PopulationDiagnostics

Introduction

In this example we’re going to just create a cohort of individuals with an ankle sprain using the Eunomia synthetic data.

library(CDMConnector)
library(CohortConstructor)
library(CodelistGenerator)
library(PatientProfiles)
library(IncidencePrevalence)
library(PhenotypeR)


con <- DBI::dbConnect(duckdb::duckdb(), 
                      CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
cdm <- CDMConnector::cdmFromCon(con = con, 
                                cdmName = "Eunomia Synpuf",
                                cdmSchema   = "main",
                                writeSchema = "main", 
                                achillesSchema = "main")

cdm$injuries <- conceptCohort(cdm = cdm,
  conceptSet = list(
    "ankle_sprain" = 81151
  ),
  name = "injuries")

We can get the incidence and prevalence of our study cohort using populationDiagnostics().

pop_diag <- populationDiagnostics(cdm$injuries)

We can quickly make tables with our results like so

tableIncidence(pop_diag,     
               groupColumn = c("cdm_name", "outcome_cohort_name"),
               hide = "denominator_cohort_name",
               settingsColumn = c("denominator_age_group",
                         "denominator_sex",
                         "denominator_days_prior_observation",
                         "outcome_cohort_name"))
Incidence start date Incidence end date Analysis interval Denominator age group Denominator sex Denominator days prior observation
Estimate name
Denominator (N) Person-years Outcome (N) Incidence 100,000 person-years [95% CI]
Eunomia Synpuf; ankle_sprain
2008-01-01 2008-12-31 years 0 to 150 Both 0 973 941.90 11 1,167.85 (582.99 - 2,089.61)
2009-01-01 2009-12-31 years 0 to 150 Both 0 947 932.17 8 858.22 (370.52 - 1,691.03)
2010-01-01 2010-12-31 years 0 to 150 Both 0 912 894.83 8 894.02 (385.98 - 1,761.58)
2008-01-01 2010-12-31 overall 0 to 150 Both 0 1,000 2,768.90 27 975.12 (642.61 - 1,418.74)
2008-12-31 2008-12-31 years 0 to 150 Both 365 898 2.46 0 0.00 (0.00 - 150,015.43)
2009-01-01 2009-12-31 years 0 to 150 Both 365 874 860.47 8 929.73 (401.39 - 1,831.93)
2010-01-01 2010-12-31 years 0 to 150 Both 365 910 894.42 8 894.44 (386.15 - 1,762.40)
2008-12-31 2010-12-31 overall 0 to 150 Both 365 968 1,757.34 16 910.46 (520.41 - 1,478.54)
2008-01-01 2008-12-31 years 0 to 150 Female 0 485 467.68 7 1,496.73 (601.76 - 3,083.84)
2009-01-01 2009-12-31 years 0 to 150 Female 0 475 466.24 2 428.96 (51.95 - 1,549.56)
2010-01-01 2010-12-31 years 0 to 150 Female 0 460 452.91 2 441.59 (53.48 - 1,595.17)
2008-01-01 2010-12-31 overall 0 to 150 Female 0 498 1,386.84 11 793.17 (395.95 - 1,419.20)
2008-12-31 years 0 to 150 Male 0 488 474.21 4 843.50 (229.83 - 2,159.69)
2009-01-01 2009-12-31 years 0 to 150 Male 0 472 465.92 6 1,287.76 (472.59 - 2,802.91)
2010-01-01 2010-12-31 years 0 to 150 Male 0 452 441.92 6 1,357.71 (498.25 - 2,955.15)
2008-01-01 2010-12-31 overall 0 to 150 Male 0 502 1,382.06 16 1,157.69 (661.72 - 1,880.02)
2008-12-31 years 18 to 64 Both 0 192 169.81 1 588.90 (14.91 - 3,281.16)
2009-01-01 2009-12-31 years 18 to 64 Both 0 154 146.70 2 1,363.35 (165.11 - 4,924.90)
2010-01-01 2010-12-31 years 18 to 64 Both 0 139 133.08 2 1,502.90 (182.01 - 5,428.99)
2008-01-01 2010-12-31 overall 18 to 64 Both 0 200 449.58 5 1,112.15 (361.11 - 2,595.39)
2008-12-31 years 65 to 150 Both 0 813 772.09 10 1,295.18 (621.09 - 2,381.88)
2009-01-01 2009-12-31 years 65 to 150 Both 0 801 785.47 6 763.87 (280.33 - 1,662.63)
2010-01-01 2010-12-31 years 65 to 150 Both 0 781 761.76 6 787.66 (289.06 - 1,714.39)
2008-01-01 2010-12-31 overall 65 to 150 Both 0 854 2,319.32 22 948.56 (594.45 - 1,436.12)
 
tablePrevalence(pop_diag,     
               groupColumn = c("cdm_name", "outcome_cohort_name"),
               hide = "denominator_cohort_name",
               settingsColumn = c("denominator_age_group",
                         "denominator_sex",
                         "denominator_days_prior_observation",
                         "outcome_cohort_name"))
Prevalence start date Prevalence end date Analysis interval Denominator age group Denominator sex Denominator days prior observation
Estimate name
Denominator (N) Outcome (N) Prevalence [95% CI]
Eunomia Synpuf; ankle_sprain
2008-01-01 2008-12-31 years 0 to 150 Both 0 973 11 0.01 (0.01 - 0.02)
2009-01-01 2009-12-31 years 0 to 150 Both 0 958 9 0.01 (0.00 - 0.02)
2010-01-01 2010-12-31 years 0 to 150 Both 0 930 8 0.01 (0.00 - 0.02)
2009-01-01 2009-12-31 years 0 to 150 Both 365 885 9 0.01 (0.01 - 0.02)
2010-01-01 2010-12-31 years 0 to 150 Both 365 928 8 0.01 (0.00 - 0.02)
2008-01-01 2008-12-31 years 0 to 150 Female 0 485 7 0.01 (0.01 - 0.03)
2009-01-01 2009-12-31 years 0 to 150 Female 0 482 2 0.00 (0.00 - 0.02)
2010-01-01 2010-12-31 years 0 to 150 Female 0 468 2 0.00 (0.00 - 0.02)
2008-01-01 2008-12-31 years 0 to 150 Male 0 488 4 0.01 (0.00 - 0.02)
2009-01-01 2009-12-31 years 0 to 150 Male 0 476 7 0.01 (0.01 - 0.03)
2010-01-01 2010-12-31 years 0 to 150 Male 0 462 6 0.01 (0.01 - 0.03)
2008-01-01 2008-12-31 years 18 to 64 Both 0 192 1 0.00 (0.00 - 0.03)
2009-01-01 2009-12-31 years 18 to 64 Both 0 155 2 0.01 (0.00 - 0.05)
2010-01-01 2010-12-31 years 18 to 64 Both 0 141 2 0.01 (0.00 - 0.05)
2008-01-01 2008-12-31 years 65 to 150 Both 0 813 10 0.01 (0.01 - 0.02)
2009-01-01 2009-12-31 years 65 to 150 Both 0 812 7 0.01 (0.00 - 0.02)
2010-01-01 2010-12-31 years 65 to 150 Both 0 797 6 0.01 (0.00 - 0.02)