Prediction and Spatial Interpretation of Meteorological Factors with Neonatal Mortality Rate

doi:10.21203/rs.3.rs-1495685/v2

Download PDF

Research Article

Prediction and Spatial Interpretation of Meteorological Factors with Neonatal Mortality Rate

https://doi.org/10.21203/rs.3.rs-1495685/v2

This work is licensed under a CC BY 4.0 License

Version 2

posted

You are reading this latest preprint version

Around a million infants die in India each year. The NMR (Neonatal Mortality Rate) reduced from 18 for every 1000 live births that is 1.8 percent in 2011 to 12 for every 1000 that is 1.2 percent in 2016. Still, the problem with it is that this decline in rate has been low, and when compared to the infant and mortality rate, it is still lower. Among all the neonatal deaths, the rate of decay of in Early "Neonatal Mortality Rate (ENMR)" is much lower than that of recent NMR according to the Registrar General of India giving the Sample Registration Statistical Report (SRS). The anthropogenic exercises of current years have encouraged the increment of elements of gases and particulate matter in the environment bringing about changes in the atmosphere influencing human wellbeing, especially the sensitive neonatal of the nation. The present paper evaluates the spatial translation of meteorological factor temperature affecting the neonatal death rate of Delhi, including the level of neonatal mortality rate and early neonatal mortality rate utilizing Multinomial logistic regression model and Pearson's Correlation coefficient for discovering the association for the equivalent.

Data Analytics

air pollution

Neonatal Mortality Rate

Meteorological Factors.

Mounting scientific evidence supports that 66% of the world's neonatal deaths happen in only ten nations, for the most part in Asia. India is number six among these nations. Contamination), preterm birth, and birth asphyxia represent 87% of neonatal death worldwide. Since reasons for neonatal death vary by government and the accessibility and nature of healthcare services, understanding neonatal mortality in connection to these elements is vital. Information accessible on neonatal deaths in India comes principally from emergency clinic studies, which have a specific referral predisposition, or from networks in which the reason for death is infrequently recorded. Mounting logical confirmations bolsters that the Greenhouse gases developed by human beings have a significant impact on the global warming scenario that prompts climate change and will probably lead to increasingly outrageous phenomena later [1]. Expressly, European researchers and the US and China studies have affirmed the harmful effects of extreme hot or cold environments on the NMR. Besides, this mortality risk changes by a few components, including the financial reason for death, area, and age [2], [3].

This venture can be utilized by specialists for tracking real air contamination causes that have emissions past a set limit. This task in a manner would help in vaporous air contamination of metro urban areas that are exceedingly influenced by the issue [15],[17]. Additionally, this venture can be utilized to limit the pollutants that are very influencing the meteorological factor temperature causing in the expansion of neonatal mortality rate along these lines influencing the rate share for the equivalent.

The majority of research on the effect of temperature extremes on neonatal mortality has been primarily directed at developed nations with a slightly older population.. Interestingly for low-income people [4] the gathering encountering higher mortality incorporates infants and kids just as the old [5]. It is vague whether the results acquired in the overall public in high asset urban areas likewise apply to rural populaces in low-income nations [6],[7]. With a populace of around 83 crores, India Rural Part is the World's second most populated nation [8],[9]. The total geological territory of India nation country part is around 31 lakhs km. The population density of the government is 382 people for every km2. There are 29 states in the nation. Uttar Pradesh Rural is the most crowded state with a rural populace of around 16 crores, and Lakshadweep Rural is the most minor crowded state with a rural populace of approximately 14 thousand. The nation's population has expanded by 12.3% in the recent ten years. In the 2001 census, the all-out populace here is around 74 crores. [10]. The nation's female populace growth rate is 12.5%, 0.4% higher than the male population growth rate of 12.1%. Our examination will appraise the extreme environmental conditions on neonatal mortality in Delhi. Unlike past literature investigating the environmental conditions—mortality relationship in Delhi, our task deals with the adjustment for [11], [12], [13]. This demonstration has the upside of disengaging the indirect and direct influences of the climate limits on neonatal mortality by misusing the relationship in slacked connections that exist inside occasionally gathered information.

Regardless of the developing portion of NMR in less than five years of age, mortality in on-going years in our country India, many of the examinations have concentrated on infants and child mortality keeping NMR on low priority index. The improvement of focused and proof-based wellbeing interventions to lessen the NMR needs a check on elements influencing it. Like this, the study endeavors to look at an individual, family unit, and network-level factors influencing the NMR in Delhi. Specialists can utilize this venture for tracking natural air contamination causes that have emissions past a set limit. This task would help in vaporous air contamination of metro urban areas that are exceedingly influenced by the issue [15]. Additionally, this venture can limit the pollutants that impact the meteorological factor temperature, causing the expansion of neonatal mortality rate along these lines affecting the rate share for the equivalent.

