Biomedical subjects
Publications and source records attributed to N Oden.
I derive two new statistics, Ipop and Ipop*, that adjust Moran's I to study clustering of disease cases in areas (for example, counties) with different, known population densities. A simulation of Lyme disease in Georgia suggests that these new statistics can be more powerful than those currently in use. This is because they consider both spatial pattern and non-binomial variance in rates as evidence supporting disease clusters.
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I applied principal components analysis to Humphrey central 24-2 threshold values from both eyes of 304 clinically normal persons selected by simple random sample from Barbados, WI. The first component, accounting for 62 per cent of the variation, is equivalent to the average threshold value within persons. The first eigenvector, when represented by grey scale maps depicting a pair of eyes, reveals that, as average threshold increases, the visual field rises and flattens, like an umbrella that, initially closed, is simultaneously opened and thrust upwards. I verify three numerical predictions based upon this umbrella description. Much less important sources of variation involve disparity between fellow eyes, and hemimeridional and other symmetric differences within eyes. I discuss briefly possible physiologic explanatory mechanisms.
One hundred consecutive first admission patients with a DSM-III-R diagnosis of schizophrenia, schizoaffective disorder, or schizophreniform disorder were compared with 100 randomly selected community controls. Childhood histories of physical, medical, and perinatal trauma, as well as physical and cognitive development, were examined by structured interviews with all available mothers of patients and controls. The prevalence of specific psychiatric disorders and several medical illnesses among first degree and more distant relatives was determined by family history questionnaires. The patient group did not have an excess of childhood head injuries, serious infections, or perinatal/birth complications compared with controls. With social class level taken into account, it was found that the acquisition of reading skills occurred significantly later in patients than controls. Family histories of schizophrenia and thyroid disorders were significantly more frequent among patients than controls. These data fail to indicate any childhood physical or medical environmental trauma that could lead to an increased risk for schizophrenia, although patients were substance abusers to a greater extent than controls. This study also confirms the already known contribution of familial factors and suggests an association of the inheritance of thyroid disorders with schizophrenia. Delayed development of reading skills suggests that precursers of illness may appear early in life before psychosis is evident.
One can roughly divide disease cluster tests into area-based (using regional data) and point-based (using exact locations). We have compared the power of two area-based methods (Moran's I and I* (pop), a new method) to that of two point-based methods (the Cuzick-Edwards test and Grimson's test), using three realistic simulations of disease (fox rabies in England, childhood leukaemia in North Humberside, England, and Lyme disease in Georgia). The naive belief that point-based methods should be better is not supported: for the complex data simulated here, I* (pop) and the Cuzick-Edwards test had higher power than Grimson's method or Moran's I. I* (pop) capitalizes on high inter-region variability, while Moran's I cannot.
This paper discusses the distributions of several cluster statistics and illustrates their utility in the analysis of disease incidence or prevalence data arising in organized or structured environments. Such environments involve primary inhabitant locations in office buildings, manufacturing plants, apartment complexes and communities of homes, for example. The methods, which include some generalizations of some existing methods as well as some new ones, will be used to test for household clustering of Trypanosoma cruzi seropositivity data.