Recent trends in the prevalence of overweight and obesity among Canadian children [Research]

Background:

Previous studies have shown an increase in the prevalence of overweight and obesity among Canadian children from 23.3% to 34.7% during 1978–2004. We examined the most recent trends by applying current definitions of overweight and obesity based on World Health Organization (WHO) body mass index (BMI) thresholds and recently validated norms for waist circumference and waist:height ratio.

Methods:

We examined directly measured height and weight data from the Canadian Community Health Survey (2004–2005) and the Canadian Health Measures Survey (2009–2013). We calculated z scores for BMI, height and weight based on the 2014 WHO growth charts for Canada, including the new extension of weight-for-age beyond 10 years. To calculate z scores for waist circumference and waist:height ratios, we used new charts from the reference population in the US NHANES III (National Health and Nutrition Examination Survey, 1988–1994).

Results:

Data were available for 14 014 children aged 3–19 years for the period 2004–2013. We observed a decline in the prevalence of overweight or obesity, from 30.7% (95% confidence interval [CI] 29.7% to 31.6%) to 27.0% (95% CI 25.3% to 28.7%) (p < 0.001) and stabilization in the prevalence of obesity at about 13%. These trends persisted after we adjusted for age, sex and race/ethnicity. Although they declined, the median z scores for BMI, weight and height were positive and higher than those in the WHO reference population. The z scores for waist circumference and waist:height ratio were negative, which indicated that the Canadian children had less central adiposity than American children in historic or contemporary NHANES cohorts.

Interpretation:

After a period of dramatic growth, BMI z scores and the prevalence of overweight or obesity among Canadian children decreased from 2004 to 2013, which attests to progress against this important public health challenge.

Women, doctors largely in sync on health concerns

Australian women are most worried about gaining weight, but their doctors think they are more concerned about their mental health, a new study has found.

Overall, however, health professionals are pretty much in touch with their female patients, the Women’s Health Survey found.

The Jean Hailes group surveyed 3035 women, average age 48, and 20 health practitioners between February and May 2016. The health practitioners included GPs, nurses, naturopaths, and community and allied health services.

Overall, the women rated their health as good or very good.

On average, they visited their doctor three to five times a year, and felt confident asking their doctor questions and discussing health issues and concerns.

They undertook regular health checks, including pap smears, breast screening, and bowel screening, but not sexual health screening for STIs.

The top five health concerns nominated by the women participants were:

Weight management, and specifically weight gain (23 per cent)
Cancer, including breast, ovarian, and skin cancer (17 per cent)
Mental and emotional health, particularly anxiety and depression (15 per cent)
Menopause (9 per cent), and
Chronic pain (8 per cent).

Asked to nominate what most concerned their female patients, the health practitioners listed:

Mental and emotional health (28 per cent)
Menopause (27 per cent)
Weight (25 per cent)
Breast cancer (17 per cent), and
Fertility (16 per cent).

Nearly half of all women surveyed said they wanted more information on healthy eating and nutrition, anxiety and worry, and weight management.

Interestingly, four in five of the health professionals said their patients needed more information on vulval irritation and painful sex, yet very few women surveyed reported needing more information on these topics.

The women were most likely to get their health information from health professionals, followed by internet searches. They rated information from commercial organisations and social media as the least trustworthy.

More than 70 per cent of women rated their health as good or very good, and 93 per cent agreed with the statement that “good health is one of the most important things in my life”.

The survey is conducted each year to identify gaps in health information, understand future health needs, and identify trends in women’s health behaviours.

Maria Hawthorne

Streamlining ethics review for multisite quality and safety initiatives: national bariatric surgery registry experience

The current ethics review process is inappropriate for clinical quality registries

Rigorous methods for assessing and improving the quality of health care have proven difficult to develop by traditional research approaches.1 Clinical quality registries (CQRs) systematically collect an agreed minimum dataset of data across multiple sites on clinically relevant outcome measures. Data are analysed, comparing procedures, providers and institutions.2 Feedback to practitioners has been shown to drive performance improvement, especially if the data are perceived to be high quality.3

Because CQRs collect and store health information, protocols require human research ethics committee (HREC) approval to ensure that they comply with the Australian Privacy Act 1988 (Cwlth). Principle 6 of this Act states that stored personal information must not be used or disclosed for a secondary purpose unless patient consent is obtained or there is a permitted health situation. Section 16B of the Act defines the permitted health situations, which include research relevant to public health or public safety. The use or disclosure of personal information must be conducted under guidelines approved under section 95A of the Act. Current National Health and Medical Research Council guidelines state that ethical review is required at each contributing site for CQRs except where multi-institutional approval is in operation.4

Bariatric surgery is burgeoning in Australia. In 2016, it is estimated that there will be over 15 000 such procedures performed in Australia at a direct cost of over $225 million. Yet there are no evidence-based guidelines directing who should be offered this surgery, nor are there any long-term community data documenting its safety and efficacy in Australia.

The Obesity Surgery Society of Australia and New Zealand (OSSANZ) partnered with Monash University Department of Epidemiology and Preventive Medicine (DEPM) to establish a national registry of all bariatric procedures with the aim of filling these knowledge gaps. The pilot commenced in 2012 and national rollout commenced in May 2014 (with federal government funding).

The Bariatric Surgery Registry (BSR) collects information on each procedure performed, the devices used, changes in patients’ weight and diabetes status, and adverse events. Data are collected primarily from surgeons and are validated against hospital International Statistical Classification of Disease and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM) discharge codes. Because the BSR is tracking and storing identifiable sensitive health information longitudinally as well as cross referencing data points to external data sources, HREC review is required at every site contributing to the BSR.4,5

We believe that there are 164 hospitals undertaking bariatric surgery in Australia. As of 30 April 2015, there were 52 hospitals (31.7%) for which HREC approval for participation in the BSR had been obtained. Private hospitals accounted for 67% of these sites, 29% were public hospitals and an additional approval was received from both the Royal Australasian College of Surgeons and Monash University. Applications for high risk projects were requested from 31.4% of private hospital and 33.3% of public hospital HRECs. Seven sites (13.5%) provided approval through an affiliated site. Fifty sites (96%) had additional governance requirements and, in 76% of cases, this was a separate process. The median time from the first application to final approval was 86 days (range, 17–414 days). The maximum numbers of queries from or changes requested by an HREC was 67.

The process of obtaining ethical approval at these initial hospital sites cost the BSR $180 698.58 in salaries and $3474.97 per application. In addition, the BSR has had to pay five sites a total of $3927.00 for ethics approval application fees.

The number of CQRs in Australia is growing rapidly in response to community demands for better monitoring of health care outcomes.6 HREC review of registry processes is one way of ensuring that the rights of individuals participating in registries are protected and complying with the Privacy Act. However, as highlighted by our experience in rolling out the BSR, the lack of a consistent process for obtaining HREC approval across multiple sites for these quality and safety initiatives creates cost and slows implementation.

HRECs are typically set up to review research projects rather than quality and safety initiatives. Unlike clinical trials, which are hypothesis driven and in which patients are given the option of participating, CQRs attempt to ensure quality through benchmarking, and so need to recruit all patients who undergo a given intervention to avoid the risk of bias. Many HRECs are not familiar with these basic and essential differences, and this can lead to confusion and delays, as the processes for clinical trials and CQRs invariably differ.7 Based on the cost and time involved in obtaining even 31.7% of the required approvals for the BSR, we call for a bespoke national process for HREC review of CQRs. This would streamline implementation and reduce costs while still protecting patient’s privacy. Examples of such a process could be having all sites apply to a single national ethics committee, as in New Zealand, or implementing specific federal legislation protecting the transfer of information to and from approved CQRs.

