class: center, middle, inverse, title-slide # Assessing coverage of Community-based Management of Acute Malnutrition --- class: inverse, center, middle ## Ernest Guevarra <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 512 512"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> ernest@guevarra.io <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 496 512"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg> ernest.guevarra.io <svg style="height:0.8em;top:.04em;position:relative;fill:white;" viewBox="0 0 448 512"><path d="M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z"/></svg> ernestguevarra 11 March 2020 --- # Outline * What is coverage? * Why is coverage important? * Measuring coverage * Coverage challenges --- class: inverse, center, middle # What is coverage? --- class: center, middle # Think of words or phrases that come to mind when you read or hear the term **coverage** --- class: center, middle `$$\Large \text{coverage} ~ = ~ \frac{\text{No. in the programme}}{\text{No. who should be in programme}}$$` ??? Here is a mathematical representation of the concept of coverage. The denominator implies that there is a criteria or a standard for eligibility to the programme. --- class: center, middle, inverse # Coverage and CMAM ??? Now we look at coverage specific to CMAM --- # Coverage estimators - old `\(\Large \text{Point coverage} ~ = ~ \frac{C_{in}}{C_{in} ~ + ~ C_{out}}\)` `\(\Large \text{Period coverage} ~ = ~ \frac{C_{in} ~ + ~ R_{in}}{C_{in} ~ + ~ C_{out} ~ + ~ R_{in}}\)` where: `\(C_{in} ~ = ~ \text{cases in the programme}\)` `\(C_{out} ~ = ~ \text{cases not in the programme}\)` `\(R_{in} ~ = ~ \text{recovering cases in the programme}\)` --- # Characteristics of estimators * **Point coverage** assesses the programme's case-finding capabilities * **Period coverage** assesses the programme's case-finding capabilities and its ability to retain a case from admission to cure. Always equal to or higher than point coverage (never lower) * Period coverage more closely approximates treatment coverage or effective coverage --- # Bias and limitations of estimators * Both estimators are unable to detect **cases who have died** and **recovering cases not in the programme** * **Point coverage** can underestimate true coverage in settings where **case prevalence is low** and **majority of cases are in recovery** * **Period coverage** can overestimate true coverage in settings where **length of stay** in programme is **prolonged** --- # Which estimators to use? * **Early days of CMAM:** reported both point and period coverage estimators * **Recent past:** recommendation has been made to report *only one* of the coverage estimators with the choice of estimator to report to be based on programme features such as length of stay and MUAC at admission among others --- # Coverage estimators - updated `\(\Large \text{Case-finding effectiveness} ~ = ~ \frac{C_{in}}{C_{in} ~ + ~ C_{out}}\)` `\(\Large \text{Treatment coverage} ~ = ~ \frac{C_{in} ~ + ~ R_{in}}{C_{in} ~ + ~ C_{out} ~ + ~ R_{in} ~ + ~ R_{out}}\)` where: `\(C_{in} ~ = ~ \text{cases in the programme}\)` `\(C_{out} ~ = ~ \text{cases not in the programme}\)` `\(R_{in} ~ = ~ \text{recovering cases in the programme}\)` `\(R_{out} ~ = ~ \text{recovering cases not in the programme}\)` --- # What estimators to use? * We now recommend that *both* estimators be reported as they describe different aspects of the programme relevant to coverage. * **Case-finding effectiveness** gives an idea of how good the programme is in case-finding – a key factor in coverage * **Treatment coverage** provides an approximation of true coverage or effective coverage --- class: inverse, center, middle # Why coverage? --- # Effective CMAM meets needs * **Efficacy** of CMAM - cure rate in ideal and controlled settings - is near 100% * **Effectiveness** of CMAM - cure rate in programme conditions - still room for improvement * Effectiveness depends on: 1. Thorough case-finding and early treatment-seeking; 2. High-level of compliance; and, 3. Good retention from admission to cure. --- # High coverage CMAM meets needs * **Coverage** of CMAM – the proportion of all children eligible to receive CMAM who actually receive it – contributes to effectiveness as well * Coverage directly depends on: 1. Thorough case-finding and early treatment-seeking; and, 2. Good retention from admission to cure. * It also indirectly depends on **compliance**. --- background-color: #FFFFFF .center[] **Meeting needs require both high effectiveness and high coverage** * Good coverage supports good effectiveness * Good effectiveness supports good coverage * Maximising coverage maximises effectiveness and met need --- class: inverse, center, middle `$$\LARGE \text{Met need} ~ = ~ \text{effectiveness} ~ \times ~ \text{coverage}$$` --- background-image: url(figures/coverage2.png) background-size: contain --- background-image: url(figures/coverage3.png) background-size: contain --- class: inverse, center, middle # How to measure coverage? --- # Indirect estimation of coverage `$$\text{coverage} ~ = ~ \frac{\text{Total cases admitted in programme}}{\text{Estimated number of cases}}$$` * **Numerator** is based on programme data * **Denominator** is a caseload estimation based on total population in programme