Chapter 1.4 · Before the first breath

Smoking in pregnancy

In the 530 large counties with a usable count, the share of births to mothers who reported smoking fell from 5.4% in 2018 to 1.9% in 2024, and within a state it is 3.9 points lower per doubling of county income when counties are weighted by births.

Author: Vishal Singh
Affiliation: NYU Stern
Published: 9 October 2026

Among births in the 530 large counties that CDC WONDER names and that publish a smoking count in every year, the share to mothers who reported smoking fell from 5.4% (95% CI 4.9% to 5.9%) in 2018 to 1.9% (95% CI 1.7% to 2.1%) in 2024, a fall of 64.6% (95% CI 63.5% to 65.7%) of the 2018 share. The share was higher in counties with lower median household income in every year, and in 2018 the lowest-income third of counties exceeded the highest-income third by 4.6 (95% CI 3.6 to 5.6) points. Within a state, each doubling of county median household income goes with a smoking share 3.9 (95% CI 2.7 to 5.5) points lower when counties are weighted by their births and 6.6 (95% CI 5.1 to 8.2) points lower when each county counts once.

Whether that slope is the same in every state depends on the weights and on the scale. Each of the 26 states with enough large counties for a slope of its own has an estimated slope below zero, but the intervals are wide and only 10 lie wholly below zero when counties are weighted by births. Only one of four tests of equal slopes rejects, the test in points when each county counts once (p = 0.009). It does not reject in points when counties are weighted by births (p = 0.108) or in proportion to a state's own smoking share (p = 0.664 weighted and 0.343 unweighted). A rejection in one scale and none in another does not show that the scales differ in heterogeneity, and the proportional tests have little power. The steeper slopes in points belong to states where more mothers smoke.

WONDER names a county only if it has 100,000 residents or more, and every county number in this chapter describes a county of that size. The 578 counties named in every year hold 79.5% of the births in the United States from 2018 to 2024, and the 530 counties used here hold 66.9%. The smaller counties are pooled by WONDER and cannot be placed on an income scale. Everything below describes counties and groups of births, not mothers. Smoking among adults is the subject of Chapter 5.1, Health behaviors and the supply of care for neighborhoods and of Chapter 5.2, Health behaviors, person by person for people, and the weight of the babies born to these mothers is the subject of Chapter 1.1, Birthweight and preterm birth.

What the tobacco item counts

The birth certificate records whether the mother used tobacco during pregnancy, and WONDER reports the answer as Yes, No, Unknown or Not Stated, or Not Reported. This chapter writes smoking for a Yes answer, although the item is named tobacco use and the documentation that comes with the data does not say which products count. A county's smoking share is its Yes births over its births with a known answer, Yes plus No, so births with an Unknown or Not Stated answer are left out of the denominator. The Unknown cells that WONDER publishes hold 0.4% of births, and WONDER withholds 1,794 of the 3,710 Unknown cells because each holds fewer than 10 births, so together they could hold at most 0.1%, the most that the left-out births could add. Not Reported, the code for reporting areas that did not collect the item, is a published zero in all 4,046 county-years. The counties used record 17.1 million births with a known answer from 2018 to 2024, and 632,733 of them are to mothers who reported smoking.

California is left out. WONDER's documentation says that every reporting area except California routinely collects the item. The California counties report a share of 1.2% for 2018, against 5.4% in the counties kept and below the share in every state kept, so the item in California cannot be compared with the item elsewhere, and the 35 California counties are in no estimate.

The smoking count is withheld whenever a county has fewer than 10 smoking births in a year, which happens in low-smoking counties. A withheld cell is missing, never zero, and nothing here is derived from a county total minus the published cells. The named counties have 68 withheld Yes cells, 33 of them in California. The other 35 sit in 13 counties, and those counties are left out of every estimate. Pooling a county over only the years in which its count is published would drop its lowest years and raise its share, so a county stays only if its count is published in every year from 2018 to 2024. The withheld cells could hold at most 315 births, or 0.05% of the smoking births in the counties kept, so they change who is in the sample and not the totals. Virginia lost 4 of its 15 large counties (Arlington, Fairfax, Loudoun and Alexandria), and 9 more counties in Florida, Georgia, Illinois, New York, Tennessee and Texas are out. The rule selects on the outcome, and the counties it removes are affluent. Of the 13 counties, 11 are above the cut for the highest-income third of the counties kept, and their median income is $109,358 against $72,077 for the counties kept. If each of the 29 withheld cells in the high-income counties held one fewer birth than the withholding floor, which is a limit and not a value for any cell, those cells could add at most 0.17% to the smoking births of the highest-income third. Adding back the 9 counties whose 2018 count is published moves the weighted within-state slope for 2018 from 4.9 to 4.8 points lower. The same check for 2024 adds only 1 county, because most of the counties left out have a withheld 2024 cell, so it says little about the later years.

Smoking fell in every income group

Ranking the 530 counties on median household income and splitting them into equal thirds gives three groups, with the lowest-income third up to $64,868 and the middle third up to $80,017. The smoking share in the lowest-income third went from 8.2% (95% CI 7.3% to 9.2%) in 2018 to 3.3% (95% CI 2.9% to 3.7%) in 2024. In the middle third it went from 5.5% (95% CI 4.4% to 6.7%) to 1.9% (95% CI 1.5% to 2.3%), and in the highest-income third from 3.6% (95% CI 3.2% to 4.0%) to 1.1% (95% CI 1.0% to 1.3%) (Figure 1). All of the lines fall in every year and the thirds keep their order, with separate intervals in each year.

Figure 1

Smoking in pregnancy fell from 8.2% to 3.3% of births in the lowest-income third of large counties and from 3.6% to 1.1% in the highest-income third

Share of births to mothers who reported smoking, among births with a known tobacco answer, by third of the 530 large counties ranked on median household income, 2018 to 2024, with 95% intervals from a bootstrap over counties

Mothers who reported smoking (% of births with a known answer)

Show data

Data for Smoking in pregnancy fell from 8.2% to 3.3% of births in the lowest-income third of large counties and from 3.6% to 1.1% in the highest-income third
conf_high	conf_low	n	series	share	year
9.17	7.31	603,928	Lowest-income third	8.18	2018
8.45	6.75	599,484	Lowest-income third	7.56	2019
7.99	6.34	580,344	Lowest-income third	7.14	2020
6.75	5.32	579,539	Lowest-income third	6.01	2021
5.43	4.26	579,736	Lowest-income third	4.81	2022
4.49	3.51	567,037	Lowest-income third	3.97	2023
3.71	2.89	567,610	Lowest-income third	3.27	2024
6.73	4.42	938,985	Middle third	5.47	2018
6.17	4.04	924,476	Middle third	5	2019
5.63	3.70	890,673	Middle third	4.58	2020
4.59	2.99	899,921	Middle third	3.72	2021
3.66	2.37	906,572	Middle third	2.96	2022
2.91	1.89	890,288	Middle third	2.36	2023
2.32	1.52	894,429	Middle third	1.89	2024
4.04	3.22	968,024	Highest-income third	3.61	2018
3.60	2.85	959,133	Highest-income third	3.21	2019
3.23	2.56	932,668	Highest-income third	2.88	2020
2.58	2.05	960,611	Highest-income third	2.30	2021
1.98	1.57	962,137	Highest-income third	1.76	2022
1.65	1.29	946,374	Highest-income third	1.46	2023
1.28	0.990	961,896	Highest-income third	1.13	2024
5.91	4.88	2,510,937	All large counties	5.40	2018
5.41	4.45	2,483,093	All large counties	4.93	2019
5	4.09	2,403,685	All large counties	4.54	2020
4.08	3.34	2,440,071	All large counties	3.71	2021
3.23	2.63	2,448,445	All large counties	2.93	2022
2.64	2.14	2,403,699	All large counties	2.38	2023
2.11	1.72	2,423,935	All large counties	1.91	2024

Every line falls in every year, and the lines keep their order, with the lowest-income third on top. The share for all large counties went from 5.4% to 1.9%. Each line plots the pooled share of the counties in a third, and the bands show how much that share depends on which counties are in the group.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: A county's smoking share is the births to mothers who reported smoking over the births with a known answer, Yes plus No. Births with an Unknown or Not Stated answer are left out of the denominator.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024. Thirds hold equal numbers of counties.Note: Intervals are 95% intervals from a bootstrap over counties (replicates and seed in the methods note). Births are full counts, so an interval shows how much a pooled share depends on which counties are in the group.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); intervals from a bootstrap over counties

Smoking fell faster in proportion where income is higher. The lowest-income third lost 60.0% (95% CI 57.7% to 62.3%) of its 2018 share and the highest-income third lost 68.8% (95% CI 67.0% to 70.4%), and the two intervals do not overlap. The gap in points between the lowest-income and highest-income thirds narrowed from 4.6 (95% CI 3.6 to 5.6) to 2.1 (95% CI 1.7 to 2.6), a narrowing of 2.4 (95% CI 1.8 to 3.1) points, while the ratio of their shares rose from 2.3 (95% CI 1.9 to 2.7) to 2.9 (95% CI 2.4 to 3.5), a rise of 0.6 (95% CI 0.4 to 0.9). A gap in points shrinks when the level falls even if the proportional difference stays the same. Here the proportional difference grew, so the gradient flattened in one scale and steepened in the other, a pattern that a later section follows year by year.