[Comment] Dietary guidelines on trial: the charges are not evidence based

Current nutrient guidelines recommend a wide acceptable range of total fat and carbohydrate intakes, emphasising quality and source rather than quantity of macronutrients, substantial restriction of free sugars, and usually restriction of saturated fat.1 Recent food-based dietary guidelines are based on similar recommendations.2 Wide dietary variation, including typical healthy dietary patterns, can be accommodated within such nutrient-based advice. And so criticisms of nutrition guidelines confuse both health professionals and the public, and provide justification for inaction by policy makers.

Doctors need to be taught how to discuss their patients’ excess weight

With 80% of adults and close to one-third of children expected to be overweight or obese by 2025, doctors are increasingly likely to be working with people who are overweight or obese.

An individual’s weight is a complex and sensitive issue, which may be related to many factors that are not only medical but social, environmental and emotional. The skills to address the issue in a way that communicates the health risks of being overweight without judgement and without inciting negative responses are not easy to acquire or universally taught.

Health professionals repeatedly report a lack of confidence in knowing how to address obesity in their patients. They report minimal, if any, training on obesity as well as limited resources for effective conversations and insufficient clinical time to be able to do this well.

Starting a conversation about weight requires not only empathy but awareness of strategies people can use to manage weight issues and an understanding of the range of local services available to assist. It has been shown that although behavioural and medical strategies can be effective, uninformed discussion in the clinic can disengage, stigmatise or shame patients, which then has negative impacts on the outcomes.

Many patients do expect weight-loss guidance from health professionals and the discussion can influence outcomes. In fact, having the conversation and formally diagnosing and documenting excess weight or obesity is the strongest predictor of having a treatment plan and weight-loss success.

Choice of language is crucial

Research has identified the terms “fat” and “fatness” are the least preferred terms. The words “obese” and “obesity” have also been found to arouse negative responses. The National Institute of Clinical Excellence in the UK suggests patients may be more receptive if the conversation is about achieving or maintaining a “healthy weight”.

The STOP Obesity Alliance in the US suggests using “people first” language such that a person “has” obesity rather than “is” obese, similar to “having” cancer or diabetes.

This is part of a debate about whether obesity should be labelled as a disease rather than a risk factor.

Regardless of how this issue is classified, doctors and patients both require the knowledge to understand effective therapies do exist and obesity treatment is not futile. Losing 5-10% of body weight can have a significant impact on risk factors such as blood pressure and can lower the risks of later health problems such as heart disease or type 2 diabetes.

This sort of weight loss also often improves other factors more immediately beneficial to the patient, such as energy levels, mood and mobility.

A communication style that encourages shared decision-making and helps people change their behaviour is key. The objective is not to solve the problem but to help the patient begin to believe change is possible and develop a plan about health goals.

Let’s take the case of a woman who presents with urinary incontinence. The woman may describe the problem of needing to wear sanitary pads because of daily leaking of urine. Factors such as obesity will worsen the problem, but the woman may not be aware of this.

The doctor might say:

“I hear you’re concerned about your loss of urine, is that correct? Let’s talk about that; and would it be OK to discuss your weight too, as that may be related?”

The practitioner might listen for a willingness to have further discussion and then pose a goal-orientated question:

“If, as part of our plan to help your urinary symptoms, you decide to work on getting to a healthier weight, what might be a first step?”

Repercussions for our kids

For men and women of reproductive age the conversation is potentially not just about their own health but also about that of their children. Women who have higher pre-conception weight and pregnancy weight gain are at increased risk of developing diabetes and heart disease in later life and are less likely to lose weight after they give birth.

This vicious cycle results in larger babies that are predisposed to short-term risks as newborns, longer-term risks of increased childhood obesity and an increased lifetime risk of obesity, diabetes and heart disease.

Between 1985 and 1995 the rate of excess weight and obesity in childhood increased by 50% and obesity tripled in Australia. Animal studies also suggest obesity in the male parent can increase the chance of their offspring developing obesity or diabetes.

The intergenerational nature of obesity therefore means until we address overweight and obesity in adults who are planning a pregnancy, it may be impossible to lower rates of childhood obesity.

The framing of the issue as a problem for patients’ own health as well as for the health of their children is even more complex. However, unless there is a greater understanding of this risk and more training of doctors in talking to patients about obesity this will be difficult to tackle.

Currently, many health professionals remain uncomfortable and unsure in this area of practice. Ensuring the workforce is skilled will also mean there is the ability to discuss weight when it is not the primary issue a patient presents with, but where an important conversation at a critical life stage may actually have lasting effects on patients’ health and that of their children.

Adrienne Gordon, Neonatal Staff Specialist, NHMRC Early Career Research Fellow, University of Sydney and Kirsten Black, Associate Professor & Joint Head of Discipline Obstetrics, Gynaecology and Neonatology, University of Sydney

This article was originally published on The Conversation. Read the original article.

Other doctorportal blogs

Striving for truly healthy growth

The limitations of political slogans – the ‘privatisation of Medicare’ or ‘jobs and growth’ – are severe. Ideas are shorn of nuance and words stripped of definition. What is meant by ‘privitisation’ and ‘Medicare’, and what by ‘growth’?

While privatising Medicare may at first blush be the phrase of greatest interest to doctors, I suggest that ‘growth’ is of deeper concern. Growth – unqualified – could be a curse and not a cure, a health hazard rather than a health promoter, a cancerous thing rather than a positive developmental pathway.

True, decades of free market-based economic growth have achieved remarkable improvements in global health. Whole nations have been lifted from poverty, death and suffering. In economically-advanced nations unimaginable affluence has been achieved with improved average life expectancy.

But with this growth have come unintended side effects. The global challenge of climate change is one such consequence. Inequality is another. In the US, the rich have become disproportionately richer without improvement in economic well-being among workers. This has substantial political effects. Commentators speak of how this inequality, present also in the UK, has contributed to Trump and Brexit.

Growth with attitude

Jeffrey Sachs, an economist at Columbia University with a long-standing passionate interest in sustainable development and health in less developed economies, wrote recently in the Boston Globe about the need for a fresh understanding of what we mean by growth. Sachs played a major part in the development of what are called the Sustainable Development Goals, or SDGs, under the auspices of the United Nations. The goals were agreed upon one year ago by more than 100 nations, including Australia.

In brief, the SDGs, to quote Sachs, aim at economic growth, but defined in a manner that promotes decency and environmental sustainability. The 17 goals involve the achievement of more than 100 specific objectives. They fall into three groups: those associated with classical economic progress; those that have to do with ensuring environmental sustainability; and those that concern justice and social fairness.

Now, almost a year later, in New York on July 20, ministers and country representatives at the annual UN High-Level Political Forum attended the launch of an index, a measuring device, designed to allow countries to assess how they stand now in relation to the SDGs, and how they can judge their progress. The index is aimed at strengthening the commitment to growth in a way that is consistent with improving human decency and honouring the environment. It provides a current assessment for 149 of the 193 UN member states. It asks each nation to rank itself on indicators of poverty, nutrition, health care, education and pollution – all elements of the SDGs.

The goals include universal education, gender equality, clean water and sanitation, affordable clean energy, decent work and economic growth, reducing inequalities and developing sustainable cities.

Three are of special interest to medical and other health professionals. They concern further efforts to reduce poverty; to do what is needed to promote health and wellbeing; and to ensure food security for all.

In one sense these goals could hardly be disputed. But the real question is whether they have enough grunt to motivate change.

Critics, including The Economist, refer to the goals as “sprawling” and not sufficiently specific, especially when compared with the much fewer (12) Millennium Development Goals that were associated with great progress in infant mortality, HIV and other forms of health promotion and disease control for example.

