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1.  Meta-Analysis of the Association of Urbanicity With Schizophrenia 
Schizophrenia Bulletin  2012;38(6):1118-1123.
The association between urbanicity and risk of schizophrenia is well established. The incidence of schizophrenia has been observed to increase in line with rising levels of urbanicity, as measured in terms of population size or density. This association is expressed as Incidence Rate Ratio (IRR), and the results are usually presented by comparing the most urban with the most rural environment. In this study, we undertook to express the effect of urbanicity on the risk of schizophrenia in a linear form and to perform a meta-analysis of all available evidence. We first employed a simple regression analysis of log (IRR) as given in each study on the urbanicity category, assuming a uniform distribution and a linear association. In order to obtain more accurate estimates, we developed a more sophisticated method that generates individual data points with simulation from the summary data presented in the original studies, and then fits a logistic regression model. The estimates from each study were combined with meta-analysis. Despite the challenges that arise from differences between studies as regards to the number and relative size of urbanicity levels, a linear association was observed between the logarithm of the odds of risk for schizophrenia and urbanicity. The risk for schizophrenia at the most urban environment was estimated to be 2.37 times higher than in the most rural environment. The same effect was found when studies measuring the risk for nonaffective psychosis were included.
PMCID: PMC3494055  PMID: 23015685
schizophrenia; psychosis; review; urban environment
2.  Association Between Prepartum Maternal Iron Deficiency and Offspring Risk of Schizophrenia: Population-Based Cohort Study With Linkage of Danish National Registers 
Schizophrenia Bulletin  2010;37(5):982-987.
Recent findings suggest that maternal iron deficiency may increase the risk of schizophrenia-spectrum disorder in offspring. We initiated this study to determine whether maternal prepartum anemia influences offspring risk of schizophrenia. We conducted a population-based study with individual record linkage of the Danish Civil Registration System, the Danish Psychiatric Central Register, and the Danish National Hospital Register. In a cohort of 1 115 752 Danish singleton births from 1978 to 1998, cohort members were considered as having a maternal history of anemia if the mother had received a diagnosis of anemia at any time during the pregnancy. Cohort members were followed from their 10th birthday until onset of schizophrenia, death, or December 31, 2008, whichever came first. Adjusted for relevant confounders, cohort members whose mothers had received a diagnosis of anemia during pregnancy had a 1.60-fold (95% confidence interval = 1.16–2.15) increased risk of schizophrenia. Although the underlying mechanisms are unknown and independent replication is needed, our findings suggest that maternal iron deficiency increases offspring risk of schizophrenia.
PMCID: PMC3160221  PMID: 20093425
Schizophrenia; epidemiology; risk factor; Denmark; maternal iron deficiency; follow-up; cohort
3.  Gestational Age, Birth Weight, Intrauterine Growth and Risk for Epilepsy 
American journal of epidemiology  2007;167(3):262-270.
The authors evaluated the association between gestational age, birth weight, intrauterine growth and epilepsy in a population-based cohort of 1.4 million singletons born in Denmark (1979-2002). A total of 14,334 individuals were registered with epilepsy in the Danish National Hospital Register as inpatients (1979-2002) and outpatients (1995-2002). Information on gestational age and birth weight was obtained from Danish Medical Birth Registry. Children small at birth were identified through two methods: 1) sex-, birth order-, and gestational-age-specific z-score, and 2) deviation from the expected birth weight estimated based on the birth weight of an older sibling. The incidence rates of epilepsy increased consistently with decreasing gestational age and birth weight. The incidence rate ratios (IRR) for epilepsy in the first year of life were more than five-fold in children born at 22-32 weeks compared with children born at 39-41 weeks, and in children with a birth weight <2,000 grams compared with children of 3,000-3,999 grams. The IRRs decreased with age, but remained elevated into early adulthood. Children identified as growth-restricted according to either of the two methods had increased IRRs for epilepsy, even among children with a ‘normal’ birth weight of 3,500-3,999 grams. Low gestational age at birth and low birth weight are associated with an increased risk of epilepsy throughout childhood and persisting into puberty. Intrauterine growth restriction is associated with an increased risk of epilepsy, even among children with a birth weight in a normal range.
PMCID: PMC2632964  PMID: 18042672
4.  Assessing fetal growth impairments based on family data as a tool for identifying high-risk babies. An example with neonatal mortality 
Low birth weight is associated with an increased risk of neonatal and infant mortality and morbidity, as well as with other adverse conditions later in life. Since the birth weight-specific mortality of a second child depends on the birth weight of an older sibling, a failure to achieve the biologically intended size appears to increase the risk of adverse outcome even in babies who are not classified as small for gestation. In this study, we aimed at quantifying the risk of neonatal death as a function of a baby's failure to fulfil its biologic growth potential across the whole distribution of birth weight.
We predicted the birth weight of 411,957 second babies born in Denmark (1979–2002), given the birth weight of the first, and examined how the ratio of achieved birth weight to predicted birth weight performed in predicting neonatal mortality.
For any achieved birth weight category, the risk of neonatal death increased with decreasing birth weight ratio. However, the risk of neonatal death increased with decreasing birth weight, even among babies who achieved their predicted birth weight.
While a low achieved birth weight was a stronger predictor of mortality, a failure to achieve the predicted birth weight was associated with increased mortality at virtually all birth weights. Use of family data may allow identification of children at risk of adverse health outcomes, especially among babies with apparently "normal" growth.
PMCID: PMC2233632  PMID: 18045458
5.  The impact of nonlinear exposure-risk relationships on seasonal time-series data: modelling Danish neonatal birth anthropometric data 
Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship.
Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns.
The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data.
In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.
PMCID: PMC2151954  PMID: 17937794

Results 1-5 (5)