ALMA Scorecard Hub

About the data

The site uses two languages, namely, English and French.
The data is presented in the form of scorecards showing several indicators that are used to assess a country’s health priorities.
Indicators may vary from country to country, countries generally choose indicators based on their national health strategies.
There are currently: 40 malaria country level tools, 1 country level scorecard tool that combines malaria and neglected tropical diseases (NTDs).
There are 3 types of scorecards available: country scorecards, community scorecards and the ALMA scorecard for accountability and action.
The country scorecard provides a snapshot of a country’s health performance.
The community scorecard provides a means for assessing the quality of health services offered at the local health facility or catchment area.
The ALMA scorecard for accountability and action displays country level performance against key indicators, highlighting successes and shortfalls.

Accessing the data

To view the available scorecards visit Scorecard management tools.
No login credentials are needed to view or download the available scorecards
Login credentials are needed if an individual wishes to undertake any of the online courses offered by ALMA.
Under the tab country scorecards, a search can be refined through the use of available filters. The filters are for country, scorecard type and year. To access the scorecards on malaria, use the filter scorecard type and choose any of the following depending on specific context (Malaria, ALMA Scorecard for Accountability on Action, Commonwealth Malaria Tracker) from the drop-down menu.
Under the tab country best practices, a search can be refined through the use of available filters. The filters are for search, topic, types of best practices, country and year. To access best practices for malaria, use the filter topic, 2 choices are available: Malaria scorecard tools and End malaria councils from the drop-down menu.
The video below demonstrates how to navigate the site:

Video demonstration of the ALMA Scorecard Hub

What does the data look like?

Some scorecards are shared as pdf files (these are downloadable)

or directly from the web platform (not downloadable, user has to scroll through the file)

Scorecards are color coded, the main colors of use are green (on track), yellow (in progress), red (not on track), grey (not applicable or no data), white (no data).
Scorecards use arrows in combination with the colors, up arrow (to show an increase) and down arrow (to show a decrease).
The ALMA scorecards for accountability and action gives the ALMA scorecard, the Africa Summary report and country specific reports as pdfs in any one of the following languages, English, Arabic, Spanish, French, Swahili and Portuguese.
Data available is for the years 2015 to 2024.

Key points to consider

Downloading the materials for the online courses is highly beneficial, as they provide a comprehensive overview on how to create scorecards. This will help towards the understanding and interpretation of the scorecards.
The scorecards are highly country specific, indicators change according to the country’s health priorities.

How to use this data

As indicators vary across countries, it is necessary to check if the indicators of interest are within the available scorecards.
It is also necessary to check if the country of interest has made the country scorecard available.
As the scorecards are available in pdf format or as a web platform, information has to be extracted manually.

How to plot this data?

Show the code

# load the libraries
#devtools::install_github("ropensci/rnaturalearthhires")

library(ggrepel)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(readxl)
library(stringr)
library(dplyr)


source(here::here("theme_health_radar.R"))

# Define the Rwanda provinces
rwanda_provinces <- c("East", "South", "West", "North", "Kigali city")

# Load the map data for Rwanda
rwanda_map <- ne_states(country = "Rwanda", returnclass = "sf") %>%
  mutate(name = rwanda_provinces)

# Read in data
rwandap_data <- read_excel("data/rwanda_q1_provinces2022.xlsx")

cases <- rwandap_data$MalariaTotalCases 

# Merge the map data with your data
rwanda_data <- rwanda_map %>%
  left_join(rwandap_data, by = c("name" = "province"))

# Create a ggplot object with the Rwanda data
ggplot(data = rwanda_data) +
  # Add a spatial plot with fill based on the number of cases
  geom_sf(aes(fill = cases), color = "lightgrey", size = 0.3) +  
  # Apply custom theme
  theme_health_radar() +
  # Apply custom fill scale for continuous data
  scale_fill_continuous_health_radar(name = "Total Cases") +  
  # Add text labels for the names, with color based on the number of cases
  geom_sf_text(
    aes(label = name, color = ifelse(cases > quantile(cases, 0.9), "black", "white")), size = 3) +  
  # Use identity scale for color
  scale_color_identity() +  
  # Customize the theme to remove axis titles and ticks
  theme(
    plot.caption.position = "plot",
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.major = element_blank(), 
    panel.grid.minor = element_blank()) +
  # Add caption and title to the plot
  labs(
    title = "Reported malaria cases in Rwanda (2022)",
    caption = str_wrap(
      "The total number of new outpatient department (OPD) cases of malaria recorded at healthcare facilities in the five provinces of Rwanda during the first quarter of 2022. The East and South provinces show the lowest and highest number of reported cases respectively. Source: ALMA Scorecard Hub, Scorecard management tool.", 
      width = 85))

