Animal welfare organisations are very good at reporting success. Dogs adopted. Dogs sterilised. Dogs vaccinated. Animals rescued. Shelters built. Campaigns completed. Food distributed. Veterinary treatments delivered.
These figures are vital. They show activity, help demonstrate how money has been spent and allow organisations and authorities to communicate what they have achieved.
But there is another set of figures we hear much less about.
Dogs returned after adoption. Sterilisation complications. Post operative deaths. Dogs who died after entering shelters. Transfers that failed. Animals lost after relocation. Vaccination targets that were missed. Population programmes that did not achieve sufficient coverage. Campaigns that ended without producing the change they were designed to create.
If an organisation reports that 500 dogs were adopted, that sounds like a success. It may well be. But if 100 of those dogs were returned within six months, that information changes our understanding of the programme. It does not mean the adoption programme failed. It means we now know something important about it.
Why were dogs returned? Were behavioural problems involved? Were adopters inadequately prepared? Were particular types of placement more likely to break down? Did dogs adopted quickly return more often than dogs whose placements involved greater preparation? Were adopters offered support when problems first appeared? Without the return data, none of those questions is likely to be asked.
The same principle applies to sterilisation. A programme may proudly announce that 10,000 dogs were sterilised. That tells us something about scale, but not necessarily about quality. How many experienced complications? How many required further treatment? How many died during or after surgery? How many were released before they had adequately recovered? How many were subsequently found with wound problems?
Those figures are not an argument against sterilisation. They are information that can make sterilisation safer.
If one clinic has a significantly higher complication rate than another, that is noteworthy. If complications increase when daily surgical numbers rise beyond a particular level, that matters. If particular post operative protocols produce better outcomes, that matters. We cannot learn any of this if the only number being collected is the number of procedures completed.
Shelters present the same problem.
Admissions are easy to announce. A municipality can say that 5,000 dogs have been removed from streets and accommodated in shelters. But admission is only the beginning of the welfare story. How many were still alive one month later? How many became sick? How many died from infectious disease? How many were euthanised? How many suffered injuries from other dogs? How many were adopted, released or transferred? How many remain there a year later?
Mortality is particularly important.
Every shelter will experience deaths. Some dogs arrive critically ill. Some are severely injured. Puppies may arrive with infectious disease. Elderly dogs die. Euthanasia may sometimes be necessary to prevent suffering. A mortality figure alone does not prove poor welfare. But the absence of mortality data prevents us from asking whether something is going wrong.
If deaths suddenly increase in one facility, that should trigger questions. If puppies are dying disproportionately, that should trigger questions. If mortality rises as occupancy increases, that should trigger questions. If one shelter consistently records very different outcomes from comparable facilities, understanding why could improve care elsewhere.
Failure data creates the possibility of learning. Without it, patterns remain hidden.
This is not only about individual dogs. Entire programmes can continue for years without anyone clearly establishing whether they have achieved their stated objective.
A campaign may announce that thousands of dogs were vaccinated. But what was the target population and what coverage was achieved?
A sterilisation programme may complete thousands of procedures. But did reproduction decline sufficiently to affect population dynamics?
A collection programme may remove thousands of dogs. But did the street population fall, or were those dogs rapidly replaced?
A shelter building programme may create thousands of places. But did overcrowding decrease, or did the new capacity simply fill?
Activity is not necessarily impact. That distinction becomes uncomfortable when organisations depend upon demonstrating success to funders, donors, politicians or the public. There is an understandable temptation to communicate the strongest figures available.
Nobody puts “we tried this and it did not work” on the front of a fundraising campaign. But perhaps animal welfare needs to become more comfortable with saying exactly that. A failed programme can be extremely valuable if somebody learns from it.
Perhaps the target was unrealistic. Perhaps the intervention was too small. Perhaps the wrong population was targeted. Perhaps resources were insufficient. Perhaps assumptions about community behaviour were wrong. Perhaps the programme worked initially but could not be sustained. Knowing that allows the next intervention to be better. Not knowing means somebody else may repeat exactly the same mistake.
There is also a difference between failure and wrongdoing. This distinction is important because fear of criticism can discourage organisations from publishing negative outcomes. A shelter that reports mortality openly may appear worse than one that publishes no mortality data at all. A rescue reporting adoption returns may appear less successful than one that simply counts dogs leaving through the door. A sterilisation provider publishing complications may attract scrutiny that a less transparent provider avoids.
That creates the wrong incentive. Transparency should not make responsible organisations look worse than organisations that tell us less. We should be far more cautious about interpreting the absence of reported problems as evidence that problems do not exist. Sometimes it simply means nobody is reporting them.
Good data should therefore include outcomes that organisations would rather were lower.
Returns. Complications. Deaths. Failed placements. Missed targets. Repeat collections. Animals lost to follow-up. Programmes that did not achieve their objectives.
None of those figures should automatically be treated as evidence of poor practice. They should be treated as information requiring context and, where necessary, investigation. Over time, they may tell us far more than success statistics alone.
They can show where systems begin to struggle. They can identify risk factors. They can expose unintended consequences. They can help organisations change protocols before the same problem affects hundreds more dogs.
Most importantly, they make it possible to distinguish between a programme that is genuinely improving and one that has simply become good at reporting its activity.
Dog Desk Animal Action believes animal welfare needs to become more interested in what did not work. Not because failure should be used to attack organisations trying to help dogs, but because dogs ultimately pay the price when unsuccessful approaches are repeated.
Success data tells us what we should continue doing. Failure data tells us what we need to change. We need both.
Because if nobody measures failure, almost every programme can eventually find a number that makes it look successful.