index.json

[{"authors":["ernestguevarra"],"categories":null,"content":"I have more than 15 years of professional experience in community and public health in humanitarian crises and developing country-settings, including as a community health worker, medical doctor, and recently as specialised expert and head of metrics, analytics and technology for Valid International. I have expertise in spatial epidemiology, geo-statistics, and statistical programming using R. In my recent role as Head of Measures for Valid International, I led the design, development and implementation of innovative surveys and assessments on health and nutrition and bespoke analytical approaches that leverage the advantages of Bayesian statistics and resampling techniques.\nI am a founding member of Katilingban, a collective of public health and nutrition experts and practitioners. I am currently part of the Oxford COVID-19 Modelling Consortium (CoMo Consortium) Philippines team or CoMo-PH along with two other public health practitioners from the Philippines. We aim to test and compare the CoMo Consortium model with existing local models that have previously been developed.\n","date":1737738000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1737738000,"objectID":"676aadfcf5944f17fb74f55e6ed17803","permalink":"https://katilingban.io/author/ernest-guevarra/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/ernest-guevarra/","section":"authors","summary":"I have more than 15 years of professional experience in community and public health in humanitarian crises and developing country-settings, including as a community health worker, medical doctor, and recently as specialised expert and head of metrics, analytics and technology for Valid International.","tags":null,"title":"Ernest Guevarra","type":"authors"},{"authors":["admin"],"categories":null,"content":"We are a collective of public health and nutrition experts and practitioners.\n","date":1554595200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1554595200,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://katilingban.io/author/katilingban/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/katilingban/","section":"authors","summary":"We are a collective of public health and nutrition experts and practitioners.","tags":null,"title":"Katilingban","type":"authors"},{"authors":["agoza"],"categories":null,"content":"Aziz has strong expertise in nutrition issues management (planning, implementation, monitoring and evaluation) and a proven experience in data management, database design and mHealth (ODK, Dimagi, Smartphone). Aziz has managed nutrition survey programmes (KAP, LQAS, SQUEAC, SMART) with strong field experience through the whole Sahel area (Senegal, Mauritania, Mali, Burkina-Faso and Niger). He has advanced knowledge in epidemiology and biostatistics and use of statistical software packages such as SPSS, Sphinx, EpiData, and ENA for SMART. Aziz has expertise in adult training and community mobilisation.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"2180b7a173ccd11589b0fbfbc93e1654","permalink":"https://katilingban.io/author/abdoul-aziz-goza/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/abdoul-aziz-goza/","section":"authors","summary":"Aziz has strong expertise in nutrition issues management (planning, implementation, monitoring and evaluation) and a proven experience in data management, database design and mHealth (ODK, Dimagi, Smartphone). Aziz has managed nutrition survey programmes (KAP, LQAS, SQUEAC, SMART) with strong field experience through the whole Sahel area (Senegal, Mauritania, Mali, Burkina-Faso and Niger).","tags":null,"title":"Abdoul-Aziz Goza","type":"authors"},{"authors":["emandalazi"],"categories":null,"content":"I am a public health professional with more than 12 years of professional experience in humanitarian crises in Sub-Saharan African and Asian regions.\nI am an expert in qualitative study approaches such as the Focused Ethnographic Studies (FES). I have contributed to the design, implementation and evaluation of the community mobilisation/outreach component of Community-based Management of Acute Malnutrition (CMAM) programmes using interactive and participatory approaches to social research in both emergency and development interventions. This involves conducting rapid socio-cultural context assessments and analysis of local socio-political structures, health attitudes and health seeking behaviours, and barriers to access to develop context-specific and culturally appropriate community mobilisation/SBCC strategies.\nI am also experienced in assessing, designing, implementing, managing, monitoring and evaluation of health and nutrition programmes using survey tools such as the Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) and Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC).\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"e1925ff2e27299d8a93664ec9c1eb4cd","permalink":"https://katilingban.io/author/emmanuel-mandalazi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/emmanuel-mandalazi/","section":"authors","summary":"I am a public health professional with more than 12 years of professional experience in humanitarian crises in Sub-Saharan African and Asian regions.\nI am an expert in qualitative study approaches such as the Focused Ethnographic Studies (FES).","tags":null,"title":"Emmanuel Mandalazi","type":"authors"},{"authors":["lfieschi"],"categories":null,"content":"Technical support and evaluation: nutrition programmes for the treatment of severe and moderate acute malnutrition. Technical assistance was provided on all areas related to the design and implementation of CTC programmes. This included training programmes for partner NGOs and local ministry of health medical and logistics staff; establishment of outpatient clinics in local health centres; advising on logistics of community outreach programmes; advising on integration of systems with local, regional and national ministry of health programme; support and evaluation of programme systems including community outreach, case-finding, programme admission, triage and referral, discharge, case follow up and patient recording systems; establishment of stabilisation centres within hospitals (TFUs); evaluation of nutrition programmes at all stages.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"528973d51410bcc9ee43dd6cec234880","permalink":"https://katilingban.io/author/lionella-fieschi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/lionella-fieschi/","section":"authors","summary":"Technical support and evaluation: nutrition programmes for the treatment of severe and moderate acute malnutrition. Technical assistance was provided on all areas related to the design and implementation of CTC programmes.","tags":null,"title":"Lionella Fieschi","type":"authors"},{"authors":null,"categories":null,"content":"Flexibility This feature can be used for publishing content such as:\nOnline courses Project or software documentation Tutorials The courses folder may be renamed. For example, we can rename it to docs for software/project documentation or tutorials for creating an online course.