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On Aug 2018




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Aug 2018




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Thanking you
With sincere regards
Dr. Rajendra Kumar Ghritlaharey, M.S., M. Ch., FAIS
Associate Professor,
Department of Paediatric Surgery, Gandhi Medical College & Associated
Kamla Nehru & Hamidia Hospitals Bhopal, Madhya Pradesh 462 001 (India)
E-mail: drrajendrak1@rediffmail.com
On May 11,2011




Dr. Shankar P.R.

"On looking back through my Gmail archives after being requested by the journal to write a short editorial about my experiences of publishing with the Journal of Clinical and Diagnostic Research (JCDR), I came across an e-mail from Dr. Hemant Jain, Editor, in March 2007, which introduced the new electronic journal. The main features of the journal which were outlined in the e-mail were extensive author support, cash rewards, the peer review process, and other salient features of the journal.
Over a span of over four years, we (I and my colleagues) have published around 25 articles in the journal. In this editorial, I plan to briefly discuss my experiences of publishing with JCDR and the strengths of the journal and to finally address the areas for improvement.
My experiences of publishing with JCDR: Overall, my experiences of publishing withJCDR have been positive. The best point about the journal is that it responds to queries from the author. This may seem to be simple and not too much to ask for, but unfortunately, many journals in the subcontinent and from many developing countries do not respond or they respond with a long delay to the queries from the authors 1. The reasons could be many, including lack of optimal secretarial and other support. Another problem with many journals is the slowness of the review process. Editorial processing and peer review can take anywhere between a year to two years with some journals. Also, some journals do not keep the contributors informed about the progress of the review process. Due to the long review process, the articles can lose their relevance and topicality. A major benefit with JCDR is the timeliness and promptness of its response. In Dr Jain's e-mail which was sent to me in 2007, before the introduction of the Pre-publishing system, he had stated that he had received my submission and that he would get back to me within seven days and he did!
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Dr. P. Ravi Shankar
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On April 2011
Anuradha

Dear team JCDR, I would like to thank you for the very professional and polite service provided by everyone at JCDR. While i have been in the field of writing and editing for sometime, this has been my first attempt in publishing a scientific paper.Thank you for hand-holding me through the process.


Dr. Anuradha
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On Jan 2020

Important Notice

Original article / research
Year : 2022 | Month : March | Volume : 16 | Issue : 3 | Page : QC18 - QC21 Full Version

Prevalence and Socio-demographic Determinants of Low Birth Weight Newborns: A Prospective Observational Study


Published: March 1, 2022 | DOI: https://doi.org/10.7860/JCDR/2022/51339.16163
Ritu Singh, Surekha Tayade, Neha Gangane, Neha Chaudhary

1. Ex Senior Resident, Department of Obstetrics and Gynaecology, AIIMS, Patna, Bihar, India. 2. Professor, Department of Obstetrics and Gynaecology, MGIMS, Wardha, Maharashtra, India. 3. Ex-Resident, Department of Obstetrics and Gynaecology, MGIMS, Sewagram, Wardha, Maharashtra, India. 4. Senior Resident, Department of Community Medicine, AIIMS, Patna, Bihar, India.

Correspondence Address :
Neha Chaudhary,
Senior Resident, Department of Community Medicine, AIIMS, Patna, Bihar, India.
E-mail: madhuchaudhary9@rediffmail.com

Abstract

Introduction: Birth weight is not only strongly associated with foetal and neonatal mortality but also with stunted growth. Low Intelligence Quotient (IQ) in childhood, obesity and diabetes in adulthood. Despite all measures taken, the prevalence of Low Birth Weight (LBW) is not significantly decreasing in Southeast Asia {from 33% in 2000 to 27% in 2015 according to United Nations Children’s Fund (UNICEF) 2019 data}. Added to the surprise in UNICEF data from countries for LBW, from A-Z data, (A-Z data is data of LBW countries whose name starts from letter A to letter Z in alphabetical order) India was not included, because data from India was partial.

