Funding. Key points. References

280 European Journal of Public Health Funding 7 Diabetes research fund, Competitive research funding from Pirkanmaa hospital district (EVO/TAYS); ...
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European Journal of Public Health

Funding

7

Diabetes research fund, Competitive research funding from Pirkanmaa hospital district (EVO/TAYS); Academy of Finland, Ministry of Education, Ministry of Social Affairs and Health.

Hyvo¨nen K. Gestaatiodiabeteksen esiintyvyys ja seulonta [Prevalence and screening of gestational diabetes]. Kuopio: Doctoral dissertation, Publications of Kuopio University, 1991: 1–165.

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Kaaja R, Luoto R. Raskauskomplikaatiot [Pregnancy complications]. In: Koponen P, Luoto R, editors. Lisa¨a¨ntymisterveys Suomessa [Sexual health in Finland]. Health 2000– survey. Publications of National Public Health Institute B5/2004: Helsinki, 2004.

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Kaaja R, Po¨yho¨nen-Alho M. Insulin resistance and sympathetic overactivity in women. J Hypertens 2006;24:131–41.

Conflicts of interest: None declared.

Key points  Increasing prevalence of GDM is an important public health issue because women with history of GDM and their offspring have a high risk of type II diabetes.  Prevalence of GDM and OGTTs varied between geographical regions in Finland.  Regional variations in GDM prevalence were higher than variation by risk factors (overweight and age over 40 years), which explained only fourth of the regional variation.

1

Dabelea D, Snell-Bergeon JK, Hartsfield CL, et al. Increasing prevalence of gestational diabetes mellitus (GDM) over time and by birth cohort. Kaiser Permanente of Colorado GDM Screening Program. Diabetes Care 2005;28:579–84.

2

Ishak M, Petocz P. Gestational diabetes among Aboriginal Australians: prevalence, time trend and comparisons with non-Aboriginal Australians. Ethn Dis 2003;13:55–60.

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Ferrara A, Kahn HS, Quesenberry C, et al. An increase in the incidence of gestational diabetes mellitus: Northern California, 1991–2000. Obstet Gynecol 2004;103:526–33.

4

Getahun D, Nath C, Ananth CV, et al. Gestational diabetes in the United States: Temporal trends 1989 through 2004. Am J Obstet Gynecol 2008;198:525.e1–5.

5

Baraban E, McCoy L, Simon P. Increasing prevalence of gestational diabetes and pregnancy-related hypertension in Los Angeles County, California, 1991–2003. Prev Chronic Dis 2008;5:A77.

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Lawrence JM, Contreras R, Chen W, Sacks DA. Trends in the prevalence of preexisting diabetes and gestational diabetes mellitus among a racially/ethnically diverse population of pregnant women, 1999–2005. Diabetes Care 2008;31:899–904.

11 Seshiah V, Balaji V, Balaji MS, et al. Prevalence of gestational diabetes mellitus in South India (Tamil nadu)- a community based study. J Assoc Physicians India 2008;56:329–33. 12 Ben-Haroush A, Hadar E, Chen R, et al. Maternal obesity is a major risk factor for large-for-gestational-infants in pregnancies complicated by gestational diabetes. Arch Gynecol Obstet 2009;279:539–43. 13 Kaaja R, Greer IA. Manifestations of chronic disease during pregnancy. JAMA 2005;294:2751–7. 14 Kim C, Newton KM, Knopp RH. Gestational diabetes and incidence of type 2 diabetes: a systematic review. Diabet Care 2002;25:1862–8. 15 Miph VA, Van der Ploeg HP, Cheung NW, et al. Sociodemographic correlates of the increasing trend in prevalence of gestational diabetes in a large population of women between 1995 and 2005. Diabet Care 2008;31:2288–93. 16 The Finnish Medical Society, an Evidence-based Gestational Diabetes Guideline. Finland: http.//www.kaypahoito.fi/web/kh/suositukset/naytaartikkelit/tunnus/hoi50068 [12 June 2008, date last accessed (22.05.2008, 09.06.2008)]. 17 Persson M, Winkvist A, Morgen I. Surprisingly low compliance to local guidelines for risk factor based screening for gestational diabetes mellitus – A population-based study. BMC Pregnancy Childbirth 2009;9:53. 18 Gissler M, Shelley J. Quality of data on subsequent events in a routine Medical Birth Register. Med Inform Internet Med 2002;27:33–7. 19 Crowther CA, Hiller JE, Moss JR, et al. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N Engl J Med 2005;352:1862–8. 20 Kinnunen TI, Pasanen M, Aittasalo M, et al. Preventing excessive weight gain during pregnancy – a controlled trial in primary health care. Eur J Clin Nutr 2007;61:884–91.

