El análisis funcional del problema clínico actual

This is a systematic review of the literature focussing on the prevalence of GDM.

Previously, available data in this field of research was summarised unsystematically or without a focus on a population based study and characteristic of screening test. There were 16 population-based studies of prevalence of GDM identified. Prevalence estimates for GDM depending on screening protocol and population ranged from 1.35% to 12.80%.

5.6.1 Selection of studies and data extraction

The prevalence rate reported for GDM is based on population-based studies that screen all pregnant women without GDM. Studies that reported trends to predict prevalence and those that reported prevalence in order to measure the accuracy of tests were omitted from this review. As one of the inclusion criterion of the systematic review was a requirement of a 95% confidence interval to estimate overall prevalence, the number of papers identified was limited to only 16. Two studies were found by a hand search out with MEDLINE and EMBASE.

5.6.2 Methodological quality assessment

This paper applied and adopted the quality assessment lists from previous systematic reviews of prevalence (Bishop et al., 2010, Prins et al., 2002), which are based on theoretical considerations as well as common sense, and can also be used for a systematic review of other conditions in the general population. These lists cover all the methodology required to construct the prevalence study. The distinction made between valid and invalid in the assessment is based on overall scores, and the use of cut-off points is arbitrary. It should be recognized; however, that some of the selection studies have a high number of negative scores (No), with a score equal to zero, as shown in Table 5.3. Additionally, the overall qualities of the studies were mixed, with more than half of the studies scoring > 50 %. One of the quality assessment lists had separate validity criteria in addition to the overall quality assessment, such as the representativeness of the study population (item d); 5 studies reported response rates lower than 70% and insufficient data were available on the representativeness of the population. However, quality seemed to be unrelated to reported prevalence. The method of study might not relate to prevalence rates.

5.6.3 Comparison of prevalence rates

This review shows that reports of prevalence for GDM vary considerably, as seen in the other systematic reviews outlined below, and that there are major population and screening test differences between the studies. It is unclear, however, whether this variance in prevalence reflects a true difference between populations, screening test procedure and screening test guideline. The differences of those characteristic precluded the comparison of prevalence rates in most of the studies. Previously, in 1998, King reported that the prevalence of women with GDM was between 1% and 16%, depending on diagnostic criteria and population studies. King’s study used standardised population-based information of diabetes in adult communities worldwide produced by WHO (King, 1998). Another multi-stage cross-sectional prevalence study reported a range of 1% to 14% for GDM in women whose ages were between 15 and 49 years, in the USA in 1988 (Engelgau et al., 1995). Many factors have caused these two papers to produce different results for prevalence, such as screening test use and population group. Another problem regarding reports of prevalence of GDM is that epidemiology data is obtained from different test techniques. Numerous screening test guidelines for GDM have been developed and published within the last decade. Most countries have their own diabetes associations, each publishing their own guidelines for GDM, which may differ slightly.

However, the screening test strategies for GDM are still debated, and no consensus has been established yet. (Metzger and Coustan, 1998)

Differences in the prevalence of GDM may be related to variations in ethnicity.

Hispanic, Native American, South or East Asian, Middle Eastern, South Asian and Black Caribbean women are in ethnic groups with relatively high rates of carbohydrate intolerance during pregnancy and of diabetes later in life (Metzger and Coustan, 1998) (Hoffman, 1998) (Bardenheier et al., 2013) and so there ethnic groups are also classified as high risk groups for GDM. Ethnic origin was the dominant influence on the prevalence of GDM in this review. A Canadian study undertaken among Cree women in the eastern James Bay region of northern Quebec, reported the highest prevalence in the reviewed papers of 12.8%, using the third international workshop conference on GDM criteria (Rodrigues et al., 1999). The authors also mention that this prevalence was at least twice as high as that reported in the general North American population. Another study in this review conducted on the same population, also reported a slightly high prevalence, about 8.5% (Godwin et al., 1999). Two additional studies also stated that the Cree ethnic group are the second highest prevalence of GDM reported in an aboriginal group worldwide (Sermer et al., 1995) (Magee et al., 1993). Similarly, In the United Arab Emirates a study of high risk populations reported a prevalence rate of about 19.3% (Agarwal et al., 2006).