Aim: to assess the prevalence and socio-demographic determinants of LBW in newborns.

Materials and Methods: A prospective hospital-based observational study was carried out in the Obstetrics and Gynaecology Department of Mahatma Gandhi Institute of Medical sciences, Sewagram, Wardha, Maharashtra India, among 500 consecutive, consenting pregnant women from December 2017 to November 2019. Sociodemographic parameters and neonate birth weight was recorded. Statistical analysis was done by inferential statistics using Chisquare test and z-test with significance value considered at <0.05. Results: Among the total 500 subjects 162 (32.4%) had LBW and 338 (67.6%) Normal Birth Weight (NBW). By using Chisquare test, statistically significant difference was found in parity, socio-economic condition, mother’s education, area of residence of both the LBW and NBW groups (χ2 =6.49, p=0.039; χ2 =51.32, p=0.0004; χ2 =12.95, p=0.012; χ2 =5.66, p=0.017), respectively.

Conclusion: The prevalence of LBW babies was 32.4%. Rural areas, low socio-economic condition, education, were significant determinants of LBW. As the parity increases birth weight increases.

Keywords

Neonatal, Predictors, Pregnancy, United nations children’s fund

The birth weight of an infant is the first weight recorded after birth, ideally measured within the first hours after birth, before significant postnatal weight loss has occurred. LBW is defined as a birth weight of less than 2500 g (upto and including 2499 g), as per the International Classification of Disease (ICD)-10 (1). It is universally acknowledged that size at birth is an important indicator of foetal and neonatal health in the context of both individuals and populations (2).

Birth weight in particular is not only strongly associated with foetal and neonatal mortality but also with stunted growth, low IQ in childhood and obesity, diabetes in adulthood (3),(4),(5). Despite all measures taken, the prevalence of LBW was not significantly decreasing in Southeast Asia from 33% in 2000 to 27% in 2015 according to UNICEF 2019 data (6). Added to the surprise in UNICEF data from countries with LBW, from A to Z data, India was not included, because data from India was partial (6).

Periods of foetal and infant growth are vital predictors of a child’s health status which are largely determined by maternal characteristics. Hence, socio-demographic factors are crucial prognosticators of pregnancy outcomes as they reflect genetic aspects, skeletal maturity and give an account of nutritional conditions (7).

This institute is placed in a rural area. Thus, the knowledge about determinants of LBW could potentially be used to plan simple public health interventions to improve pregnancy outcomes in resource-poor settings and contribute to improving maternal and child health. The study aimed to assess the prevalence and socio-demographic determinants of LBW in newborns.

Material and Methods

This hospital-based prospective observational study was conducted in Department of Obstetrics and Gynaecology at Mahatma Gandhi Institute of Medical Sciences, Sewagram, Wardha, Maharashtra, India after Institutional Ethical Committee approval over 24 months from 1st December 2017 to 30th November 2019.

Inclusion criteria: All pregnant women who visited the obstetric Outpatient Department in the first trimester and were amenable for follow-up were enrolled. All pregnant women who gave consent, regardless of age and parity, having singleton pregnancy and who wished to deliver in the concerned hospital, were recruited in the study.

Exclusion criteria: Women who are lost to follow-up, congenital anomalies foetus, intrauterine demise during subsequent follow-ups were excluded from the study.

Sample size calculation: According to a recent UNICEF global database (2019), prevalence of LBW was highest in South Asia i.e., 27% (6). Considering this value, sample size was calculated by using the following formula: considering the minimal allowable error of 15%.

n=4pq/L2

n=4×27×73/4.05×4.05

Minimum sample was calculated as 480

So, a total of 500 pregnant women were considered for the study.

A total of 574 women were recruited in the study over a period of six months and were followed over the next 8-9 months with an estimated follow-up of 80% (8),(9).