\nDelete tutorials To remove these pages, delete the courses folder and see below to delete the associated menu link.\nUpdate site menu After renaming or deleting the courses folder, you may wish to update any [[main]] menu links to it by editing your menu configuration at config/_default/menus.toml.\nFor example, if you delete this folder, you can remove the following from your menu configuration:\n[[main]] name = \u0026quot;Courses\u0026quot; url = \u0026quot;courses/\u0026quot; weight = 50 Or, if you are creating a software documentation site, you can rename the courses folder to docs and update the associated Courses menu configuration to:\n[[main]] name = \u0026quot;Docs\u0026quot; url = \u0026quot;docs/\u0026quot; weight = 50 Update the docs menu If you use the docs layout, note that the name of the menu in the front matter should be in the form [menu.X] where X is the folder name. Hence, if you rename the courses/example/ folder, you should also rename the menu definitions in the front matter of files within courses/example/ from [menu.example] to [menu.\u0026lt;NewFolderName\u0026gt;].\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"59c3ce8e202293146a8a934d37a4070b","permalink":"https://katilingban.io/courses/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/courses/example/","section":"courses","summary":"Learn how to use Academic's docs layout for publishing online courses, software documentation, and tutorials.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 2 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"74533bae41439377bd30f645c4677a27","permalink":"https://katilingban.io/courses/example/example1/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example1/","section":"courses","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":null,"title":"Example Page 1","type":"docs"},{"authors":null,"categories":null,"content":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 4 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"1c2b5a11257c768c90d5050637d77d6a","permalink":"https://katilingban.io/courses/example/example2/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example2/","section":"courses","summary":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":null,"title":"Example Page 2","type":"docs"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\nCreate slides using Academic\u0026rsquo;s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further talk details can easily be added to this page using Markdown and $\\rm \\LaTeX$ math code.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"96344c08df50a1b693cc40432115cbe3","permalink":"https://katilingban.io/talk/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example/","section":"talk","summary":"An example talk using Academic's Markdown slides feature.","tags":[],"title":"Example Talk","type":"talk"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":"After a little more than 7 years, we are pleased to announce the first CRAN release of the {oldr} package. {oldr} is an implementation of the Rapid Assessment Method for Older People or RAM-OP. HelpAge International, VALID International, and Brixton Health, with financial assistance from the Humanitarian Innovation Fund (HIF), developed RAM-OP that provides accurate and reliable estimates of the needs of older people. The method uses simple procedures, in a short time frame (i.e. about two weeks including training, data collection, data entry, and data analysis), and at considerably lower cost than other methods. The RAM-OP method is based on the following principles:\nUse of a familiar “household survey” design employing a two-stage cluster sample design optimised to allow the use of a small primary sample (m ≥ 16 clusters) and a small overall (n ≥ 192) sample.\nAssessment of multiple dimensions of need in older people (including prevalence of global, moderate and severe acute malnutrition) using, whenever possible, standard and well-tested indicators and question sets.\nData analysis performed using modern computer-intensive methods to allow estimates of indicator levels to be made with useful precision using a small sample size.\nThe {oldr} package is meant to serve as an alternative to the original software developed for RAM-OP. The original software, also built on R using the R AnalyticFlow integrated development environment (IDE), is very useful but is limited by known issues and limitations of the R AnalyticFlow IDE. The {oldr} package, on the other hand, is aimed at experienced R users who may prefer to use their own IDE when implementing a RAM-OP survey. With a few lines of code using {oldr} functions, a user can replicate everything that the original software can do. For example, using the testSVY and testPSU RAM-OP dataset included in the package, a full data processing, analysis, and reporting can be performed as follows:\nlibrary(oldr) testSVY |\u0026gt; create_op() |\u0026gt; estimate_op(w = testPSU) |\u0026gt; report_op_html( svy = testSVY, filename = file.path(tempdir(), \u0026quot;ramOPreport\u0026quot;) ) To learn more about the {oldr} package, see the website.\n","date":1737738000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1737738000,"objectID":"9d19186f43d8703200baa578cf7b030d","permalink":"https://katilingban.io/post/ram-op/","publishdate":"2025-01-24T17:00:00Z","relpermalink":"/post/ram-op/","section":"post","summary":"The `{oldr}` package contains functions that support in the data processing, analysis, and visualisation of RAM-OP survey datasets collected using the standard RAM-OP survey questionnaire.","tags":["rapid assessment method","older people","bootstrap"],"title":"Rapid Assessment Method for Older People (RAM-OP)","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":"We just released an updated version (0.3.0) of the {bbw} package on CRAN. This is {bbw}\u0026rsquo;s third CRAN release since its maiden acceptance to CRAN almost 7 years ago (17 January 2018). Key updates include the streamlining of the resampling algorithm and the addition of the option to perform bootstrap resampling in parallel for a faster and more efficient process. The package is now also able to perform stratified bootstrap resampling out-of-the-box compared to previous version where users had to write additional code to setup stratification. Finally, the package now includes a convenience function for performing weighted post-stratification estimation. Before, users had to create additional script to perform this analysis.