Infant Mortality Data Research Articles

Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes.

The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data. In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.

Suitable survival models for analyzing infant and child mortality data

Suitable survival models for analyzing infant and child mortality data

Trends and distribution patterns of infant mortality and maternal HIV positivity in South Africa: A decade review (2007 - 2016).

The infant mortality rate (IMR) (24 per 1 000 live births) remains high in South Africa (SA), well above the recommended sustainable development goal (SDG) 3 of 12 deaths per 1 000 live births. High infant mortality is a poor indicator of the health of a population and will hamper attainment of the SDGs. To investigate the trends and distribution patterns of IMR between 2007 and 2016 and its association with HIV-positive pregnant mothers in SA. This study used a cross-sectional study design by analysing secondary data on infant mortality from the 2007 and 2016 Statistics South Africa Community Surveys (CSs), as well as data from the 2007 National Antenatal Sentinel HIV and Syphilis Prevalence Survey. (Antenatal HIV Sentinel Survey - ANCHSS). Line charts with descriptive statistics were used to detail trends in IMRs, and multiple logistic regression models were used to identify risk factors for infant mortality in the 2007 and 2016 CS datasets. Spearman's rank-order correlation (rho) was used to correlate infant mortality with data from the 2007 ANCHSS. All analyses were performed with Stata version 16.0. A total sample of 87 805, comprising 43 922 males and 43 883 females, was included in the analysis. The results revealed a decline in IMR from 55 deaths per 1 000 live births in 2007 to 32 in 2016. Overall, there was a significant decrease in the mortality rate from 2007 to 2016. The infant mortality proportions by province showed KwaZulu-Natal Province having the highest IMR (17.5 deaths per 1 000 live births in 2007 and 6.3 in 2016). Males had a higher IMR (28 deaths per 1 000 live births in 2007 and 17.7 in 2016) compared with females at 26.7 deaths per 1 000 live births in 2007 and 13.8 in 2016. IMR data from the 2007 CS was correlated with the 2007 ANCHSS (28% HIV prevalence in 2007), using Spearman's rank-order correlation, which showed a moderate correlation of 0.58 (p<0.001). The study findings showed a reduction in the trends of infant mortality between 2007 and 2016 in SA; despite the reduction, health inequalities persist. There is a correlation evident between maternal HIV prevalence and IMR in SA. We recommend the use of disability-adjusted life expectancy in SA to measure population health and introduce robust data sets that can better inform policy.

Piecewise Linear Multiple Change Point Modelling for Under-Five Child Mortality

Using a piecewise linear multiple change-point model instead of more traditional survival techniques is more beneficial when dealing with mortality data. Utilizing this technique, the hazard model is estimated and the number of cut-point locations is found. Using various covariates, such as sociodemographic, biological, and proximate co-factors, this piecewise hazard model is fitted to the Infant Mortality Data of Bangladesh Demographic and Health Survey 2014. Finding the change point and the impact of covariates on the hazard rate is done using the maximum likelihood estimation process. The parameter&amp;apos;s significance is subsequently supported by the Wald test statistic. It turns out that the mother&amp;apos;s educational status, religion, mother&amp;apos;s age in years, the number of children they have ever had, currently breastfeeding, the size of the child, desire for more children, cesarean delivery, ANC visits, and birth orders are all significant factors. It is also discovered that the detected change point of the hazard rate is extremely important for the child until they reach the age of five. Through the various time cut points, it is found that a piecewise linear multiple change-point model is very important for under-five child mortality. Public health specialists, researchers, and clinicians can all benefit from this piecewise hazard model. One of the most crucial elements in lowering child mortality is time.

Comparative Analysis of Ridge, LASSO, and Elastic Net Regularization Approaches in Handling Multicollinearity for Infant Mortality Data in South Sulawesi

Infant mortality rate is a crucial indicator for assessing the health and infant care quality in a region. In the effort to reduce infant mortality rates, regression analysis serves as a tool to identify influential factors. However, regression analysis often encounters the challenge of multicollinearity, which involves high correlation among predictor variables. To address this issue, various regularization techniques can be applied, such as ridge regression, least absolute shrinkage and selection operator (LASSO), and elastic net. Ridge regression aims to control coefficient variance, while LASSO directs some coefficients to zero, functioning as variable selection. Elastic net combines the strengths of both methods by merging ridge and LASSO regularization. The objective of this research is to evaluate the performance of ridge regression, elastic net, and LASSO methods in handling multicollinearity issues, utilizing infant mortality rate data in South Sulawesi Province. The results indicate that the elastic net method outperforms both Ridge and LASSO methods. The best-performing model is obtained through elastic net with a coefficient of determination value of 60.81%, whereas ridge and LASSO methods yield coefficient of determination values of 54.11% and 58.18%, respectively. This demonstrates that the application of the elastic net method is capable of producing more accurate results in modeling the variables within the analysis of infant mortality rate data compared to other methods.

Modelling Infant Mortality Rate using Time Series Models

The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

The Effects of Race, Ethnicity, and Maternal Education on Infant Mortality.

