This document presents a sequential probit model analysis of infant mortality in Nigeria using 2003 Nigeria Demographic and Health Survey data. The analysis examined factors affecting an infant's survival (stage 1) and age at death (stage 2). For stage 1, results showed infant mortality was positively affected by birth order and breastfeeding duration, and negatively affected by mother's education, number of total children born, and place of delivery. For stage 2, only place of delivery significantly affected infant's age at death. The error terms between stages were found to be significantly correlated.
Heterogeneity in biological populations, from cancer to ecological systems, is ubiquitous. Despite this knowledge, current mathematical models in population biology often do
not account for inter-individual heterogeneity. In systems such as cancer, this means assuming cellular homogeneity and deterministic phenotypes, despite the fact that heterogeneity is thought to play a role in therapy resistance. Glioblastoma Multiforme (GBM) is an aggressive and fatal form of brain cancer notoriously difficult to predict and treat due to its heterogeneous nature. In this talk, I will discuss several approaches I have developed towards incorporating and
estimating cellular heterogeneity in partial differential equation (PDE) models of GBM growth.
An Extension of Calderón Transfer Principle and its Application to Ergodic Ma...BRNSS Publication Hub
We first prove that the well-known transfer principle of Calderón can be extended to the vector-valued setting, and then, we apply this extension to vector-valued inequalities for the Hardy–Littlewood maximal function to prove the vector-valued strong type Lp norm inequalities for 1< p < α and the vector-valued weak type (1,1) inequality for ergodic maximal function.
A controversial genetic restoration mechanism has been proposed for the model organism Arabidopsis thaliana. This theory proposes that genetic material from non-parental ancestors is used to restore genetic information that was inadvertently corrupted during reproduction. We evaluate the effectiveness of this strategy by adapting it to an evolutionary algorithm solving two distinct benchmark optimization problems. We compare the performance of the proposed strategy with a number of alternate strategies – including the Mendelian alternative. Included in this comparison are a number of biologically implausible templates that help elucidate likely reasons for the relative performance of the different templates. Results show that the proposed non- Mendelian restoration strategy is highly effective across the range of conditions investigated – significantly outperforming the Mendelian alternative in almost every situation.
Using STELLA to Explore Dynamic Single Species Models: The Magic of Making Hu...Lisa Jensen
The use of formal, mathematical models allows stakeholders, decision makers and scientists to better visualize interactions and relationships within ecological systems. This study uses STELLA, a modeling tool, to simulate simple population dynamics for the humpback whale (Megaptera novaengliae) to better understand the impacts of reproductive and mortality rates as well as alternative solution algorithms used to drive the model. A wide range of population dynamics occurred as a result of varying time increments for calculating populations and use of available solution algorithms. Populations are most likely to achieve equilibrium when reproduction and mortality result in approximately the same number of individuals.
The Probability distribution of a Simple Stochastic Infection and Recovery Pr...IOSRJM
This document discusses probability distributions of simple stochastic infection and recovery processes. It begins by introducing common probability distributions like binomial and negative binomial that are relevant for modeling infection and recovery. It then formulates general infection and recovery processes and derives conditions for the existence of a unique stationary probability distribution. Several specific infection and recovery processes are analyzed, including simple infection, simple recovery, simple infection and recovery, and simple infection and recovery with immigration. Explicit formulas are derived for the probability distributions and moment generating functions of each process.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Modeling XCS in class imbalances: Population size and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Heterogeneity in biological populations, from cancer to ecological systems, is ubiquitous. Despite this knowledge, current mathematical models in population biology often do
not account for inter-individual heterogeneity. In systems such as cancer, this means assuming cellular homogeneity and deterministic phenotypes, despite the fact that heterogeneity is thought to play a role in therapy resistance. Glioblastoma Multiforme (GBM) is an aggressive and fatal form of brain cancer notoriously difficult to predict and treat due to its heterogeneous nature. In this talk, I will discuss several approaches I have developed towards incorporating and
estimating cellular heterogeneity in partial differential equation (PDE) models of GBM growth.
An Extension of Calderón Transfer Principle and its Application to Ergodic Ma...BRNSS Publication Hub
We first prove that the well-known transfer principle of Calderón can be extended to the vector-valued setting, and then, we apply this extension to vector-valued inequalities for the Hardy–Littlewood maximal function to prove the vector-valued strong type Lp norm inequalities for 1< p < α and the vector-valued weak type (1,1) inequality for ergodic maximal function.
A controversial genetic restoration mechanism has been proposed for the model organism Arabidopsis thaliana. This theory proposes that genetic material from non-parental ancestors is used to restore genetic information that was inadvertently corrupted during reproduction. We evaluate the effectiveness of this strategy by adapting it to an evolutionary algorithm solving two distinct benchmark optimization problems. We compare the performance of the proposed strategy with a number of alternate strategies – including the Mendelian alternative. Included in this comparison are a number of biologically implausible templates that help elucidate likely reasons for the relative performance of the different templates. Results show that the proposed non- Mendelian restoration strategy is highly effective across the range of conditions investigated – significantly outperforming the Mendelian alternative in almost every situation.
Using STELLA to Explore Dynamic Single Species Models: The Magic of Making Hu...Lisa Jensen
The use of formal, mathematical models allows stakeholders, decision makers and scientists to better visualize interactions and relationships within ecological systems. This study uses STELLA, a modeling tool, to simulate simple population dynamics for the humpback whale (Megaptera novaengliae) to better understand the impacts of reproductive and mortality rates as well as alternative solution algorithms used to drive the model. A wide range of population dynamics occurred as a result of varying time increments for calculating populations and use of available solution algorithms. Populations are most likely to achieve equilibrium when reproduction and mortality result in approximately the same number of individuals.
