Norwegians ranked high with the SCORE total cholesterol model and Norwegian men low with the SCORE ratio model. Although the predictive accuracy of the SCORE models for immigrants in Norway remains to be evaluated, our findings suggest that the ratio model could be more applicable to the entire population in Norway.

Background Random effects models were used to explore how the shape of CD4 cell count responses after commencing combination antiretroviral therapy (cART) develop over time, and in particular the role of baseline and follow-up covariates. Methods Patients in APHOD who first commenced cART after January 1, 1997, and who had a baseline CD4 cell count and viral load measure and at least one follow-up measure between 6 and 24 months, were included. CD4 cell counts were determined at every 6-month period following the commencement of cART for up to 6 years. Results 1638 patients fulfilled the inclusion criteria with a median follow up time of 58 months. Lower post-cART mean CD4 cell counts were found to be associated with increasing age (p<0.001), pre-cART hepatitis C co-infection (p=0.038), prior AIDS (p=0.019), baseline viral load ≤ 100,000 copies/ml (p<0.001), and the Asia-Pacific region compared with Australia (p=0.005). A highly significant 3-way interaction between the effects of time, baseline CD4 cell count and post-cART viral burden (p<0.0001) was demonstrated. Higher long-term mean CD4 cell counts were associated with lower baseline CD4 cell count and consistently undetectable viral loads. Among patients with consistently detectable viral load CD4 cell counts appeared to converge for all baseline CD4 levels. Conclusion Our analyses suggest that the long-term shape of post-cART CD4 cell count changes depends only on a 3-way interaction between baseline CD4 cell count, viral load response and time.

Objectives To determine the prevalence and predictors of an incomplete immune response in patients with sustained viral suppression following their first or second combination antiretroviral treatment (cART) regimen. Methods All patients were recruited to the Australian HIV Observational Database (AHOD) by March 2006. Data were analysed to assess the prevalence of an incomplete immune response (<350 cells/μL) in the 12-24 months after starting the first or second cART regimen. Factors associated with an incomplete immune response were assessed using logistic regression and time to AIDS/death was assessed using survival analysis. Results Of the 2493 patients recruited to AHOD by March 2006, 590 were eligible for the analysis. Twenty eight percent of patients with a baseline CD4 count <350 cells/μL had an incomplete immune response 12-24 months after starting their first or second cART regimen. Lower baseline CD4 count prior to starting the cART regimen was predictive of an incomplete immune response. There was a non-significant trend towards faster AIDS or death in incomplete immune responders. Conclusions An incomplete immune response in patients with sustained viral suppression is associated with poorer immune function prior to starting cART. Type of cART or individual antiretroviral drugs was not associated with an incomplete immune response.

Introduction Although studies have shown reductions in mortality from AIDS after the introduction of combination antiretroviral treatment (cART), little is known about cause-specific mortality in low income settings in the cART era. We explored predictors of AIDS and non-AIDS mortality and compared cause-specific mortality across high and low income settings in the Asia Pacific region. Methods We followed patients in the Asia Pacific HIV Observational Database from the date they started cART (or cohort enrolment if cART initiation was identified retrospectively), until the date of death or last follow-up visit. Competing risks methods were used to estimate the cumulative incidence, and to investigate predictors, of AIDS and non-AIDS mortality. Results Of 4252 patients, 215 died; 89 from AIDS, 97 from non-AIDS causes and 29 from unknown causes. Age >50 years (hazard ratio (HR), 4.29; 95%CI, 2.10-8.79) and CD4 counts ≤100 cells/μL (HR, 8.59; 95%CI, 5.66-13.03) were associated with an increased risk of non-AIDS mortality. Risk factors for AIDS mortality included CD4 counts ≤100 cells/μL (HR, 34.97; 95%CI, 18.01-67.90) and HIV RNA ≥10,001 (HR 4.21; 95%CI 2.07-8.55). There was some indication of a lower risk of non-AIDS mortality in Asian high, and possibly low, income countries compared to Australia. Conclusions Immune deficiency is associated with an increased risk of AIDS and non-AIDS mortality. Older age predicts non-AIDS mortality in the cART era. Less conclusive was the association between country-income level and cause-specific mortality because of the relatively high proportion of unknown causes of death in low income settings.

