04 | 7.33 | 58 | Median household income |
| 3 | 8.43 | 7.65 | 8.01 | 58 | Median household income |
| 4 | 8.44 | 7.71 | 8.01 | 58 | Median household income |
| 5 | 8.70 | 8.03 | 8.40 | 58 | Median household income |
| 6 | 8.95 | 8.32 | 8.72 | 57 | Median household income |
| 7 | 8.87 | 8.26 | 8.56 | 58 | Median household income |
| 8 | 9.46 | 8.54 | 9.03 | 58 | Median household income |
| 9 | 10.49 | 9.55 | 10.08 | 58 | Median household income |
| 10 | 10.65 | 9.42 | 9.99 | 58 | Median household income |
| 1 | 7.95 | 7.22 | 7.55 | 57 | Bachelor's degree share |
| 2 | 8.43 | 7.66 | 8.04 | 58 | Bachelor's degree share |
| 3 | 8.41 | 7.56 | 7.95 | 58 | Bachelor's degree share |
| 4 | 8.60 | 7.79 | 8.26 | 58 | Bachelor's degree share |
| 5 | 8.93 | 8.01 | 8.46 | 58 | Bachelor's degree share |
| 6 | 9.15 | 7.99 | 8.43 | 57 | Bachelor's degree share |
| 7 | 9.42 | 8.48 | 8.96 | 58 | Bachelor's degree share |
| 8 | 9.39 | 8.56 | 9.01 | 58 | Bachelor's degree share |
| 9 | 9.66 | 8.12 | 8.85 | 58 | Bachelor's degree share |
| 10 | 8.58 | 7.76 | 8.16 | 58 | Bachelor's degree share |

Note: Least and most deprived tenth: ADI 7.4% and 9.6%, rank correlation +0.99; standardized index 7.2% and 9.7%, +0.99; median household income 7.2% and 10.0%, +0.96; bachelor's degree share 7.6% and 8.2%, +0.68.

Note: The standardized index applies the ADI's coefficients to its measures after standardizing each one, so home value and income carry less weight than in the ADI. Income and degree share are single census measures, each ranked from the highest value to the lowest.

Source: CDC WONDER natality, births in 2018-2024, and Area Deprivation Index 2023; full counts of births, county shares weighted by births (population-weighted); ADI 2023 and ACS 2019-2023 tract tables (B19013, B15003 and the 15 rebuilt ADI measures)

Data: https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/alt_rankings.json

## All estimates

Every fact in facts.json (79), sorted by id.