Where smoking in pregnancy is most common

Pooled over 2018 to 2024, the share of births to mothers who reported smoking runs from 0.8% in the county at the 5th percentile to 13.2% at the 95th (Figure 2). The counties in the top tenth by share are concentrated in Pennsylvania, Ohio, Michigan, New York, Indiana and Missouri. The counties in the bottom tenth are concentrated in Texas, New Jersey, New York, Florida and Georgia, and New York has counties in both groups.

Figure 2

The share of births to mothers who smoked is 0.8% in the county at the 5th percentile and 13.2% at the 95th percentile of large counties, and the highest shares cluster in Pennsylvania, Ohio, Michigan, New York, Indiana and Missouri

Share of births to mothers who reported smoking among births with a known tobacco answer, 2018 to 2024, 530 large counties (counties of 100,000 residents or more)

Loading county values

Mothers who reported smoking, 2018 to 2024 (% of births with a known answer)

Fewer smoking birthsMore smoking births

Values outside 0 to 20 take the end colors.

No data (3,143 counties)

Select a class to show only those places.

Find a county

Hover or tap a county for its value, or search by name. Zoom with the buttons on the map, then drag to move it.

Counties toward the high end of the key have more births to mothers who smoked, and counties toward the low end have fewer, with the low end concentrated in Texas, New Jersey, New York, Florida and Georgia. Of the 2,613 gray hatched counties, which have no value, 2,563 are under WONDER's size threshold, 35 are in California and 13 have a withheld smoking count, and the tooltip says which. The map plots each county's pooled share for 2018 to 2024, and the key runs to the highest county share; the tooltip gives the 95% interval.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: A county's smoking share is the births to mothers who reported smoking over the births with a known answer, Yes plus No. Births with an Unknown or Not Stated answer are left out of the denominator.Note: A county's interval is a Wilson score interval on its births, which treats births as independent; it describes the county's own count and not the spread across counties.Note: Connecticut is shown as its eight legacy counties, the coding of the source. Alaska is drawn. Puerto Rico is not in these data and is not drawn.Note: California's counties are gray because the source states that California does not routinely collect the item; its reported share for 2018 is 1.2%, against 5.4% in the counties kept, and lower than the share in any state kept.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; county shares are not weighted

A county's shade is its own count, so a county with few smoking births has a less stable shade than a large one, and the tooltip gives its interval. The gray counties are not a random draw of counties. Of the 2,613 gray counties, 2,563 are below WONDER's size threshold, the California counties are gray for the reason above, and the counties with a withheld smoking count are gray because their smoking births fell below the withholding floor in some year.

Smoking is lower where county income is higher

Median household income places each county on one scale. This chapter takes it from County Health Rankings, which reports a Census estimate for 2022, so its year differs from the births, which are from 2018 to 2024. The lowest-income tenth of counties has a median income of $54,059 and the highest-income tenth $107,649, a ratio of 2.0. The smoking share is 5.7% (95% CI 4.2% to 8.0%) in the lowest-income tenth and 1.7% (95% CI 1.4% to 2.1%) in the highest, and the intervals do not overlap (Figure 3). The decline is not smooth from tenth to tenth, and counties at lower incomes differ more from one another than counties at higher incomes.

Figure 3

Large counties with higher median household income have a lower share of births to mothers who smoked, from 5.7% in the lowest-income tenth to 1.7% in the highest

Share of births to mothers who reported smoking, 2018 to 2024, against county median household income (SAIPE 2022, log scale), one dot per county, with pooled shares for tenths of counties

Mothers who reported smoking (% of births with a known answer)

