Bathing water guide

This page is a knowledge crystal of subtype method. The page identifier is Op_en7961
Moderator:Nobody (see all) Click here to sign up.

Upload data {{#opasnet_base_link:Op_en7961}}

Question

How to asses the microbiological risks of natural bathing waters, and the health effects they have? Data on the water and bathing behaviour must be possible to give to the model as input.

Answer

The model below is already functional. However, there is still work to do to ensure that the input data the model uses is accurate. For example the pathogen concentrations in different bathing water classes is unclear, and obviously greatly affects the health effects of bathing in natural waters.

+ Show code - Hide code

# This is code "Op_en7961/bathingwaterguide" on page [[Bathing water guide]]
library(OpasnetUtils)
library(ggplot2)

objects.latest("Op_en7961", code_name="initialization") # [[Bathing water guide]] fetches the variables needed in the model

# Create variables from input data
RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = rawclass, Result=1))

# If microbe concentrations are given by hand, they're put into an ovariable
if (rawclass == "Hand input") {
  RawConcentration <- Ovariable(
    "RawConcentration",
    data=data.frame(
      Pathogen=c(
        "campylobacter",
        "E.coli O157:H7",
        "rotavirus",
        "norovirus",
        "cryptosporidium", 
        "giardia"),
      Result=c(
        Campylo,
        E.coli,
        Rota,
        Noro,
        Crypto,
        Giardia)
    )
  )
}

population <- Ovariable(
  "population",
  dependencies=data.frame(
    Name=c("total_population", "distribution"),
    Ident=c(NA, "Op_en7961/age_distribution")
  ),
  formula=function (...) {
    return(distribution * total_population)
  }
)

#oprint(EvalOutput(population)@output)

#exposure <- EvalOutput(exposure)

#objects.latest("Op_en2261", code_name="BoDattr2") # [[Health impact assessment]] BoDattr

BoDattr <- EvalOutput(BoDattr, forceEval=TRUE)

cat("Microbe concentrations in bathing water, microbes/liter. \n")
oprint(RawConcentration@output[,c("Pathogen", "RawConcentrationResult")])
cat("Exposure: how many of each microbe people of different ages swallow with bathing water.\n")
oprint(summary(exposure))

ggplot(
  oapply(unkeep(BoDattr, sources=TRUE), NULL, mean, "Iter")@output,
  aes(x=Exposure_agent, weight=BoDattrResult, fill=Response)) +
  geom_bar() +
  coord_flip() +
  theme_gray(base_size=24)+
  labs(
    title="Burden of disease of bathing water microbes per day",
    subtitle=paste("on a beach of", sum(result(population)), "bathers daily"),
    y = "Burden of disease (DALY/day)",
    x = "Microbe"
  )

# number of cases
cases <- BoDattr/case_burden

ggplot(
  oapply(unkeep(cases, sources=TRUE), NULL, mean, "Iter")@output,
  aes(x=Exposure_agent, weight=Result, fill=Response)) +
  geom_bar() +
  coord_flip() +
  theme_gray(base_size=24)+
  labs(
    title="Cases of illness caused daily by bathing water microbes",
    subtitle=paste("on a beach of", sum(result(population)), "bathers daily"),
    y = "Cases per day",
    x = "Microbe"
  )


plotdata <- oapply(unkeep(cases, sources=TRUE), NULL, mean, "Iter")@output
plotdata$Age <- factor(plotdata$Age, ordered=T, levels=c("0-4", "5-9", "10-14", "15-24", "25-64", "65-79", "80+"))

ggplot(
  plotdata,
  aes(x=Age, weight=Result, fill=Response)) +
  geom_bar() +
#  coord_flip() +
  theme_gray(base_size=24)+
  labs(
    title="Uimavesien mikrobien päivässä aiheuttamat tautitapaukset eri ikäryhmille",
    subtitle=paste("uimarannalla, jolla uimareita on päivässä", sum(result(population))),
    y = "Tautitapauksia päivässä",
    x = "Ikä"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust=1))

# cases as percent of beachgoers
cases_percent <- cases*100/total_population

ggplot(
  oapply(unkeep(cases_percent, sources=TRUE), NULL, mean, "Iter")@output,
  aes(x=Exposure_agent, weight=Result, fill=Response)) +
  geom_bar() +
  coord_flip() +
  theme_gray(base_size=24)+
  labs(
    title="Proportion of bathers who get ill from bathing water microbes",
    subtitle=paste("on a beach of", sum(result(population)), "bathers daily"),
    y = "Percent of bathers",
    x = "Microbe"
  )

"Run model" -button opens a new tab, on which the results will appear after the model is done running. Current running time is ~40 seconds.