The Probability distribution of a Simple Stochastic Infection and Recovery Pr...IOSRJM
This document discusses probability distributions of simple stochastic infection and recovery processes. It begins by introducing common probability distributions like binomial and negative binomial that are relevant for modeling infection and recovery. It then formulates general infection and recovery processes and derives conditions for the existence of a unique stationary probability distribution. Several specific infection and recovery processes are analyzed, including simple infection, simple recovery, simple infection and recovery, and simple infection and recovery with immigration. Explicit formulas are derived for the probability distributions and moment generating functions of each process.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Modeling XCS in class imbalances: Population size and parameter settingskknsastry
This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.
Frequency Measures Used in EpidemiologyIntroductionIn e.docxMARRY7
Frequency Measures Used in Epidemiology
Introduction
In epidemiological studies, many qualitative variables have only two possible categories, such as
Alive or dead
Case or control
Exposed and unexposed
The frequency measures for dichotomous variable include:
Ratio
Proportion
Rate
( All the above 3 measure are based on the same formula: )
Ratios, Proportion, and Rates Compared
In a ratio, the values of x and y may be completely independent from each other or x is a part of y
For example , the gender of the children attending a specific program could be compared in one of the following ways:
Proportion is a ratio in which X is included in Y
For example , the gender of the children attending a specific program
Rate is a proportion that measures the occurrence of an event in a population over time
Rate = X
Ratios, Proportion, and Rates Compared
Example 1: The following table was part of an article published by Dr. Mshana and his colleagues. The title of this study is “Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. ". Please use this table to answer the following questions.
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
4
Example 1
What is the ratio of males to females? 7 : 10
What proportion of premature babies? 12/17=0.706
What proportion of patients were discharged? 11/17=0.647
What is the ratio of prematurity to birth asphyxia ? 12 : 5
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
5
Example 2:
In 1989, 733,151 new cases of gonorrhea were reported among the United States civilian population. The 1989 mid-year U.S. civilian population was estimated to be 246,552,000. What is the 1989 gonorrhea incidence rate for the U.S. civilian population? (For these data we will use a value of 105 for 10n ).
Answer:
Incidence rate = X
Incidence rate = X = 297.4 per 100,000
6
Measures of association:
They are used to quantify the relationship between exposure and disease among two groups
They are used to compare the disease occurrence among one group with the disease occurrence in the another group
They include the following measures based on the study design:
Risk Ratio (RR):
It also called relative risk
It is used to compare the risk of health related events in two groups
The following formula cis used to calculate the RR:
A risk ratio of 1.0 indicates identical risk in the two groups
A risk ratio greater than 1.0 indicates an increased risk for the numerator group
A risk ratio greater than 1.0 ...
This document discusses fitting probability distributions to maternal mortality rate (MMR) data in Nigeria. Four distributions were fitted to the MMR data: gamma, lognormal, Weibull, and exponential. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the best fitting distribution. The exponential distribution had the lowest AIC and BIC values, indicating it was the best fitting distribution. The estimated rate parameter for the exponential distribution was 0.5853659, with this model providing the best fit for analyzing MMR in Nigeria.
Logistic Loglogistic With Long Term Survivors For Split Population ModelWaqas Tariq
Split population models are also known as mixture model . The data used in this paper is Stanford Heart Transplant data. Survival times of potential heart transplant recipients from their date of acceptance into the Stanford Heart Transplant program [3]. This set consists of the survival times, in days, uncensored and censored for the 103 patients and with 3 covariates are considered Ages of patients in years, Surgery and Transplant, failure for these individuals is death. Covariate methods have been examined quite extensively in the context of parametric survival models for which the distribution of the survival times depends on the vector of covariates associated with each individual. See [6] for approaches which accommodate censoring and covariates in the ordinary exponential model for survival. Currently, such mixture models with immunes and covariates are in use in many areas such as medicine and criminology. See for examples [4][5][7]. In our formulation, the covariates are incorporated into a split loglogistic model by allowing the proportion of ultimate failures and the rate of failure to depend on the covariates and the unknown parameter vectors via logistic model. Within this setup, we provide simple sufficient conditions for the existence, consistency, and asymptotic normality of a maximum likelihood estimator for the parameters involved. As an application of this theory, the likelihood ratio test for a difference in immune proportions is shown to have an asymptotic chi-square distribution. These results allow immediate practical applications on the covariates and also provide some insight into the assumptions on the covariates and the censoring mechanism that are likely to be needed in practice. Our models and analysis are described in section 5.
This document discusses a systematic review and meta-analysis on the relationship between dietary fat intake and breast cancer risk. The meta-analysis included 45 studies with over 25,000 breast cancer patients. It found a small increased risk of breast cancer associated with higher total fat intake. The review also discusses terms related to systematic reviews and meta-analysis such as heterogeneity statistics, I2, and the Q statistic.
This document provides an introduction and tables for determining sample sizes in various health studies. It covers one-sample situations like estimating a population proportion with absolute or relative precision and hypothesis tests for a population proportion. Two-sample situations covered include estimating the difference between two population proportions and hypothesis tests for two population proportions. It also addresses case-control studies, cohort studies, lot quality assurance sampling, and incidence-rate studies. Tables of minimum sample sizes are provided for each situation.
