Tidyndr: An R package for analysis of the Nigeria HIV National Data Repository

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# Tidyndr: An R package for analysis of the Nigeria HIV National Data Repository
### Balogun Stephen, MD, MPH

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#<span style="font-family: 'Cormorant Upright', serif; text-shadow: 2px 2px #333"> Introduction</span>

???

Hi!

I am Balogun Stephen and thanks for tuning in to my lightning talk on data analysis using the tidyndr
---

## HIV/AIDS - the global pandemic

<br/>
.pull-left[
<svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M6.1 444.3c-9.6 10.8-7.5 27.6 4.5 35.7l68.8 27.9c9.9 6.7 23.3 5 31.3-3.8l91.8-101.9-79.2-87.9-117.2 130zm435.8 0s-292-324.6-295.4-330.1c15.4-8.4 40.2-17.9 77.5-17.9s62.1 9.5 77.5 17.9c-3.3 5.6-56 64.6-56 64.6l79.1 87.7 34.2-38c28.7-31.9 33.3-78.6 11.4-115.5l-43.7-73.5c-4.3-7.2-9.9-13.3-16.8-18-40.7-27.6-127.4-29.7-171.4 0-6.9 4.7-12.5 10.8-16.8 18l-43.6 73.2c-1.5 2.5-37.1 62.2 11.5 116L337.5 504c8 8.9 21.4 10.5 31.3 3.8l68.8-27.9c11.9-8 14-24.8 4.3-35.6z"></path></svg> - First case of HIV was detected 40 years ago

In 2020:

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UNAIDS 95-95-95 target by 2030 to achieve epidemiologic control.

+ 95% of people living with HIV know their HIV status

+ 95% accessing treatment

+ 95% of persons on treatment virally suppressed 
    
]

???

- Forty years have passed since the first case of HIV/AIDS. Yet, in the year 2020, 1.5 million people were newly infected with the virus

- 690 thousand people died of AIDS-related illnesses; 37.6 million people were living with HIV/AIDS.

- While remarkable progress has been recorded in the last decade, inequity between and within countries continues to drive the epidemic. Though some countries have attained epidemic control, many are off-track with COVID-19, conflict, and humanitarian emergencies reversing the gains.
- The UNAIDS has set ambitious targets to achieve epidemic control of HIV/AIDS by 2030 through identification of at least 95% of persons living with virus, retaining 95% of those identified on treatment and achieving viral suppression in at least 95% of those on treatment.
---

## Nigeria

.pull-left[
- Estimated 1.9 million people living with HIV.

- In the year 2020:
    
    - 73% of people living with HIV had been diagnosed

- 89% of those diagnosed were accessing treatment

- 78% of those accessing treatment were virally suppressed.
    
- Need for data utilization at every level

]

.pull-right[

.footnote[
  <span style="font-size: 9px">
  <i style="font-weight: 600">Source:</i> Revised National HIV and AIDS Strategic Framework 2019-2021, Nigeria
  </span>
  ]
]

???

- Nigeria has the fourth-largest epidemic in the world with an estimated 1.9 million people living with HIV.

- At the end of 2020, the progress across the three 95s are: 73%-89%- and-78% with varying prevalence and progress across the states as shown by the map

- To achieve the UNAIDS target, granular data at community, local government and sub-population levels must continuously be made available and utilized for informed decision.
---

## The National Data Repository - NDR

- A repository of de-identified patient-level data from facilities across all the states in the country

- Contains records of over 2000 reporting facilities and over 1.3 million PLHIV currently on treatment.

- Restricted access [A public dashboard is available](https://ndr.shieldnigeriaproject.com/public/dashboard).

<br/>

.pull-left[
<img src="./snips/ndr1.png"
alt="a snapshot of the NDR public dashboard showing the distribution of persons currently on treatment in the country as at April 2021"
width="500px" />

.footnote[
  <span style="font-size: 9px">
  <i style="font-weight: 600">Source:</i> The Nigeria National Data Repository (NDR) public dashboard
  </span>
  ]
]

.pull-right[
<img src="./snips/ndr2.png"
alt="a snapshot of the NDR public dashboard as at April 2021 showing the monthly viral load suppression rate by age age and sex"
width="500px" />
]

???

- In 2016, through the PEPFAR support, the journey to a National (HIV) Data Repository started. The repository contains the de-identified records of over 1.3 million HIV clients on treatment across the country.

- This is accessible to persons within the industry to track progress and improve programming at various levels. Linelist of these clients can be downloaded for further analysis and use.

- The pictures below show snapshot of the NDR public dashboard

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## <i style="font-family: 'Cormorant Upright', serif"> "It is essential to not only capture high-quality data, but to also continuously use and analyze the data to achieve maximum program impact.The only way to improve data is to use the data” </i>
]

.pull-right[

.footnote[
  <span style="font-size: 9px">
  <i style="font-weight: 600">Source:</i> PEPFAR DATIM
  </span>
  ]

]

???

According to the PEPFAR MER version 2.5,

"It is essential to not only capture high-quality data, but to also continuously use and analyze the data to achieve maximum program impact.The only way to improve data is to use the data”

---
## NDR data use

Analysis is traditionally conducted using spreadsheets. Challenges include:

+   The software must be installed on the user’s computer.

+   The user must be familiar with the formula for calculation of the indicator of interest.

+   Can be quite heavy and difficult to manipulate

+   It is error-prone. Sometimes these errors go unnoticed

+   Periodic analysis can be quite tedious and time-consuming

???

- However, analysis of the downloaded data is traditionally conducted in spreadsheet applications.

- While these provide a great platform, the downsides include: the software must be installed on the user’s computer, and the user must be familiar with the formula for the calculation. The point-and-click nature makes it error-prone, with difficult reproducibility. Also, performing the same analysis periodically can be quite tedious and time-consuming 
---
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#<span style="font-family: 'Cormorant Upright', serif; text-shadow: 2px 2px #333"> tidyndr package</span>

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- A cross-functional team of 7 clinical and data experts came together to put together the {tidyndr} package to support analysis of the NDR patient linelist.
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## Design

.footnote[
    <span style="font-size: 9px; padding-left:50px">
        <i style="font-weight: 600">Artwork by</i> @allison_horst
    </span>
]

???

The package is designed in line with the data science workflow proposed by Hadley Wickham. Varying functions are responsible for importing, tidying, transforming, and summarizing the analysis.
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## Supporting packages
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- dplyr

- forcats

- janitor

- vroom

- tidyr

- tibble

- lubridate

- magrittr

- purrr

]
.pull-right[
<img src="./snips/tidyverse_celestial.png"
alt="a picture of logos of tidyverse packages"
height="400px" />

.footnote[
    <span style="font-size: 9px">
        <i style="font-weight: 600">Artwork by</i> @allison_horst
    </span>
    ]

]

???

Nine tidyverse and tidyverse-related packages were used in the development of the package.
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## The package

```r
# from CRAN
install.packages("tidyndr", dependencies = TRUE)

# developmental version from github
devtools::install_github("stephenbalogun/tidyndr", build_vignettes = TRUE)
```

<br/>

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#### Importing function

```
read_ndr()
```
#### Treatment function

```
1. tx_new()
2. tx_curr()
3. tx_appointment()
4. tx_ml()
5. tx_ml_outcomes()
6. tx_rtt()
7. tx_mmd()
```
]

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#### Viral Load functions

```
1. tx_vl_eligible()
2. tx_pvls_den()
3. tx_pvls_num()
4. tx_vl_unsuppressed()
```
#### Summary functions

```
1. summarise_ndr()
2. disaggregate()
```
]

???

- tidyndr can be installed from both github and CRAN

- where possible, the functions are named similar to the PEPFAR program indicators that users are already familiar with

- these functions are grouped into 1 importing function, 7 treatment-related functions, 4 viral load-related functions and 2 summary functions.
---
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## Benefits

+ Fast platform for data analysis and automation of routine projects.

+ Reproducibility - eliminate random errors

+ Scalable to support the analysis of large-volume data

+ Support easy identification of program gaps and those responsible for tailored intervention

+ Provides a foundation upon which other relevant program applications can be built.

<br/>
<i style="font-weight: 600; color: #75aadb">The NDR is still evolving, and the package is planned to evolve alongside</i>

]

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<br/>
<img src="./snips/achiever.png"
alt="a snip of the demo shinyapp dashboard built on the {tidyndr} package showing the map of Nigeria and performance summary based on some indicators"
height="250px" />

<hr/>

]

???

Benefits of the package includes:

+ It provides a fast platform for data analysis and automation of routine projects.

+ Analysis conducted using the package is reproducible

+ It can be used to analyse large volume of data

+ It supports easy identification of program gaps for tailored intervention; and

+ Provides a foundation upon which other relevant program applications can be built

+ the Shinyapps dashboard and the weekly detailed analysis shown on the right were built on the package.
---
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## Thank you! (Eshey pupo!!)

.pull-left[
![Picture of the presenter - Balogun Stephen Taiye](./snips/stbalo.jpg)

<span style="font-weight: 700">Co-authors: </span> 
Olanrewaju SC, Okunuga OO,Kolade T, Abone G, Murtala-Ibrahim F, Omuh H
]

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???

- We encourage you to try out the package, feedback, contributions and suggestions are welcomed.

- Thank you for your time