Rationale

Data

Amount of water swallowed, ml/h

Reference	Adults	Children	Notes
Dufour et al. 2006 ^[1]	0 - 70,67 (avg 21,33)	0 - 205,33 (avg 49,33)	They measured cleaning chemicals added to the pool water from the bathers' urine
Dufour et al. 2017 ^[2]	12,4 (CI95 11-14)	6-10-year-olds: 23,9 (CI95 17-33) 11-15 vuotiaat: 23,7 (CI95 19-30)	Same as previous, but with a bigger sample size
Suppes et al. 2013 ^[3]	0 - 50,9 (avg 3,5)	0,9 - 105,5 (avg 25,7)	Also measuring chemicals in urine
Suppes et al. 2016 ^[4]	0 - 60,6 (avg 6,3)	0 - 105,5 (avg 24,2)	Same methods as the previous one
Schets et al. 2011 ^[5]	20 - 30	28	Questionnaire. These values are for fresh water, but the study also looked at values for sea and pool water. Values are averages (20 for women, 30 for men).
DeFlorio-Barker et al. 2018 ^[6]	n. 0 - 114 (keskiarvo n. 16)	n. 0.7 - 144 (keskiarvo n. 27)	Quistionnaire, simulations of amounts of swallowed water based on behavior and Dufour (2017) values.

Time spent in water (min)

Reference	Adults	Children	Notes
Dufour et al. 2017	50,3 (CI95 49-52)	6-10-year.olds: 95,9 (CI95 88-104) 11-15-year-olds: 55,8 (CI95 55-59)	The study participants were asked to stay in the water for approximately one hour
Suppes et al. 2016	20 - 240 (avg 72)	30 - 480 (avg 114)
Schets et al. 2011	54 (CI95 6-220)	79
DeFlorio-Barker et al. 2018	1-360 (avg 45,4-47)	0-3-year-olds: 2 - 300 (avg 56-66,7) 4-18-year-olds: 1 - 360 (avg 64-93)

The data for the tables below is from DeFlorio-Barker et al. 2018, and is the data the model uses.

Swallowed water(l/h)
Obs	Age	Result
1	0-4	0.024(0.0005-0.087)
2	5-9	0.024(0.0005-0.087)
3	10-14	0.027(0.0005-0.113)
4	15-24	0.020(0.001-0.112)
5	25-64	0.014(0.0004-0.100)
6	65-79	0.013(0.0004-0.115)
7	80+	0.013(0.0004-0.115)

Bathing time(h)
Obs	Age	Result
1	0-4	0.5-1.5
2	5-9	0.75-2
3	10-14	0.75-2
4	15-24	0.5-2
5	25-64	0.5-1.5
6	65-79	0.5-1.5
7	80+	0.5-1.5

The table below shows an estimate of the age distribution of beachgoers. The data in the table is provided by Finnish Swimming Teaching and Lifesaving Federation, and is a very, very rough estimate of the age distribution. No data is currently collected of the age distribution of beachgoers.

Some arguments for this distribution:

0-4-year-olds only go to the beach with 25-64-year-olds (also to a much lesser extent with the 65-79-year-olds).
25-64-year-olds cover 40 years, so even though this is a group of people working and spending time at summer cottages, the total proportion is probably 0.2
Due to the lack of any real statistics, there is nothing to suggest differences in proportions between the age groups covering 5-64-year-olds.
The children and young adults between ages 5-24 might well cover 60% of the beachgoers, also depending on any possible activities on the beach, such as a tower with diving platforms.

Beachgoers' age distribution(-)
Obs	Age	Result
1	0-4	0.1
2	5-9	0.2
3	10-14	0.2
4	15-24	0.2
5	25-64	0.2
6	65-79	0.08
7	80+	0.02

Calculations

+ Show code - Hide code

# This is code "Op_en7961/swallowed_water" on page [[Bathing water guide]]
library(OpasnetUtils)

swallowedwater <- Ovariable("swallowedwater", ddata="Op_en7961", subset="Swallowed water")
bathingtime <- Ovariable("bathingtime", ddata="Op_en7961", subset="Bathing time")

Consumption <- Ovariable(
  "Consumption",
  Dependencies=data.frame(
    Names=c("bathingtime", "swallowedwater")),
  formula=function(...){
   return(bathingtime*swallowedwater) 
  }
)

objects.store(Consumption, swallowedwater, bathingtime)
#oprint(EvalOutput(Consumption)@output, show_all=TRUE)
cat("Ovariables Consumption, swallowedwater and bathingtime saved. \n")

+ Show code - Hide code

# This is code "Op_en7961/age_distibution" on page [[Bathing water guide]]
library(OpasnetUtils)

distribution <- Ovariable("distribution", ddata="Op_en7961", subset="Beachgoers' age distribution")

objects.store(distribution)
cat("Ovariable distribution saved. \n")

