Map 1 Percent of Low Birth Weight Infants by County, … of Low Birth Weight Infants by County,...

217

Map 1 Percent of Low Birth Weight Infants by County, 2010-2014 U.S. = 8.0% South Dakota = 6.4%

X Denotes that the county’s percent is significantly lower than the state percent O Denotes that the county’s percent is significantly higher than the state percent Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual percent may be higher or lower than the state percent, the difference may not be statistically significant due to the small number of people in the county. The percent of low birth weight infants is calculated based on the first weight of the newborn obtained after birth. Low birth weight infants are those born alive who weigh less than 2,500 grams (about 5 pounds 9 ounces). The U.S. percent of low birth weight infants is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

218

Map 2 Percent of Mothers Receiving Prenatal Care in the 1st Trimester by County, 2010-2014

U.S. = 71.0%* South Dakota = 71.1%

X Denotes that the county’s percent is significantly lower than the state percent. O Denotes that the county’s percent is significantly higher than the state percent. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual percent may be higher or lower than the state percent, the difference may not be statistically significant due to the small number of people in the county. The U.S. percent of mothers receiving prenatal care in the 1st trimester is based on 27 states that were using the 2003 standard birth certificate, including South Dakota. See technical notes for more complete explanations. *Due to lack of comparable data from all 50 states, U.S. and South Dakota cannot be compared. The U.S. percent of first trimester prenatal care is from 2008. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 64.3%

O Lincoln83.7%

Bon Homme 79.5%

Douglas 71.6%

O Davison78.0%

Hanson 62.2%

O Clay

79.1%

O Yankton 83.4%

O Union86.7%

Sanborn 69.4%

X Buffalo 36.3%

McCook 73.0%

O Codington 79.4%

Jerauld 68.2%

Hutchinson 67.0%

O Minnehaha 74.4%

Moody 65.1%

Hamlin 71.9%

Walworth 62.6%

O Brookings 77.4%

Campbell 78.3%

Miner 69.6%

Lake 73.1%

O Lawrence

81.1% Kingsbury 72.9%

Turner 78.4%

Deuel 81.0%

Charles Mix 65.1%

Grant 78.1%

McPherson 61.9%

Aurora 68.0%

X Hughes 60.9%

X Marshall 60.5%

Brule 67.8%

Edmunds 69.6%

Hyde 68.1%

Potter 61.7%

Gregory 60.5%

X Clark 59.9%

Jones 74.0%

Faulk 57.9%

X Roberts 54.5%

X Oglala Lakota 53.9%

X Sully 53.8%

X Bennett 43.2%

Day 65.3%

X Mellette 50.3%

X Beadle 55.0%

X Todd 40.1%

Stanley 62.8%

Hand 61.0%

Spink 69.7%

Custer 72.1% X Jackson

49.4%

Tripp 66.8%

Haakon 68.3%

X Lyman 48.1%

Pennington 72.3%

X Ziebach 48.5%

O Brown 75.5%

Butte 78.6%

Harding 74.3%

X Corson 46.0%

X Dewey 49.3%

Perkins 65.9%

O Meade 77.9%

Less than or equal to 62.2

62.3-72.3

Greater than or equal to 72.4

219

X Denotes that the county’s percent is significantly lower than the state percent. O Denotes that the county’s percent is significantly higher than the state percent. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual percent may be higher or lower than the state percent, the difference may not be statistically significant due to the small number of people in the county. The data for mothers who used tobacco while pregnant are self-reported on the birth certificates. The U.S. percent of mothers who used tobacco while pregnant is from 2008 and is based on 24 states using the 2003 standard birth certificate, including South Dakota. See technical notes for more complete explanations. *Due to lack of comparable data from all 50 states, U.S. and South Dakota cannot be compared. Source: South Dakota Department of Health, Office of Health Statistics.

