The RAM-OP Workflow is summarised in the diagram below.
The oldr package provides functions to use for all steps
after data collection. These functions were developed specifically for
the data structure created by the EpiData
or the Open Data
Kit collection tools. The data structure produced by these
collection tools is shown by the dataset testSVY included
in the oldr package.
testSVY
#> # A tibble: 192 × 90
#> ad2 psu hh id d1 d2 d3 d4 d5 f1 f2a f2b f2c
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 201 1 1 1 67 2 5 2 3 2 1 1
#> 2 1 201 2 1 1 74 1 2 2 3 2 1 1
#> 3 1 201 3 1 1 60 1 2 2 2 2 2 2
#> 4 1 201 3 2 1 60 2 2 2 3 2 2 1
#> 5 1 201 4 1 1 85 2 5 2 3 2 1 1
#> 6 1 201 5 1 2 86 1 5 1 4 2 1 1
#> 7 1 201 6 1 1 80 1 5 2 3 2 1 1
#> 8 1 201 6 2 1 60 2 5 2 3 2 2 1
#> 9 1 201 7 1 1 62 1 2 2 2 2 1 1
#> 10 1 201 8 1 1 72 2 5 2 2 2 1 1
#> # ℹ 182 more rows
#> # ℹ 77 more variables: f2d <int>, f2e <int>, f2f <int>, f2g <int>, f2h <int>,
#> # f2i <int>, f2j <int>, f2k <int>, f2l <int>, f2m <int>, f2n <int>,
#> # f2o <int>, f2p <int>, f2q <int>, f2r <int>, f2s <int>, f3 <int>, f4 <int>,
#> # f5 <int>, f6 <int>, f7 <int>, a1 <int>, a2 <int>, a3 <int>, a4 <int>,
#> # a5 <int>, a6 <int>, a7 <int>, a8 <int>, k6a <int>, k6b <int>, k6c <int>,
#> # k6d <int>, k6e <int>, k6f <int>, ds1 <int>, ds2 <int>, ds3 <int>, …Once RAM-OP data is collected, it will need to be processed and
recoded based on the definitions of the various indicators included in
RAM-OP. The oldr package provides a suite functions to
perform this processing and recoding. These functions and their syntax
can be easily remembered as the create_op_ functions as
their function names start with the create_ verb followed
by the op_ label and then followed by an indicator or
indicator set specific identifier or short name. Finally, an additional
tag for male or female can be added to the
main function to provide gender-specific outputs.
Currently, a standard RAM-OP can provide results for the 13 indicators or indicator sets for older people. The following table shows these indicators/indicator sets alongside the functions related to them:
| Indicator / Indicator Set | Related Functions |
|---|---|
| Demography and situation | create_op_demo;
create_op_demo_males;
create_op_demo_females |
| Food intake | create_op_food;
create_op_food_males;
create_op_food_females |
| Severe food insecurity | create_op_hunger;
create_op_hunger_males;
create_op_hunger_females |
| Disability | create_op_disability;
create_op_disability_males;
create_op_disability_females |
| Activities of daily living | create_op_adl;
create_op_adl_males;
create_op_adl_females |
| Mental health and well-being | create_op_mental;
create_op_mental_males;
create_op_mental_females |
| Dementia | create_op_dementia;
create_op_dementia_males;
create_op_dementia_females |
| Health and health-seeking behaviour | create_op_health;
create_op_health_males;
create_op_health_females |
| Sources of income | create_op_income;
create_op_income_males;
create_op_income_females |
| Water, sanitation, and hygiene | create_op_wash;
create_op_wash_males;
create_op_wash_females |
| Anthropometry and anthropometric screening coverage | create_op_anthro;
create_op_anthro_males;
create_op_anthro_females |
| Visual impairment | create_op_visual;
create_op_visual_males;
create_op_visual_females |
| Miscellaneous | create_op_misc;
create_op_misc_males;
create_op_misc_females |
A final function in the processing and recoding set -
create_op - is provided to perform the processing and
recoding of all indicators or indicator sets. This function allows for
the specification of which indicators or indicator sets to process and
recode which is useful for cases where not all the indicators or
indicator sets have been collected or if only specific indicators or
indicator sets need to be analysed or reported. This function also
specifies whether a specific gender subset of the data is needed.
For a standard RAM-OP implementation, this step is performed in R as follows:
## Process and recode all standard RAM-OP indicators in the testSVY dataset
create_op(svy = testSVY)which results in the following output:
#> # A tibble: 192 × 138
#> psu sex1 sex2 resp1 resp2 resp3 resp4 age ageGrp1 ageGrp2 ageGrp3
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 201 0 1 1 0 0 0 67 0 1 0
#> 2 201 1 0 1 0 0 0 74 0 0 1
#> 3 201 1 0 1 0 0 0 60 0 1 0
#> 4 201 0 1 1 0 0 0 60 0 1 0
#> 5 201 0 1 1 0 0 0 85 0 0 0
#> 6 201 1 0 0 1 0 0 86 0 0 0
#> 7 201 1 0 1 0 0 0 80 0 0 0
#> 8 201 0 1 1 0 0 0 60 0 1 0
#> 9 201 1 0 1 0 0 0 62 0 1 0
#> 10 201 0 1 1 0 0 0 72 0 0 1
#> # ℹ 182 more rows
#> # ℹ 127 more variables: ageGrp4 <dbl>, ageGrp5 <dbl>, marital1 <dbl>,
#> # marital2 <dbl>, marital3 <dbl>, marital4 <dbl>, marital5 <dbl>,
#> # marital6 <dbl>, alone <dbl>, MF <dbl>, DDS <dbl>, FG01 <dbl>, FG02 <dbl>,
#> # FG03 <dbl>, FG04 <dbl>, FG05 <dbl>, FG06 <dbl>, FG07 <dbl>, FG08 <dbl>,
#> # FG09 <dbl>, FG10 <dbl>, FG11 <dbl>, proteinRich <dbl>, pProtein <dbl>,
#> # aProtein <dbl>, pVitA <dbl>, aVitA <dbl>, xVitA <dbl>, ironRich <dbl>, …Once data has been processed and appropriate recoding for indicators has been performed, indicator estimates can now be calculated.
It is important to note that estimation procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note:
The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs).
The RAM-OP sample is not prior weighted. This means that per-PSU sampling weights are needed. These are usually the populations of the PSU.
This sample design will need to be specified to statistical analysis software being used. If no weights are provided, then the analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow).
The oldr package uses blocked weighted
bootstrap estimation approach:
Blocked : The block corresponds to the PSU or cluster.
Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates.
A total of m PSUs are sampled with-replacement from the
survey dataset where m is the number of PSUs in the survey
sample. Individual records within each PSU are then sampled
with-replacement. A total of n records are sampled
with-replacement from each of the selected PSUs where n is
the number of individual records in a selected PSU. The resulting
collection of records replicates the original survey in terms of both
sample design and sample size. A large number of replicate surveys are
taken (the standard RAM-OP software uses \(r =
399\) replicate surveys but this can be changed). The required
statistic (e.g. the mean of an indicator value) is applied to each
replicate survey. The reported estimate consists of the 50th (point
estimate), 2.5th (lower 95% confidence limit), and the 97.5th (upper 95%
confidence limit) percentiles of the distribution of the statistic
observed across all replicate surveys. The blocked weighted bootstrap
procedure is outlined in the figure below.
The principal advantages of using a bootstrap estimator are:
Bootstrap estimators work well with small sample sizes.
The method is non-parametric and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about.
The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods.
