Artikel
Prescription of drugs for pregnant women and determinants
Determinanten der Arzneimittelverordnung in der Schwangerschaft
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Autoren
Veröffentlicht: | 16. Oktober 2003 |
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Gliederung
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Background and Aim
Although prescribing drugs for pregnant women is very common in Germany, little is known about factors which influence prescribing. The aim of this study was to qualify various factors and to quantify their impact.
Material and Method
Data of 40364 members of a German health insurance who bore their child between 06-01-00 and 05-31-01 were analysed. Factors such as age, state of residence, compulsory or voluntary insurance, hardship and personal status (i.e.blue or white collar worker, student, recipient of social aid, pensioneer, self-employed or unemployed) were considered as possibly influencing the number of medicines prescribed per person. Bivariate and logistic models were performed to explore the effects on this outcome by using 50% and 75% levels as cutpoints.
Results
98,8% out of all women were prescribed drugs during pregnancy. The median number of prescribed medicines was 7 (cutpoint1,β1), 75% had up to 11 drugs prescribed (cutpoint2,β2). No general differences were seen between North, South, West or East Germany, but some between single regions: Residents of Saarland had nearly double risk of receiving medication compared to subjects from Hessen (RR1=1,5857;RR2=1,9674). There were significantly higher prescription rates in women aged 36 and above. Women who were voluntarily insured had a slightly reduced risk to receive drugs. Among all personal status groups, women who were on social aid (0,85%) showed the highest average number of drugs prescribed (10,05). 80% of these women were as well registered cases of hardship. Hardship status itself was significantly associated with increased prescription volume (β1= 0,3814;β2=0,3880). Therefore logistic models to examine the effect of being on social aid were calculated including hardship variable (β1= -0,0427;β2=0,1647) and excluding it (β1= 0,2378;β2= 0,4537).
Conclusion
Several socio-economic and regional determinants for prescribing were identified. Other factors of interest (income, profession, marital status) were missing in this data set. There is a need for more detailed data.