Map 3 Percent of Mothers Who Used Tobacco While Pregnant by County, 2010-2014

U.S. = 9.7%* South Dakota = 16.1%

O Corson20.9%

Fall River 20.6%

X Lincoln 6.4%

X Bon Homme10.3%

X Douglas9.1%

ODavison19.1%

XHanson

7.1%

XClay

10.0%

O Yankton21.0%

Sanborn 15.2%

XUnion11.2%

O Buffalo33.5%

XMcCook10.9%

Codington 23.2%

Jerauld 15.9%

X Hutchinson8.5%

X Minnehaha13.5%

OMoody20.7%

Hamlin 14.9%

Walworth 20.3%

X Brookings11.5%

Campbell LNE

Miner 10.6%

OLawrence

19.2%

X Lake11.7%

Kingsbury 13.9%

X Turner10.8%

Deuel 15.7%

Charles Mix 23.4%

XGrant12.3%

McPherson 14.0%

Aurora 13.3%

Hughes 18.8%

Marshall 17.7%

Brule 17.3%

X Edmunds10.6%

Hyde 13.0%

Potter 15.0%

Gregory 16.5%

X Clark10.4%

Jones 20.0%

ORoberts27.3%

OOglala Lakota

20.1%O Bennett

22.9%

O Day29.6%

Mellette 20.8%

X Faulk2.9%

Beadle 15.5%

X Sully8.6%

Todd 25.3%

Stanley 17.5%

Spink 16.5%

Custer 19.2% Jackson

19.4%

O Tripp21.7%

Haakon 11.0%

O Lyman28.1%

OPennington

17.5%

Ziebach 22.1%

OBrown18.3%

X Hand 8.4%

O Butte26.0%

X Harding 5.3%

O Dewey26.9%

Perkins 12.0%

Meade 15.0%


13.1-19.4


Low Number Events

O

220

Map 4 Percent of Births Less Than 37 Weeks Gestation by County, 2010-2014

U.S. = 9.6% South Dakota = 8.2%

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual percent may be higher or lower than the state percent, the difference may not be statistically significant due to the small number of people in the county. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 7.4%

Lincoln 7.6%

Bon Homme

8.5%

Douglas 5.7%

Davison 8.7%

Hanson 6.7%

Yankton 8.4%

Sanborn 5.1%

McCook 9.6%

Codington 6.9%

Buffalo 8.4%

Jerauld 7.6%

Hutchinson 8.4%

Clay 6.8%

Minnehaha 8.1%

Moody 10.8%

X Hamlin4.2%

Union 9.3%

Walworth 7.3%

X Brookings6.3%

Campbell 6.8%

Lawrence 9.6% X Kingsbury

5.3%

Turner 6.3%

Charles Mix 8.6%

X Miner2.7%

Lake 6.8%

McPherson 6.6%

Hughes 7.7%

Marshall 6.7%

Aurora 8.9%

XDeuel3.0%

X Grant4.7%

Brule 10.7%

X Edmunds4.9%

Hyde 10.1%

Gregory 6.2%

Jones 12.0%

Roberts 9.7%

Potter 5.8%

OOglala Lakota

11.7%Bennett 8.6%

Mellette 6.7%

Clark 5.4%

Faulk 7.1%

Beadle 8.3%

Sully 9.9%

Day 7.7%

Stanley 7.2%

Hand 11.1%

OJackson12.6%

X Haakon3.0%

Custer 7.4%

Todd 9.9%

Pennington 9.0%

Ziebach 12.3%

Lyman 7.8%

Spink 8.0%

Brown 7.3%

Tripp 8.4%

Harding 5.4%

Corson 11.7%

Dewey 9.4%

Perkins 6.0%

Butte 8.5%

Meade 8.0%


6.9-8.5


221

Map 5 Average Age of Mother by Resident County, 2010-2014

U.S. = N/A South Dakota = 27.4

X Denotes that the county’s age is significantly lower than the state age. O Denotes that the county’s age is significantly higher than the state age. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual age may be higher or lower than the state age, the difference may not be statistically significant due to the small number of people in the county. *Due to lack of comparable data from all 50 states, U.S. and South Dakota can not be compared. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 26.8

OLincoln

29.2

O Bon Homme

28.1

Douglas 27.6

XDavison

26.7

OHanson

29.8

Yankton 27.2

O Sanborn28.4

OMcCook

28.6

Codington 27.2

X Buffalo25.8

O Jerauld28.4

O Hutchinson28.5

O Minnehaha27.9

Hamlin 27.5

Moody 27.5

OUnion28.8

X Walworth26.3

O Brookings28.2

O Campbell28.7

Lawrence 27.5

OClay28.0

O Kingsbury28.6

O Turner28.5X

Charles Mix25.7

O McPherson29.3

O Miner28.7

Hughes 27.5

Marshall 27.8

Aurora 28.1

Deuel 27.4

O Lake28.3

Edmunds 28.0

O Grant28.0

Gregory 27.5

XRoberts

25.5

Potter 28.1

XOglala Lakota

24.3X Bennett

25.0

Brule 27.4

Jones 26.8

Hyde 27.6

XMellette

24.6

X Beadle26.6

O Faulk29.6

O Clark28.6

Stanley 28.0

Sully 28.3

Day 27.3

X Jackson25.5

O Haakon28.7

Custer 27.9

X Pennington26.9

X Ziebach25.8

X Lyman26.1

Hand 28.1

Spink 27.9

Brown 27.4

X Todd24.7

Tripp 27.1

Harding 28.4

X Corson25.4

X Dewey25.4

O Perkins28.9

X Butte26.7

Meade 27.7


27.3-28.1


222

Map 6 Teenage Birth Rate by Resident County, 2010-2014

U.S. = 12.3 South Dakota = 14.8

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The teenage birth rate is live births per 1,000 females age 15-17. The U.S. teenage birth rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 14.4

