squeacr: Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) Tools

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: stable R-CMD-check test-coverage Codecov test coverage CodeFactor DOI

In the recent past, measurement of community-based management of acute malnutrition (CMAM) programme coverage has been mainly through two-stage cluster sampled surveys either as part of a nutrition assessment or through a specific coverage survey known as Centric Systematic Area Sampling (CSAS). However, such methods are resource intensive and often only used for final programme evaluation meaning results arrive too late for programme adaptation. SQUEAC, which stands for Semi-Quantitative Evaluation of Access and Coverage, is a low resource method designed specifically to address this limitation and is used regularly for monitoring, planning and importantly, timely improvement to programme quality, both for agency and Ministry of Health (MoH) led programmes. This package provides functions for use in conducting a SQUEAC investigation.

What does the package do?

The {squeacr} package provides functions that facilitate the processing, analysis and reporting of various components of a SQUEAC investigation. The current version of the {squeacr} package currently provides the following:

• Functions to calculate CMAM programme performance metrics;

• Functions to calculate CMAM programme length of stay metrics; and,

• Functions to calculate CMAM coverage estimates.

Installation

install.packages("squeacr")

if (!require("pak")) install.packages("pak")
pak::pak("nutriverse/squeacr")

install.packages(
 "squeacr", 
 repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org')
)

Usage

Calculating CMAM programme performance metrics

Cure rate, defaulter rate, death rate, and non-response rate are the programme indicators used to monitor performance of CMAM. These indicators are calculated from routine programme monitoring data, an example of which is the monitoring dataset included in {squeacr}.

CMAM programme monitoring data for Sudan (showing first 15 rows)

The monitoring dataset is from the National CMAM programme in Sudan showing monthly programme statistics per locality. The dataset has the following fields:

We can calculate the different programme performance indicators using {squeacr}. For this example, we’ll calculate the indicators for each state per year.

library(squeacr)
library(dplyr)
monitoring |>
 group_by(State, Year) |>
 summarise(
 total_discharge = sum(`Total Discharge`, na.rm = TRUE),
 cure_rate = calculate_cured(sum(Cured, na.rm = TRUE), total_discharge),
 default_rate = calculate_default(sum(Default, na.rm = TRUE), total_discharge),
 death_rate = calculate_dead(sum(Death, na.rm = TRUE), total_discharge),
 non_response_rate = calculate_no_response(sum(`Non-Responder`, na.rm = TRUE), total_discharge),
 .groups = "drop"
 )

which results in the following:

#> # A tibble: 72 ×ばつ 7
#> State Year total_discharge cure_rate default_rate death_rate
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Blue Nile 2016 9693 0.889 0.0906 0.0151 
#> 2 Blue Nile 2017 10286 0.948 0.0399 0.00972
#> 3 Blue Nile 2018 8807 0.947 0.0404 0.00863
#> 4 Blue Nile 2019 9882 0.953 0.0366 0.00708
#> 5 Central Darfur 2016 13313 0.921 0.0440 0.0174 
#> 6 Central Darfur 2017 18098 0.935 0.0421 0.00912
#> 7 Central Darfur 2018 17600 0.939 0.0364 0.00955
#> 8 Central Darfur 2019 18573 0.952 0.0260 0.00549
#> 9 East Darfur 2016 9895 0.929 0.0550 0.0104 
#> 10 East Darfur 2017 12611 0.956 0.0327 0.00690
#> # i 62 more rows
#> # i 1 more variable: non_response_rate <dbl>

CMAM programme length-of-stay

The length-of-stay in a CMAM programme is an important metric that can provide insight into several aspects of the program’s performance and effectiveness. It is calculated from those discharged cured from outpatient care by counting the number of days between the admission date and the discharge date.

The otp_beneficiaries dataset in the package is an example of a patient record data from which length-of-stay can be calculated using the calculate_los() function:

calculate_los(otp_beneficiaries$admDate, otp_beneficiaries$disDate)

which gives the following results:

#> Warning in calculate_los(otp_beneficiaries$admDate, otp_beneficiaries$disDate):
#> Some admission date/s are not in YYYY-MM-DD format or are not available.
#> Returning NA.
#> Warning in calculate_los(otp_beneficiaries$admDate, otp_beneficiaries$disDate):
#> Some discharge dates are earlier than admisison dates. Returning NA.
#> [1] 56 42 36 49 42 51 19 75 84 49 90 70 91 20 42 50 14 13
#> [19] 21 28 107 42 42 77 77 77 31 18 18 11 35 35 14 14 14 14
#> [37] 28 11 61 73 102 71 71 112 55 71 80 22 22 63 62 44 30 42
#> [55] 35 35 28 84 28 14 42 34 47 42 45 43 23 42 105 120 105 56
#> [73] 104 42 79 90 77 28 14 14 77 28 14 54 103 78 79 70 70 98
#> [91] 78 63 58 125 42 49 44 35 89 86 60 39 41 50 47 46 48 51
#> [109] 50 44 44 46 39 50 54 140 58 84 53 56 21 54 21 28 49 18
#> [127] 56 28 28 21 54 57 29 59 50 39 91 136 127 63 93 155 35 105
#> [145] 42 28 28 35 35 70 35 82 14 17 28 168 147 112 42 35 21 97
#> [163] 35 66 35 28 126 84 70 140 22 63 42 70 94 63 63 98 70 77
#> [181] 77 60 63 63 84 56 49 91 35 42 42 49 70 57 29 64 41 21
#> [199] 93 23 31 28 30 14 21 55 65 28 21 21 88 14 22 21 21 21
#> [217] 35 63 42 28 84 48 14 18 14 14 30 35 81 76 42 28 28 28
#> [235] 56 28 56 42 98 58 35 28 39 34 33 28 49 28 64 28 29 33
#> [253] 80 77 60 42 49 56 55 42 91 98 55 92 98 112 63 63 21 63
#> [271] 63 58 56 63 126 91 119 28 72 111 42 63 91 98 91 84 15 45
#> [289] NA 29 42 49 42 49 49 14 28 44 35 49 42 84 30 14 14 9
#> [307] 112 56 112 46 28 56 14 70 70 35 28 28 28 48 123 35 14 14
#> [325] 19 14 56 32 35 131 21 47 53 64 64 39 NA NA 37 32 41 6
#> [343] 42 30 26 44 28 19 15 14 50 35 14 31 28 21 7 26 14 14
#> [361] 28 7 7 19 31 27 20 33 62 28 15 13 28 16 19 30 7 14
#> [379] 36 15 7 43 20 100 64 52 93 34 30 57 NA 56 81 52 95 63
#> [397] 49 54 37 70 84 28 28 66 56

The median length-of-stay in a CMAM programme can be calculated as follows:

calculate_los_median(otp_beneficiaries$admDate, otp_beneficiaries$disDate)

which gives the following results:

#> Warning in calculate_los(admission_date = admission_date, discharge_date =
#> discharge_date): Some admission date/s are not in YYYY-MM-DD format or are not
#> available. Returning NA.
#> Warning in calculate_los(admission_date = admission_date, discharge_date =
#> discharge_date): Some discharge dates are earlier than admisison dates.
#> Returning NA.
#> [1] 43

CMAM programme coverage

The {squeacr} provides functions to calculate programme coverage. These functions implement the single coverage estimator approach¹ . In this approach, treatment coverage is calculated in such a way that estimates the number of severe acute malnutrition (SAM) cases that have not been enrolled in the programme but have been recovering without treatment (r_out).

For example, if a coverage survey yielded 5 SAM cases in the programme, 25 cases not in the programme, and 5 recovering cases in the programme, r_out can be calculated as follows:

calculate_rout(cin = 5, cout = 25, rin = 5)
#> [1] 6

Note here that the calculate_rout() function has another argument named k which is a correction factor representing the ratio of the mean length of an untreated episode to the mean length of a CMAM treatment episode. This, by default, is set to k = 3 in the function. However, this should be adjusted based on programme data to estimate the mean length of a SAM treatment episode.

This calculation for r_out is used within calculate_tc() to estimate treatment coverage:

calculate_tc(cin = 5, cout = 25, rin = 5)
#> [1] 0.2439024

Citation

If you use the {squeacr} package in your work, please cite both the {squeacr} package and the authors and developers of the SQUEAC and SLEAC method.

A suggested citation for both is provided by a call to the citation() function as follows:

citation("squeacr")
#> To cite squeacr in publications use:
#> 
#> Ernest Guevarra, Mark Myatt (2026). _squeacr: Semi-Quantitative
#> Evaluation of Access and Coverage (SQUEAC) Tools_.
#> doi:10.5281/zenodo.7509665 <https://doi.org/10.5281/zenodo.7509665>,
#> R package version 0.1.1, <https://nutriverse.io/squeacr/>.
#> 
#> To cite the SQUEAC and SLEAC Technical Reference in publications use:
#> 
#> Mark Myatt, Ernest Guevarra, Lionella Fieschi, Allison Norris, Saul
#> Guerrero, Lilly Schofield, Daniel Jones, Ephrem Emru, Kate Sadler
#> (2012). _Semi-Quantitative Evaluation of Access and Coverage
#> (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of
#> Access and Coverage (SLEAC) Technical Reference_. FHI 360/FANTA,
#> Washington, DC.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

Community guidelines

Feedback, bug reports, and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

This is part of the nutriverse project under the Oxford iHealth initiative of the MSc in International Health and Tropical Medicine, Nuffield Department of Medicine, University of Oxford