Nevertheless, despite the ambitious spread of the SDGs, they take account of current urgent global challenges from which Australia cannot hope to remain immune.

Moving Australia toward sustainable growth

The world leaders on the SDG index are the Scandinavian countries, followed by others from Northern Europe. Canada was 13th, Australia 20th and the US 25th. Sweden’s homicide rate is around one-seventh of America’s, and its incarceration rate one-tenth. Infant and maternal mortality rates are lower, as is income inequality.

In summary, the SDGs are an international expression of an attempt to seek truly global health – for people, the environment and the planet.

While achieving these goals is a lofty ideal, we can only make progress if we use words like ‘growth’ accurately. If we mean growth that advances the economy while also promoting environmental sustainability and reducing social inequality, then we will be on a solid path to the future.

Contemporary type 1 diabetes pregnancy outcomes: impact of obesity and glycaemic control

The known Type 1 diabetes in pregnant women is associated with complications for both mother and baby. Optimal glycaemic control reduces the likelihood of these adverse outcomes.

The new The mean body mass index of Australian women with type 1 diabetes is greater than that of women without diabetes. Even with multidisciplinary specialist care and good glycaemic control, their likelihood of adverse outcomes was greater than for women without diabetes because of this additional risk factor.

The implicationsPre-conception care is important, but optimising glycaemic control is not sufficient to prevent complications associated with type 1 diabetes during pregnancy. Preventing obesity in childbearing women with type 1 diabetes requires greater attention.

Type 1 diabetes accounts for 5–10% of diabetes diagnoses, and is a well recognised and important risk factor for a number of complications during pregnancy.1 Women with type 1 diabetes have a higher risk of miscarriage, hypertensive complications and obstetric interventions, and their babies have an increased risk of congenital malformations, stillbirth, macrosomia and birth trauma.2

In 1989, the St Vincent Declaration set a 5-year goal of improving pregnancy outcomes for women with type 1 diabetes so that they approximated those of women without diabetes.3 Although the Diabetes Control and Complications Trial showed that improvements are possible,4 they have not been seen in observational studies.5,6

There are many gaps in the published literature about pregnancy outcomes for women with type 1 diabetes and their babies. Some studies have not distinguished the risks associated with type 1 and type 2 diabetes,7,8 others have used less informative composite outcomes7–9 or have not accounted for important confounders, such as maternal age, obesity and glycaemic control.5,10–12 The interaction between the effects of type 1 diabetes, glycaemic control and body mass index during pregnancy are not well understood, and there are no Australian data on this question. We accordingly aimed to compare contemporary adverse pregnancy outcomes in women with or without type 1 diabetes who were managed in a specialist maternity centre with optimal health care. Further, we explored the influence of obesity and glycaemic control on pregnancy outcomes in women with type 1 diabetes.

Methods

Study design and population

This historical cohort study included all singleton births of at least 20 weeks’ gestation at Monash Health, including Clayton, Dandenong and Casey hospitals. Monash Health is one of Australia’s largest public maternity networks (7500 births each year), providing quaternary care facilities and specialised endocrine, diabetes nurse educator, obstetric, midwifery and neonatal services.

Data were obtained from the Birthing Outcomes System (BOS) database for the period 1 January 2010 – 31 December 2013. Data were collated prospectively by midwives from the first antenatal visit until delivery and discharge. The database contains routinely recorded standardised pregnancy and neonatal health information collected for statutory data reporting, including demographic data, medical history, and information about antenatal care and complications.

More than 80% of women with type 1 diabetes attended pre-conception care, half of whom attended a pre-conception and early pregnancy clinic at our service. From 12 weeks’ gestation, all attended a specialised multidisciplinary diabetes and maternity service. Care for women without diabetes was provided by midwives and obstetric staff at general antenatal clinics. Following delivery, babies were admitted to the special care nursery if they needed specialised care and observation; this is routine for babies of women with type 1 diabetes. Babies were admitted to the neonatal intensive care unit (NICU) only if they had potentially life-threatening conditions.

Antenatal characteristics and maternal and neonatal outcomes for mothers with type 1 diabetes and women with normal glucose tolerance were compared. Women with type 2 diabetes and gestational diabetes mellitus (GDM) were excluded. All women were screened for GDM at 24–28 weeks’ gestation with a 75 g oral glucose tolerance test; GDM was diagnosed if the fasting blood glucose concentration was 5.5 mmol/L or more, or the 2-hour level was 8.0 mmol/L or more. Women with risk factors were screened early for GDM and unrecognised diabetes. “Pre-existing diabetes” was recorded if reported by the woman and validated by a clinician reviewing individual medical records for type of diabetes, treatment, and the presence of microvascular complications. Glycated haemoglobin (HbA_1c) levels were measured at booking and every 4–6 weeks thereafter by high performance liquid chromatography (HA-8160 Automatic Glycohemoglobin Analyzer, Arkray Adams; coefficient of variation, 1.4%).

Outcomes

The primary outcomes were large for gestational age (LGA; > 90th percentile) and small for gestational age babies (SGA; < 10th percentile), with weights adjusted for gestational age and sex according to Australian birthweight percentiles.13 Secondary maternal outcomes were induction of labour (IOL), caesarean delivery, pre-term birth (< 37 weeks’ gestation), gestational hypertension (new onset hypertension from 20 weeks’ gestation, with blood pressure ≥ 140/90 mmHg) and pre-eclampsia (hypertension with proteinuria > 300 mg/24 hours, spot urine protein:creatinine ratio ≥ 0.03 g/mmol, or renal, hepatic, neurological or haematological involvement). Secondary neonatal outcomes were admission to an NICU, hypoglycaemia (blood glucose level < 2.6 mmol/L), jaundice requiring phototherapy, and respiratory distress syndrome. Shoulder dystocia and Apgar scores below 7 at 5 minutes were reported for vaginal deliveries. Major congenital malformations and perinatal death (stillbirths at 20 weeks’ gestation or later, and neonatal deaths up to 28 days post partum or while the mother was an inpatient) were reported.

Statistical analyses

Maternal characteristics are reported for the two groups of women as descriptive statistics. Categorical data were compared using Pearson χ² or Fisher exact tests; continuous data were compared using Student t tests or Mann–Whitney U tests as appropriate. Multivariable logistic regression analysis generated crude and adjusted odds ratios (ORs, aORs respectively) and 95% confidence intervals (CIs) for each outcome for women with type 1 diabetes (reference category: women without diabetes). Covariates that were clinically or statistically significant (P < 0.1) in the univariable analysis were included in multivariable models. Area under the curve and the likelihood ratio test were used to determine the most parsimonious multivariable models. Potential confounders analysed included maternal age, body mass index (BMI) at the first antenatal visit, region of birth, parity, smoking status, and gestational age. We accounted for repeated measurements in an individual by adjusting analyses for clustering. A subanalysis of data for women with type 1 diabetes assessed the effect of obesity and glycaemic control. P < 0.05 (two-sided) was deemed statistically significant. Analyses were performed in Stata 12 (StataCorp).

Ethics approval

The study was approved by the Monash Health Human Research Ethics Committee (reference, 14001Q, 2013).

Results

Maternal and neonatal health characteristics

Outcomes for 107 pregnancies in 94 women with type 1 diabetes and 27 075 pregnancies in 21 370 women without diabetes were analysed. The mean BMI was higher for women with type 1 diabetes than for women without diabetes (P = 0.01) (Box 1); 66% were overweight or obese, compared with 45% of women without diabetes (data not shown). Women with type 1 diabetes were more likely to have been born in Australia (P < 0.001), but there were no significant differences in age, parity or smoking status. A greater proportion of babies born to women with type 1 diabetes were girls (65% v 49%; P = 0.002) and the mean gestation time was about 2 weeks shorter (37.3 v 39.4 weeks; P < 0.001), but there was no significant difference in mean birthweight (Box 1).