Prevalence of GDM also varies between racial and ethnic groups within the same country. Two studies in Iran included in this review presented slightly different prevalence’s, 6.3% and 4.3% (Hadaegh et al., 2005) (Hossein-Nezhad et al., 2007). The lower prevalence, reported in the study of rural populations, assumed a significantly different lifestyle (Hossein-Nezhad et al., 2007). Moreover, prevalence significantly differs for size and diversity of the population available for study in terms of geographic diversity (states, regions, and countries) and the definition used to identify women with GDM. Two studies in Australia reported a slightly high prevalence of 6.7% and 9.6% (Yue et al., 1996) (Moses et al., 2011). In the 2011 study, carried out in New South Wales City, it was not possible to record the women’s country of birth at the time of collection. However the authors refer to the Australian Institute of Health and Welfare which reports that 83.5% of women giving birth in 2009 were born in Australia or were from countries with a predominately Caucasian background (Moses et al., 2011).

The systematic review of prevalence of GDM showed no association with screening strategy. Six popular screening strategies were used world-wide, including, in order of year proposed, O’Sullivan and Mahan (O'Sullivan and Mahan, 1964), NDDG (National Diabetes Data Group, 1979), C&C (Carpenter and Coustan, 1982), WHO (WHO, 1999b), the third international workshop conference on GDM (Metzger, 1991) and the

fourth international workshop conference on GDM (Metzger and Coustan, 1998).

Screening and diagnostic test criteria have various associated screening test guidelines, which have been developed and published for GDM. Most countries have their own diabetes associations, these societies often publish guidelines for GDM, which may differ slightly. Therefore, the screening test strategies for GDM are still debated (Metzger and Coustan, 1998). Three studies that estimate the prevalence of GDM using the C&C criteria reported comparisons with the NDDG criteria. These three studies reported a similar trend, whereby prevalence derived from the C&C criteria were reported to be higher than the prevalence from NDDG (Ferrara et al., 2002) (Karcaaltincaba et al., 2009) (Hadaegh et al., 2005). The average increase in the prevalence of GDM, reported in two of the three studies, was 50% (Ferrara et al., 2002) (Ricart et al., 2005). NDDG suggested a two step screening procedure in 1979; Women are screened by 50g GCT with “hour 1”

oral glucose tolerance test and undergo a diagnostic test “hour 3”, 100g OGTT, after abnormal screening tests. On the other hand, In 1982, The C&C proposed two step screening; “hour 1”, 50 g GCT with cutoff point ≥7.8, and then diagnoses by “hour 3”, 100g OGTT; fasting, ≥5.3 mmol/l; “hour 1”, ≥10.0 mmol/l; “hour 2”, ≥8.6 mmol/l; “hour 3”, ≥7.8 mmol/l. In both sets of criteria, GDM was diagnosed by at least two plasma glucose measurements exceeding the reported cut off point during the diagnostic tests. In addition, both NDDG and C&C strategies revised the O’Sullivan and Mahan criteria, converting whole blood values to plasma values. The diagnostic criteria from NDDG have been used most often, but some maternal care clinics apply C&C criteria, which set the threshold for normal at a lower value (Turok et al., 2003) (National Diabetes Data Group, 1979).

Prevalence of GDM was therefore higher when using C&C criteria compared with NDDG criteria.

The distribution of classical risk factors in the general population of pregnant women is an important consideration in determining the optimal screening protocol.

Selective screening screens all pregnant women with more than one risk factor, showing higher prevalence than universal screening. In this review, it was shown that papers conducted on high risk groups presented significantly higher prevalence rates (Godwin et al., 1999) (Rodrigues et al., 1999) (Chanprapaph and Sutjarit, 2004) (Pedersen et al., 2010). The results of selective screening tests including all mothers with high risk factors (as confirmed by diagnostic tests) demonstrated higher prevalence rates than universal screening tests. This is evidenced in the selective screening tests performed on high risk ethnic groups in two Canadian studies (Godwin et al., 1999) (Rodrigues et al., 1999).

Consequently, the higher prevalence rates from selective screening seen in this review may have been driven by ethnicity.

Additionally, three retrieved studies reported subject ages (Schmidt et al., 2000) (Ferrara et al., 2002) (Karcaaltincaba et al., 2011). There was little evidence to conclude that GDM prevalence estimates are dependent on the age of pregnant women studied in those three studies. However, pregnant mothers over 35 years old are classified as a high-risk group for GDM. One of those presented the lowest prevalence as 1.3%, which was found in teenage pregnancies in Ankara, Turkey, for women who were aged less than 19 years. Similarly, a study in Northern California reported a lower prevalence in pregnancy age < 25, of about 1% with NDDG criteria and about 1.7% based on the C&C criteria (Ferrara et al., 2002). The prevalence of GDM increases with the increased age of pregnant women (Getahun et al., 2008). Thus, age difference of screened mothers was shown to result in various prevalence estimates of GDM.

