Open Access

U.S. congressional district cancer death rates

  • Yongping Hao1Email author,
  • Elizabeth M Ward1,
  • Ahmedin Jemal1,
  • Linda W Pickle2 and
  • Michael J Thun1
International Journal of Health Geographics20065:28

DOI: 10.1186/1476-072X-5-28

Received: 11 May 2006

Accepted: 23 June 2006

Published: 23 June 2006

Abstract

Background

Geographic patterns of cancer death rates in the U.S. have customarily been presented by county or aggregated into state economic or health service areas. Herein, we present the geographic patterns of cancer death rates in the U.S. by congressional district. Many congressional districts do not follow state or county boundaries. However, counties are the smallest geographical units for which death rates are available. Thus, a method based on the hierarchical relationship of census geographic units was developed to estimate age-adjusted death rates for congressional districts using data obtained at county level. These rates may be useful in communicating to legislators and policy makers about the cancer burden and potential impact of cancer control in their jurisdictions.

Results

Mortality data were obtained from the National Center for Health Statistics (NCHS) for 1990–2001 for 50 states, the District of Columbia, and all counties. We computed annual average age-adjusted death rates for all cancer sites combined, the four major cancers (lung and bronchus, prostate, female breast, and colorectal cancer) and cervical cancer. Cancer death rates varied widely across congressional districts for all cancer sites combined, for the four major cancers, and for cervical cancer. When examined at the national level, broad patterns of mortality by sex, race and region were generally similar with those previously observed based on county and state economic area.

Conclusion

We developed a method to generate cancer death rates by congressional district using county-level mortality data. Characterizing the cancer burden by congressional district may be useful in promoting cancer control and prevention programs, and persuading legislators to enact new cancer control programs and/or strengthening existing ones. The method can be applied to state legislative districts and other analyses that involve data aggregation from different geographic units.

Background

Cancer death rates presented by geographic boundaries such as state and county, state economic areas, and health service areas have been useful in monitoring temporal trends in allocating public health resources [1, 2], and in some instances, in generating etiological hypotheses. These rates are less useful for communicating to legislators and policy makers whose jurisdictions are not defined by state or county boundaries. There have been no published studies that attempted to measure cancer death rates within congressional districts.

Public policy and legislation play a critically important role in efforts to reduce the burden of cancer. For example, the American Cancer Society estimates that in 2006 about 170,000 of the 564,830 cancer deaths are expected to be caused by tobacco use alone [3]. Policy measures that are proven to reduce smoking prevalence include excise taxes and funding for state comprehensive tobacco control programs [46]. Declines in smoking prevalence among men as a result of public health efforts have had a major influence on the declines in cancer mortality in the last decade.

We present a method to calculate cancer death rates according to congressional district that may be useful in advocating for legislative initiatives and funding for cancer research and prevention programs.

Results and discussion

Maps of cancer death rates by congressional district were prepared for men and women, for all races combined, and for African Americans, non-Hispanic whites, and Hispanics (Figures 1, 2, 3, 4, 5); Hispanics are not mutually exclusive of whites and African Americans. Regional patterns of cancer mortality for African Americans and non-Hispanic whites were compared to previously published maps based on counties and state economic areas [1]. Although maps of cancer mortality by congressional district were also prepared for Hispanics, regional patterns are difficult to interpret because of insufficient data to calculate rates for most parts of the country. When examined at the national level, broad patterns of mortality for African Americans and non-Hispanic whites by sex and region were consistent with those previously observed [1]. Geographic variations in cancer death rates may reflect, in part, regional variations in risk factors such as smoking and obesity, early detection and screening, and access to and utilization of medical services.
https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_Fig1_HTML.jpg
Figure 1

All cancers combined death rates per 100,000 person-years by congressional district (age-adjusted 2000 US population), 1990–2001.

https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_Fig2_HTML.jpg
Figure 2

Lung cancer death rates per 100,000 person-years by congressional district (age-adjusted 2000 US population), 1990–2001.

https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_Fig3_HTML.jpg
Figure 3

Colorectal cancer death rates per 100,000 person-years by congressional district (age-adjusted 2000 US population), 1990–2001.

https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_Fig4_HTML.jpg
Figure 4

Prostate, female breast cancer death rates per 100,000 person-years by congressional district (age-adjusted 2000 US population), 1990–2001.

https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_Fig5_HTML.jpg
Figure 5

Cervical cancer death rates per 100,000 person-years by congressional district (age-adjusted 2000 US population), 1990–2001.

