Help for package oldr

Title:

An Implementation of Rapid Assessment Method for Older People

Version:

0.2.3

Description:

An implementation of the Rapid Assessment Method for Older People or RAM-OP https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/. It provides various functions that allow the user to design and plan the assessment and analyse the collected data. RAM-OP provides accurate and reliable estimates of the needs of older people.

License:

GPL-3

Depends:

R (≥ 4.1.0)

Imports:

bbw, car, withr, tibble, rmarkdown, cli, tinytex

Suggests:

testthat (≥ 3.0.0), covr, DiagrammeR, knitr, kableExtra, spelling

Encoding:

UTF-8

LazyData:

true

Language:

en-GB

RoxygenNote:

7.3.2

VignetteBuilder:

knitr

URL:

https://rapidsurveys.io/oldr/, https://github.com/rapidsurveys/oldr

BugReports:

https://github.com/rapidsurveys/oldr/issues

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-01-22 18:58:13 UTC; ernestguevarra

Author:

Mark Myatt

[aut, cph], Ernest Guevarra

[aut, cre, cph], Pascale Fritsch [aut], Katja Siling [aut], HelpAge International [cph], Elrha [fnd]

Maintainer:

Ernest Guevarra <ernest@guevarra.io>

Repository:

CRAN

Date/Publication:

2025-01-24 13:00:06 UTC

An Implementation of Rapid Assessment Method for Older People (RAM-OP)

Description

HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), have developed a Rapid Assessment Method for Older People (RAM-OP) that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods.

Details

The RAM-OP method is based on the following principles:

Use of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (m >= 16 clusters) and a small overall (n = 192) sample.
Assessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.
Data analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.

Author(s)

Maintainer: Ernest Guevarra ernest@guevarra.io (ORCID) [copyright holder]

Authors:

Mark Myatt mark@brixtonhealth.com (ORCID) [copyright holder]
Pascale Fritsch
Katja Siling

Other contributors:

HelpAge International [copyright holder]
Elrha [funder]

Plot RAM-OP indicators

Description

The plots include:

Age by sex (pyramid plot) - a wrapper function to the pyramid_plot() function to create an age by sex pyramid plot
Distribution of MUAC (overall and by sex) - histogram of MUAC distribution
Distribution of meal frequency (overall and by sex)
Distribution of dietary diversity score (overall and by sex)
Distribution of K6 (overall and by sex)
Distribution of ADL (overall and by sex)
Plot of WASH indicators
Plot of dementia screen (CSID) indicators
Plot of disability (Washington Group - WG) indicators
Plot of household hunger scale (HHS) indicators
Plot of income indicators

Usage

chart_op_age(
  x,
  save_chart = TRUE,
  filename = file.path(tempdir(), "populationPyramid")
)

chart_op_muac(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_mf(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_dds(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_k6(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_adl(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_wash(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_csid(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_wg(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_hhs(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

chart_op_income(x, save_chart = TRUE, filename = file.path(tempdir(), "chart"))

Arguments

x

Indicators dataset produced by create_op()

save_chart

Logical. Should chart be saved? Default is TRUE.

filename

Prefix to add to output chart filename or a directory path to save output to instead of working directory. Default is a path to a temporary directory and a suggested filename. Ignored if save_chart is FALSE.

Value

The respective plot in PNG format saved in the specified path if filename is a path unless when save_chart is FALSE in which case the plot is shown on current graphics device

Examples

# Create age by sex pyramid plot using indicators.ALL dataset
chart_op_age(x = indicators.ALL)

# Create MUAC histogram using indicators.ALL dataset
chart_op_muac(x = indicators.ALL)

# Create meal frequency chart using indicators.ALL dataset
chart_op_mf(x = indicators.ALL)

# Create DDS chart using indicators.ALL dataset
chart_op_dds(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_k6(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_adl(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_wash(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_csid(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_wg(x = indicators.ALL)

# Create chart using indicators.ALL dataset
chart_op_hhs(x = indicators.ALL)

# Create chart using indicators.FEMALES and indicators.MALES
# dataset
chart_op_income(x = indicators.ALL)

Check whether indicators are RAM-OP indicators

Description

Check whether indicators are RAM-OP indicators

Usage

check_indicators(indicators)

Create older people indicators dataset from survey data collected using the standard RAM-OP questionnaire.

Description

The indicator sets covered by the standard RAM-OP survey are:

Demographic indicators
Dietary intake indicators
Household hunger scale
Katz Index of Independence in Activities of Daily Living score
K6 Short form psychological distress score
Brief Community Screening Instrument for Dementia (CSID)
Health and health-seeking indicators
Income and income sources
Water, sanitation and hygiene (WASH) indicators
Anthropometry and screening
Visual impairment by "Tumbling E" method
Miscellaneous indicators
Washington Group on Disability

Usage

create_op(
  svy,
  indicators = c("demo", "food", "hunger", "disability", "adl", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc"),
  sex = c("mf", "m", "f")
)

create_op_demo(svy, sex = c("mf", "m", "f"))

create_op_food(svy, sex = c("mf", "m", "f"))

create_op_hunger(svy, sex = c("mf", "m", "f"))

create_op_adl(svy, sex = c("mf", "m", "f"))

create_op_disability(svy, sex = c("mf", "m", "f"))

create_op_mental(svy, sex = c("mf", "m", "f"))

create_op_dementia(svy, sex = c("mf", "m", "f"))

create_op_health(svy, sex = c("mf", "m", "f"))

create_op_income(svy, sex = c("mf", "m", "f"))

create_op_wash(svy, sex = c("mf", "m", "f"))

create_op_anthro(svy, sex = c("mf", "m", "f"))

create_op_oedema(svy, sex = c("mf", "m", "f"))

create_op_screening(svy, sex = c("mf", "m", "f"))

create_op_visual(svy, sex = c("mf", "m", "f"))

create_op_misc(svy, sex = c("mf", "m", "f"))

Arguments

svy

A data.frame() collected using the standard RAM-OP questionnaire.

indicators

A character vector of indicator set names. The vector may include one or more of the following: "demo", "food", "hunger", "disability", "adl", "mental", "dementia", "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc". Default is all indicator set names.

sex

A character value of "m", "f", or "mf" to indicate whether to report indicators for males, females, or both respectively. Default is "mf" for both sexes.