\nTo give you an idea of the new features, we compare the original bootBW() function to the new/alternative boot_bw() set of functions. We use the datasets for a rapid assessment method (RAM) survey on mother and child health and nutrition in three regions of Somalia included in the {bbw} package for this demonstration.\nThe indicatorsHH dataset is a survey dataset collected from a RAM survey in Bakool, Bay, and Middle Shabelle regions of Somalia. The villageData contains the list of villages/clusters that were sampled in the survey that collected the indicatorsHH dataset.\nOriginal bootstrapping workflow Bootstrap resampling with bootBW() The bootBW() function is the original bootstrap resampling function of the package. It can be used as follows:\nboot_df \u0026lt;- bootBW( x = indicatorsHH, w = villageData, statistic = bootClassic, params = c(\u0026quot;anc1\u0026quot;, \u0026quot;anc2\u0026quot;) ) This call to bootBW() takes in the survey dataset indicatorsHH as its first argument (x). This dataset is expected to have a variable labelled as psu which identifies the primary sampling unit from which data was collected during the survey and then additional variables for the indicators to be estimated. The second argument (w) is for the dataset of the list of primary sampling units that were sampled in the survey to collect the survey data specified in x. This dataset, which in this case is villageData, should have at least a variable labelled psu which identified the primary sampling unit that matches the same variable in the survey dataset and a variable labelled pop for the population size of the primary sampling unit. The statistic argument specified the type of statistic to apply to the bootstrap replicates. There are two of these functions available from the {bbw} package - bootClassic() and the bootPROBIT(). For this example, the bootClassic() function is used to get the mean value of the bootstrap replicates. This is generally useful for binomial type of indicators and for continuous variables of which to get the mean of. The params argument takes in values of the indicator names in x to be estimated. In this example, two indicator names for antenatal care are specified. Finally, the argument for replicates specify the number of replicate bootstraps to be performed. The default of 400 replicates is used here. This results in the following (showing first 10 rows):\nhead(boot_df, 10) #\u0026gt; anc1 anc2 #\u0026gt; 1 0.1864175 0.01874714 #\u0026gt; 2 0.2290978 0.02035985 #\u0026gt; 3 0.2343529 0.02641509 #\u0026gt; 4 0.2548555 0.03084955 #\u0026gt; 5 0.2698864 0.02662863 #\u0026gt; 6 0.2151356 0.01819052 #\u0026gt; 7 0.1937834 0.02677702 #\u0026gt; 8 0.2148349 0.01678766 #\u0026gt; 9 0.2593480 0.02891566 #\u0026gt; 10 0.2151414 0.01922153 The result is a data.frame() of bootstrap replicates with number of rows equal to the number or replicates and number of columns equal to the number of params specified. Hence, boot_df has 400 rows and 2 columns.\nBootstrap estimation Using boot_df containing bootstrap replicates of the indicators anc1 and anc2, estimating each indicator with a 95% confidence interval using the percentile bootstrap method. This can be simply done using the quantile() function from the stats package as follows:\nest_df \u0026lt;- lapply( X = boot_df, FUN = quantile, probs = c(0.5, 0.025, 0.975) ) |\u0026gt; do.call(rbind, args = _) The quantile() function is used to get the 50th percentile (for the estimate) and the 2.5th and the 97.5th percentile of the bootstrap replicates to get the lower confidence limit and the upper confidence limits (respectively) of the indicator estimate. This gives the following results:\nest_df #\u0026gt; 50% 2.5% 97.5% #\u0026gt; anc1 0.2316597 0.17709920 0.28849265 #\u0026gt; anc2 0.0218962 0.01347537 0.03484835 Stratified bootstrap resampling Note that the indicatorsHH dataset has geographical stratification. Specifically, the survey from which this data was collected was designed to be representative of three regions in Somalia with the regions identified through the region variable in indicatorsHH. Because of this the more appropriate bootstrap resampling approach would be to resample within each region. To do this using the original bootBW() function would require restructuring the survey dataset by region and then passing the region-stratified datasets individually to the bootBW() function. This may look something like this:\n## Split indicators by region ---- indicators_by_region \u0026lt;- split(indicatorsHH, f = indicatorsHH$region) ## Split psus by region ---- psus_by_region \u0026lt;- split(villageData, f = villageData$region) ## Bootstrap boot_df \u0026lt;- Map( f = bootBW, x = indicators_by_region, w = psus_by_region, statistic = rep(list(get(\u0026quot;bootClassic\u0026quot;)), length(indicators_by_region)), params = rep(list(c(\u0026quot;anc1\u0026quot;, \u0026quot;anc2\u0026quot;)), length(indicators_by_region)) ) The bootBW() function only accepts single data.frame inputs for x and w arguments. Hence, to resample data from within region, the datasets will have to be split into separate data.frame inputs per region and then bootBW() applied to each separately. In the example above, this is done by concatenating each of the inputs to bootBW() into a list and then using the Map() function is sent to bootBW() sequentially. This produces a list of the data.frame bootstrap resample for each region (shown below):\nclass(boot_df) #\u0026gt; [1] \u0026quot;list\u0026quot; head(boot_df$Bay, 10) #\u0026gt; anc1 anc2 #\u0026gt; 1 0.4043419 0.013568521 #\u0026gt; 2 0.3907104 0.020491803 #\u0026gt; 3 0.3224044 0.023224044 #\u0026gt; 4 0.2645862 0.016282225 #\u0026gt; 5 0.2708618 0.008207934 #\u0026gt; 6 0.3297151 0.024423338 #\u0026gt; 7 0.3627717 0.004076087 #\u0026gt; 8 0.3662551 0.016460905 #\u0026gt; 9 0.3410641 0.016371078 #\u0026gt; 10 0.2277628 0.014824798 head(boot_df$Bakool, 10) #\u0026gt; anc1 anc2 #\u0026gt; 1 0.2916667 0.17415730 #\u0026gt; 2 0.2928177 0.09497207 #\u0026gt; 3 0.3260274 0.14804469 #\u0026gt; 4 0.2747253 0.11864407 #\u0026gt; 5 0.2900552 0.11797753 #\u0026gt; 6 0.1823204 0.05849582 #\u0026gt; 7 0.4065934 0.16343490 #\u0026gt; 8 0.2727273 0.11731844 #\u0026gt; 9 0.2821918 0.06944444 #\u0026gt; 10 0.2939560 0.09749304 head(boot_df$`Middle Shabelle`, 10) #\u0026gt; anc1 anc2 #\u0026gt; 1 0.1723447 0.011055276 #\u0026gt; 2 0.2550607 0.018367347 #\u0026gt; 3 0.1330724 0.010816126 #\u0026gt; 4 0.2830189 0.024551464 #\u0026gt; 5 0.1921569 0.014792899 #\u0026gt; 6 0.2217782 0.010989011 #\u0026gt; 7 0.2117647 0.007881773 #\u0026gt; 8 0.2165156 0.019172553 #\u0026gt; 9 0.2195122 0.015625000 #\u0026gt; 10 0.2274549 0.015075377 To estimate the per region results from this bootstrap resampling, the following can be implemented:\nest_df \u0026lt;- lapply( X = boot_df, FUN = function(x) lapply( x, FUN = quantile, probs = c(0.5, 0.025, 0.975) ) |\u0026gt; do.call(rbind, args = _) ) est_df \u0026lt;- data.frame( region = names(est_df), indicators = lapply(est_df, FUN = row.names) |\u0026gt; unlist(), do.call(rbind, args = est_df) ) row.names(est_df) \u0026lt;- NULL which results in the following output:\nest_df #\u0026gt; region indicators X50. X2.5. X97.5. #\u0026gt; 1 Bakool anc1 0.30261405 0.188862799 0.41167127 #\u0026gt; 2 Bay anc2 0.11251780 0.050903865 0.19504237 #\u0026gt; 3 Middle Shabelle anc1 0.32391543 0.217411669 0.43781930 #\u0026gt; 4 Bakool anc2 0.01893172 0.002766156 0.03663750 #\u0026gt; 5 Bay anc1 0.20220114 0.134820317 0.27676772 #\u0026gt; 6 Middle Shabelle anc2 0.01724140 0.007237952 0.03006251 Alternative blocked weighted bootstrap function set From this demonstration, the bootBW() function proves to be straightforward to implement and can be easily incorporated into a user\u0026rsquo;s workflow based on their dataset and their analytic needs. However, as shown above, this flexibility requires a lot more extra coding from the user to get from resampling to indicator estimates.