BackgroundIn recent decades, there has been a shift to later childbearing in high-income countries. There is limited large-scale evidence of the relationship between maternal age and child outcomes beyond the perinatal period. The objective of this study is to quantify a child’s risk of developmental vulnerability at age five, according to their mother’s age at childbirth.Methods and findingsLinkage of population-level perinatal, hospital, and birth registration datasets to data from the Australian Early Development Census (AEDC) and school enrolments in Australia’s most populous state, New South Wales (NSW), enabled us to follow a cohort of 99,530 children from birth to their first year of school in 2009 or 2012. The study outcome was teacher-reported child development on five domains measured by the AEDC, including physical health and well-being, emotional maturity, social competence, language and cognitive skills, and communication skills and general knowledge. Developmental vulnerability was defined as domain scores below the 2009 AEDC 10th percentile cut point.The mean maternal age at childbirth was 29.6 years (standard deviation [SD], 5.7), with 4,382 children (4.4%) born to mothers aged <20 years and 20,026 children (20.1%) born to mothers aged ≥35 years. The proportion vulnerable on ≥1 domains was 21% overall and followed a reverse J-shaped distribution according to maternal age: it was highest in children born to mothers aged ≤15 years, at 40% (95% CI, 32–49), and was lowest in children born to mothers aged between 30 years and ≤35 years, at 17%–18%. For maternal ages 36 years to ≥45 years, the proportion vulnerable on ≥1 domains increased to 17%–24%. Adjustment for sociodemographic characteristics significantly attenuated vulnerability risk in children born to younger mothers, while adjustment for potentially modifiable factors, such as antenatal visits, had little additional impact across all ages. Although the multi-agency linkage yielded a broad range of sociodemographic, perinatal, health, and developmental variables at the child’s birth and school entry, the study was necessarily limited to variables available in the source data, which were mostly recorded for administrative purposes.ConclusionsIncreasing maternal age was associated with a lesser risk of developmental vulnerability for children born to mothers aged 15 years to about 30 years. In contrast, increasing maternal age beyond 35 years was generally associated with increasing vulnerability, broadly equivalent to the risk for children born to mothers in their early twenties, which is highly relevant in the international context of later childbearing. That socioeconomic disadvantage explained approximately half of the increased risk of developmental vulnerability associated with younger motherhood suggests there may be scope to improve population-level child development through policies and programs that support disadvantaged mothers and children.

Indigenous children experience a significantly higher burden of morbidity and mortality from unintentional injuries across different indigenous communities worldwide. Most of these injuries are highly preventable, presenting substantial potential to improve indigenous child health. However, there is limited evidence to illuminate the underlying risk factors for unintentional injuries in indigenous children, and this is a priority for further research.

BackgroundAustralian Aboriginal children experience a disproportionate burden of social and health disadvantage. Avoidable hospitalizations present a potentially modifiable health gap that can be targeted and monitored using population data. This study quantifies inequalities in pediatric avoidable hospitalizations between Australian Aboriginal and non-Aboriginal children.MethodsThis statewide population-based cohort study included 1 121 440 children born in New South Wales, Australia, between 1 July 2000 and 31 December 2012, including 35 609 Aboriginal children. Using linked hospital data from 1 July 2000 to 31 December 2013, we identified pediatric avoidable, ambulatory care sensitive and non-avoidable hospitalization rates for Aboriginal and non-Aboriginal children. Absolute and relative inequalities between Aboriginal and non-Aboriginal children were measured as rate differences and rate ratios, respectively. Individual-level covariates included age, sex, low birth weight and/or prematurity, and private health insurance/patient status. Area-level covariates included remoteness of residence and area socioeconomic disadvantage.ResultsThere were 365 386 potentially avoidable hospitalizations observed over the study period, most commonly for respiratory and infectious conditions; Aboriginal children were admitted more frequently for all conditions. Avoidable hospitalization rates were 90.1/1000 person-years (95 % CI, 88.9–91.4) in Aboriginal children and 44.9/1000 person-years (44.8–45.1) in non-Aboriginal children (age and sex adjusted rate ratio = 1.7 (1.7–1.7)). Rate differences and rate ratios declined with age from 94/1000 person-years and 1.9, respectively, for children aged <2 years to 5/1000 person-years and 1.8, respectively, for ages 12- < 14 years. Findings were similar for the subset of ambulatory care sensitive hospitalizations, but in contrast, non-avoidable hospitalization rates were almost identical in Aboriginal (10.1/1000 person-years, (9.6–10.5)) and non-Aboriginal children (9.6/1000 person-years (9.6–9.7)).ConclusionsWe observed substantial inequalities in avoidable hospitalizations between Aboriginal and non-Aboriginal children regardless of where they lived, particularly among young children. Policy measures that reduce inequities in the circumstances in which children grow and develop, and improved access to early intervention in primary care, have potential to narrow this gap.Electronic supplementary materialThe online version of this article (doi:10.1186/s12887-016-0706-7) contains supplementary material, which is available to authorized users.

Background: Preterm birth and developmental vulnerability are more common in Australian Aboriginal compared with non-Aboriginal children. We quantified how gestational age relates to developmental vulnerability in both populations. Methods: Perinatal datasets were linked to the Australian Early Development Census (AEDC), which collects data on five domains, including physical, social, emotional, language/cognitive, and general knowledge/ communication development. We quantified the risk of developmental vulnerability on ≥1 domains at age 5, according to gestational age and Aboriginality, for 97 989 children born in New South Wales, Australia, who started school in 2009 or 2012. Results: Seven thousand and seventy-nine children (7%) were Aboriginal. Compared with non-Aboriginal children, Aboriginal children were more likely to be preterm (5% vs. 9%), and developmentally vulnerable on ≥1 domains (20% vs. 36%). Overall, the proportion of developmentally vulnerable children decreased with increasing gestational age, from 44% at ≤27 weeks to 20% at 40 weeks. Aboriginal children had higher risks than nonAboriginal children across the gestational age range, peaking among early term children (risk difference [RD] 19.0, 95% confidence interval [CI] 16.3, 21.7; relative risk [RR] 1.91, 95% CI 1.77, 2.06). The relation of gestational age to developmental outcomes was the same in Aboriginal and non-Aboriginal children, and adjustment for socioeconomic disadvantage attenuated the risk differences and risk ratios across the gestational age range. Conclusions: Although the relation of gestational age to developmental vulnerability was similar in Aboriginal and non-Aboriginal children, Aboriginal children had a higher risk of developmental vulnerability at all gestational ages, which was largely accounted for by socio-economic disadvantage.