Package 'squeacr'

Calculate CMAM length of stay and median length of stay for a cohort of CMAM discharges

Description

Calculate CMAM length of stay and median length of stay for a cohort of CMAM discharges

Usage

calculate_los(admission_date, discharge_date)

calculate_los_median(admission_date, discharge_date, group = NULL)

Arguments

admission_date	Date of admission in YYYY-MM-DD format. If child is a kwashiorkor case, date of lowest weight (when oedema has subsided). Can be a single date value or a vector of date values.
discharge_date	Date of discharge in YYYY-MM-DD format. Can be a single date value or a vector of date values.
group	A character value/s with the same length as admission_date and discharge_data to use as grouping variable within which median length-of-stay is to be calculated. Default is NULL for no grouping.

Value

Numeric value or vector of numeric values for length-of-stay in days for calculate_los(). A numeric value for median length-of-stay in days for calculate_los_median(). If group is not NULL, a vector of numeric values for median length-of-stay in days with length equal to the number of groups.

Author(s)

Ernest Guevarra

Examples

calculate_los(admission_date = "2010-03-15",
              discharge_date = "2010-06-14")

calculate_los(admission_date = c("2010-03-15", "2010-03-16"),
              discharge_date = c("2010-06-14", "2010-06-20"))

calculate_los_median(
  otp_beneficiaries$admDate,
  otp_beneficiaries$disDate,
  group = otp_beneficiaries$locality
)

---

Calculate median MUAC-at_admissions

Description

Calculate median MUAC-at_admissions

Usage

calculate_muac_median(muac, index = NULL, na_values = NULL)

Arguments

muac	A numeric vector of mid-upper arm circumference measurements either in centimetres or millimetres.
index	A list of one or more factors each of the same length as muac which defines the grouping to be used on muac. Default is NULL (no grouping applied).
na_values	A vector of values in muac that are to be considered as NA values. Default to NULL which will use NA.

Value

A numeric value (if index = NULL) or named vector of values for median mid-upper arm circumference with names from each level of index.

Examples

calculate_muac_median(otp_beneficiaries$muac)
calculate_muac_median(otp_beneficiaries$muac, otp_beneficiaries$locality)

---

Calculate CMAM performance indicators

Description

Calculate CMAM performance indicators

Usage

calculate_performance(df, vars = NULL, add = TRUE)

calculate_cured(cured, exit)

calculate_dead(dead, exit)

calculate_default(defaulter, exit)

calculate_no_response(nr, exit)

Arguments

df	A data.frame containing programme monitoring data on number of cured, deaths, defaulters and non-response. The required data.frame holds rows of data corresponding to total counts for each performance indicator (i.e., cured, dead, defaulter and non-responder) rather than rows of individual cases.
vars	A vector of variable names specifying cured, dead, defaulter and non-responder (in this specific order) values in df. If NULL (default), typical names used for these variables will be searched and used accordingly. If search doesn't yield matching variable names, the first 4 columns of the data.frame will be used.
add	Logical. Should result be added to df. Default is TRUE.
cured	Numeric value for total number of cured cases
exit	Numeric value for total number of programme exits
dead	Numeric value for total number of cases who died
defaulter	Numeric value for total number of cases who defaulted
nr	Numeric value for total number of cases who did not respond to

Value

A tibble of performance indicator results (for calculate_performance()) or a numeric value of specified CMAM performance indicator (for calculate_cured(), calculate_default(), calculate_dead()), and calculate_no_response().

Author(s)

Ernest Guevarra

Examples

calculate_performance(df = monitoring)
calculate_cured(cured = 10, exit = 50)
calculate_dead(dead = 10, exit = 50)
calculate_default(defaulter = 10, exit = 50)
calculate_no_response(nr = 10, exit = 50)

---

CMAM coverage estimators

Description

CMAM coverage estimators

Usage

calculate_rout(cin, cout, rin, k = 3)

calculate_cf(cin, cout)

calculate_tc(cin, cout, rin, k = 3)

Arguments

cin	Cases in CMAM programme
cout	Cases not in CMAM programme
rin	Recovering cases in programme
k	Correction factor. Ratio of the mean length of an untreated episode to the mean length of a CMAM treatment episode

Value

Numeric value of required coverage estiamtor

Author(s)

Ernest Guevarra based on technical notes and equations by Mark Myatt

References

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>

Examples

calculate_rout(cin = 5, cout = 25, rin = 5, k = 3)
calculate_cf(cin = 5, cout = 20)
calculate_tc(cin = 5, cout = 20, rin = 5, k = 3)

---

Find possible variable names from a data.frame given a set of search names

Description

Find possible variable names from a data.frame given a set of search names

Usage

find_var_names(df, vars, all = FALSE)

Arguments

df	A data.frame to search variable names from
vars	A vector of terms to search for
all	Logical. Should all results of search be returned? If FALSE (default), only first value is returned.

