Package 'rSPARCS'

Generate the Case Series

Description

Estimates the daily number of cases reported by multiple grouping factors.

Usage

case.series(data,ICD,diagnosis,date,start,end,by1,by2,by3,by4,by5)

Arguments

data	a data.frame containing with each row representing a case, and each column representing the patient characteristics such as gender, age, admission date, and discharge date, etc.
ICD	a vector of ICD 9, or 10 codes, or a mix of them, which users are willing to calculate the daily numbers for; can be of length 3-6.
diagnosis	the name of the variable in the data containing the diagnostic code upon admission.
date	the name of the variable in the data showing the admission date, either in the format like "20181129" or "2018/11/29".
start, end	the start and end date for the case series to be generated.
by1, by2, by3, by4, by5	the name of the variable in the data used as grouping variables.

Details

Not limited to hospital data, but also applicable to other surveillance data.

Value

dataset	A case series will be generated for time series analysis, trend analysis and displaying, with following variables:
date	from the start date to the end date as user specified, with 1 day bin.
case	the daily number of cases diagnosed with diseases of user specified ICD codes.
others	grouping variables.

Note

When applied to other medical data without ICD code, users may arbitrarily set a ICD code, meanwhile, define the diagnosis variable in the data to the same ICD code.

Examples

set.seed(2018)

data=data.frame(
  patient=1:10000,
  primdiag=sample(390:398,10000,replace=TRUE),
  onset=sample(seq.Date(as.Date("2015/2/1"),
    as.Date("2016/2/1"),"1 day"),10000,replace=TRUE),
  sex=sample(c("M","F"),10000,replace=TRUE),
  county=sample(c("Albany","New York"),10000,replace=TRUE)
)

output.series=case.series(
  data,ICD=392:396,diagnosis="primdiag",
  date="onset",start="2015/1/1",end="2016/12/31",by1="sex")
  
head(output.series)

---

Generate the Dataset for Case Crossover Analysis

Description

Generate the dataset for case crossover analysis.

Usage

CXover.data(data,date,ID,direction,apart)

Arguments

data	a data.frame containing the date of each case.
date	the name of the variable in the data indicating the date of each case reported to the database.
ID	the name of the variable in the data indicating case ID, if not specified, it will automatically generated starting from 1.
direction	"month4" (default),"pre4" or "after4". With "pre4" (or "after4"), each case day will be matched with same weekdays in previous (or subsequent) 4 weeks. With "month4", each case day will be matched with same weekdays in the same month, which is the most common in literature.
apart	7 (default) or 14. With apart==7, each case day will be 7 days apart from control days in the same month as in the traditional case-crossover design while with apart==14, days will be 14 days apart each other.

Details

Not limited to hospital data, but also applicable to other surveillance data.

Value

dataset	A data.frame ready for the case crossover analysis, with following variables:
ID	same ID represents the same patient.
Date	one case day is matched with 3-4 control days.
status	indicating whether it is a case day or a control day.

References

Zhang W, Lin S, Hopke PK, et al. Triggering of cardiovascular hospital admissions by fine particle concentrations in New York state: Before, during, and after implementation of multiple environmental policies and a recession. Environ. Pollut. 2018;242:1404–1416.

Examples

# similated data
set.seed(2018)
dataset=data.frame(
  patient=1:1000,
  primdiag=sample(390:398,1000,replace=TRUE),
  onset=sample(seq.Date(as.Date("2015/2/1"),as.Date("2016/2/1"),"1 day"),1000,replace=TRUE),
  sex=sample(c("M","F"),1000,replace=TRUE),
  county=sample(c("Albany","New York"),1000,replace=TRUE))

out.data=CXover.data(data=dataset,date="onset",ID="patient")
head(out.data)

---

Create a dbf File for Geocoding

Description

Generate address variables and output the data as a dbf file for geocoding in ArcGIS.

Usage

DBFgeocode(data,cityname,roadaddress,mailbox,ZIP)

Arguments

data	A data.frame containing address variables that are necessry for geocoding.
cityname	The name of the variable in the data indicating city or county names.
roadaddress	The name of the variable in the data indicating home addresses.
mailbox	Optional address information such as the number of mailbox and the number of floor.
ZIP	The name of the variable in the data indicating ZIP codes.

Value

Users may output the function return to the computer as the dbf file using write.dbf ().

Note

In the dbf file, a variable named "singleline" will be used in the second step of geocoding, while variables roadaddress,cityname and ZIP will be seperately used in the first step, and the variable ZIP for the last step.

Examples

# similated data
datatest=data.frame(county=c("Albany","Albany","Albany"),
                address1=c("1 Lincoln ave","2 Lincoln ave","489 Washinton ave"),
                address2=c("1st floor","1st floor","2nd floor"),
                zip=12206
                )
DBFgeocode(data=datatest,cityname="county",roadaddress="address1",
mailbox="address2",ZIP="zip")

---

Generate a Descriptive Table

Description

Generate a comprehensive descriptive table with intergroup comparison.

