Retrospective Study of Investigation of Possible Predictors for Total Fertility Rate in India|Journal of Scientific Research and Reports

The total fertility rate (TFR) is the most frequently accepted and utilised method of determining current fertility. Because TFR is based on an age-specific fertility rate, the total number of births in various age groups, as well as the age of the female, are necessary. When the population is illiterate or elderly, age information may be subject to recall bias, misreporting digit preference, and other factors, causing TFR to deviate from reality. As a result, we’ll need to use some indirect approach to get a sense of TFR estimation. In this work, the authors attempted to discover several variables that explain TFR and to recommend the optimal combination of predictors for obtaining a TFR estimate. This study’s methodology is primarily based on the regression technique. The coefficient of determination is used to identify potential predictors and to accept them. The study is based on data from the National Family Health Survey (NFHS 4) for India’s major states.

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Retrospective Study of Investigation of Possible Predictors for Total Fertility Rate in India | Journal of Scientific Research and Reports

The total fertility rate (TFR) is the most frequently accepted and utilised method of determining current fertility. Because TFR is based on an age-specific fertility rate, the total number of births in various age groups, as well as the age of the female, are necessary. When the population is illiterate or elderly, age information may be subject to recall bias, misreporting digit preference, and other factors, causing TFR to deviate from reality. As a result, we’ll need to use some indirect approach to get a sense of TFR estimation. In this work, the authors attempted to discover several variables that explain TFR and to recommend the optimal combination of predictors for obtaining a TFR estimate. This study’s methodology is primarily based on the regression technique. The coefficient of determination is used to identify potential predictors and to accept them. The study is based on data from the National Family Health Survey (NFHS 4) for India’s major states.

Please see the link :- https://www.journaljsrr.com/index.php/JSRR/article/view/30314

Bivariate Copula Based Models for the Dependence of Maternal Mortality Ratio(MMR) on Gross Domestic Product(GDP) and Total Fertility Rate(TFR)

Global progress towards reducing high Maternal Mortality Rates (MMR) turned to be defeated by high maternal mortalities originating from developing countries. In rural Ghana, the lack of logistics, medical and laboratory equipment are among other key factors responsible for the high MMR despite several interventions structured to curb this menace. Improvement in the country’s health care delivery will require substantial investment into maternal and child health especially, in order to meet the national SDG target on MMR.This paper demonstrates societal benets of investment in maternal and child health in order to stimulate stakeholders interest in resource mobilization by the correlation of MMR with such economic and demographic indicators as Gross Domestic Product (GDP) and Total Fertility Rate (TFR). The underlying probability distributions for MMR, TFR and GDP were determined using the corrected Akaike Information Criteria (cAIC) with parameters estimated via the maximum likelihood framework. MMR and TFR showed a positive association (0.83) whilst an inverse relation exist between MMR and GDP (-0.67) and TFR and GDP (-0.76). The contour and joint density plots from appendix A and B indicate a strong lower tail dependence for the bivariate Frank copula with Gamma and Lognormal margins whereas the Gumbel copula with Gamma and Lognormal margins

shows strong upper tail dependence. Correlation gures tend to suggests that improved GDP as a consequence of improved socio-economic conditions of a Ghanaian mother tend to reduce Maternal Mortalities whilst increased fertility rates turn to increase MMR. Generally, evidence has been drawn to improvement in GDP.

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