Figure 1 shows geographic patterns of death rates for all cancer sites combined by congressional district in the United States. In men, rates range from 186.3 in Utah congressional district #3 to 343.7 in District of Columbia (Table 1) and in women, from 123.4 in Utah congressional district #1 to 217.4 in Pennsylvania congressional district #2 (Table 2). Generally, the patterns for all cancer sites combined are strikingly similar to those for lung cancer (Figure 2), reflecting the importance of lung cancer as a cause of cancer death, and the strong association of lung and cancers of several other sites with tobacco smoking. Lung cancer death rates in all races combined range from 35.7 in Utah congressional district #1 to 130.3 in Kentucky congressional district #5 for men and from 14.8 in Utah congressional district #3 to 57.9 in Kentucky congressional district #5 for women. Lung cancer death rates are the highest in congressional districts in Appalachia and the south among non-Hispanic white men and in the Midwest and the south among African American men. In contrast, among women, rates are the highest in congressional districts in the Midwest among African Americans and in the west, Appalachia, and the coastal south among non-Hispanic whites. Historically, smoking was more common in the south among men and in the west among women, especially among whites [7]. Although patterns of lung cancer mortality in the 1990's primarily reflect smoking patterns in the 1950's and 1960's, the burden of death from all cancers and lung cancer by congressional district can be used to illustrate the importance of tobacco control measures as well as to document local needs for cancer treatment and associated services.
Table 1

Age-adjusted death rates, all cancers combined, for US men by congressional district (CD), 1990–2001

