Bio-medical-sociology

In family histories we like to look on tree-like linking structures. Most frequently the choice is the descendant perspective (Top down). X, Y, Z have been the children of A and B and so on for a couple of generations. Bottom-up perspectives are equally feasible and modern patchwork families have more widespread representations of their families. Those representations were easy to do as families were lifelong bonds. Shorter family bonds, previously mainly caused by pre-mature deaths, are more common as people might have different partners and off-springs at different periods of their life course. Drawing family trees then looks more like a network structure of several families. History and literature is full of stories of how families aimed to keep their genealogy simple to the outside world. Modern days are no exception to this. Law had to adapt to these societal facts and changes thereof. Comparing decades over the last century there is, in my view, the remarkable trend to allow for more complexity in family histories, even after the 60s leading to many complete ruptures of family ties and links throughout the 70s and 80s.
With reducing fertility rates in most, not only western regions of the world, medical demography is back on the agenda. Similar to family trees, new forms of identifying promising pharmaceutical products have moved to more data-driven disease insights. Historically the local medical doctor had an overview about the likelihood of diseases following family’s medical histories over generations. Data-driven analyses, supported by data analytics and/or AI support, can learn permanently about potential and actual risks. New links of diseases are discovered this way extending the family doctor’s view of risks to watch out for in patients. Additional remedy and marketing potentials of existing drugs are also detected this way, beyond anecdotal evidence. Research published in the “Journal of biomedical semantics” by Vlietstra et al. (2020) classifies disease trajectories to construct knowledge graphs of biomolecular interactions. What previously a medical doctor in region could infer from his medical records in a less systematic way, can now be analysed on big data sets of countries, continents or even the global scale. Data is knowledge, and some already know, that this data-driven knowledge is worth a lot of money. Linking previously and seemingly unrelated facts or events, just like becoming aware of more complex family trees through DNA-analyses is the medical part of history. How we deal with this as families or societies as a whole, is the trickier part. Structural changes of societies are marked by decades-like changes, but specific events like “Fukushima”, Tschernobyl or other man-made rather than natural disasters have created new forms of contamination and the spreading of it. In addition to family trees we need broader consideration for knowledge bases to demonstrate, for example, the spread of cancer in the networked society. Additionally this evidence should have a stronger recognition in courts as prove of contamination lines. Statistical reasoning is more likely to become court-relevant. Hence, train the legal profession beyond what “statistical discrimination” is like. Causal mechanisms are manifold. Some are more likely than others. Semantic knowledge graphs remind us of the presence of reverse causality many relationships. Scientists need an optimistic state of mind to abstract from many intervening processes on health, be they tiny micro- or bigger macro-level societal effects.