Package 'oldr'

Title: An Implementation of Rapid Assessment Method for Older People
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. 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.
Authors: Mark Myatt [aut] , Ernest Guevarra [aut, cre] , Pascale Fritsch [aut], Katja Siling [aut]
Maintainer: Ernest Guevarra <[email protected]>
License: GPL-3
Version: 0.1.1
Built: 2024-10-11 03:59:45 UTC
Source: https://github.com/rapidsurveys/oldr

Help Index

Distribution of ADL (overall and by sex)

Description

Distribution of ADL (overall and by sex)

Usage

chart_adl(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar plot of ADL in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device.

Examples

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

Age by sex (pyramid plot)

Description

A wrapper function to the pyramid.plot function to create an age by sex pyramid plot

Usage

chart_age(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "populationPyramid", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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 filename starting with populationPyramid. Ignored if save_chart is FALSE.

Value

Age by sex pyramid plot in PNG format saved in the current working directory or in a specified directory if filename is a path unless when save.plot 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_age(x = indicators.ALL)

Chart dementia screen (CSID) indicators

Description

Chart dementia screen (CSID) indicators

Usage

chart_csid(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar plot of CSID in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device.

Examples

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

Distribution of DDS (overall and by sex)

Description

Distribution of DDS (overall and by sex)

Usage

chart_dds(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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 filename starting with chart. Ignored if save_chart is FALSE.

Value

Barplot of dietary diversity score in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device.

Examples

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

Chart household hunger scale (HHS) indicators

Description

Chart household hunger scale (HHS) indicators

Usage

chart_hhs(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar plot of HHS in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device.

Examples

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

Chart income indicators

Description

Chart income indicators

Usage

chart_income(
  x.male,
  x.female,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x.male

Male subset of indicator dataset
x.female

Female subset of indicator dataset
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar chart of sources of income by sex in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphics device.

Examples

# Create chart using indicators.FEMALES and indicators.MALES
  # dataset
  chart_income(x.male = indicators.MALES,
               x.female = indicators.FEMALES)

Distribution of K6 (overall and by sex)

Description

Distribution of K6 (overall and by sex)

Usage

chart_k6(x, save_chart = TRUE, filename = paste(tempdir(), "chart", sep = "/"))

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Histogram of K6 score in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphics device.

Examples

# Create chart using indicators.ALL dataset
  chart_k6(x = indicators.ALL)

Distribution of meal frequency (overall and by sex)

Description

Distribution of meal frequency (overall and by sex)

Usage

chart_mf(x, save_chart = TRUE, filename = paste(tempdir(), "chart", sep = "/"))

Arguments

x

Indicators dataset produced by create_op_all
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 filename starting with chart. Ignored if save_chart is FALSE.

Value

Barplot of meal frequency in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphics device.

Examples

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

Distribution of MUAC (overall and by sex)

Description

Distribution of MUAC (overall and by sex)

Usage

chart_muac(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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 filename starting with chart. Ignored if save_chart is FALSE.

Value

Histogram of MUAC distribution in PNG format and saved in the current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown on current graphics device.

Examples

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

Chart WASH indicators

Description

Chart WASH indicators

Usage

chart_wash(
  x,
  save_chart = TRUE,
  filename = paste(tempdir(), "chart", sep = "/")
)

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a temporary directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar plot of ADL in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device

Examples

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

Chart disability (Washington Group - WG) indicators

Description

Chart disability (Washington Group - WG) indicators

Usage

chart_wg(x, save_chart = TRUE, filename = paste(tempdir(), "chart", sep = "/"))

Arguments

x

Indicators dataset produced by create_op_all
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. Defaults to a path to a working directory and a filename starting with chart. Ignored if save_chart is FALSE.

Value

Bar plot of Disability Score in PNG format saved in current working directory or in a specified directory if filename is a path unless when save_chart is FALSE in which case chart is shown in current graphic device.

