prevalence of child acute undernutrition

[ernestguevarra]

• [x] How many children 6-59 months old in the sample are moderate acute malnourished (MAM) cases?
• [x] How many children 6-59 months old in the sample are moderate acute malnourished (MAM) cases by sex and by location?
• [x] What is the prevalence of moderate acute malnutrition (MAM) among children 6-59 months old in the sample by sex and by location?
• [x] How many children 6-59 months old in the sample are severe acute malnourished (SAM) cases?
• [x] How many children 6-59 months old in the sample are severe acute malnourished (SAM) cases by sex and by location?
• [x] What is the prevalence of severe acute malnutrition (SAM) among children 6-59 months old in the sample by sex and by location?
• [x] How many children 6-59 months old in the sample are acute malnourished cases (either MAM or SAM)?
• [x] How many children 6-59 months old in the sample are acute malnourished cases (either MAM or SAM) by sex and by location?
• [x] What is the prevalence of acute malnutrition (either MAM or SAM) among children 6-59 months old in the sample by sex and location?

[ernestguevarra]

@OxfordIHTM/pikachu

[ernestguevarra]

@OxfordIHTM/pikachu here are some notes on the questions your team and other teams have raised in today's session:

• Take note that other than just providing results by location and sex, can you also provide results by survey round (baseline and endline).

• The variables in the nut data are as follows:

variable name	variable description
survey_round	Survey round; either Baseline or Endline
survey_data	Date interview was conducted/data collected was performed
county	County location respondent comes from; either Grand Bassa or Urban Montserrado
sex	Sex of child; 1 = Male; 2 = Female
age	Age of child in whole months
muac	Mid-upper arm circumference (MUAC) measurement in centimetres
oedema	Presence or absence of nutritional oedema; 1 = Yes; 2 = No
muac_screen	In the past month, has the child's MUAC been measured? 1 = Yes; 2 = No; 99 = no answer/cannot remember
oedema_screen	In the past month, has the child been checked for nutritional oedema? 1 = Yes; 2 = No; 99 = no answer/cannot remember
cov_status	Is the child currently receiving treatment with peanut butter medicine/Plumpynut? 1 = Yes; 2 = No

• The case definition for the different types of acute malnutrition are as follows:

  • global acute malnutrition (GAM): MUAC < 12.5 cms or positive for nutritional oedema for children 6-59 months old
  • moderate acute malnutrition (MAM): MUAC < 12.5 and MUAC >= 11.5 cms and negative for nutritional oedema for children 6-59 months old
  • severe acute malnutrition (SAM): MUAC < 11.5 cms or positive for nutritional oedema for children 6-59 months old

[sophia-1-2]

Hi @ernestguevarra , I had done some other questions and committed and pushed from my R studio to github. However, I am not seeing the pull request in github? What could be the reason? Thanks!

[ernestguevarra]

Hi @ernestguevarra , I had done some other questions and committed and pushed from my R studio to github. However, I am not seeing the pull request in github? What could be the reason? Thanks!

The push goes to the same pull request because you are pushing to the same branch. So, I saw your new entries already. See my comments. You are in the right direction. Just need to focus on the little details.

[ernestguevarra]

@OxfordIHTM/pikachu see my comments to your pull request. I just have comments that I want you to think about but otherwise, you can merge your pull request to main.

For now up to our last day, what I want you to start thinking about is how to add the outputs of your analysis into the report for this hackathon.

You will notice that there is a file named coverage_assessment_report.Rmd in the repository/project. In that file, you will see a section for your part of the hackathon. Please add both text and code that reports on your results.

[ernestguevarra]

Team @OxfordIHTM/pikachu, a table that looks like this on the report will be a good summary of your analysis results:

Urban Montserrado

Severe acute malnutrition

	Baseline	Endline
Male	Number of males who are SAM at baseline (% of males who are SAM out of total males at baseline)	Number of males who are SAM at endline (% of males who are SAM out of total males at endline)
Female	Number of females who are SAM at baseline (% of females who are SAM out of total females at baseline)	Number of females who are SAM at endline (% of females who are SAM out of total females at endline)
Total	Total number who are SAM at baseline (% of total who are SAM at baseline)	Total number who are SAM at endline (% of total who are SAM at endline)

Moderate acute malnutrition

	Baseline	Endline
Male	Number of males who are MAM at baseline (% of males who are MAM out of total males at baseline)	Number of males who are MAM at endline (% of males who are MAM out of total males at endline)
Female	Number of females who are MAM at baseline (% of females who are MAM out of total females at baseline)	Number of females who are MAM at endline (% of females who are MAM out of total females at endline)
Total	Total number who are MAM at baseline (% of total who are MAM at baseline)	Total number who are MAM at endline (% of total who are MAM at endline)

Global acute malnutrition

	Baseline	Endline
Male	Number of males who are MAM or SAM at baseline (% of males who are MAM or SAM out of total males at baseline)	Number of males who are MAM or SAM at endline (% of males who are MAM or SAM out of total males at endline)
Female	Number of females who are MAM or SAM at baseline (% of females who are MAM or SAM out of total females at baseline)	Number of females who are MAM or SAM at endline (% of females who are MAM or SAM out of total females at endline)
Total	Total number who are MAM or SAM at baseline (% of total who are MAM or SAM at baseline)	Total number who are MAM or SAM at endline (% of total who are MAM or SAM at endline)

Grand Bassa

Severe acute malnutrition

	Baseline	Endline
Male	Number of males who are SAM at baseline (% of males who are SAM out of total males at baseline)	Number of males who are SAM at endline (% of males who are SAM out of total males at endline)
Female	Number of females who are SAM at baseline (% of females who are SAM out of total females at baseline)	Number of females who are SAM at endline (% of females who are SAM out of total females at endline)
Total	Total number who are SAM at baseline (% of total who are SAM at baseline)	Total number who are SAM at endline (% of total who are SAM at endline)

Moderate acute malnutrition

	Baseline	Endline
Male	Number of males who are MAM at baseline (% of males who are MAM out of total males at baseline)	Number of males who are MAM at endline (% of males who are MAM out of total males at endline)
Female	Number of females who are MAM at baseline (% of females who are MAM out of total females at baseline)	Number of females who are MAM at endline (% of females who are MAM out of total females at endline)
Total	Total number who are MAM at baseline (% of total who are MAM at baseline)	Total number who are MAM at endline (% of total who are MAM at endline)

Global acute malnutrition

	Baseline	Endline
Male	Number of males who are MAM or SAM at baseline (% of males who are MAM or SAM out of total males at baseline)	Number of males who are MAM or SAM at endline (% of males who are MAM or SAM out of total males at endline)
Female	Number of females who are MAM or SAM at baseline (% of females who are MAM or SAM out of total females at baseline)	Number of females who are MAM or SAM at endline (% of females who are MAM or SAM out of total females at endline)
Total	Total number who are MAM or SAM at baseline (% of total who are MAM or SAM at baseline)	Total number who are MAM or SAM at endline (% of total who are MAM or SAM at endline)

[ernestguevarra]

Team @OxfordIHTM/pikachu, well done! Thank you for all your contributions to the hackathon. You can now view the output report for the entire project that you contributed to here - https://oxford-ihtm.io/ihtm-hackathon-2023/coverage_assessment_report.html.

I have given direct feedback to the group and to @sophia-1-2 for the pull request that you have made. In addition to that, I am giving specific team feedback based on your efforts as a team during the hackathon.

• Your team was the first to make a pull request right on the first day of the in-person hackathon sessions! :fire: ! And that first pull request was really good and was almost close to one of the acceptable solutions. That was definitely amazing and good work on your part.
• Your team was also the first team to have all team members make their own commits and push to your individual branches. That was really good and you were all on point.
• Each individual member of this stayed on the course and completed their own branches of the project. Whilst at the last day you were a bit depleted, unfortunately, but @amalelfatih was there continuing the work and getting her final commit and push done. Amazing stuff!
• It would have been great if right from the start the whole team would have been included in the pull request. With your rapidity, you could have still waited a little bit to get everyone up to speed on that first session before making a pull request. I do think that that would have made the rest of the team be able to pick up on the work at the same pace as everyone else.
• Compared to the rest of the teams, your pull request was consistently using objects to represent the different steps in your output. I was really very happy to see this and this is going to be a good foundation for all of you moving forward.
• On the last day, I would have expected for the team to have tried to go for additional work on the outputs to use for the R Markdown. You had your main script completed but there was just not enough personnel there at the last session for your team to make really nice tables for the report and also to possibly make some plots to show your results. See the outputs of team eevee and charmander.
• There is one issue with your estimation of prevalence. You used a fixed denominator for all the children in the entire sample without delineation of survey round and location. We can't really report prevalence of MAM, SAM or GAM in two different time points. This is why the results you have are very very small proportions because you used a fixed denominator for all children 6-59 in the sample. You should have a denominator specific for each location for each time point and then estimated prevalence using that denominator accordingly.

Well done again Team @OxfordIHTM/pikachu! Look forward to the course dinner at the end of April where we will be giving away awards and individual recognition for your work on the hackathon.