| Fact id | Value | Unit | Estimand | Population | Period | Interval | n | Weights | Source | Note |
| --- | ---: | --- | --- | --- | --- | --- | ---: | --- | --- | --- |
| alt_adi_least | 7.4% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the least deprived tenth of counties ranked by Area Deprivation Index, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 7.2% to 7.7% | 57 counties | births in each county | CDC WONDER natality |   |
| alt_adi_most | 9.6% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the most deprived tenth of counties ranked by Area Deprivation Index, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 9.1% to 10.2% | 58 counties | births in each county | CDC WONDER natality |   |
| alt_adi_rho | +0.99 | rank correlation | Spearman rank correlation between bin order and bin mean, each bin counted once | the bins of counties ranked by Area Deprivation Index | births in 2018 to 2024 |   |   | each bin counted once | CDC WONDER natality |   |
| alt_degree_least | 7.6% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the least deprived tenth of counties ranked by Bachelor's degree share, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 7.2% to 7.9% | 57 counties | births in each county | CDC WONDER natality |   |
| alt_degree_most | 8.2% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the most deprived tenth of counties ranked by Bachelor's degree share, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 7.8% to 8.6% | 58 counties | births in each county | CDC WONDER natality |   |
| alt_degree_rho | +0.68 | rank correlation | Spearman rank correlation between bin order and bin mean, each bin counted once | the bins of counties ranked by Bachelor's degree share | births in 2018 to 2024 |   |   | each bin counted once | CDC WONDER natality |   |
| alt_income_least | 7.2% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the least deprived tenth of counties ranked by Median household income, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 7.0% to 7.3% | 57 counties | births in each county | CDC WONDER natality |   |
| alt_income_most | 10.0% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the most deprived tenth of counties ranked by Median household income, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 9.4% to 10.6% | 58 counties | births in each county | CDC WONDER natality |   |
| alt_income_rho | +0.96 | rank correlation | Spearman rank correlation between bin order and bin mean, each bin counted once | the bins of counties ranked by Median household income | births in 2018 to 2024 |   |   | each bin counted once | CDC WONDER natality |   |
| alt_n_places | 578 | counties | count of counties in the common sample of the ranking comparison | counties with the outcome and all four rankings | births in 2018 to 2024 |   |   | none (full count) | CDC WONDER natality |   |
| alt_rho_cut | 0.9 | absolute rank correlation | threshold between a steady and an uneven gradient | the rule for changed gradients (docs/RANKING_SWITCH_RULE.md) | births in 2018 to 2024 |   |   | none (a fixed rule) | CDC WONDER natality |   |
| alt_rho_margin | 0.1 | absolute rank correlation | smallest move in the rank correlation that counts as a change of shape | the rule for changed gradients (docs/RANKING_SWITCH_RULE.md) | births in 2018 to 2024 |   |   | none (a fixed rule) | CDC WONDER natality |   |
| alt_std_least | 7.2% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the least deprived tenth of counties ranked by Standardized index, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 7.0% to 7.3% | 57 counties | births in each county | CDC WONDER natality |   |
| alt_std_most | 9.7% | Births under 2,500 grams (% of live births) | births-weighted mean of county shares (the pooled share); 95% intervals from a bootstrap over counties | the most deprived tenth of counties ranked by Standardized index, among the counties with all four rankings | births in 2018 to 2024 | 95% CI 9.0% to 10.4% | 58 counties | births in each county | CDC WONDER natality |   |
| alt_std_rho | +0.99 | rank correlation | Spearman rank correlation between bin order and bin mean, each bin counted once | the bins of counties ranked by Standardized index | births in 2018 to 2024 |   |   | each bin counted once | CDC WONDER natality |   |
| births_2018 | 3.79 million | live births | count of live births | live births in the United States | 2018 |   |   | none (full count) | CDC WONDER natality |   |
| births_2024 | 3.63 million | live births | count of live births | live births in the United States | 2024 |   |   | none (full count) | CDC WONDER natality |   |
| births_covered | 20.5 million | live births | count of live births | live births in the counties that CDC WONDER natality names individually (counties of 100,000 residents or more) | births in 2018 to 2024 |   | 20,450,654 births | none (full count) | CDC WONDER natality |   |
| births_decline_pct | 4.3% | percent of 2018 births | fall in the count of births from 2018 to 2024 | live births in the United States | 2018 to 2024 |   |   | none (full count) | CDC WONDER natality |   |
| births_median_all_d1 | 12,055 | live births | median of the county birth counts behind the rate (CHR denominator) | the median county in the least deprived tenth of all counties with a County Health Rankings rate and an ADI | births in 2016 to 2022 |   | 304 counties | none (each county counts once) | County Health Rankings |   |
| births_median_all_d10 | 770 | live births | median of the county birth counts behind the rate (CHR denominator) | the median county in the most deprived tenth of all counties with a County Health Rankings rate and an ADI | births in 2016 to 2022 |   | 304 counties | none (each county counts once) | County Health Rankings |   |
| births_share_covered | 79.5% | percent of live births in the United States | share of births in the named counties | live births in the 50 states and DC | births in 2018 to 2024 |   | 25,709,900 births | none (full count) | CDC WONDER natality |   |
| chr_wonder_corr | 0.984 | correlation coefficient | births-weighted correlation between the county low-birthweight share in WONDER and in County Health Rankings, county-cluster bootstrap interval | the named counties | WONDER births in 2018 to 2024 and CHR births in 2016 to 2022 | 95% CI 0.980 to 0.987 | 578 counties | WONDER births in each county | County Health Rankings |   |