State

CD

Rate

State

CD

Rate

State

CD

Rate

State

CD

Rate

AL

0101

311.55

FL

1223

233.18

MN

2705

246.79

OR

4102

245.13

AL

0102

309.74

FL

1224

262.08

MN

2706

243.38

OR

4103

270.72

AL

0103

312.74

FL

1225

231.74

MN

2707

235.05

OR

4104

246.92

AL

0104

290.71

GA

1301

306.92

MN

2708

250.08

OR

4105

246.09

AL

0105

262.11

GA

1302

318.36

MS

2801

299.09

PA

4201

341.70

AL

0106

286.12

GA

1303

310.67

MS

2802

330.08

PA

4202

343.25

AL

0107

307.46

GA

1304

256.56

MS

2803

299.83

PA

4203

262.65

AK

0299

248.48

GA

1305

283.68

MS

2804

314.84

PA

4204

279.79

AZ

0401

205.84

GA

1306

271.97

MO

2901

282.13

PA

4205

250.82

AZ

0402

239.41

GA

1307

253.45

MO

2902

256.11

PA

4206

251.69

AZ

0403

229.35

GA

1308

283.26

MO

2903

298.52

PA

4207

276.22

AZ

0404

229.35

GA

1309

276.76

MO

2904

264.86

PA

4208

272.61

AZ

0405

229.35

GA

1310

276.81

MO

2905

277.15

PA

4209

253.47

AZ

0406

227.76

GA

1311

290.20

MO

2906

263.57

PA

4210

260.76

AZ

0407

211.10

GA

1312

295.19

MO

2907

272.91

PA

4211

274.08

AZ

0408

234.26

GA

1313

267.16

MO

2908

290.16

PA

4212

268.01

AR

0501

307.86

HI

1501

202.59

MO

2909

264.05

PA

4213

295.64

AR

0502

292.46

HI

1502

202.59

MT

3099

248.52

PA

4214

288.08

AR

0503

264.97

ID

1601

234.87

NE

3101

242.74

PA

4215

253.36

AR

0504

296.35

ID

1602

221.35

NE

3102

267.93

PA

4216

244.42

CA

0601

257.81

IL

1701

287.98

NE

3103

226.06

PA

4217

266.93

CA

0602

266.90

IL

1702

287.63

NV

3201

268.19

PA

4218

277.63

CA

0603

245.75

IL

1703

287.98

NV

3202

254.67

PA

4219

252.99

CA

0604

236.01

IL

1704

287.98

NV

3203

268.19

RI

4401

276.83

CA

0605

245.61

IL

1705

287.98

NH

3301

270.77

RI

4402

278.12

CA

0606

227.02

IL

1706

256.03

NH

3302

266.04

SC

4501

293.71

CA

0607

244.64

IL

1707

287.98

NJ

3401

292.38

SC

4502

279.65

CA

0608

244.76

IL

1708

265.27

NJ

3402

290.30

SC

4503

283.26

CA

0609

246.04

IL

1709

287.98

NJ

3403

277.44

SC

4504

280.26

CA

0610

242.33

IL

1710

269.31

NJ

3404

275.30

SC

4505

311.21

CA

0611

242.00

IL

1711

272.33

NJ

3405

259.29

SC

4506

313.81

CA

0612

232.65

IL

1712

296.31

NJ

3406

273.02

SD

4699

246.34

CA

0613

246.04

IL

1713

257.26

NJ

3407

260.46

TN

4701

288.63

CA

0614

216.61

IL

1714

248.91

NJ

3408

279.73

TN

4702

281.01

CA

0615

208.66

IL

1715

267.45

NJ

3409

260.33

TN

4703

293.12

CA

0616

208.66

IL

1716

266.46

NJ

3410

285.53

TN

4704

299.25

CA

0617

220.87

IL

1717

273.62

NJ

3411

253.50

TN

4705

301.32

CA

0618

248.61

IL

1718

274.38

NJ

3412

271.16

TN

4706

282.64

CA

0619

239.15

IL

1719

275.28

NJ

3413

283.59

TN

4707

295.64

CA

0620

235.22

IN

1801

297.56

NM

3501

224.30

TN

4708

299.44

CA

0621

231.25

IN

1802

273.64

NM

3502

227.97

TN

4709

323.86

CA

0622

241.10

IN

1803

264.13

NM

3503

205.63

TX

4801

298.28

CA

0623

216.41

IN

1804

278.64

NY

3601

272.33

TX

4802

302.76

CA

0624

218.17

IN

1805

265.45

NY

3602

269.70

TX

4803

251.80

CA

0625

234.12

IN

1806

271.20

NY

3603

245.27

TX

4804

280.20

CA

0626

239.12

IN

1807

310.26

NY

3604

236.48

TX

4805

296.25

CA

0627

229.74

IN

1808

287.76

NY

3605

225.59

TX

4806

281.01

CA

0628

229.74

IN

1809

286.44

NY

3606

222.78

TX

4807

277.95

CA

0629

229.74

IA

1901

259.56

NY

3607

247.39

TX

4808

282.93

CA

0630

229.74

IA

1902

250.56

NY

3608

247.21

TX

4809

302.08

CA

0631

229.74

IA

1903

256.54

NY

3609

229.07

TX

4810

242.29

CA

0632

229.74

IA

1904

242.92

NY

3610

242.88

TX

4811

272.71

CA

0633

229.74

IA

1905

244.45

NY

3611

242.94

TX

4812

272.87

CA

0634

229.74

KS

2001

236.43

NY

3612

240.42

TX

4813

267.39

CA

0635

229.74

KS

2002

254.68

NY

3613

263.79

TX