Value

A character vector of variable name/s in df

Author(s)

Ernest Guevarra

Examples

find_var_names(df = otp_beneficiaries, vars = "MUAC")

---

Routine CMAM monitoring data from Sudan

Description

Routine CMAM monitoring data from Sudan

Usage

monitoring

Format

A tibble with 8234 rows and 16 columns

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

Source

Federal Ministry of Health of Sudan

Examples

monitoring

---

MUAC at admission

Description

MUAC at admission

Usage

muac_admission

Format

A named list with 12 tibbles:

| Telkuk | MUAC at admission data for Telkuk locality | | Halfa | MUAC at admission data for Halfa locality | | Kassala | MUAC at admission data for Kassala locality | | Naher Atbara | MUAC at admission data for Naher Atbara locality | | El Fasher | MUAC at admission data for El Fasher locality | | Tawila | MUAC at admission data for Tawila locality | | Kutumu | MUAC at admission data for Kutumu locality | | Kalamendo | MUAC at admission data for Kalamendo locality | | Medani Alkupra | MUAC at admission data for Medani Alkupra locality | | South Gazira | MUAC at admission data for South Gazira locality | | Sharg Algazira | MUAC at admission data for Sharg Algazira locality | | Al Kamlin | MUAC at admission data for Al Kamlin locality |

Source

A CMAM programme evaluation in Sudan

Examples

muac_admission

---

MUAC at admission in tidy format

Description

MUAC at admission in tidy format

Usage

muac_admission_tidy

Format

A tibble with 506 rows and 3 columns

Variable	Description
muac	Mid-upper arm circumference in centimetres
district	Name of district
count	Number of cases with specific MUAC

Source

A SQUEAC survey in Lokori, Kenya

Examples

muac_admission

---

Outpatient Therapeutic Care Programme (OTP) beneficiaries data

Description

Outpatient Therapeutic Care Programme (OTP) beneficiaries data

Usage

otp_beneficiaries

Format

A tibble with 405 rows and 13 columns:

Variable	Description
index	Unique identifier
state	Name of state
locality	Name of locality
health_facility	Name of health facility
age	Age of child
muac	Mid-upper arm circumference (cms) at admission
wt	Weight (kgs) at admission
ht	Height (cms) at admission
admDate	Date of admission
disDate	Date of discharge
diswt	Weight (kgs) at discharge
attended	Number of OTP sessions attended
exitType	Type of exit (cured, dead, default or non-responder)

Source

Data collected from beneficiary cards from Kassala, North Darfur, and Algazira State, Sudan

Examples

otp_beneficiaries

---

Seasonal calendar data for Sudan

Description

Seasonal calendar data for Sudan

Usage

seasonal_calendar

Format

A tibble with 28 rows and 4 columns

Variables	Description
event	Name of seasonal calendar event or activity
start	Starting date of event/activity
end	Starting date of event/activity
group	Classification/group of activity or event

Source

https://fews.net/east-africa/sudan/seasonal-calendar/december-2013

Examples

seasonal_calendar

---

Apply median of 3 and average of 3 smoothing on a time series

Description

Apply median of 3 and average of 3 smoothing on a time series

Usage

smooth_m3a3(x)

Arguments

x	A vector of numerical information to be smoothed

Value

A vector of smoothed data

Author(s)

Ernest Guevarra

Examples

x <- aggregate(cbind(`New Admissions`, Default) ~ Month + Year,
               data = monitoring, FUN = sum)
smooth_m3a3(x = x$Default)

---

Time-to-travel to health facilities for beneficiaries and volunteers

Description

Time-to-travel to health facilities for beneficiaries and volunteers

Usage

time_to_travel

Format

A tibble with 165 rows and 9 columns:

Variable	Description
State	Name of state
Locality	Name of locality
Health Facility	Name of health facility
Category	Category of beneficiary or volunteer
30 or less	Travel time of 30 minutes or less
31 to 60	Travel time of 31 minutes to 60 minutes
61 to 90	Travel time of 61 minutes to 90 minutes
91 to 120	Travel time of 91 minutes to 120 minutes
more than 120	Travel time of more than 120 minutes

Source

Data collected from beneficiary cards from Kassala State, Sudan

Examples

time_to_travel

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