data-raw/ramOPindicators.R
In rapidsurveys/oldr: An Implementation of Rapid Assessment Method for Older People
################################################################################
#
# Read and process RAM-OP sample dataset from Addis Ababa, Ethiopia
#
################################################################################
svy <- read.csv("data-raw/testSVY.csv", header = TRUE, sep = ",")
svy$wg1 <- NA
svy$wg2 <- NA
svy$wg3 <- NA
svy$wg4 <- NA
svy$wg5 <- NA
svy$wg6 <- NA
svy <- subset(svy, select = -a9)
svyBackup <- testSVY <- svy
testSVY <- tibble::tibble(testSVY)
usethis::use_data(testSVY, overwrite = TRUE, compress = "xz")
rm(testSVY)
################################################################################
#
# Identifiers and survey administration data
#
# psu Cluster (PSU) identifier
#
psu <- svy$psu
################################################################################
#
# Demography and situation indicators
#
# Indicators are :
#
# resp1 Respondent is SUBJECT
# resp2 Respondent is FAMILY CARER
# resp3 Respondent is OTHER CARER
# resp4 Respondent is OTHER
# age Age of respondent (years)
# ageGrp1 Age of respondent is between 50 and 59 years
# ageGrp2 Age of respondent is between 60 and 69 years
# ageGrp3 Age of respondent is between 70 and 79 years
# ageGrp4 Age of respondent is between 80 and 89 years
# ageGrp5 Age of respondent is 90 years or older
# sex1 Sex = MALE
# sex2 Sex = FEMALE
# marital1 Marital status = SINGLE
# marital2 Marital status = MARRIED
# marital3 Marital status = LIVING TOGETHER
# marital4 Marital status = DIVORCED
# marital5 Marital status = WIDOWED
# marital6 Marital status = OTHER
# alone Respondent lives alone
#
resp1 <- recode(svy$d1, "1=1; 5:9=1; NA=1; else=0")
resp2 <- recode(svy$d1, "2=1; else=0")
resp3 <- recode(svy$d1, "3=1; else=0")
resp4 <- recode(svy$d1, "4=1; else=0")
age <- recode(svy$d2, "888=NA; 999=NA")
ageGrp1 <- recode(age,"50:59=1; NA=NA; else=0")
ageGrp2 <- recode(age,"60:69=1; NA=NA; else=0")
ageGrp3 <- recode(age,"70:79=1; NA=NA; else=0")
ageGrp4 <- recode(age,"80:89=1; NA=NA; else=0")
ageGrp5 <- recode(age,"90:hi=1; NA=NA; else=0")
sex1 <- recode(svy$d3, "1=1; 2=0; else=NA")
sex2 <- recode(svy$d3, "1=0; 2=1; else=NA")
marital1 <- recode(svy$d4, "1=1; else=0")
marital2 <- recode(svy$d4, "2=1; else=0")
marital3 <- recode(svy$d4, "3=1; else=0")
marital4 <- recode(svy$d4, "4=1; else=0")
marital5 <- recode(svy$d4, "5=1; else=0")
marital6 <- recode(svy$d4, "6=1; else=0")
alone <- recode(svy$d5, "1=1; else=0")
################################################################################
#
# Dietary intake indicators
#
# Indicators are :
#
# MF Meal frequency
# DDS DDS (count of 11 groups)
# FG01 Cereals
# FG02 Roots and tubers
# FG03 Fruits and vegetables
# FG04 All meat
# FG05 Eggs
# FG06 Fish
# FG07 Legumes, nuts, and seeds
# FG08 Milk and milk products
# FG09 Fats
# FG10 Sugar
# FG11 Other
# aProtein Protein rich animal sources of protein
# pProtein Protein rich plant sources of protein
# proteinRich Protein rich foods
# pVitA Plant sources of vitamin A
# aVitA Animal sources of vitamin A
# xVitA Any source of vitamin A
# ironRich Iron rich foods
# caRich Calcium rich foods
# znRich Zinc rich foods
# vitB1 Vitamin B1-rich foods
# vitB2 Vitamin B2-rich foods
# vitB3 Vitamin B3-rich foods
# vitB6 Vitamin B6-rich foods
# vitB12 Vitamin B12-rich foods
# vitBcomplex Vitamin B1/B2/B3/B6/B12-rich foods
#
# The basic approach used is described in:
#
# Kennedy G, Ballard T, Dop M C (2011). Guidelines for Measuring Household
# and Individual Dietary Diversity. Rome, FAO
#
# and extended to include indicators of probable adequate intake of a number of
# nutrients / micronutrients.
#
################################################################################
#
# Meal frequency
#
MF <- recode(svy$f1, "9=0; NA=0")
################################################################################
#
# Recode dietary diversity data
#
svy$f2a <- recode(svy$f2a, "1=1; else=0")
svy$f2b <- recode(svy$f2b, "1=1; else=0")
svy$f2c <- recode(svy$f2c, "1=1; else=0")
svy$f2d <- recode(svy$f2d, "1=1; else=0")
svy$f2e <- recode(svy$f2e, "1=1; else=0")
svy$f2f <- recode(svy$f2f, "1=1; else=0")
svy$f2g <- recode(svy$f2g, "1=1; else=0")
svy$f2h <- recode(svy$f2h, "1=1; else=0")
svy$f2i <- recode(svy$f2i, "1=1; else=0")
svy$f2j <- recode(svy$f2j, "1=1; else=0")
svy$f2k <- recode(svy$f2k, "1=1; else=0")
svy$f2l <- recode(svy$f2l, "1=1; else=0")
svy$f2m <- recode(svy$f2m, "1=1; else=0")
svy$f2n <- recode(svy$f2n, "1=1; else=0")
svy$f2o <- recode(svy$f2o, "1=1; else=0")
svy$f2p <- recode(svy$f2p, "1=1; else=0")
svy$f2q <- recode(svy$f2q, "1=1; else=0")
svy$f2r <- recode(svy$f2r, "1=1; else=0")
svy$f2s <- recode(svy$f2s, "1=1; else=0")
