PubMed · 8166792
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J L Mills. 1993-10-14. Data torturing.. https://doi.org/10.1056/nejm199310143291613
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BACKGROUND: To evaluate the impact of diabetes mellitus in hospitals costs. PATIENTS AND METHODS: In a general hospital that covers a sanitary area of 120,873 inhabitants all the hospitalizations of the year 1993 have been analyzed. The patients have been classified according to the Patient Management Categories version 5.0 system that allows the evaluation of the presence of diabetic comorbidity. The direct cost of the stay was calculated by the days of hospitalization and consumption of complementary diagnosis tests according to the Relative Score of the Patient Management Categories. RESULTS: 5% of the hospitalized patients were diabetics. They caused 6% of hospitalizations and accounted for 8% of the total expense of hospitalization. The diabetic patients had a rate of hospital admission superior to non diabetic (1.4 vs. 1.1; p < 0.05). The presence of comorbidity was associated to an increase of the risk of dying in the inpatient period (odds ratio = 3.4; p < 0.01), to an increase of the hospitalization in 3.1 days (p < 0.0001) and to an increment of the cost of 31% (p < 0.0001). In the sanitary area the patients with diabetes mellitus caused a hospital expense of 193 millions of pesetas/100,000 inhabitants and year. CONCLUSIONS: Diabetes mellitus is a very important cause of comorbidity that provokes a notable increment of the hospital expenses. The economical impact of diabetes mellitus in the costs of hospitalization is so important that the cost of the preventive measures for their control would only be a small part of the hospital expenses that it generates.
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AIM: To accurately measure the prevalence of smoking in early pregnancy by census area units (CAU) in Christchurch. METHODS: Smoking status in pregnancy was determined by serum cotinine assay for all antenatal blood samples taken over a 6 month period. CAUs in Christchurch were grouped into quartiles according to the proportion of maternal smokers. Social factors from 1991 census data were used to describe the characteristics of each quartile. RESULTS: The overall rate of smoking in pregnancy was 33.0%. Rates ranged from 10.6% to 56.9% for the census area groups. CAUs in the upper quartile (39-57% of women smoking in pregnancy) were clustered together geographically and were associated with lower socioeconomic indices. The strongest correlation was between average income with smoking rates (Pearson correlation coefficient 0.76). CONCLUSION: Smoking rates in pregnancy have remained at around 30% for at least 20 years, with some areas of the city having rates nearly double this. It would seem logical to promote smoke-free pregnancy activities in localities with the highest rates of smoking. Future evaluation of the efficacy of such programmes should be done using objective measurements.