\nStarting from v0.3.0, an alternative set of functions is available to perform blocked weighted bootstrap resampling that facilitates all the steps from resampling to estimation. Below is an example of how to use this alternative set of functions for the same tasks shown above.\nThis set of functions attempts to make the blocked weighted bootstrap algorithm more efficient through vectorisation and use of parallelisation techniques. The function syntax has been kept consistent with bootBW() for ease of transition.\nBootstrap resampling with boot_bw() The boot_bw() function is the alternative bootstrap resampling function of the package. It can be used as follows:\nboot_df \u0026lt;- boot_bw( x = indicatorsHH, w = villageData, statistic = bootClassic, params = c(\u0026quot;anc1\u0026quot;, \u0026quot;anc2\u0026quot;) ) This call to boot_bw() takes in the survey dataset indicatorsHH as its first argument (x). This dataset is expected to have a variable labelled as psu which identifies the primary sampling unit from which data was collected during the survey and then additional variables for the indicators to be estimated. The second argument (w) is for the dataset of the list of primary sampling units that were sampled in the survey to collect the survey data specified in x. This dataset, which in this case is villageData, should have at least a variable labelled psu which identified the primary sampling unit that matches the same variable in the survey dataset and a variable labelled pop for the population size of the primary sampling unit. The statistic argument specified the type of statistic to apply to the bootstrap replicates. There are two of these functions available from the {bbw} package - bootClassic() and the bootPROBIT(). For this example, the bootClassic() function is used to get the mean value of the bootstrap replicates. This is generally useful for binomial type of indicators and for continuous variables of which to get the mean of. The params argument takes in values of the indicator names in x to be estimated. In this example, two indicator names for antenatal care are specified. Finally, the argument for replicates specify the number of replicate bootstraps to be performed. The default of 400 replicates is used here. As can be noted, the boot_bw() takes on the same type of arguments as bootBW() and the syntax is exactly the same. Hence, using this alternative function will be familiar to those who have had experience using the original function.\nHowever, the output of the boot_bw() function is structured differently from the bootBW() function. The boot_bw() function produces and object of class boot_bw.\nclass(boot_df) #\u0026gt; [1] \u0026quot;boot_bw\u0026quot; The object boot_bw is a list with 4 named components: params for the values specified for the params argument, replicates for the number of bootstrap replicates performed, strata for the values specified for stratification, and boot_data which is the bootstrap results.\nnames(boot_df) #\u0026gt; [1] \u0026quot;params\u0026quot; \u0026quot;replicates\u0026quot; \u0026quot;strata\u0026quot; \u0026quot;boot_data\u0026quot; The boot_data component of the boot_bw object corresponds to the output of the bootBW() function.\nOther than the difference in the structure of the output, this alternative function also has three additional arguments for the new features it provides.\nstrata - the variable name in x that provides information on the stratification in the survey data. This is by default set to NULL signifying no stratification. This argument allows the user to perform stratified bootstrap resampling conveniently through the boot_bw() function.\nparallel - whether or not to use parallel computation for the bootstrap resampling. This is by default set to FALSE in which case bootstrap resampling is done sequentially as is with the bootBW() function. If set to TRUE, the function sets up parallel computing and utilises the machines available cores (see cores argument below).\ncores - the number of cores to use for parallel computation. This is only evaluated if parallel = TRUE. By default, this is set to 1 less the total available number of cores of the current machine.\nTo use these new features and functionality, the call to boot_bw() would look something like this:\nboot_df \u0026lt;- boot_bw( x = indicatorsHH, w = villageData, statistic = bootClassic, params = c(\u0026quot;anc1\u0026quot;, \u0026quot;anc2\u0026quot;), strata = \u0026quot;region\u0026quot;, parallel = TRUE ) This produces a boot_bw class list object with the same components as above. The only different is that the boot_data component is a list (instead of a data.frame) with each component being the data.frame bootstrap resampling output for each of the strata in the dataset.\nclass(boot_df) #\u0026gt; [1] \u0026quot;boot_bw\u0026quot; class(boot_df$boot_data) #\u0026gt; [1] \u0026quot;list\u0026quot; names(boot_df$boot_data) #\u0026gt; [1] \u0026quot;Bakool\u0026quot; \u0026quot;Bay\u0026quot; \u0026quot;Middle Shabelle\u0026quot; Bootstrap estimation The boot_bw_estimate() function can then be applied to the output of the boot_bw() function to get the indicator estimates with 95% confidence interval.\nboot_bw_estimate(boot_df) #\u0026gt; region indicator est lcl ucl #\u0026gt; 1 Bakool anc1 0.43888889 0.38881944 0.48888889 #\u0026gt; 2 Bakool anc2 0.38055556 0.32497749 0.43062500 #\u0026gt; 3 Bay anc1 0.71619066 0.63887512 0.77849135 #\u0026gt; 4 Bay anc2 0.00254615 0.00000000 0.01294677 #\u0026gt; 5 Middle Shabelle anc1 0.20757542 0.14514451 0.28293531 #\u0026gt; 6 Middle Shabelle anc2 0.05065259 0.03133757 0.07453108 #\u0026gt; se #\u0026gt; 1 0.027718319 #\u0026gt; 2 0.027983726 #\u0026gt; 3 0.036466569 #\u0026gt; 4 0.003743969 #\u0026gt; 5 0.036375151 #\u0026gt; 6 0.011463590 These two functions can be piped to each other for a single workflow from bootstrap resampling to estimation.