Primary and secondary outcomes

The odds for women with type 1 diabetes giving birth to LGA babies was higher than for women without diabetes, after adjustment for BMI and other confounders (adjusted OR [aOR], 7.9; 95% CI, 5.3–11.8). There was no difference in the likelihood of SGA births. Women with type 1 diabetes had a greater likelihood of IOL (aOR, 3.0; 95% CI, 2.0–4.5) and caesarean delivery (aOR, 4.6; 95% CI, 3.1–7.0) than those without diabetes (Box 2). Among those who gave birth to LGA babies, the proportion of women with type 1 diabetes who had caesarean deliveries was greater than for women without diabetes (62% v 35%; aOR, 3.0; 95% CI, 1.6–5.4); a significant difference was also found for women who gave birth to non-LGA babies (62% v 26%; aOR, 5.1; 95% CI, 2.9–8.9) (data not shown). Women with type 1 diabetes had a higher rate of pre-term births (aOR, 6.7; 95% CI, 4.5–10.0) (Box 2), as well as a higher rate of pre-term caesarean deliveries (39% v 11%; aOR, 4.7; 95% CI, 2.8–7.9) (data not shown) but not of pre-term IOL (20% v 11%; aOR, 1.9; 95% CI, 0.9–4.0). There was no significant difference in maternal hypertensive complications.

Babies of women with type 1 diabetes were more likely than those of women without diabetes to be admitted to an NICU (aOR, 3.4; 95% CI, 1.8–6.4), and to have hypoglycaemia (aOR, 10.3; 95% CI, 6.8–15.6), jaundice (aOR, 5.1; 95% CI, 3.3–7.7), respiratory distress (aOR, 2.5; 95% CI, 1.4–4.4) or shoulder dystocia (aOR, 8.2; 95% CI, 3.6–18.7) (Box 2). While there was no difference in the odds of NICU admission for pre-term babies of women with and without type 1 diabetes (aOR, 0.64; 95% CI, 0.29–1.42), they were higher for term babies of women with type 1 diabetes (aOR, 4.3; 95% CI, 1.3–13.8) (data not shown). There was an interaction between type 1 diabetes and gestation time for the likelihood of hypoglycaemia: the odds were higher for term babies of women with type 1 diabetes (aOR, 22; 95% CI, 13–37) than for pre-term babies of women with type 1 diabetes (aOR, 2.9; 95% CI, 1.5–5.3) (data not shown). The odds of an Apgar score under 7 at 5 minutes was not significantly different in the two groups after adjustment for gestation time (Box 2).

There was no difference in the likelihood of congenital malformations, but that of perinatal death was higher for babies of mothers with type 1 diabetes, after adjustment for congenital malformations (aOR, 5.5; 95% CI, 2.4–12.8) (Box 2). There were five stillbirths (5 per 100 live births) to women with type 1 diabetes (three terminations because of malformations, two intra-uterine deaths at 34 and 37 weeks) and two neonatal deaths (2 per 100 live births: one termination, one instance of lung disease in an extremely premature baby). Among women without diabetes, there were 284 stillbirths (1 per 100 live births) and 110 neonatal deaths (0.4 per 100 live births); of these babies, 53 and 34 respectively had malformations.

Subgroup analysis for women with type 1 diabetes

The mean HbA_1c level of women with type 1 diabetes during pregnancy was 53 mmol/mol (SD, 13). The median levels were 61 mmol/mol during the first, 52 mmol/mol during the second, and 51 mmol/mol during the third trimester. Nephropathy was documented in 14 women (15%) and retinopathy in 19 (20%), but microvascular complications were not associated with adverse outcomes. When analysed as a continuous variable, increased BMI was associated with increased odds of congenital malformations after adjustment for age and first trimester HbA_1c levels (aOR [for kg/m² difference in BMI], 1.5; 95% CI, 1.03–2.2). It was not associated with an increased likelihood of the primary outcomes, LGA and SGA babies, nor with increased odds for the secondary outcomes. When BMI was analysed as a categorical variable, however, obese women with type 1 diabetes were more likely to give birth to LGA babies than normal weight women, after adjustment for age and first trimester HbA_1c levels (aOR, 3.7; 95% CI, 1.02–13.2) (Box 3).

A one percentage point increase in mean HbA_1c level during pregnancy was associated with increased odds of pre-term birth (aOR, 1.9; 95% CI, 1.1–3.0) and perinatal death (aOR, 5.1; 95% CI, 1.5–17.5), but not with other adverse outcomes. Women with a mean HbA_1c level of 64 mmol/mol or more were less likely to give birth to LGA babies than those with levels below 53 mmol/mol (aOR, 0.20; 95% CI, 0.05–0.80) (Box 4). Pre-eclampsia and nephropathy were not associated with a change in the odds for LGA births (data not shown). Each one percentage point increase in first trimester HbA_1c level was associated with an increasing likelihood of pre-term birth (aOR, 2.5; 95% CI, 1.4–4.3) and perinatal death (aOR, 4.5; 95% CI, 1.1–18.4) and a reduced likelihood of an LGA baby (aOR, 0.62; 95% CI, 0.40–0.97) (Box 4). Second and third trimester HbA_1c levels were not correlated with adverse outcomes (data not shown).

Discussion

We compared pregnancy outcomes for women with type 1 diabetes with those for women without diabetes in a large study in a quaternary public health care setting. Mean BMI was greater and the mean duration of gestation shorter for women with type 1 diabetes than for women without diabetes; the likelihood of IOL was three times, of caesarean delivery five times, and of pre-term birth seven times that for women without diabetes. The odds of babies of women with type 1 diabetes being admitted to an NICU were three times those of neonates with mothers without diabetes; the odds of their being LGA and having hypoglycaemia, jaundice, respiratory distress, shoulder dystocia or perinatal death were also increased. In women with type 1 diabetes, obesity was associated with an increased likelihood of macrosomia and congenital malformations in their babies. Higher HbA_1c levels were associated with an increasing likelihood of pre-term birth and perinatal death, and reduced odds of an LGA birth.

Obstetric decisions about the mode of birth are largely driven by hospital protocol. We observed high rates of IOL and caesarean deliveries among women with type 1 diabetes, comparable with those reported in the United Kingdom14 and New Zealand,15 but higher than those in Nordic countries,10,12 where the reported mean BMI of women was lower. The difference in the proportions of pre-term births to women with and without type 1 diabetes (39% v 8%) was greater than reported in a recent systematic review (25% v 6%).6 While similar rates were reported in Denmark (41.7% v 6%),5 a much lower rate among women with type 1 diabetes was reported in Sweden (21% v 5%).12 Further, women in our study who gave birth before term were more likely to require a caesarean delivery. These differences may reflect the higher risk status of our cohort, given its higher proportion of overweight women, as the hospital protocol recommends earlier delivery for women at risk of adverse outcomes.

Neonatal outcomes were less than optimal for babies of women with type 1 diabetes. We found the likelihood of an LGA baby was eight times that for women without diabetes, and that it was independent of obesity, confirming the findings of an earlier study.12 Excess gestational weight gain16 and dyslipidaemia17 were associated with increased odds of giving birth to an LGA child, and this requires further study. Increased rates of hypoglycaemia, jaundice, respiratory distress and shoulder dystocia have similarly been associated with LGA births to mothers with type 1 diabetes.18 The odds of babies of women with type 1 diabetes being admitted to an NICU were three times those of other neonates; this compares favourably with the greater than 5-fold likelihood of admission reported by other Australian investigators,8 and may be related to our policy of routine special care nursery observation of such babies. LGA births and related adverse outcomes remain problems despite the modern management of pregnant women with type 1 diabetes, highlighting the importance of active monitoring.

