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International Journal of Health Geographics logoLink to International Journal of Health Geographics
. 2006 Jun 23;5:28. doi: 10.1186/1476-072X-5-28

U.S. congressional district cancer death rates

Yongping Hao 1,✉, Elizabeth M Ward 1, Ahmedin Jemal 1, Linda W Pickle 2, Michael J Thun 1
PMCID: PMC1538995  PMID: 16796732

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 [4-6]. 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.

Figure 1.

Figure 1

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

Figure 2.

Figure 2

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

Figure 3.

Figure 3

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

Figure 4.

Figure 4

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

Figure 5.

Figure 5

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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
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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
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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
...
... ... ...
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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 ri was the age-adjusted cancer death rate for block i (obtained from the corresponding county rate calculated from SEER*Stat). Suppose that aij was the population of block i within district j, and that the population for district j, Inline graphic, were known. Then the aggregated cancer death rate for district j, pj, was the summation of ri, weighted by the proportion of block population within the district,Inline graphic. 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 ni and ci were the number of deaths and the population for the county to which block i belongs, the crude death rate for block i was Inline graphic. Given aij was the population of block i within district j, then the number of deaths for block i within district j was Inline graphicaij, and the aggregated number of deaths for district j was Inline graphic. 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.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

YH, EMW, and AJ conceived the analysis and wrote the final version of the manuscript. LWP provided technical support on the method and critically revised the manuscript. MJT conceptualized and critically revised the manuscript.

Disclaimer

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

Acknowledgments

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.

Contributor Information

Yongping Hao, Email: yongping.hao@cancer.org.

Elizabeth M Ward, Email: elizabeth.ward@cancer.org.

Ahmedin Jemal, Email: ahmedin.jemal@cancer.org.

Linda W Pickle, Email: picklel@mail.nih.gov.

Michael J Thun, Email: michael.thun@cancer.org.

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