area and known prevalence estimate and known incidence * Issues mainly stem from estimation of denominator --- # Direct estimation of coverage * Coverage estimation through a survey. * Old approach: include coverage indicator in nutrition surveys that assess prevalence of undernutrition (SMART surveys) * Issues with old approach: sample size * Current approaches: surveys specifically assessing coverage --- # History of methods development * Coverage and its assessment was an integral component of the development process of CMAM (then called CTC) * **Centric Systematic Area Sampling (CSAS)** was the first coverage assessment method developed * However, **CSAS** was not as commonly used as it was deemed too costly and hard to implement --- # History of methods development * **SQUEAC** and **SLEAC** were then developed as quick and easy (hence less costly) methods to assess coverage * **SQUEAC** and to some extent **SLEAC** now widely used and considered the standard, off-the-shelf methods * With CMAM programmes getting scaled up to national scope, a wide area/large scale method such as **S3M** has been recently developed --- # CSAS: Design * **CSAS** uses a two-stage sampling design * The first stage is a **systematic spatial sample** of the entire service area to select the communities to survey. * The second stage is an **active and adaptive case-finding method** that finds all or nearly all cases in the communities being surveyed. --- # CSAS: Results * Overall coverage estimate * Local coverage estimates which can be represented as a coverage map * Ranked list of barriers --- background-color: #FFFFFF # CSAS: Results .pull-left[ Coverage map produced by CSAS surveys ] .pull-right[ Ranked list of barriers from a CSAS survey ] --- # SQUEAC: Design * **Semi-quantitative Evaluation of Access and Coverage** or **SQUEAC** is more an investigation than a survey * **Stage 1:** Semi-quantitative investigation into factors affecting coverage using the **SQUEAC** toolkit, which is a set of simple and rapid tools and methods for collecting and analysing data related to coverage. * **Stage 2:** Confirm areas of high and low coverage and other hypotheses relating to coverage identified in stage 1 through small studies, small surveys, and small-area surveys. * **Stage 3:** Estimate overall coverage using Bayesian techniques. A likelihood survey is conducted as part of this stage. This two-stage sampling design is the same as with all other coverage survey methods. * This stage is optional. Should be done if the reporting of an overall coverage estimate is a key information requirement in addition to the rich information on barriers and boosters to coverage already gained from stages 1 and 2. --- background-color: #FFFFFF # SQUEAC: Results .pull-left[ * Concept map of barriers and boosters to coverage * Coverage map using small area surveys through a “risk mapping” approach * Estimation of coverage proportion using Bayesian techniques ] .pull-right[] --- # SLEAC * **Simplified Lot Quality Assurance Evaluation of Access and Coverage (SLEAC)** is a rapid low-resource survey method that classifies coverage at the service delivery unit (SDU) level. * Identifies the category of coverage (e.g. **“low”**, **“moderate”** or **“high”**) achieved by the service delivery unit being assessed. * Relatively small sample sizes (e.g. `\(n ~ \geq ~ 40\)`) are required in order to make an accurate and reliable classification. * Can also estimate coverage over several service delivery units and is suited to wide-area use. * Coverage is still classified for the individual service delivery units, then, data from individual service delivery units are combined and overall coverage for the wide area is estimated. --- # SLEAC: Design * First stage systematic spatial sample similar to that used in CSAS. Only small sample sizes ( `\(n ~ \geq ~ 40\)` ) are required for each service delivery unit in which coverage is being classified. * Second stage sample is an active and adaptive case-finding method as with the other coverage survey methods. --- # SLEAC: Results * Indicator classifications * Can be used over wide areas to provide local indicators classifications with a map and a wide area estimate * Ranked list of barriers --- # SLEAC: Results .pull-left[ Sierra Leone National Coverage Map produced by SLEAC  ] .pull-right[ Northern Nigeria coverage map produced by SLEAC  ] --- # S3M * **Simple spatial sampling method (S3M)** is a development of **CSAS** and uses a similar sampling design. * The main difference is **S3M** uses a hexagonal grid (as compared to a square grid for **CSAS**). * Hexagonal grids address the issue of unevenness of spatial sampling created by square grids particularly at scale --- # S3M: Results .pull-left[ Coverage map of Wolayita Zone, Ethiopia produced by S3M  ] .pull-right[ Coverage map of Wollo Zone, Ethiopia produced by S3M  ] --- # Coverage challenges * Global CMAM coverage average is about 30% * Coverage assessment fatigue (?) * Same barriers and boosters are being identified * Same low levels of coverage being achieved * Better to act on what the current coverage assessments are saying about coverage to try to improve things before more coverage assessments are done * Which barriers to focus on? Is there a **"magic bullet"**?