1) The document presents on determinants of birth intervals in Tamil Nadu, India using Cox hazard models with validations and predictions. 2) Data from the 1992-1993 National Family Health Survey was analyzed to understand how factors like breastfeeding and education impacted birth intervals. 3) Results showed breastfeeding over 22 months was protective for future births, and factors like women's education, contraceptive use, and survival of the previous child decreased the likelihood of the next birth in early intervals.
This document provides an overview of key concepts in epidemiology and statistics as they relate to nutritional epidemiology. It discusses random error and how statistics are used to estimate effects and account for biases in epidemiologic studies. Specific topics covered include point estimates, confidence intervals, p-values, statistical hypothesis testing, selection bias, information bias, and confounding. Examples are provided to illustrate concepts like how selection bias can influence estimates of vaccine efficacy. The roles of statistics in estimating effects, accounting for biases, and assessing the role of chance in epidemiologic studies are also summarized.
This paper investigates the relationship between birth epoch and risk aversion using data from the Health and Retirement Study. The analysis finds that the era someone was born in has a small but significant effect on their measured level of risk aversion, with those born before 1935 showing higher risk aversion. A multinomial regression model controlling for demographic and socioeconomic factors confirms that both age and birth epoch influence risk aversion. Specifically, being born before 1935 increases the probability of extreme risk aversion by around 3 percentage points, while each additional year of age raises it by 0.2 percentage points. A binary model focusing on extreme versus other risk levels yields consistent results.
This document discusses estimating maternal mortality rates using statistical models. It outlines selecting dependent variables, processing input data from sources that provide the proportion of maternal deaths by age group, and choosing a regression model form. Key factors considered for the model are fertility, GDP, education, neonatal mortality, and HIV prevalence. A counterfactual scenario is proposed to estimate maternal mortality without the effect of HIV.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Chapter 7
Estimation
Chapter Learning Objectives
1. Explain the concepts of estimation, point estimates, confidence level, and confidence interval
2. Calculate and interpret confidence intervals for means
3. Describe the concept of risk and how to reduce it
4. Calculate and interpret confidence intervals for proportions
In this chapter, we discuss the procedures involved in estimating population means and proportions based on the
principles of sampling and statistical inference discussed in Chapter 6 (“Sampling and Sampling Distributions”).
Knowledge about the sampling distribution allows us to estimate population means and proportions from sample
outcomes and to assess the accuracy of these estimates. Consider three examples of information derived from samples.
Example 1: Based on a random sample of 1,019 U.S. adults, a March 2016 Gallup poll found that the percentage of
Americans who identify as environmentalists has decreased. Compared with the 1991 high of 78%, in 2016 only 42% of
Americans self-identified as environmentalists. In its report, Gallup attributed the decline to several factors, including the
adoption of routine environmental friendly practices and the politicization of environmental issues.1
Example 2: Every other year, the National Opinion Research Center conducts the General Social Survey (GSS) on a
representative sample of about 1,500 respondents. The GSS, from which many of the examples in this book are selected,
is designed to provide social science researchers with a readily accessible database of socially relevant attitudes,
behaviors, and attributes of a cross-section of the U.S. adult population. For example, in analyzing the responses to the
2014 GSS, researchers found that the average respondent’s education was about 13.77 years. This average probably
differs from the average of the population from which the GSS sample was drawn. However, we can establish that in most
cases the sample mean (in this case, 13.77 years) is fairly close to the actual true average in the population.
Example 3: In 2016, North Carolina legislators passed House Bill 2, prohibiting transgender people from using bathrooms
and locker rooms that do not match the gender on their birth certificate. The law quickly drew protests from civil rights and
https://jigsaw.vitalsource.com/books/9781506347219/epub/OEBPS/s9781506347189.i1831.xhtml
https://jigsaw.vitalsource.com/books/9781506347219/epub/OEBPS/s9781506347189.i3911.xhtml#s9781506347189.i4044
LGBT (lesbian, gay, bisexual, transgender) rights groups. A CNN/ORC poll of 1,001 Americans revealed that 39% of
those surveyed strongly oppose laws that require transgender individuals to use restroom facilities that correspond on
their gender at birth rather than their gender identity. Seventy-five percent favor laws guaranteeing equal protection for
transgender people in jobs, housing, and public accomodations.2
The percentage of Americans who identify as environ ...
This document describes the development of a predictive model to identify premature infants born between 33-35 weeks gestational age that are at highest risk of hospitalization due to respiratory syncytial virus (RSV) infection. The model was developed using risk factor data from a Spanish case-control study of 183 infants hospitalized with RSV compared to 371 non-hospitalized controls. Discriminant function analysis identified an initial model using 15 risk factors that discriminated between the two groups with 72% accuracy. Further refinement resulted in a final 7 variable model that predicted risk with 71% accuracy and could help optimize use of RSV prophylaxis for higher risk infants in Europe.
Viva Presentation - Fuzzy Logic and Dempster-Shafer Theory to Detect The Risk...Andino Maseleno
1. The document discusses combining fuzzy logic and Dempster-Shafer theory to detect the risk of disease spreading, and developing a web mapping to display risk levels.
2. It aims to calculate the similarity between fuzzy membership functions to determine basic probability assignments for evidence in Dempster-Shafer theory.
3. This would be used to identify risk levels for diseases like avian influenza, African trypanosomiasis, and skin diseases, taking into account population changes and densities in different areas.
Son preference and fertility behavior evidence from Viet Nam - Project statementHanh To
This project seeks to contribute to the current literature of son preference and sex imbalance in Vietnam and other developing countries by extending the measure of “son preference” to birth interval, number of children and probability of using contraceptive methods.
