1. Geographic Pattern of Type 1 Diabetes Mellitus in Children in Central Ohio: Higher risk of New Cases in Rural and Urban Areas. Sasigarn A. Bowden, MD, John R. Hayes, PhD, Domenico F. Pietropaolo, MD, Robert P. Hoffman, MD Division of Endocrinology, Department of Pediatrics, Nationwide Children’s Hospital The Ohio State University College of Medicine, Columbus, Ohio, USA
The incidence of T1DM has been increasing worldwide at a rate too rapid to be due to changes in genetic risk.
Environmental factors are likely to contribute to the increasing incidence.
There were studies in Europe that showed marked geographic variations in the incidence of T1DM in Europe.
A study in Finland found that the incidence of T1DM was the highest (41.4/100,000/year) in rural heartland areas while the increase in incidence was sharpest in urban areas.
A more recent in Austria found a higher incidence in rural area, a significant inverse negative association of population density with incidence.
BACKGROUND:
We observed the highest number of new cases in the low and high density regions, whereas, the fewest new cases were in the middle density region (p<0.001). (see Table 1)
Graph 1 shows the quadratic relationship between T1DM and population density. We used population density to define the urban, suburban and rural areas. The regression model defined in the Table 1 accounted for 29.4% of the variance in cases per 1000 children in a zip code area.
Graph 2: When total population density is used, we see the same pattern of the quadratic relationship between T1DM new cases and population.
Zip codes with higher incomes households tend to have more diagnosed cases (p=0.006).
There was a positive correlation between HbA1C at diagnosis and high child population density (p<0.001).
There was no correlation between HbA1C at diagnosis and median family household income (p=0.8).
There was no significant association between child population density and diabetic ketoacidosis (p=0.23) by logistic regression).
RESULTS:
Graph 2 shows Similar quadratic relationship between new T1DM cases and population density
To evaluate the geographic pattern of incidence of T1DM in central Ohio and to investigate the risk of T1DM according to the level of urbanization of the place of residence using Geographic mapping (Maptitude).
To investigate any correlation between clinical characteristics at diagnosis and different geographic areas.
OBJECTIVE:
Model for ln (T1DM cases)
Unstandardized coefficients
Standard Error
Standardized Coefficient t
Sig
(Constant)
5.238
0.351
14.932
.000
Ln(Child Density)
-1.374
.182
-3.332
-7.531
Ln(Child Density)2
.133
.019
3.007
6.896
Median family income
0.0896
.199
2.776
.006
There was a geographic variation in the incidence of T1DM in central Ohio—highest in rural and urban areas, lowest in suburban area.
High cases in rural areas or low population density areas may reflect increased exposure to environmental toxins (drinking water contaminated with nitrates from fertilizer or consumption of foods containing nitrates nitrites, and N-nitroso compounds); or this may reflect lower exposure to protective environmental factors, e.g. infections early in life.
More cases in urban areas than suburban areas may also reflect more exposure to environmental factors in urban areas.
The disease severity as determined by initial HbA1C was higher in the urban area.
This data suggests that some environmental risk factors in those areas may play a role in the development of T1DM.
SUMMARY AND CONCLUSION
The Nationwide Children’s Hospital at Columbus, Ohio is the only children’s hospital in central Ohio caring for children with new onset T1DM. Thus, we believe that the number of children receiving initial diabetes management and education at our facility represents nearly all of the newly diagnosed children in the area.
Our hospital’s Diabetes Team records were used to ascertain the new cases from year
Zip code of place of residence, and clinical characteristics at diagnosis were analyzed.
Cases were summarized by zip code and the counts were transformed using natural logarithms.
Similarly the natural logarithm of population density for children was computed for each zip code.
Density was calculated by dividing the number of children under 18 living in a zip code by the area of the zip code.
Natural logarithm of median family household income was computed.
Multiple polynomial regression was used to test the relationship between number of cases within a zip code and the linear and quardratic functions of child population density and median family income.
The regression analysis only includes zip codes with at least one case.
DESIGN/METHODS:
Graph 1 shows the quadratic relationship between T1DM new onset cases and child population density
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