Study Procedure

A redesigned and pretested proforma was used as a study tool to collect information. ‘Booked’ patients were those who carried documented evidence of more than or equal to three antenatal visits, with the last visit in the preceding month in the concerned hospital, and whose pregnancy care was planned.

i) Socio-demographic factors: Age of mother, mother’s education expressed as years of schooling, type of family, an income of the family, and socio-economic status according to Kuppuswamy’s classification (10).
ii) Obstetric factors: Gravidity, Parity, birth order, prior abortions.
iii) Neonatal birth weight: The naked baby was placed on an electronic weighing machine immediately after birth and measured to the nearest 10 grams.

Study participants were grouped into those giving birth to LBW babies (below 2500 g) and NBW babies (above 2500 g) for inferential statistics, as coded by National Family Health Survey-4 (NHFS-4) (11).

Statistical Analysis

The data was entered in a Microsoft Excel spreadsheet. Statistical analysis was done by inferential statistics using the Chi-square test and z-test. The softwares used in the analysis were the Statistical Package for Social Sciences (SPSS) 17.0 version and Graph Pad Prism 5.0. A two-level p-value <0.05 was considered statistically significant.

Results

A total of 574 singleton pregnant women in the first trimester, after fulfilment of inclusion and exclusion criteria, were enrolled. A total of 481 (83.8%) women came for follow-up on their own, 19 (3.3%) women turned for final follow-up after personal phone calls and home visits by auxiliary nurse midwifes. Thus, the data of 500 study participants were analysed (Table/Fig 1).

Out of 500 women, the mean weight of babies was 2636.09 g, 162 (32.4%) delivered LBW babies, and 338 (67.6%) were NBW.

Socio-demographic Determinants

Age: Mean age of the mother in LBW group was 23.46±3.51 years and in NBW group it was 23.55±2.78 years; by using the z-test, statistically no difference was found in the age of the mother in both the groups (z=0.09, p=0.760) (Table/Fig 2).

Gravida: By using the Chi-square test, statistically no difference was found in both the groups. (χ2=4.09, p=0.25) (Table/Fig 2).

Parity: In LBW, 116 (71.6%) were nulliparous, 43 (26.54%) were primipara and 3 (1.85%) were the second para compared with NBW which had 205 (60.65%), 119 (35.21%), and 14 (4.14%) women in para 0, 1 and 2 groups, respectively. The difference was statistically significant (χ2=6.49, p=0.039). It shows that as the parity increases birth weight increases (Table/Fig 2).

Booking status: In LBW, 65 (40.12%) women were booked with the hospital and 97 (59.88%) were unbooked, compared to 149 (44.08%) and 189 (55.92%) in NBW, respectively. Although in LBW number of unbooked cases are more, but it was not statistically significant by using the Chi-square test (χ2=0.70, p=0.40) (Table/Fig 2).

Residential area and type of family: Out of a total of 162 LBW, 97 (59.88%) women resided in the rural area and 65 (40.12%) in an urban area, and NBW, 164 (48.52%) in rural and 174 (51.48%) in urban area respectively. Statistical difference was found between the two (χ2=5.66, p=0.017). But joint/nuclear status difference was not statistically significant (χ2=0.16, p=0.68) (Table/Fig 2).

Mother’s education: The difference was statistically significant (χ2=12.95, p=0.012). Thus, education does play a major role in the amelioration of LBW (Table/Fig 2).

Socio-economic status (Modified Kuppuswamy scale 2019): Most women, 51.48% (174 of 338) with NBW babies belonged to lower middle class as compared to 25.93% (42 of 162) in LBW. A 16.27% (55 of 338) in NBW and 14.20% (23 of 162) in LBW belonged to the upper-middle class, respectively while 9 (2.66%) and 12 (3.55%) in NBW and 13 (8.02%) and 27 (16.67%) in LBW belonged to upper lower and lower class, respectively. The difference was found to be statistically significant. (χ2=51.32, p=0.0004) (Table/Fig 2).

