PMID- 36160303
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220928
IS  - 2352-8273 (Print)
IS  - 2352-8273 (Electronic)
IS  - 2352-8273 (Linking)
VI  - 19
DP  - 2022 Sep
TI  - Data quality of birthweight reporting in India: Evidence from cross-sectional 
      surveys and service statistics.
PG  - 101220
LID - 10.1016/j.ssmph.2022.101220 [doi]
LID - 101220
AB  - The study aims to assess the quality of birthweight data collected in two 
      surveys, including the National Family Health Survey (NFHS) and the Comprehensive 
      National Nutrition Survey (CNNS), and as reported in the statistics from the 
      Health Management Information System (HMIS). The study also aims to assess the 
      implications of the data on the estimates of low birthweight (LBW). The 
      percentage of newborns whose birthweight is missing continues to be high in the 
      recent surveys (NFHS-4: 22%, CNNS: 30%) despite an improvement from 66% in 
      NFHS-3. The under-coverage of birthweight data in HMIS is around 40%. In the 
      surveys, the percentage of missing data on birthweight is higher among newborns 
      belonging to poor households, Scheduled Tribes, and Scheduled Castes. 
      Irrespective of whether birthweights are reported from the health cards or from 
      mother's recall, there's a high reporting at multiples of 500g and heaping at 
      2,500g. The prevalence of missing data on birthweight and of heaping is higher 
      among children born at home in comparison to facility-based births. Birthweight 
      data of dead children who were more likely to have had a lower birthweight is 
      highly underreported. The paper demonstrates state-level variations in 
      birthweight reporting and inconsistencies across surveys and HMIS. In 2015-16, 
      the prevalence of LBW as per HMIS data was 12.5%, whereas during the same period, 
      NFHS-4 and CNNS reported a prevalence of 18%. The findings suggest that LBW is 
      likely to be underestimated when missing data as well as heaping at 2,500g are 
      highly prevalent. To generate robust LBW estimates in India, there is an urgent 
      need to devise methods to ensure coverage of all live births (including early 
      neo-natal deaths) as well as the stillbirths, irrespective of the facility where 
      the deliveries take place.
CI  - (c) 2022 The Author(s).
FAU - Unisa, Sayeed
AU  - Unisa S
AD  - Dept. of Biostatistics and Epidemiology, International Institute for Population 
      Sciences (IIPS), Mumbai, India.
FAU - Dhillon, Preeti
AU  - Dhillon P
AD  - Dept. of Survey Research and Data Analytics, IIPS, Mumbai, India.
FAU - Anand, Enu
AU  - Anand E
AD  - Doctoral Fellow, IIPS, Mumbai, India.
FAU - Sahoo, Harihar
AU  - Sahoo H
AD  - Dept. of Family and Generations, IIPS, Mumbai, India.
FAU - Agarwal, Praween K
AU  - Agarwal PK
AD  - IPE Global Ltd, New Delhi, India.
LA  - eng
PT  - Journal Article
DEP - 20220909
PL  - England
TA  - SSM Popul Health
JT  - SSM - population health
JID - 101678841
PMC - PMC9493382
OTO - NOTNLM
OT  - Birthweight
OT  - Data quality
OT  - Heaping
OT  - Low birthweight
OT  - Underreporting
COIS- None.
EDAT- 2022/09/27 06:00
MHDA- 2022/09/27 06:01
PMCR- 2022/09/09
CRDT- 2022/09/26 17:48
PHST- 2022/01/27 00:00 [received]
PHST- 2022/08/28 00:00 [revised]
PHST- 2022/08/29 00:00 [accepted]
PHST- 2022/09/26 17:48 [entrez]
PHST- 2022/09/27 06:00 [pubmed]
PHST- 2022/09/27 06:01 [medline]
PHST- 2022/09/09 00:00 [pmc-release]
AID - S2352-8273(22)00199-9 [pii]
AID - 101220 [pii]
AID - 10.1016/j.ssmph.2022.101220 [doi]
PST - epublish
SO  - SSM Popul Health. 2022 Sep 9;19:101220. doi: 10.1016/j.ssmph.2022.101220. 
      eCollection 2022 Sep.