XLincoln

6.1

BonHomme

LNE

Douglas 9.3

Davison 13.4

Hanson 7.2

Yankton 12.9

Sanborn LNE

McCook 7.7

X Codington8.4

OBuffalo

80.3

Jerauld LNE

X Hutchinson6.8

Minnehaha 13.7

Hamlin 14.0

Moody 18.5

XUnion

4.4

Walworth 14.1

X Brookings4.0

Campbell LNE

Lawrence 11.5 Kingsbury

LNE

XClay5.0

Turner LNEO

Charles Mix26.8

McPherson LNE

Miner LNE

X Hughes7.8

Marshall 9.2

Aurora LNE

Deuel 6.7

Lake 9.2

Edmunds 6.6

Grant 8.6

Gregory 19.5

ORoberts

34.9

Potter LNE

OOglala Lakota

55.2O Bennett

45.5

XBrule6.3

Jones LNE

Hyde LNE

O Mellette44.4

Beadle 20.9

Faulk LNE

Clark LNE

Stanley LNE

Sully LNE

Day 15.9

O Jackson45.8

Haakon LNE

Custer 8.8

Pennington 15.1

OZiebach

35.4

Lyman 31.2

Hand 9.3

Spink 8.0

X Brown10.2

OTodd56.2

Tripp 9.4

Harding LNE

O Corson34.7

O Dewey45.9

Perkins LNE

Butte 13.9

X Meade8.8


8.7-15.1


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223

Map 7 Death Rate Due to All Causes by County, 2010-2014

U.S. = 731.9 South Dakota = 704.1

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. The U.S. age-adjusted death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

O Fall River909.5

XLincoln479.4

X Bon Homme

580.4

Douglas 716.4

Davison 685.1

Hanson 683.7

OBuffalo1243.7

XYankton642.4 Clay

769.1

Sanborn 753.4

OMcCook

847.3

Codington 667.9

Jerauld 621.9

Hutchinson 665.8

Minnehaha 713.5

Union 650.5

Hamlin 760.5

Moody 643.5

O Walworth794.0

Brookings 660.4

Campbell 595.3

XLawrence

639.5 Kingsbury 765.7

Miner 697.2

X Lake627.3

Turner 724.6

Charles Mix 709.5

McPherson 669.3

X Deuel566.3

Hughes 649.2

Marshall 780.3

Aurora 648.1

Grant 666.0

X Edmunds614.1

O Gregory821.6

Hyde 657.8

Potter 614.6

O Roberts858.4

OOglala Lakota

1302.7O Bennett

939.7

Clark 743.1

Jones 591.0

X Faulk555.8

Brule 685.0

O Mellette978.5

X Sully480.9

Day 656.6

Beadle 716.4

O Todd1288.9

Stanley 585.0

O Jackson1016.9

X Hand568.9

Haakon 742.0

X Custer627.0

Spink 709.7

X Pennington645.2

Ziebach 784.6

O Lyman850.6

Tripp 716.6

Brown 718.3

O Dewey1331.7

O Corson1213.8Harding

612.4

Butte 768.2

O Perkins819.6

Meade 678.4


650.6-743.1


224

Map 8 Death Rate Due to Heart Disease by County, 2010-2014 U.S. = 169.8 South Dakota = 152.7

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Heart disease is defined as ICD-10 codes I00-I09, I11, I13, and I20-I51. The U.S. age-adjusted Heart Disease death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Douglas 147.5

ODavison

186.1Hanson 188.3

Yankton 155.4

OClay236.8

Sanborn 116.5

OMcCook

213.8

Codington 143.1

Buffalo 146.5

Jerauld 117.1

Hutchinson 149.9

Minnehaha 143.3

Union 167.8

Hamlin 147.4

Moody 152.7

Walworth 151.9

Brookings 138.4

Campbell 179.0


180.9

Miner 178.0

Lake 135.9

Turner 161.1Charles Mix

181.1

O McPherson214.3

Deuel 141.2

Hughes 134.5

Marshall 161.4

Aurora 163.5

Grant 143.5

Edmunds 127.3

O Gregory207.5

Brule 150.7

O Hyde257.9

Potter 124.5

Roberts 179.1

Oglala Lakota 154.1

Bennett 197.9

Clark 159.2

X Jones81.0

Faulk 110.3

Mellette 117.5

Day 162.8

Sully 117.7

Beadle 154.4

X Stanley89.6

Todd 206.4

Jackson 171.1

X Hand103.9

Haakon 183.6

X Custer119.1

Spink 136.0

Pennington 147.2

Ziebach 228.4

Lyman 201.8

Tripp 149.8

Brown 160.4

Harding 132.0

O Corson244.4

O Dewey252.5

O Butte 199.4

Perkins 132.9

Meade 143.8

O Fall River 205.0

XLincoln100.5

X Bon Homme 115.5Less than or equal to 143.1

143.2-167.8


225

Map 9 Death Rate Due to All Malignant Neoplasms (Cancer) by County, 2010-2014

U.S. = 163.2 South Dakota = 163.2

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Malignant Neoplasms (Cancer) are defined as ICD-10 codes C00-C97. The U.S. age-adjusted Malignant Neoplasms (Cancer) death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 188.6