Examples

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

Create older people indicators dataframe on activities of daily living from survey data collected using the standard RAM-OP questionnaire

Description

Create older people indicators dataframe on activities of daily living from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_adl(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of older people indicators on activities of daily living

Katz "Index of Independence in Activities of Daily Living" (ADL) 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

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

Author(s)

Mark Myatt

Examples

# Create activities of daily living indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_adl(testSVY)

Create female older people indicators dataframe for activities of daily living from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for activities of daily living from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_adl_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of female older people indicators on activities of daily living

Examples

# Create activities of daily living indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_adl_females(testSVY)

Create male older people indicators dataframe for activities of daily living from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for activities of daily living from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_adl_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of male older people indicators on activities of daily living

Examples

# Create activities of daily living indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_adl_males(testSVY)

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

Description

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

Usage

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

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire
indicators

A character vector of indicator names
gender

Either an "m" for male or "f" for female; Whether to report indicators for males or females. If unspecified (default), both are reported.

Value

A tibble of older people indicators

Examples

create_op_all(svy = testSVY)

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

Description

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

Usage

create_op_anthro(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on anthropometry

Anthropometry and screening

MUAC

Mid-upper arm circumference (mm)

Author(s)

Mark Myatt

Examples

# Create anthropometry indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_anthro(testSVY)

Create female older people indicators dataframe for anthropometry from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for anthropometry from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_anthro_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on anthropometry

Examples

# Create anthropometry indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_anthro_females(testSVY)

Create male older people indicators dataframe for anthropometry from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for anthropometry from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_anthro_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on anthropometry

Examples

# Create anthropometry indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_anthro_males(testSVY)

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

Description

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

Usage

create_op_dementia(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on dementia

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

DS

Probable dementia by CSID screen

Author(s)

Mark Myatt

Examples

# Create dementia indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_dementia(testSVY)

Create female older people indicators dataframe for dementia from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for dementia from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_dementia_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on dementia

Examples

# Create dementia indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_dementia_females(testSVY)

Create male older people indicators dataframe for dementia from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for dementia from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_dementia_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on dementia

Examples

# Create dementia indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_dementia_males(testSVY)

Create older people indicators dataframe for demography and situation from survey data collected using the standard RAM-OP questionnaire

Description

Create older people indicators dataframe for demography and situation from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_demo(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of older people indicators on demography and situation

Demography and situation

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

Author(s)

Mark Myatt

Examples

# Create demography and situation indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_demo(testSVY)

Create female older people indicators dataframe for demography and situation from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for demography and situation from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_demo_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of female older people indicators on demography and situation

Examples

# Create demography and situation indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_demo_females(testSVY)

create_op_demo_males

Description

Create male older people indicators dataframe for demography and situation from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_demo_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of male older people indicators on demography and situation

Examples

# Create demography and situation indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_demo_males(testSVY)

Create older people indicators dataframe on disability from survey data collected using the standard RAM-OP questionnaire

Description

Create older people indicators dataframe on disability from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_disability(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on disability

Washington Group on Disability

See:

https://www.washingtongroup-disability.com https://www.cdc.gov/nchs/washington_group/wg_documents.htm

for details.

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

Author(s)

Mark Myatt

Examples

# Create disability indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_disability(testSVY)

Create female older people indicators dataframe for disability from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for disability from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_disability_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on disability

Examples

# Create disability indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_disability_females(testSVY)

Create male older people indicators dataframe for disability from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for disability from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_disability_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on disability

Examples

# Create disability indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_disability_males(testSVY)

Create older people indicators for food intake from survey data collected using the standard RAM-OP questionnaire

Description

Create older people indicators for food intake from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_food(svy)

Arguments

svy

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

Value

A dataframe of older people indicators on food intake

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/3/i1983e/i1983e00.htm

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

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

Author(s)

Mark Myatt

Examples

# Create food intake indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_food(testSVY)

Create female older people indicators dataframe for food intake from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for food intake from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_food_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of female older people indicators on food intake

Examples

# Create food intake indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_food_females(testSVY)

Create male older people indicators dataframe for food intake from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for food intake from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_food_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of male older people indicators on food intake

Examples

# Create food intake indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_food_males(testSVY)

Create older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_health(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on health and health-seeking behaviour

Health and health-seeking indicators

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

Author(s)