................................................................................................................................. European Journal of Public Health, Vol. 22, No. 2, 280–284 ß The Author 2011. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/ckq205 Advance Access published on 18 January 2011

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Mortality from diabetic renal disease: a hidden epidemic Chalapati Rao, Timothy Adair, Chris Bain, Suhail A.R. Doi School of Population Health, University of Queensland, Brisbane, Australia Correspondence: Suhail A.R. Doi, Clinical Epidemiology Unit, School of Population Health, University of Queensland, Herston Road, Herston QLD 4006, Australia, tel: +61 7 3365 5345, fax: +61 7 3365 5599, e-mail: [email protected]

Background: Population-level mortality indicators can be useful outcome measures of diabetes care. Death registration systems serve as the main source of data for such measures. However, standard mortality indicators based on underlying causes do not adequately reflect the burden from diabetic renal disease. Methods: This article presents findings from analysis of multiple causes of death available from death registration data for Australia and USA. Both countries use an automated system that applies prescribed rules to select and code the underlying cause for each registered death. Deaths with diabetes as underlying cause were grouped according to their diabetic complications as defined by the International Classification of Diseases. Age-standardized mortality rates were calculated for the underlying cause rubric ‘diabetes with renal complications’. These were contrasted with rates calculated using additional deaths where diabetes was the underlying cause and renal failure was listed as a consequence. Results: These analyses identified that current automated programmes code three-fourths of all diabetes deaths to ‘diabetes without complications’, despite additional factors being listed. Estimated multiple cause death rates from diabetic renal disease are four to nine times higher than underlying cause rates for ‘diabetes with renal complications’ in both countries; and show a rising trend in contrast to the latter. Conclusion: These findings indicate that routine underlying cause statistics for USA and Australia grossly under estimate mortality from diabetic renal disease. Clear guidelines on the certification, coding and statistical presentation of diabetes mortality are needed for epidemiology and health policy.

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References

10 Ben-Haroush A, Yogev Y, Hod M. Epidemiology of gestational diabetes mellitus and its association with Type 2 diabetes. Diabet Med 2003;21:103–13.

Diabetic renal mortality

Introduction iabetes mellitus is an increasing global public health problem.1

DPopulation ageing, physical inactivity and obesity are the major

Methods Unit record vital registration multiple cause of death data were sourced from the Australian Bureau of Statistics (ABS) for the period 1999–2006, and from the Centers for Disease Control, USA for 1999–2004. For all these country years, deaths are coded according to the Tenth Revision of the International Classification of Diseases (ICD-10) by the automated Mortality Medical Data System (MMDS).16 The ICD-10 codes for diabetes mellitus range from E10 to E14, with fourth-character numerical subdivisions for its complications (see table 1 for details). The fourth-character extension ‘.2’ represents renal complications of

Table 1 Distribution of numbers of fourth character sub divisions assigned to deaths coded to diabetes as the underlying cause of death in Australia (1999 and 2006) and the USA (1999 and 2004) Fourth character for UCOD E10–E14

USA

1999

2004

.0—coma .1—ketoacidosis .2—renal complications .3—ophthalmic complications .4—neurological complications .5—peripheral circulatory complications .6—other specified complications .7—multiple complications .8—unspecified complications .9—without complications Total deaths with diabetes as UCOD

Australia 1999

2006

688 1894 1730 49 392 7602

464 1810 1727 31 394 7616

34 38 115 7 11 497

29 65 131 5 12 507

274 794 177 54 799 68 399

254 663 55 60 124 73 138

6 49 3 2187 2947

8 67 2838 3662

diabetes mellitus. Although the fourth-character extension ‘.9’ carries the label ‘diabetes without complications’; in practice it actually represents just a generic label for unspecified ‘diabetes mellitus’. Codes for renal failure (acute or chronic) range from N17 to N19. The MMDS includes a software program called the Automated Classification of Medical Entities (ACME), which applies rules prescribed by the World Health Organization to select the code for the underlying cause of death (UCOD).16 The ACME program is considered the international standard for this function and includes a specific set of automated criteria for selecting deaths to be coded to diabetes as the underlying cause, with specific fourth character to indicate complications17 (see Appendix 1 for criteria applied to select ‘diabetes with renal complications’ as the underlying cause). For each country year of data, deaths for which diabetes had been selected as the underlying cause by the ACME program were extracted and grouped according to the specific fourth character sub categories for complications. Subsequently, the multiple causes listings for each of these deaths were screened, for instances where renal failure was recorded as a consequence of diabetes in Part 1 of the death certificate (Part II of the death certificate lists contributory causes of death). From these data, deaths from diabetes with renal involvement were categorized as follows: (A) Reported deaths from ‘diabetes with renal complications’ = all deaths with the underlying cause coded to E10–E14 with ‘.2’ as the fourth character; (B) Deaths coded to ‘‘diabetes without complications’’ as underlying cause (i.e. E10–E14 with ‘.9’ as the fourth character) which also have renal failure listed as a consequence of diabetes in Part 1; (C) Deaths coded to diabetes as the underlying cause coded to E10–E14 with fourth characters other than ‘.2’ or ‘.9’ (i.e. diabetes with fourth character codes ‘.1’, and ‘.3’ to ‘.8’), which also have renal failure listed as a consequence of diabetes in Part 1. To examine mortality trends and differentials between the countries, we used these categories to compute three estimates of age-standardized mortality rates from renal failure due to diabetes as follows: (1) Reported Underlying Cause Rate (UCR) for ‘diabetes with renal complications’; based on deaths from (A). (2) Estimated Multiple Cause Rate 1 (MCR1) for diabetic renal disease based on deaths obtained from summing (A) and (B). (3) Estimated Multiple Cause Rate 2 (MCR 2) for diabetic renal disease based on deaths obtained from summing (A), (B) and (C). The Australian population age–sex distribution in 2006 was used as the standard population.

Results Since 1999, the total numbers and proportion of deaths attributed to diabetes as the underlying cause has increased in both countries. Diabetes was the 8th leading underlying cause of death in Australia in 2006, and 6th leading cause in the USA in 2004. Also, the listing of diabetes as either an underlying or contributory cause on death certificates has increased in the USA from 8.8% in 1999 to 9.4% in 2004; and in Australia from 7.5% in 1999 to 9.6% in 2006. These figures indicate the rising trend in the overall association of diabetes with mortality. Table 1 shows the distribution of complications that resulted in deaths coded to diabetes as the underlying cause in each country, for two time reference points. The percentage of deaths with diabetes as the underlying cause that are coded to renal complications (i.e. fourth character ‘.2’) is 3–4% in Australia, and 2% in the USA. The most striking feature in the data is that in both countries, the vast majority of deaths coded to diabetes as the underlying cause have the fourth-character extension ‘.9’; in the most recent year, this figure is 78% in Australia and 82% in the USA. This does not necessarily mean that these subjects had no complications, since further examination of the data reveals that the term ‘diabetes mellitus’ is certified as a single cause of death in only 1–3% of instances in which it has been selected as the underlying cause. Hence, diabetes mellitus is almost always listed with other causes on the death certificate, which in many cases reflects complications, and therefore

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factors contributing to the growth in diabetes prevalence in developed countries. These factors are predicted to act in conjunction with rapid urbanization, and changing lifestyle and food consumption patterns in developing countries to create a sharp increase in the global burden from diabetes in the next two decades.1,2 In addition to its chronic morbidity, diabetes is an important cause of death from its metabolic complications, or from associated vascular changes.3 Nephropathy progressing to end stage renal disease (ESRD) is a major diabetic complication. Diabetes also increases mortality risks from ESRD, whether managed by dialysis or renal transplantation.4 An increase in diabetic renal disease has been observed over the past 20 years in the USA,5 and in Australia.6 Accurate assessment of the population-level magnitude and trends in mortality from diabetic renal disease in these countries is necessary to monitor outcomes from this growing epidemic. Routine vital statistics based on underlying causes of death from national death registration systems are limited in their utility for assessing mortality from diabetes.7 Diabetes is unlikely to be listed as an underlying cause of death, because its link with the pathophysiology leading to death is often uncertain. Hence, diabetes is listed as a contributory cause of death in Part II of the death certificate (see Appendix 1) about twice as often as the instances in which it is listed as the underlying cause in Part 1.8–13 Even when diabetes is selected as the underlying cause, current ICD classification rules do not code deaths for which renal failure is listed as a consequence of diabetes to the underlying cause rubric ‘diabetes with renal complications’. Therefore, such deaths are not reflected in underlying cause of death statistics for ‘diabetes with renal complications’14–17 (See Appendix 1). In this article, we attempt to estimate the extent of under reporting of mortality from diabetes-associated renal disease in Australia and the USA, as derived from analysis of multiple cause-of-death registration data for the period 1999–2006.

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as a consequence of diabetes in Part 1 of the death certificate (table 2 and figure 1). It is likely that diabetes, being a chronic disease, is associated during the terminal illness with renal failure, the exact aetiology of which could be uncertain to certifiers. In such instances, medical certifiers are more inclined to list ‘renal failure’ as a multiple cause in addition to diabetes, rather than any of the more specific terms for renal pathology that are used to code the underlying cause to ‘diabetes with renal complications ‘(see Appendix 1). This is because renal failure can be diagnosed with minimal investigation, and is the terminal manifestation of all underlying renal pathologies. Renal diseases other than diabetic glomerulosclerosis can occur in individuals with diabetes.18 However, even if up to 10% of such renal lesions are non-diabetic in pathology,19 they would not account for the huge excess of mortality from renal failure in individuals with diabetes, as found from our analysis. In addition, the survival of individuals with diabetes and ESRD (irrespective of whether the renal lesion was diabetic nephropathy or non-diabetic nephropathy) is significantly worse when compared with the survival of individuals with nephropathy who do not have diabetes.4 This suggests that diabetes contributes to mortality from terminal renal failure regardless of the pathology of the renal lesion, thus making such a distinction less important. In some instances, however, terminal renal failure could have arisen from acute circulatory failure or septicaemia associated with the complications of diabetes,20 and hence may not be a manifestation of diabetic nephropathy per se. Hence, the calculated ‘Multiple Cause Rate 2’ (see ‘Methods’ section) is likely to be a slight overestimate. On the other hand, we did

Discussion Our data indicate that there is an increasing trend in the recording of diabetes on death certificates, which is consistent and probably a reflection of the increase in prevalence of diabetes in both countries. Concomitant to the rise in the numbers of deaths from diabetes there was no rise in deaths coded to diabetes to renal complications, but paradoxically there was a clear rise in instances where renal failure is certified

MCR2 (US)

MCR1 (US)

MCR2 (Aus)

Table 2 Numbers of deaths certified with renal failure as a consequence of diabetes according to different classifications for Australia (1999 and 2006) and USA (1999 and 2004) Deaths

USA

1999

2004

1999

2006

Diabetes with renal complications (A) Additional deaths (B)a Additional deaths (C)a Total diabetes as underlying cause

1730 11 543 2166 68 399

1727 13 739 2241 73 138

115 301 118 2947

MCR1 (Aus)

Australia UCR (US) UCR (Aus)

131 577 169 3662

a: See text for definitions

Figure 1 Trends in age-standardized death rates from renal disease due to diabetes in Australia and USA calculated using two different multiple cause definitions compared with rates based on underlying cause only

Table 3 Age-standardized death rates from renal disease due to diabetes for Australia (1999 and 2006) and USA (1999 and 2004): comparison of effects (including rate ratios) of calculating rates using two different multiple cause-based definitions with those using an underlying cause only Age-standardized death rates (per 100 000)a

USA

1999

2004

Percent change

1999

2006

Percent change

Underlying cause rate (UCR) Multiple cause Rate 1 (MCR1) Rate ratiob

0.68 5.2 7.7 (7.3–8.1) 6.1 9 (8.5–9.4)

0.62 5.6 8.9 (8.5–9.4) 6.4 10.3 (9.8–10.8)

7.7 7.3 

0.67 2.5 3.7 (3.0–4.6) 3.2 4.7 (3.9–5.8)

0.64 3.4 5.4 (4.5–6.5) 4.3 6.7 (5.6–8.0)

Multiple cause Rate 2 (MCR2) Rate ratiob

a: Standardized to Australian population in 2006 b: To the UCR

Australia

5.6 

5.3 37.7  34.2 –

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analyses of diabetes mortality should be based on multiple causes of death rather than underlying causes alone. In our efforts to quantify the potential causal association between diabetes and renal disease, we analysed the multiple causes of death listed in Part 1 of the death certificate, for those deaths where diabetes was selected as the underlying cause. In 1999, out of the 2187 deaths coded to diabetes without complications as the underlying cause in Australia, 301 (14%) also had renal failure listed as a consequence of diabetes in Part 1 of the certificate [i.e. deaths categorized to (B) in ‘Methods’ section]. This proportion had increased to 20% in 2006 (see table 2). Additionally, 18% of deaths (118/645) coded to diabetes as the UCOD with other complications (fourth-character codes other than ‘.2’ and ‘.9’) in Australia had renal failure listed as a consequence of diabetes in Part 1 [i.e. deaths categorized to (C) in ‘Methods’ section], rising to 24% in 2006 (table 2). Similar levels and trends were again observed from the multiple cause data for the USA. If these additional renal-related deaths are added to the deaths coded to ‘diabetes with renal complications’, they would result in death rates from diabetic renal disease as shown in table 3 and figure 1. At both reference time points, the rates from multiple cause analyses are markedly (4- to 9-fold) higher than underlying cause rates reported from ‘diabetes with renal complications’ in both countries, more so in the USA. Also, there is a similar relative increase in multiple cause rates in both countries over time, in contrast, to the marginal decrease in the underlying cause rates, creating an overall increase in the multiple cause rate ratios. Figure 1 shows that these trends are generally consistent over the short period of observation.

Diabetic renal mortality

Conflicts of interest: None declared.

Key points  The underlying cause rubric, ‘diabetes with renal complications’, underestimates mortality from diabetic renal disease.  The ACME program rules do not include deaths for which renal failure is listed as a consequence of diabetes within the coding rubric ‘diabetes with renal complications’.  Current coding practices mask the magnitude of diabetic renal disease as a cause of death even with the increasing epidemic of diabetes in Australia and the USA.  Multiple cause of death analysis is essential to capture mortality from diabetic renal disease to prevent spurious interpretations based on underlying cause statistics.

 Clear guidelines on the certification, coding and statistical presentation of diabetes mortality are needed for epidemiology and health policy.

References 1

King H, Aubert RE, Herman WH. Global burden of diabetes, 1995–2025: prevalence, numerical estimates, and projections. Diabetes Care 1998;21:1414–31.

2

Wild S, Roglic G, Green A, et al. Global prevalence of diabetes: estimates for the year (2000) and projections for 2030. Diabetes Care 2004;27:1047–53.

3

Morrish NJ, Wang SL, Stevens LK, et al. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia 2001;44(Suppl 2): S14–21.

4

Rabbat CG, Thorpe KE, Russell JD, Churchill DN. Comparison of mortality risk for dialysis patients and cadaveric first renal transplant recipients in Ontario, Canada. J Am Soc Nephrol 2000;11:917–22.

5

Fox CS, Muntner P. Trends in diabetes, high cholesterol, and hypertension in chronic kidney disease among U.S. adults: 1988–1994 to 1999–2004. Diabetes Care 2008;31:1337–42.

6

Villar E, Chang SH, McDonald SP. Incidences, treatments, outcomes, and sex effect on survival in patients with end-stage renal disease by diabetes status in Australia and New Zealand (1991–2005). Diabetes Care 2007;30:3070–6.

7

Roglic G, Unwin N, Bennett PH, et al. The burden of mortality attributable to diabetes: realistic estimates for the year 2000. Diabetes Care 2005;28:2130–5.

8

Goldacre MJ, Duncan ME, Cook-Mozaffari P, et al. Trends in mortality rates comparing underlying-cause and multiple-cause coding in an English population 1979–1998. J Public Health Med 2003;25:249–53.

9

Australian Bureau of Statistics: Multiple Cause of Death Analysis, 1997–2001. Canberra, Report No: 3319.0.55.001, 2003.

10 Redelings MD, Sorvillo F, Simon P. A comparison of underlying cause and multiple causes of death: US vital statistics, 2000–2001. Epidemiology 2006;17:100–103. 11 Official Statistics of Sweden: Causes of Death (2005). Centre for Epidemiology; National Board of Health and Welfare. ISBN: 978 - 91 - 85843- 90–7, 2007. 12 Barreto S, Passos V, Almeida S, Assis T. The increase of diabetes mortality burden among Brazilian adults. Pan Am J Public Health 2007;22:239–45. 13 Romon I, Jougla E, Balkau B, Fagot-Campagna A. The burden of diabetes-related mortality in France in (2002): an analysis using both underlying and multiple causes of death. Eur J Epidemiol 2008;23:327–34. 14 World Health Organization: Diabetes Mellitus (E10 - E14) In: International Statistical Classification of Diseases and Health Related Problems - Tenth Revision, Vol. 1. Geneva: World Health Organization, 1993: 276–80. 15 World Health Organization: Mortality: guidelines for certification and rules for coding. In: International Statistical Classification of Diseases and Health Related Problems - Tenth Revision, Vol. 2. Instruction Manual Geneva: World Health Organization, 1993: 30–65. 16 Centers for Disease Control and Prevention: National Center for Health Statistics: About the Mortality Medical Data System. U.S Department of Health and Human Services, 2007. 17 National Center for Health Statistics: Vital Statistics ICD-10 ACME Decision Tables for Classifying Underlying Causes of Death, 2009: Part 2c, Table E; pgs E-94 - E-106. Centers for Disease Control and Prevention, US Department of Health and Human Services, 2009. 18 Kramer HJ, Nguyen QD, Curhan G, Hsu C-Y. Renal insufficiency in the absence of albuminuria and retinopathy among adults with type 2 diabetes mellitus. JAMA 2003;289:3273–7. 19 Olsen S, Mogensen CE. How often is NIDDM complicated with non-diabetic renal disease? An analysis of renal biopsies and the literature. Diabetologia 1996;39:1638–45. 20 Schrier RW, Wang W. Acute renal failure and sepsis. N Engl J Med 2004;351:159–69. 21 Black SA. Diabetes, diversity, and disparity: what do we do with the evidence? Am J Public Health 2002;92:543–8. 22 Goldacre MJ, Duncan ME, Cook-Mozaffari P, Neil HA. Trends in mortality rates for death-certificate-coded diabetes mellitus in an English population 1979-99. Diabetes Med 2004;21:936–9. 23 Nolte E, Bain C, McKee M. Diabetes as a tracer condition in international benchmarking of health systems. Diabetes Care 2006;29:1007–11. 24 McEwen LN, Kim C, Haan M, et al. Diabetes reporting as a cause of death: results from the Translating Research Into Action for Diabetes (TRIAD) study. Diabetes Care 2006;29:247–53.

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not include in our analyses those deaths for which diabetes and renal failure were listed on different parts of the death certificate, although there may have been a direct causal relationship between them. Previous analyses using multiple cause of death data also identified statistical associations between diabetes and some selected conditions listed on death certificates, but did not explore causal relationships,9,13 as we did in this study, by focusing on data from Part 1 of death certificates. Given the known variations in the epidemiology of diabetes by ethnicity,6,21 multiple cause analyses such as presented here should be conducted to quantify mortality risks from diabetic renal disease in vulnerable subpopulations, as evidence for targeted intervention programmes. Our findings indicate an imperative to intensify screening for diabetic nephropathy, to implement early reno-protection and put in place the plans to serve the unmet need for renal replacement therapy.6 There is a pressing need for further research to validate our findings. Field studies could be undertaken in each country to review cause-of-death certification and available clinical records in a sample of deaths with renal failure listed as a consequence of diabetes in Part 1 of the death certificate. These studies could ascertain the evidence supporting diagnoses of renal failure, verify the temporal sequence of incidence of the two conditions, and identify any other evidence to confirm or refute the likelihood of diabetic renal disease. The study sample could also include a selection of deaths with diabetes and renal failure listed on different parts of the death certificate, to examine whether a causal relationship exists; and could be extended to investigate relationships between diabetes and other co-morbidities. Such studies22 would improve the measurement of diabetes mortality, and facilitate the development of clear guidelines for certification, coding and statistical presentation, including multiple cause analysis. Population-level mortality indicators from death registration data have also been proposed as useful outcome measures of diabetes care.23 However, these issues with certification and coding limit their usefulness for monitoring trends,24 and therefore improvements in diabetes mortality measurement will be required to adequately monitor the impending global diabetes epidemic. We believe that more attention should be given to the collection and analyses of multiple cause-of-death data in different populations to identify epidemiological variations as well as facilitate planned disease control strategies. In conclusion, analysis of multiple cause data provides useful insight into mortality from terminal renal failure in individuals with diabetes. Although the data presented are specific to Australia and the USA, the formal mechanistic basis for coding and statistical presentation suggests that these data represent a universal problem in correct attribution of deaths to diabetes and its complications. Also, while the estimated rate ratios indicate the potential underestimation of mortality risks from this cause, the trend analysis suggests that the observed marginal decline in the underlying cause rate for ‘diabetes with renal complications’ in both countries is likely to be spurious, and both these important findings warrant immediate attention.

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Appendix 1: Procedures for certification and coding of causes of death The international form of medical certificate of cause of death has two parts. Certifying physicians are required to record a sequence of causes originating from the underlying cause of death in Part I of the certificate, and contributory causes of death in Part II. By convention, official statistical agencies report mortality measures based on underlying causes of death. In the USA and Australia (and several other countries), data from medical certificates of cause of death is processed through the Medical Mortality Data System, developed by the Centers for Disease Control and Prevention, USA. The MMDS includes a software program called

the ACME, which applies rules prescribed by the World Health Organization to select the code for the underlying cause of death. The ACME program primarily selects the underlying cause as ‘diabetes with renal complications’ when the death certificate mentions diabetic nephropathy, intracapillary glomerulosclerosis, or Kimmelsteil–Wilson syndrome. Additionally, the ACME program assigns the code for ‘diabetes with renal complications’ as the underlying cause if any of the following conditions are mentioned in addition to diabetes: unspecified disorder of the kidney and ureter—N28.9; chronic nephritic syndrome— N03; nephrotic syndrome—N04; unspecified nephritic syndrome—N05; or unspecified contracted kidney—N26.10 The mention of renal failure as a consequence of diabetes is not included within the underlying cause rubric ‘diabetes with renal complications’.

................................................................................................................................. European Journal of Public Health, Vol. 22, No. 2, 284–289 ß The Author 2011. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/ckr001 Advance Access published on 7 March 2011

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Margot I. Witvliet1, Anton E. Kunst1, Karien Stronks1, Onyebuchi A. Arah1,2 1 Department of Public Health, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands 2 Department of Epidemiology, School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA, USA Correspondence: Margot I. Witlviet, PO Box 22660, 1100 DD Amsterdam, The Netherlands, tel: +31 20 5664828, fax: +31 20 6972316, e-mail: [email protected]

Background: Living in a particular region might affect health. We aimed to assess variations between regions in individual health. The role of socio-economic factors in the associations was also investigated. Methods: World Health Survey data were analysed on 220 487 individuals. Main outcomes included self-reported health, health complaints and disability. The main predictor variable was a modified regional classification of countries. Multilevel logistic regression was used to assess associations between individual health and regions, while accounting for individual and country-level socio-economic factors, notably occupation, education, national income and female literacy. Results: Individual health varied significantly between regions. For instance, compared with Western Europeans, Southern Asians and Western Africans reported poorer health, the odds ratios (ORs) being 2.05 [95% confidence interval (CI) 1.31–3.23] and 1.88 (95% CI 1.26–2.81), respectively. Accounting for socio-economic factors attenuated or, in a few cases, reversed the associations. For example, the OR for Southern Asia and Western Africa respectively became 0.94 (95% CI 0.37–2.37) and 0.77 (95% CI 0.26–2.25). Individuals from Central Europe and the Former Soviet Union were the most likely to report poor health, OR 1.92 (95% CI 1.07–3.44) and OR 4.17 (95% CI 1.91– 9.10) respectively. Overall, men were less likely than women to report poor health. Conclusion: Substantial regional variations in individual health exist, only partly explained by socio-economic factors. Additional policy and health research are needed to investigate Central Europe and Former Soviet Union rates that consistently lag behind Latin America, Asia and Africa.

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Introduction lobal disparities in health form a complex issue adversely affecting

G much of the world’s population. What has been found is that national income and other general socio-economic factors, for example, female literacy are strong determinants of population health.1,2 Although socio-economic factors explain a substantial amount of cross-national disparities in population health, much is left unexplained.3 This has ignited a surge in research examining other national factors that may affect health. For example, Navarro and Shi found that aggregated infant mortality rate, as a measure of population health is associated with a country’s political system.4 Furthermore, previous studies identified that Scandinavian countries, which tend to have substantial social investments, usually exhibit better overall health than other Western European countries, especially during periods of economic crisis.5,6 Despite these findings, and increased interest in national determinants of health, there is still much to be explored.7,8 For example, there is little research extending beyond Western Europe and North America, which examines the joint individual- and country-level determinants of individual health. A recent global study contrasted individual income

versus contextual income inequality effects on individual mortality.9 Another study focused on the association between individual educational attainment and self-reported health.10 A need exists for global multilevel studies of the joint influences of both the individual- and country-level exposures on individual health using data from diverse countries. We aim to contribute to the literature by investigating associations between different world regions and individual health, assessing the extent to which individual and country-level socio-economic factors could explain any such regional associations. Gender differences in regional variations in individual health are also investigated.

Methods We used cross-sectional data from the World Health Survey of the World Health Organization (WHO), implemented recently (2002–05) in 72 countries. Details of survey development, fielding, response analysis and initial findings are online.11 Countries could opt to partake in different survey versions, and data were only publicly available for 70 countries. Consequently, information on all variables was not available across all countries. Occupational status was not available for Norway

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Variations between world regions in individual health: a multilevel analysis of the role of socio-economic factors