4814

267.50

CA

0636

229.74

KS

2003

243.40

NY

3614

241.66

TX

4815

200.38

CA

0637

229.74

KS

2004

259.82

NY

3615

251.70

TX

4816

223.16

CA

0638

229.74

KY

2101

301.17

NY

3616

267.24

TX

4817

270.88

CA

0639

229.74

KY

2102

302.60

NY

3617

255.21

TX

4818

277.95

CA

0640

224.83

KY

2103

319.57

NY

3618

245.32

TX

4819

258.34

CA

0641

248.53

KY

2104

311.74

NY

3619

263.83

TX

4820

252.64

CA

0642

232.32

KY

2105

314.33

NY

3620

266.28

TX

4821

247.33

CA

0643

253.34

KY

2106

306.21

NY

3621

267.14

TX

4822

263.97

CA

0644

225.41

LA

2201

313.23

NY

3622

270.59

TX

4823

226.97

CA

0645

225.51

LA

2202

341.56

NY

3623

278.23

TX

4824

275.61

CA

0646

226.08

LA

2203

317.11

NY

3624

257.38

TX

4825

276.05

CA

0647

224.82

LA

2204

314.28

NY

3625

266.60

TX

4826

250.08

CA

0648

224.82

LA

2205

321.98

NY

3626

270.45

TX

4827

229.00

CA

0649

232.00

LA

2206

302.08

NY

3627

271.37

TX

4828

231.66

CA

0650

235.70

LA

2207

307.17

NY

3628

268.37

TX

4829

277.95

CA

0651

235.62

ME

2301

272.57

NY

3629

268.26

TX

4830

279.05

CA

0652

235.70

ME

2302

291.59

NC

3701

325.75

TX

4831

258.48

CA

0653

235.70

MD

2401

293.67

NC

3702

307.11

TX

4832

279.05

CO

0801

247.17

MD

2402

300.57

NC

3703

312.42

UT

4901

188.85

CO

0802

216.40

MD

2403

306.03

NC

3704

276.61

UT

4902

194.50

CO

0803

218.01

MD

2404

261.33

NC

3705

270.43

UT

4903

186.38

CO

0804

217.45

MD

2405

293.74

NC

3706

269.53

VT

5099

262.46

CO

0805

230.10

MD

2406

268.50

NC

3707

303.46

VA

5101

294.08

CO

0806

205.15

MD

2407

331.59

NC

3708

295.65

VA

5102

291.22

CO

0807

223.10

MD

2408

212.85

NC

3709

280.84

VA

5103

335.68

CT

0901

252.15

MA

2501

266.20

NC

3710

283.71

VA

5104

321.70

CT

0902

255.68

MA

2502

273.91

NC

3711

251.18

VA

5105

278.86

CT

0903

253.05

MA

2503

272.89

NC

3712

273.38

VA

5106

270.54

CT

0904

237.15

MA

2504

275.28

NC

3713

274.86

VA

5107

289.48

CT

0905

246.80

MA

2505

268.96

ND

3899

243.02

VA

5108

228.11

DE

1099

289.44

MA

2506

270.11

OH

3901

295.76

VA

5109

274.86

DC

1198

343.78

MA

2507

271.96

OH

3902

293.68

VA

5110

258.25

FL

1201

287.59

MA

2508

295.36

OH

3903

284.95

VA

5111

231.79

FL

1202

287.22

MA

2509

283.39

OH

3904

274.64

WA

5301

245.00

FL

1203

285.46

MA

2510

269.84

OH

3905

262.93

WA

5302

234.80

FL

1204

316.89

MI

2601

261.34

OH

3906

287.57

WA

5303

255.32

FL

1205

256.17

MI

2602

248.17

OH

3907

276.90

WA

5304

240.69

FL

1206

281.19

MI

2603

245.36

OH

3908

271.26

WA

5305

246.75

FL

1207

262.33

MI

2604

260.27

OH

3909

287.34

WA

5306

260.08

FL

1208

262.72

MI

2605

278.91

OH

3910

293.92

WA

5307

239.57

FL

1209

265.45

MI

2606

266.81

OH

3911

293.92

WA

5308

244.02

FL

1210

249.68

MI

2607

263.88

OH

3912

281.32

WA

5309

249.13

FL

1211

277.62

MI

2608

253.12

OH

3913

277.94

WV

5401

278.03

FL

1212

265.00

MI

2609

247.44

OH

3914

266.04

WV

5402

296.32

FL

1213

225.69

MI

2610

272.66

OH

3915

293.41

WV

5403

298.58

FL

1214

215.92

MI

2611

284.77

OH

3916

259.50

WI

5501

265.84

FL

1215

252.94

MI

2612

263.76

OH

3917

272.68

WI

5502

235.97

FL

1216

236.00

MI

2613

300.81

OH

3918

280.22

WI

5503

244.95

FL

1217

238.61

MI

2614

300.81

OK

4001

270.44

WI

5504

285.86

FL

1218

239.87

MI

2615

272.06

OK

4002

295.23

WI

5505

247.27

FL

1219

225.47

MN

2701

234.69

OK

4003

252.79

WI

5506

248.00

FL

1220

241.08

MN

2702

232.60

OK

4004

263.30

WI

5507

253.68

FL

1221

237.96

MN

2703

246.78

OK

4005

273.49

WI

5508

252.81

FL

1222

228.26

MN

2704

253.04

OR

4101

239.29

WY

5699

240.61

Table 2

Age-adjusted death rates, all cancers combined, for US women by congressional district (CD), 1990–2001