| counties_all_gradient | 3,040 | counties | count of counties | counties with a County Health Rankings low-birthweight rate and an ADI | births in 2016 to 2022 |   |   | none (full count) | County Health Rankings |   |
| counties_below_floor | 145 | counties | count of counties with a rate but fewer than 20 low-birthweight births, which the map leaves as no data | counties and county equivalents on the map (50 states and DC) | births in 2016 to 2022 |   |   | none (full count) | County Health Rankings |   |
| counties_mapped | 2,900 | counties | count of counties with a rate and at least 20 low-birthweight births | counties and county equivalents on the map (50 states and DC) | births in 2016 to 2022 |   |   | none (full count) | County Health Rankings |   |
| counties_no_rate | 98 | counties | count of counties for which County Health Rankings publishes no low-birthweight rate | counties and county equivalents on the map (50 states and DC) | births in 2016 to 2022 |   |   | none (full count) | County Health Rankings |   |
| counties_not_mapped | 243 | counties | count of counties with no rate or fewer than 20 low-birthweight births | counties and county equivalents on the map (50 states and DC) | births in 2016 to 2022 |   |   | none (full count) | County Health Rankings |   |
| counties_used | 578 | counties | count of counties | counties that WONDER natality names individually and that have births | births in 2018 to 2024 |   |   | none (full count) | CDC WONDER natality |   |
| deep_south_all_share | 14% | percent of counties | share of mapped counties in Alabama, Georgia, Louisiana, Mississippi and South Carolina | all mapped counties | births in 2016 to 2022 |   | 2,900 counties | none (each county counts once) | County Health Rankings |   |
| deep_south_top_share | 64% | percent of counties | share of those counties in Alabama, Georgia, Louisiana, Mississippi and South Carolina | mapped counties in the highest tenth of low-birthweight shares | births in 2016 to 2022 |   | 290 counties | none (each county counts once) | County Health Rankings |   |
| im_black | 10.3 deaths per 1,000 live births | infant deaths per 1,000 live births | crude rate from state counts, normal interval | live births to Black mothers (single race, any ethnicity) in the United States, states with a published count | births in 2018 and 2019 | 95% CI 10.10 to 10.50 | 12,953 infant deaths | none (crude rate from counts) | CDC WONDER linked birth and infant death |   |
| im_left_out_black | 13.6% | percent of live births | share of births in county-years whose deaths WONDER withholds, which the county view leaves out of deaths and births | live births to black mothers (single race, any ethnicity) in counties of 250,000 residents or more | births in 2018 and 2019 |   |   | none (full counts of births) | CDC WONDER linked birth and infant death |   |
| im_left_out_white | 5.2% | percent of live births | share of births in county-years whose deaths WONDER withholds, which the county view leaves out of deaths and births | live births to white mothers (single race, any ethnicity) in counties of 250,000 residents or more | births in 2018 and 2019 |   |   | none (full counts of births) | CDC WONDER linked birth and infant death |   |
| im_white | 4.7 | infant deaths per 1,000 live births | crude rate from state counts, normal interval | live births to white mothers (single race, any ethnicity) in the United States, states with a published count | births in 2018 and 2019 | 95% CI 4.7 to 4.8 | 26,516 infant deaths | none (crude rate from counts) | CDC WONDER linked birth and infant death |   |
| lbd_county_threshold | 250,000 | residents | population size at which WONDER names a county in the linked file (a definition) | counties that CDC WONDER linked birth and infant death names individually | births in 2018 and 2019 |   |   | none (definition) | CDC WONDER linked birth and infant death |   |
| lbw_adi_corr | 0.52 | correlation coefficient | births-weighted correlation of the county low-birthweight share with county deprivation rank, county-cluster bootstrap interval | live births in the named counties | births in 2018 to 2024 | 95% CI 0.45 to 0.58 | 578 counties | births in each county | CDC WONDER natality |   |
| lbw_all_d1 | 7.6% | percent of live births under 2,500 grams | births-weighted mean of county shares, bootstrap interval over counties | live births in the least deprived tenth of all counties with a County Health Rankings rate and an ADI | births in 2016 to 2022 | 95% CI 7.4% to 7.8% | 304 counties | births in each county (CHR denominator) | County Health Rankings |   |
| lbw_all_d10 | 10.9% | percent of live births under 2,500 grams | births-weighted mean of county shares, bootstrap interval over counties | live births in the most deprived tenth of all counties with a County Health Rankings rate and an ADI | births in 2016 to 2022 | 95% CI 10.6% to 11.3% | 304 counties | births in each county (CHR denominator) | County Health Rankings |   |
| lbw_b1_w4_diff | 4.8 percentage points | percentage points | difference of two pooled low-birthweight shares, county-cluster bootstrap interval | live births in the named counties: non-Hispanic Black mothers in the least deprived quartile minus non-Hispanic white mothers in the most deprived quartile | births in 2016 to 2022 | 95% CI 4.4 percentage points to 5.2 percentage points | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_black | 14.0% | percent of live births under 2,500 grams | pooled share of births, from the published county rates and the births they imply, bootstrap interval over counties | live births to non-Hispanic Black mothers in the named counties where County Health Rankings publishes the rate | births in 2016 to 2022 | 95% CI 13.8% to 14.2% | 549 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_black_q1 | 12.4% | percent of live births under 2,500 grams | pooled share of births, bootstrap interval over counties | live births to non-Hispanic Black mothers in the least deprived quartile of the named counties by deprivation rank | births in 2016 to 2022 | 95% CI 11.9% to 12.8% | 133 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_cutoff_grams | 2,500 | grams | birthweight below which a live birth counts as low birthweight (a definition) | live births | all years (a definition) |   |   | none (definition) | CDC WONDER natality |   |
