Developmental trajectories of paediatric headache – sex-specific analyses and predictors

Abstract

BackgroundHeadache is the most common pain disorder in children and adolescents and is associated with diverse dysfunctions and psychological symptoms. Several studies evidenced sex-specific differences in headache frequency. Until now no study exists that examined sex-specific patterns of change in paediatric headache across time and included pain-related somatic and (socio-)psychological predictors.MethodLatent Class Growth Analysis (LCGA) was used in order to identify different trajectory classes of headache across four annual time points in a population-based sample (n = 3 227; mean age 11.34 years; 51.2 % girls). In multinomial logistic regression analyses the influence of several predictors on the class membership was examined.ResultsFor girls, a four-class model was identified as the best fitting model. While the majority of girls reported no (30.5 %) or moderate headache frequencies (32.5 %) across time, one class with a high level of headache days (20.8 %) and a class with an increasing headache frequency across time (16.2 %) were identified. For boys a two class model with a ‘no headache class’ (48.6 %) and ‘moderate headache class’ (51.4 %) showed the best model fit. Regarding logistic regression analyses, migraine and parental headache proved to be stable predictors across sexes. Depression/anxiety was a significant predictor for all pain classes in girls. Life events, dysfunctional stress coping and school burden were also able to differentiate at least between some classes in both sexes.ConclusionsThe identified trajectories reflect sex-specific differences in paediatric headache, as seen in the number and type of classes extracted. The documented risk factors can deliver ideas for preventive actions and considerations for treatment programmes.

Clinical characteristics of headache in Italian adolescents aged 11–16 years: a cross-sectional questionnaire school-based study

“…Our results support recent findings that special consideration should be given to female adolescents [ 33 , 34 ]. Other studies have revealed a higher incidence of headache as well as a higher burden, clinical heterogeneity and higher psychological comorbidity such as depression and anxiety in girls as compared to boys [ 6 , 13 , 33 , 35 ]. However, this sex discrepancy seems to become relevant only from adolescence on [ 4 , 6 , 10 , 36 ].…”

“…In our study, a familiar predisposition was reported in 50.3% of students in Group 1, but this rate was significantly higher in females (54.3% vs 45.4%). Isense et al suggest that in recognizing parental headache as a risk factor, not only genetic predisposition should be considered, but also the influence of dysfunctional pain-related modelling mechanisms: in this perspective, parental perception of and coping with headache may very soon influence the child’s cognitive, emotional and behavioral coping with pain, influencing intensity, frequency and pain-related disability [ 33 ].…”

Age- and sex-related differences in the presentation of paediatric migraine: A retrospective cohort study

“…Previous studies in a similar, clinic-drawn sample of paediatric chronic have also observed increased impairment (including pain severity, depression and anxiety) with increasing age (54). Interestingly, psychiatric co-morbidities in late adolescence have been associated with a worsening or endurance of headache at eight-year follow-up (55) and in girls, but not boys, anxiety/depression was found to be a significant predictor of headaches over four years (56). Under this view it is possible that the association between psychiatric co-morbidities and the burden caused by migraine may perpetuate both conditions over time, particularly for girls (57).…”

Paediatric headache trajectories – a reappraisal after nine years

“…In a previous study, our research group used Latent Class Growth Analysis (LCGA) (30,31) to identify sex-specific headache trajectories of 3227 children and adolescents (9–14 years old at first assessment) from a population-based sample who completed four annual assessments (32). In order to find the best-fitting model, statistical fit indices (e.g.…”