XLincoln122.4

Bon Homme135.4

Douglas 185.5

Davison 172.8

Hanson 179.3

Yankton 145.0

Sanborn 183.9

McCook 206.7

Codington 175.1

Buffalo 271.8

Jerauld 173.9

X Hutchinson127.6

O Minnehaha179.5

Union 159.3

Hamlin 138.9

Moody 171.5

Walworth 175.3

Brookings 169.1

XCampbell

104.3


167.7

Miner 147.7

Lake 151.2

Clay 175.0

Turner 166.3

Charles Mix 161.0

XMcPherson

114.8

XDeuel106.5

Hughes 155.6

Marshall 174.6

Aurora 162.7

XGrant129.4

Edmunds 142.1

Gregory 170.6

Brule 154.7

Hyde 118.8

Potter 124.3

Roberts 199.1

OOglala Lakota

216.8Bennett 179.6

Clark 170.6

Jones 128.6

Faulk 161.1

Mellette 228.0

Sully 167.3

XDay

124.4

Beadle 164.9

Stanley 158.7

Todd 206.9

Jackson 178.5

Hand 132.7

Haakon 174.2

Custer 147.9

Spink 148.5

Pennington 160.6

XZiebach

57.1

Lyman 139.6

Tripp 149.6

Brown 167.1

XHarding

66.8

Corson 210.0

O Dewey230.3

Butte 174.7

Perkins 212.1

Meade 152.2


149.7-173.9


226

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Trachea, Bronchus, and Lung Cancer are defined as ICD-10 codes C33-C34. The U.S. age-adjusted Trachea, Bronchus, and Lung Cancer death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 10 Death Rate Due to Trachea, Bronchus, and Lung Cancer by County, 2010-2014


Fall River 55.0

Lincoln 36.0

Bon Homme

39.8

Douglas 34.6

Davison 53.6

Hanson 62.4

Yankton 43.1

Sanborn 37.4

McCook 67.4

Codington 49.9

Buffalo 91.5

Jerauld 28.5

X Hutchinson29.0

Minnehaha 49.5

Hamlin 27.0

Moody 30.9

Union 44.1

Walworth 48.2

Brookings 37.4

Campbell LNE

Lawrence 37.6

Clay 46.3

Kingsbury 44.1

Turner 44.4

Charles Mix 43.7

McPherson 34.3

Miner 49.1

X Hughes29.4

Marshall 35.8

Aurora 64.9

Deuel 36.0

Lake 36.6

Edmunds 27.5

Grant 30.1

Gregory 50.9

Roberts 47.2

Potter 51.6

Oglala Lakota 58.2

Bennett 31.9

Brule 45.8

Jones 53.8

Hyde 31.9

Mellette 61.3

Beadle 36.5

Faulk 37.9

Clark 38.0

Stanley 50.4

Sully 37.2

Day 30.6

Jackson 86.7

Haakon 35.3

Custer 30.1

Pennington 47.0

Ziebach LNE

Lyman 49.0

XHand24.0

Spink 45.8

Brown 48.3

Todd 38.5

XTripp25.2

Harding 30.4

Corson 32.7

Dewey 76.4

Perkins 34.5

Butte 51.6

Meade 46.0


35.9-47.0


Low Number Events

227

Map 11 Death Rate Due to Colorectal Cancer by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Colorectal Cancer is defined as ICD-10 codes C18-C21. The U.S. age-adjusted Colorectal Cancer death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 21.9

Lincoln 11.7

Bon Homme

16.0

Douglas 27.7

Davison 15.8

Hanson 30.0

Yankton 15.9

Sanborn 24.6

McCook 23.0

Codington 18.5

Buffalo 52.4

Jerauld 30.0

Hutchinson 10.3

Minnehaha 15.5

Hamlin 12.8

Moody 25.3

Union 18.8

Walworth 17.4

Brookings 18.9

Campbell 20.3


12.8

Clay 13.0

Turner 21.1

Charles Mix 16.6

McPherson 16.4

Miner 20.4

Hughes 22.4

Marshall 16.6

Aurora 11.8

Deuel LNE

Lake 11.9

Edmunds 15.0

Grant 10.2

Gregory 15.4

Roberts 16.7

Potter LNE

Oglala Lakota 24.9

Bennett 46.0

Brule 23.6

Jones LNE

Hyde LNE

Mellette 31.7

Beadle 14.4

Faulk 8.7

Clark 16.8

Stanley LNE

Sully LNE

Day 15.4

Jackson LNE

Haakon 32.9

Custer 15.5

X Pennington 11.9

Ziebach LNE

Lyman 31.7

Hand 12.8

Spink 19.7

Brown 18.0

Todd 37.6

Tripp 17.4

Harding LNE

Corson 42.1

Dewey 25.1

Perkins 31.0

Butte 14.8

Meade 14.6


15.5-21.1


Low Number Events

228

Map 12 Death Rate Due to Pancreatic Cancer by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Pancreatic Cancer is defined as ICD-10 code C25. The U.S. age-adjusted Pancreatic Cancer death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 7.4