The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes as discussed in the following literature:
World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995
Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. https://doi.org/10.1017/s1368980012003345, 2012
Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8
An estimate of GAM prevalence can be made using a classic estimator:
\[ \text{prevalence} ~ = ~ \frac{\text{Number of respondents with MUAC < 210}}{\text{Total number of respondents}} \]
On the other hand, the estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold.
Both the classic and the PROBIT methods can be thought of as estimating area:
The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method.
The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality.
The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates:
\[ \widehat{\text{GAM prevalence}} ~ = ~ \widehat{\text{GAM prevalence}} - \widehat{\text{SAM prevalence}} \]
The function estimateClassic in oldr
implements the blocked weighted bootstrap classic estimator of RAM-OP.
This function uses the bootClassic statistic to estimate
indicator values.
The estimateClassic function is used for all the
standard RAM-OP indicators except for anthropometry. The function is
used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform classic estimation on recoded data using appropriate weights provided by testPSU
classicDF <- estimate_classic(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 136 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 0.828 0.809 0.915 0.854 0.767 0.889 0.870
#> 2 resp2 0.109 0.0531 0.151 0.0676 0.0588 0.128 0.115
#> 3 resp3 0.0312 0.0115 0.0698 0.0471 0.00244 0.0958 0.0231
#> 4 resp4 0.0104 0.00104 0.0396 0.0278 0 0.0777 0
#> 5 age 70.6 69.6 73.0 70.8 68.0 72.5 70.2
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.573 0.369 0.627 0.527 0.432 0.637 0.548
#> 8 ageGrp3 0.234 0.156 0.326 0.241 0.173 0.299 0.225
#> 9 ageGrp4 0.177 0.142 0.276 0.183 0.0912 0.233 0.177
#> 10 ageGrp5 0.0417 0.0260 0.0562 0.0676 0.0391 0.104 0.0231
#> # ℹ 126 more rows
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>The function estimateProbit in oldr
implements the blocked weighted bootstrap PROBIT estimator of RAM-OP.
This function uses the probit_GAM and the
probit_SAM statistic to estimate indicator values.
The estimateProbit function is used for only the
anthropometric indicators. The function is used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform probit estimation on recoded data using appropriate weights provided by testPSU
probitDF <- estimate_probit(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 3 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GAM 0.0268 1.13e-2 0.0358 6.87e- 4 6.76e- 5 0.0189 0.0341
#> 2 MAM 0.0246 1.08e-2 0.0357 6.87e- 4 6.75e- 5 0.0189 0.0290
#> 3 SAM 0.000133 4.34e-7 0.00835 1.09e-13 2.45e-58 0.0000344 0.000801
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>The two sets of estimates are then merged using the
merge_op function as follows:
which results in:
#> # A tibble: 139 × 13
#> INDICATOR GROUP LABEL TYPE EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES
#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 Survey Resp… Prop… 0.828 8.09e-1 0.915 0.854 0.767
#> 2 resp2 Survey Resp… Prop… 0.109 5.31e-2 0.151 0.0676 0.0588
#> 3 resp3 Survey Resp… Prop… 0.0312 1.15e-2 0.0698 0.0471 0.00244
#> 4 resp4 Survey Resp… Prop… 0.0104 1.04e-3 0.0396 0.0278 0
#> 5 age Demography… Mean… Mean 70.6 6.96e+1 73.0 70.8 68.0
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 0.573 3.69e-1 0.627 0.527 0.432
#> 8 ageGrp3 Demography… Self… Prop… 0.234 1.56e-1 0.326 0.241 0.173
#> 9 ageGrp4 Demography… Self… Prop… 0.177 1.42e-1 0.276 0.183 0.0912
#> 10 ageGrp5 Demography… Self… Prop… 0.0417 2.60e-2 0.0562 0.0676 0.0391
#> # ℹ 129 more rows
#> # ℹ 4 more variables: UCL.MALES <dbl>, EST.FEMALES <dbl>, LCL.FEMALES <dbl>,
#> # UCL.FEMALES <dbl>Once indicators has been estimated, the outputs can then be used to
create relevant charts to visualise the results. A set of functions that
start with the verb chart_op_ is provided followed by the
indicator identifier to specify the type of indicator to visualise. The
output of the function is a PNG file saved in the specified filename
appended to the indicator identifier within the current working
directory or saved in the specified filename appended to the indicator
identifier in the specified directory path.
The following shows how to produce the chart for ADLs saved with filename test appended at the start inside a temporary directory:
The resulting PNG file can be found in the temporary directory
and will look something like this:
Finally, estimates can be reported through report tables. The
report_op_table function facilitates this through the
following syntax:
The resulting CSV file is found in the temporary directory
and will look something like this:
#> X X.1
#> 1 Survey
#> 2
#> 3 INDICATOR TYPE
#> 4 Respondent : SUBJECT 2
#> 5 Respondent : FAMILY CARER 2
#> 6 Respondent : OTHER CARER 2
#> 7 Respondent : OTHER 2
#> 8
#> 9 Demography and situation
#> 10
#> 11 INDICATOR TYPE
#> 12 Mean self-reported age of subject (years) 1
#> 13 Self-reported age between 50 and 59 years 2
#> 14 Self-reported age between 60 and 69 years 2
#> 15 Self-reported age between 70 and 79 years 2
#> 16 Self-reported age between 80 and 89 years 2
#> 17 Self-reported age 90 years or older 2
#> 18 Sex : MALE 2
#> 19 Sex : FEMALE 2
#> 20 Marital status : SINGLE (NEVER MARRIED) 2