The harmful effects of obesity in the general obstetric population are recognised. Scandinavian research identified that type 1 diabetes and obesity are synergistic risk factors for maternal and neonatal complications, with diabetes the stronger risk factor.11 The study found increased rates of congenital malformation in obese women with type 1 diabetes, but the authors did not examine glycaemic control.11 We found that maternal obesity in women with type 1 diabetes, after adjustment for glycaemic control, was associated with a nearly 4-fold likelihood of LGA births; further, each 1 kg/m² increase in BMI was associated with a 50% increase in the likelihood of congenital malformations after adjustment for age and first trimester HbA_1c levels. Optimal reproductive health therefore requires strategies for assisting women with type 1 diabetes to avoid excess weight prior to conception.

Type 1 diabetes is associated with an increased risk of congenital malformations and perinatal death, which may result from poor glycaemic control during conception and the first trimester of pregnancy.6,9 A systematic review reported a 2-fold risk of congenital malformations and an approximately 4-fold risk of perinatal death in women with type 1 diabetes compared with women without diabetes.6 We similarly found that the odds of perinatal death for babies of women with type 1 diabetes were four times those of other neonates, but there were no differences in the rates of congenital malformations. The women in our study had reasonable glycaemic control, and elevated HbA_1c levels during the entire pregnancy and during the first trimester were associated with an increased incidence of perinatal death, but HbA_1c levels were not predictive of congenital malformations after 20 weeks’ gestation. Comparisons with existing literature are difficult because of the differing periods during which congenital malformations were monitored.

The association between glycaemic control and other neonatal morbidity is less evident. A retrospective study of women with pre-existing diabetes found no association between first trimester HbA_1c levels and adverse maternal or fetal outcomes.7 More recently, a prospective trial of women with type 1 diabetes in the UK found that HbA_1c levels of 42–46 mmol/mol at 26 weeks’ gestation were associated with LGA births, and HbA_1c levels of 48–52 mmol/mol were associated with pre-term birth, pre-eclampsia and a need for neonatal glucose infusion.19 In our cohort, there was a continuous relationship between glycaemic control throughout gestation and during the first trimester with rates of pre-term birth and perinatal death, underscoring the importance of optimal pre-conception and early antenatal glycaemic control.

Higher first trimester HbA_1c levels were also associated with a reduced likelihood of an LGA birth. This is possibly related to closer monitoring of and earlier intervention in women with poor glycaemic control. Third trimester HbA_1c levels were not linked with neonatal hypoglycaemia, jaundice or respiratory distress in our diabetes group. It is notable that another study found no relationship between HbA_1c levels during pregnancy and neonatal hypoglycaemia or macrosomia, although maternal glucose levels during labour were negatively correlated with those of the neonate.20 We recommend intensified management in order to optimise maternal HbA_1c levels, but acknowledge the limitations of this approach during pregnancy.1 Glucose level variability may be more informative when making decisions, especially later in gestation, and this question should be investigated further.

Limitations to our study include the absence of data on pre-conception glycaemic control, diabetes duration, and gestational weight gain. As we only analysed births from 20 weeks’ gestation, we may have under-represented the proportion of pregnancies with congenital malformations that did not continue beyond 20 weeks. Odds estimates are less precise for some of the rarer outcomes, and we cannot exclude an unrecognised type 2 error. The non-matched study design may have reduced the efficiency of the study and our ability to control for known confounders. Further, the large number of women in the non-diabetes group may have led to deflation of P values; that is, the statistical significance of between-group differences may have been exaggerated by the disparate sizes of the two groups. Strengths of our study include the fact that the large number of participants enabled us to address key gaps in our knowledge, with a broad range of standardised outcomes. Attention to confounders such as obesity and glycaemic control, unlike many previous investigations, improves the generalisability of our results.

Conclusion

We have addressed gaps in the literature by investigating a contemporary cohort of women with type 1 diabetes receiving multidisciplinary care, taking both BMI and glycaemic control into consideration. We found that type 1 diabetes in pregnant women, including those with reasonable glycaemic control, was associated with an increased likelihood of adverse obstetric and neonatal outcomes even when optimally managed in a quaternary setting. Increased HbA_1c levels, even after correcting for maternal BMI, do not fully account for the increased frequency of adverse outcomes for women with type 1 diabetes. The higher BMI of pregnant women with type 1 diabetes was associated with a higher incidence of LGA births, independent of glycaemic control, highlighting the importance of controlling both weight and hyperglycaemia in these women. Further research could provide insights into how best to optimise pre-conception and antenatal care for women with type 1 diabetes in order to minimise the associated risks.

Box 1 –
Demographic and health characteristics of mothers with and without type 1 diabetes, and health characteristics of their neonates

	Women with type 1 diabetes	Women without diabetes	P

Number	107	27 075
Maternal age (years), mean (SD)	29.3 (5.3)	29.4 (5.4)	0.76
Maternal body mass index (kg/m²) at booking visit, mean (SD)	27.3 (5.0)	25.7 (5.9)	0.01
Country of birth
Australia or New Zealand	95 (89%)	12 806 (47.3%)	< 0.001
Europe or Americas	8 (7%)	1850 (6.8%)	0.79
Africa	2 (2%)	1798 (6.6%)	0.05
Asia	2 (2%)	10 620 (39.2%)	< 0.001
Parity			0.40
Primiparous	51 (48%)	11 805 (43.6%)
Parous	56 (52%)	15 269 (56.4%)
Smoker	24 (22%)	4703 (17.4%)	0.20
Neonate sex			0.002
Boy	37 (35%)	13 896 (51.3%)
Girl	70 (65%)	13 156 (48.6%)
Gestation at birth (weeks), median (IQR)	37.3 (34.6–38.1)	39.4 (38.4–40.4)	< 0.001
Birth weight (g), mean (SD)	3230 (997)	3305 (649)	0.26

Box 2 –
Maternal and neonatal adverse outcomes for women with and without type 1 diabetes

Women with type 1 diabetes

Women without diabetes

Odds ratio (95% CI)Reference category: women without diabetes

Crude odds ratio

Adjusted odds ratio

Number

107

27 075

Large for gestational age baby

47 (44%)

2087 (7.7%)

9.4 (6.4–13.8)

7.9 (5.3–11.8)*

Small for gestational age baby

7 (7%)

3964 (14.7%)

0.41 (0.19–0.88)

0.52 (0.24–1.12)^†

Induction of labour

51 (48%)

5738 (21.2%)

3.4 (2.3–5.0)

3.0 (2.0–4.5)^‡

Caesarean delivery

66 (62%)

7116 (26.3%)

4.5 (3.1–6.7)

4.6 (3.1–7.0)^‡

Pre-term birth

42 (39%)

2186 (8.1%)

7.4 (5.0–10.9)

6.7 (4.5–10.0)^†

Gestational hypertension

2 (2%)

527 (2.0%)

0.96 (0.24–3.9)

0.86 (0.21–3.5)^§

Pre-eclampsia

5 (5%)

645 (2.4%)

2.0 (0.8–5.0)

1.8 (0.7–4.5)^§

Neonatal intensive care unit admission

11 (11%)

727 (2.7%)

4.3 (2.3–8.1)

3.4 (1.8–6.4)^¶

Hypoglycaemia

41 (38%)

1074 (4.0%)

15.0 (10.1–22.3)

10.3 (6.8–15.6)**

Jaundice requiring phototherapy

40 (37%)

1737 (6.4%)

8.7 (5.9–12.9)

5.1 (3.3–7.7)

Respiratory distress requiring resuscitation

16 (15%)

1039 (3.8%)