Value

A tibble::tibble() of older people indicators.

Demographic indicators

Variable	Description
`psu`	Primary sampling unit
`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 between 90 years and older
`sex1`	Male
`sex2`	Female
`marital1`	Marital status = SINGLE
`marital2`	Marital status = MARRIED
`marital3`	Marital status = LIVING TOGETHER
`marital4`	Marital status = DIVORCED
`marital5`	Marital status = SEPARATED
`marital6`	Marital status = OTHER
`alone`	Respondent lives alone

Dietary intake indicators

These dietary intake indicators have been purpose-built for older people but 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 https://www.fao.org/4/i1983e/i1983e00.htm

and extended to include indicators of probable adequate intake of a number of nutrients / micronutrients.

Variable	Description
`MF`	Meal frequency
`DDS`	Dietary Diversity Score (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
`proteinRich`	Protein rich foods
`pProtein`	Protein rich plant sources of protein
`aProtein`	Protein rich animal sources of protein
`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

Household Hunger Scale (HHS)

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 https://www.fantaproject.org/monitoring-and-evaluation/household-hunger-scale-hhs

Variable	Description
`HHS1`	Little or no hunger in household
`HHS2`	Moderate hunger in household
`HHS3`	Severe hunger in household

Katz Index of Independence in Activities of Daily Living score

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 doi:10.1001/jama.1963.03060120024016

Katz S, Down TD, Cash HR, Grotz, RC (1970). Progress in the development of the index of ADL. The Gerontologist, 10(1), 20-30 doi:10.1093/geront/10.4_Part_1.274

Katz S (1983). Assessing self-maintenance: Activities of daily living, mobility and instrumental activities of daily living. JAGS, 31(12), 721-726 doi:10.1111/j.1532-5415.1983.tb03391.x

Variable	Description
`ADL01`	Bathing
`ADL02`	Dressing
`ADL03`	Toileting
`ADL04`	Transferring (mobility)
`ADL05`	Continence
`ADL06`	Feeding
`scoreADL`	ADL Score
`classADL1`	Severity of dependence 1
`classADL2`	Severity of dependence 2
`classADL3`	Severity of dependence 3
`hasHelp`	Have someone to help with everyday activities
`unmetNeed`	Need help but has no helper

K6 Short form psychological distress score

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 doi:10.1017/S0033291702006074

Variable	Description
`K6`	K6 score
`K6Case`	K6 score > 12 (in serious psychological distress)

Brief Community Screening Instrument for Dementia (CSID)

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 doi:10.1002/gps.2622

Variable	Description
`DS`	Probable dementia by CSID screen

Health and health-seeking indicators

Variable	Description
`H1`	Chronic condition
`H2`	Takes drugs regularly for chronic condition
`H31`	No drugs available
`H32`	Too expensive / no money
`H33`	Too old to look for care
`H34`	Use traditional medicine
`H35`	Drugs don't help
`H36`	No-one to help me
`H37`	No need
`H38`	Other
`H39`	No reason given
`H4`	Recent disease episode
`H5`	Accessed care for recent disease episode
`H61`	No drugs available
`H62`	Too expensive / no money
`H63`	Too old to look for care
`H64`	Use traditional medicine
`H65`	Drugs don't help
`H66`	No-one to help me
`H67`	No need
`H68`	Other
`H69`	No reason given

Income and income sources

Variable	Description
`M1`	Has a personal income
`M2A`	Agriculture / fishing / livestock
`M2B`	Wages / salary
`M2C`	Sale of charcoal / bricks / etc.
`M2D`	Trading (e.g. market or shop)
`M2E`	Investments
`M2F`	Spending savings / sale of assets
`M2G`	Charity
`M2H`	Cash transfer / Social security
`M2I`	Other

Water, sanitation and hygiene (WASH) indicators

These are a (core) subset of indicators from:

WHO / UNICEF (2006). Core Questions on Drinking-water and Sanitation for Household Surveys. Geneva, WHO / UNICEF https://www.who.int/publications/i/item/9241563265

Variable	Description
`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

Anthropometry and screening

Variable	Description
`MUAC`	Mid-upper arm circumference (mm)
`oedema`	Bilateral pitting oedema (may not be nutritional)
`screened`	Either MUAC or oedema checked previously

Visual impairment by "Tumbling E" method

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

Variable	Description
poorVA	Poor visual acuity (correct in < 3 of 4 tests)

Miscellaneous indicators

Variable	Description
`chew`	Problems chewing food
`food`	Anyone in HH receives a ration
`NFRI`	Anyone in HH received non-food relief item/s (NFRI) in previous month

Washington Group on Disability

See:

https://www.washingtongroup-disability.com/

for details.

Variable	Description
`wgVisionD0`	Vision domain 0
`wgVisionD1`	Vision domain 1
`wgVisionD2`	Vision domain 2
`wgVisionD3`	Vision domain 3
`wgHearingD0`	Hearing domain 0
`wgHearingD1`	Hearing domain 1
`wgHearingD2`	Hearing domain 2
`wgHearingD3`	Hearing domain 3
`wgMobilityD0`	Mobility domain 0
`wgMobilityD1`	Mobility domain 1
`wgMobilityD2`	Mobility domain 2
`wgMobilityD3`	Mobility domain 3
`wgRememberingD0`	Remembering domain 0
`wgRememberingD1`	Remembering domain 1
`wgRememberingD2`	Remembering domain 2
`wgRememberingD3`	Remembering domain 3
`wgSelfCareD0`	Self-care domain 0
`wgSelfCareD1`	Self-care domain 1
`wgSelfCareD2`	Self-care domain 2
`wgSelfCareD3`	Self-care domain 3
`wgCommunicatingD0`	Communication domain 0
`wgCommunicatingD1`	Communication domain 1
`wgCommunicatingD2`	Communication domain 2
`wgCommunicatingD3`	Communication domain 3
`wgP0`	Overall 0
`wgP1`	Overall 1
`wgP2`	Overall 2
`wgP3`	Overall 3
`wgPM`	Any disability

Examples

# Create indicators dataset from RAM-OP survey data collected from
# Addis Ababa, Ethiopia
create_op(testSVY)
create_op(testSVY, indicators = "demo")
create_op(testSVY, indicators = "hunger", sex = "m")

Construct create_op_* expression

Description

Construct create_op_* expression

Usage

create_op_indicators(indicator, svy, sex)

Apply bootstrap to RAM-OP indicators using a classical estimator.