Lincoln 7.8

Bon Homme

LNE

Douglas 8.9

Davison 11.6

Hanson LNE

Yankton 7.6

Sanborn LNE

McCook 15.0

Codington 10.8

Buffalo LNE

Jerauld LNE

Hutchinson 5.5

Minnehaha 11.0

Hamlin 15.1

Moody 14.7

Union 8.3

Walworth 5.7

Brookings 11.2

Campbell LNE


16.3

Clay 9.4

Turner 3.9Charles Mix

12.6

McPherson LNE

Miner LNE

Hughes 16.1

Marshall 24.1

Aurora LNE

Deuel LNE

Lake 10.8

Edmunds 16.3

Grant 7.5

Gregory 9.8

Roberts 7.1

Potter LNE

Oglala Lakota 17.9

Bennett 18.7

Brule 9.6

Jones LNE

Hyde LNE

Mellette LNE

Beadle 6.7

Faulk 14.2

Clark 7.9

Stanley LNE

Sully 31.0

Jackson LNE

Haakon LNE

Custer 5.7

Pennington 11.9

Ziebach LNE

Lyman LNE

Hand 19.0

Spink 9.5

Brown 11.6

Day 6.5

Todd LNE

Tripp 6.0

Harding LNE

Corson 15.4

Dewey LNE

Perkins LNE

Butte 11.6

Meade 11.2


8.4-12.6


Low Number Events

229

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Female Breast Cancer is defined as ICD-10 code C50. The U.S. age-adjusted Female Breast Cancer death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 13 Death Rate Due to Female Breast Cancer by County, 2010-2014 U.S. = 20.8 South Dakota = 20.0

Corson 33.3

Fall River 24.8

Lincoln 18.7

Bon Homme

23.0

Douglas 33.1

XDavison

10.4Hanson

LNE

Yankton 14.7

Sanborn LNE

McCook 20.5

Codington 19.0

Buffalo LNE

Jerauld LNE

Hutchinson 21.0

Minnehaha 23.1

Hamlin 9.4

Moody 11.9

Union 21.1

Walworth 19.5

Brookings 12.4

Campbell LNE

Lawrence 19.4

Clay 27.9

Kingsbury 16.3

Turner 33.5

Charles Mix 19.5

McPherson LNE

Miner LNE

Hughes 15.6

Marshall 19.9

Aurora 40.8

Deuel 16.3

Lake 21.9

Edmunds LNE

Grant 10.9

Gregory LNE

Roberts 24.8

Potter LNE

Oglala Lakota LNE

Bennett LNE

Brule 17.2

Jones LNE

Hyde LNE

Mellette LNE

Beadle 22.6

Faulk LNE

Clark 24.2

Stanley 34.6

Sully LNE

Day 32.7

Jackson LNE

Haakon LNE

Custer 19.6

Pennington 19.2

Ziebach LNE

Lyman 33.1

Hand LNE

Spink 20.5

Brown 24.9

Todd LNE

Tripp 13.0

Harding LNE

Dewey 36.5

Perkins 52.5

Butte 21.6

X Meade 8.5


19.1-23.1


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230

X Denotes that the county’s rate is significantly lower than the state rate.

O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Accidents are defined as ICD-10 codes V01-X59, Y85-Y86. The U.S. age-adjusted Accident death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 14 Death Rate Due to Accidents by County, 2010-2014


O Corson131.0

Fall River 55.0

XLincoln

24.4

Bon Homme

31.1

Douglas 42.1

Davison 36.4

Hanson 47.2

Yankton 43.4

Sanborn 61.2

McCook 54.2

Codington 39.4

Buffalo 88.6

Jerauld 38.9

Hutchinson 52.1

Minnehaha 40.4

Hamlin 47.2

Moody 40.5

Union 32.6

Walworth 39.9

X Brookings31.8

Campbell 103.9

XLawrence

34.0

Clay 33.7

Kingsbury 65.5

Turner 48.1

Charles Mix 52.1

McPherson 56.3

Miner 62.0

Hughes 38.5

Marshall 63.6

Aurora 49.5

Deuel 42.6

Lake 31.9

Edmunds 40.2

Grant 33.0

OGregory

91.8

ORoberts

78.5

Potter 58.5

OOglala Lakota

144.4Bennett

78.1

Brule 57.2

Jones LNE

Hyde LNE

Mellette 65.7

Beadle 42.1

XFaulk8.7

Clark 78.9

XStanley

18.8

Sully 28.1

Day 51.5

OJackson

106.5

Haakon 29.7

Custer 52.3

Pennington 44.0

Ziebach 105.8

O Lyman129.5

Hand 33.0

OSpink90.5

XBrown35.6

OTodd135.0

Tripp 55.8

Harding108.9

ODewey155.3

Perkins 70.3

Butte 47.7

Meade 36.7


40.3-58.5


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Map 15 Death Rate Due to Motor Vehicle Accidents by County, 2010-2014 U.S. = 10.9 South Dakota = 16.2

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Motor Vehicle Accidents are defined as ICD-10 codes (V02-V04, V09.0, V09.2, V12-V14, V19.0-V19.2, V19.4-V19.6, V20-V79, V80.3-V80.5, V81.0-V81.1, V82.0-V82.1, V83-V86, V87.0-V87.8, V88.0-V88.8, V89.0, and V89.2) The U.S. age-adjusted Motor Vehicle Accident death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Corson 32.7

