IncidencePrevalence

Estimate Incidence Rates and Prevalence in OMOP CDM

2025-06-26

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Concepts

  1. Denominator population

  2. Incidence rates

  3. Prevalence

    • Point prevalence

    • Period prevalence

Denominator population

Observation periods


Denominator population

Observation periods + study period


Denominator population

Observation periods + study period + prior history requirement


Denominator population

Observation periods + study period + prior history requirement + age (and sex) restriction


Incidence rates

Washout all history, no repetitive events


Incidence rates

No washout, no repetitive events


Incidence rates

Some washout, no repetitive events


Incidence rates

Some washout, repetitive events


Prevalence

Point prevalence


Prevalence

Period prevalence


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Required packages

install.packages("IncidencePrevalence")

generateDenominatorCohortSet()

cdm <- mockIncidencePrevalence(sampleSize = 50000)

cdm <- generateDenominatorCohortSet(cdm, name = "dpop")

cdm$dpop %>%
  glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 1, 3, 7, 8, 11, 12, 14, 21, 22, 29, 32, 33, 36, 37, 41, 42, 43, 45, 46, 47, 49, 51, 52, 5…
$ cohort_start_date    <date> 1949-10-29, 1977-03-02, 1994-07-10, 1960-05-06, 1945-08-21, 1950-04-30, 1981-06-10, 1973…
$ cohort_end_date      <date> 1957-03-20, 1982-06-05, 1996-11-16, 1963-04-12, 1951-02-11, 1954-05-09, 1982-05-31, 1975…

generateDenominatorCohortSet()

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "dpop",
  cohortDateRange = as.Date(c("2008-01-01", "2012-01-01"))
)

cdm$dpop %>%
  glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 33, 41, 52, 59, 69, 76, 79, 89, 107, 122, 126, 136, 141, 151, 156, 159, 164, 174, 175, 17…
$ cohort_start_date    <date> 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008…
$ cohort_end_date      <date> 2008-08-04, 2012-01-01, 2011-10-24, 2012-01-01, 2012-01-01, 2009-03-21, 2012-01-01, 2009…

generateDenominatorCohortSet()

cohortCount(cdm$dpop)
# A tibble: 1 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1           3575            3575
settings(cdm$dpop)
# A tibble: 1 × 11
  cohort_definition_id cohort_name    age_group sex   days_prior_observation start_date end_date   requirements_at_entry
                 <int> <chr>          <chr>     <chr>                  <dbl> <date>     <date>     <chr>                
1                    1 denominator_c… 0 to 150  Both                       0 2008-01-01 2012-01-01 FALSE                
# ℹ 3 more variables: target_cohort_definition_id <int>, target_cohort_name <chr>, time_at_risk <chr>

generateDenominatorCohortSet()

attrition(cdm$dpop)
# A tibble: 8 × 7
  cohort_definition_id number_records number_subjects reason_id reason                excluded_records excluded_subjects
                 <int>          <int>           <int>     <int> <chr>                            <int>             <int>
1                    1          50000           50000         1 Starting population                 NA                NA
2                    1          50000           50000         2 Missing year of birth                0                 0
3                    1          50000           50000         3 Missing sex                          0                 0
4                    1          50000           50000         4 Cannot satisfy age c…                0                 0
5                    1           3575            3575         5 No observation time …            46425             46425
6                    1           3575            3575         6 Doesn't satisfy age …                0                 0
7                    1           3575            3575         7 Prior history requir…                0                 0
8                    1           3575            3575        10 No observation time …                0                 0

generateDenominatorCohortSet()

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "dpop",
  cohortDateRange = as.Date(c("2008-01-01", "2012-01-01")),
  ageGroup = list(
    c(0, 49),
    c(50, 100)
  ),
  sex = c("Male", "Female")
)

cdm$dpop %>%
  glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 59, 89, 136, 200, 208, 396, 501, 579, 581, 615, 642, 665, 740, 744, 773, 834, 910, 923, 1…
$ cohort_start_date    <date> 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2009-08-30, 2008-01-01, 2008…
$ cohort_end_date      <date> 2012-01-01, 2009-08-06, 2010-02-18, 2010-12-23, 2012-01-01, 2012-01-01, 2008-12-05, 2011…

generateDenominatorCohortSet()