| lbw_d1 | 7.4% | percent of live births under 2,500 grams | births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the least deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 95% CI 7.2% to 7.7% | 57 counties | births in each county | CDC WONDER natality |   |
| lbw_d10 | 9.7% | percent of live births under 2,500 grams | births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the most deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 95% CI 9.2% to 10.3% | 58 counties | births in each county | CDC WONDER natality |   |
| lbw_gap_pp | 7.3 percentage points | percentage points | difference of pooled low-birthweight shares, county-cluster bootstrap interval | live births in the named counties, non-Hispanic Black mothers minus non-Hispanic white mothers | births in 2016 to 2022 | 95% CI 7.0 percentage points to 7.4 percentage points | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_gap_q1 | 6.2 percentage points | percentage points | difference of pooled low-birthweight shares, county-cluster bootstrap interval | live births in the least deprived quartile of the named counties, non-Hispanic Black mothers minus non-Hispanic white mothers | births in 2016 to 2022 | 95% CI 5.8 percentage points to 6.6 percentage points | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_gap_q4 | 8.0 percentage points | percentage points | difference of pooled low-birthweight shares, county-cluster bootstrap interval | live births in the most deprived quartile of the named counties, non-Hispanic Black mothers minus non-Hispanic white mothers | births in 2016 to 2022 | 95% CI 7.7 percentage points to 8.2 percentage points | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_gap_widening | 1.8 percentage points | percentage points | difference of two Black-white gaps in pooled low-birthweight shares, county-cluster bootstrap interval | live births in the named counties, gap between non-Hispanic Black and non-Hispanic white mothers in the most deprived quartile minus the gap in the least deprived quartile | births in 2016 to 2022 | 95% CI 1.3 percentage points to 2.3 percentage points | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_hispanic | 7.5% | percent of live births under 2,500 grams | pooled share of births, from the published county rates and the births they imply, bootstrap interval over counties | live births to Hispanic (any race) mothers in the named counties where County Health Rankings publishes the rate | births in 2016 to 2022 | 95% CI 7.4% to 7.7% | 575 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_natl | 8.3% | percent of live births under 2,500 grams | pooled share of births in published cells (events over births), Wilson interval | live births in the counties that CDC WONDER natality names individually (counties of 100,000 residents or more), births in published cells | births in 2018 to 2024 | 95% CI 8.3% to 8.3% | 20,406,425 births | none (full counts of births) | CDC WONDER natality | WONDER withholds cells of 1 to 9 births; a withheld cell is left out of both the count and the denominator. |
| lbw_preterm_corr | 0.84 | correlation coefficient | births-weighted correlation between the county low-birthweight share and the county preterm share, county-cluster bootstrap interval | live births in the named counties | births in 2018 to 2024 | 95% CI 0.81 to 0.87 | 578 counties | births in each county | CDC WONDER natality |   |
| lbw_range_pp | 2.3 percentage points | percentage points | difference of two births-weighted decile means of the low-birthweight share, bootstrap interval over counties | live births in the named counties, most deprived tenth minus least deprived tenth | births in 2018 to 2024 | 95% CI 1.7 percentage points to 2.9 percentage points | 578 counties | births in each county | CDC WONDER natality |   |
| lbw_ratio_bw | 2.1 | ratio of rates | ratio of pooled low-birthweight shares, county-cluster bootstrap interval | live births in the named counties, non-Hispanic Black mothers over non-Hispanic white mothers | births in 2016 to 2022 | 95% CI 2.0 to 2.1 | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_rest | 7.8% | percent of live births under 2,500 grams | pooled share of births, bootstrap interval over counties | live births in mapped counties outside the Census South region | births in 2016 to 2022 | 95% CI 7.7% to 8.0% | 1,529 counties | births in each county (CHR denominator) | County Health Rankings |   |
| lbw_slope_per_10 | 0.38 percentage points | percentage points of the low-birthweight share per ten points of county deprivation rank | births-weighted slope of the county low-birthweight share on county deprivation rank, county-cluster bootstrap interval | live births in the named counties | births in 2018 to 2024 | 95% CI 0.32 percentage points to 0.44 percentage points | 578 counties | births in each county | CDC WONDER natality |   |
| lbw_south | 9.1% | percent of live births under 2,500 grams | pooled share of births, bootstrap interval over counties | live births in mapped counties in the Census South region (16 states and DC) | births in 2016 to 2022 | 95% CI 9.0% to 9.3% | 1,371 counties | births in each county (CHR denominator) | County Health Rankings |   |
| lbw_wh_low_d1 | 1.0 | withheld cells per county-year | withheld birthweight cells below the low-birthweight cutoff, per county-year | county-years of the least deprived tenth of the named counties | births in 2018 to 2024 |   | 399 county-years | none (full count) | CDC WONDER natality |   |
| lbw_wh_low_d10 | 1.6 | withheld cells per county-year | withheld birthweight cells below the low-birthweight cutoff, per county-year | county-years of the most deprived tenth of the named counties | births in 2018 to 2024 |   | 406 county-years | none (full count) | CDC WONDER natality |   |