\nboot_bw( x = indicatorsHH, w = villageData, statistic = bootClassic, params = c(\u0026quot;anc1\u0026quot;, \u0026quot;anc2\u0026quot;), strata = \u0026quot;region\u0026quot;, parallel = TRUE ) |\u0026gt; boot_bw_estimate() #\u0026gt; region indicator est lcl ucl #\u0026gt; 1 Bakool anc1 0.438888889 0.3805556 0.49444444 #\u0026gt; 2 Bakool anc2 0.376731302 0.3138889 0.43888889 #\u0026gt; 3 Bay anc1 0.719130072 0.6487833 0.78255787 #\u0026gt; 4 Bay anc2 0.002534854 0.0000000 0.01262706 #\u0026gt; 5 Middle Shabelle anc1 0.203423968 0.1428536 0.27819673 #\u0026gt; 6 Middle Shabelle anc2 0.051256281 0.0339071 0.07622767 #\u0026gt; se #\u0026gt; 1 0.030425611 #\u0026gt; 2 0.030033802 #\u0026gt; 3 0.034273078 #\u0026gt; 4 0.003372086 #\u0026gt; 5 0.033966913 #\u0026gt; 6 0.010573679 More efficient bootstrap resampling A key feature of the most recent {bbw} update is its new function set that uses parallelisation for bootstrap resampling. This vignette explores the bootstrap resampling efficiencies gained with parallelisation.\nApplying the original and the alternative function/set to the Somalia survey dataset available from this package, bootstrap resampling is applied using the same parameters and the time the operation it takes to run is measured and compared.\nBootstrap resampling without parallelisation In this comparison, the original and alternative function/set both implement sequential bootstrap resampling with number of parameters set at varying values.\nUsing one parameter and 400 replicates Original vs Alternative bootstrap resampling function/set Sequential resampling with 1 parameter and 400 replicates User System Elapsed Original - 400 replicates - 1 parameter 31.505 0.0320000000000036 31.4860000000008 Alternative - 400 replicates - 1 parameter 25.956 0 25.8919999999998 Performing bootstrap resampling sequentially, the original function took 31.486 seconds to run while the alternative function set took 25.892 seconds to run. There was very little difference between the original and the alternative function/set.\nUsing varying number of parameters and 400 replicates Original vs Alternative bootstrap resampling function/set Sequential resampling with increasing number of parameters and 400 replicates No. of parameters User - Original System - Original Elapsed - Original User - Alternative System - Alternative Elapsed - Alternative 1 31.505 0.032 31.486 25.956 0 25.892 2 32.442 0.000 32.363 25.747 0 25.690 4 32.485 0.000 32.404 25.302 0 25.244 8 31.727 0.000 31.652 25.660 0 25.604 There are marginal gains with the alternative function set when the number of parameters more than 1 but the gains do not increase with the increase in the number of parameters.\nBootstrap resampling with parallelisation In this comparison, the alternative function/set implements parallel bootstrap resampling with number of parameters set at varying values and number of parallel cores set at varying values and then compared to performance of the original function as above.\nOriginal vs Alternative bootstrap resampling function/set Parallel resampling with increasing number of parameters and increasing number of cores No. of parameters Original Alternative - sequential Alternative - 2 cores Alternative - 4 cores Alternative - 8 cores 1 31.486 25.892 16.904 10.392 7.361 2 32.363 25.690 16.754 10.634 7.398 4 32.404 25.244 17.017 10.545 7.585 8 31.652 25.604 17.568 10.240 7.624 This updated version of the {bbw} package maintains support for the original bootstrap resampling function bootBW() so as to support existing workflows that use it. We would recommend, however, that the new boot_bw() set of functions be used for new uses and for new workflows.\n","date":1737037080,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1705414680,"objectID":"49ee6d168133b1fcb02d744cf3e9a116","permalink":"https://katilingban.io/post/bbw-update/","publishdate":"2025-01-16T14:18:00Z","relpermalink":"/post/bbw-update/","section":"post","summary":"Latest CRAN release of version 0.3.0 of {bbw} R package for performing blocked weighted bootstrap now with option for parallelisation.","tags":["bootstrap","resampling"],"title":"bbw R package update","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Announcement"],"content":"Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals.\nOur work on micronutrient surveys motivated us to develop an R package that aids in the processing and analysis of micronutrient biomarkers data taken during cross-sectional surveys. Our micronutr package has been released recently on CRAN. This package provides tools for determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found here.\nWhat does micronutr do? The micronutr package provides tools for determining select vitamin and mineral deficiencies using R. Currently, micronutr has functions for:\nDetecting haemoglobinaemia or anaemia based on an individual\u0026rsquo;s serum haemoglobin level;\nDetecting inflammation status based on c-reactive protein (CRP) and alpha(1)-acid-glycoprotein (AGP);\nDetecting iron deficiency status based on an individual\u0026rsquo;s serum ferritin level;\nDetecting iodine deficiency status based on a population\u0026rsquo;s mean urinary iodine concentration.\n","date":1713497400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1713497400,"objectID":"aacff0cb585faa3f894081249a8aab45","permalink":"https://katilingban.io/post/micronutr/","publishdate":"2024-04-19T03:30:00Z","relpermalink":"/post/micronutr/","section":"post","summary":"We are a collective of multi-disciplinary experts and practitioners of public health and nutrition.","tags":["katilingban","public health","nutrition"],"title":"Determining vitamin and mineral status in populations","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":"We just launched the tenth release (version 0.5.5) of ppitables, our R package containing Poverty Probability Index (PPI®) lookup tables for the 61 countries where PPI® can be calculated. The PPI® is a poverty measurement tool for organisations and businesses with a mission to serve the poor created by Innovations for Poverty Action (IPA).\nInitially released in March of 2018, ppitables has now been downloaded more than 27,000 times by R users averaging 375 downloads per month.\nWe developed ppitables to support our use of the PPI® for surveys and assessments we have conducted. Our main use case for PPI® is as an alternative means to classify wealth in our survey sample as opposed to the more widely used and traditional household asset listing and application of principal components analysis (PCA) for household wealth ranking used by the World Bank and by the Demographic and Health Surveys (DHS). Using the answers to just 10 questions about a household\u0026rsquo;s characteristics and asset ownership, a score is calculated and the likelihood of a household living below poverty line is computed. To learn more about the PPI®, its history and development, go here. To read more about how to use PPI®, click here.