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "dpop",
  cohortDateRange = as.Date(c("2008-01-01", "2012-01-01")),
  ageGroup = list(
    c(0, 49),
    c(50, 100)
  ),
  sex = c("Male", "Female"),
  daysPriorObservation= c(0, 180)
)

cdm$dpop %>%
  glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 59, 89, 136, 200, 208, 396, 501, 579, 581, 615, 642, 665, 740, 744, 773, 834, 910, 923, 1…
$ cohort_start_date    <date> 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2009-08-30, 2008-01-01, 2008…
$ cohort_end_date      <date> 2012-01-01, 2009-08-06, 2010-02-18, 2010-12-23, 2012-01-01, 2012-01-01, 2008-12-05, 2011…

generateDenominatorCohortSet()

settings(cdm$dpop)
# A tibble: 8 × 11
  cohort_definition_id cohort_name    age_group sex   days_prior_observation start_date end_date   requirements_at_entry
                 <int> <chr>          <chr>     <chr>                  <dbl> <date>     <date>     <chr>                
1                    1 denominator_c… 0 to 49   Male                       0 2008-01-01 2012-01-01 FALSE                
2                    2 denominator_c… 0 to 49   Male                     180 2008-01-01 2012-01-01 FALSE                
3                    3 denominator_c… 0 to 49   Fema…                      0 2008-01-01 2012-01-01 FALSE                
4                    4 denominator_c… 0 to 49   Fema…                    180 2008-01-01 2012-01-01 FALSE                
5                    5 denominator_c… 50 to 100 Male                       0 2008-01-01 2012-01-01 FALSE                
6                    6 denominator_c… 50 to 100 Male                     180 2008-01-01 2012-01-01 FALSE                
7                    7 denominator_c… 50 to 100 Fema…                      0 2008-01-01 2012-01-01 FALSE                
8                    8 denominator_c… 50 to 100 Fema…                    180 2008-01-01 2012-01-01 FALSE                
# ℹ 3 more variables: target_cohort_definition_id <int>, target_cohort_name <chr>, time_at_risk <chr>

generateDenominatorCohortSet()

cohortCount(cdm$dpop)
# A tibble: 8 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1            973             973
2                    2            959             959
3                    3            902             902
4                    4            890             890
5                    5            933             933
6                    6            925             925
7                    7            922             922
8                    8            900             900

generateDenominatorCohortSet()

attrition(cdm$dpop) %>%
  filter(cohort_definition_id == 1)
# A tibble: 9 × 7
  cohort_definition_id number_records number_subjects reason_id reason                excluded_records excluded_subjects
                 <int>          <int>           <int>     <int> <chr>                            <int>             <int>
1                    1          50000           50000         1 Starting population                 NA                NA
2                    1          50000           50000         2 Missing year of birth                0                 0
3                    1          50000           50000         3 Missing sex                          0                 0
4                    1          50000           50000         4 Cannot satisfy age c…                0                 0
5                    1           3575            3575         5 No observation time …            46425             46425
6                    1           3575            3575         6 Doesn't satisfy age …                0                 0
7                    1           3575            3575         7 Prior history requir…                0                 0
8                    1           1822            1822         8 Not Male                          1753              1753
9                    1            973             973        10 No observation time …              849               849

generateDenominatorCohortSet()

# get some people who are in more than one cohort
ids <- cdm$dpop %>% 
  group_by(subject_id) %>% 
  tally() %>% 
  collect() %>% 
  arrange(desc(n)) %>% 
  head(4) %>% 
  pull("subject_id")

generateDenominatorCohortSet()

dpop <- cdm$dpop %>%
  collect() %>%
  left_join(settings(cdm$dpop),
            by = "cohort_definition_id") %>% 
  mutate(cohort_definition_id=as.character(cohort_definition_id))

plot <- dpop %>%
  filter(subject_id %in% ids) %>%
  pivot_longer(cols = c(
    "cohort_start_date",
    "cohort_end_date"
  )) %>%
  ggplot(aes(x = as.character(subject_id), y = value, colour = cohort_definition_id, group = subject_id)) +
  facet_grid(sex + days_prior_observation ~ ., space = "free_y") +
  geom_point(position = position_dodge(width = 0.5)) +
  geom_line(position = position_dodge(width = 0.5)) +
  theme_bw() +
  theme(legend.position = "top") +
  ylab("Year") +
  coord_flip()

generateDenominatorCohortSet()

plot

Adding (time-invariant) variables for stratification

If later we want to estimate incidence or prevalence stratified for some time-invariant characteristic, we will need to add a variable to our denominator cohort table.

