Background
The use of census data to measure maternal mortality is a recent phenomenon, implemented in settings with non-functional vital registration systems and driven by needs for trend data. The 2010 round of population and housing censuses recorded a significant increase in the number of countries collecting maternal mortality data. The objective of this study was to estimate rural-urban differentials in pregnancy-related mortality in Zambia using census data.

Methods
We used data from the Zambia 2000 and 2010 censuses. Both censuses recorded the female population by age, the number of children ever born, and live births 12 months prior to the census. The 2010 census further recorded, by age, household, and pregnancy-related deaths 12 months prior to the census. We evaluated and adjusted recorded live births using the cohort Parity Fertility ratio method, and household deaths using deaths distribution methods (General Growth Balance and Synthetic Extinct Generation). Adult female mortality and pregnancy-related mortality for rural and urban areas were estimated for the period October 2009 to October 2010.

Results
Data evaluation showed errors in recorded population age, age-at-death, live births, and deaths, and appropriate adjustments were made. Adjusted adult female mortality was high; an adolescent aged 15 years had a one-in-three chance of dying before her 50th birthday in rural areas and one-in-four chance in urban areas. Pregnancy-related deaths comprised 15.3 % of all deaths among reproductive-age women overall; 17.9 % in rural areas and 9.8 % in urban areas. The pregnancy-related mortality ratio for the period was 789 deaths/100,000 live births overall: 960/100,000 live births in rural areas and 470/100,000 live births in urban areas.

Conclusions
Census-based estimates show very high adult female mortality and particularly high pregnancy-related mortality in both rural and urban areas of Zambia 12 months prior to the 2010 census. Future censuses should pay greater attention to strategies for improving data quality.