4.4 (2.6–7.5)

2.5 (1.4–4.4)

Shoulder dystocia^††

7 of 41 (17%)

498 of 19 958 (2.5%)

8.1 (3.5–18.2)

8.2 (3.6–18.7)

Apgar score under 7 at 5 min^††

7 of 40 (17%)

577 of 19 887 (2.9%)

7.1 (3.1–16.1)

2.7 (0.90–8.1)**

Congenital malformation

4 (4%)

996 (3.7%)

1.02 (0.4–2.8)

1.05 (0.39–2.9)

Perinatal death

7 (7%)

394 (1.5%)

4.7 (2.2–10.3)

4.3 (1.9–9.9)

Perinatal death, excluding congenital malformation

7 (7%)

307 (1.2%)

6.1 (2.8–13.3)

5.5 (2.4–12.8)

All outcomes adjusted for age and body mass index. Additional adjustments: * adjusted for parity, smoking status and country of birth; † adjusted for pre-eclampsia, smoking status and country of birth; ‡ adjusted for parity and pre-eclampsia; § adjusted for parity; ¶ adjusted for smoking status and country of birth; ** adjusted for gestation. †† Reported for vaginal delivery only.

Box 3 –
Association between maternal body mass index and pregnancy outcomes for 107 women with type 1 diabetes

Women with type 1 diabetes

Odds ratio (95% CI)

Crude odds ratio

Adjusted odds ratio

Body mass index as continuous variable (per 1 kg/m² difference in body mass index)

Large for gestational age baby

47 (44%)

1.06 (0.98–1.14)

1.08 (0.98–1.18)*

Small for gestational age baby

7 (8%)

1.07 (0.93–1.24)

1.06 (0.91–1.22)

Induction of labour

51 (48%)

0.98 (0.91–1.06)

0.99 (0.91–1.07)^†

Caesarean delivery

66 (62%)

1.03 (0.95–1.12)

1.03 (0.94–1.12)^†

Pre-term birth

42 (39%)

0.98 (0.91–1.06)

0.99 (0.90–1.09)^‡

Hypertensive complications^††

7 (7%)

1.08 (0.93–1.25)

1.07 (0.93–1.24)

Hypoglycaemia

41 (38%)

1.03 (0.95–1.11)

1.03 (0.92–1.14)^§

Jaundice

40 (37%)

1.01 (0.94–1.10)

0.99 (0.88–1.10)^§

Shoulder dystocia^‡‡

7 (17%)

1.07 (0.92–1.24)

1.10 (0.93–1.29)^¶

Congenital malformation

4 (4%)

1.22 (1.01–1.48)

1.51 (1.03–2.23)**

Perinatal death

7 (7%)

0.77 (0.61–0.98)

0.91 (0.70–1.17)*

Body mass index as categorical variable

Large for gestational age baby (n = 47)

Normal weight (< 25.0 kg/m²)

13 (28%)

1.00

Overweight (25.0–29.9 kg/m²)

18 (38%)

1.5 (0.6–3.6)

2.7 (0.77–9.2)

Obese (≥ 30.0 kg/m²)

16 (34%)

2.2 (0.8–5.9)

3.7 (1.02–13.2)

All variables adjusted for age. Additional adjustments: * adjusted for mean HbA_1c level; † adjusted for parity and smoking status; ‡ adjusted for mean HbA_1c level and pre-eclampsia; § adjusted for third trimester HbA_1c level; ¶ adjusted for gestation; ** adjusted for first trimester HbA_1c level. †† Pre-eclampsia and gestational hypertension. ‡‡ Reported for vaginal delivery only.

Box 4 –
Association between maternal HbA_1c levels across gestation and pregnancy outcomes for 107 women with type 1 diabetes

Women with type 1 diabetes

Odds ratio (95% CI)

Crude odds ratio

Adjusted odds ratio

HbA_1clevel as continuous variable

Large for gestational age baby

47 (44%)

0.75 (0.51–1.1)

0.74 (0.48–1.1)

Small for gestational age baby

7 (8%)

0.99 (0.47–2.1)

1.1 (0.49–2.5)

Induction of labour

51 (48%)

0.93 (0.65–1.3)

0.93 (0.61–1.4)*

Caesarean delivery

66 (62%)

0.97 (0.67–1.4)

1.1 (0.76–1.7)^†

Pre-term birth

42 (39%)

2.0 (1.3–3.2)

1.9 (1.1–3.0)^†

Hypertensive complications^§

7 (7%)

0.70 (0.28–1.7)

0.72 (0.27–1.9)

Hypoglycaemia

41 (38%)

0.94 (0.65–1.4)

0.93 (0.63–1.4)

Jaundice

40 (37%)

1.06 (0.74–1.5)

0.69 (0.41–1.2)^‡

Shoulder dystocia^¶

7 (17%)

1.01 (0.54–1.9)

1.5 (0.52–4.1)^‡

Congenital malformation

4 (4%)

1.3 (0.64–2.7)

2.0 (0.60–6.8)^‡

Perinatal death

7 (7%)

3.8 (1.4–10.3)

5.1 (1.5–17.5)

First trimester HbA_1clevel as continuous variable (per one percentage point difference in HbA_1c level)

Large for gestational age baby

47 (44%)

0.61 (0.41–0.93)

0.62 (0.40–0.97)

Pre-term birth

42 (39%)

2.3 (1.4–3.8)

2.5 (1.4–4.3)

Perinatal death

7 (7%)

2.5 (1.2–5.1)

4.5 (1.1–18.4)

HbA_1clevel as categorical variable

Large for gestational age baby (n = 47)

< 53 mmol/L

23 (52%)

53–63 mmol/L

18 (41%)

1.3 (0.51–3.1)

1.05 (0.41–2.7)

≥ 64 mmol/L

2 (7%)

0.20 (0.05–0.76)

0.20 (0.05–0.80)

All variables adjusted for age and body mass index. Additional adjustments: * adjusted for smoking status; † adjusted for parity; ‡ adjusted for gestation. § Pre-eclampsia and gestational hypertension. ¶ Reported for vaginal delivery only.

Obesity before pregnancy: new evidence and future strategies

Pre-conception weight control is important for both mother and child — and for Australia

The world is getting fatter. Between 1975 and 2014 we transitioned from a planet with a higher prevalence of malnourished individuals to one in which there are more obese than underweight people.1 The mean body mass index (BMI) of Australian women rose during this period from 23.4 kg/m² to 26.8 kg/m², so that 50% are now overweight or obese at the start of a pregnancy. This has several adverse consequences for the mother, including gestational diabetes, hypertension during pregnancy, pre-eclampsia, an increased likelihood of a caesarean delivery, and an elevated risk of cardiovascular disease in future years,2,3 all of which are potentially avoidable. Perhaps more important, however, are the increasingly recognised intergenerational effects of maternal obesity that may be manifested during pregnancy (prematurity, stillbirth, congenital anomalies, macrosomia), during childhood (obesity) or later in adult life (increased risk of metabolic disease).3,4

While pregnancy may be an opportune time to intervene, as women are often more motivated to change their behaviour during this period,5 it may not be the ideal point for trying to reduce the effects of obesity. Trials of antenatal interventions undertaken with the aim of limiting gestational weight gain by obese women have had only limited success in improving important perinatal outcomes, such as rates of caesarean delivery, large for gestational age (LGA) babies, neonatal birthweight, and macrosomia.6,7 The largest randomised control trial of limiting gestational weight gain has been the Australian LIMIT trial, which assessed a lifestyle intervention (diet and physical activity) in 2212 overweight and obese pregnant women.6 The intervention, despite appropriate statistical power, did not achieve a reduction in the primary outcome (LGA infants: birthweight above the 90th percentile), although it did find a statistically significant reduction in the secondary outcome of macrosomia (birth weight more than 4 kg) in the intervention arm (15% v 19% in the control arm; P = 0.04). There was no significant difference in maternal gestational weight gain (intervention arm, 9.39 kg [SD, 5.74]; control arm, 9.44 kg [SD, 5.77]; P = 0.89). The findings of the UPBEAT trial,7 a complex intervention involving face-to-face sessions during which information on healthy food, exercise programs and strategies for encouraging behavioural change were provided, similarly found no impact on the primary outcomes of gestational diabetes and LGA babies.

