Path
Public Health Analyst
For analysts working with routine health system data — coverage, cascades and completeness — with the epidemiological reasoning to interpret them.
Competencies
Routine health information systems
IntermediateWork confidently with DHIS2-style aggregate reporting and understand its denominators.
Coverage and cascade analysis
IntermediateCompute coverage, dropout and cascade indicators and interpret them correctly.
Completeness adjustment
AdvancedDistinguish low performance from low reporting, and adjust for it explicitly.
Before you start
- Familiarity with routine health information — a DHIS2 or EMR export, a facility register, a coverage indicator you have had to explain.
- No programming background. The Python stage starts at the install.
The route through
Stage 1
Ground the numbers
Define a coverage indicator you can defend — including what a facility that did not report does to its denominator.
Stage 2
Get fluent in Python
Read any health information system export without corrupting it, and leave behind a script that reruns next quarter without being edited.
Stage 3
Make the data trustworthy
Turn a raw export into a table you would defend line by line, and hand over a cleaning log that answers the auditor's question before it is asked.
Stage 4
Put the files together
Assemble several exports into one analysis table you would defend column by column, with every join proved, every grain stated and every denominator sourced.
Stage 5
Check it like an auditor
Assess your own data before a donor does — five dimensions with measures, a recount against the source, and a report where every finding has an owner and a date.
Stage 6
Define what you report
Write indicator definitions two analysts compute the same way, defend the denominator, and set a baseline and target that survive a mid-term review.
Stage 7
Put an interval on it
Analyse a cluster survey the way its design requires — weights from the frame, a measured design effect, and an interval that says what the sample can and cannot settle.
Stage 8
Know where it came from
Read a routine reporting system as the database it is, pull an extract you can point at months later, and answer what was counted for any figure it produces.
Stage 9
Apply it to nutrition
Take the methods into one sector — WHO growth standards, SMART plausibility, the IPC phases and the Sphere performance thresholds, on a survey and a treatment register.
Stage 10
Apply it to public health
Rates with person-time denominators, treatment cascades, coverage three ways, and an outbreak line list turned into a curve, attack rates and a case fatality you can defend.
Stage 11
Apply it to WASH
The JMP service ladders and the Sphere minimums on a household survey, then a monitoring register with repeat visits that turns one functionality rate into three and bounds each against the rounds nobody drove.
Stage 12
Apply it to food security
FCS, HHS, rCSI and the livelihood coping module built from raw components and found to disagree by a factor of seven, then a price series whose seasonality dwarfs the programme effect and the evidence table that reports both.
Stage 13
Apply it to protection
The analyses you must decline to publish, alongside the ones that matter — a consent-gated referral pathway, a nineteen-point equity gap located at a specific gate, and a caseload that explains two other tables.
Stage 14
Apply it to education
Gross against net enrolment on a projected denominator, two attendance numbers twenty-six points apart, a cohort through promotion and repetition, and two assessment rounds whose instruments differ.
Stage 15
Say how sure you are
An interval on every proportion, the right test for a comparison, an effect size beside every p-value, and the count of comparisons that turns two striking schools back into noise.
Stage 16
Model more than one thing at once
A coefficient is a comparison — which one, between which units, adjusted for what. An odds ratio your reader will misread, a covariate that removes 42% of the effect, and a model that explains three per cent and settles a targeting decision.
Stage 17
Say what caused it
A seven-point gain that is entirely the school year, a comparison group imbalanced on every characteristic measured, and the minimum detectable effect that decided the answer before any data existed.
Stage 18
Put it in front of them
The mark the comparison implies, an interval that stops a ranking, a palette that already means something to this audience, and a figure generated from the dataset so the chart and the sentence cannot drift.
Stage 19
Make it rerunnable
Raw data read-only and code the only thing edited, an environment pinned so a colleague's laptop gives your numbers, checks that stop the pipeline rather than producing a plausible wrong one, and a handover a successor can act on.
Stage 20
Put it in front of the decision
A page that answers one question rather than twenty, the definition panel that stops the monthly argument, and the report whose findings, limitations and recommendation survive being read separately.
Recommended projects
How the skill is assessed
Can adjust a coverage figure for reporting completeness and explain the adjustment.
EvidenceWritten methodology note plus the accompanying analysis.
Can build and interpret a treatment or immunisation cascade.
EvidenceCascade analysis with stated assumptions at each step.
Health system data is aggregate, late, and incomplete in patterned ways. This path is about reading it accurately anyway.
What this path still needs
Three courses are published: Data Analysis Foundations, Python for Programme Data and R and the Tidyverse for Programme Data. The routine-systems and epidemiology content sits in Routine Data and DHIS2 under Indicators and Measurement and in Public Health and Epidemiology under Sector Analysis — both on the programme roadmap, neither written yet. See the programme.