This document presents a Bayesian semiparametric framework for analyzing semicompeting risks data where the observation of time to a non-terminal event (e.g. hospital readmission) is subject to a terminal event (e.g. death). The framework models the hazards of the non-terminal and terminal events using a shared frailty illness-death model, accounting for dependence between events. It allows researchers to estimate regression parameters, characterize event dependence, and predict outcomes. The framework is applied to Medicare data on pancreatic cancer patients to investigate risks of readmission and death.
LIFE COURSE AND DELAY IN ONCOPEDIATRICS REMEDY:case of Burkitt's lymphoma in ...AJHSSR Journal
ABSTRACT :In Côte d'Ivoire, cancer pathology in children is on the rise. Until 2007, the total number was 556
cases (Effi, A.B. et al., 2012). From 2007 to 2015, the number of cases is 863 with 85.3% of burkitt’s lymphoma
(L. Couitchéré et al., 2019). However, the remission rate at the Pediatric Oncology Unit of the Treichville
University Hospital is 30% (A. J.-J. Yao, L. Couitchéré et al., 2010).Faced with this public health problem, this
study reveals that families experience delays in accessing the Pediatric Oncology Unit during the child care
itinerary. In order to understand this phenomenon, socio-anthropological approaches question the diachronic and
synchronic aspects of care in various researches. The perspective of the approach,that of the life course, is an
innovation in the works of in oncopediatrics in Côte d’Ivoire because it combines these different aspects in the
same study. To this end, 56 families affiliated with the Pediatric Oncology Unit of the Treichville University
Hospitalobtained by "snowball" whose "grain" is made up from patient files, submitted themselves to a life
calendar grid and to an interview guide. The information collected was processed with the TRAMINER module
of the R statistical software.From this analysis, it emerges that there is an institutionalization of the renunciation
of care. This institutionalization is a consequence of the institutionalization of the life course of families.
However, there is a diversification in the courses, as they are not completely homogeneous.
This contribution is an aid to the construction of a program to reduce infant mortality due to Burkitt’s lymphoma
in Côte d'Ivoire.
KEYWORDS: Life course - pediatric burkitt lymphoma – institutionalization – delay in seeking care-AbidjanCôte d’Ivoire
Modeling the Effect of Variation of Recruitment Rate on the Transmission Dyna...IOSR Journals
In this Paper, the effect of the variation of recruitment rate on the transmission dynamics of
tuberculosis was studied by modifying an existing model. While the recruitment rate into the susceptible class of
the existing model is constant, in our modified model we used a varying recruitment rate. The models were
analyzed analytically and numerically and these results were compared. The Disease Free Equilibrium (DFE)
state of the existing model was found to be
,0,0,0
, the DFE of the modified model was found to be
( ,0,0,0) * S where * S is arbitrary. While all the eigenvalue of the existing model are negative, one of the
eigenvalues of the modified model is zero. The basic reproduction number o R of both models are established to
be the same. The numerical experiments show a gradual decline in the infected and exposed populations as the
recruitment rates increase in both models but the decline is more in the modified model than in the existing
model. This implies that eradication will be achieved faster using the model with a varying recruitment rate.
Effects of Socio - Economic Factors on Children Ever Born in India: Applicati...inventionjournals
This paper aims at identity the socio – economic determinants of cumulative fertility number of children ever born to women at the end of their reproductive period. The first step is to determine explanatory variables likely to impact the children ever born using multiple regression analysis. The path analysis if used to find out the direct and indirect implied effects of the selected socio demographic factors on children ever born (CEB). The zero order correlation coefficients of various socio economic and demographic variables on CEB are estimated. Percentages of the total absolute effect on CEB through endogenous and exogenous variables are estimated. Direct, Indirect and implied effect of the selected explanatory variables on CEB are obtained by using path analysis.
Ovid 'kangaroo mother care' to prevent neonatal deaths due to preterm birth ...Rosalinda Acuña
This review found that Kangaroo Mother Care (KMC), which involves continuous skin-to-skin contact between a mother and premature baby, substantially reduces neonatal mortality and morbidity compared to conventional care. The review included 15 studies with over 10,000 participants. Meta-analyses found that KMC significantly reduced the risk of death by 51% based on RCTs and 32% based on observational studies. It also significantly reduced the risk of serious health issues like respiratory distress by 66%. While some results depended on modeling assumptions, the authors concluded KMC is highly effective in improving outcomes for preterm babies.
Frequency Measures Used in EpidemiologyIntroductionIn e.docxMARRY7
Frequency Measures Used in Epidemiology
Introduction
In epidemiological studies, many qualitative variables have only two possible categories, such as
Alive or dead
Case or control
Exposed and unexposed
The frequency measures for dichotomous variable include:
Ratio
Proportion
Rate
( All the above 3 measure are based on the same formula: )
Ratios, Proportion, and Rates Compared
In a ratio, the values of x and y may be completely independent from each other or x is a part of y
For example , the gender of the children attending a specific program could be compared in one of the following ways:
Proportion is a ratio in which X is included in Y
For example , the gender of the children attending a specific program
Rate is a proportion that measures the occurrence of an event in a population over time
Rate = X
Ratios, Proportion, and Rates Compared
Example 1: The following table was part of an article published by Dr. Mshana and his colleagues. The title of this study is “Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. ". Please use this table to answer the following questions.