Usage

desc.comp(data,variables,by,margin,avg.num,test.num)

Arguments

data	a data.frame containing the variables to be described and a group variable
variables	a numeric variable indicating the columns of variables to be described.
by	a number indicating the column of the group variable
margin	calculate the proportion for categorical variables by 1 (row) or 2 (column).
avg.num	"mean", describe continuous variables with mean and standard deviation; "median", describe continuous variables with median and interquantile range; otherwise, normal distribution test will be conducted, for normal distributed variables, "mean" will be used, otherwise, "median" will be used.
test.num	"metric", t test or anova will be used for intergroup comparison; "nonmetric", Wilcoxon rank sum test or Kruskal-Wallis test will be used; otherwise, normal distribution test will be conducted, for normal distributed variables, "metric" will be used, otherwise, "nonmetric" will be used.

Details

Not limited to hospital data, but also applicable to other surveillance data.

Value

A comprehensive descriptive table with statistics and P value for intergroup comparisons.

Examples

desc.comp(CO2,variables=2:5,by=1,margin=1)

---

Identify Duplicates and Re-admissions

Description

Identify the duplicates and re-admissions in hospital data with subject identifications.

Usage

dupl.readm(data,UniqueID,date,period)

Arguments

data	a data.frame containing "UniqueID" and "date"
UniqueID	the name of the variable in the data indicating case ID.
date	the name of the variable in the data indicating the admission/onset date.
period	the time period used to define an re-admission; period=365 by default.

Details

Not limited to hospital data, but also applicable to other surveillance data with "UniqueID" and "date".

Value

id.dupl	indicating whether it is a duplicated record with exactly the same "UniqueID" and "date" as a previous record. In some hospital data,some patients may be reported twice or even more due to insurance issues. For most studies, researchers may remove this kind of duplicates to avoid potential overcounting problems.
onlyone	indicating whether this is the only record with this ID.
Period	the time period between the current visit and the previous one for a patient; 0 for the 1st visit; and NA for those with only one record.
Nadmission	indicating the times of admission, e.g. 1st, 2nd admission; a patient may have more than one 1st admissions if some periods between two visits are greater than e.g. 365 days.

Examples

dataset=data.frame(
  ID=c(1,3,4,2,4,6,3,5,7,1),
  onset=c("2015/1/1","2016/1/2","2015/5/9",
          "2015/12/1","2016/8/2","2015/5/9",
		  "2015/11/1","2016/3/2","2016/5/9","2015/9/9")
)

out.data=dupl.readm(data=dataset,
                    UniqueID="ID",date="onset",period=365)
head(out.data)

---

Calculate Individual and Cumulative Lags for Exposure

Description

Calculate individual and cumulative lag exposure for specific variables. Cumulative lag exposure was calculated by using moving average.

Usage

exposure_lag(data,var,maxlag,ID,Date,lag_suffix)

Arguments

data	A dataframe.
var	Variable names in the dataframe to specify variables to be used for the lag calculation.
maxlag	A number. The max day for calculating the lag exposure.
ID	A variable name. The exposure station ID.
Date	A variable name. A variable indicating the date of exposure measurement.
lag_suffix	A two-length vector indicating the cumulative lag or the individual lag. The first was the suffix for cumulative lag exposure. The second was for individual lag exposure. Default: c('_cu_lag','_si_lag')

Value

It returns a dataframe with calculated individual and cumulative lag exposures. 'var_cu_lag5' means the moving average from lag 0 to lag 5 days. 'var_si_lag5' means the exposure 5 days ago.

References

Deng X, Friedman S, Ryan I, et al. The independent and synergistic impacts of power outages and floods on hospital admissions for multiple diseases [published online ahead of print, 2022 Mar 5]. Sci Total Environ. 2022;828:154305. doi:10.1016/j.scitotenv.2022.154305

Examples

data=data.frame(
  ID=rep(1:5,each=5),
  Date=seq(as.Date('2022-01-01'),as.Date('2022-01-05'),by='1 day'),
  x=rnorm(25)
)

exposure_lag(data,var='x',maxlag=3,ID='ID',Date='Date')

---

Determine the Area that Each Record Is Located in

Description

Identify the residential county/city/census tract for each case, and add county/city/census tract ID.

Usage

FIPS.name(data,ID.case,long.case,lat.case,map,state.map,level.map,areaID)

Arguments

data	A data.frame containing the ID and coordinates of cases
ID.case	Name of the variable in the data indicating the case ID.
long.case	Name of the variable in the data indicating the longitude of cases.
lat.case	Name of the variable in the data indicating the latitude of cases.
map	The reference map containing the boundary of county/city/census tract. Do not have to specify for study areas within the U.S. A map for a region outside the U.S. can be imported as a "spatialpolygonsdataframe" object.
state.map	State FIPS code for the study area, e,g, "36" for the New York State. Ignored if readers' own map is being used.
level.map	"county" or "tract", determine whether cases will be macthed to counties or census tracts. Ignored if readers' own map is being used.
areaID	Name of the variable in the map indicating the area ID. Use the default if the study is within the U.S.

