Smooth Breaks and Mean Reversion in Infant Mortality Rates

This paper, the second of four, examines patterns and trends in infant mortality during the period 1538–1837 when the principal source available to examine these issues is parish registers. It explains how to calculate infant mortality rates from parish registers, identifies trends and discusses possible explanations for the patterns of change identified. The paper also shows how new estimates of infant mortality can be readily undertaken and ends with suggestions for future research.

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The Joy of Statistics

10.1093/oso/9780198833444.001.0001 ◽

2019 ◽

Cited By ~ 1

Author(s):

Steve Selvin

Keyword(s):

Coronary Heart Disease ◽

Short Stories ◽

Heart Disease ◽

Infant Mortality ◽

Statistical Methods ◽

Ethnic Differences ◽

Coronary Heart Disease Risk ◽

Disease Risk ◽

Mortality Rates ◽

Heart Disease Risk

The Joy of Statistics consists of a series of 42 “short stories,” each illustrating how elementary statistical methods are applied to data to produce insight and solutions to the questions data are collected to answer. The text contains brief histories of the evolution of statistical methods and a number of brief biographies of the most famous statisticians of the 20th century. Also throughout are a few statistical jokes, puzzles, and traditional stories. The level of the Joy of Statistics is elementary and explores a variety of statistical applications using graphs and plots, along with detailed and intuitive descriptions and occasionally using a bit of 10th grade mathematics. Examples of a few of the topics are gambling games such as roulette, blackjack, and lotteries as well as more serious subjects such as comparison of black/white infant mortality rates, coronary heart disease risk, and ethnic differences in Hodgkin’s disease. The statistical description of these methods and topics are accompanied by easy to understand explanations labeled “how it works.”

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Health capabilities and the determinants of infant mortality in Brazil, 2004–2015: an innovative methodological framework

BMC Public Health ◽

10.1186/s12889-021-10903-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Alexandre Bugelli ◽

Roxane Borgès Da Silva ◽

Ladislau Dowbor ◽

Claude Sicotte

Keyword(s):

Infant Mortality ◽

Educational Attainment ◽

Health Professionals ◽

Capita Income ◽

Mortality Rates ◽

Fertility Rate ◽

Employment Rate ◽

Infant Deaths ◽

Bolsa Familia ◽

Per Capita

Abstract Background Despite the implementation of a set of social and health policies, Brazil has experienced a slowdown in the decline of infant mortality, regional disparities and persistent high death levels, raising questions about the determinants of infant mortality after the implementation of these policies. The objective of this article is to propose a methodological approach aiming at identifying the determinants of infant mortality in Brazil after the implementation of those policies. Method A series of multilevel panel data with fixed effect nested within-clusters were conducted supported by the concept of health capabilities based on data from 26 Brazilian states between 2004 and 2015. The dependent variables were the neonatal, the infant and the under-five mortality rates. The independent variables were the employment rate, per capita income, Bolsa Família Program coverage, the fertility rate, educational attainment, the number of live births by prenatal visits, the number of health professionals per thousand inhabitants, and the access to water supply and sewage services. We also used different time lags of employment rate to identify the impact of employment on the infant mortality rates over time, and household income stratified by minimum wages to analyze their effects on these rates. Results The results showed that in addition to variables associated with infant mortality in previous studies, such as Bolsa Família Program, per capita income and fertility rate, other factors affect child mortality. Educational attainment, quality of prenatal care and access to health professionals are also elements impacting infant deaths. The results also identified an association between employment rate and different infant mortality rates, with employment impacting neonatal mortality up to 3 years and that a family income below 2 minimum wages increases the odds of infant deaths. Conclusion The results proved that the methodology proposed allowed the use of variables based on aggregated data that could hardly be used by other methodologies.

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Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽

10.3390/math9030282 ◽

2021 ◽

Vol 9 (3) ◽

pp. 282

Author(s):

Mabel Morales-Otero ◽

Vicente Núñez-Antón

Keyword(s):

Infant Mortality ◽

Count Data ◽

Spatial Dependence ◽

Poisson Model ◽

Mortality Rates ◽

Estimation Methods ◽

Neighborhood Structure ◽

Specific Context ◽

Count Data Models ◽

Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

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Determinants of neonatal, postneonatal and maternal mortality on models of primary health

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.857 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

A B Guerra ◽

L M Guerra ◽

L F Probst ◽

B V Castro Gondinho ◽

G M Bovi Ambrosano ◽

...

Keyword(s):

Health Care ◽

Infant Mortality ◽

Maternal Mortality ◽

Public Policies ◽

Family Health ◽

Mortality Rates ◽

Sao Paulo ◽

The State ◽

Care Organization ◽

São Paulo

Abstract Background The state of São Paulo recorded a significant reduction in infant mortality, but the desired reduction in maternal mortality was not achieved. Knowledge of the factors with impact on these indicators would be of help in formulating public policies. The aims of this study were to evaluate the relations between socioeconomic and demographic factors, health care model and both infant mortality and maternal mortality in the state of São Paulo, Brazil. Methods In this ecological study, data from national official open sources were used. Analyzed were 645 municipalities in the state of São Paulo, Brazil. For each municipality, the infant mortality and maternal mortality rates were calculated for every 1000 live births, 2013. The association between these rates, socioeconomic variables, demographic models and the primary care organization model in the municipality were verified. We used the zero-inflated negative binomial model. Gross analysis was performed and then multiple regression models were estimated. For associations, we adopted “p” at 5%. Results The increase in the HDI of the city and proportion of Family Health Care Strategy implemented were significantly associated with the reduction in both infant mortality (neonatal + post-neonatal) and maternal mortality rates. In turn, the increase in birth and caesarean delivery rates were associated with the increase in infant and maternal mortality rates. Conclusions It was concluded that the Family Health Care Strategy model that contributed to the reduction in infant (neonatal + post-neonatal) and maternal mortality rates, and so did actors such as HDI and cesarean section. Thus, public health managers should prefer this model. Key messages Implementation of public policies with specific focus on attenuating these factors and making it possible to optimize resources, and not interrupting the FHS. Knowledge of the factors with impact on these indicators would be of help in formulating public policies.

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