#> 21 Marital status : MARRIED 2
#> 22 Marital status : LIVING TOGETHER 2
#> 23 Marital status : DIVORCED 2
#> 24 Marital status : WIDOWED 2
#> 25 Marital status : OTHER 2
#> 26 Subject lives alone 2
#> 27
#> 28 Diet
#> 29
#> 30 INDICATOR TYPE
#> 31 Meal frequency (i.e. number of meals and snacks in previous 24 hours) 1
#> 32 Dietary diversity (count from 11 food groups) 1
#> 33 Consumed CEREALS (in previous 24 hours) 2
#> 34 Consumed ROOTS / TUBERS (in previous 24 hours) 2
#> 35 Consumed FRUITS / VEGETABLES (in previous 24 hours) 2
#> 36 Consumed MEAT (in previous 24 hours) 2
#> 37 Consumed EGGS (in previous 24 hours) 2
#> 38 Consumed FISH (in previous 24 hours) 2
#> 39 Consumed LEGUMES / NUTS / SEEDS (in previous 24 hours) 2
#> 40 Consumed MILK / MILK PRODUCTS (in previous 24 hours) 2
#> 41 Consumed FATS (in previous 24 hours) 2
#> 42 Consumed SUGARS (in previous 24 hours) 2
#> 43 Consumed OTHER (in previous 24 hours) 2
#> 44
#> 45 Nutrients
#> 46
#> 47 INDICATOR TYPE
#> 48 PROTEIN rich foods in diet 2
#> 49 Protein rich plant sources of protein in diet 2
#> 50 Protein rich animal sources of protein in diet 2
#> 51 Plant sources of Vitamin A in diet 2
#> 52 Animal sources of Vitamin A in diet 2
#> 53 Any source of Vitamin A 2
#> 54 IRON rich foods in diet 2
#> 55 CALCIUM rich foods in diet 2
#> 56 ZINC rich foods in diet 2
#> 57 Vitamin B1 rich foods in diet 2
#> 58 Vitamin B2 rich foods in diet 2
#> 59 Vitamin B3 rich foods in diet 2
#> 60 Vitamin B6 rich foods in diet 2
#> 61 Vitamin B12 rich foods in diet 2
#> 62 Vitamin B1 / B2 / B3 / B6 / B12 rich foods in diet 2
#> 63
#> 64 Food Security
#> 65
#> 66 INDICATOR TYPE
#> 67 Little or no hunger in household (HHS = 0 / 1) 2
#> 68 Moderate hunger in household (HHS = 2 / 3) 2
#> 69 Severe hunger in household (HHS = 4 / 5 / 6) 2
#> 70
#> 71 Disability (WG)
#> 72
#> 73 INDICATOR TYPE
#> 74 Vision : D0 : None 2
#> 75 Vision : D1 : Any 2
#> 76 Vision : D2 : Moderate or severe 2
#> 77 Vision : D3: Severe 2
#> 78 Hearing : D0 : None 2
#> 79 Hearing : D1 : Any 2
#> 80 Hearing : D2 : Moderate or severe 2
#> 81 Hearing : D3: Severe 2
#> 82 Mobility : D0 : None 2
#> 83 Mobility : D1 : Any 2
#> 84 Mobility : D2 : Moderate or severe 2
#> 85 Mobility : D3: Severe 2
#> 86 Remembering : D0 : None 2
#> 87 Remembering : D1 : Any 2
#> 88 Remembering : D2 : Moderate or severe 2
#> 89 Remembering : D3: Severe 2
#> 90 Self-care : D0 : None 2
#> 91 Self-care : D1 : Any 2
#> 92 Self-care : D2 : Moderate or severe 2
#> 93 Self-care : D3: Severe 2
#> 94 Communicating : D0 : None 2
#> 95 Communicating : D1 : Any 2
#> 96 Communicating : D2 : Moderate or severe 2
#> 97 Communicating : D3: Severe 2
#> 98 No disability in Washington Group domains 2
#> 99 At least 1 domain with any disability (P1) 2
#> 100 At least 1 domain with moderate or severe disability (P2) 2
#> 101 At least 1 domain with severe disability (P3) 2
#> 102 Multiple disability : More than one domain with any disability (PM) 2
#> 103
#> 104 Activities of daily living
#> 105
#> 106 INDICATOR TYPE
#> 107 Independent : Bathing 2
#> 108 Independent : Dressing 2
#> 109 Independent : Toileting 2
#> 110 Independent : Transferring (mobility) 2
#> 111 Independent : Continence 2
#> 112 Independent : Feeding 2
#> 113 Katz ADL score 1
#> 114 Independent (Katz ADL score = 5/6) 2
#> 115 Partial dependency (Katz ADL score = 3/4) 2
#> 116 Severe dependency (Katz ADL score = 0/1/2) 2
#> 117 Subject has someone to help them with activities of daily living 2
#> 118 Subject has ADL needs (ADL < 6) but has no helper 2
#> 119
#> 120 Mental health
#> 121
#> 122 INDICATOR TYPE
#> 123 K6 psychological distress score 1
#> 124 Serious psychological distress (K6 > 12) 2
#> 125 Probable dementia by brief CSID screen 2
#> 126
#> 127 Health
#> 128
#> 129 INDICATOR TYPE
#> 130 Long term disease requiring regular medication 2
#> 131 Takes medication for long term disease requiring regular medication 2
#> 132 Not taking drugs for long term disease : NO DRUGS AVAILABLE 2
#> 133 Not taking drugs for long term disease : TOO EXPENSIVE / NO MONEY 2
#> 134 Not taking drugs for long term disease : TOO OLD TO LOOK FOR CARE 2
#> 135 Not taking drugs for long term disease : USE OF TRADITIONAL MEDICINE 2
#> 136 Not taking drugs for long term disease : DRUGS DON'T HELP 2
#> 137 Not taking drugs for long term disease : NO-ONE TO HELP ME 2
#> 138 Not taking drugs for long term disease : NO NEED 2
#> 139 Not taking drugs for long term disease : OTHER 2
#> 140 Not taking drugs for long term disease : NO REASON GIVEN 2
#> 141 Recent illness (i.e. in the previous 2 weeks) 2
#> 142 Accessed care for recent illness 2
#> 143 Not accessing care for recent illness : NO DRUGS AVAILABLE 2
#> 144 Not accessing care for recent illness : TOO EXPENSIVE / NO MONEY 2
#> 145 Not accessing care for recent illness : TOO OLD TO LOOK FOR CARE 2
#> 146 Not accessing care for recent illness : USE OF TRADITIONAL MEDICINE 2
#> 147 Not accessing care for recent illness : DRUGS DON'T HELP 2
#> 148 Not accessing care for recent illness : NO-ONE TO HELP ME 2
#> 149 Not accessing care for recent illness : NO NEED 2
#> 150 Not accessing care for recent illness : OTHER 2
#> 151 Not accessing care for recent illness : NO REASON GIVEN 2
#> 152 Bilateral pitting oedema (may not be nutritional) 2
#> 153 Visual impairment (visual acuity < 6 / 12) by tumbling E method 2
#> 154 Problems chewing food (self-report) 2
#> 155
#> 156 Income
#> 157
#> 158 INDICATOR TYPE
#> 159 Has a personal source of income 2
#> 160 Source of income : Agriculture / fishing / livestock 2