\nThe ppitables package is aimed at R users whose work and/or research includes the use of the PPI®. The package facilitates the conversion of country-specific household PPI® score into a poverty likelihood value for a household. Users can immediately write appropriate scripts to convert data they may have of a sample of households in a particular country into the respective poverty probabilities using the country-specific lookup tables.\nIn this tenth iteration of the package, we have added new tables released by IPA since 2020 which use the current approach for calculating poverty probabilities. For more information about ppitables and how to use it, visit the package website. To view the package source code, see the package\u0026rsquo;s GitHub repository.\nIf you have used ppitables before or have used it recently, we\u0026rsquo;d love to hear from you for feedback and comments. If you find a bug or error or would like to request additional feature/s, file an issue here.\n","date":1713490020,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1713490020,"objectID":"4c082b09a52dfd20c6ed9ba40783768b","permalink":"https://katilingban.io/post/ppitables-update/","publishdate":"2024-04-19T01:27:00Z","relpermalink":"/post/ppitables-update/","section":"post","summary":"Latest version release of ppitables R package that provides lookup tables for estimating the likelihood of poverty using the Poverty Probability Index (previously called Progress out of Poverty Index) or PPI from country-specific indicators collected from cross-sectional surveys.","tags":["poverty","ppi"],"title":"ppitables R package update","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":" National Information Platforms for Nutrition (NiPN) is an initiative of the European Commission to provide support to countries to strengthen their information systems for nutrition and to improve the analysis of data so as to better inform the strategic decisions they are faced with to prevent malnutrition and its consequences.\nAs part of this mandate, NiPN has commissioned work on the development of a toolkit to assess the quality of various nutrition-specific and nutrition-related data. This is a companion R package to the toolkit of practical analytical methods that can be applied to variables in datasets to assess their quality.\nThe focus of the toolkit is on data required to assess anthropometric status such as measurements of weight, height or length, MUAC, sex and age. The focus is on anthropometric status but many of presented methods could be applied to other types of data. NiPN may commission additional toolkits to examine other variables or other types of variables.\nData quality is assessed by:\nRange checks and value checks to identify univariate outliers\nScatterplots and statistical methods to identify bivariate outliers\nUse of flags to identify outliers in anthropometric indices\nExamining the distribution and the statistics of the distribution of measurements and anthropometric indices\nAssessing the extent of digit preference in recorded measurements\nAssessing the extent of age heaping in recorded ages\nExamining the sex ratio\nExamining age distributions and age by sex distributions\nTo read more about nipnTK, visit the package website where you can read more about the package and learn how the data quality assessment is performed in R.\n","date":1606696200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606779000,"objectID":"ad65307b5ac7c3a7783466239c1cbb8d","permalink":"https://katilingban.io/post/nipn-toolkit/","publishdate":"2020-11-30T00:30:00Z","relpermalink":"/post/nipn-toolkit/","section":"post","summary":"The focus of the toolkit is on data required to assess anthropometric status such as measurements of weight, height or length, MUAC, sex and age. The focus is on anthropometric status but many of presented methods could be applied to other types of data. NiPN may commission additional toolkits to examine other variables or other types of variables.","tags":["nutrition","nipn","toolkit"],"title":"National Information Platforms for Nutrition Data Quality Toolkit","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":" en-net is the go to online forum for field practitioners requiring prompt technical advice for operational challenges for which answers are not readily accessible in current guidelines. The questions and the corresponding answers raised within en-net online forum can provide insight into what the key topics of discussion are within the nutrition sector. This package provides utility functions for the extraction, processing and analysis of text data from the online forum.\nWhat does ennet do? The ennet package has two key sets of functions:\nThe first set of functions facilitates the extraction of text data from the en-net online forum.\nThe second set of functions supports the analysis of the extracted datasets to produce summary measures and statistics of the en-net online forum.\nUsage The ennet data extraction workflow ennet package provides a set of functions that extracts information from the en-net online forum. This set of functions was built on top of the rvest package which provides robust and performant web scraping functions and the dplyr package which provides a full suite of data manipulation functions. The ennet package was designed to be able to interact with how the en-net online forum has been structured.\nen-net website structure The en-net online forum website has a very clear and clean structure. The opening page is a list of thematic areas which are linked to each of their respective webpages. In each of these thematic area webpages is another list, this time a list of topics raised within the thematic area. These topics are the text that an online user provides as the title for the question she/he is going to ask. Each of the topics are then again linked to their respective webpages that show the actual full question raised and the ensuing responses and discussion stemming from that question.\nBased on this structure, the following functions are available from the ennet package for extracting text data:\nget_themes - function to get a list of thematic areas in the forum;\nget_theme_topics and get_themes_topics - functions to get list of topics for a specific thematic area or thematic areas; and,\nget_topic_discussions and get_topics_discussions - functions to get list of discussions for a specific topic or topics,\nFor a more detailed discussion of the data extraction workflow via the ennet package, read Utilities to Extract Text Data from en-net.