Fall River 17.7

XLincoln

6.1

Bon Homme

23.5

Douglas LNE

XDavison

7.8Hanson

23.8

Yankton 16.3

Sanborn 43.2

McCook 34.6

Codington 12.3

Buffalo 26.5

Jerauld 16.3

Hutchinson 16.6

X Minnehaha 10.7

Hamlin 15.4

Moody 15.6

Union 9.1

Walworth 14.6

Brookings 10.3

Campbell LNE

Lawrence 10.9 O

Kingsbury48.4

Clay 13.0

Turner 29.2

Charles Mix 8.5

McPherson 22.4

Miner 38.0

Hughes 13.2

Marshall 25.8

Aurora LNE

Deuel 14.2

Lake 15.7

Edmunds 15.6

Grant 15.0

OGregory

63.7

ORoberts

40.7

Potter LNE

OOglala Lakota

61.7Bennett

45.2

Brule 8.1

Jones LNE

Hyde LNE

Mellette 56.2

Beadle 9.2

Faulk LNE

Clark 18.5

Stanley 18.8

Sully LNE

Day 17.8

OJackson

67.7

Haakon 19.4

Custer 25.6

X Pennington 9.3

Ziebach 33.0

OLyman75.9

Hand LNE

Spink 28.5

Brown 11.0

OTodd74.6

Tripp 21.2

Harding LNE

ODewey

82.8

Perkins 24.4

Butte 18.6

Meade 12.3


15.1-25.6


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232

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Alzheimer’s Disease is defined as ICD-10 code G30. The U.S. age-adjusted Alzheimer’s Disease death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 16 Death Rate Due to Alzheimer’s Disease by County, 2010-2014


Fall River 25.1

Lincoln 32.9

Bon Homme

30.5

Douglas 47.0

Davison 23.5

Hanson 24.7

Yankton 32.7

OSanborn

98.5

OMcCook

67.2

Codington 37.1

Buffalo 63.1

OJerauld

77.1

Hutchinson 31.0

OMinnehaha

43.7

O Hamlin105.8

OMoody63.6

Union 31.3

OWalworth

66.3

Brookings 38.8

Campbell 19.2

Lawrence 25.6

XClay19.6

Kingsbury 32.8

OTurner64.3

Charles Mix 51.5

McPherson 21.4

OMiner70.3

Hughes 32.8

Marshall 29.2

Aurora 26.0

Deuel 23.4

Lake 49.5

Edmunds 24.6

Grant 41.9

Gregory 22.6

Roberts 27.2

Potter 31.1

XOglala Lakota

15.3Bennett

LNE

Brule 30.4

Jones 26.6

Hyde 22.1

Mellette 23.7

Beadle 39.9

Faulk 39.1

Clark 35.1

Stanley LNE

Sully LNE

Jackson 19.1

Haakon LNE

Custer 24.6

Pennington 27.7

Ziebach LNE

Lyman 55.8

XHand9.8

Spink 47.8

OBrown48.5

Day 27.0

XTodd13.1

Tripp 53.1

Harding LNE

Corson 40.2

Dewey 22.6

Perkins 36.9

Butte 33.1

XMeade20.2


26.1-39.1


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X

233

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Cerebrovascular disease is defined as ICD-10 code I60-I69. The U.S. age-adjusted Cerebrovascular disease death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 17 Death Rate Due to Cerebrovascular Disease by County, 2010-2014


Fall River 31.3

Lincoln 31.3

Bon Homme

37.5

Douglas 38.6

Davison 41.1

Hanson 28.3

XYankton

26.9

Sanborn 34.9

McCook 55.1

Codington 33.2

Buffalo LNE

Jerauld 64.5

Hutchinson 47.8

Minnehaha 42.1

Hamlin 47.1

Moody 41.4

XUnion 22.4

Walworth 55.5

Brookings 48.5

Campbell 52.4

Lawrence 33.1

Clay 42.7

Kingsbury 41.2

X Turner 20.7

Charles Mix 26.3

McPherson 60.6

Miner 48.3

Hughes 53.6

O Marshall 72.6

Aurora 61.4

Deuel 36.7

Lake 54.5

X Edmunds 17.5

Grant 48.8

Gregory 40.6

Roberts 49.9

X Potter 21.0

Oglala Lakota 53.6

Bennett 44.9

Brule 27.9

Jones LNE

Hyde 69.0

Mellette 62.3

Beadle 43.5

Faulk 23.6

Clark 56.7

Stanley 30.8

Sully LNE

Day 55.7

Jackson LNE

Haakon 43.2

Custer 40.4

X Pennington 29.5

Ziebach 44.6

Lyman 44.4

Hand 38.4

Spink 31.3

Brown 39.7

O Todd 75.4

Tripp 28.9

Harding 67.4

Corson 32.2

Dewey 36.3

Perkins 33.2

Butte 31.1

Meade 43.0


33.3-47.1


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234

Map 18 Death Rate Due to Chronic Lower Respiratory Diseases by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Chronic Lower Respiratory disease is defined as ICD-10 code J40-J47. The U.S. age-adjusted Chronic Lower Respiratory disease death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Corson 46.5