| lbw_wh_max_d1 | 0.2% | percent of live births | largest share of births the withheld birthweight cells could hold: 9 births in each withheld cell, over all births in these counties | live births in the least deprived tenth of the named counties | births in 2018 to 2024 |   |   | none (an upper limit from the cell count) | CDC WONDER natality |   |
| lbw_wh_max_d10 | 1.3% | percent of live births | largest share of births the withheld birthweight cells could hold: 9 births in each withheld cell, over all births in these counties | live births in the most deprived tenth of the named counties | births in 2018 to 2024 |   |   | none (an upper limit from the cell count) | CDC WONDER natality |   |
| lbw_white | 6.7% | percent of live births under 2,500 grams | pooled share of births, from the published county rates and the births they imply, bootstrap interval over counties | live births to non-Hispanic white mothers in the named counties where County Health Rankings publishes the rate | births in 2016 to 2022 | 95% CI 6.6% to 6.8% | 578 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_white_q4 | 7.6% | percent of live births under 2,500 grams | pooled share of births, bootstrap interval over counties | live births to non-Hispanic white mothers in the most deprived quartile of the named counties by deprivation rank | births in 2016 to 2022 | 95% CI 7.5% to 7.7% | 145 counties | births to mothers of the group in each county | County Health Rankings |   |
| lbw_withheld_cells | 11,250 | withheld cells | count of cells with 1 to 9 births, which WONDER withholds | county-year cells of the WONDER birthweight cut in the named counties | births in 2018 to 2024 |   |   | none (full count) | CDC WONDER natality |   |
| lbw_withheld_max_share | 0.5% | percent of live births | largest share of births the withheld birthweight cells could hold: 9 births in each withheld cell, over all births in the counties | live births in the named counties | births in 2018 to 2024 |   |   | none (an upper limit from the cell count) | CDC WONDER natality |   |
| map_floor_births | 20 | low-birthweight births | fewest low-birthweight births for which a county is drawn (a floor set for this book) | counties on the map | births in 2016 to 2022 |   |   | none (definition) | County Health Rankings |   |
| preterm_adi_corr | 0.69 | correlation coefficient | births-weighted correlation of the county preterm share with county deprivation rank, county-cluster bootstrap interval | live births in the named counties | births in 2018 to 2024 | 95% CI 0.64 to 0.73 | 578 counties | births in each county | CDC WONDER natality |   |
| preterm_cutoff_weeks | 37 | completed weeks of gestation | gestational age below which a live birth counts as preterm, obstetric estimate (a definition) | live births | all years (a definition) |   |   | none (definition) | CDC WONDER natality |   |
| preterm_hi | 12.1% | percent of live births before 37 weeks | births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the most deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 95% CI 11.3% to 12.7% | 58 counties | births in each county | CDC WONDER natality |   |
| preterm_lo | 8.8% | percent of live births before 37 weeks | births-weighted mean of county shares (the pooled share), bootstrap interval over counties | live births in the least deprived tenth of the named counties by county deprivation rank | births in 2018 to 2024 | 95% CI 8.6% to 9.0% | 57 counties | births in each county | CDC WONDER natality |   |
| preterm_natl | 10.1% | percent of live births before 37 weeks | pooled share of births in published cells (events over births), Wilson interval | live births in the counties that CDC WONDER natality names individually (counties of 100,000 residents or more), births in published cells | births in 2018 to 2024 | 95% CI 10.1% to 10.1% | 20,418,250 births | none (full counts of births) | CDC WONDER natality |   |
| preterm_range_pp | 3.2 percentage points | percentage points | difference of two births-weighted decile means of the preterm share, bootstrap interval over counties | live births in the named counties, most deprived tenth minus least deprived tenth | births in 2018 to 2024 | 95% CI 2.5 percentage points to 3.9 percentage points | 578 counties | births in each county | CDC WONDER natality |   |
| preterm_slope_per_10 | 0.49 percentage points | percentage points of the preterm share per ten points of county deprivation rank | births-weighted slope of the county preterm share on county deprivation rank, county-cluster bootstrap interval | live births in the named counties | births in 2018 to 2024 | 95% CI 0.44 percentage points to 0.55 percentage points | 578 counties | births in each county | CDC WONDER natality |   |
| preterm_withheld_cells | 8,704 | withheld cells | count of cells with 1 to 9 births, which WONDER withholds | county-year cells of the WONDER gestational-age cut in the named counties | births in 2018 to 2024 |   |   | none (full count) | CDC WONDER natality |   |
| preterm_withheld_max_share | 0.4% | percent of live births | largest share of births the withheld gestational-age cells could hold: 9 births in each withheld cell, over all births in the counties | live births in the named counties | births in 2018 to 2024 |   |   | none (an upper limit from the cell count) | CDC WONDER natality |   |
| south_all_share | 47% | percent of counties | share of mapped counties in the Census South region (16 states and DC) | all mapped counties | births in 2016 to 2022 |   | 2,900 counties | none (each county counts once) | County Health Rankings |   |
| south_top_share | 89% | percent of counties | share of those counties in the Census South region (16 states and DC) | mapped counties in the highest tenth of low-birthweight shares | births in 2016 to 2022 |   | 290 counties | none (each county counts once) | County Health Rankings |   |
| wonder_suppression_floor | 10 | births | a cell with fewer births than this is withheld (a definition) | cells of the WONDER natality files | births in 2018 to 2024 |   |   | none (definition) | CDC WONDER natality |   |
| wonder_threshold | 100,000 | residents | population size at which WONDER names a county (a definition) | counties that CDC WONDER natality names individually | births in 2018 to 2024 |   |   | none (definition) | CDC WONDER natality |   |