################################################################################
#
# Dietary diversity
#
# Recode to ELEVEN standard food groups :
#
# Var Food group From ...
# ---- --------------------------------------- ---------------
# FG01 Cereals (f2c)
# FG02 Roots and tubers (f2g)
# FG03 Fruits and vegetables (f2d, f2f, f2i)
# FG04 All meat (f2j, f2k, f2q)
# FG05 Eggs (f2n)
# FG06 Fish (f2l)
# FG07 Legumes, nuts, and seeds (f2h)
# FG08 Milk and milk products (f2a, f2m)
# FG09 Fats (f2e, f2o)
# FG10 Sugar (f2r)
# FG11 Other (f2b, f2p, f2s)
# ---- --------------------------------------- ---------------
#
# and sum into 'DDS' ...
#
FG01 <- svy$f2c
FG02 <- svy$f2g
FG03 <- ifelse(svy$f2d == 1 | svy$f2f == 1 | svy$f2i == 1, 1, 0)
FG04 <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2q == 1, 1, 0)
FG05 <- svy$f2n
FG06 <- svy$f2l
FG07 <- svy$f2h
FG08 <- ifelse(svy$f2a == 1 | svy$f2m == 1, 1, 0)
FG09 <- ifelse(svy$f2e == 1 | svy$f2o == 1, 1, 0)
FG10 <- svy$f2r
FG11 <- ifelse(svy$f2b == 1 | svy$f2p == 1 | svy$f2s == 1, 1, 0)
#
# Sum food groups to 'DDS'
#
DDS <- FG01 + FG02 + FG03 + FG04 + FG05 + FG06 + FG07 + FG08 + FG09 + FG10 + FG11
################################################################################
#
# Protein rich foods in diet from aminal, plant, and all sources
#
aProtein <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2q ==1 | svy$f2n == 1 | svy$f2a == 1 | svy$f2m == 1, 1, 0)
pProtein <- ifelse(svy$f2h == 1 | svy$f2p == 1, 1, 0)
proteinRich <- ifelse(aProtein == 1 | pProtein == 1, 1, 0)
################################################################################
#
# Micronutrient intake (vitamin A, iron, calcium, zinc)
#
# pVitA Respondent consumes plant sources of vitamin A
# aVitA Respondent consumes animal sources of vitamin A
# xVitA Respondent consumes any source of vitamin A
# ironRich Respondent consumes iron rich foods
# caRich Respondent consumes calcium rich foods
# znRich Respondent consumes zinc rich foods
#
pVitA <- ifelse(svy$f2d == 1 | svy$f2e == 1 | svy$f2f == 1, 1, 0)
aVitA <- ifelse(svy$f2a == 1 | svy$f2j == 1 | svy$f2m == 1 | svy$f2n == 1, 1, 0)
xVitA <- ifelse(pVitA == 1 | aVitA == 1, 1, 0)
ironRich <- ifelse(svy$f2f == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
caRich <- ifelse(svy$f2a == 1 | svy$f2m == 1, 1, 0)
znRich <- ifelse(svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2p == 1 | svy$f2q == 1, 1, 0)
#################################################################################
#
# Micronutrient intake (B vitamins)
#
# vitB1 Respondent consumes vitamin B1-rich foods
# vitB2 Respondent consumes vitamin B2-rich foods
# vitB3 Respondent consumes vitamin B3-rich foods
# vitB6 Respondent consumes vitamin B6-rich foods
# vitB12 Respondent consumes vitamin B12-rich foods
# vitBcomplex Respondent consumes vitamin B1/B2/B3/B6/B12-rich foods
#
vitB1 <- ifelse(svy$f2a == 1 | svy$f2e == 1 | svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1 | svy$f2n == 1 | svy$f2p == 1, 1, 0)
vitB2 <- ifelse(svy$f2a == 1 | svy$f2f == 1 | svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1, 1, 0)
vitB3 <- ifelse(svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
vitB6 <- ifelse(svy$f2d == 1 | svy$f2f == 1 | svy$f2h == 1 | svy$f2i == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
vitB12 <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1 | svy$f2n == 1, 1, 0)
vitBsources <- vitB1 + vitB2 + vitB3 + vitB6 + vitB12
vitBcomplex <- ifelse(vitBsources == 5, 1, 0)
################################################################################
#
# Clean-up
#
rm(vitBsources)
################################################################################
#
# Household Hunger Scale (HHS)
#
# Indicators are :
#
# HHS1 Little or no hunger in household
# HHS2 Moderate hunger in household
# HHS3 Severe hunger in household
#
# The HHS is described in :
#
# Ballard T, Coates J, Swindale A, Deitchler M (2011). Household Hunger
# Scale: Indicator Definition and Measurement Guide. Washington DC,
# FANTA-2 Bridge, FHI 360
#
################################################################################
#
# Recode component variables
#
svy$f3 <- recode(svy$f3, "1=1; 2=1; 3=2; else=0")
svy$f4 <- recode(svy$f4, "1=1; 2=1; 3=2; else=0")
svy$f5 <- recode(svy$f5, "1=1; 2=1; 3=2; else=0")
################################################################################
#
# Sum components and classify hunger into three separate indicator variables
#
sumHHS <- svy$f3 + svy$f4 + svy$f5
HHS1 <- recode(sumHHS, "0:1=1; else=0")
HHS2 <- recode(sumHHS, "2:3=1; else=0")
HHS3 <- recode(sumHHS, "4:6=1; else=0")
###############################################################################
#
# Clean-up
#
rm(sumHHS)
################################################################################
#
# Katz "Index of Independence in Activities of Daily Living" (ADL) score
#
# Indicators are :
#
# ADL01 Bathing
# ADL02 Dressing
# ADL03 Toileting
# ADL04 Transferring (mobility)
# ADL05 Continence
# ADL06 Feeding
# scoreADL ADL score
# classADL1 Severity of dependence = INDEPENDENT
# classADL2 Severity of dependence = PARTIAL DEPENDENCY
# classADL3 Severity of dependence = SEVERE DEPENDENCY
# hasHelp Has someone to help with ADL
# unmetNeed Unmet need (dependency with NO helper)
#
#
# The Katz ADL score is described in :
#
# Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW (1963). Studies
# of illness in the aged. The Index of ADL: a standardized measure of
# biological and psychosocial function. JAMA, 1963, 185(12):914-9
#
# Katz S, Down TD, Cash HR, Grotz, RC (1970). Progress in the development
# of the index of ADL. The Gerontologist, 10(1), 20-30
#
# Katz S (1983). Assessing self-maintenance: Activities of daily living,
# mobility and instrumental activities of daily living. JAGS, 31(12),
# 721-726
#
################################################################################
#
# Recode ADL (activities of daily living) score data
#
# Each item is scored :
#
# 1 = Independence
# 0 = Needs assistance or supervision
#
ADL01 <- recode(svy$a1, "2=1; else=0") # Bathing
ADL02 <- recode(svy$a2, "2=1; else=0") # Dressing
ADL03 <- recode(svy$a3, "2=1; else=0") # Toileting
ADL04 <- recode(svy$a4, "2=1; else=0") # Transferring (mobility)
ADL05 <- recode(svy$a5, "2=1; else=0") # Continence
ADL06 <- recode(svy$a6, "2=1; else=0") # Feeding
################################################################################
#
# Create ADL score (items summed over all six activities / dimensions)
#
scoreADL <- ADL01 + ADL02 + ADL03 + ADL04 + ADL05 + ADL06
################################################################################
#
# Severity of dependence (from Katz ADL score)
#
# Indicator Degree of severity Related Scores
# --------- ------------------ ----------------
# classADL1 Independent scoreADL = 5,6
# classADL2 Partial dependency scoreADL = 3,4
# classADL3 Severe dependency scoreADL = 0,1,2
# --------- ------------------ ----------------
#
classADL1 <- recode(scoreADL, "5:6=1; else=0")
classADL2 <- recode(scoreADL, "3:4=1; else=0")
classADL3 <- recode(scoreADL, "0:2=1; else=0")
################################################################################
#
# Does the subject have someone to help with everyday activities?
#
hasHelp <- recode(svy$a7, "1=1; else=0")
################################################################################
#
# Does the subject need help but has no helper?
#
# Note : Denominator is entire sample so the indicator is the proportion of
# the population with unmet ADl help needs
#
unmetNeed <- ifelse(scoreADL < 6 & hasHelp == 0, 1, 0)
################################################################################
#
# K6 : Short form psychological distress score
#
# Indicators are :
#
# K6 K6 score
# K6Case K6 score > 12 (in serious psychological distress)
#
# The K6 score is described in :
#
# Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT,
# et al. (2002). Short screening scales to monitor population prevalences
# and trends in non-specific psychological distress. Psychological
# Medicine, 32(6), 959–976
#
################################################################################
#
# Recode DON'T KNOW, REFUSED, NA and MISSING values to 5 (NONE)
#
svy$k6a <- recode(svy$k6a, "6:9=5")
svy$k6b <- recode(svy$k6b, "6:9=5")
svy$k6c <- recode(svy$k6c, "6:9=5")
svy$k6d <- recode(svy$k6d, "6:9=5")
svy$k6e <- recode(svy$k6e, "6:9=5")
svy$k6f <- recode(svy$k6f, "6:9=5")
################################################################################
#
# Reverse coding & create K6 score (i.e. as the sum of individual item scores)
#
svy$k6a <- 5 - svy$k6a
svy$k6b <- 5 - svy$k6b
svy$k6c <- 5 - svy$k6d
svy$k6d <- 5 - svy$k6d
svy$k6e <- 5 - svy$k6e
svy$k6f <- 5 - svy$k6f
K6 <- svy$k6a + svy$k6b + svy$k6c + svy$k6d + svy$k6e + svy$k6f
