Characterizing Social Determinants of Maternal and Child Health: A Qualitative Community Health Needs Assessment in Underserved Areas.
Sara Rizvi JafreeGulzar Hussain ShahRubeena ZakarAnam MuzamillHumna AhsanSyeda Khadija BurhanAmbreen JavedRana Rubab DurraniPublished in: Healthcare (Basel, Switzerland) (2023)
This study aimed to identify social determinants of maternal and child health (SDoH) in Pakistan. Using a qualitative study design, data were collected from community members in seven underserved areas of Lahore City, Pakistan. A total of 22 qualitative in-depth interviews and 10 focus group discussions (FGDs) were conducted. The participants included basic health unit healthcare staff, women of reproductive ages, male family members, mothers-in-law, and religious leaders. We found that maternal and child health is adversely affected by the following socioeconomic and environmental barriers: (i) poor housing quality and sanitation; (ii) inadequate food supply and safety; (iii) unsatisfactory public sector school services; (iv) a lack of safety and security; (v) scarce poverty alleviation efforts and loan schemes; (vi) unsatisfactory transport and internet services; and (vii) inadequate health services. The targets for maternal and child health in Pakistan cannot be met without close coordination between the primary health sector, local governance, and macro state structures, which collectively must monitor and improve housing adequacy, food security, public sector services (primary healthcare services, public schooling, public transport, and public internet access), overall safety, and poverty emergence.
Keyphrases
- healthcare
- mental health
- pregnancy outcomes
- health information
- birth weight
- mental illness
- human health
- tertiary care
- public health
- global health
- systematic review
- primary care
- high resolution
- type diabetes
- quality improvement
- pregnant women
- drinking water
- weight gain
- machine learning
- tyrosine kinase
- adipose tissue
- risk assessment
- climate change
- metabolic syndrome
- artificial intelligence
- deep learning
- weight loss
- adverse drug
- life cycle