Dot area: Births with a known answer, 2018 to 202445,000160,000450,000

Show data

Data for Large counties with higher median household income have a lower share of births to mothers who smoked, from 5.7% in the lowest-income tenth to 1.7% in the highest
births	county	income	share
17160	Baldwin County, AL	71704	5.15
9085	Calhoun County, AL	52772	11.61
8291	Etowah County, AL	55487	9.20
9150	Houston County, AL	56842	7.64
56274	Jefferson County, AL	61996	2.81
13383	Lee County, AL	59637	2.79
30871	Madison County, AL	80426	4.14
36143	Mobile County, AL	54315	5.40
20991	Montgomery County, AL	56862	3.02
10183	Morgan County, AL	62260	9.38
16052	Shelby County, AL	98263	2.70
17613	Tuscaloosa County, AL	60875	3.37
25397	Anchorage Borough, AK	99162	5.15
8855	Cochise County, AZ	55254	6.15
9343	Coconino County, AZ	64016	2.24
354234	Maricopa County, AZ	83668	2.41
12222	Mohave County, AZ	54842	10.94
8894	Navajo County, AZ	49948	5.73
70533	Pima County, AZ	64007	4.14
34615	Pinal County, AZ	76232	4.17
12334	Yavapai County, AZ	64187	8.72
20395	Yuma County, AZ	54209	1.73
26446	Benton County, AR	86374	4.39
10357	Faulkner County, AR	63429	7.01
34300	Pulaski County, AR	57057	4.08
9524	Saline County, AR	70330	6.72
11184	Sebastian County, AR	52679	13.26
22486	Washington County, AR	62100	4.57
43713	Adams County, CO	89849	3.90
52605	Arapahoe County, CO	93525	3.38
17177	Boulder County, CO	96584	3.05
63854	Denver County, CO	87619	2.94
25857	Douglas County, CO	140768	1.44
63657	El Paso County, CO	82248	2.95
38524	Jefferson County, CO	102413	3.57
21451	Larimer County, CO	88338	4.69
10630	Mesa County, CO	69578	7.97
11938	Pueblo County, CO	58314	8.55
32098	Weld County, CO	90770	5.16
69264	Fairfield County, CT	100703	0.800
62361	Hartford County, CT	79958	2.61
9943	Litchfield County, CT	84689	5
9096	Middlesex County, CT	94735	2.86
59057	New Haven County, CT	75094	3.15
17367	New London County, CT	78552	4.35
8040	Tolland County, CT	86430	3.40
7251	Windham County, CT	71581	7.06
14677	Kent County, DE	71866	7.02
42417	New Castle County, DE	85427	4.37
16347	Sussex County, DE	81188	7.18
58922	District of Columbia, DC	99897	1.59
18307	Alachua County, FL	58783	3.30
14566	Bay County, FL	65521	4.82
36399	Brevard County, FL	74899	5.84
145116	Broward County, FL	70834	0.440
7347	Charlotte County, FL	65040	7.51
7206	Citrus County, FL	52534	12.14
15562	Clay County, FL	84364	5.49
88927	Duval County, FL	70203	3.97
26002	Escambia County, FL	61634	7.33
11396	Hernando County, FL	60582	6.90
118938	Hillsborough County, FL	74091	2.68
8688	Indian River County, FL	69462	4.52
24964	Lake County, FL	67637	3.71
50754	Lee County, FL	71607	2.27
19699	Leon County, FL	61560	2.56
25681	Manatee County, FL	72176	3.82
25082	Marion County, FL	54517	6.04
8810	Martin County, FL	80678	2.95
203848	Miami-Dade County, FL	66952	0.330
18394	Okaloosa County, FL	76663	6.73
113897	Orange County, FL	72129	0.960
32984	Osceola County, FL	63782	0.880
103462	Palm Beach County, FL	76713	0.900
37949	Pasco County, FL	65933	6.02
52244	Pinellas County, FL	66427	5.59
60104	Polk County, FL	61941	3.99
16550	St. Johns County, FL	103757	2.76
23841	St. Lucie County, FL	64038	3.04
13753	Santa Rosa County, FL	82537	7.42
20203	Sarasota County, FL	78309	3.20
31335	Seminole County, FL	80296	1.63
34159	Volusia County, FL	64601	5.03
9290	Bartow County, GA	80017	5.87
14374	Bibb County, GA	48539	5.57
11113	Carroll County, GA	69924	6.63
25199	Chatham County, GA	64029	2.04
19635	Cherokee County, GA	99932	1.95
9263	Clarke County, GA	49832	3.57
24517	Clayton County, GA	57625	1.51
61734	Cobb County, GA	96798	0.640
11456	Columbia County, GA	94224	1.55
11488	Coweta County, GA	87618	2.15
70132	DeKalb County, GA	76736	0.550
11749	Douglas County, GA	79242	2.55
86437	Fulton County, GA	89798	0.810
76767	Gwinnett County, GA	83918	0.530
18680	Hall County, GA	76789	2.17
19450	Henry County, GA	82379	1.84
13798	Houston County, GA	71237	3.26
11235	Lowndes County, GA	52623	3.90
18898	Muscogee County, GA	53740	3.33
12678	Paulding County, GA	87459	2.07
18963	Richmond County, GA	50079	3.09
8845	Whitfield County, GA	61680	8.64
13721	Hawaii County, HI	71143	5.29
78177	Honolulu County, HI	96304	0.980
11460	Maui County, HI	89794	2.34
35094	Ada County, ID	87748	3.21
12619	Bonneville County, ID	74505	5.42
22754	Canyon County, ID	70594	5.05
13213	Kootenai County, ID	75583	5.87
14447	Champaign County, IL	61205	4.98
383990	Cook County, IL	76614	1.23
7336	DeKalb County, IL	71882	4.57
68155	DuPage County, IL	102383	0.910
40030	Kane County, IL	93475	1.52
8124	Kankakee County, IL	70494	8.03
47165	Lake County, IL	101621	1.47
7651	LaSalle County, IL	64711	11.93
21307	McHenry County, IL	99128	2.45
11768	McLean County, IL	71825	6.26
8211	Macon County, IL	60648	11.53
18669	Madison County, IL	67441	8.96
15369	Peoria County, IL	65421	9.19
11015	Rock Island County, IL	62652	7.46
19069	St. Clair County, IL	67185	8.16
14427	Sangamon County, IL	73684	11.01
9039	Tazewell County, IL	73285	9.55
48876	Will County, IL	97076	2.09
23007	Winnebago County, IL	62557	9.35
36434	Allen County, IN	65967	6.36
10024	Clark County, IN	68493	8.82
7485	Delaware County, IN	54059	14.83
20565	Elkhart County, IN	63777	6.54
26730	Hamilton County, IN	117068	1.08
12470	Hendricks County, IN	94760	3.70
13190	Johnson County, IN	83036	7.22
37599	Lake County, IN	66169	4.72
7988	La Porte County, IN	66336	14.20
9959	Madison County, IN	58926	14.33
98302	Marion County, IN	62776	5.89
7574	Monroe County, IN	64299	9.69
11244	Porter County, IN	84352	5.55
22894	St. Joseph County, IN	61028	6.36
13904	Tippecanoe County, IN	56088	7.40
14931	Vanderburgh County, IN	57997	9.65
7850	Vigo County, IN	52286	14.76
11181	Black Hawk County, IA	62020	10.06
11741	Johnson County, IA	74909	2.67
18297	Linn County, IA	74134	7.81
45236	Polk County, IA	77369	6.53
13715	Scott County, IA	68576	8.09
9940	Woodbury County, IA	66830	11.03
7172	Douglas County, KS	67482	7.11
48396	Johnson County, KS	99174	1.51
46079	Sedgwick County, KS	64720	7.31
13791	Shawnee County, KS	61255	11
16991	Wyandotte County, KS	58701	4.74
11860	Boone County, KY	90265	7.10
26174	Fayette County, KY	63667	7.25
9385	Hardin County, KY	61920	12.66
66206	Jefferson County, KY	64719	7.48
15267	Kenton County, KY	77284	11.12
12523	Warren County, KY	60651	5.40
11564	Ascension Parish, LA	81284	1.79
11284	Bossier Parish, LA	63812	4.33
19927	Caddo Parish, LA	47699	5.03
17588	Calcasieu Parish, LA	61797	3.29
38804	East Baton Rouge Parish, LA	61376	1.83
37142	Jefferson Parish, LA	61157	1.77
23015	Lafayette Parish, LA	58641	2.65
12889	Livingston Parish, LA	72887	3.41
29212	Orleans Parish, LA	51913	1.57
13883	Ouachita Parish, LA	47283	5.01
11340	Rapides Parish, LA	55870	5.91
20165	St. Tammany Parish, LA	78629	4.81
13379	Tangipahoa Parish, LA	54942	2.92
9366	Terrebonne Parish, LA	57376	4.49
8063	Androscoggin County, ME	62848	10.62
19382	Cumberland County, ME	88571	4.16
7569	Kennebec County, ME	63191	10.13
8985	Penobscot County, ME	60761	10.92
12761	York County, ME	81315	7.14
47232	Anne Arundel County, MD	112525	2.98
65127	Baltimore County, MD	86198	3.64
11842	Carroll County, MD	105096	4.52
7980	Cecil County, MD	84513	12.29
12626	Charles County, MD	112932	3.08
21532	Frederick County, MD	116796	3.27
18269	Harford County, MD	100334	5.16
22263	Howard County, MD	133068	1.07
81230	Montgomery County, MD	118020	0.680
77654	Prince George's County, MD	93833	0.930
9508	St. Mary's County, MD	112154	5.57
11317	Washington County, MD	69271	11.33
50682	Baltimore city, MD	54735	5.46
10378	Barnstable County, MA	89774	5.69
6394	Berkshire County, MA	71102	10.76
39208	Bristol County, MA	80702	5.39
59374	Essex County, MA	92118	1.94
33261	Hampden County, MA	64030	4.36
6421	Hampshire County, MA	81878	4.06
112463	Middlesex County, MA	118494	1.49
49207	Norfolk County, MA	115770	1.39
36705	Plymouth County, MA	102647	3.06
59159	Suffolk County, MA	84548	1.12
59265	Worcester County, MA	86078	4.64
8704	Allegan County, MI	75893	8.95
6344	Bay County, MI	56388	15.61
10619	Berrien County, MI	61790	10.17
10267	Calhoun County, MI	59652	13.60
7231	Eaton County, MI	71499	8.39
30202	Genesee County, MI	57443	13.24
20064	Ingham County, MI	60571	9.30
11293	Jackson County, MI	61299	14.49
19251	Kalamazoo County, MI	69689	11.28
56939	Kent County, MI	76923	2.97
11820	Livingston County, MI	101227	4.42
61113	Macomb County, MI	73927	5.40
10327	Monroe County, MI	72166	10.08
13230	Muskegon County, MI	58303	12.49
87631	Oakland County, MI	90801	2.97
21658	Ottawa County, MI	85968	3.42
13662	Saginaw County, MI	54064	12.71
10247	St. Clair County, MI	64894	14.59
22242	Washtenaw County, MI	80941	4.21
147992	Wayne County, MI	55928	5.38
28534	Anoka County, MN	92249	4.04
34738	Dakota County, MN	97727	2.62
103273	Hennepin County, MN	89418	2.57
13730	Olmsted County, MN	88032	4.29
47518	Ramsey County, MN	75041	3.96
12496	St. Louis County, MN	63141	12.86
11743	Scott County, MN	115687	2.73
14058	Stearns County, MN	72378	6.62
19167	Washington County, MN	107649	2.69
12703	Wright County, MN	95259	4.09
15318	DeSoto County, MS	79967	4.69
17326	Harrison County, MS	55916	6.20
19281	Hinds County, MS	47479	4.05
10864	Jackson County, MS	61515	7.29
12150	Rankin County, MS	72906	4.06
13590	Boone County, MO	63981	7.45
20781	Clay County, MO	78930	6.28
7934	Franklin County, MO	65779	13.12
22914	Greene County, MO	55751	11.69