Description

Apply bootstrap to RAM-OP indicators using a classical estimator.

Usage

estimate_classic(
  x,
  w,
  statistic = bbw::bootClassic,
  indicators = c("demo", "food", "hunger", "adl", "disability", "mental", "dementia",
    "health", "oedema", "screening", "income", "wash", "visual", "misc"),
  params = get_variables(indicators),
  outputColumns = params,
  replicates = 399
)

Arguments

x

Indicators dataset produced by create_op() with primary sampling unit (PSU) in column named "psu".

w

A data frame with primary sampling unit (PSU) in column named "psu" and survey weight (i.e. PSU population) in column named "pop".

statistic

A function operating on data in x. Fixed to bbw::bootClassic() function for means.

indicators

A character vector of indicator set names to estimate. Indicator set names are "demo", "food", "hunger", "disability", "adl", "mental", "dementia", "health", "income", "wash", "visual", and "misc". Default is all indicator sets.

params

Parameters (named columns in x) passed to the function specified in statistic. This is equivalent to variables corresponding to the indicator sets specified in indicators. The function get_variables() is used to specify these variables.

outputColumns

Names of columns in output data frame. This defaults to values specified in params.

replicates

Number of bootstrap replicates

Value

A tibble::tibble() of boot estimates using bbw::bootClassic() mean function

Examples

test <- estimate_classic(
  x = indicators.ALL, w = testPSU, replicates = 9
)

test

Estimate all standard RAM-OP indicators

Description

Estimate all standard RAM-OP indicators

Usage

estimate_op(
  x,
  w,
  indicators = c("demo", "anthro", "food", "hunger", "adl", "disability", "mental",
    "dementia", "health", "oedema", "screening", "income", "wash", "visual", "misc"),
  replicates = 399
)

Arguments

x

Indicators dataset produced by create_op() with primary sampling unit (PSU) in column named "psu"'

w

A data frame with primary sampling unit (PSU) in column named "psu" and survey weight (i.e. PSU population) in column named "pop".

indicators

A character vector of indicator set names to estimate. Indicator set names are "demo", "anthro", "food", "hunger", "disability", "adl", "mental", "dementia", "health", "income", "wash", "visual", and "misc". Default is all indicator sets.

replicates

Number of bootstrap replicates. Default is 399.

Value

A tibble::tibble() of boot estimates for all specified standard RAM-OP indicators.

Examples

estimate_op(x = create_op(testSVY), w = testPSU, replicates = 9)

Apply bootstrap to RAM-OP indicators using a PROBIT estimator.

Description

Apply bootstrap to RAM-OP indicators using a PROBIT estimator.

Usage

estimate_probit(
  x,
  w,
  gam.stat = probit_gam,
  sam.stat = probit_sam,
  params = "MUAC",
  outputColumns = params,
  replicates = 399
)

Arguments

x

Indicators dataset produced by create_op() with primary sampling unit (PSU) in column named "psu".

w

A data frame with primary sampling unit (PSU) in column named "psu" and survey weight (i.e. PSU population) in column named "pop".

gam.stat

A function operating on data in x to estimate GAM prevalence for older people. Fixed to probit_gam().

sam.stat

A function operating on data in x to estimate SAM prevalence for older people. Fixed to probit_sam().

params

Parameters (named columns in x) passed to the function specified in statistic; fixed to "MUAC" as indicator amenable to probit estimation.

outputColumns

Names of columns in output data frame.

replicates

Number of bootstrap replicate case and non-case.

Value

A tibble::tibble() of boot estimates using PROBIT.

Examples

test <- estimate_probit(x = indicators.ALL, w = testPSU, replicates = 3)

test

Fill out a one-dimensional table to include a specified range of values

Description

Fill out a one-dimensional table to include a specified range of values

Usage

fullTable(x, values)

Arguments

x

A vector to tabulate

values

A vector of values to be included in a table

Value

A one-dimensional table with specified values

Author(s)

Mark Myatt

Get appropriate RAM-OP indicator variable names given a specified indicator set

Description

Get appropriate RAM-OP indicator variable names given a specified indicator set

Usage

get_variables(
  indicators = c("demo", "food", "hunger", "adl", "disability", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc")
)

Arguments

indicators

A character vector of indicator set names. Indicator set names are "demo", "food", "hunger", "disability", "adl", "mental", "dementia", "health", "income", "wash", "anthro", "screening", "visual", and "misc". Default is all indicator sets.