## Definitions

- Low birthweight: A live birth weighing less than 2,500 grams, the cutoff used in vital statistics.
- Preterm birth: A birth before 37 weeks of pregnancy.
- Area Deprivation Index (ADI): A score that ranks neighborhoods by income, education, employment and housing conditions drawn from the American Community Survey. In this book a higher national percentile means a more deprived neighborhood.
- Suppression: The withholding of a number because the count behind it is too small to publish without risking the identification of a person. A suppressed value is missing, not zero.
- Weathering: The hypothesis that chronic stress from disadvantage and discrimination wears down health faster, so that the same outcomes appear at younger ages in affected groups. Arline Geronimus proposed it in studies of birth outcomes.
- EITC: The Earned Income Tax Credit, a refundable federal tax credit for low and moderate income workers. It grows with earnings up to a limit and then phases out.

## Methods

The county deprivation comparison uses births in 2018 to 2024 in the 578 counties that CDC WONDER names individually, the counties of 100,000 residents or more, which hold 79.5% of births. Smaller counties are not part of the WONDER figures. The comparisons by mother's race use other sources and birth periods: County Health Rankings rates for births in 2016 to 2022, and WONDER linked birth and infant death records for 2018 and 2019. Coverage and denominators differ across these sources, so each is described separately below.

### The county comparison uses only counties that WONDER names

Low birthweight is a live birth under 2,500 grams and preterm birth one before 37 completed weeks (obstetric estimate).

Counts come from CDC WONDER natality for 2018 to 2024, for the 578 counties that WONDER names individually (counties of 100,000 residents or more, 79.5% of births); WONDER pools every other county in a state into one Unidentified Counties row, which is not used. WONDER's 2024 natality data are final.

Explains Table 2.

### Rates by mother's race and the map use other sources and birth periods

Race-specific low birthweight comes from County Health Rankings county rates for births in 2016 to 2022, for non-Hispanic Black, non-Hispanic white and Hispanic (any race) mothers: CHR publishes each rate with a 95% interval, from which the births behind the rate are recovered, and the rates are pooled by births over the counties where a rate is published.

Infant mortality by mother's race comes from WONDER linked birth and infant death for 2018 and 2019, the only years with mother's race, with mothers counted by single race of any ethnicity: national rates pool the state files, and the county view covers counties of 250,000 residents or more, with county-years that have withheld deaths left out of both deaths and births.