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
4
Example 1
What is the ratio of males to females? 7 : 10
What proportion of premature babies? 12/17=0.706
What proportion of patients were discharged? 11/17=0.647
What is the ratio of prematurity to birth asphyxia ? 12 : 5
Source: Mshana SE, Gerwing L, Minde M, Hain T, Domann E, Lyamuya E, et al. Outbreak of a novel Enterobacter sp. carrying blaCTX-M-15 in a neonatal unit of a tertiary care hospital in Tanzania. International journal of antimicrobial agents. 2011;38(3):265-9.
5
Example 2:
In 1989, 733,151 new cases of gonorrhea were reported among the United States civilian population. The 1989 mid-year U.S. civilian population was estimated to be 246,552,000. What is the 1989 gonorrhea incidence rate for the U.S. civilian population? (For these data we will use a value of 105 for 10n ).
Answer:
Incidence rate = X
Incidence rate = X = 297.4 per 100,000
6
Measures of association:
They are used to quantify the relationship between exposure and disease among two groups
They are used to compare the disease occurrence among one group with the disease occurrence in the another group
They include the following measures based on the study design:
Risk Ratio (RR):
It also called relative risk
It is used to compare the risk of health related events in two groups
The following formula cis used to calculate the RR:
A risk ratio of 1.0 indicates identical risk in the two groups
A risk ratio greater than 1.0 indicates an increased risk for the numerator group
A risk ratio greater than 1.0 ...
This document discusses fitting probability distributions to maternal mortality rate (MMR) data in Nigeria. Four distributions were fitted to the MMR data: gamma, lognormal, Weibull, and exponential. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the best fitting distribution. The exponential distribution had the lowest AIC and BIC values, indicating it was the best fitting distribution. The estimated rate parameter for the exponential distribution was 0.5853659, with this model providing the best fit for analyzing MMR in Nigeria.
Logistic Loglogistic With Long Term Survivors For Split Population ModelWaqas Tariq
Split population models are also known as mixture model . The data used in this paper is Stanford Heart Transplant data. Survival times of potential heart transplant recipients from their date of acceptance into the Stanford Heart Transplant program [3]. This set consists of the survival times, in days, uncensored and censored for the 103 patients and with 3 covariates are considered Ages of patients in years, Surgery and Transplant, failure for these individuals is death. Covariate methods have been examined quite extensively in the context of parametric survival models for which the distribution of the survival times depends on the vector of covariates associated with each individual. See [6] for approaches which accommodate censoring and covariates in the ordinary exponential model for survival. Currently, such mixture models with immunes and covariates are in use in many areas such as medicine and criminology. See for examples [4][5][7]. In our formulation, the covariates are incorporated into a split loglogistic model by allowing the proportion of ultimate failures and the rate of failure to depend on the covariates and the unknown parameter vectors via logistic model. Within this setup, we provide simple sufficient conditions for the existence, consistency, and asymptotic normality of a maximum likelihood estimator for the parameters involved. As an application of this theory, the likelihood ratio test for a difference in immune proportions is shown to have an asymptotic chi-square distribution. These results allow immediate practical applications on the covariates and also provide some insight into the assumptions on the covariates and the censoring mechanism that are likely to be needed in practice. Our models and analysis are described in section 5.
This document discusses a systematic review and meta-analysis on the relationship between dietary fat intake and breast cancer risk. The meta-analysis included 45 studies with over 25,000 breast cancer patients. It found a small increased risk of breast cancer associated with higher total fat intake. The review also discusses terms related to systematic reviews and meta-analysis such as heterogeneity statistics, I2, and the Q statistic.
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Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
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Estimation
Chapter Learning Objectives
1. Explain the concepts of estimation, point estimates, confidence level, and confidence interval
2. Calculate and interpret confidence intervals for means
3. Describe the concept of risk and how to reduce it
4. Calculate and interpret confidence intervals for proportions
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2014 GSS, researchers found that the average respondent’s education was about 13.77 years. This average probably
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On a Sequential Probit Model of Infant Mortality in Nigeria, by K.T. Amzat and S.A. Adeosun
1. International Journal of Mathematics and Statistics Invention (IJMSI)
E-ISSN: 2321 – 4767 P-ISSN: 2321 - 4759
www.ijmsi.org || Volume 2 || Issue 3 || March 2014 || PP-89-94
www.ijmsi.org 89 | P a g e
On A Sequential Probit Model of Infant Mortality in Nigeria
K. T. Amzat1
, S. A. Adeosun1
1
Department of Mathematical Sciences, Crescent University, Abeokuta, Nigeria.
ABSTRACT: This paper presents and analyzed the nature of relationship between infant mortality and some
socioeconomic and demographic variables, and the proximate covariate that influence the survival of an infant
using the 2003 Nigeria Demographic and Health Survey Data (NDHS). We used sequential probit model to
examine the relationship between the dependent variables (infant’s death and age at death) and predictor
variables for both correlated and uncorrelated error terms. The results of the analysis showed that in both of
the situation with correlated and uncorrelated error terms, infant’s being alive or death is positively affected by
education, birth order number, duration of breast feeding and negatively affected by both total children born
and place of delivery. There are significant differences among the predictor variables on the probability of
infant’s death at neonatal and post neonatal period. The correlation between the error terms is significant. It
is needed to be examined two stages together.
KEYWORDS: Convergence, Discrete choice, Homoscedasticity, Infant Mortality, Sequential probit model.
I. INTRODUCTION
By nature, individual enters the human world by birth and leaves by death. Births and deaths are two
facts opposite to one another. In statistical terms, a distinction exists between births and infant mortality. They
have two things in common; they are both events that have a date and they occur only once for every man.