#> 161 Source of income : Wages / salary 2
#> 162 Source of income : Sale of charcoal / bricks / etc. 2
#> 163 Source of income : Trading (e.g. market or shop) 2
#> 164 Source of income : Investments 2
#> 165 Source of income : Spending savings / sales of assets 2
#> 166 Source of income : Charity 2
#> 167 Source of income : Cash transfer / social security / welfare 2
#> 168 Source of income : Other source(s) of income 2
#> 169
#> 170 WASH
#> 171
#> 172 INDICATOR TYPE
#> 173 Improved source of drinking water 2
#> 174 Safe drinking water 2
#> 175 Improved sanitation facility 2
#> 176 Improved non-shared sanitation facility 2
#> 177
#> 178 Relief
#> 179
#> 180 INDICATOR TYPE
#> 181 Previously screened (MUAC or oedema) 2
#> 182 Anyone in household receives a ration 2
#> 183 Received non-food relief items in previous month 2
#> 184
#> 185 Anthropometry
#> 186
#> 187 INDICATOR TYPE
#> 188 Global acute malnutrition : GAM 2
#> 189 Moderate acute malnutrition : MAM 2
#> 190 Severe acute malnutrition : SAM 2
#> X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10
#> 1
#> 2 ALL MALES FEMALES
#> 3 EST LCL UCL EST LCL UCL EST LCL UCL
#> 4 0.8281 0.8094 0.9146 0.8537 0.7669 0.8885 0.8699 0.8019 0.9021
#> 5 0.1094 0.0531 0.1510 0.0676 0.0588 0.1278 0.1154 0.0717 0.1579
#> 6 0.0312 0.0115 0.0698 0.0471 0.0024 0.0958 0.0231 0.0016 0.0397
#> 7 0.0104 0.0010 0.0396 0.0278 0.0000 0.0777 0.0000 0.0000 0.0086
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL UCL
#> 12 70.5938 69.6198 72.9552 70.7805 68.0342 72.5137 70.1694 69.0897 71.3545
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 0.5729 0.3688 0.6271 0.5270 0.4325 0.6366 0.5484 0.4882 0.6745
#> 15 0.2344 0.1562 0.3260 0.2405 0.1727 0.2994 0.2250 0.1113 0.3237
#> 16 0.1771 0.1417 0.2760 0.1829 0.0912 0.2330 0.1774 0.1360 0.2868
#> 17 0.0417 0.0260 0.0563 0.0676 0.0391 0.1036 0.0231 0.0016 0.0398
#> 18 0.4062 0.3427 0.4875 1.0000 1.0000 1.0000 0.0000 0.0000 0.0000
#> 19 0.5938 0.5125 0.6573 0.0000 0.0000 0.0000 1.0000 1.0000 1.0000
#> 20 0.0312 0.0260 0.0417 0.0244 0.0138 0.0481 0.0488 0.0173 0.0847
#> 21 0.3177 0.2240 0.3375 0.5278 0.3721 0.6420 0.1290 0.0935 0.1731
#> 22 0.1146 0.0823 0.1688 0.1818 0.0742 0.2293 0.0692 0.0364 0.0854
#> 23 0.0625 0.0292 0.0719 0.1176 0.0321 0.1981 0.0526 0.0314 0.0912
#> 24 0.4948 0.4135 0.5750 0.1477 0.1018 0.2346 0.7097 0.6602 0.7456
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 0.1198 0.0750 0.1719 0.1707 0.0930 0.2486 0.1463 0.0806 0.1744
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL UCL
#> 31 2.5729 2.5323 2.7302 2.4783 2.2714 2.5538 2.6167 2.4629 2.7099
#> 32 4.6146 4.4146 4.9052 4.4412 4.2014 5.0853 4.6810 4.4401 4.8432
#> 33 0.9167 0.8979 0.9531 0.9324 0.8853 0.9454 0.9138 0.8964 0.9494
#> 34 0.5260 0.4729 0.6271 0.5122 0.3456 0.6258 0.5593 0.4374 0.6587
#> 35 0.5990 0.5104 0.6427 0.4865 0.4013 0.5919 0.6271 0.5515 0.7075
#> 36 0.0729 0.0448 0.0969 0.0294 0.0043 0.0908 0.0593 0.0078 0.0863
#> 37 0.0208 0.0031 0.0615 0.0294 0.0115 0.0850 0.0177 0.0018 0.0688
#> 38 0.3385 0.2885 0.3781 0.4118 0.3780 0.5187 0.2389 0.1732 0.3109
#> 39 0.4271 0.3927 0.4917 0.4051 0.2773 0.4854 0.4336 0.3603 0.4633
#> 40 0.0156 0.0052 0.0344 0.0000 0.0000 0.0354 0.0484 0.0176 0.0869
#> 41 0.1927 0.1729 0.2479 0.2353 0.1390 0.3597 0.2258 0.1333 0.2683
#> 42 0.5260 0.4323 0.5927 0.4239 0.3058 0.5807 0.5615 0.3969 0.6655
#> 43 0.9792 0.9438 0.9938 0.9878 0.9437 1.0000 0.9823 0.9443 0.9900
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL UCL
#> 48 0.5052 0.4719 0.5667 0.4565 0.3181 0.5244 0.4951 0.4451 0.5483
#> 49 0.4271 0.3927 0.4917 0.4051 0.2773 0.4854 0.4336 0.3603 0.4633
#> 50 0.1094 0.0896 0.1708 0.0735 0.0445 0.1585 0.1532 0.0511 0.1892
#> 51 0.6094 0.5417 0.6896 0.5366 0.4325 0.5692 0.6408 0.6109 0.7207
#> 52 0.0469 0.0104 0.0854 0.0541 0.0134 0.0850 0.0813 0.0281 0.1467
#> 53 0.6458 0.5479 0.7260 0.5610 0.4560 0.6307 0.6846 0.6372 0.7845
#> 54 0.6562 0.5833 0.7365 0.6081 0.4948 0.7073 0.6903 0.6558 0.7733
#> 55 0.0156 0.0052 0.0344 0.0000 0.0000 0.0354 0.0484 0.0176 0.0869
#> 56 0.6354 0.5781 0.6844 0.6765 0.5661 0.7911 0.5776 0.5002 0.6180
#> 57 0.6615 0.6094 0.7406 0.6912 0.5874 0.8066 0.6452 0.5880 0.6778
#> 58 0.8229 0.7698 0.8885 0.7703 0.6984 0.9136 0.8615 0.7999 0.9179
#> 59 0.6354 0.5781 0.6844 0.6765 0.5661 0.7911 0.5776 0.5002 0.6180
#> 60 0.8750 0.8115 0.9281 0.8784 0.8040 0.9751 0.8692 0.8112 0.9290
#> 61 0.4010 0.3344 0.4708 0.4756 0.3953 0.5811 0.3398 0.2590 0.4045
#> 62 0.3958 0.3240 0.4708 0.4583 0.3931 0.5811 0.3204 0.2530 0.3843
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL UCL
#> 67 0.7552 0.7177 0.7948 0.7647 0.6990 0.9003 0.8154 0.7280 0.8870
#> 68 0.1875 0.1479 0.2094 0.1932 0.0997 0.2381 0.1154 0.0597 0.1850
#> 69 0.0260 0.0062 0.0542 0.0217 0.0000 0.0980 0.0308 0.0000 0.0575
#> 70
#> 71
#> 72 ALL MALES FEMALES
#> 73 EST LCL UCL EST LCL UCL EST LCL UCL
#> 74 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 ALL MALES FEMALES
#> 106 EST LCL UCL EST LCL UCL EST LCL UCL
#> 107 0.9688 0.9271 0.9865 0.9412 0.8946 0.9754 0.9912 0.9563 1.0000
#> 108 0.9948 0.9594 0.9948 0.9873 0.9401 1.0000 1.0000 0.9860 1.0000
#> 109 0.9948 0.9594 0.9948 0.9873 0.9401 1.0000 1.0000 0.9860 1.0000
#> 110 0.9583 0.9479 0.9740 0.9773 0.9401 1.0000 0.9741 0.8808 0.9984
#> 111 0.7396 0.7104 0.7812 0.7765 0.7026 0.8336 0.7097 0.5744 0.7671
#> 112 1.0000 0.9781 1.0000 1.0000 0.9644 1.0000 1.0000 1.0000 1.0000
#> 113 5.6510 5.5219 5.7062 5.6765 5.4098 5.7664 5.6228 5.5601 5.7348
#> 114 0.9688 0.9479 0.9885 0.9873 0.9401 1.0000 0.9914 0.9449 1.0000