The map shows County Health Rankings rates for every county with at least 20 low-birthweight births.

Explains Table 4, Table 3 and Table 1.

### How counties are ranked and grouped

A county's deprivation rank is the population-weighted mean of the Area Deprivation Index national ranks of its census tracts, weighted by 2019-2023 tract population; Connecticut tracts are assigned to the eight legacy counties.

Deciles and quartiles hold equal numbers of the 578 large counties. Decile means are births-weighted means of county shares.

Explains Table 2 and Table 4.

### How uncertainty is measured

Pooled WONDER shares carry Wilson score intervals on the counts. Decile and quartile means, pooled rates by mother's race, differences, ratios, correlations and slopes carry 95% percentile intervals from a bootstrap that resamples whole counties (2,000 draws, seed 12345); gaps between groups use the same draws for both groups.

### Withheld cells are excluded and never reconstructed

WONDER withholds cells of 1 to 9 births. A withheld cell is missing: it leaves both the count below the cutoff and the denominator, which is the births in the published cells, and nothing is derived from the county totals.

The birthweight cut has 11,250 withheld cells in these counties and the gestational-age cut 8,704; at 9 births each they could hold at most 0.5% and 0.4% of births.

### Four rankings of the same places

The comparison follows the book's ranking switch rule, fixed on October 28, 2026 before any of these gradients was computed. The main gradient of the chapter is recomputed under four rankings of the same places, with the outcome, period, weights, bins and intervals unchanged: the published ADI; a standardized index that applies the ADI's coefficients to its measures after standardizing each one across tracts; median household income; and the share of adults 25 and older with a bachelor's degree or more. The three alternatives come from ACS tract estimates for 2019 to 2023, the period of the ADI.

All four rankings use the same places, the 578 counties that have the outcome and all four rankings, so the ADI panel can differ slightly from the chapter's main figure. Tracts enter only if they have every input of the standardized index, which leaves out tracts without a median rent, home value or mortgage cost. A county takes the population-weighted mean of the ranks of its common-sample tracts, weighted by 2019-2023 tract population, with Connecticut tracts assigned to the eight legacy counties that WONDER uses.

A pattern counts as changed when the worse end switches sides, when the two ends differ under one ranking and not the other, or when the rank correlation of bin order and bin mean falls on the other side of 0.9 and moves by at least 0.1.

Explains Table 5.

## What these data cannot show

The county figures describe places, and a county rate is an average over the births in it. A county ranked as least deprived can hold deprived neighborhoods, and the Area Deprivation Index understates deprivation in high-cost cities (Chapter 1.1, What the deprivation index measures (https://vishalsingh.org/zipcode/anatomy-of-deprivation)). A pattern across counties does not describe the babies in any one of them.

The WONDER figures cover only counties of 100,000 or more residents, which hold 79.5% of births. Births in smaller counties are pooled by WONDER and cannot be placed on the deprivation scale, and the check against County Health Rankings shows that the pattern is less regular across all counties than across large ones.

The sources differ in period and definition. The deprivation index draws on the American Community Survey for 2019 to 2023, while the births span 2018 to 2024. Rates by mother's race come from County Health Rankings for births in 2016 to 2022, for non-Hispanic Black, non-Hispanic white and Hispanic mothers, and they are pooled over the counties where a rate is published. Infant mortality by mother's race comes from 2018 and 2019 only, for single races of any ethnicity, so its white group includes Hispanic mothers and is not the group in the low-birthweight rates. The infant mortality county view covers only larger counties and leaves out county-years in which WONDER withholds the deaths, which are 13.6% of Black births and 5.2% of white births there. The intervals leave out those differences in source, period and group.

Finally, none of these comparisons identifies a mechanism. Deprivation and race are tied to smoking, prenatal care, chronic stress, pollution, maternal age and education, and to the quality of the nearest hospital. Birthweight is associated with all of them. The two policy studies above are the only evidence in this chapter on what changes the numbers, and they speak to income and coverage, not to the rest. A county map cannot settle the mechanism behind any one county's rate or behind the gap between groups.