60264	Jackson County, MO	62509	7.23
10728	Jasper County, MO	53548	11.36
16677	Jefferson County, MO	78297	11.52
28851	St. Charles County, MO	98925	4.23
72521	St. Louis County, MO	79609	4.59
24881	St. Louis city, MO	52278	7.78
7491	Missoula County, MT	68210	7.74
12828	Yellowstone County, MT	78216	13.21
55993	Douglas County, NE	74424	4.89
26001	Lancaster County, NE	68358	5.90
16778	Sarpy County, NE	98619	3.64
175327	Clark County, NV	70838	2.53
34800	Washoe County, NV	80245	3.30
28953	Hillsborough County, NH	96415	5.46
8961	Merrimack County, NH	84898	5.99
18894	Rockingham County, NH	107445	2.88
7901	Strafford County, NH	82845	8.10
19276	Atlantic County, NJ	76108	6.15
61993	Bergen County, NJ	113885	0.640
31600	Burlington County, NJ	100188	3.37
42040	Camden County, NJ	81372	4.21
12773	Cumberland County, NJ	60892	6.73
69450	Essex County, NJ	74747	1.39
20188	Gloucester County, NJ	97851	4.81
65455	Hudson County, NJ	82361	0.770
7241	Hunterdon County, NJ	137334	1.84
28477	Mercer County, NJ	94832	2.14
59370	Middlesex County, NJ	102073	1.24
42622	Monmouth County, NJ	117699	1.37
33345	Morris County, NJ	131562	0.700
68988	Ocean County, NJ	81152	1.57
43760	Passaic County, NJ	78779	1.30
22098	Somerset County, NJ	134735	1.27
9249	Sussex County, NJ	111308	3.80
46241	Union County, NJ	96483	0.730
7177	Warren County, NJ	97729	4.82
45975	Bernalillo County, NM	65034	3.68
17941	Dona Ana County, NM	52011	2.31
9258	Sandoval County, NM	82862	2.59
9165	San Juan County, NM	51898	5.72
7868	Santa Fe County, NM	72836	3.34
19642	Albany County, NY	76997	6.03
124230	Bronx County, NY	45864	0.610
12582	Broome County, NY	60533	12.14
8649	Chautauqua County, NY	55644	12.90
18274	Dutchess County, NY	90740	3.05
65097	Erie County, NY	69888	6.82
12213	Jefferson County, NY	58763	9.42
243533	Kings County, NY	73244	0.230
51812	Monroe County, NY	68169	5.89
96131	Nassau County, NY	135528	0.420
103761	New York County, NY	95514	0.340
13749	Niagara County, NY	63565	12.44
16428	Oneida County, NY	63894	9.73
33510	Onondaga County, NY	70939	7.68
6712	Ontario County, NY	76601	8.05
37791	Orange County, NY	88532	1.83
8126	Oswego County, NY	59335	16.21
175588	Queens County, NY	80180	0.310
10754	Rensselaer County, NY	80040	7.86
35382	Richmond County, NY	91582	0.840
6920	St. Lawrence County, NY	59451	14.29
14110	Saratoga County, NY	93341	5.07
12440	Schenectady County, NY	74206	3.62
108290	Suffolk County, NY	119253	1.47
4697	Tompkins County, NY	72025	8.11
10433	Ulster County, NY	79340	6.58
69003	Westchester County, NY	108037	0.520
13463	Alamance County, NC	61034	5.30
7779	Brunswick County, NC	73536	10.09
16798	Buncombe County, NC	67906	6.48
18401	Cabarrus County, NC	79649	3.74
11683	Catawba County, NC	63934	9.60
9020	Craven County, NC	63076	6.43
36657	Cumberland County, NC	58013	5.94
12394	Davidson County, NC	63590	10.56
28827	Durham County, NC	79524	1.67
30890	Forsyth County, NC	62992	4.78
18836	Gaston County, NC	65430	9.20
41136	Guilford County, NC	63822	4.19
13131	Harnett County, NC	65242	6.06
7163	Henderson County, NC	65252	6.91
13881	Iredell County, NC	73446	6.37
18759	Johnston County, NC	80612	1.88
104441	Mecklenburg County, NC	80645	1.24
15155	New Hanover County, NC	70995	4.47
26602	Onslow County, NC	62190	3.71
8043	Orange County, NC	87780	2.90
14419	Pitt County, NC	57319	5.62
10891	Randolph County, NC	58037	8.76
12735	Robeson County, NC	39931	12.40
11120	Rowan County, NC	61105	9.38
17327	Union County, NC	95079	2.68
90404	Wake County, NC	97099	1.31
11364	Wayne County, NC	55635	5.10
16501	Cass County, ND	70002	5.67
8560	Allen County, OH	60384	13.19
7213	Ashtabula County, OH	52468	17.37
30275	Butler County, OH	74841	9.08
11086	Clark County, OH	57264	13.77
15560	Clermont County, OH	79492	10.49
6770	Columbiana County, OH	54167	19.70
91896	Cuyahoga County, OH	60808	5.11
14885	Delaware County, OH	121528	3.07
11746	Fairfield County, OH	82447	9.12
123475	Franklin County, OH	69762	6.16
11884	Greene County, OH	82769	7.20
72529	Hamilton County, OH	67033	6.84
14711	Lake County, OH	76163	7.22
13273	Licking County, OH	76596	10.69
22139	Lorain County, OH	67124	10.23
34681	Lucas County, OH	57971	8.80
16265	Mahoning County, OH	54774	17.37
11543	Medina County, OH	89108	5.09
8098	Miami County, OH	72887	9.95
43181	Montgomery County, OH	62794	7.89
9489	Portage County, OH	72965	10.39
9476	Richland County, OH	54906	14.90
27672	Stark County, OH	64557	12
38632	Summit County, OH	66034	8.82
13986	Trumbull County, OH	54406	18.90
16762	Warren County, OH	104523	4.95
9852	Wayne County, OH	70121	8.09
8697	Wood County, OH	67254	6.17
13869	Canadian County, OK	81761	3.17
19195	Cleveland County, OK	70923	5.55
11392	Comanche County, OK	55600	6.77
77162	Oklahoma County, OK	61580	4.16
61069	Tulsa County, OK	63442	6.06
26210	Clackamas County, OR	96915	3.64
12969	Deschutes County, OR	84281	4.83
6962	Douglas County, OR	55837	14.02
14669	Jackson County, OR	68239	7.53
21179	Lane County, OR	64192	7.80
10064	Linn County, OR	67265	9.08
27793	Marion County, OR	70861	4.88
52225	Multnomah County, OR	79432	3.67
42730	Washington County, OR	98706	1.64
6311	Adams County, PA	76554	7.68
84377	Allegheny County, PA	71973	6.21
10503	Beaver County, PA	67135	11.60
31618	Berks County, PA	72157	5.17
8107	Blair County, PA	57884	14.60
38785	Bucks County, PA	105202	4.32
11971	Butler County, PA	81507	7.97
8256	Cambria County, PA	54612	15.42
7626	Centre County, PA	67654	5.76
37871	Chester County, PA	117326	2.68
18379	Cumberland County, PA	81295	5.94
22887	Dauphin County, PA	67813	7.02
43218	Delaware County, PA	83856	4.01
18412	Erie County, PA	56489	12.92
8259	Fayette County, PA	51494	19.22
11879	Franklin County, PA	73709	8.28
14059	Lackawanna County, PA	61168	12.14
48303	Lancaster County, PA	82434	4.37
10896	Lebanon County, PA	69490	6.65
27840	Lehigh County, PA	72590	5.17
22049	Luzerne County, PA	59358	10.59
7855	Lycoming County, PA	61729	14.41
7091	Mercer County, PA	57702	15.13
9632	Monroe County, PA	79824	8.13
58583	Montgomery County, PA	106743	2.96
19365	Northampton County, PA	75468	5.68
132998	Philadelphia County, PA	56385	3.77
8821	Schuylkill County, PA	63680	15.63
13560	Washington County, PA	70751	11.75
19863	Westmoreland County, PA	71152	10.76
33202	York County, PA	79938	6.98
10172	Kent County, RI	84230	4.49
47909	Providence County, RI	74190	3
5881	Washington County, RI	99015	4.06
12958	Aiken County, SC	67728	5.78
15927	Anderson County, SC	62708	9.42
13125	Beaufort County, SC	83687	2.65
20728	Berkeley County, SC	79259	4.68
34628	Charleston County, SC	79969	2.09
13622	Dorchester County, SC	74730	4.77
11111	Florence County, SC	52650	5.86
44972	Greenville County, SC	71833	4.05
22821	Horry County, SC	61375	8.01
22711	Lexington County, SC	73022	3.87
8508	Pickens County, SC	55333	8.02
32196	Richland County, SC	61010	1.17
28596	Spartanburg County, SC	58535	5.81
9377	Sumter County, SC	56937	3.55
20749	York County, SC	83692	4.07
19479	Minnehaha County, SD	75623	5.87
9955	Pennington County, SD	67573	10.60
9054	Blount County, TN	69713	11.70
70452	Davidson County, TN	72561	3.48
30527	Hamilton County, TN	75127	5.74
36932	Knox County, TN	70013	8.33
25144	Montgomery County, TN	67478	6.31
30515	Rutherford County, TN	77320	4.44
86476	Shelby County, TN	61452	3.14
10394	Sullivan County, TN	54361	15.76
15744	Sumner County, TN	87453	7.40
8668	Washington County, TN	64270	12.01
12421	Wilson County, TN	88462	6.09
42118	Bell County, TX	62412	2.56
183122	Bexar County, TX	65839	0.730
32604	Brazoria County, TX	86197	2.06
18809	Brazos County, TX	60355	1.64
85710	Collin County, TX	113943	0.490
12761	Comal County, TX	92979	1.83
257839	Dallas County, TX	70871	0.520
75897	Denton County, TX	102711	0.790
20859	Ector County, TX	64868	3.33
18034	Ellis County, TX	89314	1.79
78428	El Paso County, TX	53441	0.600
64701	Fort Bend County, TX	105583	0.370
27343	Galveston County, TX	75947	3.09
11900	Grayson County, TX	66141	4.61
11611	Gregg County, TX	60932	5.27
13957	Guadalupe County, TX	86043	1.58
454216	Harris County, TX	68748	0.640
96678	Hidalgo County, TX	48825	0.260
22497	Jefferson County, TX	55065	4.38
16371	Johnson County, TX	76747	4.19
16578	Kaufman County, TX	92814	1.74
28325	Lubbock County, TX	59138	3.22
23327	McLennan County, TX	63160	3.55
20997	Midland County, TX	81187	2.76
56694	Montgomery County, TX	95241	2.02
30350	Nueces County, TX	60250	1.62
12350	Parker County, TX	95277	3.38
11012	Potter County, TX	51028	8.70
11715	Randall County, TX	77066	5.63
21030	Smith County, TX	67978	2.18
191010	Tarrant County, TX	76285	1.45
13649	Taylor County, TX	61798	5.19
10398	Tom Green County, TX	60348	3.65
110483	Travis County, TX	95151	0.760
10856	Wichita County, TX	56634	7.51
53621	Williamson County, TX	101891	0.840
14399	Cache County, UT	76631	1.11
33922	Davis County, UT	103143	1.70
105389	Salt Lake County, UT	91713	1.94
83649	Utah County, UT	95085	0.770
15947	Washington County, UT	75572	1.52
24434	Weber County, UT	83465	3.07
9528	Chittenden County, VT	86579	5.78
28141	Chesterfield County, VA	100149	2.13
25974	Henrico County, VA	82187	2.69
43962	Prince William County, VA	119051	0.710
11100	Spotsylvania County, VA	96322	4.94
12619	Stafford County, VA	134456	2.60
19782	Chesapeake city, VA	87127	2.85
11050	Hampton city, VA	64086	4.24
17847	Newport News city, VA	63350	4.93
22033	Norfolk city, VA	60030	3.92
20909	Richmond city, VA	58719	3.95