Value

A vector of variable names

RAM-OP Indicators Dataset - ALL

Description

Indicators dataset calculated from a dataset collected from a RAM-OP survey conducted in Addis Ababa, Ethiopia in early 2014

Usage

indicators.ALL

Format

A data frame with 138 columns and 192 rows:

psu: Cluster (PSU) identifier
resp1: Respondent is SUBJECT
resp2: Respondent is FAMILY CARER
resp3: Respondent is OTHER CARER
resp4: Respondent is OTHER
age: Age of respondents (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
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
proteinRich: Protein rich animal sources of protein
pProtein: Protein rich plant sources of protein
aProtein: Protein rich animal sources of protein
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
HHS1: Little or no hunger in household
HHS2: Moderate hunger in household
HHS3: Severe hunger in household
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)
K6: K6 score
K6Case: K6 score > 12 (in serious psychological distress)
DS: Probable dementia by CSID screen
H1: Chronic condition
H2: Takes drugs regularly for chronic condition
H31: Main reason for not taking drugs for chronic condition: No drugs available
H32: Main reason for not taking drugs for chronic condition: Too expensive / no money
H33: Main reason for not taking drugs for chronic condition: Too old to look for care
H34: Main reason for not taking drugs for chronic condition: Use traditional medicine
H35: Main reason for not taking drugs for chronic condition: Drugs don't help
H36: Main reason for not taking drugs for chronic condition: No one to help me
H37: Main reason for not taking drugs for chronic condition: No need
H38: Main reason for not taking drugs for chronic condition: Other
H39: Main reason for not taking drugs for chronic condition: No reason given
H4: Recent disease episode
H5: Accessed care for recent disease episode
H61: Main reason for not accessing care for recent disease episode: No drugs available
H62: Main reason for not accessing care for recent disease episode: Too expensive / no money
H63: Main reason for not accessing care for recent disease episode: Too old to look for care
H64: Main reason for not accessing care for recent disease episode: Use traditional medicine
H65: Main reason for not accessing care for recent disease episode: Drugs don't help
H66: Main reason for not accessing care for recent disease episode: No one to help me
H67: Main reason for not accessing care for recent disease episode: No need
H68: Main reason for not accessing care for recent disease episode: Other
H69: Main reason for not accessing care for recent disease episode: No reason given
M1: Has a personal income
M2A: Agriculture / fishing / livestock
M2B: Wages / salary
M2C: Sale of charcoal / bricks / etc
M2D: Trading (e.g. market or shop)
M2E: Investments
M2F: Spending savings / sale of assets
M2G: Charity
M2H: Cash transfer / Social security
M2I: Other
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
MUAC: Mid-upper arm circumference (mm)
oedema: Presence of oedema
screened: Screened with oedema check and MUAC measurement in previous month
poorVA: Poor visual acuity
chew: Problems chewing food
food: Anyone in household receives a ration
NFRI: Anyone in HH received non-food relief item(s) in previous month
wgVisionD0: Vision domain 0
wgVisionD1: Vision domain 1
wgVisionD2: Vision domain 2
wgVisionD3: Vision domain 3
wgHearingD0: Hearing domain 0
wgHearingD1: Hearing domain 1
wgHearingD2: Hearing domain 2
wgHearingD3: Hearing domain 3
wgMobilityD0: Mobility domain 0
wgMobilityD1: Mobility domain 1
wgMobilityD2: Mobility domain 2
wgMobilityD3: Mobility domain 3
wgRememberingD0: Remembering domain 0
wgRememberingD1: Remembering domain 1
wgRememberingD2: Remembering domain 2
wgRememberingD3: Remembering domain 3
wgSelfCareD0: Self-care domain 0
wgSelfCareD1: Self-care domain 1
wgSelfCareD2: Self-care domain 2
wgSelfCareD3: Self-care domain 3
wgCommunicatingD0: Communicating domain 0
wgCommunicatingD1: Communicating domain 1
wgCommunicatingD2: Communicating domain 2
wgCommunicatingD3: Communicating domain 3
wgP0: Overall prevalence 0
wgP1: Overall prevalence 1
wgP2: Overall prevalence 2
wgP3: Overall prevalence 3
wgPM: Overall prevalence

Examples

indicators.ALL

RAM-OP Indicators Dataset - FEMALES

Description

Indicators dataset calculated from a dataset collected from a RAM-OP survey conducted in Addis Ababa, Ethiopia in early 2014. This indicator dataset is from the subset of women/females of the total sample.

Usage

indicators.FEMALES

Format

A data frame with 138 columns and 113 rows:

psu: Cluster (PSU) identifier
resp1: Respondent is SUBJECT
resp2: Respondent is FAMILY CARER
resp3: Respondent is OTHER CARER
resp4: Respondent is OTHER
age: Age of respondents (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
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
proteinRich: Protein rich animal sources of protein
pProtein: Protein rich plant sources of protein
aProtein: Protein rich animal sources of protein
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
HHS1: Little or no hunger in household
HHS2: Moderate hunger in household
HHS3: Severe hunger in household
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)
K6: K6 score
K6Case: K6 score > 12 (in serious psychological distress)
DS: Probable dementia by CSID screen
H1: Chronic condition
H2: Takes drugs regularly for chronic condition
H31: Main reason for not taking drugs for chronic condition: No drugs available
H32: Main reason for not taking drugs for chronic condition: Too expensive / no money
H33: Main reason for not taking drugs for chronic condition: Too old to look for care
H34: Main reason for not taking drugs for chronic condition: Use traditional medicine
H35: Main reason for not taking drugs for chronic condition: Drugs don't help
H36: Main reason for not taking drugs for chronic condition: No one to help me
H37: Main reason for not taking drugs for chronic condition: No need
H38: Main reason for not taking drugs for chronic condition: Other
H39: Main reason for not taking drugs for chronic condition: No reason given
H4: Recent disease episode
H5: Accessed care for recent disease episode
H61: Main reason for not accessing care for recent disease episode: No drugs available
H62: Main reason for not accessing care for recent disease episode: Too expensive / no money
H63: Main reason for not accessing care for recent disease episode: Too old to look for care
H64: Main reason for not accessing care for recent disease episode: Use traditional medicine
H65: Main reason for not accessing care for recent disease episode: Drugs don't help
H66: Main reason for not accessing care for recent disease episode: No one to help me
H67: Main reason for not accessing care for recent disease episode: No need
H68: Main reason for not accessing care for recent disease episode: Other
H69: Main reason for not accessing care for recent disease episode: No reason given
M1: Has a personal income
M2A: Agriculture / fishing / livestock
M2B: Wages / salary
M2C: Sale of charcoal / bricks / etc
M2D: Trading (e.g. market or shop)
M2E: Investments
M2F: Spending savings / sale of assets
M2G: Charity
M2H: Cash transfer / Social security
M2I: Other
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
MUAC: Mid-upper arm circumference (mm)
oedema: Presence of oedema
screened: Screened with oedema check and MUAC measurement in previous month
poorVA: Poor visual acuity
chew: Problems chewing food
food: Anyone in household receives a ration
NFRI: Anyone in HH received non-food relief item(s) in previous month
wgVisionD0: Vision domain 0
wgVisionD1: Vision domain 1
wgVisionD2: Vision domain 2
wgVisionD3: Vision domain 3
wgHearingD0: Hearing domain 0
wgHearingD1: Hearing domain 1
wgHearingD2: Hearing domain 2
wgHearingD3: Hearing domain 3
wgMobilityD0: Mobility domain 0
wgMobilityD1: Mobility domain 1
wgMobilityD2: Mobility domain 2
wgMobilityD3: Mobility domain 3
wgRememberingD0: Remembering domain 0
wgRememberingD1: Remembering domain 1
wgRememberingD2: Remembering domain 2
wgRememberingD3: Remembering domain 3
wgSelfCareD0: Self-care domain 0
wgSelfCareD1: Self-care domain 1
wgSelfCareD2: Self-care domain 2
wgSelfCareD3: Self-care domain 3
wgCommunicatingD0: Communicating domain 0
wgCommunicatingD1: Communicating domain 1
wgCommunicatingD2: Communicating domain 2
wgCommunicatingD3: Communicating domain 3
wgP0: Overall prevalence 0
wgP1: Overall prevalence 1
wgP2: Overall prevalence 2
wgP3: Overall prevalence 3
wgPM: Overall prevalence

