Two countries reporting the same indicator may be measuring different things. A dataset that drops the footnote turns that into a ranking.
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WHO pages, filters, fields and a schedule
Built, monitored and repaired by our team
API, JSON feed or CSV, XLSX and JSON files
ScrapeIt runs the collection as a managed service. You name the indicators, countries or topics; we build the pipeline with comparability notes and data type on every row, definition breaks flagged, and hand back CSV, JSON, Excel or a push into your warehouse.
Gaps stay gaps. We do not interpolate missing country-years, because the gaps correlate with what is being measured and filling them biases the result while hiding that it did.
We collect only publicly available data - indicators, fact sheets and country profiles, everything a visitor can see on the WHO website - and we collect it legally, with requests paced.
Indicator records carry the indicator name and code, country, region, year, value, unit, and the disaggregation where published - sex, age group, urban or rural.
Comparability notes travel with the value rather than in separate documentation. Where the source qualifies a figure - estimated rather than reported, different definition, incomplete coverage - that qualification is a field on the row. It is the single most important design decision on this source.
Data type is recorded: reported by the country, estimated by the organisation, or modelled. Those three are different epistemic objects and treating them as one number produces analysis that cannot be defended.
Fact sheet records carry the topic, section structure, publication and update dates, and the text, kept separate from the statistical records because guidance and measurement are different things.
Every row carries the collection timestamp and the source address.
Time series assembly is the main deliverable and it needs definition breaks flagged rather than smoothed. A series annotated with its methodology changes is usable; one that looks continuous and is not will eventually produce a wrong conclusion nobody can trace.
Regional and income group aggregates are published alongside country figures and are worth collecting as their own records, since they are computed by the organisation rather than derived by a user and carry their own methodology.
Fact sheet monitoring tracks how guidance on a disease or risk factor changes, which for public health communication work is a different and useful product from the statistics.
Every value keeps the fields that make it defensible - indicator code, country, year, unit, disaggregation, data type and comparability note - so a figure pulled into a report can always be traced back to what the source said about it.
The World Health Organization publishes global health statistics, disease fact sheets, guidelines and country profiles. Its Global Health Observatory carries indicators covering mortality, disease burden, health systems, risk factors and coverage, by country and year.
The defining problem with this data is comparability. Countries differ in how they collect, define and report health statistics, and the organisation documents those differences in notes attached to the data. Those notes are where the analytical honesty lives, and they are exactly what a careless pipeline drops on the way to a tidy table.
A dataset of indicator values without its caveats produces country rankings that look authoritative and compare, in some cases, genuinely different measurements. That is a serious error in a field where the numbers inform funding and policy.
The data and fact sheet sections respond directly, and everything a visitor can see there - indicator values, country profiles and fact sheets - can be collected. We pace requests as part of the service.
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan detailsGlobal health data is the clearest case in this catalogue where the footnotes matter as much as the figures.
A country with a functioning civil registration system reports mortality from records. A country without one has its mortality estimated from surveys and models. Both appear as a number against a country and a year. Ranked side by side without their data type, they produce a comparison that a health statistician would not make and that a spreadsheet makes effortlessly.
The same applies to definitions. Indicator definitions are revised over time and adopted by countries at different moments, so a multi-year series can contain a definitional break that looks exactly like an epidemiological trend.
The third issue is coverage. Not every country reports every indicator every year, and gaps are not random - they correlate with the health system capacity being measured. A dataset that silently interpolates or drops missing values biases the result in a predictable direction and hides that it did so.
The fourth is that the real difficulty lies in handling this data responsibly, because the numbers inform funding and policy - and that is where the effort belongs.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, tracks indicator definition changes as they are published, and repairs the collector before a multi-year series acquires an unexplained break.
You see a sample first, in your format, over the indicators you actually use, with data type and caveats attached so you can see on real countries what a bare value would have hidden.
Still unsure? Write to info@scrapeit.io
Because they are what stops a comparison being wrong. A figure reported from civil registration and one estimated from surveys look identical as numbers and are different objects - ranked side by side without that field, they produce a comparison no health statistician would make.
No. Gaps correlate with the health system capacity being measured, so interpolating biases the result in a predictable direction while hiding that it happened. Gaps stay gaps and are marked as such.
Yes, with definition breaks flagged rather than smoothed. Indicator definitions are revised and adopted by countries at different times, so a break can look exactly like an epidemiological trend to anyone reading a continuous-looking line.
No, they are computed by the organisation with their own methodology, which is why we collect them as their own records rather than deriving them. A user-computed aggregate and the published one can legitimately differ.
Yes. The ScrapeIt WHO API returns indicator name and code, country, year, value, data type and comparability notes as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
You share your needs, expectations, and desired timeframe. We'll suggest the best solution based on your request and budget.
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Tell us which sites, which fields and how often. A couple of lines is enough to start.
WHO data from €169/month. Free project assessment, reply within 1 business day.