Rising referrals, progress or displacement?

Rhoda, I would be cautious about interpreting an increase in referrals as a positive outcome on its own. Referral numbers tell us that movement through the referral pathway is occurring, but they do not tell us whether women actually received the service they were referred for. The same increase could reflect improved identification and access, or repeated referrals, incomplete handovers, or women moving between services without successful linkage.

From the service dataset alone, I would therefore want to follow the referral cascade rather than stopping at referral volume: number referred → referral received by the destination service → appointment/assessment completed → service actually received → follow-up or case resolution, where appropriate. Ideally, these indicators should be disaggregated by facility, service type and relevant client characteristics while protecting confidentiality.

I would also look for unique client identifiers or safe linkage mechanisms, referral dates, destination and receiving-service confirmation, time from referral to service, referral completion rates, repeat referrals, missed appointments and documented reasons for non-completion. Where linkage is not possible, a combination of routine records, client follow-up and qualitative interviews with service providers and women can help explain what is happening.

So, if all we have is “referrals increased by 30%,” we can confidently say referral activity increased—but we cannot automatically conclude that access or service uptake improved. To make that inference, we need evidence further down the care pathway.

Building on Dr. Magoba's cascade point — there's
also a structural reason referral volume gets read as progress rather than
interrogated: it's the easiest number to report. Referral counts require no
follow-up infrastructure, no linkage tracking, no confidentiality-protected
identifiers across services — so they become the default metric not because
they're the best proxy for access, but because they're the cheapest to produce.
That creates a quiet incentive for both funders and implementers to treat
throughput as outcome, since the alternative (tracking the full cascade Dr.
Magoba outlined) is expensive and often politically inconvenient if it reveals
leakage.

So the question isn't only "what additional
data would tell us the difference" — it's also "who currently
benefits from not collecting it." A rising-referrals number that goes
unquestioned often does so because no one downstream is incentivized to
complicate the story.

Charles, I agree with your point that this is not simply a question of having more data; it is also a question of what the system chooses to measure, reward, and report.

Referral counts are attractive because they are relatively easy and inexpensive to generate, but their simplicity can create a measurement blind spot: activity becomes a proxy for outcome. Once referral volume is incorporated into performance reporting or funding narratives, there may be little incentive to invest in the more resource-intensive tracking needed to establish whether referrals translate into completed services and meaningful outcomes.

This is why I think the M&E question should go beyond “Did referrals increase?” to “What happened after the referral, for whom, and where did the pathway break down?” The referral cascade can make that leakage visible while maintaining confidentiality and proportionality. Even a small set of indicators—referral received, service accessed, time to service, non-completion, and reason for non-completion—could substantially change what the programme understands as success.

Your question of “who benefits from not collecting it?” is therefore important. It shifts the conversation from data availability to data governance, accountability, and incentives. Ultimately, an indicator should not become a measure of success simply because it is convenient to collect. The real value of M&E is to make the pathway—and its gaps—visible enough to support corrective action.