Mark Myatt

Examples

# Create health and health-seeking behaviour indicators dataset from RAM-OP
# survey data collected from Addis Ababa, Ethiopia
create_op_health(testSVY)

Create female older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_health_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on health and health-seeking behaviours

Examples

# Create health and health-seeking behaviours indicators dataset from RAM-OP
# survey data collected from Addis Ababa, Ethiopia
create_op_health_females(testSVY)

Create male older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for health and health-seeking behaviours from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_health_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on health and health-seeking behaviours

Examples

# Create health and health-seeking behaviours indicators dataset from RAM-OP
# survey data collected from Addis Ababa, Ethiopia
create_op_health_males(testSVY)

Create older people indicators for severe food insecurity from survey data collected using the standard RAM-OP questionnaire

Description

Create older people indicators for severe food insecurity from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_hunger(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of older people indicators on household hunger

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

HHS1

Little or no hunger in household

HHS2

Moderate hunger in household

HHS3

Severe hunger in household

Author(s)

Mark Myatt

Examples

# Create household hunger indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_hunger(testSVY)

Create female older people indicators dataframe for household hunger from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for household hunger from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_hunger_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of female older people indicators on household hunger

Examples

# Create household hunger indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_hunger_females(testSVY)

Create male older people indicators dataframe for household hunger from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for household hunger from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_hunger_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A dataframe of male older people indicators on household hunger

Examples

# Create household hunger indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_hunger_males(testSVY)

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

Description

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

Usage

create_op_income(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on income

Income and income sources

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

Author(s)

Mark Myatt

Examples

# Create income indicators dataset from RAM-OP survey data collected from
# Addis Ababa, Ethiopia
create_op_income(testSVY)

Create female older people indicators dataframe for income from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for income from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_income_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on income

Examples

# Create income indicators dataset from RAM-OP survey data collected from
# Addis Ababa, Ethiopia
create_op_income_females(testSVY)

Create male older people indicators dataframe for income from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for income from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_income_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on income

Examples

# Create income indicators dataset from RAM-OP survey data collected from
# Addis Ababa, Ethiopia
create_op_income_males(testSVY)

Create older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_mental(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on mental health

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

K6

K6 score

K6Case

K6 score > 12 (in serious psychological distress)

Author(s)

Mark Myatt

Examples

# Create mental health indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_mental(testSVY)

Create female older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_mental_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on mental health

Examples

# Create mental health indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_mental_females(testSVY)

Create male older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for mental health from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_mental_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on mental health

Examples

# Create mental health indicators dataset from RAM-OP survey data collected
# from Addis Ababa, Ethiopia
create_op_mental_males(testSVY)

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

Description

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

Usage

create_op_misc(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people miscellaneous indicators

Miscellaneous indicators

chew

Problems chewing food

food

Anyone in HH receives a ration

NFRI

Anyone in HH received non-food relief item(s) in previous month

Author(s)

Mark Myatt

Examples

# Create miscellaneous indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_misc(testSVY)

Create female older people indicators dataframe for miscellaneous indicators from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for miscellaneous indicators from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_misc_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people miscellaneous indicators

Examples

# Create miscellaneous indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_misc_females(testSVY)

Create male older people indicators dataframe for miscellaneous indicators from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for miscellaneous indicators from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_misc_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people miscellaneous indicators

Examples

# Create miscellaneous indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_misc_males(testSVY)

Create older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_oedema(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on oedema prevalence

Oedema prevalence

oedema

Bilateral pitting oedema (may not be nutritional)

Author(s)

Mark Myatt

Examples

# Create oedema prevalence indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_oedema(testSVY)

Create female older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_oedema_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on oedema prevalence

Examples

# Create oedema prevalence indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_oedema_females(testSVY)

Create male older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for oedema prevalence from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_oedema_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on oedema prevalence

Examples

# Create oedema prevalence indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_oedema_males(testSVY)

Create older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_screening(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on screening coverage

Screening Coverage

screened

Either MUAC or oedema checked previously

Author(s)

Mark Myatt

Examples

# Create screening coverage indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_screening(testSVY)

Create female older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_screening_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on screening coverage