## References

1. Almond, Douglas; Currie, Janet (2011). Killing Me Softly: The Fetal Origins Hypothesis. Journal of Economic Perspectives 25(3): 153-172. https://doi.org/10.1257/jep.25.3.153
2. Aizer, Anna; Currie, Janet (2014). The Intergenerational Transmission of Inequality: Maternal Disadvantage and Health at Birth. Science 344(6186): 856-861. https://doi.org/10.1126/science.1251872
3. Kind, Amy J. H.; Buckingham, William R. (2018). Making Neighborhood-Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine 378(26): 2456-2458. https://doi.org/10.1056/NEJMp1802313
4. David, Richard J.; Collins, James W., Jr. (1997). Differing Birth Weight among Infants of U.S.-Born Blacks, African-Born Blacks, and U.S.-Born Whites. New England Journal of Medicine 337(17): 1209-1214. https://doi.org/10.1056/NEJM199710233371706
5. Geronimus, Arline T. (1992). The Weathering Hypothesis and the Health of African-American Women and Infants: Evidence and Speculations. Ethnicity & Disease 2(3): 207-221.
6. Hoynes, Hilary; Miller, Doug; Simon, David (2015). Income, the Earned Income Tax Credit, and Infant Health. American Economic Journal: Economic Policy 7(1): 172-211. https://doi.org/10.1257/pol.20120179
7. Currie, Janet; Gruber, Jonathan (1996). Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women. Journal of Political Economy 104(6): 1263-1296. https://doi.org/10.1086/262059

## Provenance

- Pipeline version: 0.1.0
- Build script: book2/chapters/birthweight-and-preterm-birth/build.py (sha256 15908823ab9d370445413480d8ba74adfabfeb7a1e1ba498f748384d128e0e4e)
- Built on: 2026-10-07
- Site commit: c2b1ff3309cb4a10833f884da91ac0057a66ad0a

Inputs:

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| --- | ---: | --- | --- |
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| data/staged/births/wonder_natality_county_gestational_age_2018_2024.parquet | 104,715 | 4e24d1ed2292840cbf08b8939b316ae7ecb029187bfbfc9390da95721a484dd4 | 4e24d1ed2292840cbf08b8939b316ae7ecb029187bfbfc9390da95721a484dd4 |
| data/staged/births/wonder_natality_county_total_2018_2024.parquet | 41,774 | e85d351fa72c022a05415d332185113b6badcaff1119da1d6f45c76ffe16d86d | e85d351fa72c022a05415d332185113b6badcaff1119da1d6f45c76ffe16d86d |
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Outputs:

| File | SHA-256 |
| --- | --- |
| facts.json | 9332a0015b74d0cb3babb0d72c1b75129ee521b3719bb6e2d1abf87321e9308a |
| figures/alt_rankings.json | 2b4facd8450b8f46d33c77517e55ab89b6e87e3383c8cbbc365b108150ce1206 |
| figures/birth_outcomes_by_race.json | 31c49d004799b6689056601578601d1a566275ad0addd032f1f6ceb22e229076 |
| figures/lbw_by_deprivation.json | d98d50597d29c7dc3a12ce69ff8d5abf68a205051a62824ffb30791b0c827796 |
| figures/lbw_map.json | df63b4e72329eb8b87443c32a0fd49aeaea240377fa620c7fb8f0e104b73493b |
| figures/weathering.json | 02bc86d58e45065993f13cbe2fd4e4aec07d9cc051d2ee4e82ddf44712b50d06 |
| meta.json | a41f90bf2fceb47a0878676b734993cb0fdbd657183877356b12098162593b23 |
| public/data/birthweight-and-preterm-birth/lbw_map.json | bd8bbce4271837be5dd0fa8d06df21722f86b1ad86e0ee4e17eafd7426199912 |
| refs.json | 257338ba1458f99dd66dd41995198798df5660ed8da0bd35a2966fb23ad0e8b3 |

## Machine formats

- Report (HTML): https://vishalsingh.org/zipcode/birthweight-and-preterm-birth/report
- Report (Markdown): https://vishalsingh.org/zipcode/birthweight-and-preterm-birth/report.md
- Every estimate (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/facts.json
- Table 1, figure "lbw_map" (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/lbw_map.json
- Table 2, figure "lbw_by_deprivation" (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/lbw_by_deprivation.json
- Table 3, figure "birth_outcomes_by_race" (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/birth_outcomes_by_race.json
- Table 4, figure "weathering" (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/weathering.json
- Table 5, figure "alt_rankings" (JSON): https://vishalsingh.org/zipcode/report/birthweight-and-preterm-birth/figures/alt_rankings.json
- Payload of "lbw_map" (JSON): https://vishalsingh.org/zipcode/data/birthweight-and-preterm-birth/lbw_map.json
- Index of every report: https://vishalsingh.org/zipcode/llms.txt; all reports in one file: https://vishalsingh.org/zipcode/llms-full.txt