| Information component |
Page 4 Health summary – Indicator no. 22 |
| Subject category / domain(s) |
How long we live and what we die of |
| Indicator name (* Indicator title in health profile) |
Infant deaths |
| PHO with lead responsibility |
LHO |
| Date of PHO dataset creation |
18 Dec 2006 |
| Indicator definition |
Infant deaths, crude rate, persons, aged less than 1 year, 2003-05, per 1,000 live births |
| Geography |
England, GOR, Local Authority: Counties, County Districts, Metropolitan County Districts, Unitary Authorities, London Boroughs |
| Timeliness |
The Compendium infant mortality indicator is updated annually, usually around September of the following year. |
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Rationale:What this indicator purports to measure
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This indicator measures the level of infant deaths in the area. |
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Rationale:Public Health Importance
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Infant mortality is an indicator of the general health of an entire population. It reflects the relationship between causes of infant mortality and upstream determinants of population health such as economic, social and environmental conditions. Deaths occurring during the first 28 days of life (the neonatal period) in particular, are considered to reflect the health and care of both mother and newborn. |
| Rationale: Purpose behind the inclusion of the indicator |
There is a national health inequalities target for infant mortality which aims to reduce the gap between the infant mortality rate in the ‘routine and manual classes’ and the population as a whole. However, this target is difficult to monitor at local level as the number of infant deaths in any given local authority or primary care trust (PCT) among a particular social class group is very small and subject to random fluctuations from year to year. Therefore we have chosen to include overall infant mortality as an indicator. There are wide inequalities in infant mortality rates by local authority in England and monitoring these inequalities is essential to understanding trends in inequalities in infant mortality. |
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Rationale:Policy relevance
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There is a national health inequalities target for infant mortality which aims to reduce the gap between the infant mortality rate in the ‘routine and manual classes’ and the population as a whole. |
| Interpretation: What a high / low level of indicator value means |
A high indicator value (red circle in health summary chart) represents a statistically significant higher level of infant deaths for that local authority when compared to the national value.A low indicator value (amber circle in health summary chart) represents a statistically significant lower level of infant deaths for that local authority when compared to the national value. A reduction in the infant death rate indicates a reduction in the number of deaths, relative to the number of live births. However, as the number of infant deaths in any given area is small, fluctuations from year to year are possible and may not indicate a statistically significant trend. |
| Interpretation: Potential for error due to type of measurement method |
Coverage can be considered to be complete as the registration of deaths is a legal requirement. Data quality for the relevant fields (age and area of residence) is extremely high. The small number of infant deaths at a local authority level means that pooling of three years data is required. Even with pooled rates, however, numbers may still be small and large random fluctuations possible. |
| Interpretation: Potential for error due to bias and confounding |
The rates are not standardised or adjusted to take into account any potential confounding variables such as the age or ethnicity of the mother. Whether or not such variables need to be considered depends on the purpose to which the indicator is being put. |
| Confidence Intervals: Definition and purpose |
A confidence interval is a range of values that is normally used to describe the uncertainty around a point estimate of a quantity, for example, a mortality rate. This uncertainty arises as factors influencing the indicator are subject to chance occurrences that are inherent in the world around us. These occurrences result in random fluctuations in the indicator value between different areas and time periods. In the case of indicators based on a sample of the population, uncertainty also arises from random differences between the sample and the population itself.The stated value should therefore be considered as only an estimate of the true or ‘underlying’ value. Confidence intervals quantify the uncertainty in this estimate and, generally speaking, describe how much different the point estimate could have been if the underlying conditions stayed the same, but chance had led to a different set of data. The wider is the confidence interval the greater is the uncertainty in the estimate.Confidence intervals are given with a stated probability level. In Health Profiles 2007 this is 95%, and so we say that there is a 95% probability that the interval covers the true value. The use of 95% is arbitrary but is conventional practice in medicine and public health. The confidence intervals have also been used to make comparisons against the national value. For this purpose the national value has been treated as an exact reference value rather than as an estimate and, under these conditions, the interval can be used to test whether the value is statistically significantly different to the national. If the interval includes the national value, the difference is not statistically significant and the value is shown on the health summary chart with a white symbol. If the interval does not include the national value, the difference is statistically significant and the value is shown on the health summary chart with a red or amber symbol depending on whether it is worse or better than the national value respectively. |