\nThe ennet analytics functions The ennet package also includes analytic functions that summarises the text data available from the en-net online forum. Currently, there are four analytic functions available from ennet:\ncount_topics - function to count the number of topics or questions by theme and date;\ncount_authors - function to count the number of topics attributed to a specific author;\narrange_views - function to arrange topics by number of views; and,\narrange_replies - function to arrange topics by number of replies.\nFor a more detailed discussion of the analytics functions available from the ennet package, read Summarising en-net online forum statistics.\nUtilities and datasets In addition to these two sets of key functions, ennet package also includes a function - update_topics - that extracts the en-net online forum dataset and updates it at a given time interval. This is a convenience wrapper function to get_themes_topics that is potentially useful for those who wants to build dashboards or applications that uses data from the en-net online forum.\nTwo datasets are also included in the en-net package. The first dataset is a data.frame of en-net online forum themes and the second dataset is a data.frame of en-net online forum topics.\nPractical applications The en-net online forum is a rich resource for understanding the community of users that participate in it. And given how an online forum is designed, that resource can be tapped relatively easily given that the documentation of the interaction and discussion between its users happens in real-time. The ennet package facilitates the access to that information through the statistical analysis tool R with which further levels of analysis can be applied to generate meaningful and valuable understanding of this specific community and to some extent the greater nutrition sector at large.\nFollowing are a few practical and meaningful applications of the information generated by the en-net online forum.\nAssess effectiveness of the en-net online forum The data from the en-net online forum can be used to assess effectiveness of the forum. Effectiveness can be defined as whether the forum has been able to achieve its stated aims/objectives when it was started. Effectiveness can also be expressed in terms of indicators or metrics that reflect overarching principles, ideals or values that those who started the forum adhere to or that the community of users and the wider sector or society believe in. These may include values of inclusion, participation, scientific rigour among others. Given that the forum has been in existence for many years now, information is available over the same period allowing for assessing temporal variation in effectiveness (as defined). This application is a more normative approach and will involve creating or developing metrics or taking relevant metrics from other sectors and applying those to this case.\nIdentify gaps in information, knowledge and/or skills Given the nature of the en-net online forum as a quick point of recourse for field practitioners to seek answers to practical questions and challenges faced, it can be expected that the data from the forum contains information on what these topics are. These information can then be used to identify most common or most important information, knowledge and skills that have been asked about. By identifying these gaps in information, knowledge and/or skills and by understanding the evolution of these needs over time, we can potentially predict training needs in the near term and over time. This application is a more formative approach in that we let the data tell us what information it holds.\nSummary The ennet package provides R users access to text data from the en-net online forum which can be used for various purposes. In the coming weeks, we\u0026rsquo;ll be using the ennet package to draw data from the en-net online forum to analyse the topics and discussions in the forum and answer questions that may offer a deeper insight into the community that the online forum fosters.\nIf you have ideas of questions to explore through the ennet package, let us know here.\n","date":1606177800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606260600,"objectID":"4c2ed53235fc0ceb13d81540081f2b53","permalink":"https://katilingban.io/post/extract-analyse-ennet-forum/","publishdate":"2020-11-24T00:30:00Z","relpermalink":"/post/extract-analyse-ennet-forum/","section":"post","summary":"R package that provides utility functions for the extraction, processing and analysis of text data from the en-net online forum","tags":["enn","text","nlp","nutrition"],"title":"ennet: Utilities to Extract and Analyse Text Data from the Emergency Nutrition Network (en-net) Forum","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Software","R"],"content":"We just launched the eighth release (version 0.5.3) of ppitables, our R package containing Poverty Probability Index (PPI®) lookup tables for the 61 countries where PPI® can be calculated. The PPI® is a poverty measurement tool for organisations and businesses with a mission to serve the poor created by Innovations for Poverty Action (IPA).\nInitially released in March of 2018, ppitables has now been downloaded by more than 12,000 R users averaging more than 500 downloads per month.\nWe developed ppitables to support our use of the PPI® for surveys and assessments we have conducted. Our main use case for PPI® is as an alternative means to classify wealth in our survey sample as opposed to the more widely used and traditional household asset listing and application of principal components analysis (PCA) for household wealth ranking used by the World Bank and by the Demographic and Health Surveys (DHS). Using the answers to just 10 questions about a household\u0026rsquo;s characteristics and asset ownership, a score is calculated and the likelihood of a household living below poverty line is computed. To learn more about the PPI®, its history and development, go here. To read more about how to use PPI®, click here.\nThe ppitables package is aimed at R users whose work and/or research includes the use of the PPI®. The package facilitates the conversion of country-specific household PPI® score into a poverty likelihood value for a household. Users can immediately write appropriate scripts to convert data they may have of a sample of households in a particular country into the respective poverty probabilities using the country-specific lookup tables.\nIn this eighth iteration of the package, we have added the most recently released lookup tables for Malawi that uses IPA\u0026rsquo;s current approach to calculating poverty probabilities. For more information about ppitables and how to use it, visit the package website. To view the package source code, see the package\u0026rsquo;s GitHub repository.\nIf you have used ppitables before or have used it recently, we\u0026rsquo;d love to hear from you for feedback and comments. If you find a bug or error or would like to request additional feature/s, file an issue here.