Examples

indicators.FEMALES

RAM-OP Indicators Dataset - MALES

Description

Indicators dataset calculated from a dataset collected from a RAM-OP survey conducted in Addis Ababa, Ethiopia in early 2014. This indicator dataset is from the subset of men/males of the total sample.

Usage

indicators.MALES

Format

A data frame with 138 columns and 113 rows:

psu: Cluster (PSU) identifier
resp1: Respondent is SUBJECT
resp2: Respondent is FAMILY CARER
resp3: Respondent is OTHER CARER
resp4: Respondent is OTHER
age: Age of respondents (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
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
proteinRich: Protein rich animal sources of protein
pProtein: Protein rich plant sources of protein
aProtein: Protein rich animal sources of protein
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
HHS1: Little or no hunger in household
HHS2: Moderate hunger in household
HHS3: Severe hunger in household
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)
K6: K6 score
K6Case: K6 score > 12 (in serious psychological distress)
DS: Probable dementia by CSID screen
H1: Chronic condition
H2: Takes drugs regularly for chronic condition
H31: Main reason for not taking drugs for chronic condition: No drugs available
H32: Main reason for not taking drugs for chronic condition: Too expensive / no money
H33: Main reason for not taking drugs for chronic condition: Too old to look for care
H34: Main reason for not taking drugs for chronic condition: Use traditional medicine
H35: Main reason for not taking drugs for chronic condition: Drugs don't help
H36: Main reason for not taking drugs for chronic condition: No one to help me
H37: Main reason for not taking drugs for chronic condition: No need
H38: Main reason for not taking drugs for chronic condition: Other
H39: Main reason for not taking drugs for chronic condition: No reason given
H4: Recent disease episode
H5: Accessed care for recent disease episode
H61: Main reason for not accessing care for recent disease episode: No drugs available
H62: Main reason for not accessing care for recent disease episode: Too expensive / no money
H63: Main reason for not accessing care for recent disease episode: Too old to look for care
H64: Main reason for not accessing care for recent disease episode: Use traditional medicine
H65: Main reason for not accessing care for recent disease episode: Drugs don't help
H66: Main reason for not accessing care for recent disease episode: No one to help me
H67: Main reason for not accessing care for recent disease episode: No need
H68: Main reason for not accessing care for recent disease episode: Other
H69: Main reason for not accessing care for recent disease episode: No reason given
M1: Has a personal income
M2A: Agriculture / fishing / livestock
M2B: Wages / salary
M2C: Sale of charcoal / bricks / etc
M2D: Trading (e.g. market or shop)
M2E: Investments
M2F: Spending savings / sale of assets
M2G: Charity
M2H: Cash transfer / Social security
M2I: Other
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
MUAC: Mid-upper arm circumference (mm)
oedema: Presence of oedema
screened: Screened with oedema check and MUAC measurement in previous month
poorVA: Poor visual acuity
chew: Problems chewing food
food: Anyone in household receives a ration
NFRI: Anyone in HH received non-food relief item(s) in previous month
wgVisionD0: Vision domain 0
wgVisionD1: Vision domain 1
wgVisionD2: Vision domain 2
wgVisionD3: Vision domain 3
wgHearingD0: Hearing domain 0
wgHearingD1: Hearing domain 1
wgHearingD2: Hearing domain 2
wgHearingD3: Hearing domain 3
wgMobilityD0: Mobility domain 0
wgMobilityD1: Mobility domain 1
wgMobilityD2: Mobility domain 2
wgMobilityD3: Mobility domain 3
wgRememberingD0: Remembering domain 0
wgRememberingD1: Remembering domain 1
wgRememberingD2: Remembering domain 2
wgRememberingD3: Remembering domain 3
wgSelfCareD0: Self-care domain 0
wgSelfCareD1: Self-care domain 1
wgSelfCareD2: Self-care domain 2
wgSelfCareD3: Self-care domain 3
wgCommunicatingD0: Communicating domain 0
wgCommunicatingD1: Communicating domain 1
wgCommunicatingD2: Communicating domain 2
wgCommunicatingD3: Communicating domain 3
wgP0: Overall prevalence 0
wgP1: Overall prevalence 1
wgP2: Overall prevalence 2
wgP3: Overall prevalence 3
wgPM: Overall prevalence

Examples

indicators.MALES

Concatenate classic and PROBIT estimates into a single data.frame

Description

Concatenate classic and PROBIT estimates into a single data.frame

Usage

merge_op(x, y, prop2percent = FALSE)

Arguments

x

Classic estimates data frame

y

Probit estimates data frame

prop2percent

Logical. Should proportion type indicators be converted to percentage? Default is FALSE.