+ Show code - Hide code

# This is code Op_en7961/initialization on page [[Bathing water guide]]
library(OpasnetUtils)
library(ggplot2)

openv.setN(1)

# Create ovariables from user input data
RawClass <- Ovariable("RawClass", data=data.frame(RawWaterClass = 'Surface water - low contamination', Result=1))

population <- Ovariable("population", data=data.frame(Result=100))
# population <- EvalOutput(population)

objects.latest("Op_en7947", code_name="caseburden")
#case_burden <- EvalOutput(case_burden)

incidence <- 1 # Number per 1py # Potentially everyone can get the disease. PAF is the fraction of this that actually occurs

exposure <- Ovariable(
  "exposure",
  dependencies=data.frame(
    Name=c("Consumption", "RawConcentration"),
    Ident=c("Op_en7961/swallowed_water", "Op_en7953/variable")
  ),
  formula = function(...) {
    out <- RawConcentration * Consumption
    colnames(out@output)[colnames(out@output)=="Pathogen"] <- "Exposure_agent"
    return(out)
  }
)

ERF <- Ovariable(
  "ERF",
  dependencies=data.frame(Name="ERF_micr",Ident="Op_en7948/ERF_micr2"),
  formula = function(...) return(ERF_micr)
)

objects.latest("Op_en2261", code_name="BoDattr2") # [[Health impact assessment]] BoDattr
BoDattr <- EvalOutput(BoDattr, verbose=TRUE)

oprint(summary(BoDattr, marginals=c("Response","Exposure_agent")))

oprint(summary(exposure))

objects.store(list=setdiff(ls(), "wiki_username"))
cat("The whole model stored (", setdiff(ls(), "wiki_username"), ").\n")

ggplot(
  oapply(unkeep(BoDattr, sources=TRUE), NULL, mean, "Iter")@output,
  aes(x=Exposure_agent, weight=BoDattrResult, fill=Response)) +
  geom_bar() +
  coord_flip() +
  labs(
    title="Disease burden of waterborne microbes",
    subtitle=paste("in a population of", sum(result(population))),
    y = "Disease burden (DALY/incident)"
  )

if(FALSE) {
  ######################## Diagnostics
  
  objects.latest("Op_en6007", code_name="diagnostics")
  oprint(showLoctable())
  oprint(showind())
  
  ################ Insight network
  
  gr <- scrape(type="assessment")
  objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
  gr <- makeGraph(gr)
  #export_graph(gr, "ruori.svg")
  #render_graph(gr)
}

Viitteet

↑ Dufour et al. 2006. Water ingestion during swimming activities in a pool: a pilot study. J Water Health (2006) 4 (4): 425-430. [1]
↑ Dufour et al. 2017. Ingestion of swimming pool water by recreational swimmers. J Water Health (2017) 15 (3): 429-437. [2]
↑ Suppes et al. 2013. Assessment of swimmer behaviors on pool water ingestion. J Water Health (2013) 12 (2): 269-279. [3]
↑ Suppes et al. 2016. Cryptosporidium risk from swimming pool exposures. International Journal of Hygiene and Environmental Health 219(8):915-919 [4]
↑ Schets et al. 2011. Exposure assessment for swimmers in bathing waters and swimming pools. Water Research 45:2392-2400. [5]
↑ DeFlorio-Barker et al. 2018. Child environmental exposures to water and sand at the beach: Findings from studies of over 68,000 subjects at 12 beaches. Journal of Exposure Science and Environmental Epidemiology 28:93–100 [6]

[1] Dufour et al. 2006. Water ingestion during swimming activities in a pool: a pilot study. J Water Health (2006) 4 (4): 425-430. [1]

[2] Dufour et al. 2017. Ingestion of swimming pool water by recreational swimmers. J Water Health (2017) 15 (3): 429-437. [2]

[3] Suppes et al. 2013. Assessment of swimmer behaviors on pool water ingestion. J Water Health (2013) 12 (2): 269-279. [3]

[4] Suppes et al. 2016. Cryptosporidium risk from swimming pool exposures. International Journal of Hygiene and Environmental Health 219(8):915-919 [4]

[5] Schets et al. 2011. Exposure assessment for swimmers in bathing waters and swimming pools. Water Research 45:2392-2400. [5]

[6] DeFlorio-Barker et al. 2018. Child environmental exposures to water and sand at the beach: Findings from studies of over 68,000 subjects at 12 beaches. Journal of Exposure Science and Environmental Epidemiology 28:93–100 [6]

[1]

[2]

[3]

[4]

[5]

[6]

Bathing water guide

Contents

Question

Answer

Rationale

Data

Calculations

See also

Viitteet

Navigation menu