As per studies the major reasons behind the neonatal deaths have been found are as following [17]:

Birth Asphyxia and Birth Injuries
Pneumonia
LBW/Prematurity
Other infections and Sepsis
Diarrhea
Congenital Malformation
Other Perinatal Cause
Syndrome of Sudden Infant Death
Others

It is a procedure of investigating informational indexes and make assumptions concerning the existing information with specific frameworks. Data Analytics is exceptionally valuable in business enterprises. It is useful to settle on better business choices. The researchers likewise use data analytics strategies to favor or discredit scientific models, speculations, and theories. It is a subjective just as quantitative system to upgrade the efficiency and concentrate at the patterns [8],[19]. Information examination is grouping of applications like Business Intelligence (BI) and Online Analytical Processing (OLAP) [9],[10]. Data analytics can be divided into two halves:

Quantitative Data Analytics can be characterized as the kind of examination which can be utilized when we have numerical information with quantifiable variables, and we need to gauge it factually [14], [21].

Qualitative Data Analytics, which is an interpretive methodology which centers according to the non-numerical information. This information examination can be utilized for understanding the pictures, text, and video substance. Steps involved in Data analytics are:

Search, select and approve data on the chosen topic
Identify information hotspots for investigations; comprehend the information structure and information meaning.
Formulate theories and check them dependent on accessible informational collections and explanatory strategies.
Analyze distinctive informational indexes utilizing essential measurable ideas.
Closely audit and perform elucidation step.
Understand contamination estimations information and relationship with different elements.
Generalize distinctive discoveries and make conclusions.

For the present investigation, information from six progressive years from 2011-16 have been contemplated which is acquired from the Registrar General of India giving the Sample Registration Statistical Report (SRS) involving neonatal mortality rate of Delhi including the percentage share of neonatal deaths by living arrangement, 2011-16 and the early neonatal death rates and rate offer of early neonatal passing’s by residence [16], [21], 2011-16. The variety in estimations of meteorological factor temperature has been found by utilizing the Pearson's correlation Coefficient on the data acquired from Central Pollution Control Board, ITO station for desired air contaminations and Center for Development of Glass Industries (CDGI); the temperature information is taken from National Centers for Environmental Information (NOAA) and National Weather Service for corresponding the acquired qualities according to the information. From this information, we additionally gathered neonatal mortality data before the age (neonates of less than 30 days vs. Infants who are more than 30 days of age and less than 153 days) from the period of January 2011 to December 2016 [19], [22]. Detailed data for temperatures for the same duration of period was also sourced from the Meteorological Department of Delhi. Initially, all the data of temperature and mortality were collected and relation for the same has been found between them. Scientific methodology - The relapse with Autoregressive Integrated Moving Averages (ARIMA) approach of errors was applied for investigating the connection between mortalities and temperature. This strategy basically joins standard least squares and ARIMA models to address for sequential connection regularly present in the error term of relapses including time series variables. [16],[24]. The standard architecture of the model is as follows:

yt = b1x1,t + ……. + bk xk,t + nt

Where nt is assumed to follow an ARIMA model. When yt and xtare differenced once, the combined regression with ARIMA (1,1,1) errors model is:

y′t = b1x′1,t + …….bk x′k,t + n′t

(1 − ∅1 L)n′t = (1 + θ1 L)et

Where y′t = yt − yt − 1, x′t,i = xt,i − xt − 1, n′t = nt − nt − 1 and et is a white noise series [17]. Average temperature is the best indicator of the relationship between temperature and mortality when contrasted with different measures of temperature [23].

This paper centers according to the graphical investigation with the pattern analysis and measurable strategy for Pearson Correlation Coefficient to discover the relation among the high temperature and the air pollutants. Data Analysis has been opted as the tool and the information is used for validating the outcomes according to the research.

Table 1. Table depicting Neonatal Mortality in Delhi from 2000-2016.

2000	5.1	2009	4.4
2001	5.1	2010	4.2
2002	5.1	2011	4.3
2003	5	2012	4.2
2004	4.7	2013	4.1
2005	4.6	2014	3.8
2006	4.7	2015	3.6
2007	4.8	2016	4
2008	4.8

The table 1 gives an overview of neonatal deaths from the year 2000–2016 with the values and we have seen a decrease in the death rate.