#> 115 0.0104 0.0000 0.0427 0.0000 0.0000 0.0000 0.0086 0.0000 0.0551
#> 116 0.0052 0.0010 0.0406 0.0127 0.0000 0.0599 0.0000 0.0000 0.0000
#> 117 0.5938 0.5448 0.6635 0.5122 0.3710 0.6314 0.5825 0.4523 0.6833
#> 118 0.0990 0.0760 0.1375 0.1471 0.0860 0.2566 0.0923 0.0533 0.1566
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL UCL
#> 123 12.0156 10.5948 12.2219 11.3472 9.7589 12.1610 12.6525 12.2230 13.2469
#> 124 0.4792 0.3688 0.5240 0.3902 0.2981 0.5208 0.5167 0.4432 0.5698
#> 125 0.1979 0.1479 0.2490 0.1304 0.0523 0.1919 0.2119 0.1687 0.2680
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL UCL
#> 130 0.4583 0.3896 0.4844 0.3750 0.2917 0.4599 0.5439 0.3761 0.5680
#> 131 0.7742 0.6876 0.8564 0.6667 0.5352 0.8407 0.7973 0.6710 0.9697
#> 132 0.1429 0.0167 0.2649 0.0000 0.0000 0.4182 0.0909 0.0000 0.8667
#> 133 0.5000 0.2026 0.6583 0.3750 0.0909 0.8762 0.5000 0.0625 0.6627
#> 134 0.0741 0.0000 0.1549 0.0000 0.0000 0.0000 0.0625 0.0000 0.4188
#> 135 0.0714 0.0000 0.2444 0.3333 0.0738 0.6000 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.1214 0.0000 0.0000 0.0000 0.0000 0.0000 0.1705
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 0.1154 0.0143 0.5727 0.1667 0.0000 0.3950 0.0909 0.0000 0.4933
#> 141 0.8750 0.8125 0.9177 0.8293 0.8052 0.9308 0.8793 0.8134 0.9373
#> 142 0.8061 0.7833 0.8969 0.6892 0.6370 0.7459 0.9100 0.7685 0.9383
#> 143 0.0571 0.0057 0.1393 0.0417 0.0000 0.4019 0.0625 0.0000 0.2747
#> 144 0.8611 0.7277 0.9342 0.8636 0.4700 0.9433 0.7083 0.5333 0.8482
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0286 0.0000 0.0971 0.1200 0.0083 0.1358 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0476 0.0000 0.1102 0.0000 0.0000 0.0000 0.2000 0.0154 0.3111
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0457 0.0000 0.0000 0.0000 0.0000 0.0000 0.0967
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0208 0.0052 0.0469 0.0122 0.0000 0.0352 0.0177 0.0016 0.0525
#> 153 0.4167 0.3635 0.4990 0.4634 0.3720 0.5419 0.3097 0.2010 0.4463
#> 154 0.3073 0.2875 0.3990 0.2973 0.1669 0.3592 0.2966 0.1654 0.4372
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL UCL
#> 159 0.5677 0.4906 0.6021 0.6463 0.5490 0.7226 0.5000 0.3927 0.5944
#> 160 0.3333 0.2896 0.4344 0.4583 0.4148 0.5634 0.2833 0.2601 0.4487
#> 161 0.1146 0.0833 0.1771 0.1829 0.1455 0.3660 0.0462 0.0106 0.1336
#> 162 0.0208 0.0010 0.0385 0.0380 0.0000 0.0877 0.0081 0.0000 0.0191
#> 163 0.0573 0.0427 0.0823 0.0139 0.0000 0.0649 0.0763 0.0308 0.1625
#> 164 0.0000 0.0000 0.0146 0.0000 0.0000 0.0000 0.0000 0.0000 0.0440
#> 165 0.0104 0.0052 0.0375 0.0366 0.0051 0.0731 0.0000 0.0000 0.0000
#> 166 0.0208 0.0104 0.0312 0.0435 0.0000 0.0566 0.0083 0.0000 0.0336
#> 167 0.3333 0.2969 0.4188 0.3472 0.2381 0.4122 0.3390 0.2285 0.4038
#> 168 0.0208 0.0062 0.0312 0.0000 0.0000 0.0268 0.0081 0.0000 0.0140
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL UCL
#> 173 0.6094 0.5500 0.6427 0.5556 0.4953 0.6691 0.6210 0.5246 0.7100
#> 174 0.6979 0.6771 0.7500 0.6098 0.5089 0.7713 0.7542 0.6713 0.8157
#> 175 0.2396 0.1990 0.2771 0.2317 0.1271 0.2930 0.1935 0.1602 0.3152
#> 176 0.2344 0.1906 0.2750 0.2317 0.1271 0.2930 0.1864 0.1412 0.3012
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL UCL
#> 181 0.0312 0.0062 0.0573 0.0000 0.0000 0.0518 0.0244 0.0017 0.0697
#> 182 0.0417 0.0219 0.0688 0.0244 0.0000 0.0568 0.0431 0.0206 0.0759
#> 183 0.0260 0.0156 0.0490 0.0147 0.0000 0.0694 0.0259 0.0039 0.0454
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL UCL
#> 188 0.0268 0.0113 0.0358 0.0007 0.0001 0.0189 0.0341 0.0134 0.0919
#> 189 0.0246 0.0108 0.0357 0.0007 0.0001 0.0189 0.0290 0.0128 0.0864
#> 190 0.0001 0.0000 0.0083 0.0000 0.0000 0.0000 0.0008 0.0000 0.0090The oldr package functions were designed in such a way
that they can be piped to each other to provide the desired output.
Below we use the base R pipe operator |>.
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_table(filename = file.path(tempdir(), "TEST"))This results in a CSV file TEST.report.csv in the
temporary directory
with the following structure:
#> X X.1
#> 1 Survey
#> 2
#> 3 INDICATOR TYPE
#> 4 Respondent : SUBJECT 2
#> 5 Respondent : FAMILY CARER 2
#> 6 Respondent : OTHER CARER 2
#> 7 Respondent : OTHER 2
#> 8
#> 9 Demography and situation
#> 10
#> 11 INDICATOR TYPE
#> 12 Mean self-reported age of subject (years) 1
#> 13 Self-reported age between 50 and 59 years 2
#> 14 Self-reported age between 60 and 69 years 2
#> 15 Self-reported age between 70 and 79 years 2
#> 16 Self-reported age between 80 and 89 years 2
#> 17 Self-reported age 90 years or older 2
#> 18 Sex : MALE 2
#> 19 Sex : FEMALE 2
#> 20 Marital status : SINGLE (NEVER MARRIED) 2
#> 21 Marital status : MARRIED 2
#> 22 Marital status : LIVING TOGETHER 2
#> 23 Marital status : DIVORCED 2
#> 24 Marital status : WIDOWED 2
#> 25 Marital status : OTHER 2
#> 26 Subject lives alone 2
#> 27
#> 28 Diet
#> 29
#> 30 INDICATOR TYPE
#> 31 Meal frequency (i.e. number of meals and snacks in previous 24 hours) 1
#> 32 Dietary diversity (count from 11 food groups) 1
#> 33 Consumed CEREALS (in previous 24 hours) 2
#> 34 Consumed ROOTS / TUBERS (in previous 24 hours) 2
#> 35 Consumed FRUITS / VEGETABLES (in previous 24 hours) 2
#> 36 Consumed MEAT (in previous 24 hours) 2
#> 37 Consumed EGGS (in previous 24 hours) 2
#> 38 Consumed FISH (in previous 24 hours) 2
#> 39 Consumed LEGUMES / NUTS / SEEDS (in previous 24 hours) 2
#> 40 Consumed MILK / MILK PRODUCTS (in previous 24 hours) 2
#> 41 Consumed FATS (in previous 24 hours) 2