Examples

# Create screening coverage indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_screening_females(testSVY)

Create male older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for screening coverage from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_screening_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on screening coverage

Examples

# Create screening coverage indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_screening_males(testSVY)

Create older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_visual(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on visual impairment

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

poorVA

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

Author(s)

Mark Myatt

Examples

# Create visual impairment indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_visual(testSVY)

Create female older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_visual_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on visual impairment

Examples

# Create visual impairment indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_visual_females(testSVY)

Create male older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for visual impairment from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_visual_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on visual impairment

Examples

# Create visual impairment indicators dataset from RAM-OP survey data
# collected from Addis Ababa, Ethiopia
create_op_visual_males(testSVY)

Create older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire.

Description

Create older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire.

Usage

create_op_wash(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of older people indicators on water, sanitation and hygiene

Water, sanitation and hygiene (WASH) indicators

These are a (core) subset of indicators from: https://washdata.org/monitoring/methods/core-questions

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

Author(s)

Mark Myatt

Examples

# Create water, sanitation and hygiene indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_wash(testSVY)

Create female older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire

Description

Create female older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_wash_females(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of female older people indicators on water, sanitation and hygiene

Examples

# Create water, sanitation and hygiene indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_wash_females(testSVY)

Create male older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire

Description

Create male older people indicators dataframe for water, sanitation and hygiene from survey data collected using the standard RAM-OP questionnaire

Usage

create_op_wash_males(svy)

Arguments

svy

A dataframe collected using the standard RAM-OP questionnaire

Value

A tibble of male older people indicators on water, sanitation and hygiene

Examples

# Create water, sanitation and hygiene indicators dataset from RAM-OP survey
# data collected from Addis Ababa, Ethiopia
create_op_wash_males(testSVY)

Function to apply bootstrap to RAM-OP indicators using a classical estimator.

Description

Function to 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_all 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 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

Tibble of boot estimates using 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_all(
  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_all 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

Tibble of boot estimates for all specified standard RAM-OP indicators

Examples

estimate_op_all(x = create_op_all(testSVY),
                w = testPSU,
                replicates = 9)

Function to apply bootstrap to RAM-OP indicators using a PROBIT estimator.

Description

Function to 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 = "MUAC",
  replicates = 399
)

Arguments

x

Indicators dataset produced by create_op_all 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; fixed to MUAC
replicates

Number of bootstrap replicate case and non-case

Value

Dataframe of boot estimates using bootPROBIT function

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

Examples

xTable <- fullTable(x = sample(x = 5,
                                 size = 100,
                                 replace = TRUE),
                      values = 1:5)
  xTable

Function to get appropriate RAM-OP indicator variable names given a specified indicator set

Description

Function to 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

Examples

get_variables(indicators = c("demo", "food"))

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_estimates(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

Data frame of combined classic and probit estimates

Author(s)

Ernest Guevarra

Examples

#
  ## Not run: 
  test <- merge_estimates(x = classicEstimates,
                          y = probitEstimates)
  
## End(Not run)

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)

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.

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 statistics function for bootstrap estimation of older people SAM

Description

PROBIT statistics function for bootstrap estimation of older people SAM

Usage

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 an older people with SAM to those with no SAM. Default is set at 185.

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_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 report chunk for activities of daily living indicators

Description

Create a report chunk for activities of daily living indicators

Usage

report_op_adl(format = "html")

Arguments

format

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

Value

A reporting chunk for ADL indicators

Author(s)

Ernest Guevarra

Examples

report_op_adl()

Create a report chunk for anthropometric indicators

Description

Create a report chunk for anthropometric indicators

Usage

report_op_anthro(format = "html")

Arguments

format

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

Value

A reporting chunk for anthropometric indicators

Author(s)

Ernest Guevarra

Examples

report_op_anthro()

Create a report chunk for dementia indicators

Description

Create a report chunk for dementia indicators

Usage

report_op_dementia(format = "html")

Arguments

format

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

Value

A reporting chunk for dementia indicators

Author(s)

Ernest Guevarra

Examples

report_op_dementia()

Create a report chunk for demography indicators

Description

Create a report chunk for demography indicators

Usage

report_op_demo(format = "html")

Arguments

format

Either html, docx or odt. Defaults to html.