State

CD

Rate

State

CD

Rate

State

CD

Rate

State

CD

Rate

AL

0101

178.15

FL

1223

166.84

MN

2705

167.95

OR

4102

167.81

AL

0102

169.16

FL

1224

171.40

MN

2706

159.96

OR

4103

181.38

AL

0103

173.01

FL

1225

148.24

MN

2707

149.49

OR

4104

175.60

AL

0104

160.11

GA

1301

171.36

MN

2708

167.49

OR

4105

170.49

AL

0105

158.39

GA

1302

164.99

MS

2801

163.41

PA

4201

216.57

AL

0106

166.72

GA

1303

160.00

MS

2802

178.71

PA

4202

217.49

AL

0107

173.12

GA

1304

158.33

MS

2803

162.39

PA

4203

171.06

AK

0299

177.59

GA

1305

174.21

MS

2804

173.19

PA

4204

177.43

AZ

0401

150.54

GA

1306

168.46

MO

2901

184.42

PA

4205

167.87

AZ

0402

160.42

GA

1307

156.76

MO

2902

172.86

PA

4206

170.48

AZ

0403

155.51

GA

1308

166.01

MO

2903

191.43

PA

4207

185.30

AZ

0404

155.51

GA

1309

160.49

MO

2904

167.09

PA

4208

182.33

AZ

0405

155.51

GA

1310

158.41

MO

2905

180.75

PA

4209

162.09

AZ

0406

154.84

GA

1311

168.71

MO

2906

167.65

PA

4210

169.23

AZ

0407

143.81

GA

1312

169.92

MO

2907

166.90

PA

4211

175.65

AZ

0408

155.45

GA

1313

166.59

MO

2908

173.10

PA

4212

169.55

AR

0501

176.39

HI

1501

132.18

MO

2909

168.23

PA

4213

195.46

AR

0502

167.22

HI

1502

132.18

MT

3099

164.72

PA

4214

185.54

AR

0503

159.68

ID

1601

159.32

NE

3101

154.37

PA

4215

167.46

AR

0504

171.75

ID

1602

145.79

NE

3102

172.54

PA

4216

166.84

CA

0601

180.93

IL

1701

187.65

NE

3103

148.99

PA

4217

171.04

CA

0602

179.84

IL

1702

187.39

NV

3201

185.55

PA

4218

179.77

CA

0603

173.30

IL

1703

187.65

NV

3202

178.47

PA

4219

164.61

CA

0604

171.91

IL

1704

187.65

NV

3203

185.55

RI

4401

176.99

CA

0605

174.43

IL

1705

187.65

NH

3301

184.05

RI

4402

181.40

CA

0606

174.68

IL

1706

171.34

NH

3302

177.78

SC

4501

168.40

CA

0607

172.79

IL

1707

187.65

NJ

3401

197.38

SC

4502

169.68

CA

0608

160.82

IL

1708

183.94

NJ

3402

194.20

SC

4503

160.68

CA

0609

171.62

IL

1709

187.65

NJ

3403

187.01

SC

4504

163.56

CA

0610

171.36

IL

1710

184.25

NJ

3404

189.04

SC

4505

170.43

CA

0611

166.00

IL

1711

176.79

NJ

3405

181.48

SC

4506

170.50

CA

0612

163.35

IL

1712

182.42

NJ

3406

189.45

SD

4699

155.91

CA

0613

171.63

IL

1713

170.69

NJ

3407

175.40

TN

4701

163.70

CA

0614

155.60

IL

1714

173.48

NJ

3408

186.04

TN

4702

166.03

CA

0615

150.41

IL

1715

169.81

NJ

3409

179.94

TN

4703

170.24

CA

0616

150.41

IL

1716

173.31

NJ

3410

190.31

TN

4704

166.86

CA

0617

159.08

IL

1717

169.35

NJ

3411

178.32

TN

4705

181.74

CA

0618

167.37

IL

1718

175.46

NJ

3412

185.32

TN

4706

166.05

CA

0619

160.90

IL

1719

171.91

NJ

3413

185.44

TN

4707

171.72

CA

0620

160.07

IN

1801

187.73

NM

3501

152.60

TN

4708

172.99

CA

0621

155.17

IN

1802

174.13

NM

3502

148.21

TN

4709

191.57

CA

0622

167.90

IN

1803

171.04

NM

3503

145.39

TX

4801

170.48

CA

0623

156.79

IN

1804

175.19

NY

3601

193.45

TX

4802

179.62

CA

0624

159.18

IN

1805

174.37

NY

3602

192.13

TX

4803

158.43

CA

0625

165.29

IN

1806

173.27

NY

3603

180.21

TX

4804

171.21

CA

0626

167.46

IN

1807

195.50

NY

3604

175.92

TX

4805

174.87

CA

0627

163.44

IN

1808

174.00

NY

3605

159.07

TX

4806

173.74

CA

0628

163.44

IN

1809

174.32

NY

3606

154.59

TX

4807

174.78

CA

0629

163.44

IA

1901

167.19

NY

3607

167.00

TX

4808

175.14

CA

0630

163.44

IA

1902

160.01

NY

3608

169.61

TX

4809

184.12

CA

0631

163.44

IA

1903

166.60

NY

3609

157.90

TX

4810

161.79

CA

0632

163.44

IA

1904

155.81

NY

3610

165.33

TX

4811

162.37

CA

0633

163.44

IA

1905

158.63

NY

3611

165.35

TX

4812

173.24

CA

0634

163.44

KS

2001

150.79

NY

3612

164.75

TX

4813

166.63

CA

0635

163.44

KS

2002

164.11

NY

3613

180.01

TX

4814

161.32

CA

0636

163.44

KS

2003

162.29

NY

3614

168.07

TX

4815

130.06

CA

0637

163.44

KS

2004

167.47

NY

3615

173.80

TX

4816

150.47

CA

0638

163.44

KY

2101

169.41

NY

3616

175.64

TX

4817

163.78

CA

0639

163.44

KY

2102

175.24

NY

3617

173.76

TX

4818

174.78

CA

0640

158.89

KY

2103

193.34

NY

3618

170.09

TX

4819

158.33

CA

0641

171.99

KY

2104

188.93

NY

3619

184.37

TX

4820

159.07

CA

0642

162.99

KY

2105

194.13

NY

3620

182.40

TX

4821

156.84

CA

0643

173.93

KY

2106

182.99

NY

3621

181.17

TX

4822

163.94

CA

0644

162.59

LA

2201

185.99

NY

3622

184.58

TX

4823

145.28

CA

0645

163.17

LA

2202

195.03

NY

3623

181.55

TX

4824

173.40

CA

0646

160.07

LA

2203

183.13

NY

3624

172.75

TX

4825

173.48

CA

0647

158.89

LA

2204

181.02

NY

3625

177.64

TX

4826

166.78

