Mumtaz Begum1,2,3, Rhiannon M Pilkington1,2, Catherine R Chittleborough1,2, John W Lynch4,5,6, Megan Penno2,7, Lisa G Smithers8,9. 1. School of Public Health, The University of Adelaide, Level 9, AHMS Building, North Terrace, Adelaide, SA, 5005, Australia. 2. Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia. 3. Department of Food and Nutrition, College of Home Economics, University of Peshawar, Peshawar, Pakistan. 4. School of Public Health, The University of Adelaide, Level 9, AHMS Building, North Terrace, Adelaide, SA, 5005, Australia. john.lynch@adelaide.edu.au. 5. Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia. john.lynch@adelaide.edu.au. 6. Population Health Sciences, University of Bristol, Bristol, UK. john.lynch@adelaide.edu.au. 7. Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia. 8. School of Public Health, The University of Adelaide, Level 9, AHMS Building, North Terrace, Adelaide, SA, 5005, Australia. lisa.smithers@adelaide.edu.au. 9. Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia. lisa.smithers@adelaide.edu.au.
Abstract
AIMS/HYPOTHESIS: Evidence of an association between maternal smoking during pregnancy (prenatal smoking) and childhood type 1 diabetes is mixed. Previous studies have been small and potentially biased due to unmeasured confounding. The objectives of this study were to estimate the association between prenatal smoking and childhood type 1 diabetes, assess residual confounding with a negative control design and an E-value analysis, and summarise published effect estimates from a meta-analysis. METHODS: This whole-of-population study (births from 1999 to 2013, participants aged ≤15 years) used de-identified linked administrative data from the South Australian Early Childhood Data Project. Type 1 diabetes was diagnosed in 557 children (ICD, tenth edition, Australian Modification [ICD-10-AM] codes: E10, E101-E109) during hospitalisation (2001-2014). Families not given financial assistance for school fees was a negative control outcome. Adjusted Cox proportional HRs were calculated. Analyses were conducted on complete-case (n = 264,542, type 1 diabetes = 442) and imputed (n = 286,058, type 1 diabetes = 557) data. A random-effects meta-analysis was used to summarise the effects of prenatal smoking on type 1 diabetes. RESULTS: Compared with non-smokers, children exposed to maternal smoking only in the first or second half of pregnancy had a 6% higher type 1 diabetes incidence (adjusted HR 1.06 [95% CI 0.73, 1.55]). Type 1 diabetes incidence was 24% lower (adjusted HR 0.76 [95% CI 0.58, 0.99]) among children exposed to consistent prenatal smoking, and 16% lower for exposure to any maternal smoking in pregnancy (adjusted HR 0.84 [95% CI 0.67, 1.08]), compared with the unexposed group. Meta-analytic estimates showed 28-29% lower risk of type 1 diabetes among children exposed to prenatal smoking compared with those not exposed. The negative control outcome analysis indicated residual confounding in the prenatal smoking and type 1 diabetes association. E-value analysis indicated that unmeasured confounding associated with prenatal smoking and childhood type 1 diabetes, with a HR of 1.67, could negate the observed effect. CONCLUSIONS/ INTERPRETATION: Our best estimate from the study is that maternal smoking in pregnancy was associated with 16% lower childhood type 1 diabetes incidence, and some of this effect was due to residual confounding.
AIMS/HYPOTHESIS: Evidence of an association between maternal smoking during pregnancy (prenatal smoking) and childhood type 1 diabetes is mixed. Previous studies have been small and potentially biased due to unmeasured confounding. The objectives of this study were to estimate the association between prenatal smoking and childhood type 1 diabetes, assess residual confounding with a negative control design and an E-value analysis, and summarise published effect estimates from a meta-analysis. METHODS: This whole-of-population study (births from 1999 to 2013, participants aged ≤15 years) used de-identified linked administrative data from the South Australian Early Childhood Data Project. Type 1 diabetes was diagnosed in 557 children (ICD, tenth edition, Australian Modification [ICD-10-AM] codes: E10, E101-E109) during hospitalisation (2001-2014). Families not given financial assistance for school fees was a negative control outcome. Adjusted Cox proportional HRs were calculated. Analyses were conducted on complete-case (n = 264,542, type 1 diabetes = 442) and imputed (n = 286,058, type 1 diabetes = 557) data. A random-effects meta-analysis was used to summarise the effects of prenatal smoking on type 1 diabetes. RESULTS: Compared with non-smokers, children exposed to maternal smoking only in the first or second half of pregnancy had a 6% higher type 1 diabetes incidence (adjusted HR 1.06 [95% CI 0.73, 1.55]). Type 1 diabetes incidence was 24% lower (adjusted HR 0.76 [95% CI 0.58, 0.99]) among children exposed to consistent prenatal smoking, and 16% lower for exposure to any maternal smoking in pregnancy (adjusted HR 0.84 [95% CI 0.67, 1.08]), compared with the unexposed group. Meta-analytic estimates showed 28-29% lower risk of type 1 diabetes among children exposed to prenatal smoking compared with those not exposed. The negative control outcome analysis indicated residual confounding in the prenatal smoking and type 1 diabetes association. E-value analysis indicated that unmeasured confounding associated with prenatal smoking and childhood type 1 diabetes, with a HR of 1.67, could negate the observed effect. CONCLUSIONS/ INTERPRETATION: Our best estimate from the study is that maternal smoking in pregnancy was associated with 16% lower childhood type 1 diabetes incidence, and some of this effect was due to residual confounding.
Entities:
Keywords:
Childhood; E-value; Maternal smoking; Negative control outcome; Population-based birth cohort; Pregnancy; Type 1 diabetes
Authors: Yuxia Wei; Tomas Andersson; Jessica Edstorp; Josefin E Löfvenborg; Mats Talbäck; Maria Feychting; Sofia Carlsson Journal: BMC Med Date: 2022-08-12 Impact factor: 11.150