#> 42 Consumed SUGARS (in previous 24 hours) 2
#> 43 Consumed OTHER (in previous 24 hours) 2
#> 44
#> 45 Nutrients
#> 46
#> 47 INDICATOR TYPE
#> 48 PROTEIN rich foods in diet 2
#> 49 Protein rich plant sources of protein in diet 2
#> 50 Protein rich animal sources of protein in diet 2
#> 51 Plant sources of Vitamin A in diet 2
#> 52 Animal sources of Vitamin A in diet 2
#> 53 Any source of Vitamin A 2
#> 54 IRON rich foods in diet 2
#> 55 CALCIUM rich foods in diet 2
#> 56 ZINC rich foods in diet 2
#> 57 Vitamin B1 rich foods in diet 2
#> 58 Vitamin B2 rich foods in diet 2
#> 59 Vitamin B3 rich foods in diet 2
#> 60 Vitamin B6 rich foods in diet 2
#> 61 Vitamin B12 rich foods in diet 2
#> 62 Vitamin B1 / B2 / B3 / B6 / B12 rich foods in diet 2
#> 63
#> 64 Food Security
#> 65
#> 66 INDICATOR TYPE
#> 67 Little or no hunger in household (HHS = 0 / 1) 2
#> 68 Moderate hunger in household (HHS = 2 / 3) 2
#> 69 Severe hunger in household (HHS = 4 / 5 / 6) 2
#> 70
#> 71 Disability (WG)
#> 72
#> 73 INDICATOR TYPE
#> 74 Vision : D0 : None 2
#> 75 Vision : D1 : Any 2
#> 76 Vision : D2 : Moderate or severe 2
#> 77 Vision : D3: Severe 2
#> 78 Hearing : D0 : None 2
#> 79 Hearing : D1 : Any 2
#> 80 Hearing : D2 : Moderate or severe 2
#> 81 Hearing : D3: Severe 2
#> 82 Mobility : D0 : None 2
#> 83 Mobility : D1 : Any 2
#> 84 Mobility : D2 : Moderate or severe 2
#> 85 Mobility : D3: Severe 2
#> 86 Remembering : D0 : None 2
#> 87 Remembering : D1 : Any 2
#> 88 Remembering : D2 : Moderate or severe 2
#> 89 Remembering : D3: Severe 2
#> 90 Self-care : D0 : None 2
#> 91 Self-care : D1 : Any 2
#> 92 Self-care : D2 : Moderate or severe 2
#> 93 Self-care : D3: Severe 2
#> 94 Communicating : D0 : None 2
#> 95 Communicating : D1 : Any 2
#> 96 Communicating : D2 : Moderate or severe 2
#> 97 Communicating : D3: Severe 2
#> 98 No disability in Washington Group domains 2
#> 99 At least 1 domain with any disability (P1) 2
#> 100 At least 1 domain with moderate or severe disability (P2) 2
#> 101 At least 1 domain with severe disability (P3) 2
#> 102 Multiple disability : More than one domain with any disability (PM) 2
#> 103
#> 104 Activities of daily living
#> 105
#> 106 INDICATOR TYPE
#> 107 Independent : Bathing 2
#> 108 Independent : Dressing 2
#> 109 Independent : Toileting 2
#> 110 Independent : Transferring (mobility) 2
#> 111 Independent : Continence 2
#> 112 Independent : Feeding 2
#> 113 Katz ADL score 1
#> 114 Independent (Katz ADL score = 5/6) 2
#> 115 Partial dependency (Katz ADL score = 3/4) 2
#> 116 Severe dependency (Katz ADL score = 0/1/2) 2
#> 117 Subject has someone to help them with activities of daily living 2
#> 118 Subject has ADL needs (ADL < 6) but has no helper 2
#> 119
#> 120 Mental health
#> 121
#> 122 INDICATOR TYPE
#> 123 K6 psychological distress score 1
#> 124 Serious psychological distress (K6 > 12) 2
#> 125 Probable dementia by brief CSID screen 2
#> 126
#> 127 Health
#> 128
#> 129 INDICATOR TYPE
#> 130 Long term disease requiring regular medication 2
#> 131 Takes medication for long term disease requiring regular medication 2
#> 132 Not taking drugs for long term disease : NO DRUGS AVAILABLE 2
#> 133 Not taking drugs for long term disease : TOO EXPENSIVE / NO MONEY 2
#> 134 Not taking drugs for long term disease : TOO OLD TO LOOK FOR CARE 2
#> 135 Not taking drugs for long term disease : USE OF TRADITIONAL MEDICINE 2
#> 136 Not taking drugs for long term disease : DRUGS DON'T HELP 2
#> 137 Not taking drugs for long term disease : NO-ONE TO HELP ME 2
#> 138 Not taking drugs for long term disease : NO NEED 2
#> 139 Not taking drugs for long term disease : OTHER 2
#> 140 Not taking drugs for long term disease : NO REASON GIVEN 2
#> 141 Recent illness (i.e. in the previous 2 weeks) 2
#> 142 Accessed care for recent illness 2
#> 143 Not accessing care for recent illness : NO DRUGS AVAILABLE 2
#> 144 Not accessing care for recent illness : TOO EXPENSIVE / NO MONEY 2
#> 145 Not accessing care for recent illness : TOO OLD TO LOOK FOR CARE 2
#> 146 Not accessing care for recent illness : USE OF TRADITIONAL MEDICINE 2
#> 147 Not accessing care for recent illness : DRUGS DON'T HELP 2
#> 148 Not accessing care for recent illness : NO-ONE TO HELP ME 2
#> 149 Not accessing care for recent illness : NO NEED 2
#> 150 Not accessing care for recent illness : OTHER 2
#> 151 Not accessing care for recent illness : NO REASON GIVEN 2
#> 152 Bilateral pitting oedema (may not be nutritional) 2
#> 153 Visual impairment (visual acuity < 6 / 12) by tumbling E method 2
#> 154 Problems chewing food (self-report) 2
#> 155
#> 156 Income
#> 157
#> 158 INDICATOR TYPE
#> 159 Has a personal source of income 2
#> 160 Source of income : Agriculture / fishing / livestock 2
#> 161 Source of income : Wages / salary 2
#> 162 Source of income : Sale of charcoal / bricks / etc. 2
#> 163 Source of income : Trading (e.g. market or shop) 2
#> 164 Source of income : Investments 2
#> 165 Source of income : Spending savings / sales of assets 2
#> 166 Source of income : Charity 2
#> 167 Source of income : Cash transfer / social security / welfare 2
#> 168 Source of income : Other source(s) of income 2
#> 169
#> 170 WASH
#> 171
#> 172 INDICATOR TYPE
#> 173 Improved source of drinking water 2
#> 174 Safe drinking water 2
#> 175 Improved sanitation facility 2
#> 176 Improved non-shared sanitation facility 2
#> 177
#> 178 Relief
#> 179
#> 180 INDICATOR TYPE
#> 181 Previously screened (MUAC or oedema) 2
#> 182 Anyone in household receives a ration 2
#> 183 Received non-food relief items in previous month 2