CA

0648

158.89

LA

2205

178.98

NY

3626

178.41

TX

4827

145.93

CA

0649

165.98

LA

2206

180.49

NY

3627

181.68

TX

4828

145.40

CA

0650

167.74

LA

2207

187.87

NY

3628

178.66

TX

4829

174.78

CA

0651

164.39

ME

2301

184.93

NY

3629

181.02

TX

4830

173.69

CA

0652

167.74

ME

2302

183.09

NC

3701

174.50

TX

4831

160.07

CA

0653

167.74

MD

2401

188.54

NC

3702

165.66

TX

4832

173.69

CO

0801

162.28

MD

2402

192.89

NC

3703

174.10

UT

4901

123.40

CO

0802

153.27

MD

2403

196.95

NC

3704

170.87

UT

4902

131.73

CO

0803

147.33

MD

2404

174.28

NC

3705

155.94

UT

4903

127.35

CO

0804

147.02

MD

2405

189.44

NC

3706

162.13

VT

5099

172.62

CO

0805

153.67

MD

2406

169.34

NC

3707

168.11

VA

5101

180.85

CO

0806

153.08

MD

2407

205.58

NC

3708

169.12

VA

5102

184.69

CO

0807

153.73

MD

2408

150.96

NC

3709

168.36

VA

5103

197.48

CT

0901

167.11

MA

2501

174.47

NC

3710

157.81

VA

5104

186.12

CT

0902

169.98

MA

2502

178.16

NC

3711

158.78

VA

5105

163.70

CT

0903

172.06

MA

2503

178.00

NC

3712

166.97

VA

5106

163.67

CT

0904

167.64

MA

2504

179.23

NC

3713

165.79

VA

5107

176.21

CT

0905

167.56

MA

2505

179.67

ND

3899

156.30

VA

5108

165.17

DE

1099

190.49

MA

2506

179.61

OH

3901

193.84

VA

5109

166.02

DC

1198

203.38

MA

2507

181.29

OH

3902

190.45

VA

5110

170.94

FL

1201

170.38

MA

2508

190.60

OH

3903

185.18

VA

5111

168.97

FL

1202

177.20

MA

2509

188.59

OH

3904

171.76

WA

5301

171.78

FL

1203

180.69

MA

2510

184.62

OH

3905

164.79

WA

5302

169.26

FL

1204

187.94

MI

2601

170.39

OH

3906

179.70

WA

5303

176.09

FL

1205

165.25

MI

2602

161.41

OH

3907

182.23

WA

5304

163.04

FL

1206

174.62

MI

2603

163.09

OH

3908

177.92

WA

5305

166.08

FL

1207

170.67

MI

2604

164.71

OH

3909

184.70

WA

5306

180.48

FL

1208

172.08

MI

2605

177.98

OH

3910

188.77

WA

5307

166.68

FL

1209

168.29

MI

2606

172.69

OH

3911

188.77

WA

5308

169.06

FL

1210

159.50

MI

2607

173.30

OH

3912

187.23

WA

5309

171.64

FL

1211

172.59

MI

2608

169.43

OH

3913

180.65

WV

5401

178.91

FL

1212

160.52

MI

2609

171.89

OH

3914

177.31

WV

5402

186.23

FL

1213

150.69

MI

2610

175.35

OH

3915

191.61

WV

5403

191.78

FL

1214

144.66

MI

2611

185.66

OH

3916

168.52

WI

5501

173.85

FL

1215

166.13

MI

2612

173.14

OH

3917

174.30

WI

5502

160.12

FL

1216

159.77

MI

2613

191.34

OH

3918

176.73

WI

5503

156.93

FL

1217

155.30

MI

2614

191.34

OK

4001

174.42

WI

5504

183.35

FL

1218

152.52

MI

2615

181.41

OK

4002

175.25

WI

5505

164.99

FL

1219

163.40

MN

2701

150.21

OK

4003

157.63

WI

5506

163.77

FL

1220

167.15

MN

2702

161.35

OK

4004

162.63

WI

5507

158.81

FL

1221

152.17

MN

2703

167.91

OK

4005

175.18

WI

5508

157.81

FL

1222

164.56

MN

2704

172.77

OR

4101

169.53

WY

5699

164.81

Historically, female breast cancer death rates have been elevated in the Northeastern and North Central regions; North-South differences have diminished over time as female breast cancer death rates decreased in the Northeast but increased in the South [8]. For all races combined, female breast cancer death rates vary from 20.6 in Hawaii to 39.4 in District of Columbia. Among African American women, breast cancer death rates are highest in congressional districts in the south, Midwest, and west coast, while among non-Hispanic whites, breast cancer mortality is highest in congressional districts in the Northeast and west coast (Figure 4, right panel). Patterns of breast cancer mortality partly reflect the influence of known risk factors as well as access to and utilization of cancer screening and treatment. Important cancer control measures include access to mammography for the uninsured and under-insured, and availability of Medicaid coverage for diagnosis and treatment.

Colorectal cancer death rates are highest overall in the Northeast and parts of the South and Midwest. Generally, death rates range from 18.4 in Texas congressional district #15 to 37.1 in Pennsylvania congressional district #1 for men and from 11.3 in Texas congressional district #15 to 24.1 in District of Columbia for women (Figure 3). Although a strong geographic pattern for colorectal cancer mortality has existed since the 1950's, the reasons are not well-understood [1]. The current priority for colorectal cancer control is to increase the proportion of individuals over 50 who receive recommended screening tests. Illustrating colorectal cancer mortality by legislative district may be influential in encouraging legislative support for mandated insurance coverage of colorectal screening tests and for programs to provide testing for the uninsured and under-insured.