\n","date":1600648200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600731000,"objectID":"fac20497634f2c5989404b1479798ce0","permalink":"https://katilingban.io/post/ppitables/","publishdate":"2020-09-21T00:30:00Z","relpermalink":"/post/ppitables/","section":"post","summary":"Lookup tables for estimating the likelihood of poverty using the Poverty Probability Index (previously called Progress out of Poverty Index) or PPI from country-specific indicators collected from cross-sectional surveys. These lookup tables are extracted from documentation of the PPI found at https://www.povertyindex.org.","tags":["poverty","ppi"],"title":"Poverty Probability Index","type":"post"},{"authors":["Ernest Guevarra"],"categories":["Announcement"],"content":" katilingban - [ka-ti-ling-ban]: (n.) A Cebuano word meaning 1) a group or a community of people; 2) society, organisation or club.\nIt is at times of extreme social struggle and difficulty that we are reminded of the primacy of the collective and the value of working towards the common good. The current global health crisis brought about by the COVID-19 pandemic along with the social upheaval caused by the continuing and persistent racial injustice in the United States recently sparked by the death of George Floyd at the hands of a white police officer and which has grown into a worldwide Black Lives Matter movement are the signs of the times. And these are the times when the power of collective action towards the common good is even more crucial.\nIt is against this backdrop that I, together with three other colleagues, have founded Katilingban, a collective of multi-disciplinary experts and practitioners in public health and nutrition. Though coming from different nationalities and diverse backgrounds and experiences, one of our main commonalities is our community-based orientation - from a doctor who ran a community-based mental health programme for civil war-affected children and youth in the Philippines, to a public health practitioner who studies and works with communities to understand their context to inform health and nutrition programming, to a community health and nutrition expert who has performed multiple coverage assessments and evaluations of community-based nutrition programmes in the past decade illustrating how the community aspect of these programmes has mostly been poorly done, and to a nutrition data expert who has implemented numerous nutrition surveys to aid in the design and planning of nutrition programmes advocating for more community participation in programme design and planning. And it is this community-based orientation that inspired us to form our own collective, to form Katilingban.\nFor the remainder of 2020, we would like to once again emphasise the importance of coverage of health and nutrition programming more so in the light of the COVID-19 pandemic. The pandemic has disrupted health and nutrition in more ways than just the infection itself. The global focus on COVID-19 has potentially further widened disparities in other health and nutrition outcomes particularly in low and middle income countries through a variety of mechanisms. We would like to collaborate with other organisations and researchers who are keen on assessing the impact of the pandemic on various aspects of health and nutrition in low and middle income countries. And given the limitations posed by the pandemic, we would like to explore the use of relatively new or less utilised modalities of primary data collection and to test and utilise other analytical techniques on already existing secondary data and routine programme data taht would allow us to still examine and understand the on-going health and nutrition situation other than COVID-19.\nInterested in collaborating? Contact us.\n","date":1594341000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1594337400,"objectID":"2edf16e58cdfa00c690a340858d4cf84","permalink":"https://katilingban.io/post/we-are-katilingban/","publishdate":"2020-07-10T00:30:00Z","relpermalink":"/post/we-are-katilingban/","section":"post","summary":"We are a collective of multi-disciplinary experts and practitioners of public health and nutrition.","tags":["katilingban","public health","nutrition"],"title":"We are Katilingban","type":"post"},{"authors":["Katilingban"],"categories":null,"content":" Click the Slides button above to demo Academic\u0026rsquo;s Markdown slides feature. Supplementary notes can be added here, including code and math.\n","date":1554595200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554595200,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"https://katilingban.io/publication/preprint/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example preprint / working paper","type":"publication"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Academic Academic | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne **Two** Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}} Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://katilingban.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"d1311ddf745551c9e117aa4bb7e28516","permalink":"https://katilingban.io/project/external-project/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/external-project/","section":"project","summary":"An example of linking directly to an external project website using `external_link`.","tags":["Demo"],"title":"External Project","type":"project"},{"authors":null,"categories":null,"content":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"8f66d660a9a2edc2d08e68cc30f701f7","permalink":"https://katilingban.io/project/internal-project/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/internal-project/","section":"project","summary":"An example of using the in-built project page.","tags":["Deep Learning"],"title":"Internal Project","type":"project"},{"authors":["Katilingban","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Click the Slides button above to demo Academic\u0026rsquo;s Markdown slides feature. Supplementary notes can be added here, including code and math.\n","date":1441065600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1441065600,"objectID":"966884cc0d8ac9e31fab966c4534e973","permalink":"https://katilingban.io/publication/journal-article/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/journal-article/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example journal article","type":"publication"},{"authors":["Katilingban","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Click the Slides button above to demo Academic\u0026rsquo;s Markdown slides feature. Supplementary notes can be added here, including code and math.\n","date":1372636800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372636800,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://katilingban.io/publication/conference-paper/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example conference paper","type":"publication"}]