################################################################################
#
# Apply case-definition for serious psychological distress(i.e. K6 > 12)
#
K6Case <- recode(K6, "0:12=0; 13:hi=1")
################################################################################
#
# Brief Community Screening Instrument for Dementia (CSID)
#
# Indicators are :
#
# DS Probable dementia by CSID screen
#
# The CSID dementia screening tool is described in :
#
# Prince M, et al. (2010). A brief dementia screener suitable for use
# by non-specialists in resource poor settings - The cross-cultural
# derivation and validation of the brief Community Screening Instrument
# for Dementia. International Journal of Geriatric Psychiatry, 26(9),
# 899–907
#
################################################################################
#
# Recode scored components to 0 / 1 (with 1 = correct)
#
svy$ds1 <- recode(svy$ds1, "1=1; else=0") # Nose
svy$ds2 <- recode(svy$ds2, "1=1; else=0") # Hammer
svy$ds3 <- recode(svy$ds3, "1=1; else=0") # Day of week
svy$ds4 <- recode(svy$ds4, "1=1; else=0") # Season
svy$ds5 <- recode(svy$ds5, "1=1; else=0") # Point to window then door
svy$ds6a <- recode(svy$ds6a, "1=1; else=0") # Memory "CHILD"
svy$ds6b <- recode(svy$ds6b, "1=1; else=0") # Memory "HOUSE"
svy$ds6c <- recode(svy$ds6c, "1=1; else=0") # Memory "ROAD"
################################################################################
#
# Sum correct items into CSID score
#
scoreCSID <- svy$ds1 + svy$ds2 + svy$ds3 + svy$ds4 + svy$ds5 + svy$ds6a + svy$ds6b + svy$ds6c
#################################################################################
#
# Classify dementia :
#
# 0 = Normal (not probable dementia)
# 1 = Probable dementia
#
DS <- recode(scoreCSID, "0:4=1; 5:8=0")
################################################################################
#
# Clean-up
#
rm(scoreCSID)
################################################################################
#
# Health and health-seeking indicators
#
# Indicators are :
#
# H1 Chronic condition
#
# H2 Takes drugs regularly for chronic condition
#
# H3x Main reason for not taking drugs for chronic condition where :
#
# x = 1 No drugs available
# x = 2 Too expensive / no money
# x = 3 Too old to look for care
# x = 4 Use traditional medicine
# x = 5 Drugs don't help
# x = 6 No-one to help me
# x = 7 No need
# x = 8 Other
# x = 9 No reason given
#
# H4 Recent disease episode
#
# H5 Accessed care for recent disease episode
#
# H6x Main reason for not accessing care for recent disease episode where :
#
# x = 1 No drugs available
# x = 2 Too expensive / no money
# x = 3 Too old to look for care
# x = 4 Use traditional medicine
# x = 5 Drugs don't help
# x = 6 No-one to help me
# x = 7 No need
# x = 8 Other
# x = 9 No reason given
#
################################################################################
#
# Health indicators : CHRONIC CONDITIONS
#
svy$h1 <- recode(svy$h1, "1=1; else=2")
H1 <- recode(svy$h1, "1=1; else=0")
H2 <- ifelse(H1 == 0, NA, recode(svy$h2, "1=1; else=0"))
H3 <- ifelse(H2 == 1, NA, recode(svy$h3, "NA=9"))
#
# Indicators for main reason for NOT taking drugs for chronic condition
#
H31 <- recode(H3, "1=1; NA=NA; else=0")
H32 <- recode(H3, "2=1; NA=NA; else=0")
H33 <- recode(H3, "3=1; NA=NA; else=0")
H34 <- recode(H3, "4=1; NA=NA; else=0")
H35 <- recode(H3, "5=1; NA=NA; else=0")
H36 <- recode(H3, "6=1; NA=NA; else=0")
H37 <- recode(H3, "7=1; NA=NA; else=0")
H38 <- recode(H3, "8=1; NA=NA; else=0")
H39 <- recode(H3, "9=1; NA=NA; else=0")
################################################################################
#
# Health indicators : RECENT DISEASE EPISODE
#
svy$h4 <- recode(svy$h4, "1=1; else=2")
H4 <- recode(svy$h4, "1=1; else=0")
H5 <- ifelse(H4 == 0, NA, recode(svy$h5, "1=1; else=0"))
H6 <- ifelse(H5 == 1, NA, recode(svy$h6, "NA=9"))
#
#
# Indicators for main reason for NOT accessing care for recent disease episode
#
H61 <- recode(H6, "1=1; NA=NA; else=0")
H62 <- recode(H6, "2=1; NA=NA; else=0")
H63 <- recode(H6, "3=1; NA=NA; else=0")
H64 <- recode(H6, "4=1; NA=NA; else=0")
H65 <- recode(H6, "5=1; NA=NA; else=0")
H66 <- recode(H6, "6=1; NA=NA; else=0")
H67 <- recode(H6, "7=1; NA=NA; else=0")
H68 <- recode(H6, "8=1; NA=NA; else=0")
H69 <- recode(H6, "9=1; NA=NA; else=0")
#
# Clean-up
#
rm(H3, H6)
###############################################################################
#
# Income and income sources
#
# Indicators are :
#
# M1 Has a personal income
# M2A Agriculture / fishing / livestock
# M2B Wages / salary
# M2C Sale of charcoal / bricks / &c.
# M2D Trading (e.g. market or shop)
# M2E Investments
# M2F Spending savings / sale of assets
# M2G Charity
# M2H Cash transfer / Social security
# M2I Other
#
################################################################################
#
# Create binary indicators
#
M1 <- recode(svy$m1, "1=1; else=0") # Has a personal income
M2A <- recode(svy$m2a, "1=1; else=0") # Agriculture / fishing / livestock
M2B <- recode(svy$m2b, "1=1; else=0") # Wages / salary
M2C <- recode(svy$m2c, "1=1; else=0") # Sale of charcoal / bricks / &c.
M2D <- recode(svy$m2d, "1=1; else=0") # Trading (e.g. market or shop)
M2E <- recode(svy$m2e, "1=1; else=0") # Investments
M2F <- recode(svy$m2f, "1=1; else=0") # Spending savings / sale of assets
M2G <- recode(svy$m2g, "1=1; else=0") # Charity
M2H <- recode(svy$m2h, "1=1; else=0") # Cash transfer / social security
M2I <- recode(svy$m2i, "1=1; else=0") # Other
################################################################################
#
# Check for any income (return 'correct' result in M1)
#
checkForIncome <- M1 + M2A + M2B + M2C + M2D + M2E + M2F + M2G + M2H + M2I
M1 <- ifelse(checkForIncome > 0, 1, 0)
################################################################################
#
# Clean-up
#
rm(checkForIncome)
################################################################################
#
# Water, Sanitation, and Hygiene (WASH) indicators
#
# Indicators are :
#
# W1 Improved source of drinking water
# W2 Safe drinking water (improved source OR adequate treatment)
# W3 Improved sanitation facility
# W4 Improved non-shared sanitation facility
#
# These are a (core) subset of indicators from :
#
# WHO / UNICEF (2006). Core Questions on Drinking-water and Sanitation
# for Household Surveys. Geneva, WHO / UNICEF
#
################################################################################
#
# Recode WASH data
#
svy$w1 <- recode(svy$w1, "1=1; else=0")
svy$w2 <- recode(svy$w2, "1=1; else=0")
svy$w3 <- recode(svy$w3, "1=1; else=0")
svy$w4 <- recode(svy$w4, "1=1; else=0")
################################################################################
#
# Create WASH indicators
#
W1 <- svy$w1
W2 <- ifelse(svy$w1 == 1 | svy$w2 == 1, 1, 0)
W3 <- svy$w3
W4 <- ifelse(svy$w3 == 1 & svy$w4 != 1, 1, 0)
################################################################################
#
# Anthropometry and screening
#
# Indicators are :
#
# MUAC MUAC
# oedema Bilateral pitting oedema (may not be nutritional)
# screened Either MUAC or oedema checked previously
#
################################################################################
#
# Censor REFUSAL, NOT APPLICABLE, and MISSING values codes in MUAC and Oedema
#
MUAC <- recode(svy$as1, "777=NA; 888=NA; 999=NA")
oedema <- recode(svy$as3, "1=1; else=0")
################################################################################
#
# Screening for GAM, MAM, SAM (i.e. either MUAC or oedema checked previously)
#
screened <- ifelse(svy$as2 == 1 | svy$as4 == 1, 1, 0)
################################################################################
#
# Visual impairment by "Tumbling E" method
#
# Indicators are :
#
# poorVA Poor visual acuity (correct in < 3 of 4 tests)
#
# Refusal and blindness are treated as test failures
#
# The "Tumbling E" method is described in :
#
# Taylor HR (1978). Applying new design principles to the construction of an
# illiterate E Chart. Am J Optom & Physiol Optics 55:348
#
################################################################################
#
# Create binary indicators
#
svy$va2a <- recode(svy$va2a, "1=1; else=0")
svy$va2b <- recode(svy$va2b, "1=1; else=0")
svy$va2c <- recode(svy$va2c, "1=1; else=0")
svy$va2d <- recode(svy$va2d, "1=1; else=0")
sumVA <- svy$va2a + svy$va2b + svy$va2c + svy$va2d
poorVA <- ifelse(sumVA < 3, 1, 0)
###############################################################################
#
# Clean-up
#
rm(sumVA)
################################################################################
#
# Miscellaneous indicators
#
# Indicators are :
#
# chew Problems chewing food
# food Anyone in HH receives a ration
# NFRI Anyone in HH received non-food relief item(s) in previous month
#
chew <- recode(svy$a8, "1=1; else=0")
food <- recode(svy$f6, "1=1; else=0")
NFRI <- recode(svy$f7, "1=1; else=0")