The Fig. 3 is thegraphical representation has been obtained from the data collected on the air pollutants and they have been validated and graphs are formed using graphical tools.

The graph shows all the variants of neonatal mortality rate from the year 1970–2015.

This graph shows that the neonatal mortality rate has significantly reduced with per year 1970 to 2015. As a result, we can conclude that the temperature has no influence on the neonatal mortality rate.

Table 2. Table indicating values of correlation between temperature and particular air pollutant [6]

(2010-2018)	NO	CO	NO2	O3	SO2	NOx
JAN	-0.85	0.52	-0.03	0.74	0.49	-0.81
FEB	-0.87	0.33	0.07	0.54	0.50	-0.56
MAR	-0.44	0.56	-0.08	-0.19	0.37	-0.22
APR	0.48	-0.69	-0.09	-0.46	-0.32	0.82
MAY	0.88	-0.03	-0.02	-0.69	-0.42	0.46
JUNE	0.89	-0.18	0.08	-0.83	-0.33	0.64
JULY	0.93	-0.27	-0.08	-0.83	-0.47	0.67
AUG	0.92	-0.16	-0.27	-0.80	-0.56	0.54
SEPT	0.86	0.03	-0.26	-0.59	-0.47	0.36
OCT	0.70	-0.39	-0.24	-0.93	-0.63	0.52
NOV	-0.83	0.54	0.37	0.55	0.69	-0.65
DEC	-0.92	0.34	0.29	0.79	0.59	-0.65

Relationship between NO and Temperature: As found in Table 2, commonly, values are positive and are near 1, which demonstrates a solid connection between NO and temperature. The qualities on the negative side are likewise near to - 1, which gives us a solid negative relationship too. Thus, there is a solid relationship between NO and temperature. Connection estimations of CO and temperature, there are an equivalent number of positive and negative qualities that demonstrate a moderate negative relationship between CO and temperature. Relationship between NO(2) and temperature, most qualities appear in the negative relationship and are not near to - 1 which demonstrates a negative connection among NO(2) and temperature that implies it doesn't influence legitimately but dangerous when shaped after the response. The relation between ground level Ozone and temperature likewise demonstrates most qualities on the negative side appearing negative connection. What is more, the qualities are not incredibly near - 1 which indicates that there is again a negative connection between Ozone and temperature. Now, considering SO(2) and temperature again, values lie more in the negative part, indicating solid hostile relations. Finally, NOx and temperature generally lie on the positive scale, demonstrating a solid positive association among NOx and temperature appearing positive relationships.

No proof could be found that survival of neonates is adversely affected by month-to-month temperature extremes in Delhi. It may more enhance the sensitivity of babies to hypothermia than hyperthermia in this condition. Taking everything into account, our outcomes demonstrate that the neonatal mortality hazard to kids from outrageous temperatures is greater in the extreme cool temperature than its extreme hot counterpart. This proposes environmental change related general wellbeing intercessions should concentrate on winter seasons in tropical countries like India. Other marvels related with environmental change, for example, increased floods, precipitation, and poverty ought to be explored in future research since they might be longer term aspects of environmental change that may clarify infant and child mortality in Delhi. Despite the growing share of “NMR in newborn under 5 years of age”, mortality in the continuous decades in India, most examinations have focused on newborn children and child mortality putting neonatal mortality on the lesser need contemplates. The improvement of focused and confirmation-based prosperity mediations to reduce neonatal mortality warrants an examination of components affecting it. In this way, this study tried to take a gander at individual, nuclear family, and system level components impacting neonatal mortality in Delhi.