Value

A data.frame() of combined classic and probit estimates.

Author(s)

Ernest Guevarra

Examples

indicators <- c(
  "demo", "anthro", "food", "hunger", "adl", "disability",
  "mental", "dementia", "health", "oedema", "screening", "income",
  "wash", "visual", "misc"
)

classicIndicators <- indicators[indicators != "anthro"]

## Bootstrap classic
classicEstimates <- estimate_classic(
  x = indicators.ALL, w = testPSU, 
  indicators = classicIndicators, replicates = 9
)

probitEstimates <- estimate_probit(
  x = indicators.ALL, w = testPSU, replicates = 9
)

merge_op(x = classicEstimates, y = probitEstimates)

PROBIT statistics function for bootstrap estimation of older people GAM

Description

PROBIT statistics function for bootstrap estimation of older people GAM

Usage

probit_gam(x, params, threshold = 210)

probit_sam(x, params, threshold = 185)

Arguments

x

A data frame with primary sampling unit (PSU) in column named "psu" and with data column/s containing the continuous variable/s of interest with column names corresponding to params values

params

A vector of column names corresponding to the continuous variables of interest contained in x

threshold

cut-off value for continuous variable to differentiate case and non-case. Default is set at 210 for probit_gam() and 185 for probit_sam().

Value

A numeric vector of the PROBIT estimate of each continuous variable of interest with length equal to length(params).

Examples


# Example call to bootBW function:
probit_gam(x = indicators.ALL, params = "MUAC", threshold = 210)
probit_sam(x = indicators.ALL, params = "MUAC", threshold = 185)

Function to create a pyramid plot

Description

Function to create a pyramid plot

Usage

pyramid_plot(
  x,
  g,
  main = paste("Pyramid plot of", deparse(substitute(x)), "by", deparse(substitute(g))),
  xlab = paste(deparse(substitute(g)), "(", levels(g)[1], "/", levels(g)[2], ")"),
  ylab = deparse(substitute(x))
)

Arguments

x

A vector (numeric, factor, character) holding age-groups

g

A binary categorical variable (usually sex)

main

Plot title

xlab

x-axis label

ylab

y-axis label

Value

Pyramid plot

Author(s)

Mark Myatt

Examples

pyramid_plot(
  x = cut(
    testSVY$d2, 
    breaks = seq(from = 60, to = 105, by = 5),
    include.lowest = TRUE
  ),
  g = testSVY$d3
)

Create a DOCX report document containing RAM-OP survey results

Description

Create a DOCX report document containing RAM-OP survey results

Usage

report_op_docx(
  estimates,
  svy,
  indicators = c("demo", "food", "hunger", "disability", "adl", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc"),
  filename = paste(tempdir(), "ramOPreport", sep = "/"),
  title = "RAM-OP Report",
  view = FALSE
)

Arguments

estimates

A data.frame of RAM-OP results produced by merge_op().

svy

A data.frame collected using the standard RAM-OP questionnaire

indicators

A character vector of indicator names

filename

Filename for output document. Can be specified as a path to a specific directory where to output report document. Defaults to a path to a temporary directory and a filename ramOPreport.

title

Title of report

view

Logical. Open report in current environment? Default is FALSE.

Value

An DOCX in the working directory or if filename is a path, to a specified directory.

Author(s)

Ernest Guevarra

Examples

classicResults <- estimate_classic(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

probitResults <- estimate_probit(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

resultsDF <- merge_op(x = classicResults, y = probitResults)

report_op_docx(
  svy = testSVY, estimates = resultsDF, indicators = "mental",
  filename = paste(tempdir(), "report", sep = "/")
)

Create an HTML report document containing RAM-OP survey results

Description

Create an HTML report document containing RAM-OP survey results

Usage

report_op_html(
  estimates,
  svy,
  indicators = c("demo", "food", "hunger", "disability", "adl", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc"),
  filename = paste(tempdir(), "ramOPreport", sep = "/"),
  title = "RAM-OP Report",
  view = FALSE
)

Arguments

estimates

A data.frame of RAM-OP results produced by merge_op().

svy

A data.frame collected using the standard RAM-OP questionnaire

indicators

A character vector of indicator names

filename

Filename for output document. Can be specified as a path to a specific directory where to output report document. Defaults to a path to a temporary directory and a filename 'ramOPreport“.

title

Title of report

view

Logical. Open report in current browser? Default is FALSE.

Value

An HTML document in the working directory or if filename is a path, to a specified directory.

Author(s)

Ernest Guevarra

Examples

classicResults <- estimate_classic(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

probitResults <- estimate_probit(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

resultsDF <- merge_op(x = classicResults, y = probitResults)

report_op_html(
  svy = testSVY, estimates = resultsDF, indicators = "mental",
  filename = paste(tempdir(), "report", sep = "/")
)

Create a ODT report document containing RAM-OP survey results

Description

Create a ODT report document containing RAM-OP survey results

Usage

report_op_odt(
  estimates,
  svy,
  indicators = c("demo", "food", "hunger", "disability", "adl", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc"),
  filename = paste(tempdir(), "ramOPreport", sep = "/"),
  title = "RAM-OP Report",
  view = FALSE
)

Arguments

estimates

A data.frame of RAM-OP results produced by merge_op().

svy

A data.frame collected using the standard RAM-OP questionnaire

indicators

A character vector of indicator names

filename

Filename for output document. Can be specified as a path to a specific directory where to output report document. Defaults to a path to a temporary directory and a filename ramOPreport.

title

Title of report

view

Logical. Open report in current environment? Default is FALSE.

Value

An ODT in the working directory or if filename is a path, to a specified directory.