################################################################################
#
# Washington Group (WG) short set of question designed to identify people with a
# disability in a census or survey format.
#
# See:
#
# http://www.washingtongroup-disability.com
#
# https://www.cdc.gov/nchs/washington_group/wg_documents.htm
#
# for details.
#
################################################################################
#
# Missing values
#
svy$wg1 <- recode(svy$wg1, "9=0; NA=0")
svy$wg2 <- recode(svy$wg2, "9=0; NA=0")
svy$wg3 <- recode(svy$wg3, "9=0; NA=0")
svy$wg4 <- recode(svy$wg4, "9=0; NA=0")
svy$wg5 <- recode(svy$wg5, "9=0; NA=0")
svy$wg6 <- recode(svy$wg6, "9=0; NA=0")
################################################################################
#
# Vision domain
#
wgVisionD0 <- ifelse(svy$wg1 == 0, 1, 0)
wgVisionD1 <- ifelse(svy$wg1 == 1 | svy$wg1 == 2 | svy$wg1 == 3, 1, 0)
wgVisionD2 <- ifelse(svy$wg1 == 2 | svy$wg1 == 3, 1, 0)
wgVisionD3 <- ifelse(svy$wg1 == 3, 1, 0)
################################################################################
#
# Hearing domain
#
wgHearingD0 <- ifelse(svy$wg2 == 0, 1, 0)
wgHearingD1 <- ifelse(svy$wg2 == 1 | svy$wg2 == 2 | svy$wg2 == 3, 1, 0)
wgHearingD2 <- ifelse(svy$wg2 == 2 | svy$wg2 == 3, 1, 0)
wgHearingD3 <- ifelse(svy$wg2 == 3, 1, 0)
################################################################################
#
# Mobility domain
#
wgMobilityD0 <- ifelse(svy$wg3 == 0, 1, 0)
wgMobilityD1 <- ifelse(svy$wg3 == 1 | svy$wg3 == 2 | svy$wg3 == 3, 1, 0)
wgMobilityD2 <- ifelse(svy$wg3 == 2 | svy$wg3 == 3, 1, 0)
wgMobilityD3 <- ifelse(svy$wg3 == 3, 1, 0)
################################################################################
#
# Remembering domain
#
wgRememberingD0 <- ifelse(svy$wg4 == 0, 1, 0)
wgRememberingD1 <- ifelse(svy$wg4 == 1 | svy$wg4 == 2 | svy$wg4 == 3, 1, 0)
wgRememberingD2 <- ifelse(svy$wg4 == 2 | svy$wg4 == 3, 1, 0)
wgRememberingD3 <- ifelse(svy$wg4 == 3, 1, 0)
################################################################################
#
# Self-care domain
#
wgSelfCareD0 <- ifelse(svy$wg5 == 0, 1, 0)
wgSelfCareD1 <- ifelse(svy$wg5 == 1 | svy$wg5 == 2 | svy$wg5 == 3, 1, 0)
wgSelfCareD2 <- ifelse(svy$wg5 == 2 | svy$wg5 == 3, 1, 0)
wgSelfCareD3 <- ifelse(svy$wg5 == 3, 1, 0)
################################################################################
#
# Communicating domain
#
wgCommunicatingD0 <- ifelse(svy$wg6 == 0, 1, 0)
wgCommunicatingD1 <- ifelse(svy$wg6 == 1 | svy$wg6 == 2 | svy$wg6 == 3, 1, 0)
wgCommunicatingD2 <- ifelse(svy$wg6 == 2 | svy$wg6 == 3, 1, 0)
wgCommunicatingD3 <- ifelse(svy$wg6 == 3, 1, 0)
################################################################################
#
# Overall prevalence
#
wgP0 <- ifelse(wgVisionD0 + wgHearingD0 + wgMobilityD0 + wgRememberingD0 + wgSelfCareD0 + wgCommunicatingD0 == 6, 1, 0)
wgP1 <- ifelse(wgVisionD1 + wgHearingD1 + wgMobilityD1 + wgRememberingD1 + wgSelfCareD1 + wgCommunicatingD1 > 0, 1, 0)
wgP2 <- ifelse(wgVisionD2 + wgHearingD2 + wgMobilityD2 + wgRememberingD2 + wgSelfCareD2 + wgCommunicatingD2 > 0, 1, 0)
wgP3 <- ifelse(wgVisionD3 + wgHearingD3 + wgMobilityD3 + wgRememberingD3 + wgSelfCareD3 + wgCommunicatingD3 > 0, 1, 0)
wgPM <- ifelse(wgVisionD1 + wgHearingD1 + wgMobilityD1 + wgRememberingD1 + wgSelfCareD1 + wgCommunicatingD1 > 1, 1, 0)
################################################################################
#
# Make summary data.frame for ALL respondents
#
indicators.ALL <- data.frame(psu, resp1, resp2, resp3, resp4,
age, ageGrp1, ageGrp2, ageGrp3, ageGrp4, ageGrp5, sex1, sex2,
marital1, marital2, marital3, marital4, marital5, marital6,
alone,
MF, DDS,
FG01, FG02, FG03, FG04, FG05, FG06, FG07, FG08, FG09, FG10, FG11,
proteinRich, pProtein, aProtein,
pVitA, aVitA, xVitA,
ironRich,
caRich,
znRich,
vitB1, vitB2, vitB3, vitB6, vitB12, vitBcomplex,
HHS1, HHS2, HHS3,
ADL01, ADL02, ADL03, ADL04, ADL05, ADL06, scoreADL, classADL1, classADL2, classADL3, hasHelp, unmetNeed,
K6, K6Case,
DS,
H1, H2, H31, H32, H33, H34, H35, H36, H37, H38, H39,
H4, H5, H61, H62, H63, H64, H65, H66, H67, H68, H69,
M1, M2A, M2B, M2C, M2D, M2E, M2F, M2G, M2H, M2I,
W1, W2, W3, W4,
MUAC, oedema, screened,
poorVA,
chew, food, NFRI,
wgVisionD0, wgVisionD1, wgVisionD2, wgVisionD3,
wgHearingD0, wgHearingD1, wgHearingD2, wgHearingD3,
wgMobilityD0, wgMobilityD1, wgMobilityD2, wgMobilityD3,
wgRememberingD0, wgRememberingD1, wgRememberingD2, wgRememberingD3,
wgSelfCareD0, wgSelfCareD1, wgSelfCareD2, wgSelfCareD3,
wgCommunicatingD0, wgCommunicatingD1, wgCommunicatingD2, wgCommunicatingD3,
wgP0, wgP1, wgP2, wgP3, wgPM)
indicators.ALL <- tibble::tibble(indicators.ALL)
usethis::use_data(indicators.ALL, overwrite = TRUE, compress = "xz")
#
# Subset summary data.frames for MALES and FEMALES
#
indicators.MALES <- subset(indicators.ALL, sex1 == 1)
indicators.FEMALES <- subset(indicators.ALL, sex2 == 1)
usethis::use_data(indicators.MALES, overwrite = TRUE, compress = "xz")
usethis::use_data(indicators.FEMALES, overwrite = TRUE, compress = "xz")
#
# Clean-up
#
rm(psu, resp1, resp2, resp3, resp4,
age, ageGrp1, ageGrp2, ageGrp3, ageGrp4, ageGrp5, sex1, sex2,
marital1, marital2, marital3, marital4, marital5, marital6,
alone,
MF, DDS,
FG01, FG02, FG03, FG04, FG05, FG06, FG07, FG08, FG09, FG10, FG11,
proteinRich, pProtein, aProtein,
pVitA, aVitA, xVitA,
ironRich,
caRich,
znRich,
vitB1, vitB2, vitB3, vitB6, vitB12, vitBcomplex,
HHS1, HHS2, HHS3,
ADL01, ADL02, ADL03, ADL04, ADL05, ADL06, scoreADL, classADL1, classADL2, classADL3, hasHelp, unmetNeed,
