Premium
Measuring regional and district variations in the incidence of pregnancy‐induced hypertension in Ghana: challenges, opportunities and implications for maternal and newborn health policy and programmes
Author(s) -
Antwi Edward,
KlipsteinGrobusch Kerstin,
Quansah Asare Gloria,
Koram Kwadwo A.,
Grobbee Diederick,
Agyepong Irene A.
Publication year - 2016
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/tmi.12626
Subject(s) - medicine , incidence (geometry) , pregnancy , environmental health , obesity , obstetrics , pediatrics , demography , genetics , physics , sociology , optics , biology
Objectives The objectives were to assess the quality of health management information system ( HMIS ) data needed for assessment of local area variation in pregnancy‐induced hypertension ( PIH ) incidence and to describe district and regional variations in PIH incidence. Methods A retrospective review of antenatal and delivery records of 2682 pregnant women in 10 district hospitals in the Greater Accra and Upper West regions of Ghana was conducted in 2013. Quality of HMIS data was assessed by completeness of reporting. The incidence of PIH was estimated for each district. Results Key variables for routine assessment of PIH such as blood pressure ( BP ) at antenatal visits, weight and height were 95–100% complete. Fundal height, gestational age and BP at delivery were not consistently reported. The incidence of PIH differed significantly between Greater Accra region (6.1%) and Upper West region (3.2%). Prevalence of obesity among pregnant women in Greater Accra region (13.9%) was significantly higher than that of women in Upper West region (2.2%). Conclusions More attention needs to be given to understanding local area variations in PIH and possible relationships with urbanisation and lifestyle changes that promote obesity, to inform maternal and newborn health policy. This can be done with good quality routine HMIS data.