To adequately measure population health a health information system is essential 1. The main rationale for collecting routine data on population health is to provide information and evidence for designing and assessing health programs and to ensure that their objectives are being met 23. Such data might also be used to generate or support observations about population health transition 2. Among the available data generated by health information systems, data on mortality are the most commonly used, not only as indicators of health development, but also as broader measures of socioeconomic development.

Most, if not all countries possess legislation for vital registration systems to collect mortality data to generate various summary measures of population health 3. However, a functioning vital registration system which yields valid and timely data on mortality requires considerable resources such as skilled manpower and technology, as well as processes to ensure that death data are reported, validated and used 4. Developing countries usually do not have complete death registration and to estimate the level and trend of mortality based on available data, various demographic methods (e.g. Brass Growth Balance method) are used to adjust the data, particularly data on adult mortality 35.

There are various standard criteria that have been used elsewhere to assess the completeness and data quality of a death registration system 36. Differential mortality information, (e.g. by age, sex, ethnicity, occupational category, place of residence) is a key output of a functioning death registration system 7. Health decision makers are interested in the comparative health status of various segments of the population, and in different regions, to better target health programmes. Over the past three decades, research has been carried out to establish differential mortality patterns according to socioeconomic status (e.g. social class, income, ethnicity) and geography (e.g. rural and urban areas), based on data from various mortality information systems 78. However, given the problems of death registration systems in developing countries, the majority of these studies have been conducted in developed countries. The few studies that have been carried out in developing countries are generally based on child mortality from health or demographic surveys 79.

In Iran there are three climatic zones; (a) arid/semi-arid regions, (b) mountainous extensions and (c) the Caspian region. Administratively, the country is divided into 30 provinces with different levels of socioeconomic development. The variations in socioeconomic condition, climate and the environment across the country may imply differences in the level and distribution of health-related indices between regions and provinces 10. Table 1 summarizes the variation in socioeconomic conditions across provinces according to various data sources. If socioeconomic status and geographic conditions are also strong predictors of mortality in Iran, as elsewhere, one would expect corresponding provincial differences in survival. However, this has not yet been established.

The objective of this paper is to estimate the level of mortality in terms of probability of child (₅q₀) and adult (₄₅q₁₅) death, and life expectancy at birth, among the various provinces in Iran, and also to determine to what extent these mortality indices are consistent with the socioeconomic characteristics of provinces in Iran.

In Iran, two organizations, the National Organization for Civil Registration (NOCR) and the Ministry of Health and Medical Education (MOH&ME) currently operate death registration systems. The NOCR is legally responsible for the registration of four vital events (birth, death, marriage and divorce) 11. It is generally agreed that of the four events registered by the NOCR, mortality data are the least complete and of the lowest quality 12. Although data from 1995 show that this organization has made progress in capturing and registering deaths, there remains substantial delayed registration and inaccurate recording and reporting of the cause of death 13. Thus, these data have not been widely used for analysing mortality in previous studies in Iran.

The MOH&ME (Deputy of Research and Technology) collected data on mortality during the period 1965–2001 1415. The main aim of this data collection system was to obtain information on the structure of causes of death in Iran 16171819. This data source was based on data on causes of death from cemeteries and was discontinued in 2002 with the introduction of a new death registration system by MOH&ME (Deputy of Health) 16171819.

Recognizing that in Iran accurate data on causes of death were not available, the Deputy of Health in the MOH&ME initiated a new comprehensive program for death registration in order to improve the capacity of district health networks for registering deaths by age, sex, cause, and place of residence. This project started in one province (Bushehr) as a pilot in 1997 20. To improve the level and quality of registration, data from different sources (e.g. NOCR, cemetery, and hospital) have been integrated and cross-checked to remove duplication and to improve registration coverage. Moreover, registration is active, in that health workers at each health facility are responsible for identifying deaths among the population (see Figure 1) 13. In 2001, a comparison with other sources (e.g. the Iranian NOCR and Deputy of Research and Technology of the MOH&ME) revealed that both completeness and classification of causes of death were better than other sources 13. This system was then progressively implemented in several provinces in subsequent years and by 2006, 29 provinces were covered by the system. It is expected that all 30 provinces (including Tehran province) will be covered by the end of 2007 (See Table 2).

To estimate the key mortality indices (₅q₀, ₄₅q₁₅ and life expectancy at birth) by province, data from the death registration system operated by the MOH&ME (Deputy of Health) in 2004 by age, sex and province were obtained. This was the latest year for which data were available. Data on deaths by age, sex and cause were available for 29 (out of 30) provinces, the exception being Tehran province (12 million people) which was not yet covered by this death registration system. To estimate the level of mortality in Tehran province, the latest available data on mortality (2001) from the death registration system operated by the MOH&ME (Deputy of Research and Technology) were used (see below).

Khorasan province was divided into three provinces in 2004. However, since data on population size and socioeconomic status (e.g. literacy and GDP) for the new provinces were not available for this study, we have aggregated the data for these provinces into Khorasan province.

To estimate mortality rates, midyear population data by age and sex for each province are required as denominators. As the last census was carried out in Iran in 1996, provincial population data for 2004 were estimated as follows. At the beginning of each year a census of households is conducted in rural areas by health workers from local rural health units. Population data for those rural areas not covered by health units are collected by mobile health worker teams. We collated these sources to obtain population estimates by age and sex for rural areas of Iran in 2004 21.

For urban populations, as for rural areas, a census of households covered by urban health units is carried out each year 21. For several large cities (capital cities of provinces) for which the coverage of the health network is not complete, populations by age and sex were estimated. To do this, the total population size of each city in 2004 was first obtained from the Statistical Centre of Iran, based on trends in population growth rates between 1991 and 1996 (based on the 1991 inter-censal survey and the census of 1996) 22. The proportional age-sex distribution of the population for each city was then obtained from the Iranian Demographic and Health Survey in 2000 and was used to estimate the population of males and females in 5 year age groups in urban areas in 2004 23.

As the data from the death registration system were not complete, we needed to adjust them to estimate mortality rates and create life tables for each province 24. Mortality data for children and adults were adjusted separately. Since there are no previous estimates of ₅q₀ by province, we used the latest available estimates of infant mortality (₁q₀) for each province in 2001 obtained from the Statistical Centre of Iran (SCI), based on the results of the multi-round Population Growth Survey (1998–1999), using data on children ever-born and children surviving and analysed according to the Brass method 2526.

In order to assess the socioeconomic status of each province, Gross Domestic Product (GDP) and Literacy were used. The last available data on GDP per capita by province, estimated by the Statistical Centre of Iran, was for the period 2000–2003. The annual average over this period was used for this study 27. Literacy levels (in %) for the population aged 15 years and over, by province, were obtained from the Population Data Sheet of Iran in 2001, which in turn was based on the results of the Labour Force Survey of 2001 carried out by the SCI 28. In this survey a literate person was defined as: "Anyone who can read and write a simple text in Farsi or any other language irrespective of formal certification" 29.

Generally, in situations where mortality data, particularly data from death registration, are poor, there are two approaches to correct the data: indirect and direct 30. There are various indirect demographic methods available for estimating the completeness of death registration data (e.g. Brass Growth Balance method, Bennett-Horiuchi method) 31. In these techniques, the age distribution of reported deaths is compared with the age distribution of the population, under certain assumptions 32. Completeness can also be assessed directly using the "capture-recapture" approach. That is, deaths reported in an independent survey of mortality are compared to deaths reported in the death registration system for the same population, from which unmatched and unrecorded deaths can be identified and estimated 30. This is a more expensive means to assess completeness since a sufficiently large population sample is required.

The first analytical step in this study was the computation of age-specific death rates by province. Firstly, data on deaths by 5 year age groups were reviewed. Those deaths for which there was no information on age (1,533 deaths) and sex (637 deaths) were proportionately redistributed across age and sex categories based on observed data. In addition, data on 6,245 deaths (out of 240,754 deaths in 2004) where the place of usual residence (districts) of the deceased was unknown were proportionally redistributed across all provinces.

To correct for under-enumeration of deaths, the following methods were used.

This study provides comprehensive comparative estimates of mortality at the sub-national level in Iran. For the first time, age-specific death rates have been estimated at the provincial level of Iran based on empirical data. As data from the death registration system were not complete, different approaches such as the Brass Growth Balance Method and regression models based on literacy were used to correct the registered data on adult deaths. From the corrected mortality data, life tables were constructed for each province.

The Statistical Centre of Iran has grouped provinces into five regions based on their mortality (particularly child mortality) and fertility rates 26. Figure 11 shows the map of Iran based on this classification. The first region includes provinces with the lowest levels of mortality and fertility, and the fifth region consists of provinces with the highest mortality and fertility levels. It has been claimed that these five regions correspond closely to a classification of the level of development from highest to lowest. However, this classification is based only on these two demographic indicators, and does not adequately reflect the social and economic development of provinces. We have prepared an alternative classification of the socioeconomic development of provinces based on literacy and GDP per capita (see Figure 12).

Our study suggests that there are important variations in child mortality (₅q₀) and adult mortality (₄₅q₁₅) across the country. We also found a close correlation between child mortality and socioeconomic status as measured by GDP per capita (except for the oil producing provinces) and literacy. However, for adult mortality this association is less obvious.

Sistan and Baluchistan province, which is located near to the Afghanistan-Iran-Pakistan borders (AIP region), has the highest level of mortality. The health status of the population living in this area, particularly children and women, is known to be relatively poor 41. Provinces with a high literacy rate and GDP per capita (e.g. Tehran and Esfahan) tend to have low levels of mortality, confirming similar findings elsewhere.

The three oil producing provinces, Kohgilooye and Boyer-Ahmad, Khuzestan and Bushehr rank first to third among provinces according to GDP per capita. However, Kohgilooye and Boyer-Ahmad province is classified as a "low development" province 42. About 55% of the population of the province live in rural areas and also this province has a comparatively high nomadic population (86,677 in 2001) 43. While the Iranian government is working to improve the situation of the population in rural areas, there is still a gap in mortality levels between the rural and urban areas. Part of this difference might be due to the fact that the revenues of the Iranian government from exporting oil products are spent disproportionately on development projects in urban areas than in rural areas 44.

It is important to remember that for the larger provinces in particular, socioeconomic status, and hence mortality patterns, can vary substantially. This is likely to be the case for the more urbanized provinces such as Khorasan, East Azerbaijan, Fars and Kerman. The extent to which the nomadic population form part of the provincial total will also affect the distribution of socioeconomic status. These variations need to be borne in mind when interpreting the strength of the relationship with mortality at the provincial level.

Similar to results from previous studies that have demonstrated a high correlation between child mortality (₅q₀) and socioeconomic status (e.g. literacy), our findings also revealed a strong relationship between the level of child mortality and literacy as a key socioeconomic indicator 45. However, for adult mortality this association is more complex. Non-communicable diseases, such as cardiovascular diseases, or injury are the most important causes of adult deaths and are related to multiple risk factors. As a result, the association between the risk of adult death and socioeconomic factors is not straightforward. This is not the case with infectious diseases which have a more direct association with socioeconomic status.

This study was based on official data from the death registration system operated by the Iranian MOH&ME (Deputy of Health). The coverage of this death registration system has increased in several provinces of Iran over the past five years. Nevertheless, the completeness of data on death registration varies among provinces. Detailed population data by age and sex from a recent census were not available for several large cities, and hence population estimates for urban areas are uncertain. To estimate child mortality (₅q₀), we used data on ₁q₀ since there is no information on ₅q₀ by province. Since the Iranian MOH&ME's death registration system (Deputy of Health) was not operational in Tehran province (12 million population), we have had to use data on death registration from the Iranian MOH&ME (Deputy of Research and Technology) in 2001 for this province, with known limitations.

In this study, to evaluate the completeness of data on death registration among adults, the Brass Growth Balance Method has been used. The method has several limitations. First, it might be that the assumption of a linear relationship between the partial birth rate and the partial death rate is violated. This could be due to several reasons such as misreporting of age, particularly at advanced ages 34. When these points are excluded, different estimates of completeness will be obtained. A second source of bias for this method is that the completeness of death registration might vary by age which cannot be assessed from this method 30. Perhaps most importantly, the method is sensitive to violation of the assumption that the population is stable and closed to migration. This is very rarely the case 30. In addition, the method is highly sensitive to the estimated slope of the regression line of partial birth and death rates, which in turn depends on the statistical rigour of the method used to estimate the regression model.

Results of the Brass Growth Balance method for some provinces were considered implausible. It is highly likely that migration between provinces, which is non-negligible 46, and under-enumeration of the population data for these provinces were the main reasons for this. There is thus uncertainty around our estimates of completeness of death registration based on this method. Where the method yielded estimates of incompleteness that were relatively high (60–80%), we have accepted that as plausible based on other comparative data and information about the socioeconomic development of provinces.

These adjustments and assumptions were necessary in order to estimate differential mortality in Iran. While this information is undoubtedly useful for health development policies in Iran, the limitations noted above suggest caution in interpreting our findings, particularly small differences between provinces. Further research is necessary to confirm our findings once better provincial mortality data become available.

This study has used routine data on mortality to estimate the level of mortality for provinces of Iran. While we have identified important differences in survival among provinces, the extent of variation is lower than observed in other developing countries where such studies have been carried out. The various patterns of provincial mortality are closely related to the level of socioeconomic development of provinces. In particular, we found that literacy is a good predictor of the level of mortality in those provinces where indirect demographic methods to adjust the data were inapplicable. While the completeness of data on death registration by the Iranian MOH&ME is reasonable (≥ 75%) in the majority of provinces, further efforts are needed to improve the quality of data on mortality and also to extend the death registration system to include Tehran province.

The author(s) declare that they have no competing interests.

AK: Contribution to conception and design; acquisition, analysis and interpretation of data; drafting of manuscript.

RT: Contribution to conception and design; interpretation of data; critical revision of manuscript.

MN: Contribution to conception and design; acquisition of data.

ADL: Contribution to conception and design; interpretation of data; critical revision of manuscript.

All authors have read and approved the final manuscript.

The Iranian government has provided a scholarship to support Mr. Khosravi's doctoral studies in Australia.