Package 'BHAI' reference manual

Package 'BHAI'

Title:	Estimate the Burden of Healthcare-Associated Infections
Description:	Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Authors:	Benedikt Zacher [aut, cre]
Maintainer:	Benedikt Zacher <[email protected]>
License:	GPL-3
Version:	0.99.2
Built:	2024-10-31 06:31:46 UTC
Source:	CRAN

Title:

Estimate the Burden of Healthcare-Associated Infections

Description:

Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.

Authors:

Benedikt Zacher [aut, cre]

Maintainer:

Benedikt Zacher <[email protected]>

License:

GPL-3

Version:

0.99.2

Built:

2024-10-31 06:31:46 UTC

Source:

CRAN

Help Index

Main function of the package to estimation of the burden of healthcare-associated infections

Description

Estimation of the burden of healthcare-associated infections

Usage

bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)

## S4 method for signature 'PPS'
bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)
bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)

## S4 method for signature 'PPS'
bhai(pps, nsim = 1000, pop.sampling = TRUE,
  sample_distr = "rbetamix", estimate_loi_fun = bootstrap_mean_gren,
  stratified_sampling = FALSE, summarize_strata = TRUE,
  use_prior = TRUE)

Arguments

`pps`	The PPS object containing the data.
`nsim`	Number of Monte Carlo simulations, default: 1000.
`pop.sampling`	Specifying whether parameters of the disease outcome trees should be sampled on population level, default: TRUE.
`sample_distr`	Distribution used for prevalence sampling, default: "rbetamix".
`estimate_loi_fun`	Function used for estimation of the length of infection, default: bootstrap_mean_gren (recommended!).
`stratified_sampling`	Specifying whether stratified sampling should be done.
`summarize_strata`	Specifying whether stratum-specific summary statistics should be computed.
`use_prior`	Specifying whether Prior distributions should be used for computations.

Value

A PPS class object.

Examples


data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
bhai(german_pps_repr, nsim=10)

data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
bhai(german_pps_repr, nsim=10)

BHAI:

Description

The BHAI package

BHAI functions

bhai:

Barplot of cases, deaths and DALYs.

Description

Barplot of cases, deaths and DALYs.

Usage

bhai.barplot(..., what, infections=NULL, cols1=NULL, cols2=NULL, ylab=NULL, ylim=NULL, 
legend_labs=NULL, main="", names.inf=TRUE, cex.names=1, border=par("fg"), lwd.errors=2)
bhai.barplot(..., what, infections=NULL, cols1=NULL, cols2=NULL, ylab=NULL, ylim=NULL, 
legend_labs=NULL, main="", names.inf=TRUE, cex.names=1, border=par("fg"), lwd.errors=2)

Arguments

`...`	Further plotting arguments
`what`	One of c("Cases", "Deaths", "DALY")
`infections`	If sepcified only a subset of infections in `bhai_summary` is plotted.
`cols1`	Color used to fill the bars.
`cols2`	Specifies colors of YLDs when plotting DALYs.
`ylab`	Y-axis labels.
`ylim`	Limits of y-axis.
`legend_labs`	Labels of legend.
`main`	Title of plot
`names.inf`	Specifying whether names of infections should be plotted.
`cex.names`	Font size of labels.
`border`	The color to be used for the border of the bars, default: par("fg").
`lwd.errors`	Line width of error bars.

Examples


data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result_ger = bhai(german_pps_repr, nsim=10)

bhai.barplot(result_ger, what="Cases")

data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result_ger = bhai(german_pps_repr, nsim=10)

bhai.barplot(result_ger, what="Cases")

Summary plot of number of infections, deaths and DALYs

Description

Summary plot of number of infections, deaths and DALYs

Usage

bhai.circleplot(pps, infections=NULL, main="", xlim=NULL, ylim=NULL)
bhai.circleplot(pps, infections=NULL, main="", xlim=NULL, ylim=NULL)

Arguments

`pps`	The PPS object containing the data.
`infections`	Infections to be plotted.
`main`	Title of plot.
`xlim`	Limits of x-axis.
`ylim`	Limits of y-axis.

