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Population diagnostics

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)

This function builds on IncidencePrevalence R package to perform the following analyses:

All analyses are performed for:

Visualising the results

We can use IncidencePrevalence package to visualise the results obtained.

Incidence

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 (229.82 - 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)
 
results <- pop_diag |> 
  omopgenerics::filterSettings(result_type == "incidence") |>
  visOmopResults::filterAdditional(analysis_interval == "years")
plotIncidence(results,
              colour = "denominator_age_group",
              facet = c("denominator_sex", "denominator_days_prior_observation"))

Prevalence

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)
2008-01-01 2010-12-31 overall 0 to 150 Both 0 1,000 27 0.03 (0.02 - 0.04)
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)
2008-12-31 2010-12-31 overall 0 to 150 Both 365 979 17 0.02 (0.01 - 0.03)
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 - 0.01)
2010-01-01 2010-12-31 years 0 to 150 Female 0 468 2 0.00 (0.00 - 0.02)
2008-01-01 2010-12-31 overall 0 to 150 Female 0 498 11 0.02 (0.01 - 0.04)
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 2010-12-31 overall 0 to 150 Male 0 502 16 0.03 (0.02 - 0.05)
2008-12-31 years 18 to 64 Both 0 192 1 0.01 (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 2010-12-31 overall 18 to 64 Both 0 200 5 0.03 (0.01 - 0.06)
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)
2008-01-01 2010-12-31 overall 65 to 150 Both 0 855 22 0.03 (0.02 - 0.04)
 
results <- pop_diag |> 
  omopgenerics::filterSettings(result_type == "prevalence") |>
  visOmopResults::filterAdditional(analysis_interval == "years")
plotPrevalence(results,
               colour = "denominator_age_group",
               facet = c("denominator_sex", "denominator_days_prior_observation"))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.