Showing the first 500 of 530 rows.

Dots drift down from left to right, and the line through the tenth-of-county shares falls but not at every step. Each dot is one of 530 large counties, with area in proportion to its births with a known answer. The line plots the pooled share in each tenth of counties ranked on income, with 95% intervals from a bootstrap over counties.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: A county's smoking share is the births to mothers who reported smoking over the births with a known answer, Yes plus No. Births with an Unknown or Not Stated answer are left out of the denominator.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Note: Intervals are 95% intervals from a bootstrap over counties (replicates and seed in the methods note). Births are full counts, so an interval shows how much a pooled share depends on which counties are in the group. Tenths hold equal numbers of counties.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); tenths pooled by births, intervals from a bootstrap over counties

A second measure checks the choice of income. The Area Deprivation Index orders neighborhoods by income, education, employment and housing, and a county's rank here is the average of its tracts' ranks, weighted by tract population.[1] The check is not independent, since income is among the index's inputs. Within a state, the smoking share is higher by 1.3 (95% CI 0.9 to 1.6) points for each ten-point step in a county's rank. The correlation between a county's rank and the log of its median income is −0.79 (95% CI −0.90 to −0.67), so the two measures place counties alike, which shows agreement and not independent confirmation, and the chapter uses income.

The slope within states is close to the slope across all counties

A slope across all counties can come from comparing states that differ in both income and smoking. Figure 4 separates the two. The first slope is a least-squares line through all counties. The second gives each state its own intercept, a fixed-effects slope that compares counties only inside a state, and the third compares state averages. The slopes are in points of the smoking share for a doubling of county median household income.

Figure 4

Within a state, a doubling of county income goes with a smoking share 3.9 points lower when counties are weighted by births, and 6.6 points lower when each county counts once

Change in the share of births to mothers who smoked, in percentage points per doubling of county median household income (SAIPE 2022), across all counties, within states and between states, 2018 to 2024, with 95% intervals from a bootstrap over states

Weighted by births
Each county counts once

Percentage points per doubling of county median household income

Show data

Data for Within a state, a doubling of county income goes with a smoking share 3.9 points lower when counties are weighted by births, and 6.6 points lower when each county counts once
conf_high	conf_low	estimate	n	scope	weighting
−2.71	−5.91	−4.12	49	Across all counties	Weighted by births
−2.75	−5.53	−3.88	45	Within states	Weighted by births
−1.82	−8.33	−4.71	49	Between states	Weighted by births
−4.57	−8	−6.24	49	Across all counties	Each county counts once
−5.11	−8.20	−6.63	45	Within states	Each county counts once
−2.19	−8.69	−5.29	49	Between states	Each county counts once

All six dots lie left of zero, and within each weighting the three slopes are close: the within-state slope differs from the slope across the counties of the same states by +0.3 points when weighted and −0.4 points when not, and both intervals include zero. The weighted within-state slope is shallower than the unweighted one by 2.8 points. The dots plot least-squares slopes; the within-state slope gives each state its own intercept.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Note: Intervals are 95% intervals from a bootstrap over states (replicates and seed in the methods note), which allows for counties in one state resembling each other. The between-state slope compares 49 state means, so its interval is the widest; 45 states have two or more counties and contribute to the within-state slope.Note: The weighted slopes weight each county by its births with a known answer. The unweighted slopes count each county once.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); slopes weighted by births or unweighted as labeled; intervals from a bootstrap over states

When counties are weighted by their births with a known answer, the slope across all counties is 4.1 (95% CI 2.7 to 5.9) points, the slope within states 3.9 (95% CI 2.7 to 5.5) and the slope between states 4.7 (95% CI 1.8 to 8.3). The within-state slope differs from the slope across the counties of the same 45 states, those with two or more counties, by +0.3 (95% CI −0.7 to 1.1) points, an interval that includes zero. When each county counts once, the three are 6.2 (95% CI 4.6 to 8.0), 6.6 (95% CI 5.1 to 8.2) and 5.3 (95% CI 2.2 to 8.7), and the within-state slope differs from that comparison slope by −0.4 (95% CI −1.3 to 0.5) points, again an interval that includes zero. The between-state slope is the least precise because it rests on 49 state averages. The gap between higher-income and lower-income counties therefore appears inside states about as it appears across them.

The weights matter. The weighted within-state slope is shallower than the unweighted one by 2.8 (95% CI 1.3 to 3.9) points. The counties at or above the median in births hold 82.2% of the births, and among them the within-state slope is 3.7 (95% CI 2.6 to 5.1) points against 8.3 (95% CI 6.3 to 10.2) among the counties below the median, a difference of 4.6 (95% CI 2.5 to 6.6) points. A weighted slope leans on large counties, where the gradient is shallower in points. Expressed as the proportional difference in the smoking share for a 10% higher county income, the smoking share is 12.0% (95% CI 8.2% to 17.0%) lower within a state when counties are weighted and 16.7% (95% CI 14.4% to 19.0%) lower when each county counts once. The scope figure shows both weightings, the figure of state slopes has a view for each, and the other state figures are weighted by births.

One slope for each state with enough counties

A state gets a slope of its own if it has at least 8 complete counties. That leaves 26 states and 449 counties, and the other states have too few large counties to fit a line (Figure 5). Across these states the common within-state slope is 3.8 (95% CI 2.6 to 5.6) points per doubling. Every state's estimated slope is below zero, 26 of 26. The interval lies wholly below zero in 10 of them (Colorado, Georgia, Illinois, Indiana, Massachusetts, Missouri, North Carolina, Ohio, Oregon and Virginia), and no interval lies wholly above zero (0 states). With each county counting once, the estimated slope is below zero in 26 states, and the interval lies wholly below zero in 12, with no interval wholly above zero (0 states), as the second view of the figure shows.

Figure 5

In all 26 states with a slope of their own the estimated slope is below zero, and the 95% interval lies below zero in 10 when counties are weighted by births

Change in the share of births to mothers who smoked per doubling of county median household income (SAIPE 2022), one dot per state with at least 8 complete large counties, 2018 to 2024, with 95% intervals; four views by scale and by weighting