Infant mortality refers to the death of an infant during the first year of life (number of deaths among infants
under one year old per 1,000 live births in a given year). Historically, there has been a negative relationship
between infant mortality rates (IMR) and economic factors as rightly pointed by some scholars (Zerai [1], Suwal
[2]). This relationship is likely caused indirectly by several variables both exogenous and endogenous. Since
mothers and infants are among the most vulnerable members of society, infant mortality is a measure of a
population’s health. In addition, disparities in infant mortality by race/ethnicity and socioeconomic status are an
important measure of the inequalities in a society.
Some have argued that racial and economic disparities is a reflection of the long-standing disparity
between black and white populations, with the infant mortality rate among black Americans consistently twice
that of white Americans. Others cite the wide inequalities between the wealthiest and poorest segments of the
society. Whereas, in Africa and Nigeria in particular, mortality as an aspect of demographic studies has not been
given as much attention as fertility, the reason is not far-fetched, most researchers who ventured into the area are
partially if not wholly, discouraged by the poor responses to child mortality questions. People see questions
about their mortality experiences as too private. Much more important is the fact that these experiences are
interpreted as uncontrollable by human force. There is also the belief that a woman faced with the problem of
constants child deaths is being visited by the same child several times, only to be recalled back to the spirit
world on each occasion (Fadipe [3]). The Infant Mortality Rate (IMR) is a public health indicator of a complex
societal problem. Numerous frameworks have been used to help understand the multiple determinants of infant
mortality in a society and to identify interventions to reduce infant mortality. While the root social causes of
infant mortality- persistent poverty, pervasive and subtle racism, and the chronic stresses associated with them-
may not be easy to address, it is still possible to understand the risks of infant death by examining the biological
pathways through which these societal forces act.Therefore, this paper presents the conceptual basis for the
sequential probit model. Theoretical background and parameter estimation method are given. More so, the
impact of various demographic and socioeconomic attributes on the probability of infant mortality are estimated
via two stages (neonatal phase and post neonatal phase) sequential probit model for both correlated and
uncorrelated error terms. Thereafter, estimates of parameters for the infant mortality data are given.
Many studies on infant mortality have suggested different variables indirectly affecting infant
mortality (Suwal [2], Turrel and Mengersen [4], Agha [5]). However, hypothesis about indirect effect are not
adequately represented by conventional methods. Sequential probit model is a more appropriate statistical
technique for this type of situation because of its usefulness in the inclusion of dependent variables into a model
2. On A Sequential Probit Model of Infant Mortality in Nigeria
www.ijmsi.org 90 | P a g e
and provides estimates of independent variables’ marginal effect (Grooraert and Patrins [6], Orzlem and Hatice
[7]).
Basic Probit Model:
Probit regression, also called a probit model, is used to model dichotomous or binary outcome
variables. In the model, the inverse standard normal distribution of the probability is model as a linear
combination of the predictors. The model can be represented as follow:
, where (1)
and is the inverse of the cumulative distribution function (cdf) of the standard normal distribution. Binary
response models directly describe the response probability of the dependent variable . The
probability that the dependent variables take value 1 is modeled as where is
a dimensional column vector of parameters. The probit model assumes that the transformation function
is the cumulative density function of the standard normal distribution. The response probabilities are
then . The model also assumes that there is a latent or unobserved variable that is
linearly depends on given as . This latent variable is the utility difference between
choosing
. (2)
Sequential Probit Model:
A sequential probit model is used in analyzing discrete choice problems. In the sequential model, each
decision is made sequentially according to a binary probit model. Whether or not an alternative is selected is
determined before an alternative is considered. The latent variable model is given by
(3)
where for , and is a scalar constant term, is a coefficient vector.
Then the sequential probit model is defined as follows:
.
For this paper, the sequential probit model consists in assuming that infant’s age at death occurs about three
options in a sequential manner, namely:
Infant lives
Infant is died between 0 – 1 month (neonatal)
Infant is died between 1 – 12 months (post neonatal).
A sequential model is considered with two qualitative variables and , which are observed sequentially. This
is illustrated in the figure 1.1 below (Ozlem and Hatice [7]).
Figure 1.1: Sequential probit model
If , outcome is observed, otherwise depending on the value of , there are two additional outcomes:
10 10
3. On A Sequential Probit Model of Infant Mortality in Nigeria
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In other words, the first observation corresponds to outcome and the next observation
corresponds to outcome or depending on the value of . We associate with stage a latent
variable such that
(4)
The continuous latent variables are modeled as follows:
for
for (5)
where , and are vectors of the parameters to be estimated, and are vectors of the error terms
(Ozarci [8], Waelbroeck [9]).
For estimation of the parameters, maximum likelihood estimation (MLE) was adopted. The
probabilities of the different options are written as follows:
(6)
where and are cumulative distribution function of the univariate and bivariate standard normal
distribution respectively. If we assume that and are independent, their joint probability is the product of
their marginal probabilities. We then have
(7)
Using the probabilities above, the likelihood function of the sequential probit model is given by
(8)
Taking the natural logarithm of the likelihood function , we obtain (see Eklof and Kalsson [10])
(9)
If the error terms are independent , natural logarithm of the likelihood functions yields
(10)
The natural logarithm of maximum likelihood function with correlated error term is as follows:
(11)
The estimators of sequential probit model were derived by maximizing likelihood function given above. However,
the maximum likelihood estimator possesses a number of attractive asymptotic properties, for many problems; they are:
Consistency: the estimator converges in probability to the value being estimated.