#> 184
#> 185 Anthropometry
#> 186
#> 187 INDICATOR TYPE
#> 188 Global acute malnutrition : GAM 2
#> 189 Moderate acute malnutrition : MAM 2
#> 190 Severe acute malnutrition : SAM 2
#> X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9
#> 1
#> 2 ALL MALES FEMALES
#> 3 EST LCL UCL EST LCL UCL EST LCL
#> 4 84.3750 79.6875 86.2500 83.5616 79.7967 90.3743 84.4828 77.9286
#> 5 11.4583 7.9167 14.2708 7.3171 2.6662 14.2226 13.6752 9.9969
#> 6 4.1667 0.7292 6.1458 5.8824 3.4884 8.6209 1.6667 0.0000
#> 7 1.0417 0.1042 2.5000 2.1978 0.2439 4.7407 0.8621 0.0000
#> 8
#> 9
#> 10 ALL MALES FEMALES
#> 11 EST LCL UCL EST LCL UCL EST LCL
#> 12 70.4792 70.0875 72.1906 70.8293 70.0312 72.8752 71.3661 70.1139
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 53.6458 43.2292 58.5417 48.7500 42.2955 55.8747 51.2821 46.3793
#> 15 24.4792 19.2708 28.9583 26.5060 21.4205 32.9583 21.6667 14.9244
#> 16 17.7083 14.0625 25.8333 15.0685 9.3316 26.7646 24.1071 16.3030
#> 17 4.6875 2.0833 8.4375 3.8961 1.2108 11.7582 3.4483 0.0000
#> 18 42.7083 33.4375 54.0625 100.0000 100.0000 100.0000 0.0000 0.0000
#> 19 57.2917 45.9375 66.5625 0.0000 0.0000 0.0000 100.0000 100.0000
#> 20 4.1667 1.2500 6.5625 1.4706 0.0000 6.1258 3.4188 2.6047
#> 21 31.7708 25.5208 37.2917 50.6849 40.7973 59.4098 16.1017 8.6001
#> 22 10.4167 5.7292 15.0000 16.4384 8.3356 25.7792 6.3063 3.4483
#> 23 6.2500 3.7500 12.3958 8.7500 2.8998 18.8863 5.0847 0.8698
#> 24 45.3125 39.8958 56.1458 19.7531 11.0924 27.6291 70.2479 61.4802
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 14.0625 9.5833 18.1250 17.2840 9.3316 24.3474 9.9099 7.0690
#> 27
#> 28
#> 29 ALL MALES FEMALES
#> 30 EST LCL UCL EST LCL UCL EST LCL
#> 31 2.5625 2.4437 2.7073 2.5926 2.2986 2.6895 2.6577 2.4653
#> 32 4.4792 4.3625 4.8083 4.4787 4.0865 4.9376 4.6942 4.5209
#> 33 92.1875 89.7917 98.3333 89.7059 83.6254 94.3748 93.2203 88.0000
#> 34 51.5625 46.6667 61.2500 44.1558 33.3957 61.0760 56.0345 48.0296
#> 35 57.2917 44.6875 65.9375 58.5366 39.4646 66.8449 59.4828 56.5104
#> 36 6.2500 2.9167 8.2292 5.8824 2.9570 10.2565 9.8214 1.9890
#> 37 1.5625 0.1042 3.9583 6.2500 1.6583 9.3734 0.8475 0.0000
#> 38 31.7708 28.2292 38.3333 44.6809 32.1605 49.7059 28.8288 23.4070
#> 39 40.1042 39.0625 45.0000 37.5000 28.4607 46.1618 41.3793 35.7966
#> 40 3.1250 0.6250 4.1667 0.0000 0.0000 2.4045 2.5862 1.0315
#> 41 20.8333 15.1042 22.2917 22.8916 17.0681 32.2549 20.8333 12.8487
#> 42 47.3958 43.9583 56.3542 48.9362 35.0130 53.3280 58.0357 44.8718
#> 43 96.3542 94.3750 98.8542 96.2963 94.4722 100.0000 97.4138 92.9453
#> 44
#> 45
#> 46 ALL MALES FEMALES
#> 47 EST LCL UCL EST LCL UCL EST LCL
#> 48 46.8750 43.8542 53.4375 45.0000 35.4489 56.0158 50.0000 40.2989
#> 49 40.1042 39.0625 45.0000 37.5000 28.4607 46.1618 41.3793 35.7966
#> 50 10.9375 6.3542 13.8542 13.2353 5.6943 22.0344 13.5593 6.8410
#> 51 59.8958 52.2917 65.0000 58.5366 47.3767 68.2811 62.9310 59.6523
#> 52 4.1667 2.2917 7.9167 6.2500 1.6583 11.7779 4.5045 1.2069
#> 53 62.5000 53.6458 67.0833 61.7284 48.4405 70.8938 65.8333 60.4187
#> 54 66.6667 61.3542 76.5625 62.6374 52.6049 68.9111 68.1034 63.2273
#> 55 3.1250 0.6250 4.1667 0.0000 0.0000 2.4045 2.5862 1.0315
#> 56 58.3333 56.1458 65.6250 67.5325 52.9815 75.6343 62.8099 49.6036
#> 57 63.5417 61.0417 67.6042 69.5122 56.4444 76.3572 66.3793 54.5766
#> 58 82.2917 79.4792 84.6875 79.1209 72.5679 85.4926 86.6071 75.0175
#> 59 58.3333 56.1458 65.6250 67.5325 52.9815 75.6343 62.8099 49.6036
#> 60 85.9375 82.2917 90.2083 86.8132 84.1605 93.6074 89.2562 77.3503
#> 61 37.5000 31.6667 44.4792 49.3506 39.1481 54.6168 36.2069 29.0026
#> 62 36.4583 31.5625 43.6458 49.3506 36.9012 51.4204 36.2069 28.1690
#> 63
#> 64
#> 65 ALL MALES FEMALES
#> 66 EST LCL UCL EST LCL UCL EST LCL
#> 67 76.0417 69.7917 85.2083 76.4706 65.5749 84.2881 76.0684 70.9661
#> 68 17.7083 11.8750 23.6458 20.0000 12.4363 28.2820 16.0714 8.7147
#> 69 2.0833 1.5625 3.1250 2.9412 1.1108 6.7140 3.3333 1.2315
#> 70
#> 71
#> 72 ALL MALES FEMALES
#> 73 EST LCL UCL EST LCL UCL EST LCL
#> 74 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 ALL MALES FEMALES
#> 106 EST LCL UCL EST LCL UCL EST LCL
#> 107 96.3542 94.3750 98.4375 96.2963 88.4502 99.7059 97.3214 95.0287
#> 108 98.4375 97.3958 99.4792 96.2963 92.2198 100.0000 100.0000 96.4865
#> 109 98.4375 97.3958 99.4792 96.2963 92.2198 100.0000 100.0000 96.4865
#> 110 96.3542 92.2917 98.7500 96.2963 90.6453 100.0000 94.6429 91.0579
#> 111 72.9167 69.6875 77.3958 79.0123 74.2845 85.1984 68.3333 62.5627
#> 112 100.0000 98.5417 100.0000 98.7013 93.5567 100.0000 100.0000 100.0000
#> 113 5.6302 5.5323 5.6844 5.6049 5.3423 5.8149 5.6198 5.5009
#> 114 97.3958 94.3750 98.9583 96.2963 92.2198 100.0000 96.5812 94.2715
#> 115 1.5625 0.1042 3.5417 0.0000 0.0000 0.0000 3.4188 0.1724
#> 116 1.0417 0.1042 2.5000 3.7037 0.0000 7.7802 0.0000 0.0000
#> 117 54.6875 48.9583 68.7500 56.2500 44.0185 69.1979 64.6552 46.9231
#> 118 11.9792 7.7083 14.0625 12.9870 6.3926 19.0462 10.7143 4.3103
#> 119
#> 120
#> 121 ALL MALES FEMALES
#> 122 EST LCL UCL EST LCL UCL EST LCL
#> 123 11.7917 10.9604 12.3812 11.9877 10.5406 12.3535 12.4380 11.0107
#> 124 47.3958 36.9792 50.7292 51.6484 38.8861 54.8115 49.5868 42.6219
#> 125 18.2292 13.3333 21.6667 21.9178 15.1793 30.4772 24.1379 16.1068
#> 126
#> 127
#> 128 ALL MALES FEMALES
#> 129 EST LCL UCL EST LCL UCL EST LCL
#> 130 42.7083 35.3125 50.7292 39.3617 24.4897 45.4945 49.1071 36.9938
#> 131 76.5432 69.9255 85.8000 72.7273 53.8095 86.7568 84.0000 69.5699
#> 132 24.0000 1.2500 40.6061 28.5714 0.0000 61.3333 12.5000 0.0000