Variation in the prevalence of GDM can be explained by screening tests and population characteristics. Reports of prevalence among different ethnic groups in the Scottish population are not available in any known studies. In the UK, NICE reports the estimated incidence of gestational diabetes as 3.5% based on assuming age-standardised prevalence of 3.5% with type 2 DM and on a population of 39,57,157 aged 18 years or older (NICE, 2008a). Dornhorst and colleagues reported that women from ethnic groups other than white had a higher frequency of gestational diabetes than white women (2.9% vs 0.4%, p < 0.001) in the UK. Compared to white women, the relative risk of gestational diabetes in the other ethnic groups in the UK, was: Black 3.1 %, South East Asian 7.6 %, Indian 11.3 %, and miscellaneous 5.9 % (Dornhorst et al., 1992). This study confirms that different ethnic groups in the UK have dissimilar susceptibility with regard to the development of GDM. This result clearly shows that women in ethnic minority groups have an increased risk of GDM in UK. The largest ethnic population group in Scotland, at around 80% of the population, is white, and is similar to England & Wales (The Scottish Government, 2011) (Office for National Statistics, 2012).Therefore, prevalence of GDM in Scotland is taken to be approximately 3.5%.

5.6.4 Limitation and recommendation for future research

Publication bias was not investigated in this review, as funnel plots were not considered appropriate due to the variation across studies. It is unlikely that the set of published papers are biased with respect to the prevalence reported. However, it is possible that some studies were not identified in the searches if they were not published in mainstream journals. However, studies brought together in a systematic review will differ (Glasziou and Sanders, 2002). In this systematic review it was not possible to pool prevalence estimates of GDM because of the considerable heterogeneity across individual studies. There are many factors which impact the heterogeneity of GDM. Those

factors may include socio-demographic features of the population such as: age, the ethnic group and screening test procedure. Another factor that may have had an impact is the number of papers identified, which was limited to only 16. This was because one of the inclusion criteria of the systematic review was the requirement of a 95% confidence interval to estimate overall prevalence, and many papers are poor at reporting prevalence within a 95% confidence interval.

Variability in ethnicity of those screened for GDM resulted in wide differences in prevalence. Even more concerning is that the diversity in screening test criteria for GDM usually results in varying prevalence. Moreover, unclear risk factors for GDM in each study such as family history of diabetes mellitus before pregnancy, history of macrosomia in previous pregnancy, history of preeclampsia during gestation and weight gain during pregnancy may have resulted in heterogeneity in this study. A move towards the application of similar strategies for screening tests for similar populations may reduce the heterogeneity of prevalence for GDM.

5.7 Conclusion

This review has shown some of the influences on variation of prevalence for GDM.

The present review indentified several studies. These studies were heterogeneous in methodological quality and results. Variation in over half of the studies can be explained by ethnicity and screening test strategies. Other important factors were whether universal or selective screening protocols for GDM were adopted and whether 75g or 100g OGTT were used. The impact of these identified factors on prevalence estimates should be further investigated as they may be acting as proxies for other influences on prevalence.

For example, although the literature on prevalence and the incidence of GDM is varied, there should be a general consensus for GDM screening strategies with regard to population. Accurate prevalence for GDM is as important as health-care planning and epidemiological research, as they provide essential knowledge to assess the burden of a condition within a population. Documentation of GDM‘s impact on quality of life and costs help to inform public health planning. Moreover, it is essential to begin base line prevalence rates, so that researchers can monitor trends.

Table 5.5 Description of the characteristics of the study Author Year Country Study design Source of

population

(Yue et al., 1996) 1996 Australia Retrospective study

1999 Canada Retrospective Medical chart who give birth at

2000 Brazil Cohort study Prenatal care clinics of National

NR = not reported, Organisation (C&C) Indicated Carpenter and Constant, (NDDG) National Diabetes Data Group, (WHO) World Health Organisation, (ADIPS) The Australasian Diabetes in Pregnancy Society, ( 3^rd)Third International work shop Conference on GDM, (4^th) Forth International Workshop Conference on GDM, (ECDC) The Expert Committee on the Diagnosis and Classification of DM in USA

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Table 5.5 (Continue)

Author Year Country Study design Source of population

NR = not reported, Organisation (C&C) Indicated Carpenter and Constant, (NDDG) National Diabetes Data Group, (WHO) World Health Organisation, (ADIPS) The Australasian Diabetes in Pregnancy Society, ( 3^rd)Third International work shop Conference on GDM, (4^th) Forth International Workshop Conference on GDM, (ECDC) The Expert Committee on the Diagnosis and Classification of DM in USA

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Chapter 6 Literature review of economic