There is a growing realisation by researchers, clinicians, nutritionists, consumers and other health professionals in the area of obesity that intervening during pregnancy may be too little too late.8 Targeting obesity before pregnancy potentially offers a greater benefit. Two recent publications about large population-based cohorts highlight the particular effects that a change in pre-pregnancy weight can have on critical pregnancy outcomes. The first report, from Canada and based on a cohort of 225 000 pregnancies, found that a 10% reduction in pre-pregnancy BMI reduced the likelihood of stillbirth by 10%.9 The second report, from the Swedish birth registry and encompassing 456 711 women, documented a dose–response association between inter-pregnancy weight gain and the risk of stillbirth (relative risk [RR] associated with a BMI increase ≥ 4 kg/m² [v a change of –1 to < 1 kg/m²], 1.55; 95% CI, 1.23–1.96).10 Reassuringly, neonatal mortality was reduced for overweight women who lost weight before a second pregnancy (RR associated with a BMI reduction > 2 kg/m², 0.49; 95% CI, 0.27–0.88). A recent Cochrane review of targeted pre-conception interventions in overweight or obese women, however, found no controlled trials relevant to this aim, and called urgently for research in this area.11

Laboratory research has indicated that the risk of intergenerational obesity is not limited to maternal obesity, with recent work finding that, in rats, a high fat diet for either parent renders it more likely that their offspring will develop obesity and diabetes.12 If these results can be transferred to humans, they suggest that epigenetic factors account for some of the risk of offspring becoming obese, with the consequence that attempts to reduce rates of childhood obesity are unlikely to be wholly successful unless significant attention is shifted to parental obesity.

Pre-conception care involves providing health care to women of reproductive age and their partners that optimises their physical, social and emotional wellbeing prior to conception, with the aim of improving health-related outcomes for women and their children. A United States epidemiologic study that used nationally representative data to assess the potential beneficial effects on birth outcomes of universal pre-conception care for women with pre-gestational diabetes estimated that it might avert 8397 (90% prediction interval [PI], 5252–11 449) pre-term deliveries, 3725 (90% PI, 3259–4126) birth defects, and 1872 (90% PI, 1239–2415) perinatal deaths annually.13 The discounted lifetime health costs averted for the affected cohort of children was estimated to be as high as US$4.3 billion (90% PI, US$3.4–5.1 billion).

This type of extensive modelling has not been performed specifically for pre-pregnancy obesity in Australia, but a Queensland-based study found that maternal pre-pregnancy obesity was associated with an increase in the mothers’ hospital costs above those of normal weight women of $5 002 283.14

In 2006, the US Centers for Disease Control launched the world’s first national policy on pre-conception care.15 It focuses on individual responsibility for pre-conception health, as well as on the roles of clinicians, government and health care systems. National policies have also been published in the Netherlands, the United Kingdom and Italy. In Australia, only South Australia has released a specific policy, although pre-conception care is addressed in guidance documents produced by the Royal Australian and New Zealand College of Obstetricians and Gynaecologists16 and the Royal Australian College of General Practitioners.17 Implementing these policies is challenging, as capturing those who will become pregnant is not straightforward: one-third of pregnancies that continue to antenatal care are unplanned.18 Further, changes that require complex behavioural modification prior to pregnancy, such as weight loss, are less likely to be implemented than simple measures, such as folic acid supplementation.19 Another significant problem is the lack of evidence on which to base recommendations about weight loss and lifestyle interventions before pregnancy for improving maternal and neonatal outcomes; randomised trials concerned with pre-pregnancy weight have thus far focused on obesity and reduced fertility.20

Despite a lack of robust evidence about optimal pre-conception programs and interventions, a number of international bodies, including the US Academy of Nutrition and Dietetics,21 have published position papers advocating that women be better informed about the risks associated with pre-pregnancy obesity, and that behavioural counselling be provided to improve the diet and physical activity of obese women of reproductive age. We recognise that understanding of the maternal and neonatal consequences of obesity in Australia is deficient.22 A pragmatic approach may be to start with programs that enhance a woman’s knowledge of the consequences of obesity during pregnancy and the post partum period, and to inform women about postnatal programs that can reduce the risk of post partum weight retention, and therefore obesity during subsequent pregnancies. The most effective interventions include both dietary and exercise components.23 This approach will require hospitals and health services to take a more long term perspective of pregnancy and the post partum period, as well as government investment in these public health strategies.

Obesity, however, involves a complex interplay of biological and social vulnerabilities in different environments and population groups and at different life stages. Interventions at the level of the individual will need to be combined with system-level changes that promote preference for healthy foods, including increasing taxes on beverages with a high sugar content and controlling the marketing of unhealthy foods. There is some evidence that these strategies can reduce the consumption of less healthy products and lead to re-formulations that reduce their sugar, fat or salt content, but no studies have reported a population impact on obesity.24

If current trends persist, about 80% of all Australian adults and one-third of Australian children will be overweight or obese by 2025.25 Supporting research into the most effective pre-conception interventions and strategies to arrest this trend is a key recommendation of the World Health Organization in the Report of the Commission on Ending Childhood Obesity, and this deserves thoughtful consideration.8

The Paleo diet and diabetes

Studies are inconclusive about the benefits of the Paleo diet in patients with type 2 diabetes

Type 2 diabetes is characterised by fasting hyperglycaemia as a result of insulin resistance and defects in insulin secretion. Obesity is the major risk factor for the development of the condition and a number of studies — including the Diabetes Prevention Program, the Da Qing IGT and Diabetes Study, and the Finnish Diabetes Prevention Study — have shown that lifestyle modification (diet and exercise) can significantly prevent the progression of glucose intolerance (prediabetes) to diabetes by up to 58%.1–3 In addition, a recent study showed that a very-low-calorie diet for 8 weeks resulted in remission of type 2 diabetes for at least 6 months in 40% of the participants.4 As such, clinical guidelines prescribe lifestyle modification as first-line treatment for type 2 diabetes and indeed throughout the management of the disease process.5 Therefore, it is clear that dietary intervention is a critical component of the glucose-lowering strategy in diabetes.

The Paleolithic or hunter–gatherer diet is currently popular for weight loss, diabetes management and general wellbeing. It recommends avoidance of processed food, refined sugars, legumes, dairy, grains and cereals, and instead it advocates for grass-fed meat, wild fish, fruit, vegetables, nuts and “healthy” saturated fat. In the early 1980s, O’Dea showed that 7 weeks of living as hunter–gatherers and consuming a high-protein, low-fat diet with an energy intake of 5020 kJ per person per day significantly improved or normalised the metabolic abnormalities of Indigenous Australians with type 2 diabetes.6 Thus, in its purest sense, the focus on fresh foods and avoidance of processed foods seems reasonable and consistent with dietary guidelines worldwide. However, what constitutes a Paleolithic diet is often skewed by individual interpretation or bias. This lack of a standard definition further complicates research evidence for or against this dietary approach and is often supported by individual self-reported benefits on health and wellbeing in popular social media channels. Is there scientific evidence that the Paleolithic diet is better for diabetes management than any other diet that advocates reducing energy intake?

Given its popularity, it was somewhat surprising that a PubMed search using the terms “Paleolithic diet and diabetes” resulted in only 23 articles, with many being reviews or commentaries. This is a similar outcome to a recently published systematic review of Paleolithic nutrition and metabolic syndrome.7 Clinical studies in patients with type 2 diabetes have only been performed by two research groups. Lindeberg and colleagues, from Sweden, published a randomised crossover study of the effects of a 3-month Paleolithic diet compared with a diabetes diet (according to current guidelines) in 13 obese (body mass index [BMI] of 30 ± 7 kg/m²) well controlled (glycated haemoglobin [HbA_1c], 48.6 ± 1.5 mmol/mol) patients with type 2 diabetes.8 The data showed that while both diets resulted in a reduction in BMI and HbA_1c, the Paleolithic diet achieved a significantly lower absolute value for these parameters. However, it is important to note that the patients on the Paleolithic diet had a lower BMI and HbA_1c at baseline and at the 3-month crossover, so it is not clear whether the relative reductions were similar with these diets. In addition, although there was no significant difference in oral glucose tolerance, the high-density lipoprotein levels were higher and triglyceride levels and diastolic pressure were lower with the Paleolithic diet. It is interesting that, based on a 4-day diet diary halfway through the intervention, the patients on the Paleolithic diet consumed less total energy. A follow-up study suggested that the Paleolithic diet may well be more satiating in patients with type 2 diabetes.9 In support of these results, Frassetto and colleagues showed, in a 14-day study of patients with type 2 diabetes, that both the Paleolithic diet (including canola oil and honey; n = 14) and standard diet (according to the American Diabetes Association recommendations; n = 10)10 resulted in a small reduction in HbA_1c levels, with no differences in insulin resistance (as assessed with a euglycaemic–hyperinsulinaemic clamp), blood pressure or blood lipids between the diets.11 There was, however, a beneficial effect of the Paleolithic diet only when compared with baseline for fasting plasma glucose, fructosamine, lipid levels and insulin sensitivity. It is important to note that canola oil is generally not considered a component of a Paleolithic diet. Moreover, this study was designed to maintain body weight at the baseline level in both groups of patients, with the result being a small but significant weight loss of 2.1 ± 1.9 kg and 2.4 ± 0.7 kg in the standard and Paleolithic diets respectively. In summary, these small and short-term studies tend to indicate some benefit but do not convincingly show that a Paleolithic diet is effective for weight loss and glycaemic control in type 2 diabetes.

In addition to the above studies of patients with type 2 diabetes, the Paleolithic diet has also been studied in healthy normal-weight individuals.12 Compared with a reference meal (based on the World Health Organization guidelines),13 there was very little effect on plasma glucose and insulin levels during an oral glucose tolerance test, but statistically significant increases were found in plasma glucagon-like peptide-1, glucose-dependent insulinotropic peptide and peptide YY. These hormone changes were associated with a higher satiety score. One of the Paleolithic meals used in this study caused an increase in the glucose excursion associated with a reduction in the insulin excursion during the glucose tolerance test.12 Similarly, in nine overweight healthy individuals, a Paleolithic diet for 10 days resulted in no change in fasting plasma glucose or insulin levels, but it showed reduced plasma lipid levels and blood pressure compared with the baseline usual diet.14 It is interesting that, while insulin levels during an oral glucose tolerance test were lower with the Paleolithic diet compared with baseline, the authors did not report the glycaemic excursions during this test. Moreover, a 2-week study in obese patients (n = 18) with the metabolic syndrome did not show an effect on glucose tolerance, but it resulted in reduced blood pressure and plasma lipid levels associated with a small but significant decrease in weight.15 In patients with ischaemic heart disease plus either glucose intolerance or type 2 diabetes (n = 14), a Paleolithic diet for 12 weeks resulted in reduced glucose and insulin excursions during the glucose tolerance test and was associated with a 26% reduction in energy intake, compared with a Mediterranean-style diet (n = 15).16 Again, in the absence of changes in weight or energy intake, the Paleolithic diet is as effective in improving the above metabolic parameters as a standard diet.

Thus, given that even very short deficits in energy balance can improve metabolic parameters,17 it is difficult to make strong conclusions about the long term benefits of the Paleolithic diet in type 2 diabetes (or any other condition), because of the short duration of the interventions (less than 12 weeks), the lack of a proper control group in some instances, and the small sample size (less than 20 individuals) of the above studies. While it makes sense that the Paleolithic diet promotes avoidance of refined and extra sugars and processed energy dense food, clearly more randomised controlled studies with more patients and for a longer period of time are required to determine whether it has any beneficial effect over other dietary advice.

This is where the health system fails

The effect of where you live on your health is nowhere more apparent than on Palm Island.

Inhabitants of the small island just north of Townsville are being hospitalised for chronic obstructive pulmonary disease at almost 21 times the rate of other Queenslanders, are being admitted for epilepsy and the bacterial skin infection cellulitis at 12 times the state-wide rate, are in hospital for diabetes complications at almost nine times the state-wide rate, and are six times more likely to be admitted for a urinary tract infection.

Leading health economist Professor Stephen Duckett says these figures show a community that is being failed by the health system.

“When people end up in hospital for diabetes, tooth decay or other conditions that should be treatable or manageable out of hospital, it’s a warning sign of system failure. Australia’s health system is consistently failing some communities,” he says.

Palm Island is among 63 locations in two states – Queensland and Victoria – identified by Professor Duckett and his colleagues at the Grattan Institute in their report Perils of place: identifying hotspots of health inequalitywhere rates of preventable hospitalisation are at least 50 per cent above the state-wide average for a decade or more. These include conditions such as asthma, diabetes, high blood pressure and malnutrition.

“Persistently high rates of potentially preventable hospitalisations are not normal,” the health economist says. “They are a signal that the existing health policies are not working or are insufficient.”

What causes these areas to have such high rates of health disadvantage are as individual as the places themselves, and influences include air and water quality, housing standards, employment, services like schools, clinics, roads and public transport, crime and community cohesion.

Professor Duckett says that while these areas tend to be more disadvantaged, “we found that potentially preventable hospitalisations are actually generally widely spread, and the places where hospitalisations are most concentrated are quite different for different diseases”.

The complex picture means that policy prescriptions have to be tailored to the individual characteristics of each location: “There is no single solution. The driving forces will be different in each place”.

But just because they defy generalisation and a one-size-fits-all solution, that is no reason not to address the issue, and the rewards in improved health and lower expenditure are considerable – Professor Duckett calculates that reducing preventable hospitalisation rates in the 63 areas identified in the Grattan Institute report to the state-wide average would, conservatively, save between $10 and $15 million a year in direct health costs alone, without taking into account indirect savings from fewer sick days and improved workforce participation.

Professor Duckett says the Commonwealth should fund trials, led by local Primary Health Networks, to test solutions and, crucially, commission rigorous and independent evaluations to identify what works and what does not.

PHNs should also develop tools to more precisely identify and target preventable hospitalisation hotspots. As data from trials is accumulated and lessons drawn, PHNs should use this information and experience to strengthen and expand their efforts.

Professor Duckett admits the priority areas identified in his report represent only a fraction of the problem, and “prevention efforts in these areas alone will not substantially reduce the overall burden of potentially preventable hospitalisations”.

“But,” he added, “they will help to efficiently reduce the worst health inequalities and will build the evidence base for how to address health inequalities more broadly.”

The bottom 10

The nation’s worst preventable hospitalisation hotspots

Palm Island

Yarrabah

Mount Isa

Mount Morgan

Northern Peninsula

Donald

Langwarrin South and Baxter

Broadmeadows

Frankston North

Kingaroy

Source: Grattan Institute

Adrian Rollins