K6, K6Case,
DS,
H1, H2, H31, H32, H33, H34, H35, H36, H37, H38, H39,
H4, H5, H61, H62, H63, H64, H65, H66, H67, H68, H69,
M1, M2A, M2B, M2C, M2D, M2E, M2F, M2G, M2H, M2I,
W1, W2, W3, W4,
MUAC, oedema, screened,
poorVA,
chew, food, NFRI,
wgVisionD0, wgVisionD1, wgVisionD2, wgVisionD3,
wgHearingD0, wgHearingD1, wgHearingD2, wgHearingD3,
wgMobilityD0, wgMobilityD1, wgMobilityD2, wgMobilityD3,
wgRememberingD0, wgRememberingD1, wgRememberingD2, wgRememberingD3,
wgSelfCareD0, wgSelfCareD1, wgSelfCareD2, wgSelfCareD3,
wgCommunicatingD0, wgCommunicatingD1, wgCommunicatingD2, wgCommunicatingD3,
wgP0, wgP1, wgP2, wgP3, wgPM)
gc()
#
# Restore 'svy' from back-up copy (i.e. to undo recoding used when making indicators)
#
svy <- svyBackup
rm(svyBackup)
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################################################################################
#
# Read and process RAM-OP sample dataset from Addis Ababa, Ethiopia
#
################################################################################
svy <- read.csv("data-raw/testSVY.csv", header = TRUE, sep = ",")
svy$wg1 <- NA
svy$wg2 <- NA
svy$wg3 <- NA
svy$wg4 <- NA
svy$wg5 <- NA
svy$wg6 <- NA
svy <- subset(svy, select = -a9)
svyBackup <- testSVY <- svy
testSVY <- tibble::tibble(testSVY)
usethis::use_data(testSVY, overwrite = TRUE, compress = "xz")
rm(testSVY)
################################################################################
#
# Identifiers and survey administration data
#
# psu Cluster (PSU) identifier
#
psu <- svy$psu
################################################################################
#
# Demography and situation indicators
#
# Indicators are :
#
# resp1 Respondent is SUBJECT
# resp2 Respondent is FAMILY CARER
# resp3 Respondent is OTHER CARER
# resp4 Respondent is OTHER
# age Age of respondent (years)
# ageGrp1 Age of respondent is between 50 and 59 years
# ageGrp2 Age of respondent is between 60 and 69 years
# ageGrp3 Age of respondent is between 70 and 79 years
# ageGrp4 Age of respondent is between 80 and 89 years
# ageGrp5 Age of respondent is 90 years or older
# sex1 Sex = MALE
# sex2 Sex = FEMALE
# marital1 Marital status = SINGLE
# marital2 Marital status = MARRIED
# marital3 Marital status = LIVING TOGETHER
# marital4 Marital status = DIVORCED
# marital5 Marital status = WIDOWED
# marital6 Marital status = OTHER
# alone Respondent lives alone
#
resp1 <- recode(svy$d1, "1=1; 5:9=1; NA=1; else=0")
resp2 <- recode(svy$d1, "2=1; else=0")
resp3 <- recode(svy$d1, "3=1; else=0")
resp4 <- recode(svy$d1, "4=1; else=0")
age <- recode(svy$d2, "888=NA; 999=NA")
ageGrp1 <- recode(age,"50:59=1; NA=NA; else=0")
ageGrp2 <- recode(age,"60:69=1; NA=NA; else=0")
ageGrp3 <- recode(age,"70:79=1; NA=NA; else=0")
ageGrp4 <- recode(age,"80:89=1; NA=NA; else=0")
ageGrp5 <- recode(age,"90:hi=1; NA=NA; else=0")
sex1 <- recode(svy$d3, "1=1; 2=0; else=NA")
sex2 <- recode(svy$d3, "1=0; 2=1; else=NA")
marital1 <- recode(svy$d4, "1=1; else=0")
marital2 <- recode(svy$d4, "2=1; else=0")
marital3 <- recode(svy$d4, "3=1; else=0")
marital4 <- recode(svy$d4, "4=1; else=0")
marital5 <- recode(svy$d4, "5=1; else=0")
marital6 <- recode(svy$d4, "6=1; else=0")
alone <- recode(svy$d5, "1=1; else=0")
################################################################################
#
# Dietary intake indicators
#
# Indicators are :
#
# MF Meal frequency
# DDS DDS (count of 11 groups)
# FG01 Cereals
# FG02 Roots and tubers
# FG03 Fruits and vegetables
# FG04 All meat
# FG05 Eggs
# FG06 Fish
# FG07 Legumes, nuts, and seeds
# FG08 Milk and milk products
# FG09 Fats
# FG10 Sugar
# FG11 Other
# aProtein Protein rich animal sources of protein
# pProtein Protein rich plant sources of protein
# proteinRich Protein rich foods
# pVitA Plant sources of vitamin A
# aVitA Animal sources of vitamin A
# xVitA Any source of vitamin A
# ironRich Iron rich foods
# caRich Calcium rich foods
# znRich Zinc rich foods
# vitB1 Vitamin B1-rich foods
# vitB2 Vitamin B2-rich foods
# vitB3 Vitamin B3-rich foods
# vitB6 Vitamin B6-rich foods
# vitB12 Vitamin B12-rich foods
# vitBcomplex Vitamin B1/B2/B3/B6/B12-rich foods
#
# The basic approach used is described in:
#
# Kennedy G, Ballard T, Dop M C (2011). Guidelines for Measuring Household
# and Individual Dietary Diversity. Rome, FAO
#
# and extended to include indicators of probable adequate intake of a number of
# nutrients / micronutrients.
#
################################################################################
#
# Meal frequency
#
MF <- recode(svy$f1, "9=0; NA=0")
################################################################################
#
# Recode dietary diversity data
#
svy$f2a <- recode(svy$f2a, "1=1; else=0")
svy$f2b <- recode(svy$f2b, "1=1; else=0")
svy$f2c <- recode(svy$f2c, "1=1; else=0")
svy$f2d <- recode(svy$f2d, "1=1; else=0")
svy$f2e <- recode(svy$f2e, "1=1; else=0")
svy$f2f <- recode(svy$f2f, "1=1; else=0")
svy$f2g <- recode(svy$f2g, "1=1; else=0")
svy$f2h <- recode(svy$f2h, "1=1; else=0")
svy$f2i <- recode(svy$f2i, "1=1; else=0")
svy$f2j <- recode(svy$f2j, "1=1; else=0")
svy$f2k <- recode(svy$f2k, "1=1; else=0")
svy$f2l <- recode(svy$f2l, "1=1; else=0")
svy$f2m <- recode(svy$f2m, "1=1; else=0")
svy$f2n <- recode(svy$f2n, "1=1; else=0")
svy$f2o <- recode(svy$f2o, "1=1; else=0")
svy$f2p <- recode(svy$f2p, "1=1; else=0")
svy$f2q <- recode(svy$f2q, "1=1; else=0")
svy$f2r <- recode(svy$f2r, "1=1; else=0")
svy$f2s <- recode(svy$f2s, "1=1; else=0")
