check_coverage_homogeneity

In the recent past, measurement of 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. SLEAC, which stands for Simplified Lot Quality
Assurance Sampling 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. SLEAC is designed to complement the Semi-quantitative Evaluation
of Access and Coverage (SQUEAC) method. This package provides functions for
use in conducting a SLEAC assessment.

Ernest Guevarra

sleacr

Simplified Lot Quality Assurance Sampling Evaluation of Access
and Coverage (SLEAC) Tools

Mark Myatt

check_coverage_homogeneity function

<dl><dt>cov_df</dt>
<dd>A <code><a href="/link/data.frame()?package=sleacr&version=0.1.3" data-mini-rdoc="sleacr::data.frame()">data.frame()</a></code> of stratified coverage survey data to get
overall coverage estimates of. <code>cov_df</code> should have a variable named
<code>cases_in</code> for number of SAM or MAM cases in the programme found during the
survey, <code>cases_out</code> for number SAM or MAM cases not in the programme found
during the survey, and <code>rec_in</code> for children recovering from SAM or MAM who
are in the programme found during the survey. A final required variable
should be one that contains identifying geographical information
corresponding to the location from which each row of the survey data was
collected from.</dd>
<dt>k</dt>
<dd>Correction factor. Ratio of the mean length of an untreated episode
to the mean length of a CMAM treatment episode</dd>
<dt>p</dt>
<dd>Minimum p-value to test statistic. Default is 0.05.</dd></dl>

Arguments

Check coverage distribution — check_coverage_homogeneity

<dl>

<dt>cov_df</dt>
<dd>A <code><a href='https://rdrr.io/r/base/data.frame.html'>data.frame()</a></code> of stratified coverage survey data to get
overall coverage estimates of. <code>cov_df</code> should have a variable named
<code>cases_in</code> for number of SAM or MAM cases in the programme found during the
survey, <code>cases_out</code> for number SAM or MAM cases not in the programme found
during the survey, and <code>rec_in</code> for children recovering from SAM or MAM who
are in the programme found during the survey. A final required variable
should be one that contains identifying geographical information
corresponding to the location from which each row of the survey data was
collected from.</dd>


<dt>k</dt>
<dd>Correction factor. Ratio of the mean length of an untreated episode
to the mean length of a CMAM treatment episode</dd>


<dt>p</dt>
<dd>Minimum p-value to test statistic. Default is 0.05.</dd>

</dl>

check_coverage_homogeneity: Check coverage distribution

Description

Usage

Value

Arguments

Examples