Incidence rates per 100,000 persons

[May Blake]

Hi, I have the following data below in patients who have all experience an event of thrombosis. What I am trying to do is get incidence of thrombosis per 100,000 persons using the total population data stratified by gender. Secondly, I want to repeat that analysis and adjust for age. I am not sure how to remotely start this process. I started to use a poisson regression model but it seems this gives incidence rates based on individual observations and can't estimate per 100,000 people. Thank you for your help.

---------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. For more info, type help dataex
clear
input long id float age str34 sex str113 race str7 survived long censustractid str8 medianhousehold str4 medianage long(totalpopulation white)
102472 55 "Male"   "White"                            "No"  31532 "28611"  "31"    4355  1725
103810 72 "Male"   "Black/African-American"           "No"  34213 "71250"  "44.7"  2602  2082
103864 94 "Female" "White"                            "No"  13010 "72813"  "42.9"  2928  2769
103867 87 "Male"   "Unknown"                          "No"  15748 "31442"  "41.7"  1381   589
103869 70 "Male"   "White"                            "No"  25732 "43883"  "31.8"  3701  3241
103882 27 "Male"   "White"                            "No"  22839 "48365"  "41.2"  4431  4035
103885 67 "Female" "Black/African-American"           "Yes" 22852 "15720"  "23.6"  1499   376
103972 22 "Male"   "Unknown"                          "No"   6661 "36280"  "41.1"  3963  1268
103987 54 "Female" "Unknown"                          "No"  29532 "48405"  "38.4"  5962  5403
103989 81 "Female" "White"                            "No"  29458 "74167"  "53.3"  4314  3927
103999 84 "Female" "White"                            "No"  25711 "70169"  "36.1"  3784  3377
104003 56 "Female" "Unknown"                          "Yes"  6470 "91023"  "39.8"  5684  5053
104050 55 "Female" "Unknown"                          "No"  12866 "11397"  "21.6"  3709   218
104052 77 "Male"   "Black/African-American"           "No"  14864 "31607"  "20.4"  2483   505
104053 44 "Female" "White"                            "No"  14850 "15172"  "21.8"  3523  1222
104054 75 "Female" "Unknown"                          "No"  14975 "50655"  "47.5"  3153  2402
104055 69 "Female" "Black/African-American"           "No"  14912 "46116"  "33.4"  9627    26
104056 54 "Male"   "White"                            "Yes" 14965 "62745"  "28.9"  4962  2975
104068 59 "Female" "Asian"                            "Yes" 15900 "60310"  "37.6"  5659  1097
104069 92 "Male"   "Asian"                            "Yes" 15941 "93750"  "34.5"  1948   650
104070 82 "Male"   "Unknown"                          "Yes" 16161 "47806"  "40.9"  4982   823
104073 73 "Female" "White"                            "Yes" 34279 "47273"  "33.5"  3673  2639
104075 82 "Female" "Native Hawaiian/Pacific Islander" "No"  22831 "35074"  "42.2"  4011  2600
104077 80 "Female" "White"                            "No"  22821 "43309"  "56"    2189  2037
104180 60 "Female" "White"                            "No"  29415 "44196"  "38.9"   946   754
104188 44 "Male"   "White"                            "No"  29300 "43542"  "31.5"  5803  4301
104192 98 "Female" "Black/African-American"           "No"  29294 "59286"  "35.1"  3645  2449
104204 73 "Male"   "Black/African-American"           "No"  29297 "66216"  "39"    4303  3039
104256 73 "Male"   "White"                            "No"  25523 "62796"  "27"    2856  1566
104259 43 "Male"   "White"                            "No"   6671 "18231"  "32.8"  3271  2405
104264 26 "Female" "Unknown"                          "Yes"  8645 "65214"  "34.3"  5123  3770
104271 55 "Female" "White"                            "No"      . ""       ""         .     .
