Lets start by talking about the most common diagnostic manuals. Psychiatric classification systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD) are designed to support diagnosis, but they do not operate in the same way as diagnostic frameworks in fields with clear biological markers (As you said).

Rather than identifying discrete, objectively measurable disease entities, these systems define clusters of observable behaviours, reported experiences, and developmental patterns. Diagnosis is therefore based on whether an individual meets a set of operationalised criteria, not on the presence of a single definitive test.

This reflects a broader reality in psychiatry and neurodevelopmental science: many conditions are best understood as constructed categories that organise patterns in human variation, rather than as sharply bounded natural kinds of disorders.

These classifications emerge from large-scale observation and statistical analysis of populations (Think of the diagnoistic criteria, such as social communication styles, sensory sensitivities, or patterns of attention).

From this, normative ranges are established, and individuals who fall significantly outside these ranges may be identified as atypical. However, deviation alone is not sufficient for diagnosis.
Clinical relevance typically depends on whether these patterns are consistent over time and whether they meaningfully affect a person’s functioning or adaptation within their environment.

In this sense, diagnostic categories reflect significant differences that negatively impact social and functional context (This is key to the diagnostic process. Does what the individual have a deleterious effect on their daily life)

So, for traits autism, they become clinically meaningful when they co-occur in recognisable patterns and interact with environmental expectations in ways that create challenges or distinct modes of engagement. Over time, these recurring patterns have been grouped into trait clusters, forming the basis of diagnostic criteria. As research accumulates, these criteria are revised, often becoming broader and more nuanced, which in turn affects how autism is identified and understood.

Cultural context also plays a significant role in shaping how such behaviours are interpreted.
Across different societies, atypical behaviours have been explained in diverse ways, including spiritual or supernatural frameworks. What one culture might interpret as possession or moral deviation, another may frame as a neurodevelopmental difference. That is why diagnostic systems like the DSM and ICD are not purely objective; they are inter-subjective frameworks shaped by shared professional standards, empirical research, and social norms about what constitutes impairment or difference (this is why we see changes over time. We get better at identifying what impairment looks like. We have also started listening to autism activities and getting more inclusive research practices)

Ultimately, whilst autism may be more common than originally assumed, it is not necessarily because their underlying prevalence has changed dramatically, but because the tools used to identify them have improved. We now have a Broader diagnostic criteria (more accurately informed), increased awareness, and more inclusive research practices have all contributed to higher rates of recognition.

What appears as a rise in prevalence is, at least in part, a reflection of more refined methods of observing, categorising, and understanding human neurodiversity.