################################################################################
#
# Dietary diversity
#
# Recode to ELEVEN standard food groups :
#
# Var Food group From ...
# ---- --------------------------------------- ---------------
# FG01 Cereals (f2c)
# FG02 Roots and tubers (f2g)
# FG03 Fruits and vegetables (f2d, f2f, f2i)
# FG04 All meat (f2j, f2k, f2q)
# FG05 Eggs (f2n)
# FG06 Fish (f2l)
# FG07 Legumes, nuts, and seeds (f2h)
# FG08 Milk and milk products (f2a, f2m)
# FG09 Fats (f2e, f2o)
# FG10 Sugar (f2r)
# FG11 Other (f2b, f2p, f2s)
# ---- --------------------------------------- ---------------
#
# and sum into 'DDS' ...
#
FG01 <- svy$f2c
FG02 <- svy$f2g
FG03 <- ifelse(svy$f2d == 1 | svy$f2f == 1 | svy$f2i == 1, 1, 0)
FG04 <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2q == 1, 1, 0)
FG05 <- svy$f2n
FG06 <- svy$f2l
FG07 <- svy$f2h
FG08 <- ifelse(svy$f2a == 1 | svy$f2m == 1, 1, 0)
FG09 <- ifelse(svy$f2e == 1 | svy$f2o == 1, 1, 0)
FG10 <- svy$f2r
FG11 <- ifelse(svy$f2b == 1 | svy$f2p == 1 | svy$f2s == 1, 1, 0)
#
# Sum food groups to 'DDS'
#
DDS <- FG01 + FG02 + FG03 + FG04 + FG05 + FG06 + FG07 + FG08 + FG09 + FG10 + FG11
################################################################################
#
# Protein rich foods in diet from aminal, plant, and all sources
#
aProtein <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2q ==1 | svy$f2n == 1 | svy$f2a == 1 | svy$f2m == 1, 1, 0)
pProtein <- ifelse(svy$f2h == 1 | svy$f2p == 1, 1, 0)
proteinRich <- ifelse(aProtein == 1 | pProtein == 1, 1, 0)
################################################################################
#
# Micronutrient intake (vitamin A, iron, calcium, zinc)
#
# pVitA Respondent consumes plant sources of vitamin A
# aVitA Respondent consumes animal sources of vitamin A
# xVitA Respondent consumes any source of vitamin A
# ironRich Respondent consumes iron rich foods
# caRich Respondent consumes calcium rich foods
# znRich Respondent consumes zinc rich foods
#
pVitA <- ifelse(svy$f2d == 1 | svy$f2e == 1 | svy$f2f == 1, 1, 0)
aVitA <- ifelse(svy$f2a == 1 | svy$f2j == 1 | svy$f2m == 1 | svy$f2n == 1, 1, 0)
xVitA <- ifelse(pVitA == 1 | aVitA == 1, 1, 0)
ironRich <- ifelse(svy$f2f == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
caRich <- ifelse(svy$f2a == 1 | svy$f2m == 1, 1, 0)
znRich <- ifelse(svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2p == 1 | svy$f2q == 1, 1, 0)
#################################################################################
#
# Micronutrient intake (B vitamins)
#
# vitB1 Respondent consumes vitamin B1-rich foods
# vitB2 Respondent consumes vitamin B2-rich foods
# vitB3 Respondent consumes vitamin B3-rich foods
# vitB6 Respondent consumes vitamin B6-rich foods
# vitB12 Respondent consumes vitamin B12-rich foods
# vitBcomplex Respondent consumes vitamin B1/B2/B3/B6/B12-rich foods
#
vitB1 <- ifelse(svy$f2a == 1 | svy$f2e == 1 | svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1 | svy$f2n == 1 | svy$f2p == 1, 1, 0)
vitB2 <- ifelse(svy$f2a == 1 | svy$f2f == 1 | svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1, 1, 0)
vitB3 <- ifelse(svy$f2h == 1 | svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
vitB6 <- ifelse(svy$f2d == 1 | svy$f2f == 1 | svy$f2h == 1 | svy$f2i == 1 | svy$f2k == 1 | svy$f2l == 1, 1, 0)
vitB12 <- ifelse(svy$f2j == 1 | svy$f2k == 1 | svy$f2l == 1 | svy$f2m == 1 | svy$f2n == 1, 1, 0)
vitBsources <- vitB1 + vitB2 + vitB3 + vitB6 + vitB12
vitBcomplex <- ifelse(vitBsources == 5, 1, 0)
################################################################################
#
# Clean-up
#
rm(vitBsources)
################################################################################
#
# Household Hunger Scale (HHS)
#
# Indicators are :
#
# HHS1 Little or no hunger in household
# HHS2 Moderate hunger in household
# HHS3 Severe hunger in household
#
# The HHS is described in :
#
# Ballard T, Coates J, Swindale A, Deitchler M (2011). Household Hunger
# Scale: Indicator Definition and Measurement Guide. Washington DC,
# FANTA-2 Bridge, FHI 360
#
################################################################################
#
# Recode component variables
#
svy$f3 <- recode(svy$f3, "1=1; 2=1; 3=2; else=0")
svy$f4 <- recode(svy$f4, "1=1; 2=1; 3=2; else=0")
svy$f5 <- recode(svy$f5, "1=1; 2=1; 3=2; else=0")
################################################################################
#
# Sum components and classify hunger into three separate indicator variables
#
sumHHS <- svy$f3 + svy$f4 + svy$f5
HHS1 <- recode(sumHHS, "0:1=1; else=0")
HHS2 <- recode(sumHHS, "2:3=1; else=0")
HHS3 <- recode(sumHHS, "4:6=1; else=0")
###############################################################################
#
# Clean-up
#
rm(sumHHS)
################################################################################
#
# Katz "Index of Independence in Activities of Daily Living" (ADL) score
#
# Indicators are :
#
# ADL01 Bathing
# ADL02 Dressing
# ADL03 Toileting
# ADL04 Transferring (mobility)
# ADL05 Continence
# ADL06 Feeding
# scoreADL ADL score
# classADL1 Severity of dependence = INDEPENDENT
# classADL2 Severity of dependence = PARTIAL DEPENDENCY
# classADL3 Severity of dependence = SEVERE DEPENDENCY
# hasHelp Has someone to help with ADL
# unmetNeed Unmet need (dependency with NO helper)
#
#
# The Katz ADL score is described in :
#
# Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW (1963). Studies
# of illness in the aged. The Index of ADL: a standardized measure of
# biological and psychosocial function. JAMA, 1963, 185(12):914-9
#
# Katz S, Down TD, Cash HR, Grotz, RC (1970). Progress in the development
# of the index of ADL. The Gerontologist, 10(1), 20-30
#
# Katz S (1983). Assessing self-maintenance: Activities of daily living,
# mobility and instrumental activities of daily living. JAGS, 31(12),
# 721-726
#
################################################################################
#
# Recode ADL (activities of daily living) score data
#
# Each item is scored :
#
# 1 = Independence
# 0 = Needs assistance or supervision
#
ADL01 <- recode(svy$a1, "2=1; else=0") # Bathing
ADL02 <- recode(svy$a2, "2=1; else=0") # Dressing
ADL03 <- recode(svy$a3, "2=1; else=0") # Toileting
ADL04 <- recode(svy$a4, "2=1; else=0") # Transferring (mobility)
ADL05 <- recode(svy$a5, "2=1; else=0") # Continence
ADL06 <- recode(svy$a6, "2=1; else=0") # Feeding
################################################################################
#
# Create ADL score (items summed over all six activities / dimensions)
#
scoreADL <- ADL01 + ADL02 + ADL03 + ADL04 + ADL05 + ADL06
################################################################################
#
# Severity of dependence (from Katz ADL score)
#
# Indicator Degree of severity Related Scores
# --------- ------------------ ----------------
# classADL1 Independent scoreADL = 5,6
# classADL2 Partial dependency scoreADL = 3,4
# classADL3 Severe dependency scoreADL = 0,1,2
# --------- ------------------ ----------------
#
classADL1 <- recode(scoreADL, "5:6=1; else=0")
classADL2 <- recode(scoreADL, "3:4=1; else=0")
classADL3 <- recode(scoreADL, "0:2=1; else=0")
################################################################################
#
# Does the subject have someone to help with everyday activities?
#
hasHelp <- recode(svy$a7, "1=1; else=0")
################################################################################
#
# Does the subject need help but has no helper?
#
# Note : Denominator is entire sample so the indicator is the proportion of
# the population with unmet ADl help needs
#
unmetNeed <- ifelse(scoreADL < 6 & hasHelp == 0, 1, 0)
################################################################################
#
# K6 : Short form psychological distress score
#
# Indicators are :
#
# K6 K6 score
# K6Case K6 score > 12 (in serious psychological distress)
#
# The K6 score is described in :
#
# Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek, DK, Normand SLT,
# et al. (2002). Short screening scales to monitor population prevalences
# and trends in non-specific psychological distress. Psychological
# Medicine, 32(6), 959–976
#
################################################################################
#
# Recode DON'T KNOW, REFUSED, NA and MISSING values to 5 (NONE)
#
svy$k6a <- recode(svy$k6a, "6:9=5")
svy$k6b <- recode(svy$k6b, "6:9=5")
svy$k6c <- recode(svy$k6c, "6:9=5")
svy$k6d <- recode(svy$k6d, "6:9=5")
svy$k6e <- recode(svy$k6e, "6:9=5")
svy$k6f <- recode(svy$k6f, "6:9=5")
################################################################################
#
# Reverse coding & create K6 score (i.e. as the sum of individual item scores)
#
svy$k6a <- 5 - svy$k6a
svy$k6b <- 5 - svy$k6b
svy$k6c <- 5 - svy$k6d
svy$k6d <- 5 - svy$k6d
svy$k6e <- 5 - svy$k6e
svy$k6f <- 5 - svy$k6f
K6 <- svy$k6a + svy$k6b + svy$k6c + svy$k6d + svy$k6e + svy$k6f