cdm$dpop <- cdm$dpop %>% 
  mutate(group = if_else(as.numeric(subject_id)  < 20, "first", "second")) 

cdm$dpop |> 
  glimpse()
Rows: ??
Columns: 5
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 59, 89, 136, 200, 208, 396, 501, 579, 581, 615, 642, 665, 740, 744, 773, 834, 910, 923, 1…
$ cohort_start_date    <date> 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2008-01-01, 2009-08-30, 2008-01-01, 2008…
$ cohort_end_date      <date> 2012-01-01, 2009-08-06, 2010-02-18, 2010-12-23, 2012-01-01, 2012-01-01, 2008-12-05, 2011…
$ group                <chr> "second", "second", "second", "second", "second", "second", "second", "second", "second",…

generateTargetDenominatorCohortSet()

When we want to stratify on a time-varying characteristic, we will do this by first creating a cohort for it. Once we have the cohort we will will use it when creating our denominator cohort.

generateTargetDenominatorCohortSet()

personTable <- tibble(
  person_id = c("1", "2", "3", "4", "5"),
  gender_concept_id = c(rep("8507", 2), rep("8532", 3)),
  year_of_birth = 2000,
  month_of_birth = 06,
  day_of_birth = 01
)
observationPeriodTable <- tibble(
  observation_period_id = "1",
  person_id = c("1", "2", "3", "4", "5"),
  observation_period_start_date = c(
    as.Date("2010-12-19"),
    as.Date("2005-04-01"),
    as.Date("2009-04-10"),
    as.Date("2010-08-20"),
    as.Date("2010-01-01")
  ),
  observation_period_end_date = c(
    as.Date("2011-06-19"),
    as.Date("2005-11-29"),
    as.Date("2016-01-02"),
    as.Date("2011-12-11"),
    as.Date("2015-06-01")
  )
)

acute_asthma <- tibble(
  cohort_definition_id = rep("1", 5),
  subject_id = c("3", "3", "5", "5", "2"),
  cohort_start_date = c(
    as.Date("2012-01-01"),
    as.Date("2015-06-01"),
    as.Date("2014-10-01"),
    as.Date("2010-06-01"),
    as.Date("2005-08-20")
  ),
  cohort_end_date = c(
    as.Date("2013-01-01"),
    as.Date("2015-12-31"),
    as.Date("2015-04-01"),
    as.Date("2010-06-01"),
    as.Date("2005-09-20")
  )
)

# mock database
cdm <- mockIncidencePrevalence(
  personTable = personTable,
  observationPeriodTable = observationPeriodTable,
  targetCohortTable = acute_asthma
)

generateTargetDenominatorCohortSet()

cdm <- generateTargetDenominatorCohortSet(
  cdm = cdm, 
  name = "denominator_acute_asthma",
  targetCohortTable = "target"
)

cdm$denominator_acute_asthma |> 
  dplyr::glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1, 1, 1
$ subject_id           <chr> "2", "3", "5", "3", "5"
$ cohort_start_date    <date> 2005-08-20, 2015-06-01, 2010-06-01, 2012-01-01, 2014-10-01
$ cohort_end_date      <date> 2005-09-20, 2015-12-31, 2010-06-01, 2013-01-01, 2015-04-01

generateTargetDenominatorCohortSet()

We can add demographic requirements like before. But it is important to note that these are applied at the cohort start date of the target cohort.

cdm <- generateTargetDenominatorCohortSet(
  cdm = cdm, 
  name = "denominator_acute_asthma_2",
  ageGroup = list(c(11, 15)),
  sex = "Female",
  daysPriorObservation = 0,
  targetCohortTable = "target"
)
cdm$denominator_acute_asthma_2 |> 
  dplyr::glimpse()
Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1/:memory:]
$ cohort_definition_id <int> 1, 1, 1
$ subject_id           <chr> "3", "3", "5"
$ cohort_start_date    <date> 2015-06-01, 2012-01-01, 2014-10-01
$ cohort_end_date      <date> 2015-12-31, 2013-01-01, 2015-04-01

estimateIncidence()

cdm <- mockIncidencePrevalence(
  sampleSize = 50000,
  outPre = 0.5
)