OFall River

93.6

XLincoln

28.1

Bon Homme

37.9

Douglas 57.1

Davison 31.5

XHanson

12.6

Yankton 48.9

Sanborn 41.0

XMcCook

26.9

Codington 45.7

Buffalo LNE

Jerauld 42.0

Hutchinson 39.0

Minnehaha 47.1

Hamlin 46.7

XMoody21.9

Union 46.8

Walworth 43.0

XBrookings

30.9

Campbell LNE

OLawrence

62.1

Clay 37.8

Kingsbury 29.9

OTurner64.8

Charles Mix 29.9

McPherson 29.0

Miner 46.3

Hughes 45.8

Marshall 52.8

XAurora21.5

Deuel 45.3

Lake 41.6

Edmunds 47.0

Grant 48.7

Gregory 60.5

Roberts 38.3

Potter 34.8

Oglala Lakota 57.2

Bennett 39.3

Brule 28.1

Jones 45.2

Hyde LNE

Mellette 58.3

Beadle 37.3

Faulk 49.0

Clark 39.7

OStanley109.3

Sully LNE

Jackson 84.2

Haakon 54.1

Custer 41.1

X Pennington35.2

Ziebach 68.5

Lyman 55.1

Hand 50.8

Spink 62.4

Brown 42.8

Day 48.0

OTodd83.1

Tripp 37.9

Harding 42.3

Dewey 42.6

Perkins 28.6

Butte 43.4

Meade 52.9


39.1-47.1


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235

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Diabetes Mellitus is defined as ICD-10 codes E10-E14. The U.S. age-adjusted Diabetes Mellitus death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Map 19 Death Rate Due to Diabetes Mellitus by County, 2010-2014


OCorson

65.4

OFall River

47.6

XLincoln

9.4

BonHomme

20.3

Douglas 15.5

Davison 18.3

Hanson LNE

Yankton 25.1

Sanborn 19.0

McCook 25.4

Codington 24.7

Buffalo 66.3

Jerauld LNE

Hutchinson 19.4

XMinnehaha

17.2

Hamlin 22.1

Moody 32.8

XUnion11.4

Walworth 31.7

XBrookings

15.4

Campbell 29.8

Lawrence 25.0

Clay 31.2

Kingsbury 23.8

Turner 21.0

Charles Mix 33.2

McPherson 25.3

Miner 11.0

Hughes 20.5

Marshall 34.8

Aurora 31.9

Deuel 12.1

Lake 17.5

OEdmunds

52.8

Grant 35.2

Gregory 41.4

Roberts 37.9

Potter 34.9

OOglala Lakota

91.2Bennett

47.0

Brule 34.6

Jones LNE

Hyde LNE

Mellette 79.2

OBeadle

37.4

Faulk 34.8

Clark 21.4

Stanley 20.2

Sully LNE

Day 23.5

OTodd100.1

Jackson 44.3

Haakon LNE

XCuster11.3

X Pennington 10.5

Ziebach 54.6

Lyman 22.5

Hand 33.3

Spink 27.6

Brown 29.2

Tripp 16.9

Harding 51.4

ODewey

99.9

Perkins 16.7

Butte 29.6

Meade 20.2


21.1-33.3


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236

Map 20 Death Rate Due to Influenza and Pneumonia by County, 2010-2014 U.S. = 15.9 South Dakota = 16.2

X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Influenza and Pneumonia are defined as ICD-10 codes J09-J18. The U.S. age-adjusted Influenza and Pneumonia death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 21.3

XLincoln

10.3

Bon Homme

11.1

Douglas 14.4

Davison 24.0

Hanson 16.8

Yankton 15.3

Sanborn 19.7

McCook 17.0

Codington 14.7

Buffalo LNE

Jerauld LNE

Hutchinson 20.5

XMinnehaha

12.4

Hamlin 20.3

Moody LNE

Union 16.1

Walworth 14.7

Brookings 13.6

Campbell LNE

XLawrence

9.6

Clay 19.3

Kingsbury 26.5

Turner 9.8

Charles Mix 19.3

McPherson 8.6

Miner 17.1

Hughes 14.6

Marshall 22.8

Aurora 21.3

Deuel 18.4

XLake7.2

Edmunds 18.7

Grant 17.0

XGregory

7.4

Roberts 14.0

Potter 18.7

OOglala Lakota

49.2Bennett

48.6

Brule 20.7

Jones LNE

Hyde LNE

Mellette LNE

Beadle 23.8

Faulk 11.5

Clark 22.9

Stanley LNE

Sully LNE

Day 11.7

Jackson 21.2

OHaakon

46.7

Custer 14.7

Pennington 15.9

Ziebach LNE

Lyman 17.5

Hand 7.9

Spink 24.9

Brown 17.4

Todd 29.7

Tripp 23.5

Harding LNE

Corson 26.3

ODewey

54.2

Perkins 22.1

Butte 22.0

Meade 13.7


14.7-20.5


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Map 21 Death Rate Due to Intentional Self-Harm (suicide) by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Intentional Self-Harm (suicide) is defined as ICD-10 codes *U03,X60-X84,Y87.0. The U.S. age-adjusted Intentional Self-Harm (suicide) death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