104277 65 "Female" "Asian"                            "Yes"  6615 "97829"  "49.7"  6830  5862
104282 63 "Male"   "White"                            "No"  15598 "54345"  "39.4"  9700  3507
104283 53 "Male"   "White"                            "Yes" 33345 "56818"  "34.2"  7693  5917
104284 77 "Female" "Hispanic/Latino"                  "No"  12877 "22880"  "30.4"  3483   937
104285 69 "Male"   "White"                            "No"  14856 "56190"  "39.5"  1997   625
104287 85 "Male"   "White"                            "No"  22908 "62975"  "34.8"  5276  4954
104288 76 "Male"   "White"                            "No"  16118 "67422"  "38.5"  6605  1772
104289 61 "Female" "Unknown"                          "No"  16072 "91086"  "42.8"  5496   758
104290 37 "Male"   "Asian"                            "No"  15978 "57544"  "39.6"  4942   816
104291 81 "Male"   "Unknown"                          "No"  16129 "61558"  "37.8"  8233  4417
104292 80 "Male"   "White"                            "No"  22964 "63586"  "42.7"  7643  7166
104294 51 "Male"   "White"                            "No"  14790 "65185"  "35.7"  8920  4311
104295 54 "Female" "White"                            "No"  14777 "80386"  "43"    8241  7418
104298 68 "Male"   "White"                            "No"  11169 "57537"  "41"    7241  2256
104299 57 "Female" "White"                            "No"  11056 "31740"  "27.1"  3892  1307
104306 81 "Female" "White"                            "Yes" 11117 "50025"  "49.7"  3586  3159
104312 38 "Female" "White"                            "No"  11125 "54672"  "32.6"  5599  3135
104353 65 "Male"   "White"                            "No"  34434 "33370"  "33.7"  4419  3189
104357 35 "Male"   "Unknown"                          "No"   8635 "38229"  "43.2"  2721  2408
104364 88 "Male"   "Unknown"                          "No"   8711 "88730"  "38.4"  9872  6267
104378 66 "Male"   "Unknown"                          "No"   8591 "57500"  "30.7"  6579  4091
104499 60 "Female" "White"                            "No"   8355 "35280"  "37.3"  4384  3205
104505 86 "Male"   "Unknown"                          "No"   8595 "67227"  "30.1"  3542  2067
104511 74 "Male"   "Hispanic/Latino"                  "No"   6757 "59667"  "35.1"  8160  6307
104513 78 "Male"   "White"                            "No"   6727 "62813"  "36.1"  5677  4420
104516 80 "Female" "Unknown"                          "No"  29467 "48047"  "46.3"  2315  2077
104532 47 "Male"   "Black/African-American"           "Yes" 12864 "18602"  "32.9"  5113   757
104533 56 "Male"   "Black/African-American"           "No"  14863 "18722"  "37.8"  2532   204
104534 77 "Male"   "White"                            "Yes" 14899 "17545"  "40"    3580   269
104537 64 "Male"   "White"                            "No"  27528 "16955"  "34.1"  2647   511
104538 60 "Female" "Black/African-American"           "No"  27575 "33727"  "37.3"  4534  2899
104540 56 "Male"   "Unknown"                          "No"  16034 "35938"  "30.2"  6158   615
104553 44 "Male"   "White"                            "No"  16503 "38365"  "40.8"  4978  4733
104568 65 "Male"   "White"                            "Yes" 33508 "87301"  "35.4"  9155  5716
104569 83 "Female" "Unknown"                          "No"  15866 "87546"  "50.1"  5894   677
104570 59 "Male"   "Unknown"                          "No"  15896 "94875"  "43.3"  4111   922
104571 49 "Female" "Unknown"                          "No"  15986 "90167"  "31.6"  7364   954
104573 55 "Female" "Unknown"                          "Yes" 16065 "72940"  "41"    4377   649
104577 69 "Female" "White"                            "No"  25484 "39056"  "35.7"  2783  1950
104582 23 "Male"   "Black/African-American"           "No"  33383 "69718"  "34.3"  5285  3975