Details

Not limited to hospital data, but also applicable to other surveillance data.

Value

areaID

The area unique ID such as FIPS code and ZIP code will be added to the original data.

Examples

set.seed(2018)
dataset=data.frame(Patient=1:2,lat=rnorm(2,42,0.5),long=rnorm(2,-76,1))
data.out=FIPS.name(data=dataset,ID.case="Patient",long.case="long",
lat.case="lat",state.map="36",level.map="tract",areaID="GEOID")

---

Mediating Analysis

Description

This function provides convenient algorithm to calculate total effect, mediation effect, direct effect and the proportion of mediation effect.

Usage

mediationking(dataset,outcome,mediator,exposure,n.sim)

Arguments

dataset	The dataset that is used for analysis.
outcome	The name of the outcome variable in the dataset.
mediator	The name of the mediator in the dataset.
exposure	The name of the exposure factor in the dataset.
n.sim	Times of simulation to estimate 95% confidence intervals.

Details

Please use set.seed() if you want to get a consistent result; this function will be expended to allow more covariates shortly.

Value

Total effect	The total effect of the exposure on the outcome variable.
Indirect effect	The effect of the exposure on the outcome variable that is caused by mediator.
Direct effect	The effect of the exposure on the outcome variable that is caused by factors other than the mediator.
Meditation.proportion	The proportion of the mediation effect.

Examples

set.seed(1)
exposure<-rnorm(20,0,1)
mediator<-rnorm(20,10,1)
outcome<-rnorm(20,10,1)
dataset<-data.frame(outcome,mediator,exposure)
mediationking(dataset,"outcome","mediator","exposure")

---

Select cases within certain distance around a site

Description

Identify the closest site (e.g. monitoring sites) for each case, and select cases within certain distance around a site, e.g. 15 miles buffer.

Usage

pick.cases(data,long.case,lat.case,long.sites,lat.sites,radius)

Arguments

data	a data.frame containing the coordinates of cases.
long.case	the name of variable in the data indicating the longitude of cases.
lat.case	the name of variable in the data indicating the latitude of cases.
long.sites	a numeric vector containing the longitude of sites.
lat.sites	a numeric vector containing the latitude of sites.
radius	radius of the buffer, e.g."15 miles", "20 kms".

Details

Not limited to hospital data, but also applicable to other surveillance data.

Value

which.site	the closest site to the case.
minDIST	the distance of the case to the closest site; in the same unit as "radius".
Select	an indicator of whether a case was within the buffer.

References

Zhang W, Lin S, Hopke PK, et al. Triggering of cardiovascular hospital admissions by fine particle concentrations in New York state: Before, during, and after implementation of multiple environmental policies and a recession. Environ. Pollut. [electronic article]. 2018;242:1404–1416.

Examples

set.seed(2018)
data=data.frame(Patient=1:100,lat=rnorm(100,41,0.5),long=rnorm(100,-76,1))

long.monitor=c(-73.75464,-78.80953,-73.902,-73.82153,-77.54817)
lat.monitor=c(42.64225,42.87691,40.81618,40.73614,43.14618)

data.out=pick.cases(data,long.case="long",lat.case="lat",
long.sites=long.monitor,lat.sites=lat.monitor,radius="30 miles")
data.out

---

Extract Values from a Raster Map

Description

Crop the raster with the boundary of areas of your interest, and extract the values from the raster to each of these areas.

Usage

raster_extract(rastermap,refmap,ID.var,ID.code,cutpoint)

Arguments

rastermap	a raster map containing the information you need, such as the National Land Cover Database 2011.
refmap	"SpatialPolygonsDataFrame" object. A reference map containing the boundary information of your study areas.
ID.var	the name of variable in the refmap indicating the unique ID for each of your study areas.
ID.code	a character vector containing the unique ID for areas that you want to extract the values to. ID.code=ALL" by default where all areas in the reference map are of interest.
cutpoint	a number to dichotomize the values in the raster; specified ONLY when those values are continuous.

Details

Usually for extracting data which are available as rasters such as the land coverage or land usage data.

Value

ID.code	the column indicating the unique ID for each area, followed by the number of cells for each category/colour within that area.
Total cells	the total number of cells within each area.

Examples

library(raster)
set.seed(4715)
rast=raster(matrix(rnorm(500),100,100))
extent(rast)=c(50,100,10,60)
crs(rast)=CRS("+proj=longlat +datum=WGS84")

ref=cbind(x=c(60,80,80,70), y=c(20,25,40,30))
p=Polygon(ref)
ps=Polygons(list(p),ID="ID")
ref=SpatialPolygons(list(ps))
data=data.frame(value=1, ID="10086",row.names="ID")
ref=SpatialPolygonsDataFrame(ref,data)
proj4string(ref)=CRS("+proj=longlat +datum=WGS84")

raster_extract(rastermap=rast,refmap=ref,ID.var="ID",ID.code="ALL",cutpoint=0.5)

---