Competing interest’s policy

I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Funding

No Funding received

Stevens, Sonya M., James E. Gray, Douglas K. Richardson, Marie C. McCormick, and Donald A. Goldmann. "Estimating neonatal mortality risk: an analysis of clinicians' judgments." Pediatrics 93, no. 6 (1994): 945–950.
Gray, James E., Douglas K. Richardson, Marie C. McCormick, Kathryn Workman-Daniels, and Donald A. Goldmann. "Neonatal therapeutic intervention scoring system: a therapy-based severity-of-illness index." Pediatrics 90, no. 4 (1992): 561–567.
Richardson, Douglas K., John D. Corcoran, Gabriel J. Escobar, and Shoo K. Lee. "SNAP-II and SNAPPE-II: simplified newborn illness severity and mortality risk scores." The Journal of pediatrics 138, no. 1 (2001): 92–100.
Yang, Chun-Chieh, Shiv O. Prasher, and Guy R. Mehuys. "An artificial neural network to estimate soil temperature." Canadian Journal of Soil Science 77, no. 3 (1997): 421–429.
Frize, M., D. Ibrahim, H. Seker, R. Walker, M. O. Odetayo, Dobrila Petrovic, and R. N. G. Naguib. "Predicting clinical outcomes for newborns using two artificial intelligence approaches." In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 3202–3205. IEEE, 2004.
Mathur, Sandeep, Aditya Shantanu, and Ajay Rana. "Estimating the Imaging in Medical Science Using Image Processing Techniques." In Journal of Physics: Conference Series, vol. 1714, no. 1, p. 012007. IOP Publishing, 2021.
Verma, Kritika, Sandeep Mathur, and S. K. Khatri. "Study on Temperature Variation Pattern Based on Data Analytics." In 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 106–109. IEEE, 2019.
M. Villarroel, A. Guazzi, J. Jorge et al., "Continuous non-contact vital sign monitoring in neonatal intensive care unit", Healthc. Technol. Lett., vol. 1, no. 3, pp. 87–91, 2014.
Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE et al. Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 2015; 385(9966): 430–440.
Registrar General of India, Sample registration system (SRS) statistical report 2013. New Delhi: 2013.
Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet 2012; 379: 2151–2161.
Bassani DG, Kumar R, Awasthi S, Morris SK, Paul VK, Shet A et al. Causes of neonatal and child mortality in India: a nationally representative mortality survey. Lancet 2010; 376: 1853–1860.
Sharma, Shally, and Sandeep Mathur. "Analyzing the Patterns of Delhi’s Air Pollution." In Advances in Data Sciences, Security and Applications, pp. 33–44. Springer, Singapore, 2020.
Murthi M, Guio A-C, Dreze J. Mortality, fertility, and gender bias in India: a district-level analysis. Popul Dev Rev 1995; 21: 745–782.
Sankar, M. J., S. B. Neogi, J. Sharma, M. Chauhan, R. Srivastava, P. K. Prabhakar, A. Khera, R. Kumar, S. Zodpey, and V. K. Paul. "State of newborn health in India." Journal of Perinatology 36, no. 3 (2016): S3-S8.
Willis JR, Kumar V, Mohanty S, Singh P, Singh V, Baqui AH et al. Gender differences in perception and care-seeking for illness of newborns in rural Uttar Pradesh, India. J Health Popul Nutr 2009; 27(1): 62.
Singh A, Kumar A, Kumar A. Determinants of neonatal mortality in rural India. 2007–2008. Peer J2013; 1: e75.
Upadhyay RP, Dwivedi PR, Rai SK, Misra P, and Kalaivani M, Krishnan A. Determinants of neonatal mortality in rural Haryana: a retrospective population based study. Indian Pediatrics 2012; 49(4): 291–294.
Victora CG, Wagstaff A, Schellenberg JA, Gwatkin D, Claeson M, Habicht JP. Applying an equity lens to child health and mortality: more of the same is not enough. Lancet 2003; 362(9379): 233–241.
Fewtrell L, Kaufmann RB, Kay D, Enanoria W, Haller L, Colford JM Jr. Water, sanitation, and hygiene interventions to reduce diarrhea in less developed countries: a systematic review and meta-analysis. Lancet Infect Dis 2005; 5(1): 42–52.
United Nations Children Fund (UNICEF), State of World Children: Maternal and Newborn Health. UNICEF: New York, USA, 2009.
Mathur, Sandeep. "Modeling the stubble burning generated airborne contamination with air pollution components through MATLAB." Earth Science Informatics (2022): 1–10.
Paul VK, Sachdev HS, Mavalankar D, Ramachandran P, Sankar MJ, Bhandari N et al. Reproductive health, and child health and nutrition in India: meeting the challenge. Lancet 2011; 377: 332–349.

No competing interests reported.

Download PDF

Version 2

posted

You are reading this latest preprint version

Prediction and Spatial Interpretation of Meteorological Factors with Neonatal Mortality Rate

Status:

Version 2

Abstract

Figures

Introduction

I. OBJECTIVES

II. Effect of Climate

III. MOTIVATION

IV. MAJOR CAUSES OF NEONATAL DEATHS

V. DATA ANALYTICS AND THE STEPS INVOLVED

VI. DATABASE AND INVOLVED METHODOLOGY

VII. RESULT AND DISCUSSION

VIII. CONCLUSION

Declarations

References

Additional Declarations

Status:

Version 2