Methods and tools to evaluate the quality of vital statistics
Using fertility rates to assess birth data quality
Birth data from registration is used to calculate additional fertility indicators such as the total fertility rate (TFR) and age-specific fertility rates (ASFRs), which are key indicators in development planning and monitoring health situations in countries. (The age-specific fertility rate is the number of live births per 1000 women in a particular age group over a given period of time, usually one year. The total fertility rate is the average number of children a woman would be expected to have in her lifetime.) Teenage fertility rates are also an important component of SDG goal 3 which is to ‘ensure healthy lives and promote well-being for all at all ages.’
Recording the correct age of the mother at the time of registration is a critical component in calculating both total and age-specific fertility rates (ASFRs) from vital statistics data. The percentage of births with unspecified age of mother should be very low and is an indicator of poor quality data
The quality of the reporting of age of mother can be assessed from the plausibility of age specific fertility rates (ASFRs). In populations with mid to high fertility generally where total fertility rate (TFR) is > 3), ASFRs tend to peak at age 20–24 or 25–29. In countries where TFR is 4 or greater, they remain high in both these age groups. In countries with low fertility rates (TFR ≤ 2), ASFRs often peak in the 30–34-year age range. A deviation from these patterns may suggest poor data quality.
A typical ASFR pattern in a country with high fertility would look similar to that shown in the figure below. The peak in fertility is around age 25–29, and ages 20–29 have rates of more than 200 births per 1000 women.
Implausible patterns of fertility might include a peak in fertility at ages 15–19 or 35 and above, or levels of fertility at ages 15–19, similar to that of ages 20–24. Additionally, a peak in fertility at ages 30–34 may also signal poor data quality, where the TFR is > 3, as would relatively high fertility levels in women age 40 and above (see figure below).
The TFR can also be used to check the plausibility of the data. TFRs generated from vital statistics should be compared with TFRs derived from census and survey data in the same time period. If the TFR derived from CRVS data is significantly lower than the TFR from other sources, this may indicate incomplete registration and signal poor data quality. Additionally, although TFRs tend to decline slowly over time, a large drop compared with previous years or other data sources should alert users that the data need to be reviewed in more detail to assess data quality and completeness.