Interval includes zero
Interval below zero

Show data

Data for In all 26 states with a slope of their own the estimated slope is below zero, and the 95% interval lies below zero in 10 when counties are weighted by births
State	Scale	Counties	Slope	Lower limit of the 95% interval	Upper limit of the 95% interval	Complete counties	Counties left out for a withheld count	State share (%)
Alabama	points	Weighted by births	−3.28	−7.14	0.58	12	0	4.6
Arizona	points	Weighted by births	−4.97	−13.64	3.70	9	0	3.2
Colorado	points	Weighted by births	−4.35	−8.56	−0.14	11	0	3.7
Connecticut	points	Weighted by births	−6.42	−16.92	4.08	8	0	2.6
Florida	points	Weighted by births	−3.97	−10.03	2.09	32	1	2.7
Georgia	points	Weighted by births	−3.34	−4.95	−1.73	22	2	1.8
Illinois	points	Weighted by births	−8.37	−16.40	−0.33	19	1	2.9
Indiana	points	Weighted by births	−7.17	−10.21	−4.12	17	0	6.7
Louisiana	points	Weighted by births	−0.81	−6.94	5.31	14	0	3.1
Maryland	points	Weighted by births	−4.04	−9.51	1.43	13	0	3.1
Massachusetts	points	Weighted by births	−4.24	−7.52	−0.97	11	0	2.8
Michigan	points	Weighted by births	−7.45	−24.40	9.51	20	0	6.6
Minnesota	points	Weighted by births	−7.80	−19.54	3.93	10	0	3.7
Missouri	points	Weighted by births	−6.23	−12.12	−0.34	10	0	7.2
New Jersey	points	Weighted by births	−1.86	−4.47	0.75	19	0	1.9
New York	points	Weighted by births	−2.36	−9.13	4.41	27	1	2.3
North Carolina	points	Weighted by births	−8.20	−11.15	−5.25	27	0	4.2
Ohio	points	Weighted by births	−7.34	−13.71	−0.98	28	0	8.3
Oregon	points	Weighted by births	−9.98	−15.26	−4.70	9	0	4.8
Pennsylvania	points	Weighted by births	−5.10	−15.68	5.47	31	0	6.4
South Carolina	points	Weighted by births	−4.51	−10.99	1.97	15	0	4.5
Tennessee	points	Weighted by births	−0.91	−28.66	26.85	11	1	5.5
Texas	points	Weighted by births	−0.63	−2.10	0.83	36	3	1.3
Virginia	points	Weighted by births	−2.72	−5.20	−0.25	11	4	2.8
Washington	points	Weighted by births	−5.69	−14.20	2.83	12	0	3.7
Wisconsin	points	Weighted by births	−3.37	−20.54	13.80	15	0	5.5
Alabama	points	Each county counts once	−5.47	−11.26	0.33	12	0	4.6
Arizona	points	Each county counts once	−4.59	−13.11	3.92	9	0	3.2
Colorado	points	Each county counts once	−5.89	−9.84	−1.94	11	0	3.7
Connecticut	points	Each county counts once	−8.16	−18.13	1.81	8	0	2.6
Florida	points	Each county counts once	−4.49	−10.73	1.75	32	1	2.7
Georgia	points	Each county counts once	−2.96	−4.84	−1.07	22	2	1.8
Illinois	points	Each county counts once	−11.40	−14.72	−8.09	19	1	2.9
Indiana	points	Each county counts once	−9.50	−13.06	−5.93	17	0	6.7
Louisiana	points	Each county counts once	−1.54	−6.01	2.93	14	0	3.1
Maryland	points	Each county counts once	−5.80	−17.26	5.67	13	0	3.1
Massachusetts	points	Each county counts once	−6.41	−14.99	2.16	11	0	2.8
Michigan	points	Each county counts once	−12.63	−18.78	−6.48	20	0	6.6
Minnesota	points	Each county counts once	−9.52	−22.26	3.22	10	0	3.7
Missouri	points	Each county counts once	−5.82	−12.76	1.12	10	0	7.2
New Jersey	points	Each county counts once	−2.87	−6.11	0.37	19	0	1.9
New York	points	Each county counts once	−8.60	−16.15	−1.06	27	1	2.3
North Carolina	points	Each county counts once	−7.86	−10.18	−5.54	27	0	4.2
Ohio	points	Each county counts once	−11.15	−15.57	−6.73	28	0	8.3
Oregon	points	Each county counts once	−12.13	−20.36	−3.90	9	0	4.8
Pennsylvania	points	Each county counts once	−11.72	−16.34	−7.11	31	0	6.4
South Carolina	points	Each county counts once	−4.71	−9.31	−0.10	15	0	4.5
Tennessee	points	Each county counts once	−9.43	−29.00	10.13	11	1	5.5
Texas	points	Each county counts once	−2.73	−5.19	−0.26	36	3	1.3
Virginia	points	Each county counts once	−2.09	−4.23	0.05	11	4	2.8
Washington	points	Each county counts once	−4.69	−15.06	5.67	12	0	3.7
Wisconsin	points	Each county counts once	−6.69	−15.34	1.95	15	0	5.5
Alabama	proportion	Weighted by births	−7.62	−17.55	3.51	12	0	4.6
Arizona	proportion	Weighted by births	−13.80	−42.48	29.20	9	0	3.2
Colorado	proportion	Weighted by births	−14.86	−24.34	−4.20	11	0	3.7
Connecticut	proportion	Weighted by births	−37.17	−76.01	64.60	8	0	2.6
Florida	proportion	Weighted by births	−9.96	−39.70	34.45	32	1	2.7
Georgia	proportion	Weighted by births	−20.60	−27.42	−13.14	22	2	1.8
Illinois	proportion	Weighted by births	−24.70	−43.86	1.01	19	1	2.9
Indiana	proportion	Weighted by births	−21.06	−37.44	−0.40	17	0	6.7
Louisiana	proportion	Weighted by births	−2.35	−25.39	27.80	14	0	3.1
Maryland	proportion	Weighted by births	−17.27	−33.50	2.91	13	0	3.1
Massachusetts	proportion	Weighted by births	−17.13	−28.02	−4.60	11	0	2.8
Michigan	proportion	Weighted by births	−15.68	−37.23	13.26	20	0	6.6
Minnesota	proportion	Weighted by births	−18.02	−32.93	0.21	10	0	3.7
Missouri	proportion	Weighted by births	−12.11	−20.52	−2.81	10	0	7.2
New Jersey	proportion	Weighted by births	−11.04	−23.37	3.28	19	0	1.9
New York	proportion	Weighted by births	−7.29	−27.80	19.05	27	1	2.3
North Carolina	proportion	Weighted by births	−26.20	−33.19	−18.48	27	0	4.2
Ohio	proportion	Weighted by births	−10.74	−19.29	−1.28	28	0	8.3
Oregon	proportion	Weighted by births	−25.39	−40.79	−5.98	9	0	4.8
Pennsylvania	proportion	Weighted by births	−9.71	−27.57	12.55	31	0	6.4
South Carolina	proportion	Weighted by births	−9.25	−30.76	18.96	15	0	4.5
Tennessee	proportion	Weighted by births	5.28	−39.93	84.52	11	1	5.5
Texas	proportion	Weighted by births	−0.52	−15.83	17.58	36	3	1.3
Virginia	proportion	Weighted by births	−16.02	−33.86	6.62	11	4	2.8
Washington	proportion	Weighted by births	−21.40	−49.98	23.49	12	0	3.7
Wisconsin	proportion	Weighted by births	−10.92	−43.04	39.32	15	0	5.5
Alabama	proportion	Each county counts once	−12.06	−21.15	−1.91	12	0	4.6
Arizona	proportion	Each county counts once	−10.74	−31.45	16.21	9	0	3.2
Colorado	proportion	Each county counts once	−17.41	−21.63	−12.96	11	0	3.7
Connecticut	proportion	Each county counts once	−33.42	−63.91	22.83	8	0	2.6
Florida	proportion	Each county counts once	−11.39	−26.28	6.51	32	1	2.7
Georgia	proportion	Each county counts once	−15.69	−23.22	−7.43	22	2	1.8
Illinois	proportion	Each county counts once	−31.15	−37.93	−23.62	19	1	2.9
Indiana	proportion	Each county counts once	−21.07	−33.11	−6.85	17	0	6.7
Louisiana	proportion	Each county counts once	−5.57	−22.73	15.41	14	0	3.1
Maryland	proportion	Each county counts once	−17.22	−37.28	9.25	13	0	3.1
Massachusetts	proportion	Each county counts once	−20.85	−35.58	−2.75	11	0	2.8
Michigan	proportion	Each county counts once	−20.74	−29.38	−11.04	20	0	6.6
Minnesota	proportion	Each county counts once	−19.90	−33.80	−3.08	10	0	3.7
Missouri	proportion	Each county counts once	−10.92	−21.19	0.69	10	0	7.2
New Jersey	proportion	Each county counts once	−12.55	−24.87	1.79	19	0	1.9
New York	proportion	Each county counts once	−24.26	−42.67	0.05	27	1	2.3
North Carolina	proportion	Each county counts once	−20.97	−28.79	−12.29	27	0	4.2
Ohio	proportion	Each county counts once	−15.14	−18.88	−11.24	28	0	8.3
Oregon	proportion	Each county counts once	−24.96	−36.84	−10.85	9	0	4.8
Pennsylvania	proportion	Each county counts once	−17.99	−23.00	−12.65	31	0	6.4
South Carolina	proportion	Each county counts once	−11.04	−23.82	3.89	15	0	4.5
Tennessee	proportion	Each county counts once	−10.64	−36.17	25.12	11	1	5.5
Texas	proportion	Each county counts once	−11.50	−25.12	4.60	36	3	1.3
Virginia	proportion	Each county counts once	−11.24	−25.06	5.14	11	4	2.8
Washington	proportion	Each county counts once	−13.76	−38.91	21.75	12	0	3.7
Wisconsin	proportion	Each county counts once	−17.17	−34.81	5.24	15	0	5.5

Every dot is left of zero in the first view, and the intervals are wide: most include zero, and none lies right of it. The vertical rule marks the common slope across these states. Each dot is a least-squares line through one state's counties, with HC3 standard errors and t intervals. The views give the slope in points and as a percent difference in the smoking share for a 10% higher income, with counties weighted by births or each counting once. Rows are ordered by the estimate to make the figure readable, and the order is not a ranking, since the intervals overlap.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Note: A state gets a slope when it has at least 8 complete counties, which gives 26 states and 449 counties. Intervals are 95% intervals from a t distribution with the state's counties minus two degrees of freedom and heteroskedasticity-robust (HC3) standard errors, so a state with few counties has a wide interval and the interval may be too narrow where one county dominates.Note: Virginia has 4 of its 15 large counties left out for a withheld smoking count, and counties left out in other states are listed in the data table. A state with counties left out rests on the rest.Note: Counties differ by less than a doubling of income in a typical state: the median span between a state's lowest and highest county income is 0.9 doublings, so the slopes extend beyond what most states' own counties cover.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); state slopes weighted by births with a known answer or unweighted as labeled, 95% t intervals

The intervals are wide because a state's line rests on a handful of counties that span less than a doubling of income. The median state's highest-income county has 0.9 doublings more income than its lowest-income county, so a slope per doubling extends past what most states' own counties cover. The figure orders its rows by the estimate only to make it readable, and the order is not a ranking. A state's dot moves with the counties it happens to hold, the intervals overlap widely, and a state is said to differ from another only where the intervals separate. Virginia has 4 of its 15 large counties left out for a withheld count, and those counties are among the highest in income of all the large counties, so its slope describes the counties that remain.

Whether the slope is the same everywhere

A visual impression of spread in a figure of intervals does not test equality, so the chapter tests it. The test compares the spread of the state slopes with the spread their own intervals allow, which is Cochran's Q. With a handful of counties per state the usual reference distribution for Q rejects too often, so each p-value here is the share of bootstrap draws, generated on the assumption that every state has the same slope, in which Q is at least as large as the observed one. The test is run in points and in proportion, with counties weighted by births and with each county counting once.

In points the answer depends on the weights. With each county counting once, p = 0.009 and the estimated standard deviation of the true state slopes is 3.0 (95% CI 1.6 to 3.6) points, with 70% of the variation in the slopes beyond what sampling allows. With counties weighted by births, p = 0.108 and the standard deviation is 1.9 (95% CI 0.0 to 2.7) points, an interval that reaches zero, with 52% of the variation beyond sampling. The standard deviation is the DerSimonian and Laird estimate used in meta-analysis.[2] The steeper slopes also belong to states with more smoking. Across the states, the correlation between a state's pooled smoking share and its slope is −0.62 (95% CI −0.83 to −0.20), weighted by births (Figure 6).