Author(s)

Ernest Guevarra

Examples

classicResults <- estimate_classic(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

probitResults <- estimate_probit(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

resultsDF <- merge_op(x = classicResults, y = probitResults)

report_op_odt(
  svy = testSVY, estimates = resultsDF, indicators = "mental",
  filename = paste(tempdir(), "report", sep = "/")
)

Create a PDF report document containing RAM-OP survey results

Description

Create a PDF report document containing RAM-OP survey results

Usage

report_op_pdf(
  estimates,
  svy,
  indicators = c("demo", "food", "hunger", "disability", "adl", "mental", "dementia",
    "health", "income", "wash", "anthro", "oedema", "screening", "visual", "misc"),
  filename = "ramOPreport",
  title = "RAM-OP Report",
  view = FALSE
)

Arguments

estimates

A data.frame of RAM-OP results produced by merge_op().

svy

A data.frame collected using the standard RAM-OP questionnaire

indicators

A character vector of indicator names

filename

Filename for output document. Can be specified as a path to a specific directory where to output report document

title

Title of report

view

Logical. Open report in current PDF reader? Default is FALSE.

Value

A PDF document in the working directory or if filename is a path, to a specified directory.

Examples

classicResults <- estimate_classic(
  x = create_op(testSVY), w = testPSU, replicates = 3
)

probitResults <- estimate_probit(
  x = create_op(testSVY), w = testPSU, replicates = 3
)

resultsDF <- merge_op(x = classicResults, y = probitResults)


  report_op_pdf(
    svy = testSVY, estimates = resultsDF, indicators = "mental",
    filename = paste(tempdir(), "report", sep = "/")
  )

Create table and report chunk of RAM-OP results

Description

Create table and report chunk of RAM-OP results

Usage

report_op_table(estimates, filename = paste(tempdir(), "ramOP", sep = "/"))

report_op_demo(output_format = c("html", "docx", "odt", "pdf"))

report_op_food(output_format = c("html", "docx", "odt", "pdf"))

report_op_hunger(output_format = c("html", "docx", "odt", "pdf"))

report_op_disability(output_format = c("html", "docx", "odt", "pdf"))

report_op_adl(output_format = c("html", "docx", "odt", "pdf"))

report_op_mental(output_format = c("html", "docx", "odt", "pdf"))

report_op_dementia(output_format = c("html", "docx", "odt", "pdf"))

report_op_health(output_format = c("html", "docx", "odt", "pdf"))

report_op_oedema(output_format = c("html", "docx", "odt", "pdf"))

report_op_anthro(output_format = c("html", "docx", "odt", "pdf"))

report_op_screen(output_format = c("html", "docx", "odt", "pdf"))

report_op_visual(output_format = c("html", "docx", "odt", "pdf"))

report_op_income(output_format = c("html", "docx", "odt", "pdf"))

report_op_wash(output_format = c("html", "docx", "odt", "pdf"))

report_op_misc(output_format = c("html", "docx", "odt", "pdf"))

Arguments

estimates

A data.frame of RAM-OP results produced by merge_op().

filename

Prefix to append to report output filename. Can be specified as a path to a specific directory where to output tabular results CSV file. Defaults to a path to a temporary directory with a filename starting with ramOP.

output_format

Either "html", "docx", "odt", or "pdf". Defaults to "html".

Value

Report of tabulated estimated results saved in CSV format in current working directory or in the specified path or a reporting chunk for specific indicators.

Author(s)

Mark Myatt and Ernest Guevarra

Examples

##
x <- estimate_classic(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

y <- estimate_probit(
  x = create_op(testSVY), w = testPSU, replicates = 9
)

z <- merge_op(x, y, prop2percent = TRUE)
report_op_table(z)

report_op_demo()
report_op_hunger()
report_op_food()
report_op_disability()

Subset data.frame to required sex

Description

Subset data.frame to required sex

Usage

subset_by_sex(df, sex)

RAM-OP Population Dataset

Description

This is a short and narrow file with one record per PSU and just two variables

Usage

testPSU

Format

A data frame with 2 columns and 16 rows:

psu: The PSU identifier. This must use the same coding system used to identify the PSUs that is used in the main RAM-OP dataset
pop: The population of the PSU

The PSU dataset is used during data analysis to weight data by PSU population.

Examples

testPSU

RAM-OP Survey Dataset

Description

Dataset collected from a RAM-OP survey conducted in Addis Ababa, Ethiopia in early 2014

Usage

testSVY

Format

A data frame with 91 columns and 192 rows:

ad2: Team number
psu: PSU (cluster) number
hh: Household identifier
id: Person identifier
d1: Who is answering these questions?
d2: Age in years
d3: Sex
d4: Marital status
d5: Do you live alone?
f1: How many meals did you eat since this time yesterday?
f2a: Tinned, powdered or fresh milk?
f2b: Sweetened or flavoured water, soda drink, alcoholic drink, beer, tea or infusion, coffee, soup, or broth?
f2c: Any food made from grain such as millet, wheat, barley, sorghum, rice, maize, pasta, noodles, bread, pizza, porridge?
f2d: Any food made from fruits or vegetables that have yellow or orange flesh such as carrots, pumpkin, red sweet potatoes, mangoes, and papaya?
f2e: Any food made with red palm oil or red palm nuts?
f2f: Any dark green leafy vegetables such as cabbage, broccoli, spinach, moringa leaves, cassava leaves?
f2g: Any food made from roots or tubers such as white potatoes, white yams, false banana, cassava, manioc, onions, beets, turnips, and swedes?
f2h: Any food made from lentils, beans, peas, groundnuts, nuts, or seeds?
f2i: Any other fruits or vegetables such as banana, plantain, avocado, cauliflower, coconut?
f2j: Liver, kidney, heart, black pudding, blood, or other organ meats?
f2k: Any meat such as beef, pork, goat, lamb, mutton, veal, chicken, camel, or bush meat?
f2l: Fresh or dried fish, shellfish, or seafood?
f2m: Cheese, yoghurt, or other milk products?
f2n: Eggs?
f2o: Any food made with oil, fat, butter, or ghee?
f2p: Any mushrooms or fungi?
f2q: Grubs, snails, insects?
f2r: Sugar, honey and foods made with sugar or honey such as sweets, candies, chocolate, cakes, and biscuits?
f2s: Salt, pepper, herbs, spices, or sauces (hot sauce, soy sauce, ketchup)?
f3: In the past four weeks, how often was there ever no food to eat of any kind in your home because of lack of resources to get food?
f4: In the past four weeks, how often did you go to sleep at night hungry because there was not enough food?
f5: In the past four weeks, how often did you go a whole day and night without eating anything at all because there was not enough food?
f6: Are you or anyone in your household receiving a food ration on a regular basis?
f7: Have you or another member of your household received non-food relief items such as soap, bucket, water container, bedding, mosquito net, clothes, or plastic sheet in the previous four weeks?
a1: Have you or another member of your household received non-food relief items such as soap, bucket, water container, bedding, mosquito net, clothes, or plastic sheet in the previous four weeks?
a2: Do you need help getting dressed partially or completely (not including tying of shoes)?
a3: Do you need help going to the toilet or cleaning yourself after using the toilet or do you use a commode or bed-pan?
a4: Do you need someone (i.e. not a walking aid) to help you move from a bed to a chair?
a5: Are you partially or totally incontinent of bowel or bladder?
a6: Do you need partial or total help with eating?
a7: Is someone taking care of you or helping you with everyday activities such as shopping, cooking, bathing and dressing?
a8: Do you have problems chewing food?
k6a: About how often during the past four weeks did you feel nervous – all of the time, most of the time, some of the time, a little of the time, or none of the time?
k6b: During the past four weeks, about how often did you feel hopeless – all of the time, most of the time, some of the time, a little of the time, or none of the time?
k6c: During the past four weeks, about how often did you feel restless or fidgety – all of the time, most of the time, some of the time, a little of the time, or none of the time?
k6d: During the past four weeks, about how often did you feel so depressed that nothing could cheer you up – all of the time, most of the time, some of the time, a little of the time, or none of the time?
k6e: During the past four weeks, about how often did you feel that everything was an effort – all of the time, most of the time, some of the time, a little of the time, or none of the time?
k6f: During the past four weeks, about how often did you feel worthless – all of the time, most of the time, some of the time, a little of the time, or none of the time?
ds1: Point to nose and ask "What do you call this?"
ds2: What do you do with a hammer?
ds3: What day of the week is it?
ds4: What is the season?
ds5: Please point first to the window and then to the door.
ds6a: Child
ds6b: House
ds6c: Road
h1: Do you suffer from a long term disease that requires you to take regular medication?
h2: Do you take drugs regularly for this?
h3: Why not?
h4: Have you been ill in the past two weeks?
h5: Did you go to the pharmacy, dispensary, health centre, health post, clinic, or hospital?
h6: Why not?
m1: Do you have a personal source of income or money?
m2a: Where does your income or money come from?: Agriculture, livestock, or fishing
m2b: Where does your income or money come from?: Wages or salary
m2c: Where does your income or money come from?: Sale of charcoal, bricks, firewood, poles, etc.
m2d: Where does your income or money come from?: Trading (e.g. market, shop)
m2e: Where does your income or money come from?: Private pension, investments, interest, rents, etc.
m2f: Where does your income or money come from?: Spending savings; Sale of household goods, personal goods, or jewellery; Sale of livestock, land, or other assets
m2g: Where does your income or money come from?: Aid, gifts, charity (e.g. from church, mosque, temple), begging, borrowing, or sale of food aid or relief items
m2h: Where does your income or money come from?: Cash transfer (NGO, UNO, government); State pension, social security, benefits, welfare program
m2i: Where does your income or money come from?: Other
w1: What is your main source of drinking water?
w2: What do you usually do to the water to make it safer to drink?
w3: What kind of toilet facility do members of your household usually use?
w4: Do you share this toilet facility with other households?
as1: Mid-upper arm circumference (mm)
as2: Has someone measured your arm like this in the previous month?
as3: Bilateral pitting oedema
as4: Has someone examined your feet like this in the previous month?
va2a: Tumbling Es: first time
va2b: Tumbling Es: second time
va2c: Tumbling Es: third time
va2d: Tumbling Es: fourth time
wg1: Do you have difficulty seeing, even if wearing glasses?
wg2: Do you have difficulty hearing, even if using a hearing aid?
wg3: Do you have difficulty walking or climbing steps?
wg4: Do you have difficulty remembering or concentrating?
wg5: Do you have difficulty with self-care such as washing all over or dressing?
wg6: Using your usual (customary) language, do you have difficulty communicating, for example understanding or being understood?

Examples

testSVY

An Implementation of Rapid Assessment Method for Older People (RAM-OP)

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See Also

Plot RAM-OP indicators

Description

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Examples

Check whether indicators are RAM-OP indicators

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Create older people indicators dataset from survey data collected using the standard RAM-OP questionnaire.

Description

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Demographic indicators

Dietary intake indicators

Household Hunger Scale (HHS)

Katz Index of Independence in Activities of Daily Living score

K6 Short form psychological distress score

Brief Community Screening Instrument for Dementia (CSID)

Health and health-seeking indicators

Income and income sources

Water, sanitation and hygiene (WASH) indicators

Anthropometry and screening

Visual impairment by "Tumbling E" method

Miscellaneous indicators

Washington Group on Disability

Examples

Construct create_op_* expression

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Usage

Apply bootstrap to RAM-OP indicators using a classical estimator.

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Estimate all standard RAM-OP indicators

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Examples

Apply bootstrap to RAM-OP indicators using a PROBIT estimator.

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Examples

Fill out a one-dimensional table to include a specified range of values

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Author(s)

Get appropriate RAM-OP indicator variable names given a specified indicator set

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Value

RAM-OP Indicators Dataset - ALL

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Format

Examples

RAM-OP Indicators Dataset - FEMALES

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Usage

Format

Examples

RAM-OP Indicators Dataset - MALES

Description

Usage

Format

Examples