################################################################################
#
# Apply case-definition for serious psychological distress(i.e. K6 > 12)
#
K6Case <- recode(K6, "0:12=0; 13:hi=1")
################################################################################
#
# Brief Community Screening Instrument for Dementia (CSID)
#
# Indicators are :
#
# DS Probable dementia by CSID screen
#
# The CSID dementia screening tool is described in :
#
# Prince M, et al. (2010). A brief dementia screener suitable for use
# by non-specialists in resource poor settings - The cross-cultural
# derivation and validation of the brief Community Screening Instrument
# for Dementia. International Journal of Geriatric Psychiatry, 26(9),
# 899–907
#
################################################################################
#
# Recode scored components to 0 / 1 (with 1 = correct)
#
svy$ds1 <- recode(svy$ds1, "1=1; else=0") # Nose
svy$ds2 <- recode(svy$ds2, "1=1; else=0") # Hammer
svy$ds3 <- recode(svy$ds3, "1=1; else=0") # Day of week
svy$ds4 <- recode(svy$ds4, "1=1; else=0") # Season
svy$ds5 <- recode(svy$ds5, "1=1; else=0") # Point to window then door
svy$ds6a <- recode(svy$ds6a, "1=1; else=0") # Memory "CHILD"
svy$ds6b <- recode(svy$ds6b, "1=1; else=0") # Memory "HOUSE"
svy$ds6c <- recode(svy$ds6c, "1=1; else=0") # Memory "ROAD"
################################################################################
#
# Sum correct items into CSID score
#
scoreCSID <- svy$ds1 + svy$ds2 + svy$ds3 + svy$ds4 + svy$ds5 + svy$ds6a + svy$ds6b + svy$ds6c
#################################################################################
#
# Classify dementia :
#
# 0 = Normal (not probable dementia)
# 1 = Probable dementia
#
DS <- recode(scoreCSID, "0:4=1; 5:8=0")
################################################################################
#
# Clean-up
#
rm(scoreCSID)
################################################################################
#
# Health and health-seeking indicators
#
# Indicators are :
#
# H1 Chronic condition
#
# H2 Takes drugs regularly for chronic condition
#
# H3x Main reason for not taking drugs for chronic condition where :
#
# x = 1 No drugs available
# x = 2 Too expensive / no money
# x = 3 Too old to look for care
# x = 4 Use traditional medicine
# x = 5 Drugs don't help
# x = 6 No-one to help me
# x = 7 No need
# x = 8 Other
# x = 9 No reason given
#
# H4 Recent disease episode
#
# H5 Accessed care for recent disease episode
#
# H6x Main reason for not accessing care for recent disease episode where :
#
# x = 1 No drugs available
# x = 2 Too expensive / no money
# x = 3 Too old to look for care
# x = 4 Use traditional medicine
# x = 5 Drugs don't help
# x = 6 No-one to help me
# x = 7 No need
# x = 8 Other
# x = 9 No reason given
#
################################################################################
#
# Health indicators : CHRONIC CONDITIONS
#
svy$h1 <- recode(svy$h1, "1=1; else=2")
H1 <- recode(svy$h1, "1=1; else=0")
H2 <- ifelse(H1 == 0, NA, recode(svy$h2, "1=1; else=0"))
H3 <- ifelse(H2 == 1, NA, recode(svy$h3, "NA=9"))
#
# Indicators for main reason for NOT taking drugs for chronic condition
#
H31 <- recode(H3, "1=1; NA=NA; else=0")
H32 <- recode(H3, "2=1; NA=NA; else=0")
H33 <- recode(H3, "3=1; NA=NA; else=0")
H34 <- recode(H3, "4=1; NA=NA; else=0")
H35 <- recode(H3, "5=1; NA=NA; else=0")
H36 <- recode(H3, "6=1; NA=NA; else=0")
H37 <- recode(H3, "7=1; NA=NA; else=0")
H38 <- recode(H3, "8=1; NA=NA; else=0")
H39 <- recode(H3, "9=1; NA=NA; else=0")
################################################################################
#
# Health indicators : RECENT DISEASE EPISODE
#
svy$h4 <- recode(svy$h4, "1=1; else=2")
H4 <- recode(svy$h4, "1=1; else=0")
H5 <- ifelse(H4 == 0, NA, recode(svy$h5, "1=1; else=0"))
H6 <- ifelse(H5 == 1, NA, recode(svy$h6, "NA=9"))
#
#
# Indicators for main reason for NOT accessing care for recent disease episode
#
H61 <- recode(H6, "1=1; NA=NA; else=0")
H62 <- recode(H6, "2=1; NA=NA; else=0")
H63 <- recode(H6, "3=1; NA=NA; else=0")
H64 <- recode(H6, "4=1; NA=NA; else=0")
H65 <- recode(H6, "5=1; NA=NA; else=0")
H66 <- recode(H6, "6=1; NA=NA; else=0")
H67 <- recode(H6, "7=1; NA=NA; else=0")
H68 <- recode(H6, "8=1; NA=NA; else=0")
H69 <- recode(H6, "9=1; NA=NA; else=0")
#
# Clean-up
#
rm(H3, H6)
###############################################################################
#
# Income and income sources
#
# Indicators are :
#
# M1 Has a personal income
# M2A Agriculture / fishing / livestock
# M2B Wages / salary
# M2C Sale of charcoal / bricks / &c.
# M2D Trading (e.g. market or shop)
# M2E Investments
# M2F Spending savings / sale of assets
# M2G Charity
# M2H Cash transfer / Social security
# M2I Other
#
################################################################################
#
# Create binary indicators
#
M1 <- recode(svy$m1, "1=1; else=0") # Has a personal income
M2A <- recode(svy$m2a, "1=1; else=0") # Agriculture / fishing / livestock
M2B <- recode(svy$m2b, "1=1; else=0") # Wages / salary
M2C <- recode(svy$m2c, "1=1; else=0") # Sale of charcoal / bricks / &c.
M2D <- recode(svy$m2d, "1=1; else=0") # Trading (e.g. market or shop)
M2E <- recode(svy$m2e, "1=1; else=0") # Investments
M2F <- recode(svy$m2f, "1=1; else=0") # Spending savings / sale of assets
M2G <- recode(svy$m2g, "1=1; else=0") # Charity
M2H <- recode(svy$m2h, "1=1; else=0") # Cash transfer / social security
M2I <- recode(svy$m2i, "1=1; else=0") # Other
################################################################################
#
# Check for any income (return 'correct' result in M1)
#
checkForIncome <- M1 + M2A + M2B + M2C + M2D + M2E + M2F + M2G + M2H + M2I
M1 <- ifelse(checkForIncome > 0, 1, 0)
################################################################################
#
# Clean-up
#
rm(checkForIncome)
################################################################################
#
# Water, Sanitation, and Hygiene (WASH) indicators
#
# Indicators are :
#
# W1 Improved source of drinking water
# W2 Safe drinking water (improved source OR adequate treatment)
# W3 Improved sanitation facility
# W4 Improved non-shared sanitation facility
#
# These are a (core) subset of indicators from :
#
# WHO / UNICEF (2006). Core Questions on Drinking-water and Sanitation
# for Household Surveys. Geneva, WHO / UNICEF
#
################################################################################
#
# Recode WASH data
#
svy$w1 <- recode(svy$w1, "1=1; else=0")
svy$w2 <- recode(svy$w2, "1=1; else=0")
svy$w3 <- recode(svy$w3, "1=1; else=0")
svy$w4 <- recode(svy$w4, "1=1; else=0")
################################################################################
#
# Create WASH indicators
#
W1 <- svy$w1
W2 <- ifelse(svy$w1 == 1 | svy$w2 == 1, 1, 0)
W3 <- svy$w3
W4 <- ifelse(svy$w3 == 1 & svy$w4 != 1, 1, 0)
################################################################################
#
# Anthropometry and screening
#
# Indicators are :
#
# MUAC MUAC
# oedema Bilateral pitting oedema (may not be nutritional)
# screened Either MUAC or oedema checked previously
#
################################################################################
#
# Censor REFUSAL, NOT APPLICABLE, and MISSING values codes in MUAC and Oedema
#
MUAC <- recode(svy$as1, "777=NA; 888=NA; 999=NA")
oedema <- recode(svy$as3, "1=1; else=0")
################################################################################
#
# Screening for GAM, MAM, SAM (i.e. either MUAC or oedema checked previously)
#
screened <- ifelse(svy$as2 == 1 | svy$as4 == 1, 1, 0)
################################################################################
#
# Visual impairment by "Tumbling E" method
#
# Indicators are :
#
# poorVA Poor visual acuity (correct in < 3 of 4 tests)
#
# Refusal and blindness are treated as test failures
#
# The "Tumbling E" method is described in :
#
# Taylor HR (1978). Applying new design principles to the construction of an
# illiterate E Chart. Am J Optom & Physiol Optics 55:348
#
################################################################################
#
# Create binary indicators
#
svy$va2a <- recode(svy$va2a, "1=1; else=0")
svy$va2b <- recode(svy$va2b, "1=1; else=0")
svy$va2c <- recode(svy$va2c, "1=1; else=0")
svy$va2d <- recode(svy$va2d, "1=1; else=0")
sumVA <- svy$va2a + svy$va2b + svy$va2c + svy$va2d
poorVA <- ifelse(sumVA < 3, 1, 0)
###############################################################################
#
# Clean-up
#
rm(sumVA)
################################################################################
#
# Miscellaneous indicators
#
# Indicators are :
#
# chew Problems chewing food
# food Anyone in HH receives a ration
# NFRI Anyone in HH received non-food relief item(s) in previous month
#
chew <- recode(svy$a8, "1=1; else=0")
food <- recode(svy$f6, "1=1; else=0")
NFRI <- recode(svy$f7, "1=1; else=0")