OCorson

64.0

Fall River 17.0

XLincoln

8.9

Bon Homme

12.7

Douglas LNE

Davison 18.6

Hanson LNE

Yankton 18.4

Sanborn LNE

McCook 13.3

Codington 14.8

Buffalo 36.6

Jerauld LNE

Hutchinson LNE

Minnehaha 16.7

Hamlin 13.1

Moody 22.2

XUnion

8.8

Walworth 13.1

XBrookings

8.6

Campbell LNE

Lawrence 17.1

Clay 16.5

Kingsbury 27.8

Turner 18.3

Charles Mix 19.5

McPherson LNE

Miner LNE

Hughes 16.6

Marshall 18.4

Aurora LNE

Deuel 19.8

Lake 16.0

Edmunds LNE

Grant 9.4

Gregory LNE

Roberts 23.4

Potter LNE

OOglala Lakota

37.7Bennett

22.0

Brule 17.0

Jones LNE

Hyde LNE

Mellette 31.2

XBeadle

7.9

Faulk LNE

Clark LNE

Stanley 41.3

Sully LNE

Day 10.7

Jackson 31.9

Haakon LNE

Custer 11.5

Pennington 16.7

Ziebach LNE

Lyman LNE

Hand LNE

Spink LNE

Brown 11.6

OTodd52.7

Tripp LNE

Harding LNE

Dewey 41.8

Perkins 24.1

Butte 23.8

OMeade29.8


16.1-22.0


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Map 22 Death Rate Due to Chronic Liver Disease and Cirrhosis by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. The death rate is age-adjusted per 100,000 population. This eliminates age difference between populations, making them easier to compare. Chronic Liver Disease and Cirrhosis are defined as ICD-10 codes K70, K73-K74. The U.S. age-adjusted Chronic Liver Disease and Cirrhosis death rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Fall River 12.9

XLincoln

2.4

Bon Homme

LNE

Douglas 24.3

XDavison

5.3Hanson

LNE

XYankton

4.8

Sanborn LNE

McCook LNE

XCodington

5.1

O Buffalo 96.3

Jerauld LNE

Hutchinson LNE

Minnehaha 10.4

Hamlin LNE

Moody LNE

Union 9.3

Walworth 26

X Brookings4.1

Campbell LNE


12.3

Clay 7.4

Turner LNE

Charles Mix 8.9

McPherson LNE

Miner LNE

Hughes 11.8

Marshall 10.2

Aurora LNE

Deuel LNE

Lake 11.8

Edmunds LNE

Grant LNE

Gregory 24.7

Roberts 28.9

Potter LNE

OOglala Lakota

120.4Bennett

44.4

Brule LNE

Jones LNE

Hyde LNE

OMellette

79.0

Beadle 10.0

Faulk LNE

Clark LNE

Stanley LNE

Sully LNE

OJackson

66.2

Haakon LNE

Custer 10.0

Pennington 12.0

Ziebach 49.6

Lyman 19.0

Hand 12.7

Spink 15.7

XBrown

7.5

Day 7.8

OTodd70.8

Tripp 33.7

Harding LNE

OCorson

58.8

ODewey

98.0

Perkins LNE

Butte 11.9

Meade 11.6


10.1-24.3


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Map 23 Infant Mortality Rate by County, 2010-2014


X Denotes that the county’s rate is significantly lower than the state rate. O Denotes that the county’s rate is significantly higher than the state rate. Note: “Significantly” refers to statistical significance at the 0.05 level. Although a county’s actual rate may be higher or lower than the state rate, the difference may not be statistically significant due to the small number of people in the county. Infant mortality is calculated as the number of deaths to babies (less than 1 year old) per 1,000 live births. The U.S. infant mortality rate is from 2013. See technical notes for more complete explanations. Source: South Dakota Department of Health, Office of Health Statistics.

Corson 7.01

Fall River LNE

Lincoln 4.55

Bon Homme

9.40

Douglas LNE

Davison 5.31

Hanson 23.53

Yankton 6.85

Sanborn LNE

McCook LNE

Codington 5.25

Buffalo 18.94

Jerauld LNE

Hutchinson LNE

XMinnehaha

4.45

Hamlin LNE

Moody 8.81

Union 3.68

Walworth 8.67

Brookings 7.49

Campbell LNE


13.20

Clay 8.00

Turner LNE

Charles Mix 5.01

McPherson LNE

Miner LNE

Hughes 6.80

Marshall 16.67

Aurora LNE

Deuel LNE

Lake 7.68

Edmunds LNE

Grant LNE

Gregory 16.46

Brule 16.04

Roberts 7.78

Potter LNE

OOglala Lakota

15.05 OBennett25.28

Jones LNE

Hyde LNE

Mellette LNE

Beadle 11.89

Faulk LNE

Clark LNE

Stanley LNE

Sully LNE

Day LNE

OTodd14.58

Jackson 16.62

Haakon LNE

Custer LNE

Pennington 7.64

Ziebach 14.56

Lyman LNE

Hand LNE

Spink LNE

Brown 5.78

Tripp 9.01

Harding LNE

Dewey 11.20

Perkins LNE

Butte 9.30

Meade 4.34


6.86-11.20


Low Number Events

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