Asymptotic normality: as the sample size increases, the distribution of the MLE tends to the Gaussian distribution with
mean and covariance matrix equal to the inverse of Fisher information matrix (Myung [10])
4. On A Sequential Probit Model of Infant Mortality in Nigeria
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Efficiency: it achieves the Cramer-Rao lower bound when the sample size tends to infinity. This means that no
asymptotically unbiased estimator has lower asymptotic mean square error than the MLE.
Remark: In a sequential probit model, the log likelihood is globally concave and the latent error term is normally
distributed and homoscedastic. The maximum likelihood is inconsistent in the presence of Heteroscedasticity. Typically, we
used a bivariate normal distribution for two standard normally distributed errors and the joint density would be
where is a “correlation parameter” denoting the extent to which the two errors covary.
Application and Analysis:
At the first stage, the factors affecting both the mortality and survival of infants born within 1998 – 2003 in
Nigeria were examined. At the second stage of the sequential process, we investigated the factors affected the infant’s
age at death. The data used in the study obtained from the answers of the married women, between 15 – 48 years, who
joined the 2003 Nigeria Demographic and Health Survey Data (NDHS) conducted by the Federal Office of Statistics
with the aim of gathering reliable information on fertility, family planning, infant and child mortality, vaccination
status, breastfeeding and nutrition, etc. In the sequential process, data of 5138 infants have been used for each of the
two stages. The variables used in this paper are given in Table 1 and subsequently followed with explainable and
necessary tables.
Table 1: Definitions of Variables
Variables Type of Variable Stage
Infant’s being alive or death Dependent 1
Infant’s birth order number Independent 1
Woman’s total children born Independent 1
Duration of breastfeeding Independent 1
Woman’s highest education Independent 1
Place of delivery Independent 1
Infant’s age at death Dependent 2
Duration of breastfeeding Independent 2
Place of delivery Independent 2
Delivery by Caesarian Section (CS) Independent 2
Age of woman at first birth Independent 2
Table 2: The estimation results of sequential probit model when
Variable Coefficient Standard error Z P> 95% Confidence Interval
First Stage
Total children born -0.4122 0.0069 -11.70 0.0000 -0.4813 -0.3432
Birth order number 0.3784 0.0353 10.73 0.0000 0.3093 0.4476
Place of delivery -0.0110 0.0017 -6.54 0.0000 -0.0143 -0.0077
Highest education 0.2213 0.0286 7.74 0.0000 0.1653 0.2774
Duration of breastfeeding 0.0054 0.0007 8.05 0.0000 0.0042 0.0068
Second Stage
Age of first birth 0.0070 0.0069 -1.10 0.312 -0.0066 0.0205
Place of delivery -0.0066 0.0018 -3.75 0.000 -0.0101 -0.0032
Delivery by CS -0.0036 0.0285 -0.13 0.895 -0.0594 0.0522
From TABLE 2 above, at the first stage of the model, the dependent variable (infant’s being alive or death) is
affected by total children born, birth order number, place of delivery, highest education and duration of breastfeeding. At the
second stage, infant’s age at death is affected by place of delivery. To determine the magnitude of these effects, marginal
effects can be calculated which indicate that for every one unit increase in a variable causes an increase on the probability of
the dependent variable.
Table 3: Marginal effects of independent variables of sequential probit model
Variable Coefficient Standard error Z P> 95% Confidence Interval
First Stage
Total children born -0.0874 0.0073 -12.00 0.0000 -0.1006 -0.0718
Birth order number 0.0803 0.0073 10.96 0.0000 0.0648 0.0936
Place of delivery -0.0023 0.0004 -6.58 0.0000 -0.0030 -0.0016
Highest education 0.0470 0.0061 7.78 0.0000 0.0341 0.0580
Duration of breastfeeding 0.0012 0.0001 8.09 0.0000 0.0009 0.0014
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Second Stage
Age of first birth 0.0011 0.0010 1.01 0.3120 -0.0008 0.0026
Place of delivery -0.0010 0.0003 -3.76 0.0000 -0.0013 -0.0004
Delivery by CS -0.0005 0.0043 -0.13 0.8990 -0.0076 0.0067
According to TABLE 3, at the first stage of the model, place of delivery and total children born, negatively
affected the probability of infant’s death. The total children born decrease the probability of infant’s death by 8.7%. Birth
order number is the most effective positive variable which indicates that increase of one unit in this variable (birth order
number) causes an increase in the probability of infant’s death by 8.03%, closely followed by highest education with 4.7%
and duration of breastfeeding. At the second stage of the model, place of delivery negatively affect the probability of infant’s
age at death.
Table 4: Estimates of parameters of sequential probit model for
Variable Coefficient Standard error Z P> 95% Confidence Interval
First Stage
Total children born -0.2951 0.0301 -9.78 0.0000 -0.3542 -0.2359
Birth order number 0.2753 0.0300 9.18 0.0000 0.2165 0.3340
Place of delivery -0.0108 0.0016 -6.64 0.0000 -0.0140 -0.0076
Highest education 0.1959 0.0240 8.16 0.0000 0.1488 0.2429
Duration of breastfeeding 0.0053 0.0006 9.47 0.0000 0.0042 0.0064
Second Stage
Age of first birth -0.0025 0.0069 -0.37 0.7120 -0.0159 0.0109
Place of delivery -0.0072 0.0018 -3.98 0.0000 -0.0107 -0.0036
Delivery by CS -0.0357 0.0321 -1.11 0.2660 -0.0985 0.0271
rho ( ) 0.9100 0.0142
From the TABLE 4, at the first stage of the model, education, total number of children born, place of
delivery, birth order number and duration of breastfeeding are significant. At the second stage, place of delivery
significantly affect infant’s age at death.