################################################################################
#
# Washington Group (WG) short set of question designed to identify people with a
# disability in a census or survey format.
#
# See:
#
# http://www.washingtongroup-disability.com
#
# https://www.cdc.gov/nchs/washington_group/wg_documents.htm
#
# for details.
#
################################################################################
#
# Missing values
#
svy$wg1 <- recode(svy$wg1, "9=0; NA=0")
svy$wg2 <- recode(svy$wg2, "9=0; NA=0")
svy$wg3 <- recode(svy$wg3, "9=0; NA=0")
svy$wg4 <- recode(svy$wg4, "9=0; NA=0")
svy$wg5 <- recode(svy$wg5, "9=0; NA=0")
svy$wg6 <- recode(svy$wg6, "9=0; NA=0")
################################################################################
#
# Vision domain
#
wgVisionD0 <- ifelse(svy$wg1 == 0, 1, 0)
wgVisionD1 <- ifelse(svy$wg1 == 1 | svy$wg1 == 2 | svy$wg1 == 3, 1, 0)
wgVisionD2 <- ifelse(svy$wg1 == 2 | svy$wg1 == 3, 1, 0)
wgVisionD3 <- ifelse(svy$wg1 == 3, 1, 0)
################################################################################
#
# Hearing domain
#
wgHearingD0 <- ifelse(svy$wg2 == 0, 1, 0)
wgHearingD1 <- ifelse(svy$wg2 == 1 | svy$wg2 == 2 | svy$wg2 == 3, 1, 0)
wgHearingD2 <- ifelse(svy$wg2 == 2 | svy$wg2 == 3, 1, 0)
wgHearingD3 <- ifelse(svy$wg2 == 3, 1, 0)
################################################################################
#
# Mobility domain
#
wgMobilityD0 <- ifelse(svy$wg3 == 0, 1, 0)
wgMobilityD1 <- ifelse(svy$wg3 == 1 | svy$wg3 == 2 | svy$wg3 == 3, 1, 0)
wgMobilityD2 <- ifelse(svy$wg3 == 2 | svy$wg3 == 3, 1, 0)
wgMobilityD3 <- ifelse(svy$wg3 == 3, 1, 0)
################################################################################
#
# Remembering domain
#
wgRememberingD0 <- ifelse(svy$wg4 == 0, 1, 0)
wgRememberingD1 <- ifelse(svy$wg4 == 1 | svy$wg4 == 2 | svy$wg4 == 3, 1, 0)
wgRememberingD2 <- ifelse(svy$wg4 == 2 | svy$wg4 == 3, 1, 0)
wgRememberingD3 <- ifelse(svy$wg4 == 3, 1, 0)
################################################################################
#
# Self-care domain
#
wgSelfCareD0 <- ifelse(svy$wg5 == 0, 1, 0)
wgSelfCareD1 <- ifelse(svy$wg5 == 1 | svy$wg5 == 2 | svy$wg5 == 3, 1, 0)
wgSelfCareD2 <- ifelse(svy$wg5 == 2 | svy$wg5 == 3, 1, 0)
wgSelfCareD3 <- ifelse(svy$wg5 == 3, 1, 0)
################################################################################
#
# Communicating domain
#
wgCommunicatingD0 <- ifelse(svy$wg6 == 0, 1, 0)
wgCommunicatingD1 <- ifelse(svy$wg6 == 1 | svy$wg6 == 2 | svy$wg6 == 3, 1, 0)
wgCommunicatingD2 <- ifelse(svy$wg6 == 2 | svy$wg6 == 3, 1, 0)
wgCommunicatingD3 <- ifelse(svy$wg6 == 3, 1, 0)
################################################################################
#
# Overall prevalence
#
wgP0 <- ifelse(wgVisionD0 + wgHearingD0 + wgMobilityD0 + wgRememberingD0 + wgSelfCareD0 + wgCommunicatingD0 == 6, 1, 0)
wgP1 <- ifelse(wgVisionD1 + wgHearingD1 + wgMobilityD1 + wgRememberingD1 + wgSelfCareD1 + wgCommunicatingD1 > 0, 1, 0)
wgP2 <- ifelse(wgVisionD2 + wgHearingD2 + wgMobilityD2 + wgRememberingD2 + wgSelfCareD2 + wgCommunicatingD2 > 0, 1, 0)
wgP3 <- ifelse(wgVisionD3 + wgHearingD3 + wgMobilityD3 + wgRememberingD3 + wgSelfCareD3 + wgCommunicatingD3 > 0, 1, 0)
wgPM <- ifelse(wgVisionD1 + wgHearingD1 + wgMobilityD1 + wgRememberingD1 + wgSelfCareD1 + wgCommunicatingD1 > 1, 1, 0)
################################################################################
#
# Make summary data.frame for ALL respondents
#
indicators.ALL <- data.frame(psu, resp1, resp2, resp3, resp4,
age, ageGrp1, ageGrp2, ageGrp3, ageGrp4, ageGrp5, sex1, sex2,
marital1, marital2, marital3, marital4, marital5, marital6,
alone,
MF, DDS,
FG01, FG02, FG03, FG04, FG05, FG06, FG07, FG08, FG09, FG10, FG11,
proteinRich, pProtein, aProtein,
pVitA, aVitA, xVitA,
ironRich,
caRich,
znRich,
vitB1, vitB2, vitB3, vitB6, vitB12, vitBcomplex,
HHS1, HHS2, HHS3,
ADL01, ADL02, ADL03, ADL04, ADL05, ADL06, scoreADL, classADL1, classADL2, classADL3, hasHelp, unmetNeed,
K6, K6Case,
DS,
H1, H2, H31, H32, H33, H34, H35, H36, H37, H38, H39,
H4, H5, H61, H62, H63, H64, H65, H66, H67, H68, H69,
M1, M2A, M2B, M2C, M2D, M2E, M2F, M2G, M2H, M2I,
W1, W2, W3, W4,
MUAC, oedema, screened,
poorVA,
chew, food, NFRI,
wgVisionD0, wgVisionD1, wgVisionD2, wgVisionD3,
wgHearingD0, wgHearingD1, wgHearingD2, wgHearingD3,
wgMobilityD0, wgMobilityD1, wgMobilityD2, wgMobilityD3,
wgRememberingD0, wgRememberingD1, wgRememberingD2, wgRememberingD3,
wgSelfCareD0, wgSelfCareD1, wgSelfCareD2, wgSelfCareD3,
wgCommunicatingD0, wgCommunicatingD1, wgCommunicatingD2, wgCommunicatingD3,
wgP0, wgP1, wgP2, wgP3, wgPM)
indicators.ALL <- tibble::tibble(indicators.ALL)
usethis::use_data(indicators.ALL, overwrite = TRUE, compress = "xz")
#
# Subset summary data.frames for MALES and FEMALES
#
indicators.MALES <- subset(indicators.ALL, sex1 == 1)
indicators.FEMALES <- subset(indicators.ALL, sex2 == 1)
usethis::use_data(indicators.MALES, overwrite = TRUE, compress = "xz")
usethis::use_data(indicators.FEMALES, overwrite = TRUE, compress = "xz")
#
# Clean-up
#
rm(psu, resp1, resp2, resp3, resp4,
age, ageGrp1, ageGrp2, ageGrp3, ageGrp4, ageGrp5, sex1, sex2,
marital1, marital2, marital3, marital4, marital5, marital6,
alone,
MF, DDS,
FG01, FG02, FG03, FG04, FG05, FG06, FG07, FG08, FG09, FG10, FG11,
proteinRich, pProtein, aProtein,
pVitA, aVitA, xVitA,
ironRich,
caRich,
znRich,
vitB1, vitB2, vitB3, vitB6, vitB12, vitBcomplex,
HHS1, HHS2, HHS3,
ADL01, ADL02, ADL03, ADL04, ADL05, ADL06, scoreADL, classADL1, classADL2, classADL3, hasHelp, unmetNeed,
K6, K6Case,
DS,
H1, H2, H31, H32, H33, H34, H35, H36, H37, H38, H39,
H4, H5, H61, H62, H63, H64, H65, H66, H67, H68, H69,
M1, M2A, M2B, M2C, M2D, M2E, M2F, M2G, M2H, M2I,
W1, W2, W3, W4,
MUAC, oedema, screened,
poorVA,
chew, food, NFRI,
wgVisionD0, wgVisionD1, wgVisionD2, wgVisionD3,
wgHearingD0, wgHearingD1, wgHearingD2, wgHearingD3,
wgMobilityD0, wgMobilityD1, wgMobilityD2, wgMobilityD3,
wgRememberingD0, wgRememberingD1, wgRememberingD2, wgRememberingD3,
wgSelfCareD0, wgSelfCareD1, wgSelfCareD2, wgSelfCareD3,
wgCommunicatingD0, wgCommunicatingD1, wgCommunicatingD2, wgCommunicatingD3,
wgP0, wgP1, wgP2, wgP3, wgPM)
gc()
#
# Restore 'svy' from back-up copy (i.e. to undo recoding used when making indicators)
#
svy <- svyBackup
rm(svyBackup)
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