104584 73 "Female" "Black/African-American"           "No"  33489 "78092"  "41.8"  3344  2266
104586 77 "Female" "Black/African-American"           "No"  33297 "60069"  "27"    4364  3264
104593 51 "Female" "White"                            "No"  29554 "54776"  "40.5"  5037  3999
104594 70 "Female" "Unknown"                          "No"  29443 "61857"  "41.2"  5270  4917
104596 91 "Female" "Unknown"                          "Yes" 29455 "71453"  "47.2"  3318  2853
104597 89 "Male"   "Unknown"                          "No"  29455 "71453"  "47.2"  3318  2853
104598 80 "Male"   "White"                            "Yes" 29443 "61857"  "41.2"  5270  4917
104599 71 "Male"   "Unknown"                          "No"  29458 "74167"  "53.3"  4314  3927
104600 71 "Male"   "Black/African-American"           "No"  14848 "19912"  "32.4"  1933    83
104601 57 "Female" "Black/African-American"           "No"  14919 "57784"  "48.8"  5517   116
104602 76 "Male"   "Black/African-American"           "Yes" 14891 "26157"  "31.7"  4190   114
104603 62 "Male"   "Unknown"                          "No"  16125 "62778"  "43.4"  5091  1268
104605 61 "Male"   "Unknown"                          "No"  16023 "47822"  "34"    5526   737
104606 38 "Male"   "Unknown"                          "No"  15930 "105508" "32.9"  3014   524
104609 68 "Male"   "White"                            "No"  14779 "61341"  "38.3"  7755  4956
104626 89 "Female" "White"                            "No"  11117 "50025"  "49.7"  3586  3159
104628 59 "Male"   "White"                            "No"  11127 "67433"  "42.1"  1829  1463
104631 53 "Male"   "White"                            "No"  11062 "45482"  "34.4"  3695  2223
104640 63 "Male"   "White"                            "No"  25711 "70169"  "36.1"  3784  3377
104652 88 "Male"   "Hispanic/Latino"                  "No"  41995 "70972"  "32.6" 10795 10341
104654 67 "Female" "Unknown"                          "No"   8671 "70200"  "36.3"  5330  3603
104656 78 "Female" "White"                            "Yes" 22924 "76979"  "31.5"  5755  4244
104658 62 "Male"   "White"                            "No"  22838 "27250"  "31.2"  5622  4467
104661 65 "Male"   "White"                            "No"  22974 "47671"  "34.6"  8377  6050
104662 61 "Male"   "Black/African-American"           "No"  42457 "33222"  "39.6"  4049  1171
104676 92 "Male"   "Unknown"                          "No"   8632 "38072"  "29.1"  6999  4523
104687 55 "Male"   "White"                            "No"  42808 "94865"  "33"   20376 17812
104694 67 "Male"   "Unknown"                          "Yes"  6625 "34614"  "31.9"  7851  6497
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 307 observations
Use the count() option to list more

.

[Maarten Buis]

In order to get the incidence of thrombosis, you need an estimate of the number of people with thrombosis in the population and an estimate of the number of people without thrombosis in the population. How do you expect to get these from a dataset with only people with thrombosis?

[May Blake]

Hi Maarten, I do have a numerator and denominator for the dataset that I forgot to include in the dateex. My numerator actually has a variable that is the number of thrombotic events in each census. The code I have successful used to arcuate the incidence rates is below but I am not sure how to adjust the rates and still get rates per 100,000 persons. I can use the poisson model but then it doesn't give me per 100k persons.

gen incidence_rate = (sum_thrombotic_evets / totalpopulation) * 100000

[Clyde Schechter]

Assuming you have correctly implemented a Poisson model, you can get the adjusted incidence rate per 100,000 by following it with:

margins, expression(100000*predict(ir))