Table 5: Marginal effect of independent variable for (Neonatal)
Variable Coefficient Standard error Z P> 95% Confidence Interval
First Stage
Total children born -0.0338 0.0165 -2.05 0.040 -0.0660 -0.0015
Birth order number 0.0311 0.0155 2.00 0.045 0.0007 0.0615
Place of delivery -0.0021 0.0023 -1.81 0.071 -0.0043 0.0002
Highest education 0.0189 0.0085 2.22 0.026 0.0022 0.0356
Duration of breastfeeding 0.0007 0.0003 2.30 0.021 0.0001 0.0012
Second Stage
Age of first birth -0.0001 0.0001 -1.09 0.275 -0.0001 0.0000
Place of delivery -0.0001 0.0011 -1.78 0.073 -0.0032 0.0001
Delivery by CS -0.0002 0.0004 -0.49 0.623 -0.0010 0.0006
Table 6: Marginal effect of independent variable for (Post neonatal)
Variable Coefficient Standard error Z P> 95% Confidence Interval
First Stage
Total children born -0.0273 0.0045 -6.01 0.000 -0.0363 -0.0184
Birth order number 0.0252 0.0042 5.92 0.000 0.0169 0.0335
Place of delivery -0.0018 0.0004 -4.98 0.000 -0.0025 -0.0011
Highest education 0.0153 0.0027 5.65 0.000 0.0010 0.0206
Duration of breastfeeding 0.0005 0.0001 9.44 0.000 0.0004 0.0006
Second Stage
Age of first birth -0.0001 0.0001 -1.00 0.315 -0.0004 0.0001
Place of delivery -0.0003 0.0002 -4.68 0.075 -0.0026 -0.0011
Delivery by CS -0.0005 0.0011 -0.49 0.625 -0.0026 0.0016
From TABLE 5 and TABLE 6 above, at the first stage, all the variables are significantly affect the infant’s age at
death (neonatal) except place of delivery. Birth order number is the most effective positive variable that increases the
probability of infant’s age at death (neonatal) by 3.11%, followed by education with 1.89%. Total number of children
decreases the probability of infant’s age at death (neonatal) by 3.36%. Similarly, at the second stage of the model, delivery
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by caesarian section and age of the woman at first birth are not significant while that of duration of breastfeeding positively
affect the probability of infant’s age at death. Place of delivery decreases the probability of infant’s age at death (post
neonatal).
II. CONCLUSION
In this paper, the nature of relationship between infant mortality and some demographic socioeconomic and health
variables were studied. The data on infant’s being alive or death and age at death used for the study were obtained from 2003
data files. In both of the situations with correlated and uncorrelated error terms, infant’s being alive or death is positively
affected by education, birth order, duration of breastfeeding and negatively by both total children born and place of delivery.
Also, infant’s age at death is affected negatively by place of delivery. This indicated that the related independent variables
decrease the probability of infant’s age at death at post neonatal period. More so, the correlation coefficient, , is statistically
significant ( ). That is, infant’s age at death and infant’s being alive or death are related. There are significant
differences among the variables on the probability of infant’s death at neonatal and post neonatal period.
This paper provides assessment of the relative importance of factors associated with neonatal and post
neonatal in Nigeria. The results showed that; there is a relationship between infant’s death and age at death.
Birth order number, mother’s education and duration of breastfeeding are the most possible variables that
influence the survival of a child. To alleviate fears of women towards caesarian section and to support medical
experts’ point of view, the study indicated that age at first birth and delivery by caesarian section not effective in
determining infant’s age at death. More so, breastfeeding should be encouraged among nursing mothers and
especially working class mothers, as it helps in bringing down infant mortality. It is hope that the findings of this
paper will guide the key policy planners to achieve the goals of Nigeria’s development plan. All government
parastatals and private organizations responsible for different survey should be consistent and make necessary
follow up at regular intervals, so that there will be no chance of missing events.
REFERENCES
[1] Zerai, A., Preventive health strategies and infant survival in Zimbabwe, African Population Studies 11(1), 1996, 29-62.
[2] Suwal, J.V., 2001. The Main Determinants of Infant Mortality in Nepal. Social Sci. Med., 53: 1667-1681.
[3] Fadipe, N. A. 1970. The Sociology of the Yoruba. Ibadan: Ibadan University Press.
[4] Turrel, G. and Mengersen K., 2000. Socioeconomic Status and Infant Mortality in Australia. A national Study of Small Urban Areas
1985-89. Social Sci. Med., 50: 1209-1225.
[5] Agha, S., 2000.The Determinant of Infant Mortality in Pakistan. Social Sci. Med., 51: 199-208
[6] Grooraert, C. and Patrinos H. A., 1999. A Four Country Comparative Study of Child labour, Policy Analysis of Child Labour:
AComparative Study, St. Martin Press, New York.
[7] Ozlem Alpu and Hatice Fidan, Sequential Probit Model for Infant Mortality Modeling in Turkey, Journal of Applied Sciences4(4),
2004, 590-595.
[8] Ozarici, O., Bivariate probit model with full observability and Heteroscedasticity and an application. Ph.D. Thesis, Osmangazi
University, Turkey. 2002.
[9] Waelbroeck, P., 2000. Econometric Analysis of the Sequential Probit Model, GREQUAM, 25.