Discussion

In the present study, the proportion of LBW babies was 32.4%. In 2015, nearly 20.5 million newborns, an estimated 14.6% of all babies born globally that year, had LBW, (UNICEF 2019) with more than half from Asia (6). According to NFHS-4 (2015-16) prevalence of LBW in India was 18% (11).

Kramer MS in his meta-analysis on determinants of LBW had observed low maternal age as an important risk factor and its causal effect was established (12). Similar findings have been observed by various studies (13),(14),(15),(16),(17),(18). But some studies support that age was not a significant determinant consistent with the present study (11),(13),(14),(15),(16),(17),(18),(19),(20). In NFHS-4, 20.6% of women had LBW at age <20 years and 18.2% had LBW if age was 35-49 years which was not statistically significant (11) (Table/Fig 3).

Studies done by many including NFHS-4 has not identified parity as a significant risk factor for LBW babies (11),(15),(19),(21). However, studies were done by Kader M and Perera NKP, Dharmalingam A et al., and Bisai S et al., showed that parity was a significant risk factor of LBW which was consistent with our study (13),(17),(18). In the present study, a higher proportion of LBW belonged to joint families (61.73%) than nuclear families (38.27%). Vijayalaxmi KG and Urooj A conducted a study in Bangalore and concluded that most women who delivered LBW babies lived in joint families (54.0%) (22). But the difference between LBW and NBW was not significant in the present study, as consistent with other studies (14),(19).

In the present study, the difference in the mother’s education status was statistically significant between LBW and NBW (χ2=12.95, p=0.012). The level of mother’s education influences birth weight of the baby. Thus, having some amount of maternal education has a protective effect against LBW (13),(15),(23). Low socio-economic status is one of the strongest predictors of LBW in low-income countries, consistent with the present study (19),(23). But Kader M and Perera NKP also reported that perhaps despite poor socio-economic status if a woman could maintain a good nutritional status and avoid potential medical complications during pregnancy, giving birth to an NBW baby might be a possibility (13).

Limitation(s)

The present study had a few limitations. Most important was that it was a hospital-based study, thus the chances of getting referred cases were high. The majority of women included in the study lived in the surrounding area. The result of this study, therefore, may not be completely applicable to women living and delivering their babies in more remote areas of the district.

Conclusion

The prevalence of LBW in a tertiary hospital of rural central India was 32.4%. Rural areas, low socio-economic conditions, mother’s education are significant determinants of LBW. According to this study, if women were residing in rural area, having low socio-economic status and mother’s education was low then they will have more number of LBW babies. But other parameters like age, gravida, booking status, family type, father’s education was not statistically significant. Further Systemic review and meta-analysis are required to add to the data needed for UNICEF.

References

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Christian P, Lee SE, Donahue Angel M, Adair LS, Arifeen SE, Ashorn P, et al. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low-and middle-income countries. International Journal of Epidemiology. 2013;42(5):1340-55. [crossref] [PubMed]
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Jornayvaz FR, Vollenweider P, Bochud M, Mooser V, Waeber G, Marques-Vidal P. Low birth weight leads to obesity, diabetes and increased leptin levels in adults: the CoLaus study. Cardiovascular Diabetology. 2016;15(1):01-10. [crossref] [PubMed]
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Low birth weight [Internet]. UNICEF DATA. [cited 2021 May 26]. Available from: https://data.unicef.org/topic/nutrition/low-birthweight/ Last accessed 13/12/2021.
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DOI and Others

DOI: 10.7860/JCDR/2022/51339.16163

Date of Submission: Jul 11, 2021
Date of Peer Review: Nov 17, 2021
Date of Acceptance: Jan 11, 2022
Date of Publishing: Mar 01, 2022

AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? Yes
• For any images presented appropriate consent has been obtained from the subjects. NA

PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Jul 19, 2021
• Manual Googling: Dec 29, 2021
• iThenticate Software: Jan 18, 2022 (19%)

ETYMOLOGY: Author Origin

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