For all races combined, prostate cancer death rates range from 23.8 in Texas congressional district #15 and Hawaii to 58.2 in District of Columbia. Generally, rates are highest in congressional districts in the mid-Atlantic and Southern coastal areas, reflecting in large part the higher proportion of the African American men in the population of these areas (Figure 4, left panel). Death rates for African American men are more than twice the rates for non-Hispanic white men, reflecting higher incidence, later stage at diagnosis and poorer survival among African American men. Among non-Hispanic whites, rates are highest in congressional districts in the Rocky Mountain region; high rate (40.2) is observed in Hispanics in Texas congressional district #13. A recent study suggested that 10% to 30% of the geographic variation in prostate cancer death rates might relate to variations in access to medical care [9]. Although cancer control measures for prostate cancer are less well-defined than measures for some other cancer sites, illustrating prostate cancer mortality by congressional district may be helpful in advocating for funding of research on the prevention, early detection and treatment of prostate cancer and highlighting the importance of access to medical care for African American men.

Mortality from cervical cancer in all races combined is highest in congressional districts in Appalachia, in the South and parts of the Southwest, with rates ranging from 1.4 in Minnesota congressional district #2 to 5.7 in New York congressional district #16 (Figure 5). Among African American women, rates are highest in congressional districts in the south and southeast, among non-Hispanic whites, rates are highest in congressional districts in Appalachia, and in Hispanics rates are highest in congressional districts in the coastal parts of California and Texas and in Colorado congressional district #3. Important cancer control measures include access to Pap tests for the uninsured and under-insured, and availability of Medicaid coverage for diagnosis and treatment.

Conclusion

The cancer mortality patterns by congressional district are generally similar to the patterns seen using other geographic boundaries. However, the patterns by congressional district may be useful to cancer control advocates to illustrate the importance of cancer control measures (prevention, early detection, and treatment) for their constituents. The method can be applied to state legislative districts and other analyses that involve data aggregation from different geographic units. Further research is needed to validate the estimates using mortality data geocoded to the lower geographic level such as block.

Methods

Death rates for U.S. states and counties

Mortality data were obtained from the National Center for Health Statistics (NCHS). We computed annual average age-adjusted death rates for all cancer sites combined, the four major cancers (lung and bronchus, prostate, female breast, and colorectal cancer) and cervical cancer from 1990–2001 for 50 states, District of Columbia, and all counties using SEER*Stat [10]. Death rates, counts (number of deaths), and populations for counties were directly obtained for men and women, for all races combined, and for African Americans, non-Hispanic whites, and Hispanics. Except for the years of 1990 and 2000, the intercensal populations computed by the Census Bureau were used to obtain the total populations for the study time period. Since county designation for Alaska and Hawaii was not available from NCHS, death rates for Alaska and Hawaii reflect state rates. Rates were standardized to the 2000 U.S. population and expressed per 100,000 person-years.

Death rates for U.S. congressional districts

There are 436 (excluding Puerto Rico) federal congressional districts in the U.S. [11]. Among these, eight congressional districts followed state boundaries or their equivalent (Alaska, District of Columbia, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming). Further, since county-specific mortality data were not provided for Hawaii in SEER*Stat, we assigned the state death rate to both congressional districts. For congressional districts whose boundaries did not follow state and county boundaries (n = 426), death rates were calculated by assigning county-level age-adjusted death rates to census block and then aggregating death rates over blocks by congressional district using GIS [12] and SAS [13]. By doing so, we assume that blocks within a county have same death rates.

There are three major areal interpolation methods (area weighting, surface smoothing, and dasymetric technique) for generating estimates for target zones from data available for source zones when the two geographic units are not comparable. Areal weighting assumes that data are homogeneously distributed across geographic units, which is generally unrealistic; it also involves the direct superimposition of source zones and target zones [14], which often leads to a lot of geographic boundary-line discrepancies [15]. Surface smoothing models data available for source zones as a continuous surface across the adjacent zones, assuming that the density declines with distance, taking into account the proximity of neighboring centroids [16, 17]. Dasymetric technique uses ancillary information to refine uneven data distributions across geographic units. Land cover from remote sensing [18] and the street layer [15, 19] have been used as subzone ancillary information. A recent study uses parish level (the lowest administrative unit) population data to derive weights [20]. However, there is no universal rule to construct areal interpolation, and the best solution depends on various factors: the variables of interest, the spatial relationships between source zones and target zones, and the availability of ancillary information related to both.

In this study, we constructed a dasymetric method based on the hierarchical spatial relationships between blocks and counties and between blocks and congressional districts. Generally, congressional district and county share census block as a common basic spatial unit (Table 3) [21, 22]. We used block level sex- and race- specific population to devise a dasymetric approach that assigns county-level measures such as cancer death rates to census block and then aggregates census blocks at the congressional district level, using block population as a weighting factor. We did not use area weighting because of its unrealistic homogeneity assumption and boundary-line discrepancies associated with direct superimposition of two incomparable geographic units. Surface smoothing gives reliable estimates when smoothness is the real property of the density. However, the occurrence of cancer rarely follows a smooth distance-decay surface because major risk factors that affect cancer occurrence do not have smooth paths from the centroid to its adjacent neighboring centroids.
Table 3

The hierarchical spatial relationships between blocks and counties and between blocks and congressional districts

County

Block

Congressional district

County A

Block A1

 
 

Block A2

 
 

Block A3

 
  

Congressional district #1

 

...

