Knowledge of Delivery Warning Signs
English: % of [select: pregnant women / pregnant women’s mothers / …] who are able to state at least three warning signs during labour and delivery
French: % de [sélectionnez: femmes enceintes / mères de femmes enceintes / ... ] qui sont capables de citer au moins trois signes d'alerte pendant le travail et l'accouchement
Czech: % [určete: těhotných žen / matek těhotných žen / … ] schopných uvést alespoň tři rizikové příznaky v průběhu porodu
What is its purpose?
The indicator assesses the proportion of the target population aware of at least three warning signs of complicated or dangerous delivery – a key pre-condition for seeking appropriate and timely referral.
How to Collect and Analyse the Required Data
Collect the following data by conducting individual interviews with a representative sample of your target population members:
RECOMMENDED SURVEY QUESTIONS (Q) AND POSSIBLE ANSWERS (A)
Q1: Do you know any warning signs showing that a woman’s labour or delivery is not going well and the woman urgently needs professional health care?
A1: yes / no
(ask the following question only if the previous answer is YES)
Q2: What are all the most important warning signs you know?
A2: (multiple answers possible; do not read the options)
1) vaginal bleeding during pregnancy
2) severe vaginal bleeding after delivery
3) obstructed labour taking longer than 12 hours
4) convulsions or fits
5) placenta is retained for more than 1 hour
6) swelling of face, fingers and feet (pre-eclampsia/ eclampsia)
7) baby’s head not down or not coming first
8) high fever
9) fast or difficult breathing
10) severe, persistent abdominal pain
11) problems urinating (i.e. painful urination, insufficient urination)
Note: If less than three signs are stated, keep probing: “Are there any other warning signs you know?”
Calculate the indicator’s value by dividing the respondents aware of at least three of the pre-defined warning signs by the total number of respondents and multiplying the result by 100.
1) Consider disaggregating the data by location (rural/ urban), socio-economic characteristics (education level) and the number of pregnancies (first pregnancy, second pregnancy, etc.).