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = as.Date(c("2008-01-01", "2012-01-01")),
  ageGroup = list(
    c(0, 30),
    c(31, 50),
    c(51, 70),
    c(71, 100)
  )
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "years",
  outcomeWashout = Inf,
  repeatedEvents = FALSE
)

estimateIncidence()

inc %>%
  glimpse()
Rows: 288
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "denominator_cohort_name &&& outcome_cohort_name", "denominator_cohort_name &&& outcome_cohor…
$ group_level      <chr> "denominator_cohort_1 &&& cohort_1", "denominator_cohort_1 &&& cohort_1", "denominator_cohort…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Denominator", "Outcome", "Denominator", "Denominator", "Outcome", "Outcome", "Outcome", "Den…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ estimate_name    <chr> "denominator_count", "outcome_count", "person_days", "person_years", "incidence_100000_pys", …
$ estimate_type    <chr> "integer", "integer", "numeric", "numeric", "numeric", "numeric", "numeric", "integer", "inte…
$ estimate_value   <chr> "662", "63", "189075", "517.659", "12170.174", "9351.896", "15570.939", "624", "64", "180488"…
$ additional_name  <chr> "incidence_start_date &&& incidence_end_date &&& analysis_interval", "incidence_start_date &&…
$ additional_level <chr> "2008-01-01 &&& 2008-12-31 &&& years", "2008-01-01 &&& 2008-12-31 &&& years", "2008-01-01 &&&…

estimateIncidence()

inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "quarters",
  outcomeWashout = 365,
  repeatedEvents = TRUE
)
inc %>%
  glimpse()
Rows: 624
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "denominator_cohort_name &&& outcome_cohort_name", "denominator_cohort_name &&& outcome_cohor…
$ group_level      <chr> "denominator_cohort_1 &&& cohort_1", "denominator_cohort_1 &&& cohort_1", "denominator_cohort…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Denominator", "Outcome", "Denominator", "Denominator", "Outcome", "Outcome", "Outcome", "Den…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ estimate_name    <chr> "denominator_count", "outcome_count", "person_days", "person_years", "incidence_100000_pys", …
$ estimate_type    <chr> "integer", "integer", "numeric", "numeric", "numeric", "numeric", "numeric", "integer", "inte…
$ estimate_value   <chr> "732", "13", "62234", "170.387", "7629.69", "4062.489", "13047.002", "718", "17", "61093", "1…
$ additional_name  <chr> "incidence_start_date &&& incidence_end_date &&& analysis_interval", "incidence_start_date &&…
$ additional_level <chr> "2008-01-01 &&& 2008-03-31 &&& quarters", "2008-01-01 &&& 2008-03-31 &&& quarters", "2008-01-…

plotIncidence()

plotIncidence(inc,
              facet = "denominator_age_group")

estimateIncidence()