Show the code

# PDF containing data available at: https://alma2030.org/our-work/scorecard-tools/alma-scorecard-for-accountability-and-action/list-of-alma-scorecards/

# Creating the data frame
Country <- c("Angola", "Angola", "Angola",
             "Botswana", "Botswana", "Botswana",
             "Eswatini", "Eswatini", "Eswatini",
             "Mozambique", "Mozambique", "Mozambique",
             "Namibia", "Namibia", "Namibia",
             "South Africa", "South Africa", "South Africa",
             "Zambia", "Zambia", "Zambia",
             "Zimbabwe", "Zimbabwe", "Zimbabwe")

Year <- rep(2023, 24)  # Same year for all entries
Quarter <- rep(1:3, times = 8)  # Three quarters for each country
Operational_LLINS_IRS <- c(58, 62, 56,  # Angola
                           0, 66, 66,   # Botswana
                           90, 91, 91,  # Eswatini
                           100, 100, 100, # Mozambique
                           64, 64, 64,   # Namibia
                           70, 86, 86,   # South Africa
                           88, 100, 100, # Zambia
                           94, 94, 94)   # Zimbabwe

# Create the data frame
dataframe <- data.frame(Country, Year, Quarter, Operational_LLINS_IRS)

# Display the dataframe
knitr::kable(head(dataframe))

Country	Year	Quarter	Operational_LLINS_IRS
Angola	2023	1	58
Angola	2023	2	62
Angola	2023	3	56
Botswana	2023	1	0
Botswana	2023	2	66
Botswana	2023	3	66

Tracking indicator perfomance

The plot given below highlights how tracking indicator performance can be achieved.
Several country performances are highlighted using data obtained from ALMA scorecards for accountability and action.
The implementation indicator on operational vector control (LLINs/IRS) coverage was considered.

When visualising similar data over a much longer period of time, a line plot is useful. Here, the coverage by an LLIN and/or IRS in each of the Frontline 4 countries from 2015 to 2024 is visualised.

Show the code

# Read in data
E8_data <- read_excel("data/E8_implementation.xlsx")
E8_data$LLINs <- as.numeric(E8_data$LLINs)  

# Filter the data for specific countries
E8_data |> 
   # Keep rows where Country is one of the Frontline 4
  filter(Country %in% c("Botswana", "Namibia", "South Africa", "Eswatini")) |> 
  # Create a ggplot object with the filtered data
  ggplot(aes(x = Year, y = as.numeric(LLINs), group = Country)) +
  # Customize the x-axis to show yearly breaks
  scale_x_continuous(breaks = seq(floor(min(E8_data$Year)), ceiling(max(E8_data$Year)), by = 1)) +  
  # Customize the y-axis to show breaks from 0 to 100 in increments of 10
  scale_y_continuous(breaks = seq(0, 100, by = 10)) +  
  # Add lines to the plot, coloured by Country
  geom_line(aes(color = Country), lwd = 1) +  
  # Add points to the plot, coloured by Country
  geom_point(aes(color = Country), size = 2) +
  # Apply custom colour scale
  scale_colour_manual_health_radar() +  
  # Apply custom theme
  theme_health_radar() +
  # Add labels and title to the plot
  labs(
    title = "Operational LLIN or IRS coverage for the Frontline 4 (2015-2024)",
    x = "Year",
    y = "Coverage (%)",
    color = "Country",
    caption = str_wrap(
      "Operational long-lasting insecticidal nets (LLINs) or indoor residual spraying (IRS) coverage as a percentage of the at-risk population in the Frontline 4 countries. Solid lines represent the coverage trends taken during each quarter from 2015 to the end of 2023. Namibia experienced a large drop in coverage at the start of 2020, and only began to recover in 2023. South Africa and Eswatini appear to have consistently achieved above 70% coverage. Source: ALMA Scorecard Hub, ALMA Scorecard for Accountability on Action.", 
      width = 85
    )
  )

How can this data be used in disease modelling?

The data obtained from the scorecards can be used to:

Estimate the parameter values used in malaria transmission models
Inform quarterly changes to intervention coverage and access, which in turn allows for time-varying parameters that reflect changes in the health system over time
Represent regional differences with spatially explicit models
Evaluate different scenarios that are linked to the scorecard indicators

Generally, model outputs may complement the ALMA scorecards by forecasting key indicators and generating policy-relevant projections.