Figure 6

States with a higher share of births to mothers who smoked have steeper slopes in points, a correlation of −0.62, with no detectable relation in proportion, where the correlation is −0.19

Slope of the smoking share on county median household income against the state's pooled smoking share, one dot per state with a slope of its own, 2018 to 2024; slopes in percentage points per doubling of income or as a percent difference per 10% higher income

Slope (percentage points per doubling of county median household income)

Dot area: Births with a known answer, 2018 to 2024220,000760,0002,200,000

Show data

Data for States with a higher share of births to mothers who smoked have steeper slopes in points, a correlation of −0.62, with no detectable relation in proportion, where the correlation is −0.19
births	level	measure	slope	state
245196	4.57	points	−3.28	Alabama
531425	3.19	points	−4.97	Arizona
381504	3.68	points	−4.35	Colorado
242379	2.62	points	−6.42	Connecticut
1416167	2.71	points	−3.97	Florida
565701	1.80	points	−3.34	Georgia
777655	2.90	points	−8.37	Illinois
359143	6.66	points	−7.17	Indiana
269558	3.08	points	−0.810	Louisiana
437262	3.07	points	−4.04	Maryland
471835	2.79	points	−4.24	Massachusetts
580836	6.58	points	−7.45	Michigan
297960	3.72	points	−7.80	Minnesota
279141	7.17	points	−6.23	Missouri
691343	1.88	points	−1.86	New Jersey
1319857	2.30	points	−2.36	New York
621319	4.22	points	−8.20	North Carolina
704336	8.32	points	−7.34	Ohio
214801	4.75	points	−9.98	Oregon
802576	6.36	points	−5.10	Pennsylvania
312029	4.49	points	−4.51	South Carolina
336327	5.53	points	−0.910	Tennessee
2167850	1.27	points	−0.630	Texas
249880	2.82	points	−2.72	Virginia
491503	3.66	points	−5.69	Washington
284970	5.53	points	−3.37	Wisconsin
245196	4.57	proportion	−7.62	Alabama
531425	3.19	proportion	−13.80	Arizona
381504	3.68	proportion	−14.86	Colorado
242379	2.62	proportion	−37.17	Connecticut
1416167	2.71	proportion	−9.96	Florida
565701	1.80	proportion	−20.60	Georgia
777655	2.90	proportion	−24.70	Illinois
359143	6.66	proportion	−21.06	Indiana
269558	3.08	proportion	−2.35	Louisiana
437262	3.07	proportion	−17.27	Maryland
471835	2.79	proportion	−17.13	Massachusetts
580836	6.58	proportion	−15.68	Michigan
297960	3.72	proportion	−18.02	Minnesota
279141	7.17	proportion	−12.11	Missouri
691343	1.88	proportion	−11.04	New Jersey
1319857	2.30	proportion	−7.29	New York
621319	4.22	proportion	−26.20	North Carolina
704336	8.32	proportion	−10.74	Ohio
214801	4.75	proportion	−25.39	Oregon
802576	6.36	proportion	−9.71	Pennsylvania
312029	4.49	proportion	−9.25	South Carolina
336327	5.53	proportion	5.28	Tennessee
2167850	1.27	proportion	−0.520	Texas
249880	2.82	proportion	−16.02	Virginia
491503	3.66	proportion	−21.40	Washington
284970	5.53	proportion	−10.92	Wisconsin

In the first view the dots slope down to the right, so states with more smoking have slopes farther below zero; in the second view the dots show no clear direction. Dot area is the state's births with a known answer. The correlations weight states by those births, and their intervals, from a bootstrap over states, are in the chapter text.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Note: Dots are the same 26 states as in the previous figure, with counties weighted by births, and the state slopes carry the wide intervals shown there; the correlation interval allows for the states drawn.Note: The state's smoking share is the births to mothers who reported smoking over births with a known answer, pooled over its complete counties.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); state slopes weighted by births with a known answer; correlations weighted by births, intervals from a bootstrap over states

The same proportional gradient gives a larger difference in points in a state with more smoking, so a steeper slope in points is expected there even if the slope in proportion is the same. The second view of the figure tests that. Written as the proportional difference in the smoking share for a 10% higher county income, the smoking share is 11.9% (95% CI 8.0% to 17.5%) lower. The tests of equal slopes give p = 0.664 weighted and 0.343 unweighted, the correlation with the state's smoking level is −0.19 (95% CI −0.56 to 0.37), an interval that includes zero. Of the 26 estimated slopes, 25 are below zero in proportion and 8 have an interval wholly below zero, and with each county counting once the counts are 26 and 12. The spread of the true proportional slopes is estimated at 2.1% (95% CI 0.0% to 6.5%) weighted and 2.7% (95% CI 0.0% to 7.0%) unweighted, in the units of the proportional slope, so the data are compatible with state slopes that differ moderately around the common value and do not show that they do. The proportional tests have little power: the upper limit of the interval for the unweighted spread sits beside a within-state slope of 16.7% lower, and a rejection in points with no rejection in proportion does not show that the scales differ in heterogeneity.

How the minimum number of counties changes the results

The floor of 8 counties is a choice. A lower floor adds states with a few counties, whose lines are unstable, and a higher floor drops states (Figure 7). Moving the floor from 3 to 10 counties changes the number of states from 41 to 23. The number of states with an interval wholly below zero stays between 9 and 11, and at no floor does an interval lie wholly above zero (0 states).

Figure 7

Raising the minimum number of counties from 3 to 10 leaves 9 to 11 states with an interval below zero and none above it, and the test of equal proportional slopes never rejects

States with a slope of their own, states with an interval below or above zero, and wild-bootstrap p-values for equal slopes in percentage points and in proportion, by the fewest complete counties a state needs, 2018 to 2024

Fewest complete counties	States	Counties	States with interval below zero	p, points, weighted	p, points, each county once	p, proportion, weighted	p, proportion, each county once
3	41	518	11	0.109	0.264	0.788	0.784
4	37	506	11	0.121	0.006	0.790	0.710
5	35	498	11	0.118	0.006	0.743	0.648
6	30	473	10	0.117	0.006	0.639	0.344
8	26	449	10	0.108	0.009	0.664	0.343
10	23	423	9	0.165	0.0005	0.601	0.319

Show data

Data for Raising the minimum number of counties from 3 to 10 leaves 9 to 11 states with an interval below zero and none above it, and the test of equal proportional slopes never rejects
below	counties	cutoff	p_points_unweighted	p_points_weighted	p_prop_unweighted	p_prop_weighted	states
11	518	3	0.264	0.109	0.784	0.788	41
11	506	4	0.006	0.121	0.710	0.790	37
11	498	5	0.006	0.118	0.648	0.743	35
10	473	6	0.006	0.117	0.344	0.639	30
10	449	8	0.009	0.108	0.343	0.664	26
9	423	10	0.0005	0.165	0.319	0.601	23

Read across a row for one cutoff. The count of states with an interval below zero barely moves, no interval lies above zero at any cutoff, and the p-values for proportional slopes stay above 0.319 in every row. In points the p-value depends on the weights: it is small for every cutoff above 3 counties when each county counts once and larger when counties are weighted by births.Note: A p-value tests that every state in the row has the same slope. It is the share of wild-bootstrap draws (replicates and seed in the methods note) in which Cochran's Q is at least as large as observed, so the smallest value it can take is one over the number of draws plus one.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); state slopes weighted by births or unweighted as labeled; p-values from a wild bootstrap

The tests depend on the weights more than on the floor. In proportion the smallest p-value across all floors and both weightings is 0.319. In points, with counties weighted by births, p runs from 0.108 to 0.165. With each county counting once p is at most 0.009 at every floor above 3 counties, and it is 0.264 at a floor of 3 counties, where the lines of the smallest states add noise. The conclusion that the state slopes differ in points rests on counting each county once.

The slope in points shrank and the slope in proportion grew

The within-state slope can be measured year by year for the same counties (Figure 8). In 2018 a doubling of county income went with a smoking share 4.9 (95% CI 3.5 to 7.0) points lower, and in 2024 with a share 2.4 (95% CI 1.7 to 3.4) points lower, so the slope became shallower by 2.5 (95% CI 1.7 to 3.7) points. In proportion the slope went the other way. A 10% higher county income went with a smoking share 10.1% (95% CI 6.3% to 15.1%) lower in 2018 and 14.7% (95% CI 11.6% to 19.3%) lower in 2024, a steepening of 4.5 (95% CI 3.3 to 5.8) points.

Figure 8

The within-state slope in points shrank from 4.9 to 2.4 points per doubling of county income from 2018 to 2024 while the slope in proportion grew from 10.1% to 14.7%

Within-state slope of the smoking share on county median household income (SAIPE 2022), by year of birth, 2018 to 2024, in percentage points per doubling of income or as a percent difference per 10% higher income, with 95% intervals from a bootstrap over states

Percentage points per doubling of county median household income

Show data

Data for The within-state slope in points shrank from 4.9 to 2.4 points per doubling of county income from 2018 to 2024 while the slope in proportion grew from 10.1% to 14.7%
conf_high	conf_low	measure	n	slope	year
−3.45	−6.98	points	45	−4.89	2018
−3.31	−6.71	points	45	−4.70	2019
−3.25	−6.62	points	45	−4.65	2020
−2.80	−5.76	points	45	−4.02	2021
−2.32	−4.78	points	45	−3.31	2022
−1.99	−4.03	points	45	−2.81	2023
−1.70	−3.36	points	45	−2.39	2024
−6.33	−15.12	proportion	45	−10.13	2018
−6.88	−15.88	proportion	45	−10.87	2019
−7.56	−16.68	proportion	45	−11.73	2020
−7.69	−17.47	proportion	45	−12.18	2021
−9.10	−18.22	proportion	45	−12.86	2022
−10.07	−18.86	proportion	45	−13.82	2023
−11.61	−19.29	proportion	45	−14.67	2024

In the first view the line climbs toward zero each year as smoking falls, and in the second it falls each year, so the gradient flattens in points and steepens in proportion. Each point is a state fixed-effects slope with counties weighted by births with a known answer; the same 530 counties are in every year.Note: Large counties are the counties that CDC WONDER names individually: those with 100,000 residents or more. WONDER pools the births of every smaller county in a state into one row, so smaller counties are not in these data. Of the 578 large counties named in every year from 2018 to 2024, California (35) and 13 counties with a withheld smoking count are left out, which leaves 530.Note: County median household income is the County Health Rankings 2024 release, a Census Small Area Income and Poverty Estimates value for 2022. Its year differs from the births, which are from 2018 to 2024.Note: Intervals are 95% intervals from a bootstrap over states (replicates and seed in the methods note), which allows for counties in one state resembling each other. The same counties are used in every year, and each is published in every year.Note: The slopes are negative: a value farther below zero means a lower smoking share for a doubling of county income.Source: CDC WONDER natality, tobacco use during pregnancy, births in 2018-2024; full counts of births; counties ranked on County Health Rankings 2024 median household income (Census SAIPE 2022); slopes weighted by births with a known answer, intervals from a bootstrap over states

What moves smoking in pregnancy

Two studies of state policy, each reported under its own design, bear on what changes smoking in pregnancy. Neither measures the county gradient in this chapter.