Incidence start date Incidence end date Denominator age group Denominator sex
Estimate name
Denominator (N) Person-years Outcome (N) Incidence 100,000 person-years [95% CI]
mock; cohort_1
2008-01-01 2008-03-31 0 to 30 Both 732 170.39 13 7,629.69 (4,062.49 - 13,047.00)
2008-04-01 2008-06-30 0 to 30 Both 718 167.26 17 10,163.57 (5,920.66 - 16,272.87)
2008-07-01 2008-09-30 0 to 30 Both 708 167.39 18 10,753.52 (6,373.22 - 16,995.20)
2008-10-01 2008-12-31 0 to 30 Both 715 165.77 15 9,048.63 (5,064.45 - 14,924.33)
2009-01-01 2009-03-31 0 to 30 Both 695 160.15 15 9,366.51 (5,242.36 - 15,448.64)
2009-04-01 2009-06-30 0 to 30 Both 695 159.13 22 13,825.43 (8,664.33 - 20,931.88)
2009-07-01 2009-09-30 0 to 30 Both 670 157.35 15 9,533.07 (5,335.59 - 15,723.35)
2009-10-01 2009-12-31 0 to 30 Both 656 156.67 12 7,659.22 (3,957.63 - 13,379.11)
2010-01-01 2010-03-31 0 to 30 Both 626 147.78 14 9,473.28 (5,179.13 - 15,894.56)
2010-04-01 2010-06-30 0 to 30 Both 600 142.42 10 7,021.34 (3,367.00 - 12,912.49)
2010-07-01 2010-09-30 0 to 30 Both 570 136.14 16 11,752.61 (6,717.63 - 19,085.50)
2010-10-01 2010-12-31 0 to 30 Both 530 126.99 9 7,087.17 (3,240.71 - 13,453.66)
2011-01-01 2011-03-31 0 to 30 Both 497 117.62 8 6,801.33 (2,936.33 - 13,401.34)
2011-04-01 2011-06-30 0 to 30 Both 470 112.61 4 3,551.99 (967.80 - 9,094.50)
2011-07-01 2011-09-30 0 to 30 Both 447 106.78 6 5,619.08 (2,062.10 - 12,230.38)
2011-10-01 2011-12-31 0 to 30 Both 418 99.00 8 8,080.97 (3,488.79 - 15,922.74)
2008-01-01 2008-03-31 31 to 50 Both 694 162.12 13 8,018.95 (4,269.75 - 13,712.65)
2008-04-01 2008-06-30 31 to 50 Both 702 165.28 10 6,050.19 (2,901.30 - 11,126.52)
2008-07-01 2008-09-30 31 to 50 Both 712 167.91 14 8,337.70 (4,558.30 - 13,989.25)
2008-10-01 2008-12-31 31 to 50 Both 695 163.34 13 7,959.10 (4,237.89 - 13,610.31)
2009-01-01 2009-03-31 31 to 50 Both 682 158.32 10 6,316.24 (3,028.88 - 11,615.79)
2009-04-01 2009-06-30 31 to 50 Both 689 160.18 12 7,491.67 (3,871.05 - 13,086.43)
2009-07-01 2009-09-30 31 to 50 Both 699 164.70 18 10,929.09 (6,477.27 - 17,272.68)
2009-10-01 2009-12-31 31 to 50 Both 697 161.48 16 9,908.29 (5,663.44 - 16,090.44)
2010-01-01 2010-03-31 31 to 50 Both 655 153.79 19 12,354.27 (7,438.08 - 19,292.72)
2010-04-01 2010-06-30 31 to 50 Both 621 146.93 6 4,083.58 (1,498.60 - 8,888.23)
2010-07-01 2010-09-30 31 to 50 Both 589 142.36 14 9,834.02 (5,376.35 - 16,499.81)
2010-10-01 2010-12-31 31 to 50 Both 568 135.00 9 6,666.76 (3,048.47 - 12,655.60)
2011-01-01 2011-03-31 31 to 50 Both 535 124.54 7 5,620.46 (2,259.72 - 11,580.29)
2011-04-01 2011-06-30 31 to 50 Both 504 121.08 9 7,432.86 (3,398.78 - 14,109.88)
2011-07-01 2011-09-30 31 to 50 Both 486 114.72 11 9,588.90 (4,786.74 - 17,157.19)
2011-10-01 2011-12-31 31 to 50 Both 455 108.55 6 5,527.51 (2,028.50 - 12,031.06)
2008-01-01 2008-03-31 51 to 70 Both 662 153.18 12 7,834.18 (4,048.03 - 13,684.73)
2008-04-01 2008-06-30 51 to 70 Both 655 151.24 18 11,901.69 (7,053.70 - 18,809.80)
2008-07-01 2008-09-30 51 to 70 Both 656 154.21 15 9,727.06 (5,444.16 - 16,043.30)
2008-10-01 2008-12-31 51 to 70 Both 646 154.67 11 7,112.05 (3,550.31 - 12,725.43)
2009-01-01 2009-03-31 51 to 70 Both 658 152.59 15 9,830.52 (5,502.07 - 16,213.95)
2009-04-01 2009-06-30 51 to 70 Both 666 153.96 16 10,392.44 (5,940.18 - 16,876.68)
2009-07-01 2009-09-30 51 to 70 Both 667 155.18 22 14,176.99 (8,884.65 - 21,464.14)
2009-10-01 2009-12-31 51 to 70 Both 660 155.92 11 7,054.76 (3,521.71 - 12,622.92)
2010-01-01 2010-03-31 51 to 70 Both 633 149.84 10 6,673.92 (3,200.40 - 12,273.58)
2010-04-01 2010-06-30 51 to 70 Both 619 147.54 12 8,133.39 (4,202.64 - 14,207.39)
2010-07-01 2010-09-30 51 to 70 Both 598 142.71 8 5,605.77 (2,420.18 - 11,045.61)
2010-10-01 2010-12-31 51 to 70 Both 574 137.31 8 5,826.27 (2,515.37 - 11,480.08)
2011-01-01 2011-03-31 51 to 70 Both 547 127.41 15 11,773.39 (6,589.48 - 19,418.41)
2011-04-01 2011-06-30 51 to 70 Both 525 122.76 10 8,145.98 (3,906.31 - 14,980.74)
2011-07-01 2011-09-30 51 to 70 Both 498 119.00 8 6,722.97 (2,902.50 - 13,246.93)
2011-10-01 2011-12-31 51 to 70 Both 482 114.96 9 7,829.01 (3,579.92 - 14,861.91)
2008-01-01 2008-03-31 71 to 100 Both 553 129.06 12 9,298.00 (4,804.41 - 16,241.74)
2008-04-01 2008-06-30 71 to 100 Both 568 130.07 22 16,913.58 (10,599.65 - 25,607.36)
2008-07-01 2008-09-30 71 to 100 Both 565 133.99 13 9,702.00 (5,165.91 - 16,590.71)
2008-10-01 2008-12-31 71 to 100 Both 572 135.16 17 12,577.22 (7,326.69 - 20,137.35)
2009-01-01 2009-03-31 71 to 100 Both 581 132.86 15 11,289.91 (6,318.88 - 18,620.99)
2009-04-01 2009-06-30 71 to 100 Both 585 137.14 12 8,750.25 (4,521.38 - 15,284.92)
2009-07-01 2009-09-30 71 to 100 Both 586 139.15 16 11,498.71 (6,572.51 - 18,673.19)
2009-10-01 2009-12-31 71 to 100 Both 610 141.44 6 4,242.23 (1,556.82 - 9,233.55)
2010-01-01 2010-03-31 71 to 100 Both 586 138.06 16 11,589.25 (6,624.26 - 18,820.21)
2010-04-01 2010-06-30 71 to 100 Both 563 134.34 12 8,932.29 (4,615.45 - 15,602.92)
2010-07-01 2010-09-30 71 to 100 Both 553 132.16 13 9,836.64 (5,237.59 - 16,820.95)
2010-10-01 2010-12-31 71 to 100 Both 522 127.57 12 9,406.38 (4,860.41 - 16,431.05)
2011-01-01 2011-03-31 71 to 100 Both 512 119.66 15 12,535.73 (7,016.15 - 20,675.78)
2011-04-01 2011-06-30 71 to 100 Both 493 118.44 8 6,754.19 (2,915.98 - 13,308.45)
2011-07-01 2011-09-30 71 to 100 Both 483 115.92 11 9,489.06 (4,736.90 - 16,978.54)
2011-10-01 2011-12-31 71 to 100 Both 468 111.96 6 5,358.86 (1,966.61 - 11,663.99)