 

County B

Block B1

 
 

Block B2

 
 

Block B3

 
 

...

 

County C

Block C1

Congressional district #2

 

Block C2

 
 

Block C3

 
 

...

 

...

...

...

To make the calculations, the following steps were taken:

1. The number of people living within each census block by sex and race was determined from the 2000 U.S. census (covering 42 states, 426 congressional districts). Therefore, block population is sex- and race- specific.

2. Block population was spatially assigned to congressional districts by block centroids.

3. The age-adjusted cancer death rates for counties by sex and race were assigned to block by county FIPS (Federal Information Processing Standards) codes; FIPS codes are a standardized set of numeric or alphabetic codes issued by the National Institute of Standards and Technology (NIST) to ensure uniform identification of geographic entities through all federal government agencies [23].

4. Cancer death rate for each congressional district by sex and race was calculated by aggregating sex- and race- specific cancer death rates over blocks. Taking non-Hispanic white men as an example, suppose that r i was the age-adjusted cancer death rate for block i (obtained from the corresponding county rate calculated from SEER*Stat). Suppose that a ij was the population of block i within district j, and that the population for district j, https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_IEq1_HTML.gif , were known. Then the aggregated cancer death rate for district j, p j , was the summation of r i , weighted by the proportion of block population within the district, https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_IEq2_HTML.gif . Other sex- and race-specific cancer death rates were calculated similarly.

5. The number of cancer deaths for each congressional district by sex and race was calculated by aggregating the sex- and race- specific number of cancer deaths over blocks. The number of cancer deaths for a block was the product of crude death rate for the block (inherited from the corresponding county, which is the number of deaths for the county divided by the county population) and the block population. Again, taking non-Hispanic white men as an example, suppose that n i and c i were the number of deaths and the population for the county to which block i belongs, the crude death rate for block i was https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_IEq3_HTML.gif . Given a ij was the population of block i within district j, then the number of deaths for block i within district j was https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_IEq3_HTML.gif a ij , and the aggregated number of deaths for district j was https://static-content.springer.com/image/art%3A10.1186%2F1476-072X-5-28/MediaObjects/12942_2006_Article_105_IEq4_HTML.gif . Other sex- and race- specific number of cancer deaths were calculated in a similar way.

6. The aggregated cancer death rates and the number of cancer deaths for the congressional districts (n = 426) from step 4 & 5 were exported back to GIS and linked with the other ten congressional districts (Alaska, District of Columbia, Delaware, Montana, North Dakota, South Dakota, Vermont, Wyoming, and two Hawaii districts) for producing maps. The estimates of the number of deaths were not presented separately. Instead, they were used as the criteria when mapping death rates across congressional districts. Death rates based on the small number of deaths (< 20) for the study time period were considered not reliable and thus excluded.

7. Maps were generated using ArcGIS [12]. For all cancer sites combined and for each cancer site, the maps for all races combined were created by categorizing the rates into five groups. Cut points for the lowest and highest groups are approximately the 10th and 90th percentiles, except for cervical cancer which are 20th and 80th percentiles. Intervening groups are set at equal length between the lower bound cut point of 90th or 80th and the upper bound of 10th or 20th. Thus each interval represents the same absolute change over the middle range of rates, while the most extreme rates fall into the first and fifth categories. For each cancer site, to allow comparison among ethnic subgroups, the cut points for all races combined are used for race specific maps if rates are in the same range as those for all races combined. When the race specific rates fall out of the range of rates for all races combined, cut points for the exceeded portion are equally set at the length of rates in the highest category for all races combined. Cancer death rates based on the small number of deaths (< 20) are considered unstable and congressional districts with such rates are marked with hatches.

In describing the cancer burden by congressional district, we used direct age adjustment instead of indirect age adjustment because direct method is more statistically correct when the rates are being compared [24]. Direct age-adjusted death rates describe the cancer death rate each congressional district would have if it had the age-sex-race distribution of the U.S. in the year 2000. In so far as congressional districts have age-sex-race compositions different from the U.S. in 2000, the need for resources to eliminate disparities between districts might be more or less than that suggested by the results described in this paper.

Disclaimer

The views and opinions expressed in this article do not necessarily reflect those of the National Cancer Institute.

Declarations

Acknowledgements

We gratefully acknowledge Dr. Lance A Waller from Rollins School of Public Health at Emory University for his comments and suggestions on the early version of the manuscript.

Authors’ Affiliations

(1)
American Cancer Society,
(2)
NCI/DCCPS,

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