Value

A reporting chunk for demographic indicators

Author(s)

Ernest Guevarra

Examples

report_op_demo()

Create a report chunk for disability indicators

Description

Create a report chunk for disability indicators

Usage

report_op_disability(format = "html")

Arguments

format

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

Value

A reporting chunk for disability indicators

Author(s)

Ernest Guevarra

Examples

report_op_disability()

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_estimates function.
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_all(testSVY),
                                     w = testPSU,
                                     replicates = 9)

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

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

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

Create a report chunk for food indicators

Description

Create a report chunk for food indicators

Usage

report_op_food(format = "html")

Arguments

format

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

Value

A reporting chunk for food indicators

Author(s)

Ernest Guevarra

Examples

report_op_food()

Create a report chunk for health and health-seeking behaviour indicators

Description

Create a report chunk for health and health-seeking behaviour indicators

Usage

report_op_health(format = "html")

Arguments

format

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

Value

A reporting chunk for health and health-seeking behaviour indicators

Author(s)

Ernest Guevarra

Examples

report_op_health()

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_estimates function.
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_all(testSVY),
                                     w = testPSU,
                                     replicates = 9)

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

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

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

Create a report chunk for activities of food security indicators

Description

Create a report chunk for activities of food security indicators

Usage

report_op_hunger(format = "html")

Arguments

format

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

Value

A reporting chunk for food security indicators

Author(s)

Ernest Guevarra

Examples

report_op_hunger()

Create a report chunk for income

Description

Create a report chunk for income

Usage

report_op_income(format = "html")

Arguments

format

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

Value

A reporting chunk for income

Author(s)

Ernest Guevarra

Examples

report_op_income()

Create a report chunk for mental health indicators

Description

Create a report chunk for mental health indicators

Usage

report_op_mental(format = "html")

Arguments

format

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

Value

A reporting chunk for mental health indicators

Author(s)

Ernest Guevarra

Examples

report_op_mental()

Create a report chunk for miscellaneous indicators

Description

Create a report chunk for miscellaneous indicators

Usage

report_op_misc(format = "html")

Arguments

format

Either html, docx or odt. Defaults to html.

Value

A reporting chunk for miscellaneous indicators

Author(s)

Ernest Guevarra

Examples

report_op_misc()

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_estimates function.
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_all(testSVY),
                                     w = testPSU,
                                     replicates = 9)

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

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

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

Create a report chunk for oedema

Description

Create a report chunk for oedema

Usage

report_op_oedema(format = "html")

Arguments

format

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

Value

A reporting chunk for oedema indicators

Author(s)

Ernest Guevarra

Examples

report_op_oedema()

Create a report chunk for screening indicators

Description

Create a report chunk for screening indicators

Usage

report_op_screen(format = "html")

Arguments

format

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

Value

A reporting chunk for screening indicators

Author(s)

Ernest Guevarra

Examples

report_op_screen()

Create table of RAM-OP results

Description

Create table of RAM-OP results

Usage

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

Arguments

estimates

A data.frame of RAM-OP results produced by merge_estimates function.
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.

Value

Report of tabulated estimated results saved in CSV format in current working directory or in the specified path

Author(s)

Mark Myatt

Examples

##
  x <- estimate_classic(x = create_op_all(testSVY),
                        w = testPSU,
                        replicates = 9)
  y <- estimate_probit(x = create_op_all(testSVY),
                       w = testPSU,
                       replicates = 9)
  z <- merge_estimates(x, y, prop2percent = TRUE)
  report_op_table(z)

Create a report chunk for visual acuity

Description

Create a report chunk for visual acuity

Usage

report_op_visual(format = "html")

Arguments

format

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

Value

A reporting chunk for visual acuity

Author(s)

Ernest Guevarra

Examples

report_op_visual()

Create a report chunk for water, sanitation and hygiene

Description

Create a report chunk for water, sanitation and hygiene

Usage

report_op_wash(format = "html")

Arguments

format

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

Value

A reporting chunk for water, sanitation and hygiene

Author(s)

Ernest Guevarra

Examples

report_op_wash()

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

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