estimatePointPrevalence() and estimatePeriodPrevalence()

cdm <- mockIncidencePrevalence(
  sampleSize = 50000,
  outPre = 0.5
)

cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = as.Date(c("2008-01-01", "2012-01-01")),
  ageGroup = list(
    c(0, 30),
    c(31, 50),
    c(51, 70),
    c(71, 100)
  )
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "Years"
)

estimatePointPrevalence() and estimatePeriodPrevalence()

prev %>%
  glimpse()
Rows: 276
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "denominator_cohort_name &&& outcome_cohort_name", "denominator_cohort_name &&& outcome_cohor…
$ group_level      <chr> "denominator_cohort_1 &&& cohort_1", "denominator_cohort_1 &&& cohort_1", "denominator_cohort…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Denominator", "Outcome", "Outcome", "Outcome", "Outcome", "Denominator", "Outcome", "Outcome…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ estimate_name    <chr> "denominator_count", "outcome_count", "prevalence", "prevalence_95CI_lower", "prevalence_95CI…
$ estimate_type    <chr> "integer", "integer", "numeric", "numeric", "numeric", "integer", "integer", "numeric", "nume…
$ estimate_value   <chr> "755", "1", "0.00132", "0.00023", "0.00746", "708", "1", "0.00141", "0.00025", "0.00796", "67…
$ additional_name  <chr> "prevalence_start_date &&& prevalence_end_date &&& analysis_interval", "prevalence_start_date…
$ additional_level <chr> "2008-01-01 &&& 2008-01-01 &&& years", "2008-01-01 &&& 2008-01-01 &&& years", "2008-01-01 &&&…

