Gestational Diabetes Mellitus in Far North Queensland, Australia, 2004 to 2010: midwives' perinatal data most accurate source

Time trends and projections in the prevalence of gestational diabetes mellitus in Queensland, Australia, 2009-2030: Evidence from the Queensland Perinatal Data Collection

BACKGROUND

Gestational diabetes mellitus (GDM) is the fastest-growing type of diabetes in Australia. We aimed to assess the time trends during 2009-2018 and projections of GDM in Queensland, Australia up to 2030.

MATERIALS AND METHODS

The study data were from the Queensland Perinatal Data Collection (QPDC) and included data on 606 662 birth events with the births reported from at least 20 weeks gestational age or birth weight at least 400 g. Bayesian regression model was used to assess the trends in the prevalence of GDM.

RESULTS

The prevalence of GDM increased from 5.47 to 13.62% from 2009 to 2018 (average annual rate of change, AARC = +10.71%). If the trend remains the same, the projected prevalence will increase to 42.04% (95% uncertainty interval = 34.77-48.96) by 2030. Observing AARC across different subpopulations, we found that the trend of GDM increased markedly among women living in inner regional areas (AARC = +12.49%), were non-Indigenous (AARC = +10.93%), most disadvantaged (AARC = +11.84%), aged either of two age groups (AARC = +18.45% and + 15.17% for <20 years and 20-24 years, respectively), were with obesity (AARC = +11.05%) and smoked during pregnancy (AARC = +12.26%).

CONCLUSIONS

Overall, the prevalence of GDM has sharply increased in Queensland, and if this trend continues, about 42% of pregnant women will experience GDM by 2030. The trends vary across different subpopulations. Therefore, targeting the most vulnerable subpopulations is vital to prevent the development of GDM.

A Comprehensive Systematic Review of Data Linkage Publications on Diabetes in Australia

Aims

Our study aimed to identify the common themes, knowledge gaps and to evaluate the quality of data linkage research on diabetes in Australia.

Methods

This systematic review was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (the PRISMA Statement). Six biomedical databases and the Australian Population Health Research Network (PHRN) website were searched. A narrative synthesis was conducted to comprehensively identify the common themes and knowledge gaps. The guidelines for studies involving data linkage were used to appraise methodological quality of included studies.

Results

After screening and hand-searching, 118 studies were included in the final analysis. Data linkage publications confirmed negative health outcomes in people with diabetes, reported risk factors for diabetes and its complications, and found an inverse association between primary care use and hospitalization. Linked data were used to validate data sources and diabetes instruments. There were limited publications investigating healthcare expenditure and adverse drug reactions (ADRs) in people with diabetes. Regarding methodological assessment, important information about the linkage performed was under-reported in included studies.

Conclusions

In the future, more up to date data linkage research addressing costs of diabetes and its complications in a contemporary Australian setting, as well as research assessing ADRs of recently approved antidiabetic medications, are required.

Growth of linked hospital data use in Australia: a systematic review

Objective The aim of the present study was to quantify and understand the utilisation of linked hospital data for research purposes across Australia over the past two decades. Methods A systematic review was undertaken guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist. Medline OVID, PsycINFO, Embase, EconLit and Scopus were searched to identify articles published from 1946 to December 2014. Information on publication year, state(s) involved, type of data linkage, disease area and purpose was extracted. Results The search identified 3314 articles, of which 606 were included; these generated 629 records of hospital data linkage use across all Australian states and territories. The major contributions were from Western Australia (WA; 51%) and New South Wales (NSW; 32%) with the remaining states and territories having significantly fewer publications (total contribution only 17%). WA's contribution resulted from a steady increase from the late 1990s, whereas NSW's contribution is mostly from a rapid increase from 2010. Current data linkage is primarily used in epidemiological research (73%). Conclusion More than 80% of publications were from WA and NSW, whereas other states significantly lag behind. The observable growth in these two states clearly demonstrates the underutilised opportunities for data linkage to add value in health services research in the other states. What is known about the topic? Linking administrative hospital data to other data has the potential to be a cost-effective method to significantly improve health policy. Over the past two decades, Australia has made significant investments in improving its data linkage capabilities. However, several articles have highlighted the many barriers involved in using linked hospital data. What does this paper add? This paper quantitatively evaluates the performance across all Australian states in terms of the use of their administrative hospital data for research purposes. The performance of states varies considerably, with WA and NSW the clear stand-out performers and limited outputs currently seen for the other Australian states and territories. What are the implications for practitioners? Given the significant investments made into data linkage, it is important to continue to evaluate and monitor the performance of the states in terms of translating this investment into outputs. Where the outputs do not match the investment, it is important to identify and overcome those barriers limiting the gains from this investment. More generally, there is a need to think about how we improve the effective and efficient use of data linkage investments in Australia.

The prevalence of gestational diabetes mellitus among Aboriginal and Torres Strait Islander women in Australia: a systematic review and meta-analysis

INTRODUCTION

Gestational diabetes mellitus (GDM) is an important and increasing health problem. This study aims to investigate and explain the marked variation in reported GDM prevalence among Australian Indigenous women.

MATERIALS AND METHODS

We searched five databases to August 2013 for studies of GDM prevalence; two people independently assessed search results, extracted data, and appraised risk of bias. Meta-analysis was conducted, and between-study heterogeneity examined using subgroup analyses. Within-study findings were synthesized narratively.

RESULTS

The pooled GDM prevalence from 23 of the 25 total studies (5.74%, 4.78-6.71) was similar to that reported in national studies, but heterogeneity was substantial (I(2)  = 97%), making conclusions from between-study comparisons difficult. The greatest reductions in heterogeneity were seen within subgroups using localized diagnostic criteria (I(2)  = 43%, 3 studies), universal screening (I(2)  = 58%) and some jurisdictions, probably reflecting proxy measures of increased consistency in diagnostic and screening methods. Insufficient data were available to assess the effect of factors such as rurality, diagnostic criteria, study design and data sources on prevalence. Synthesis of within-study findings showed: higher age-adjusted prevalences of GDM in Indigenous versus non-Indigenous women; Indigenous women have greater increases in prevalence with maternal age; and non-Indigenous women appear to have a steeper increase in GDM prevalence over time. Prevalence increased almost fourfold in two studies following introduction of universal screening when compared with selective risk-based screening, although numbers were small.

DISCUSSION/CONCLUSIONS

The published GDM prevalence among Indigenous women varies markedly, probably due to variation in diagnostic and screening practices.