Causal evidenceMatched comparison states around large tax increases, with an instrumental variable for birth weight

Cigarette taxes and smoking in pregnancy

Lien and Evans studied 4 states that adopted large cigarette tax increases and chose control groups of states by a statistical matching procedure. Smoking participation among pregnant women fell in the states with the increases relative to their matched controls.[3] The paper also uses the tax increases as an instrument for smoking in equations for birth weight, pooled across the experiments, and estimates that smoking in pregnancy raises the chance of a low-birthweight birth to 2 times its level without smoking. These statements come from the paper's abstract. The study estimates the effect of higher cigarette taxes on smoking and does not speak to why smoking differs between higher-income and lower-income counties.

Causal evidenceDifference-in-differences

State income support and maternal smoking

Markowitz and colleagues compared births in states with and without state Earned Income Tax Credit laws from 1995 to 2013, in a multi-state, multi-year difference-in-differences analysis with state and year-by-quarter fixed effects, standard errors clustered by state and a credit measured in the year before conception.[4] For tobacco use during pregnancy, a low state credit with no refund went with a probability of smoking 1.6 points lower, a high credit with no refund with a probability 0.9 points higher that is significant only at the weakest of the usual levels, and credits with a refund with no significant change. The authors find little difference in maternal health behaviors associated with the credits and write that the policies do not appear to reduce smoking. They find small improvements in birth weight and gestation in states with credits. The estimates are for mothers with a high-school education or less and for the laws in force from 1995 to 2013.

What these data cannot show

The comparison is between counties. A county with higher median household income has a lower smoking share, and that says nothing about the income of the mothers who smoked, which these data do not hold. The pattern is ecological and need not hold for the mothers inside the counties. The county tables staged for this book carry one stratifier each, so mother's education cannot be crossed with the tobacco item.

The item is reported at birth. The fall in the share from 5.4% to 1.9% can reflect less smoking, a change in how or whether the item is recorded, or a change in who gives birth, and these data cannot separate them. The item is named tobacco use and may include products other than cigarettes.

The counties are the 530 large counties that remain after the rules above, and they hold 66.9% of the births in the United States. California is out because its item is not collected routinely, and 13 counties are out because their smoking count is withheld in some year, a rule that selects on the outcome. Smaller counties are not in these data, so the chapter says nothing about them. The income measure is an estimate for 2022 and the births are from 2018 to 2024, so a county's income stands in for its income in earlier years. The Area Deprivation Index describes neighborhoods in 2019 to 2023.

State slopes rest on 8 counties or more, and the states with a slope hold 449 counties in all. The intervals use a t distribution and standard errors that allow for unequal variance, and they can be too narrow where one county dominates the line. The tests have little power to detect moderate differences, their p-values depend on how counties are weighted, and a test that finds no difference does not show equality. A slope per doubling of income is a statement about a difference larger than most states' own counties span. None of this identifies why smoking is less common where incomes are higher, and the studies in the box above are the only evidence here on what changes it.

Data sources

CDC WONDER natalityCDC WONDER natality, county by tobacco use during pregnancy and county totals, births in 2018 to 2024 (consolidated pulls); 2024 is finalYears: 2018-2024 · Weights: none (full counts of births); shares pooled over counties, births-weighted or unweighted as stated · License: Public domain (NCHS, CDC); WONDER data-use restrictions apply
County Health RankingsCounty Health Rankings 2024 release, median household income (Census Small Area Income and Poverty Estimates, 2022)Years: 2024 release · Weights: none (published county values) · License: CC BY 4.0 (University of Wisconsin Population Health Institute)
Area Deprivation IndexNeighborhood Atlas Area Deprivation Index 2023 (v4.0.1), tract national rank, averaged to counties with 2019-2023 tract population; used as a check on the income measureYears: 2023 release (version 4.0.1; built on ACS 2019-2023) · Weights: 2019-2023 tract population · License: Free for non-commercial use; cite the Neighborhood Atlas and Kind and Buckingham
Published estimates from cited studiesFindings of two studies of state policy and maternal smoking: the abstract of Lien and Evans (2005) and Table 2 of Markowitz and coauthors (2017)Years: as reported in each study · Weights: not applicable · License: Quoted from published research for commentary

All data sources

Methods

This chapter compares the share of births with tobacco use reported during pregnancy across counties and states, using county median household income (a 2022 estimate) as the predictor for births in 2018 to 2024, so the outcome and the predictor come from different periods. The counties are those that CDC WONDER names (100,000 residents or more) in every year and that publish every smoking count: California is left out because it does not routinely collect the item, and counties with a withheld smoking count are left out, which removes counties where smoking in pregnancy was rarest. Slopes are shown weighted by births and with each county counting once, and the two weightings differ in size. A state's own slope rests on a handful of complete counties, so state intervals are wide.

Which births, counties and years are used

Births are counts from CDC WONDER natality for 2018 to 2024 (2024 is final), by county of the mother's residence and by tobacco use during pregnancy, a birth-certificate item with the answers Yes, No, Unknown or Not Stated and Not Reported.

WONDER names a county only if it has 100,000 residents or more. The universe is the 578 counties named in every year, which hold 79.5% of births in the United States in 2018 to 2024. California (35 counties) is left out because WONDER states that California does not routinely collect the item, and its reported share for 2018 is 1.2% against 5.4% in the counties kept.

WONDER withholds cells of 1 to 9 births. A withheld cell is missing, and nothing is derived from a county total minus the published cells. 13 counties outside California have a withheld Yes cell in at least one year (35 cells, which could hold at most 315 births, or 0.05% of smoking births) and are left out of every estimate, so that no county is pooled over only its published years. The 530 complete counties are therefore selected on having at least 10 smoking births in every year, which removes counties where smoking in pregnancy was rarest.

Figure explained here: Figure 2

How the smoking share and county income are defined

A county's smoking share is the Yes births over the Yes plus No births, so births with an Unknown or Not Stated answer are left out of the denominator; the Unknown cells that WONDER publishes hold 0.4% of births, and 1,794 of 3,710 Unknown cells are withheld and could hold at most 0.1%. Not Reported is a published zero in all 4,046 county-years.

County income is the County Health Rankings 2024 release of median household income, a Census Small Area Income and Poverty Estimates value for 2022; a slope is per doubling (log base 2) of income. A slope in proportion is the slope of the natural log of the share, shown as the proportional difference for a 10% higher income. The income measure is checked against the county Area Deprivation Index, the population-weighted mean of tract national ranks.

Figures explained here: Figure 1 and Figure 3

How counties are weighted and mapped

Slopes across counties, within states (state fixed effects) and between states (state means) are least-squares slopes, weighted by births with a known answer or unweighted.

Maps draw every county of the book's county map; counties that WONDER does not name, California counties and counties with a withheld count have no value.

Figures explained here: Figure 4 and Figure 2

How each state's slope is estimated

A state's own slope is a weighted least-squares line through its complete counties with HC3 standard errors and a t interval with the counties minus two degrees of freedom, for states with at least 8 complete counties, with cutoffs of 3 to 10 as a check.

Figures explained here: Figure 5 and Figure 7

How uncertainty is measured

Shares and differences of shares pool births over counties and carry 95% percentile intervals from a bootstrap that resamples whole counties (2,000 replicates, seed 12345) with the same draws for every statistic, so a difference or a ratio has its own interval.

The slopes across counties, within states and between states carry percentile intervals from a bootstrap that resamples whole states with the same replicate count and seed.

How the test of equal state slopes is run

The test that every state has the same slope is Cochran's Q of the state slopes with their HC3 variances.

Q is too liberal with a handful of counties per state, so its p-value comes from a restricted wild bootstrap (2,000 draws, seed 12345): residuals from a model with a state intercept and one common slope are multiplied by random signs within county, each state's slope and error are refit, and Q is recomputed.

The spread of the true state slopes (tau) is the DerSimonian-Laird estimate with a percentile interval from resampling states.

Figure explained here: Figure 6

References

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. doi:10.1056/NEJMp1802313
DerSimonian, Rebecca; Laird, Nan (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7(3): 177-188. doi:10.1016/0197-2456(86)90046-2
Lien, Diana S.; Evans, William N. (2005). Estimating the Impact of Large Cigarette Tax Hikes: The Case of Maternal Smoking and Infant Birth Weight. Journal of Human Resources 40(2): 373-392. doi:10.3368/jhr.xl.2.373
Markowitz, Sara; Komro, Kelli A.; Livingston, Melvin D.; Lenhart, Otto; Wagenaar, Alexander C. (2017). Effects of state-level Earned Income Tax Credit laws in the U.S. on maternal health behaviors and infant health outcomes. Social Science & Medicine 194: 67-75. doi:10.1016/j.socscimed.2017.10.016

How to cite

@misc{singh2026smokinginpregnancy,
  author = {Singh, Vishal},
  title = {Smoking in pregnancy},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/smoking-in-pregnancy},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}