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Keywords:

  • fatal outcome;
  • female;
  • incidence;
  • mortality;
  • myocardial infarction;
  • socio-economic factors

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Abstract. Tydén P, Engström G, Hansen O, Hedblad B, Janzon L (Malmö University Hospital, Lund University, Malmö, Sweden). Geographical pattern of female deaths from myocardial infarction in an urban population: fatal outcome out-of-hospital related to socio-economic deprivation. J Intern Med 2001; 250: 201–207.

Objective. This study of myocardial infarction (MI) amongst urban women has sought to assess whether there are differences in fatal outcome, in-hospital respectively out-of-hospital, between residential areas defined in terms of socio-economic circumstances.

Design. Register-based surveillance study 1986–95.

Setting. Seventeen residential areas in the city of Malmö, Sweden.

Subjects. Women 20–74 years of age.

Main outcome measures. Differences in fatal outcome, in-hospital respectively out-of-hospital, between residential areas were expressed in terms of age-adjusted odds ratios (ORs), calculated by means of logistic regression. Socio-economic circumstances in the areas were expressed in terms of a composite score.

Results. Between residential areas there were marked and statistically significant differences in incidence (range 124–328/105, < 0.001, d.f.=16) and mortality (range 38–132/105, < 0.005, d.f.=16). Area rates of mortality covaried with incidence (r=0.85, < 0.001) and with odds ratios of fatal outcome out-of-hospital (r=0.52, P=0.031) but not in-hospital. The odds ratios of fatal outcome out-of-hospital decreased in a statistically significant stepwise fashion from areas in the lowest socio-economic quintile (reference) to areas in the highest socio-economic quintile (OR: 0.67, 95% CI: 0.48–0.94). There was no corresponding association with the odds ratios of fatal outcome in-hospital.

Conclusions. The high rate of mortality from MI amongst women in areas with deprived socio-economic circumstances was related to deaths occurring out-of-hospital. In order to assess the preventive potential there is a need for further studies that may clarify to what extent the association with socio-economic circumstances can be explained by other factors and conditions known to influence the probability of survival.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

With regard to incidence and mortality rates, myocardial infarction (MI) may be considered a less serious problem in women than in men. Yet it remains the leading female cause of death in most industrialized countries [1, 2]. Between and within countries there are, however, marked differences in the rates of mortality that can only partly be accounted for by the covariance with incidence of disease.

In the city of Malmö in Sweden, there are about 120 000 women. Between 300 and 400 of them die per year in acute MI. The rate of mortality is highest in residential areas which in terms of their socio-economic circumstances deviate unfavourably from the city average [3].

Between 30 and 40% of patients with an acute MI die suddenly [4,5] and the mortality rate in hospital is around 15% [5,6]. The objective in this study has been to assess to what extent differences in terms of fatal outcome, in-hospital respectively out-of-hospital, contribute to the pattern of mortality and whether differences in these respects have any relationship with the socio-economic environment.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Nonfatal cases

Malmö University Hospital is the single referral unit for patients with acute MI. From the patient administrative register it is possible to retrieve name, 10-digit personal number, diagnosis and dates of admittance and discharge for each patient. Since 1972 information has been transferred to the Malmö MI register for patients with ICD (International Classification of Diseases) codes 410.00–410.99, respectively, 410A–410X according to the 8th and 9th versions. Area of residence, which is based on a code added to the 10-digit personal number, was introduced in 1986. From 1986 to 1995 there were 1025 events of nonfatal MI, 358 of which occurred in women below 65 years of age. Methods for retrieval and ascertainment of cases remained unchanged during the entire period. At least two of the following three criteria were required for the diagnosis: (i) central chest pain, lung oedema or shock, (ii) electrocardiograph changes indicating acute MI and (iii) elevated serum levels of cardiac enzymes [7]. One individual may have contributed two or more events given that the interval between events exceeds 28 days.

Fatal cases

The National Cause of Death Register was used to trace those who, between 1986 and 1995 had died of ischaemic heart disease, i.e. 410–414 according to the ICD code versions 8 and 9. In all there were 803 cases, 199 of whom were below 65 years of age. Cases who according to the death certificate died of ischaemic heart disease and who according to the MI register had not been hospitalized for MI were counted as fatal events out-of-hospital. Those who, according to the death certificate, died whilst in hospital and who had been recorded in the MI register were counted as fatal events in-hospital.

In order to reduce the number of cases who were no longer living in their own home and hence could not be allocated to any specific residential area, the study has been limited to subjects below 75 years of age.

Socio-economic circumstances in residential areas

Within the city there are 18 residential areas which in terms of their socio-economic circumstances, and morbidity and mortality patterns are very different [8]. The harbour area has not been included in this study because of the small number of people living there. A comprehensive socio-economic score (SES), was used for the comparison of socio-economic circumstances. For the computation of this score, which is based on information supplied by Malmö City Council and Statistics, Sweden [9], we used data on the rate of migration, percentage of residents with foreign citizenship amongst the ones with foreign background, number of people receiving social welfare support (negative signs) and rate of employment (positive sign). These parameters were selected in order to cover different aspects of the concept of socio-economic deprivation in Sweden today. The area value for each of these four variables was standardized by subtracting from it the mean value for the city and dividing the difference by the standard deviation for all the 17 areas [10]. The sum of these standardized values is the SES of the residential area. This score, which has been used in several other studies [11,12], correlates with other well-known measures of socio-economic conditions, e.g. mean income (r=0.8) and percentage of blue-collar workers (r=−0.7).

Values on the percentage of people who in, each area, were receiving social welfare support are from 1991. The average value that year was 11%, ranging from 0.8 to 28.6% between areas. Area specific rates of migration, which is the percentage of people who per year move within or out from a residential area, is similarly based on values from that year. Between areas the percentages range from 5.9 to 22.6% with an average of 15%. Foreign background was defined as foreign citizens, Swedish citizens who were born as foreign citizens, or children under 18 years of age with one or two foreign-born parents. The percentage of residents with foreign citizenship as a proportion of all citizens with foreign background was used as a measure of the social integration of immigrants. In 1992, there were 48% who met these criteria, with a range of 22.6–63.2% between areas. In 1991, the rate of employment, which is the percentage of all inhabitants between 20 and 64 years employed in the free labour market, was 79% in Malmö, ranging from 63.1 to 94.4% in residential areas.

Statistical methods

Differences in incidence and mortality between residential areas.

Incidence and mortality rates in the residential areas were compared after age standardization. Women who were between 20 and 74 years and who in 1990 were registered as citizens in Malmö were used as a standard population. For the age stratification we used an age interval of 5 years.

Kruskal–Wallis nonparametric test was used to assess whether differences between the 17 residential areas [degrees of freedom (d.f.)=16] with regard to annual incidence and mortality rates were statistically significant, i.e. corresponding to P-values <0.05.

Odds ratio of fatal outcome, in- and out-of-hospital, in relation to the SES.

For each area, we calculated the odds of fatal outcome out-of-hospital (all events) and in-hospital (if hospitalized for MI). This odds is the number of fatal events in relation to the number of nonfatal events. These odds were used in the logistic regression analysis of outcome in quintiles of residential areas, defined in terms of the SES. Differences between quintiles are expressed in terms of age-adjusted odds ratios. Quintile 1, i.e. areas with the lowest SES, was used as the Ref. [13].

Age-adjusted odds ratios of fatal outcome, in-hospital respectively out-of-hospital, were calculated in a similar fashion by logistic regression analysis for each of the 17 residential areas. Pearson’s correlation coefficient was used to illustrate to what extent these area-specific odds ratios of fatal outcome, in-hospital respectively out-of-hospital, covaried with the socio-economic area score. This analysis was weighted for the number of cases in each area.

Intra-urban differences with regard to rate of mortality in relation to incidence respectively fatal outcome, in-hospital and out-of-hospital.

Pearson’s correlation coefficient was used to assess to what extent differences in mortality from ischaemic heart disease between residential areas could be accounted for by covariance with incidence of MI respectively odds ratios of fatal outcome in-hospital and out-of-hospital. These analyses were weighted for the number of cases in each area.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

Differences in incidence and mortality between residential areas

The annual age-adjusted incidence of fatal events out-of-hospital ranged between the 17 residential areas from 33 to 102/105 (P=0.014, d.f.=16), incidence of fatal events in-hospital from 0 to 46/105 (P=0.005, d.f.=16). The age-adjusted mortality in ischaemic heart disease, i.e. based on all fatal cases, ranged from 38 to 132/105 per year (< 0.005, d.f.=16). The annual total incidence varied between 124 and 328/105 (< 0.001, d.f.=16; Table 1).

Table 1.   Area specific age adjusted incidences of myocardial infarction in relation to the socio-economic score (SES) Thumbnail image of

Amongst women below 65 years of age, the adjusted incidence of fatal events out-of-hospital ranged from 2 to 36/105 per year (P=0.007, d.f.=16), incidence of fatal events in-hospital from 0 to 14/105 (P=0.162, d.f.=16) and total incidence from 36 to 124/105 (P=0.007, d.f.=16).

Odds ratio of fatal outcome, in- and out-of-hospital, in relation to the SES

There was a statistically significant inverse correlation between the socio-economic area score and the area specific odds ratio of fatal outcome out-of-hospital but not in-hospital (r=−0.54, P=0.025 and r=−0.17, P=0.507, respectively; Fig. 1).

image

Figure 1.  Odds ratios (ORs) of fatal outcome out-of-hospital in relation to the socio-economic score (SES) of residential areas. Every marking in area symbol represents 11 events of myocardial infarction.

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The odds ratio of fatal outcome out-of-hospital was 33% lower in the quintile of areas with the highest SES (OR: 0.67, 95% CI: 0.48–0.94) than it was in the quintile of areas with the lowest score. This inverse relationship was even more marked in women below 65 years of age (Table 2).

Table 2.   Age adjusted odds ratios (OR) of fatal outcome in relation to the socio-economic area score (SES) Thumbnail image of

Intra-urban differences with regard to rate of mortality in relation to incidence respectively fatal outcome, in-hospital and out-of-hospital

Area rates of mortality from ischaemic heart disease covaried in a statistically significant fashion with incidence of MI (r=0.85, r2=0.73, < 0.001) and with the odds ratio of fatal outcome out-of-hospital but not in-hospital (r=0.52, r2=0.27, P=0.031 and r=0.38, r2=0.15, P=0.127, respectively; Fig. 2). Approximately 25% of the intra-urban variance in mortality from ischaemic heart disease could be accounted for by differences in fatal outcome out-of-hospital.

image

Figure 2.  Odds ratios (ORs) of fatal outcome out-of-hospital in relation to the age-adjusted mortality of ischaemic heart disease in residential areas. Every marking in area symbol represents 11 events of myocardial infarction.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

In the city of Malmö like in many other urban settings, MI is the leading female cause of death [1–3]. There are marked differences in the rates of mortality between residential areas defined in terms of their socio-economic circumstances, however. It is the conclusion in this study that this association, to a substantial degree, is related to differences with regard to fatal events out-of-hospital. The true nature of this association remains to be evaluated, i.e. whether the high case fatality rate amongst women from areas with inferior socio-economic circumstances may be because of other factors and conditions of importance for the immediate prognosis, e.g. severity of coronary atherosclerosis, level of exposure to major risk factors, the patient’s delay and time for transportation to the emergency unit. The important issue from a preventive perspective is, however, that there is a pattern of fatal events out-of-hospital and that areas with high rates can be described in terms of their socio-economic circumstances.

Primary health care and private physicians in collaboration with the university hospital supply medical care in the city. It has been shown in previous studies that exposure to major risk factors for cardiovascular disease is more common in areas with inferior socio-economic circumstances [12]. Whether women who die outside hospital have been identified and been treated appropriately as risk individuals for acute MI remains to be evaluated. According to some studies of patients with coronary heart disease (CHD) the risk of sudden death is related to the number of risk factors the patient is exposed to [14,15]. Women with high education seem to have a greater ability to quit and remain free from smoking [16]. Prevalence of hypertension, hyperlipidaemia, diabetes and obesity seem similarly to be inversely related to level of education and annual income [12].

Patients with acute chest symptoms need no referral note to be admitted to the hospital. The emergency unit can be reached from any part of the city within minutes. Yet about 30% of the patients die out-of-hospital. It is conceivable that within this group of patients there is a distribution in time from onset of symptoms until death, which may range from seconds to hours. Considering the documented benefits associated with early restoration of coronary blood flow it is likely that a certain proportion of these women could have been saved had they reached the hospital in time. Further studies are needed to explore to what extent differences with regard to fatal outcome out-of-hospital may be related to the patient’s delay and time for transportation to the emergency unit, and whether these premises have any relationship with the socio-economic environment.

The average in-hospital mortality/incidence ratio in Malmö, 17%, is similar to what has been reported from many other hospitals [6,17–20]. Differences in outcome between groups of patients may be related to site and extension of the infarction, the patient’s delay and concomitant diseases. Our results do not indicate that the rate of survival in hospital would be lower for patients from areas with inferior socio-economic circumstances. Results from studies of case fatality rate in relation to socio-demographic markers are however, not consistent. In a study on MI from Scotland socio-economic deprivation was associated with an increased overall case fatality rate. Outcome in hospital was, however, similar for patients from all social classes [21]. In other studies, short education and unemployment have been identified as markers associated with unfavourable outcome in hospital [22,23]. To what extent this may be related to differences in the use of thrombolytic agents, aspirin and β-blockers remains controversial. Studies from northern Ireland and Canada on care and evaluation of patients with CHD have shown that those who are economically deprived have to wait longer to receive coronary angiography [24,25]. Other epidemiological features associated with treatment and severity of the infarction, i.e. variations with regard to when (time of day, week and season) the patient is hospitalized and variations with regard to patients socio-demographic circumstances, have received little scientific attention.

Some methodological issues should be considered. Around 50% of the death certificates in each of the areas were based on autopsy. The validity of the diagnosis for nonfatal cases has been estimated at 90–95% [26]. As there is only one hospital for treatment of MI within the city of Malmö, we consider it unlikely that patterns of incidence and mortality could have been confounded by biased retrieval and ascertainment of cases. It has, however, been claimed that incidence figures that are based on hospitalizations and deaths may underestimate the true incidence because older patients especially, in the absence of characteristic or more severe symptoms, may avoid seeking medical advice [27].

In this city, in which acute MI is the leading cause of death in females, there were marked differences in the rates of mortality between residential areas defined in terms of their socio-demographic circumstances. It is concluded that this association, to a substantial degree, is related to differences with regard to fatal events out-of-hospital. In order to assess the preventive potential there is a need for studies that may clarify to what extent the association with socio-economic circumstances can be explained by covariance with other factors and conditions known to influence the probability of survival.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References

This study was supported by grants from the Swedish Council for Social Research.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. References
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