IJE Advance Access originally published online on March 13, 2008
International Journal of Epidemiology 2008 37(3):573-582; doi:10.1093/ije/dyn039
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International correlations between indicators of prevalence, hospital admissions and mortality for asthma in children
1Division of Community Health Sciences, St George's, University of London, UK.
2ISAAC International Data Centre, Department of Paediatrics, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand.
3Royal Children's Hospital, Parkville, Victoria, Australia.
*Corresponding author. Division of Community Health Sciences, St George's, University of London, Cranmer Terrace, London, SW18 0RE, UK. E-mail: r.anderson{at}sgul.ac.uk
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Abstract |
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Background There are extensive data on the prevalence of childhood asthma world-wide but the relationships between asthma symptom prevalence, mortality and hospital admissions have not been investigated.
Methods The International Study of Asthma and Allergies in Childhood (ISAAC) used a standard questionnaire to measure the 12-month period prevalence of asthma symptoms by parental report in 6–7 year olds in 40 countries, and by self-report in 13–14 year olds in 60 countries. The initial survey was in the mid 1990s (Phase One) and this was repeated in the early 2000s (Phase Three). We correlated the prevalence values of any wheeze and severe wheeze with national data on mortality and hospital admissions for asthma in 5–14 year olds.
Results All correlations with prevalence were positive. In 13–14 year olds, the correlations between severe wheeze in Phase One and contemporaneous mortality and hospital admission rates were r = 0.32 (P = 0.047) and r = 0.73 (P = 0.003), respectively. In 6–7 year olds in Phase One, the correlation with severe wheeze and mortality was r = 0.42 (P = 0.024). In 14 countries the correlation between admission and mortality rates in the 5–14 year age group was r = 0.53 (P = 0.054).
Conclusions There are consistently positive associations between asthma symptom prevalence, admissions and mortality. The prevalence of asthma symptoms in children obtained from local questionnaire studies may provide a guide to estimate the incidence of severe episodes of asthma in countries with incomplete data on hospital admissions or mortality, or vice versa.
Keywords Adolescent, asthma, epidemiology, child, prevalence, hospitalization, questionnaires, world health, mortality
Accepted 7 February 2008
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Background |
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The severity of asthma ranges from minimal symptoms with little or no health impact through to life-threatening attacks. The main available indicators of asthma at the population level are prevalence of asthma symptoms, hospital admissions and mortality, and rates for these vary greatly throughout the world.1–4 The severity pyramid has a very broad base. In 5–14 year-old children in the United Kingdom, for example, the current ratio of 12-month prevalence of wheeze symptom to the annual risk of asthma admission is 56 to 1, and to mortality is 66 400 to 1.5 These three indicators represent a hierarchy of severity among people with asthma but in practice there are various reasons why this relationship might be difficult to discern: (i) variations in the availability and quality of care; (ii) variations in the balance between primary and secondary care, and within secondary care between overnight admissions, day stay and emergency room visits; (iii) admissions and mortality data are usually based on national data while prevalence estimates are usually based on localized samples of the population; (iv) variations in diagnosis or definition and (v) random variation, especially with mortality which is a rare event in children. A positive correlation between hospital admissions for asthma and adult asthma prevalence within one country has been reported,6 but we are unaware of any evidence concerning the intercorrelations of asthma prevalence, hospital admissions and mortality in children at an international level.
In the mid 1990s, Phase One of the International Study of Asthma and Allergies in Childhood (ISAAC) used a standardized protocol to measure the prevalence of asthma symptoms in large number of countries from all the regions of the world. Participants were drawn from a sample of schools in one or more, mostly urban centres within a country. Phase Three repeated the surveys in many of the same centres and in many new centres in the early 2000s. In this article we examine the correlations at a country level between the prevalence of asthma symptoms reported by ISAAC and national mortality asthma and hospital admission rates obtained from national registries. Evidence of positive associations would constitute a validation of the ISAAC prevalence estimates in terms of independently measured extremes of severity not measurable with any accuracy in prevalence studies. Quantification of these associations would provide guidance to public health authorities wishing to estimate the burden of childhood asthma in their populations with incomplete data.
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Data sources
Details of the ISAAC protocol and results for the prevalence of asthma symptoms have been published elsewhere.1,2,7–10 Each centre was required to obtain data on 13–14 year old children, by self-completed questionnaire in the classroom. Optionally, each centre also obtained data on 6–7 year old children by parent-completed questionnaire. Using a variety of sampling approaches, the schools were selected to represent a defined geographical area. The target for completed questionnaires was 3000 for each age group. ISAAC Phase One refers to the surveys carried out in the mid 1990s. ISAAC Phase Three A refers to the Phase One centres that repeated the ISAAC Study in the early 2000s to investigate time trends. For correlations with Phase One ISAAC data, the individual child data were obtained from the ISAAC International Data Centre (Auckland, New Zealand) for 6–7 year old children from 40 countries (274 905 children) and for the 13–14 year old children from 60 countries (506 849 children). These numbers differ in detail from those in the initial Phase One report1 because they include, in addition, some centres that had been too late for inclusion in the initial Phase One analysis as well as subsequent updated data from certain centres. From these data we calculated the 12-month prevalence of any wheeze (wheeze symptom of any severity), which was based on a single question: ‘have you had wheezing or whistling in the chest in the last 12 months?’ The 12-month prevalence of moderate to severe wheezing was based on responses to branches of the above question and comprised one or more of: (i) four or more attacks of wheeze; (ii) woken by wheeze on one or more nights per week or (iii) wheezing severe enough to limit speech to only one or two words at a time, between breaths. For brevity this will be referred to as ‘severe wheeze’.
For analysis of cross-sectional correlations with Phase Three A data and of correlations over time, we used published data for 193 404 6–7 year old children in 66 centres from 37 countries and for 304 679 13–14 year old children in 106 centres from 56 countries.2
The number of deaths from asthma in countries represented by ISAAC was obtained from the World Health Organization (WHO).3 The age group 5–14 years was used, because it encompassed the ages of the ISAAC subjects. Under the Ninth Revision of the International Classification of Diseases (ICD9), asthma (ICD9 493) was included in the WHO Basic Tabulation List (BTL) together with unspecified bronchitis (ICD9 490), chronic bronchitis (ICD9 491) and emphysema (ICD9 492) in a broader category coded as BTL code B323. Following the introduction of ICD10, asthma deaths were reported separately (J45-46) in some countries. We investigated the proportion of these chronic lower respiratory deaths accounted for by asthma by comparing, for the 29 countries that had implemented ICD10, the numbers of deaths from asthma (J45-46) with those for the equivalent to BTL B323 (ICD10 J40-J43, J45-46). We found that asthma accounted for 89.8% of deaths in the B323 category in 5–14 year olds. We also observed that the proportion of deaths in the B323 category labelled as asthma increased over time from an average of 78.5% in 1995 to 99.8% in 2004. We therefore used the BTL B323 category or its equivalent in ICD10 (J40-43, J45-46) as a proxy for asthma deaths for all countries and all years available. As the death rate in the 5–14 age group is low, 3-year average mortality rates, centred over the exact year of the relevant prevalence study, were used for the cross-sectional correlations where possible. Additionally, to increase the number of countries, we also included six where we had mortality data 1 year before or after the year of the prevalence study in that country. Finally, longitudinal analysis using B323 and the ICD10 equivalent showed that the introduction of ICD10 was associated with a small step change in the trend in mortality. To adjust for this we fitted a linear regression model of mortality (using all the raw mortality data available) with a term for the ICD change and used the residuals from this model in place of the raw mortality rates in both the cross-sectional and trend analyses.
Data on hospital admissions for asthma (also coded using ICD9 and ICD10) in 5–14 year olds were obtained on a country-by-country basis by direct enquiry.4
Population denominators of 5–14 year olds for estimating rates were obtained from the US Census Bureau.11 These compared well with those available from the WHO but were preferred because they were more complete in respect of years. In China, where the WHO mortality data referred to a sub-national sample, WHO population denominators were used. In Italy, where the admission rates referred only to the region of Lazio, Lazio population denominators were used. For six countries with no more than 40% missing years, the latter were filled using ordinary linear regression models.
Statistical analysis
For each analysis, all countries with available data were included. We used Spearman correlation to investigate: (i) the association between the prevalence of asthma symptoms and the average hospital admission rates estimated for 3 years around the relevant year of the ISAAC survey; (ii) the association between the prevalence of asthma symptoms and the average mortality rates estimated for 3 years around the relevant year of the ISAAC survey; (iii) the association between prevalence and mortality rates after adjusting for the ICD change with a linear regression model; and (iv) the association between admission rates and mortality rates both estimated for 3 years spanning the year of the prevalence survey. The association between admission rates and mortality rates was also investigated by fitting a linear regression model of the logits of mortality rates with admissions rates, allowing for clustering in time by country and adjusting for the ICD change, using data for all available years. This followed several tests, which confirmed that a linear regression model was appropriate.
We investigated the correlations between trends in prevalence between Phase One and Phase Three A and trends in mortality and admissions by comparing the percentage change per year in prevalence with that in both mortality and admissions in each country using Spearman correlation. The statistical analyses were done using STATA.12
Results
The individual country data are shown in Table 1 and the correlations with prevalence are shown in Table 2 and Figures 1 and 2. The Spearman correlations are shown for both the raw series and following adjustment of mortality for the ICD change. In both age groups, the correlations became stronger when the data were adjusted for the ICD change. The number of observations is sometimes higher in the unadjusted analysis because this included some countries for which mortality data were only available for the year before or year after the year of the prevalence survey.
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For 6–7 year olds in Phase One there were 30 countries with data on mortality and prevalence and 11 with data on admissions and prevalence (Table 1). For Phase Three A there were 23 countries with data on mortality and prevalence and seven countries with data on admissions and prevalence. The correlations were all positive, ranging from r = 0.21 for Phase Three A wheeze prevalence and mortality (unadjusted for the ICD change) to r = 0.42 for Phase One severe wheeze prevalence and mortality (adjusted for the ICD change) (Table 2). Correlations between wheeze and mortality were consistent between Phases One and Three A. There was a slightly greater correlation between severe wheeze and mortality than between any wheeze and mortality.
For 13–14 year olds in Phase One there were 39 countries with data on mortality and prevalence, and 14 with data on admissions and prevalence. For Phase Three A, there were 29 countries with data on mortality and prevalence, and eight with data on admissions and prevalence. The correlations were all positive and ranged from r = 0.13 to r = 0.73. The correlations between Phase One prevalence and admissions were moderately strong both for any wheeze (r = 0.67, P = 0.008) and severe wheeze (r = 0.73, P = 0.003). The correlations between Phase One prevalence and mortality were also moderately positive (r = 0.32, P = 0.047, mortality adjusted for the ICD change). As observed in the 6–7 year age group there was a greater correlation between severe wheeze and mortality than between any wheeze and mortality.
For the Phase One period, for which there were sufficient data for analysis, there was a moderate cross-sectional correlation between admission and mortality rates (r = 0.53, P = 0.054) (Figure 3).
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The correlations over time between prevalence and admissions and mortality are shown in Supplementary Table A. As the variability in trends was small in comparison with other sources of variability, there was insufficient power to exclude or confirm a relationship. The correlation over time between trends in prevalence and trends in mortality was slightly negative but consistent with chance in the younger and older age groups (–0.01, P < 0.96 and –0.20, P < 0.24, respectively). There was little evidence of any correlation between trends in the prevalence of wheeze and trends in admissions in 6–7 year olds (r = 0.08, P = 0.79) though in 13–14 year olds the correlation was 0.39 (P = 0.16) and was stable after the exclusion of outliers. Analysis of the association between the trend in admission rates compared with mortality also showed little evidence of a correlation (r = 0.21, P = 0.44) and after excluding two countries with very different patterns this was negative (r = –0.18, P = 0.53).
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Discussion |
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We found positive correlations between the prevalence of wheeze in 6–7 and 13–14 year old children, based on local studies using standardized methods, and national rates for asthma mortality and asthma admissions for the 5–14 age group. Correlations of the prevalence of wheeze were similar with admissions and mortality in the 6–7 year group. In the 13–14 year group there was inconsistent evidence of stronger correlations between wheeze and admissions than between wheeze and mortality. The strongest association overall was between severe wheeze and admissions in the 13–14 year group. We also found a moderate positive correlation between national admissions and mortality rates for the 5–14 age group. Further investigation, performed by fitting a linear regression model of the logits of mortality rates with admissions rates, allowing for clustering in time by country and adjusting for the ICD change, confirmed and strengthened this association. However, there was little evidence of an association between trends in admissions and mortality over time or between trends in prevalence and admissions or prevalence and mortality.
The prevalence data were obtained using a standardized protocol for the sampling of children, administering the questionnaire and checking and analysing the data. The sample sizes for each centre were sufficient to obtain reasonably precise estimates for prevalence, including the prevalence of more severe symptoms.9 Questions which related to asthma severity enabled us to derive prevalence estimates for clinically important levels of asthma symptoms. The ISAAC questionnaire has been validated favourably against physician diagnosis of asthma in both English and non-English speaking children.13–16 Similar correlations were obtained using symptom prevalences obtained from parental questionnaire to 6–7 year olds and self-completed questionnaire to 13–14 year olds independently in the same centres. Weaknesses of the prevalence data include the likelihood of international variability due to differences in understanding the questions on asthma symptoms due to cultural and translation factors. It has been previously suggested that the tendency of ISAAC prevalence rates to be higher in English speaking countries may be partly explained by a greater awareness and understanding of the term ‘wheeze’ in English language countries compared with others.1 However, similar patterns of variation in asthma prevalence have been found with the video questionnaire, which does not depend on written language and the term ‘wheeze’.1 Moreover, some high prevalence centres did not use English, e.g. in Latin America. Another potential problem with the prevalence data was the use of one (or no more than a few) centres to ‘represent’ the whole country. However, variability within country was small in comparison with between-country variability.1 These various sources of variability would be expected to reduce the size and statistical significance of associations.
Death rates have their own sources of error and international variability. The certification of asthma is notoriously difficult, especially where there is coexisting chronic obstructive pulmonary disease. Most investigators have found that the certification of asthma as a cause of death is reasonably accurate below the age of 50 years,17,18 but there is also evidence that certification of asthma death varies within and between countries.19 One advantage of our study is that the potential for diagnostic transfer is probably low in the 5–14 age group, amongst whom deaths from other chronic lung diseases such as bronchiectasis are rare. The relative rarity of asthma death in this age group led to considerable random variation, especially in less populated countries. Where possible we tried to reduce random variability by combining adjacent years. Another problem was that prior to the introduction of ICD10, data on asthma deaths were available from WHO only as an aggregate of asthma and bronchitis. We addressed this by using the WHO combined chronic lower respiratory category B323, of which asthma accounted for an estimated 90% of deaths, for the whole period spanning the ICD change. Even so, we observed an additional effect that was associated with the ICD change itself and additional adjustment for this increased the strength of some of the associations.
Hospital admission statistics are generally based on a diagnosis at discharge, which considers the clinical history, investigations and response to treatment. It has been found to be reasonably accurate for asthma in the United Kingdom.4,20 Though studied less than death certification, it is likely that some between-country variations may be explained by differences in diagnostic customs. There are also likely to be international variations in the balance between being treated in primary care or at hospital, and in the latter case between overnight admissions, day stay and emergency room visits, especially in children who generally have short episodes.
The relationship between asthma symptoms prevalence and mortality (case-fatality) is likely to be affected by health care factors such as accessibility to care and the quality of the care received. This varies considerably within and between countries. Similar considerations apply to the relationship between asthma symptom prevalence and hospital admission.4
In the face of these numerous sources of variation, and the large differences in frequency within countries between the three indicators, we find it remarkable that positive and in some instances moderately strong associations between these indicators were observed.
In conclusion, our analysis suggests that in spite of limitations of the questionnaire method, and dependence on localized samples, ISAAC asthma symptom prevalence estimates often reflect national levels of severe or fatal asthma in children and this holds strongest in the relationship between self-reported symptoms and hospital admission in adolescents. It also suggests that ISAAC data may provide a guide to estimate the burden of severe asthma where there are no admissions or mortality data. Likewise, where there are no asthma prevalence data, hospital admission or mortality rates may provide a guide to the prevalence of asthma symptoms. It is clear that any such estimates would be subject to considerable error.
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Appendix 1: Members of the ISAAC Steering Committee |
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ISAAC Steering Committee—N Aït-Khaled* (Union Internationale Contre la Tuberculose et les Maladies Respiratoires, Paris, France), HR Anderson (Division of Community Health Sciences, St George's, University of London, UK), MI Asher (Department of Paediatrics, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand), R Beasley* (Medical Research Institute of New Zealand, Wellington, New Zealand), B Björkstén* (Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden), B Brunekreef (Institute of Risk Assessment Sciences, Universiteit Utrecht, The Netherlands), W Cookson (Department of Respiratory Medicine, Imperial College London, UK), J Crane (Wellington Asthma Research Group, Wellington School of Medicine, New Zealand), P Ellwood (Department of Paediatrics, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand), S Foliaki* (Ministry of Health, Nuku'alofa, Kingdom Of Tonga), L García-Marcos (Instituto de Salud Respiratoria, Universidad de Murcia, Spain), U Keil* (Institut für Epidemiologie und Sozialmedizin, Westfälische Wilhelms Universität, Münster, Germany), CKW Lai* (Department of Medicine and Therapeutics, The Chinese University of Hong Kong, SAR China), J Mallol* (Department of Pediatric and Respiratory Medicine, Hospital CRS El Pino, University of Santiago de Chile, Chile), EA Mitchell (Division of Paediatrics, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand), S Montefort* (Department of Medicine, University of Malta, Malta), JA Odhiambo* (Centre Respiratory Diseases Research Unit, Kenya Medical Research Institute, Nairobi, Kenya), N Pearce (Centre for Public Health Research, Massey University, Wellington, New Zealand), C Robertson (Department of Respiratory Medicine, Royal Children's Hospital, Melbourne, Australia), J Shah* (Jaslok Hospital & Research Centre, Mumbai, India), AW Stewart (Division of Community Health, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand), DP Strachan (Division of Community Health Sciences, St George's, University of London, UK), E von Mutius (Dr von Haunerschen Kinderklinik de Universität München, Germany), SK Weiland (Department of Epidemiology, University of Ulm, Germany)**, H Williams (Centre for Evidence Based Dermatology, Queen's Medical Centre, University Hospital, Nottingham, UK), G Wong (Department of Paediatrics, Prince of Wales Hospital, Hong Kong Special Administrative Region, China).
*Regional coordinators: ISAAC International Data Centre—Mr Tadd Clayton (Department of Paediatrics, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand).
**Deceased.
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Supplementary Data |
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Supplementary data are available at IJE online.
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Acknowledgements |
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We thank the children and parents who participated in ISAAC Phases One and Three, the school staff for their assistance, the Phase One and Phase Three principal investigators (listed in reference 2) and the many funding bodies who have supported the work in the collaborating centres and at the ISAAC International Data Centre. We thank Maria Kirwen for her assistance with the collection of hospital admissions data. We acknowledge the use of WHO mortality data but we alone are responsible for the analysis and interpretation of these data given in this paper.
Conflict of interest: None declared.
KEY MESSAGES
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See Appendix 1 for the Members of The ISAAC Steering Committee 
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20 Dixon J, Sanderson C, Elliott P, Walls P, Jones J, Petticrew M. Assessment of the reproducibility of clinical coding in routinely collected hospital activity data: a study in two hospitals. J Public Health Med (1998) 20:63–69.
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