estimatePointPrevalence() and estimatePeriodPrevalence()

prev <- estimatePeriodPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "quarters"
)

prev %>%
  glimpse()
Rows: 496
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "denominator_cohort_name &&& outcome_cohort_name", "denominator_cohort_name &&& outcome_cohor…
$ group_level      <chr> "denominator_cohort_1 &&& cohort_1", "denominator_cohort_1 &&& cohort_1", "denominator_cohort…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Denominator", "Outcome", "Outcome", "Outcome", "Outcome", "Denominator", "Outcome", "Outcome…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ estimate_name    <chr> "denominator_count", "outcome_count", "prevalence", "prevalence_95CI_lower", "prevalence_95CI…
$ estimate_type    <chr> "integer", "integer", "numeric", "numeric", "numeric", "integer", "integer", "numeric", "nume…
$ estimate_value   <chr> "790", "14", "0.01772", "0.01059", "0.02953", "770", "18", "0.02338", "0.01484", "0.03665", "…
$ additional_name  <chr> "prevalence_start_date &&& prevalence_end_date &&& analysis_interval", "prevalence_start_date…
$ additional_level <chr> "2008-01-01 &&& 2008-03-31 &&& quarters", "2008-01-01 &&& 2008-03-31 &&& quarters", "2008-01-…

plotPrevalence()

plotPrevalence(prev,
              facet = "denominator_age_group")

plotPrevalence()

Prevalence start date Prevalence end date Denominator age group Denominator sex
Estimate name
Denominator (N) Outcome (N) Prevalence [95% CI]
mock; cohort_1
2008-01-01 2008-03-31 0 to 30 Both 790 14 0.02 (0.01 - 0.03)
2008-04-01 2008-06-30 0 to 30 Both 770 18 0.02 (0.01 - 0.04)
2008-07-01 2008-09-30 0 to 30 Both 757 18 0.02 (0.02 - 0.04)
2008-10-01 2008-12-31 0 to 30 Both 759 16 0.02 (0.01 - 0.03)
2009-01-01 2009-03-31 0 to 30 Both 734 16 0.02 (0.01 - 0.04)
2009-04-01 2009-06-30 0 to 30 Both 735 22 0.03 (0.02 - 0.04)
2009-07-01 2009-09-30 0 to 30 Both 716 15 0.02 (0.01 - 0.03)
2009-10-01 2009-12-31 0 to 30 Both 705 12 0.02 (0.01 - 0.03)
2010-01-01 2010-03-31 0 to 30 Both 670 14 0.02 (0.01 - 0.03)
2010-04-01 2010-06-30 0 to 30 Both 636 11 0.02 (0.01 - 0.03)
2010-07-01 2010-09-30 0 to 30 Both 602 16 0.03 (0.02 - 0.04)
2010-10-01 2010-12-31 0 to 30 Both 566 12 0.02 (0.01 - 0.04)
2011-01-01 2011-03-31 0 to 30 Both 530 9 0.02 (0.01 - 0.03)
2011-04-01 2011-06-30 0 to 30 Both 501 4 0.01 (0.00 - 0.02)
2011-07-01 2011-09-30 0 to 30 Both 469 6 0.01 (0.01 - 0.03)
2011-10-01 2011-12-31 0 to 30 Both 435 8 0.02 (0.01 - 0.04)
2008-01-01 2008-03-31 31 to 50 Both 735 13 0.02 (0.01 - 0.03)
2008-04-01 2008-06-30 31 to 50 Both 742 11 0.01 (0.01 - 0.03)
2008-07-01 2008-09-30 31 to 50 Both 748 15 0.02 (0.01 - 0.03)
2008-10-01 2008-12-31 31 to 50 Both 730 15 0.02 (0.01 - 0.03)
2009-01-01 2009-03-31 31 to 50 Both 717 11 0.02 (0.01 - 0.03)
2009-04-01 2009-06-30 31 to 50 Both 716 13 0.02 (0.01 - 0.03)
2009-07-01 2009-09-30 31 to 50 Both 727 18 0.02 (0.02 - 0.04)
2009-10-01 2009-12-31 31 to 50 Both 732 16 0.02 (0.01 - 0.04)
2010-01-01 2010-03-31 31 to 50 Both 694 20 0.03 (0.02 - 0.04)
2010-04-01 2010-06-30 31 to 50 Both 665 6 0.01 (0.00 - 0.02)
2010-07-01 2010-09-30 31 to 50 Both 623 14 0.02 (0.01 - 0.04)
2010-10-01 2010-12-31 31 to 50 Both 603 9 0.01 (0.01 - 0.03)
2011-01-01 2011-03-31 31 to 50 Both 564 7 0.01 (0.01 - 0.03)
2011-04-01 2011-06-30 31 to 50 Both 535 9 0.02 (0.01 - 0.03)
2011-07-01 2011-09-30 31 to 50 Both 513 12 0.02 (0.01 - 0.04)
2011-10-01 2011-12-31 31 to 50 Both 484 7 0.01 (0.01 - 0.03)
2008-01-01 2008-03-31 51 to 70 Both 694 13 0.02 (0.01 - 0.03)
2008-04-01 2008-06-30 51 to 70 Both 682 18 0.03 (0.02 - 0.04)
2008-07-01 2008-09-30 51 to 70 Both 691 18 0.03 (0.02 - 0.04)
2008-10-01 2008-12-31 51 to 70 Both 684 12 0.02 (0.01 - 0.03)
2009-01-01 2009-03-31 51 to 70 Both 691 16 0.02 (0.01 - 0.04)
2009-04-01 2009-06-30 51 to 70 Both 702 18 0.03 (0.02 - 0.04)
2009-07-01 2009-09-30 51 to 70 Both 707 23 0.03 (0.02 - 0.05)
2009-10-01 2009-12-31 51 to 70 Both 708 14 0.02 (0.01 - 0.03)
2010-01-01 2010-03-31 51 to 70 Both 678 10 0.01 (0.01 - 0.03)
2010-04-01 2010-06-30 51 to 70 Both 658 13 0.02 (0.01 - 0.03)
2010-07-01 2010-09-30 51 to 70 Both 630 8 0.01 (0.01 - 0.02)
2010-10-01 2010-12-31 51 to 70 Both 601 8 0.01 (0.01 - 0.03)
2011-01-01 2011-03-31 51 to 70 Both 572 15 0.03 (0.02 - 0.04)
2011-04-01 2011-06-30 51 to 70 Both 555 10 0.02 (0.01 - 0.03)
2011-07-01 2011-09-30 51 to 70 Both 527 9 0.02 (0.01 - 0.03)
2011-10-01 2011-12-31 51 to 70 Both 513 10 0.02 (0.01 - 0.04)
2008-01-01 2008-03-31 71 to 100 Both 583 14 0.02 (0.01 - 0.04)
2008-04-01 2008-06-30 71 to 100 Both 597 24 0.04 (0.03 - 0.06)
2008-07-01 2008-09-30 71 to 100 Both 605 14 0.02 (0.01 - 0.04)
2008-10-01 2008-12-31 71 to 100 Both 615 19 0.03 (0.02 - 0.05)
2009-01-01 2009-03-31 71 to 100 Both 628 15 0.02 (0.01 - 0.04)
2009-04-01 2009-06-30 71 to 100 Both 629 12 0.02 (0.01 - 0.03)
2009-07-01 2009-09-30 71 to 100 Both 628 17 0.03 (0.02 - 0.04)
2009-10-01 2009-12-31 71 to 100 Both 651 6 0.01 (0.00 - 0.02)
2010-01-01 2010-03-31 71 to 100 Both 621 18 0.03 (0.02 - 0.05)
2010-04-01 2010-06-30 71 to 100 Both 598 12 0.02 (0.01 - 0.03)
2010-07-01 2010-09-30 71 to 100 Both 583 13 0.02 (0.01 - 0.04)
2010-10-01 2010-12-31 71 to 100 Both 562 12 0.02 (0.01 - 0.04)
2011-01-01 2011-03-31 71 to 100 Both 549 16 0.03 (0.02 - 0.05)
2011-04-01 2011-06-30 71 to 100 Both 534 10 0.02 (0.01 - 0.03)
2011-07-01 2011-09-30 71 to 100 Both 516 14 0.03 (0.02 - 0.05)
2011-10-01 2011-12-31 71 to 100 Both 501 7 0.01 (0.01 - 0.03)

Oveview

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Package paper

https://onlinelibrary.wiley.com/doi/10.1002/pds.5717

IncidencePrevalence

👉 Packages website
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👉 Manual

📧 edward.burn@ndorms.ox.ac.uk