#> 133 40.0000 19.8086 57.6431 44.4444 11.3333 79.6078 44.4444 35.2381
#> 134 8.0000 0.0000 32.6797 0.0000 0.0000 0.0000 23.8095 12.5000
#> 135 3.7037 0.0000 23.7500 16.6667 0.0000 50.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 7.4667 0.0000 0.0000 0.0000 0.0000 0.0000
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 16.0000 0.7407 45.1675 14.2857 0.0000 39.3333 0.0000 0.0000
#> 141 85.9375 80.2083 90.9375 80.8824 76.1373 90.6294 89.8305 79.1379
#> 142 82.6087 75.9257 86.5794 77.6316 65.2870 80.8882 85.8586 82.4611
#> 143 9.3023 0.0000 24.2029 7.1429 0.0000 29.4154 7.1429 0.0000
#> 144 86.2069 72.0807 97.6744 85.7143 50.5231 94.3791 85.7143 60.8088
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 0.0000 10.6404 5.8824 0.0000 21.1888 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 11.5987 0.0000 0.0000 0.0000 0.0000 0.0000
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 3.4783 0.0000 0.0000 0.0000 0.0000 0.0000
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 1.5625 1.0417 3.9583 0.0000 0.0000 3.9925 4.3103 1.0476
#> 153 39.0625 35.5208 49.7917 44.5783 35.5588 60.7343 38.8430 31.7514
#> 154 28.1250 19.0625 32.6042 25.9740 14.0349 34.0142 33.6207 26.2999
#> 155
#> 156
#> 157 ALL MALES FEMALES
#> 158 EST LCL UCL EST LCL UCL EST LCL
#> 159 56.2500 48.1250 61.1458 60.2740 48.3904 69.8061 53.3898 43.5181
#> 160 37.5000 31.5625 49.7917 45.0000 25.8145 52.0882 33.3333 19.1394
#> 161 13.0208 6.9792 16.5625 21.9512 13.4906 24.5453 5.3571 2.6731
#> 162 2.0833 1.1458 3.5417 5.1948 1.2378 8.3842 0.0000 0.0000
#> 163 4.6875 2.8125 7.9167 0.0000 0.0000 5.6815 9.8214 5.6255
#> 164 0.0000 0.0000 1.3542 0.0000 0.0000 0.0000 0.0000 0.0000
#> 165 1.5625 0.5208 4.0625 2.5000 0.2410 6.2907 0.0000 0.0000
#> 166 1.5625 0.7292 3.9583 1.4706 0.0000 7.0638 0.8621 0.0000
#> 167 33.8542 26.1458 36.5625 26.5060 16.4759 38.0828 36.6667 31.5525
#> 168 1.0417 0.1042 2.5000 2.1277 0.0000 3.9470 0.0000 0.0000
#> 169
#> 170
#> 171 ALL MALES FEMALES
#> 172 EST LCL UCL EST LCL UCL EST LCL
#> 173 62.5000 58.4375 66.8750 61.5385 54.2246 69.0073 60.3306 56.2458
#> 174 71.8750 66.8750 75.5208 65.8537 55.9436 75.4230 75.8621 67.2147
#> 175 27.0833 19.8958 30.2083 29.2683 21.2206 39.2762 23.9669 16.4933
#> 176 27.0833 18.5417 29.5833 29.2683 21.2206 39.2762 22.2222 14.1233
#> 177
#> 178
#> 179 ALL MALES FEMALES
#> 180 EST LCL UCL EST LCL UCL EST LCL
#> 181 3.1250 0.6250 4.5833 3.8961 0.0000 8.0568 4.3103 1.0230
#> 182 4.1667 3.1250 7.0833 6.0241 1.2818 7.6036 4.3103 0.6612
#> 183 2.6042 1.6667 4.1667 2.5000 0.0000 5.4594 2.5862 0.1786
#> 184
#> 185
#> 186 ALL MALES FEMALES
#> 187 EST LCL UCL EST LCL UCL EST LCL
#> 188 1.8130 1.0630 5.2885 0.3554 0.0563 1.1869 3.8523 1.4438
#> 189 1.6398 0.9404 5.2042 0.3553 0.0553 1.1868 3.8503 0.9093
#> 190 0.0697 0.0000 0.3752 0.0000 0.0000 0.0066 0.3502 0.0252
#> X.10
#> 1
#> 2
#> 3 UCL
#> 4 87.8223
#> 5 16.7381
#> 6 4.7816
#> 7 2.5648
#> 8
#> 9
#> 10
#> 11 UCL
#> 12 72.5655
#> 13 0.0000
#> 14 56.5198
#> 15 26.9906
#> 16 28.1640
#> 17 6.6839
#> 18 0.0000
#> 19 100.0000
#> 20 9.8023
#> 21 22.8767
#> 22 8.5632
#> 23 6.9109
#> 24 75.6897
#> 25 0.0000
#> 26 17.8974
#> 27
#> 28
#> 29
#> 30 UCL
#> 31 2.8276
#> 32 4.8290
#> 33 97.4993
#> 34 64.7216
#> 35 69.1783
#> 36 15.4052
#> 37 4.0855
#> 38 36.2349
#> 39 54.7164
#> 40 5.2774
#> 41 25.3325
#> 42 62.5762
#> 43 99.8347
#> 44
#> 45
#> 46
#> 47 UCL
#> 48 61.1273
#> 49 54.7164
#> 50 21.3492
#> 51 68.4500
#> 52 7.6252
#> 53 71.4900
#> 54 76.0232
#> 55 5.2774
#> 56 66.8742
#> 57 72.7320
#> 58 92.9443
#> 59 66.8742
#> 60 94.3236
#> 61 46.3680
#> 62 42.7966
#> 63
#> 64
#> 65
#> 66 UCL
#> 67 84.8977
#> 68 22.7648
#> 69 6.9761
#> 70
#> 71
#> 72
#> 73 UCL
#> 74 100.0000
#> 75 0.0000
#> 76 0.0000
#> 77 0.0000
#> 78 100.0000
#> 79 0.0000
#> 80 0.0000
#> 81 0.0000
#> 82 100.0000
#> 83 0.0000
#> 84 0.0000
#> 85 0.0000
#> 86 100.0000
#> 87 0.0000
#> 88 0.0000
#> 89 0.0000
#> 90 100.0000
#> 91 0.0000
#> 92 0.0000
#> 93 0.0000
#> 94 100.0000
#> 95 0.0000
#> 96 0.0000
#> 97 0.0000
#> 98 100.0000
#> 99 0.0000
#> 100 0.0000
#> 101 0.0000
#> 102 0.0000
#> 103
#> 104
#> 105
#> 106 UCL
#> 107 99.8305
#> 108 100.0000
#> 109 100.0000
#> 110 99.6694
#> 111 75.3759
#> 112 100.0000
#> 113 5.6854
#> 114 99.8276
#> 115 5.7285
#> 116 0.0000
#> 117 77.8818
#> 118 17.6757
#> 119
#> 120
#> 121
#> 122 UCL
#> 123 13.7706
#> 124 57.7069
#> 125 30.3897
#> 126
#> 127
#> 128
#> 129 UCL
#> 130 53.8506
#> 131 86.7390
#> 132 40.9524
#> 133 72.5000
#> 134 42.2222
#> 135 0.0000
#> 136 0.0000
#> 137 0.0000
#> 138 10.0000
#> 139 0.0000
#> 140 25.0000
#> 141 92.1402
#> 142 90.0758
#> 143 23.5294
#> 144 100.0000
#> 145 0.0000
#> 146 0.0000
#> 147 0.0000
#> 148 23.1092
#> 149 0.0000
#> 150 5.7143
#> 151 0.0000
#> 152 8.0989
#> 153 46.4780
#> 154 43.8621
#> 155
#> 156
#> 157
#> 158 UCL
#> 159 68.9369
#> 160 54.4020
#> 161 10.6999
#> 162 2.5690
#> 163 15.0196
#> 164 2.3153
#> 165 0.0000
#> 166 4.7143
#> 167 46.7599
#> 168 0.8874
#> 169
#> 170
#> 171
#> 172 UCL
#> 173 68.8978
#> 174 79.8030
#> 175 28.2759
#> 176 27.2414
#> 177
#> 178
#> 179
#> 180 UCL
#> 181 7.1217
#> 182 5.3958
#> 183 3.4366
#> 184
#> 185
#> 186
#> 187 UCL
#> 188 6.6098
#> 189 5.9258
#> 190 1.0052If the preferred output is a report with combined charts and tables of results, the following piped operations can be performed:
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_html(
svy = testSVY, filename = file.path(tempdir(), "ramOPreport")
)which results in an HTML file saved in the specified output directory that looks something like this:
