Socioeconomic Inequality in Infant Mortality in Iran and across Its Provinces

Article excerpt

Introduction

More than 10 million children die each year in the world (1). That is why child mortality has received renewed attention as part of the United Nation's Millennium Development Goals (2). Furthermore, evidence worldwide suggests that children in households with a lower socioeconomic status have higher mortality rates (3-9). Few studies have been carried out on inequalities in infant and child mortality in developing countries before the last decade, but this situation has recently begun to change. Recent attention to the differences in health status between the poor and rich has led to more research on the health of different groups in developing countries (10). The inter-country projects initiated by WHO and World Bank provide basic information on the health status of different groups (11, 12).

Over the last two decades in Iran there has been a significant declining trend in infant mortality rates with 63.5, 43.5, and 26.7 per 1000 livebirths in 1988, 1994 and 2000, respectively (13, 14). However, no studies have been done on differences in infant mortality rates across socioeconomic groups in Iran. We measured the socioeconomic inequality in infant mortality in Iran overall, as well as in each of its provinces.

Methods

Data

We extracted data from the Demographic and Health Survey (DHS), which was conducted in Iran in 2000 (14). The sample population of DHS constituted 4000 households (2000 rural and 2000 urban) from 28 provinces of the country, plus 2000 households in the capital, Tehran. The sampling design consisted of a stratified single stage (equal size) duster sampling with unequal sampling probabilities. The specific design and sample size (4000 households) make this survey representative at the sub-national level (14).

In addition, we studied 110 751 households to define the socioeconomic status of households in Iran. To define the socioeconomic inequality in infant mortality, we analysed 47 896 livebirths from 1995 to 1999 at the national level and 187 292 livebirths from 1985 to 1999 at the provincial level.

Selection of a five-year observation period at the national level and a fifteen-year observation period at the provincial level is a compromise between providing recent estimates and ensuring enough births to reduce the effects of sampling error (15).

Analysis

We used a dichotomous hierarchical ordered probit (DIHOPIT) model to develop an indicator of the long-running economic status of households (16). It is based on the premise that wealthier households are more likely to own a given set of assets, thus providing an estimate for economic status index for households. We used the following indicators: owning a refrigerator, a television, a telephone, a car, a motorcycle, a bicycle, a bathroom, a toilet, type of heating system, use of natural gas for cooking and heating, number of rooms per capita, type of bathroom effluent disposal, status of toilet sanitation, type of solid garbage disposal, and main source of drinking water. Furthermore variables considered as predictors of the household's economic status included age, sex, education, marital status of the head of the household and migration history of the household five years prior to the interview.

In addition to creating a national index of household economic status, we estimated economic indices for each province separately based on the assumption that province-specific information would produce a more effective and accurate index. Therefore we calculated separate economic indices to discover the socioeconomic status of the sampled populations within each province.

We selected a binary outcome variable: namely whether or not each of the live born infants of the women interviewed was still alive or not in the 12 months following birth. We estimated the infant mortality rate (number of deaths among children below one year of age divided by 1000 livebirths reported during the above-mentioned periods) from birth histories for the entire country and for each province separately. …