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.
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.
The {squeacr} package is not yet available on
CRAN but can be installed from the
nutriverse R Universe as follows:
install.packages(
"squeacr",
repos = c('https://nutriverse.r-universe.dev', 'https://cloud.r-project.org')
)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}.
| State | Locality | Beginning Of Month | New Admissions | Male | Female | Cured | Death | Default | Non-Responder | Total Discharge | Rutf Consumed | Screening | Sites | Month | Year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gazera | El Qurashi | 16 | 16 | 8 | 8 | 23 | 0 | 3 | 0 | 26 | 80 | 49 | NA | Jan | 2016 |
| Gazera | El Qurashi | 56 | 24 | 11 | 13 | 0 | 0 | 0 | 0 | 0 | -46 | 298 | NA | Apr | 2016 |
| Gazera | El Qurashi | 80 | 41 | 16 | 25 | 22 | 0 | 2 | 0 | 24 | 16 | 225 | NA | May | 2016 |
| Gazera | El Qurashi | 81 | 43 | 21 | 22 | 29 | 0 | 0 | 0 | 29 | 22 | 215 | NA | Jun | 2016 |
| Gazera | El Qurashi | 93 | 51 | 31 | 30 | 36 | 2 | 0 | 0 | 38 | 14 | 0 | NA | Jul | 2016 |
| Gazera | El Qurashi | 103 | 59 | 34 | 25 | 3 | 0 | 0 | 0 | 3 | 12 | 289 | NA | Aug | 2016 |
| Gazera | El Qurashi | 163 | 69 | 34 | 35 | 8 | 0 | 12 | 2 | 22 | 8 | 0 | NA | Sep | 2016 |
| Gazera | El Qurashi | 104 | 108 | 56 | 40 | 6 | 0 | 47 | 0 | 53 | -40 | 0 | NA | Oct | 2016 |
| Gazera | El Qurashi | 275 | 123 | 61 | 62 | 111 | 0 | 81 | 2 | 194 | 32 | 0 | NA | Nov | 2016 |
| Gazera | El Qurashi | 204 | 81 | 39 | 40 | 52 | 0 | 8 | 2 | 62 | 52 | 293 | NA | Dec | 2016 |
| Gazera | El Kamlin | 8 | 8 | 3 | 5 | 0 | 0 | 0 | 0 | 0 | 4 | 8 | NA | Jan | 2016 |
| Gazera | El Kamlin | 119 | 19 | 11 | 8 | 2 | 0 | 2 | 1 | 5 | 16 | 7 | NA | Mar | 2016 |
| Gazera | El Kamlin | 133 | 8 | 5 | 3 | 18 | 0 | 2 | 1 | 21 | 18 | 182 | NA | Apr | 2016 |
| Gazera | El Kamlin | 120 | 22 | 15 | 7 | 8 | 0 | 0 | 1 | 9 | 6 | 552 | NA | May | 2016 |
| Gazera | El Kamlin | 134 | 9 | 5 | 4 | 15 | 0 | 13 | 0 | 28 | 15 | 285 | NA | Jun | 2016 |
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:
| Variable | Description |
|---|---|
| State | Name of state |
| Locality | Name of locality |
| Beginning of Month | Cases in programme at beginning of month |
| New Admissions | New cases admitted within the month |
| Male | New male cases admitted within the month |
| Female | New female cases admitted within the month |
| Cured | Number of cured cases within the month |
| Death | Number of cases who died within the month |
| Default | Number of cases who defaulted within the month |
| Non-Responder | Number of non-responder cases within the month |
| Total Discharge | Total number of discharges within the month |
| RUTF Consumed | Number of RUTF consumed |
| Screening | Screening |
| Sites | Sites |
| Month | Month |
| Year | Year |
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 × 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 Da… 2016 13313 0.921 0.0440 0.0174
#> 6 Central Da… 2017 18098 0.935 0.0421 0.00912
#> 7 Central Da… 2018 17600 0.939 0.0364 0.00955
#> 8 Central Da… 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>
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
#> [17] 14 13 21 28 107 42 42 77 77 77 31 18 18 11 35 35
#> [33] 14 14 14 14 28 11 61 73 102 71 71 112 55 71 80 22
#> [49] 22 63 62 44 30 42 35 35 28 84 28 14 42 34 47 42
#> [65] 45 43 23 42 105 120 105 56 104 42 79 90 77 28 14 14
#> [81] 77 28 14 54 103 78 79 70 70 98 78 63 58 125 42 49
#> [97] 44 35 89 86 60 39 41 50 47 46 48 51 50 44 44 46
#> [113] 39 50 54 140 58 84 53 56 21 54 21 28 49 18 56 28
#> [129] 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
#> [161] 21 97 35 66 35 28 126 84 70 140 22 63 42 70 94 63
#> [177] 63 98 70 77 77 60 63 63 84 56 49 91 35 42 42 49
#> [193] 70 57 29 64 41 21 93 23 31 28 30 14 21 55 65 28
#> [209] 21 21 88 14 22 21 21 21 35 63 42 28 84 48 14 18
#> [225] 14 14 30 35 81 76 42 28 28 28 56 28 56 42 98 58
#> [241] 35 28 39 34 33 28 49 28 64 28 29 33 80 77 60 42
#> [257] 49 56 55 42 91 98 55 92 98 112 63 63 21 63 63 58
#> [273] 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
#> [305] 14 9 112 56 112 46 28 56 14 70 70 35 28 28 28 48
#> [321] 123 35 14 14 19 14 56 32 35 131 21 47 53 64 64 39
#> [337] NA NA 37 32 41 6 42 30 26 44 28 19 15 14 50 35
#> [353] 14 31 28 21 7 26 14 14 28 7 7 19 31 27 20 33
#> [369] 62 28 15 13 28 16 19 30 7 14 36 15 7 43 20 100
#> [385] 64 52 93 34 30 57 NA 56 81 52 95 63 49 54 37 70
#> [401] 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
The {squeacr} provides functions to calculate programme coverage.
These functions implement the single coverage estimator approach[1].
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] 6Note 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] 0.2439024If you use {squeacr} in your work, please cite using the suggested
citation provided by a call to the citation function as follows:
citation("squeacr")
#> To cite sleacr 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.
#> <https://www.fantaproject.org/sites/default/files/resources/SQUEAC-SLEAC-Technical-Reference-Oct2012_0.pdf>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Book{,
#> title = {Semi-Quantitative Evaluation of Access and Coverage ({SQUEAC})/Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage ({SLEAC}) Technical Reference},
#> author = {{Mark Myatt} and {Ernest Guevarra} and {Lionella Fieschi} and {Allison Norris} and {Saul Guerrero} and {Lilly Schofield} and {Daniel Jones} and {Ephrem Emru} and {Kate Sadler}},
#> year = {2012},
#> publisher = {FHI 360/FANTA},
#> address = {Washington, DC},
#> url = {https://www.fantaproject.org/sites/default/files/resources/SQUEAC-SLEAC-Technical-Reference-Oct2012_0.pdf},
#> }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.
- Safari Balegamire, Katja Siling, Jose Luis Alvarez Moran, Ernest Guevarra, Sophie Woodhead, Alison Norris, Lionella Fieschi, Paul Binns, and Mark Myatt (2015). A single coverage estimator for use in SQUEAC, SLEAC, and other CMAM coverage assessments. Field Exchange 49, March 2015. p81. <www.ennonline.net/fex/49/singlecoverage>