Examples


data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.circleplot(pps=result)

data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.circleplot(pps=result)

Create summary table

Description

Create BHAI summary table

Usage

bhai.prettyTable(pps, pop_norm=FALSE, conf.int=TRUE)
bhai.prettyTable(pps, pop_norm=FALSE, conf.int=TRUE)

Arguments

`pps`	The PPS object containing the data.
`pop_norm`	Indicating whether statistics should be computed per 100,000 population, default: TRUE.
`conf.int`	Specifying whether confidence intervals should be computed, default: TRUE.

Value

A data.frame containing the summarised results.

Examples


data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.prettyTable(result)
  
data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.prettyTable(result)

Stratified barplot of cases, deaths and DALYs.

Description

Stratified barplot of cases, deaths and DALYs.

Usage

bhai.strataplot(pps, infection, what, col=NULL, errors=TRUE, lwd.errors=2, xlab=NULL, ...)
bhai.strataplot(pps, infection, what, col=NULL, errors=TRUE, lwd.errors=2, xlab=NULL, ...)

Arguments

`pps`	The PPS object containing the data.
`infection`	Infection to be plotted.
`what`	One of c("Cases", "Deaths", "DALY")
`col`	Color used to fill the bars.
`errors`	Specifying whether error bars should be plotted, default: TRUE.
`lwd.errors`	Line width of error bars.
`xlab`	X-axis labels.
`...`	Further plotting arguments

Examples

 
data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.strataplot(pps=result, infection="HAP", what="Cases")

data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

set.seed(3)
# The following example is run only for illustratory reasons
# Note that you should never run the function with only 10 Monte-Carlo simulations in practice!
result = bhai(german_pps_repr, nsim=10)
bhai.strataplot(pps=result, infection="HAP", what="Cases")

Aggregated data of the ECDC PPS 2010-2011.

Description

Aggregated data of the ECDC PPS 2010-2011.

Usage

data(eu_pps_2011)
data(eu_pps_2011)

Format

A PPS object.

Aggregated data of the german PPS 2010-2011 (convenience sample).

Description

Aggregated data of the german PPS 2010-2011 (convenience sample).

Usage

data(german_pps_2011_conv)
data(german_pps_2011_conv)

Format

A PPS object.

Hospital discharges in Germany (2011)

Description

Hospital discharges in Germany (2011)

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Average length of stay of survey patients in german PPS 2011 (representative sample)

Description

Average length of stay of survey patients in german PPS 2011 (representative sample)

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

A list containing length of infections from all patients in the german PPS 2011 representative sample.

Description

A list containing length of infections from all patients in the german PPS 2011 representative sample.

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Named list containing remaining life expectancies for each McCabe score (NONFATAL, ULTFATAL, RAPFATAL).

Description

Named list containing remaining life expectancies for each McCabe score (NONFATAL, ULTFATAL, RAPFATAL).

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

The observed McCabe scores (counts) for each infection, age and gender stratum from the ECDC PPS 2011-2012.

Description

The observed McCabe scores (counts) for each infection, age and gender stratum from the ECDC PPS 2011-2012.

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Number of cases for each infection in the german PPS 2011 (representative sample)

Description

Number of cases for each infection in the german PPS 2011 (representative sample)

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Stratified number of cases for each infection in the german PPS 2011 (representative sample)

Description

Stratified number of cases for each infection in the german PPS 2011 (representative sample)

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Stratified number of cases for each infection in the german PPS 2011 (convenience sample). This distribution is used as a Prior for the representative sample.

Description

Stratified number of cases for each infection in the german PPS 2011 (convenience sample). This distribution is used as a Prior for the representative sample.

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Number of survey patients in the german PPS 2011 (representative sample).

Description

Number of survey patients in the german PPS 2011 (representative sample).

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Population size of Germany in 2011.

Description

Population size of Germany in 2011.

Usage

data(german_pps_2011_repr)
data(german_pps_2011_repr)

Format

A PPS object.

Create a PPS object

Description

This function creates a PPS object.

Usage

PPS(num_hai_patients = NULL, num_survey_patients = NULL,
  length_of_stay = NULL, loi_pps = NULL, hospital_discharges = NULL,
  num_hai_patients_by_stratum = NULL,
  num_hai_patients_by_stratum_prior = NULL, mccabe_scores_distr = NULL,
  mccabe_by_stratum_prior = NULL, mccabe_life_exp = NULL,
  num_survey_patients_by_stratum = NULL, population = NULL,
  country = "")
PPS(num_hai_patients = NULL, num_survey_patients = NULL,
  length_of_stay = NULL, loi_pps = NULL, hospital_discharges = NULL,
  num_hai_patients_by_stratum = NULL,
  num_hai_patients_by_stratum_prior = NULL, mccabe_scores_distr = NULL,
  mccabe_by_stratum_prior = NULL, mccabe_life_exp = NULL,
  num_survey_patients_by_stratum = NULL, population = NULL,
  country = "")

Arguments

`num_hai_patients`	Named numeric containing patients having healthcare-associated infections.
`num_survey_patients`	Number of patients in point prevalence survey.
`length_of_stay`	Length of stay of all patients in hospitals. This is need for the prevalence to incidence conversion with the Rhame-Sudderth formula.
`loi_pps`	A list containing length of infections from all patients in the PPS. The length of infection of all healthcare-associated infections. In PPS this is usually approximated as the time from infection onset until the date of the survey.
`hospital_discharges`	The number of hospital discharges.
`num_hai_patients_by_stratum`	A list containing for each infection the number of patients in each age and gender stratum.
`num_hai_patients_by_stratum_prior`	The prior weight (counts) for each infection, age and gender stratum. This is used for smooting the age and gender distribution when small numbers are observed.
`mccabe_scores_distr`	The observed McCabe scores (counts) for each infection, age and gender stratum from the PPS.
`mccabe_by_stratum_prior`	The prior weight (counts) for each infection, McCabe score, age and gender stratum. This is used for smooting the age and gender distribution when small numbers are observed.
`mccabe_life_exp`	Named list containing remaining life expectancies for each McCabe score (NONFATAL, ULTFATAL, RAPFATAL).
`num_survey_patients_by_stratum`	Number of survey patients stratified by infection, age and gender. If this parameter is provided the methodology described in Cassini et al. (2016) <doi:https://doi.org/10.1371/journal.pmed.1002150> is applied.
`population`	Population size.
`country`	Name of the country.

Value

A PPS class object.

Examples


data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

data(german_pps_2011_repr)
german_pps_repr = PPS(num_hai_patients = num_hai_patients,
    num_hai_patients_by_stratum = num_hai_patients_by_stratum,
    num_hai_patients_by_stratum_prior = num_hai_patients_by_stratum_prior,
    num_survey_patients = num_survey_patients,
    length_of_stay = length_of_stay,
    loi_pps = loi_pps,
    mccabe_scores_distr = mccabe_scores_distr,
    mccabe_life_exp = mccabe_life_exp,
    hospital_discharges = hospital_discharges,
    population = population,
    country="Germany (representative sample)")
german_pps_repr

This class is a generic container for PPS data sets.

Description

This class is a generic container for PPS data sets.

Slots

infections: Character vector storing names of infections in PPS
num_hai_patients: Named numeric containing patients having healthcare-associated infections.
num_survey_patients: Number of patients in point prevalence survey.
length_of_stay: Length of stay of all patients in hospitals. This is need for the prevalence to incidence conversion with the Rhame-Sudderth formula.
loi_pps: A list containing length of infections from all patients in the PPS. In PPS this is usually calculated as the time from infection onset until the date of the survey.
hospital_discharges: The number of hospital discharges.
num_hai_patients_by_stratum: A list containing for each infection the number of patients in each age and gender stratum.
num_hai_patients_by_stratum_prior: The prior weight (counts) for each infection, age and gender stratum. This is used for smooting the age and gender distribution when small numbers are observed.
mccabe_scores_distr: The observed McCabe scores (counts) for each infection, age and gender stratum from the PPS.
mccabe_by_stratum_prior: The prior weight (counts) for each infection, McCabe score, age and gender stratum. This is used for smooting the age and gender distribution when small numbers are observed.
mccabe_life_exp: Named list containing remaining life expectancies for each McCabe score (NONFATAL, ULTFATAL, RAPFATAL).
num_survey_patients_by_stratum: Number of survey patients stratified by infection, age and gender. If this parameter is provided the methodology described in Cassini et al. (2016) <doi:https://doi.org/10.1371/journal.pmed.1002150> is applied.
population: Population size
country: Name of the country in which PPS was conducted
bhai_options: Options with which bhai was run. If bhai was not run yet, this is an empty list.
bhai_summary: Summary statistics of bhai. If bhai was not run yet, this is an empty list.

Simulate PPS data

Description

Simulate PPS data

Usage

sample.pps(pps_data, num_survey_patients)
sample.pps(pps_data, num_survey_patients)

Arguments

`pps_data`	The PPS object containing the data. Parameters for simulations are extracted from this data.
`num_survey_patients`	Numeric vector indicating sample sizes for simulations.

Value

A simulated PPS object.

Examples


# Specify the number of survey patients
sim_survey_patients = 10000
# Subsample data sets from european PPS
sim_pps = sample.pps(eu_pps, num_survey_patients = sim_survey_patients)

# Specify the number of survey patients
sim_survey_patients = 10000
# Subsample data sets from european PPS
sim_pps = sample.pps(eu_pps, num_survey_patients = sim_survey_patients)

Simulated/subsampled data sets from european PPS

Description

Simulated/subsampled data sets from european PPS

Usage

data(simulations)
data(simulations)

Format

A PPS object.

BHAI with default options was applied to simulated/subsampled data sets from european PPS

Description

BHAI with default options was applied to simulated/subsampled data sets from european PPS

Usage

data(simulations)
data(simulations)

Format

A PPS object.

BHAI with prior was applied to simulated/subsampled data sets from european PPS

Description

BHAI with prior was applied to simulated/subsampled data sets from european PPS

Usage

data(simulations)
data(simulations)

Format

A PPS object.

BHAI with stratified sampling was applied to simulated/subsampled data sets from european PPS

Description

BHAI with stratified sampling was applied to simulated/subsampled data sets from european PPS

Usage

data(simulations)
data(simulations)

Format

A PPS object.

Package 'BHAI'

Help Index

Main function of the package to estimation of the burden of healthcare-associated infections

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BHAI:

Description

BHAI functions

Barplot of cases, deaths and DALYs.

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Summary plot of number of infections, deaths and DALYs

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Create summary table

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Examples

Stratified barplot of cases, deaths and DALYs.

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Examples

Aggregated data of the ECDC PPS 2010-2011.

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Aggregated data of the german PPS 2010-2011 (convenience sample).

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Hospital discharges in Germany (2011)

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Average length of stay of survey patients in german PPS 2011 (representative sample)

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A list containing length of infections from all patients in the german PPS 2011 representative sample.

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Named list containing remaining life expectancies for each McCabe score (NONFATAL, ULTFATAL, RAPFATAL).

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The observed McCabe scores (counts) for each infection, age and gender stratum from the ECDC PPS 2011-2012.

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Number of cases for each infection in the german PPS 2011 (representative sample)

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Stratified number of cases for each infection in the german PPS 2011 (representative sample)

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Stratified number of cases for each infection in the german PPS 2011 (convenience sample). This distribution is used as a Prior for the representative sample.

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Number of survey patients in the german PPS 2011 (representative sample).

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Usage