Methodology

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Data SourcesData Sources

Statistics South Africa

  • Community Survey, 2016
  • Census, 2011

Health Systems Trust

  • South African Health Review, 2019
  • District Health Barometer, 2015/16
  • District Health Barometer, 2018/19

Kenya Ministry of Health

  • Kenya Master Health Facility List, 2020
  • Kenya Harmonized Health Facility Assessment (KHFA), 2018/2019
  • Kenya Service Availability and Readiness Assessment Mapping (SARAM), 2013

Kenya National Bureau of Statistics

  • Kenya Integrated Household Budget Survey, 2016
  • Kenya Population and Housing Census, 2019

Kenya Healthcare Federation

  • Covid19 Treatment Centers, 2020

Institute for Health Metrics and Evaluation (IHME)

  • Respiratory infections under 5-year-olds, 2017
  • HIV prevalence estimates 15-49 year old, 2017

African Covid-19 Vulnerability Index (ACVI)African Covid-19 Vulnerability Index (ACVI)

The African Covid-19 Vulnerability Index (ACVI) is replicated from the Covid-19 Community Vulnerability Index (CCVI). The purpose of the index is to identify the geographic regions less resilient to the impacts of Covid-19. The index is built off the Centres for Disease Control and Prevention’s (CDC) Social Vulnerability Index (SVI). As defined by SVI,  social vulnerability refers to the resilience of communities when confronted by external stresses on human health, stresses such as natural or human-caused disasters, or disease outbreaks.

The African Covid-19 Vulnerability Index examined the CCVI themes and indicators and adapted them to the African context. For example, the lack of water, sanitation and hygiene are major problems experienced in Africa and contributes to the risk of Covid-19, which is why these indicators were included in the index for African countries.

The methods and calculations used in the ACVI are taken from the CCVI methodology and SVI data documentation respectively.

Themes and Indicators 

The ACVI is calculated for various African countries covering four themes:

  • Epidemiological factors
  • Healthcare systems
  • Demographics
  • Water, Sanitation and Hygiene (WASH) 

Theme 1: Epidemiological factors

Identifies communities with health issues that make them more vulnerable to the effects of Covid-19 such as HIV, Tuberculous and diabetes.

Theme 2:  Healthcare systems 

Covers topics such as health worker density and number of hospital beds among others. 

Theme 3: Demographics

Identifies areas with a high density and high percentage of elderly people aged 65 and over who are more vulnerable to the effects of Covid-19.

Theme 4:  WASH 

Identifies areas with no access to water and sanitation. 

The data used in the index has been sourced from various surveys, reports and estimates listed here [link to data sources page]

All raw data and calculations can be found here [link to openAFRICA with all ACVI data]

A detailed list of indicators used are represented in the table below:

ThemeVariableCountryIndicatorGeo PrecisionSource
Epidemiological FactorsCardiovascular conditionsSouth AfricaCardiovascular Prevention (prevalence of non-raised blood pressure)District MunicipalityDistrict Health Barometer, 2018/19
Epidemiological FactorsRespiratory conditionsSouth AfricaRespiratory infections under 5 year oldsDistrict MunicipalityInstitute for Health Metrics and Evaluation (IHME), 2017
Epidemiological FactorsDiabetesSouth AfricaDiabetes prevalence 15 years and olderDistrict MunicipalitySouth African Health Review, 2019
Epidemiological FactorsHIV/AidsSouth AfricaHIV prevalence estimates 15-49 year oldDistrict MunicipalityInstitute for Health Metrics and Evaluation (IHME), 2017
Epidemiological FactorsTuberculousSouth AfricaIncidence per 100,000 populationDistrict MunicipalityDistrict Health Barometer, 2015/16
Epidemiological FactorsTobacco smokingSouth AfricaPercent smoking tobaccoDistrict MunicipalityDistrict Health Barometer, 2018/19
Healthcare SystemsNumber of hospital bedsSouth AfricaPublic sector hospital bed density per 1000 uninsured populationDistrict MunicipalitySouth African Health Review, 2019
Healthcare SystemsNumber of healthworkersSouth AfricaPublic sector ealth professionals per 100,000 uninsured populationDistrict MunicipalitySouth African Health Review, 2019
Healthcare SystemsHealth insuranceSouth AfricaMedical scheme coverageDistrict MunicipalitySouth African Health Review, 2019
Healthcare SystemsAccess to medicineSouth AfricaProportion of public sector health facilities with essential medicinesDistrict MunicipalityDistrict Health Barometer, 2018/19
Healthcare SystemsAccess to pharmaciesSouth AfricaPublic sector pharmacists per 100,000 uninsured populationDistrict MunicipalitySouth African Health Review, 2019
DemographicsAgeSouth AfricaPopulation by age groupLocal MunicipalityCommunity Survey, 2016
DemographicsPopulation densitySouth AfricaPopulation densityLocal MunicipalityCommunity Survey, 2016
WASHAccess to sanitationSouth AfricaMain type of toilet facility usedLocal MunicipalityCommunity Survey, 2016
WASHAccess to waterSouth AfricaMain source of water for drinkingLocal MunicipalityCommunity Survey, 2016
Epidemiological FactorsCardiovascular conditionsKenyaPopulation reporting having heart problemCountyKenya Integrated Household Budget Survey, 2016
Epidemiological FactorsRespiratory conditionsKenyaPopulation reporting having respiratory infectionsCountyKenya Integrated Household Budget Survey, 2016
Epidemiological FactorsDiabetesKenyaPopulation reporting having diabetesCountyKenya Integrated Household Budget Survey, 2016
Epidemiological FactorsHIV/AidsKenyaHIV prevalence estimates 15-49 year oldSubcountyInstitute for Health Metrics and Evaluation (IHME), 2017
Epidemiological FactorsTuberculosisKenyaPopulation reporting having TBCountyKenya Integrated Household Budget Survey, 2016
Healthcare SystemsICUsKenyaCovid-19 Treatment CentersCountyCovid19 Treatment Centers, 2020
Healthcare SystemsVentilatorsKenyaPercent type of emergency breathing interventionCountyKenya Harmonized Health Facility Assessment (KHFA), 2018/2019
Healthcare SystemsNumber of hospital bedsKenyaNumber of hospital bedsSubcountyKenya Master Health Facility List, 2020
Healthcare SystemsNumber of healthworkersKenyaCore health workforce per 10,000 populationCountyKenya Harmonized Health Facility Assessment (KHFA), 2018/2019
Healthcare SystemsHealth insuranceKenyaPercent with health insuranceCountyKenya Integrated Household Budget Survey, 2016
Healthcare SystemsAccess to medicineKenyaPercent of health facilities with essential medicineCountyKenya Harmonized Health Facility Assessment (KHFA), 2018/2019
Healthcare SystemsAccess to pharmaciesKenyaNumber of pharmaciesSubcountyKenya Master Health Facility List, 2020
Healthcare SystemsAccess to pharmaciesKenyaNumber of Community Health UnitsSubcountyKenya Master Health Facility List, 2020
DemographicsAgeKenyaPopulation by age groupSubcountyKenya Population and Housing Census, 2019
DemographicsPopulation densityKenyaPopulation densitySubcountyKenya Population and Housing Census, 2019
WASHAccess to sanitationKenyaType of toilet facilitiesSubcountyKenya Population and Housing Census, 2019
WASHAccess to waterKenyaMain source of water for drinkingSubcountyKenya Population and Housing Census, 2019
Table 1. Indicators used to calculate ACVI 

Calculating the ACVI

A vulnerability score has been created for each of the four themes at each administrative level. The overall ACVI has been calculated using the vulnerability score calculated for each theme. Each indicator was ranked against all the geographies in that particular administrative level. The rankings for each indicator were summed up for each geography and the sum was ranked against all geographies. The resulting value is the vulnerability score used to identify the vulnerability rating.

The overall ACVI consists of adding up the vulnerability score for each theme and ranking the sum against all geographies. The resulting value was the vulnerability score used to identify the vulnerability rating. This process was done for each administrative level. 

All themes were weighted equally as based on the CDC SV model. The vulnerability rating is classified according to the CCVI model:

Score (%)Rating
<20Very Low
20-40Low
40-60Moderate
60-80High
>80Very High

Data sourcing and HURUmap visualisationsData sourcing and HURUmap visualisations

Code for Africa’s data team sorted through each country’s government statistics and national surveys and reports to find the required datasets. As this was a relatively large data collection task involving various countries, Google Sheets was used to track the status of the process. This allowed the assignment of tasks to team members and changed the status of the tasks easily.

A lot of the datasets were in pdf, but for the purpose of this project the datasets needed to be in machine-readable format. We scraped the data tables from the pdf reports using tools such as Tableau and Cometdocs to generate .csv files. 

Some datasets were downloaded in spreadsheet format from interactive data dashboards, such as the Statistics South Africa SuperWEB

The datasets often needed additional cleaning as scraping tools aren’t always entirely accurate – and to remove data not required. The datasets were cleaned using Google Sheets.

Each dataset was then uploaded as .csv files to openAFRICA. From past experience, reports or datasets are sometimes taken down or misplaced on a website resulting in broken links. This problem is solved by uploading all datasets used on openAFRICA.  openAFRICA built by Code for Africa is Africa’s largest open data portal that allows an user to upload, search and download datasets in various file formats.

The dataset needs to be arranged in standardised format in order to be visualised in HURUmap. The dataset is required in long data format and the appropriate geographic data, such as the administrative level and codes need to be added. For this formatting purpose, an open source online tool called Workbench was used. We created a workflow with the relevant steps to produce the data into the required format.

Each dataset is presented into a separate .csv file which is uploaded to the PostgreSQL  database. Once the datasets are uploaded to the database, the data visualisations were easily created using the HURUmap visual plugin on the website’s dashboard. The HURUmap visual plugin has features to select the dataset, chart type and chart title which creates the visualisations seen on the website. 

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This Code for Africa initiative was made possible with support from the Pulitzer Center on Crisis Reporting.
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This Code for Africa initiative was made possible with support from the Pulitzer Center on Crisis Reporting.
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