NUTRITRAK PLATFORM

A platform built to watch the trend,
not just take the measurement.

NutriTrak is ZMF's longitudinal growth monitoring and predictive analytics platform — designed to follow the same children and mothers repeatedly over time, link their growth data to the conditions they live in, and surface decline while it's still early enough to act on simply and cheaply.

HOW IT WORKS

From repeated measurement to early warning

A single visit produces a data point. NutriTrak's job starts where that point ends — turning a series of routine visits into a trajectory a community health worker or researcher can actually read.

WHAT'S COLLECTED
  • Anthropometric readings (MUAC, weight-for-age)
  • Illness history & dietary indicators
  • Maternal nutrition & infant feeding data
  • Household food security, livelihoods, shocks
  • Water access & sanitation conditions
NUTRITRAK ENABLES
  • Links each visit to the child's full history
  • Builds individual growth trajectories over time
  • Connects trajectories to household conditions
  • Flags faltering as a trend, not a single reading
  • Early identification of nutritional risk
  • Relapse & recurrence surveillance
THE CORE MECHANISM

Why a single reading isn't enough

A child screened today might have a reading that looks stable on its own. But that single number can mean two very different things.

It could belong to a child who has been steady for months — or to a child who has been slowly losing ground for months, and is now several measurements into a decline that hasn't yet crossed the threshold that would get them noticed.

The signal isn't in any one visit. It's in the line connecting them.

Sample child · 14 months
● MONITORING ACTIVE
JANMARMAYJUL
DEVIATION ALERT
−1.2 SD
flagged 6 weeks early
NEXT VISIT
In 4 days
auto-scheduled
COHORT DESIGN

Two cohorts, one continuous view

NutriTrak is built around following two linked groups over time, rather than surveying a population once.

Child Cohort

Every child under five

All children under five years of age within an enrolled household are followed through regular visits. Repeated measurements build individual growth trajectories — making it possible to track deterioration, recovery, and relapse over time, including differences between siblings in the same household.

Maternal & Birth Cohort

From pregnancy onward

Pregnant women are followed through pregnancy and the postnatal period; their newborns are enrolled and monitored from birth. This extends observation into the first 1,000 days — the window where maternal nutrition, early growth, and infant feeding practices shape much of what follows.

PLATFORM CAPABILITIES

What NutriTrak does

Continuous Growth Monitoring

Repeated anthropometric measurement linked over time into individual growth trajectories, rather than isolated checkpoints.

Maternal & Infant Tracking

Monitoring from pregnancy through the postnatal period and into early childhood, covering the full first 1,000 days.

CMAM Continuity & Post-Discharge Follow-Up

Tracking children through admission, treatment, and recovery — and continuing to watch their trajectory after discharge, rather than ending visibility there.

Relapse Surveillance

Connecting episodes of deterioration over time, so recovery and recurrence are visible as one trajectory rather than disconnected events.

Household-Linked Vulnerability Monitoring

Periodic household assessments — food security, livelihoods, shocks, water and sanitation — linked to the same children's growth data.

Growth Trajectory Analysis

Reading a child's measurements as a trend over time, so a slow decline is visible as a slope rather than a sudden surprise.

Early Nutritional Risk Identification

Surfacing rising risk — using growth trends, illness history, seasonality, and household conditions together — while a child is not yet malnourished.

HOW IT FITS

Designed to plug into existing systems, not replace them.

NutriTrak is not designed to compete with screening, treatment, or surveillance systems already in place. It's designed to sit alongside them — adding the one dimension most conventional nutrition data structurally lacks:

Time. The same children, watched repeatedly, with their growth data linked to the household conditions they're living in.