The state of New Jersey has a large Black/African American (AA) versus White racial disparity in infant mortality and educational level at childbirth. This disparity, measured by rate ratio, increases with greater maternal education among varied racial-ethnic groups. The nature of this disparity measured by rate differences has not been explored. Infant birth and mortality data were used to examine whether racial or ethnic disparities in infant mortality increased with greater maternal education, comparing rate differences and rate ratios. Racial and ethnic variations in the association between maternal education and infant mortality were examined. Data were from the New Jersey State Health Assessment Data for all New Jersey births between 2014 and 2018 stratified by race and ethnicity, maternal education, and infant mortality ( n = 481,333). R software was used to create a data set and estimate additive and multiplicative interactions, rate differences, and rate ratios for infant mortality by maternal race/ethnicity and educational levels among four racial-ethnic groups. Infant mortality was significantly greater for Black/AA and Hispanic mothers than for White mothers. At all educational levels, Black/AA mothers had the highest prevalence of infant mortality compared to other racial or ethnic groups. Rate differences in infant mortality showed a decrease in Black/AA-White differences for mothers with a high school education or less compared to mothers with a college degree. However, rate ratios showed an increase in Black/AA-White ratio with increasing education levels for mothers with high school education or less than mothers with a college degree. Risk ratios comparing infant mortality for Black/AA versus Hispanic or Asian mothers showed more than a twofold greater risk at all education levels for Black/AA infants. Finally, college-educated Black/AA mothers had significantly higher rates of infant mortality than White or Hispanic mothers with a high school education or less. Black/AA mothers with a college degree had a higher infant mortality rate than White, Hispanic, or Asian mothers with a high school education or less. Future research should address contextual/systemic contributors to this disparity.

Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability

ObjectiveThe major objective of this project is to find the best suitable model for district-wise infant mortality rate (IMR) data of Bangladesh over the period 2014–2020 that captures the regional...

Mobile phone coverage and infant mortality in sub-Saharan Africa

Infant mortality is still high in Sub-Saharan Africa. Mobile phone technology has the potential to reduce mortality by facilitating the exchange of information. To test for effects, we combine georeferenced information on mobile phone signal coverage with infant mortality data on 1,268,041 children born in 30 Sub-Saharan African countries between 1999–2016. Our results reveal that infant mortality risk drops substantially as mobile phone coverage expands. Infants are 0.9 percentage points less likely to die within the first year after birth compared to their sibling(s) when mobile phone signal is available. In line with this result, we also find that fertility rates decline with the rollout of mobile coverage. Suggestive evidence indicates that improved health knowledge is relevant in explaining our findings. Mobile-phone related changes in access to in-person healthcare services or improvements in income opportunities, on the other hand, are unlikely to play an important role.

Seasonal Variation On Infant Mortality: A Study Based On Pauri Garhwal, Uttarakhand

Infant mortality is a key indicator of national health, as it is linked to many factors such as maternal health, quality of life and access to health care facilities, economic conditions, and public health systems. The infant mortality rate (IMR), expressed as the rate per 1000 children born within a year and die before reaching the age of one, has been the subject of many studies and public policies. Uttarakhand’s infant mortality rate was decreased 42 to, 40 and 39.1as per NFHS-3 (2005-06), NFHS-4 (2015-16) and NFHS-5 (2020-21) respectively. Infant mortality is considered to be the best indicator of how a society meets the needs of its people. The purpose of this study was to determine the effect of seasonal changes on infant mortality in selected areas in Pauri Garhwal (Uttarakhand). In this study, infant mortality data was collected for six consecutive years (2017- 2022) from various health centres in Pauri Garhwal Nagar Palika. Seasonal variation in total infant mortality was analysed using monthly mortality data collected during the study period. Regression models are used to measure seasonal effects on total infant mortality. Data analysis was performed using SPSS software. The frequency of infant deaths in winter month December was very high compared to other seasons. The majority of infant deaths were reported due to cold weather in this study area, especially in November and December. This study concludes that seasonal differences significantly affect infant mortality in Pauri-Garhwal, Uttarakhand, India. Because children are most sensitive to environmental conditions in the first year of life, policy makers should consider the effect of seasonality on infant mortality.

MATERNAL MORTALITY IN GROBOGAN DURING COVID-19 PANDEMIC 2020-2021

Background: Maternal mortality is one of the health statuses that is considered global. Central Java Province has the highest maternal mortality rate in Indonesia, and one of the regencies with a high MMR (Maternal Mortality Rate) is Grobogan. Purpose: This research wants to describe maternal mortality in Grobogan in 2020-2021. In further research, we can determine the relationship between risk factors that cause maternal death and use them to develop program decisions to reduce maternal mortality in Grobogan. Methods: This study used a descriptive-quantitative study using Maternal and Infant Mortality data from the Health Office of Grobogan in 2020-2021 and analyzed averages and proportions.Results: This study showed that 103 mothers died, 69% mothers at the age of 20-35 years, 46.60% mothers had Junior High School, 8% were in the Purwodadi 2 area, 73% with gestational age &gt;28 weeks, 52% had 2-3 parity, 87.38% of mothers never abortion, 57% of mothers died during the puerperium, and 39% caused by being infected of COVID-19. Conclusion: Maternal mortality in Grobogan Regency during the 2020-2021 COVID-19 pandemic, most of the causes of death were due to COVID-19 infection, which mainly occurred in mothers aged 20-35 years, last educated at junior high school, at gestational age &gt;28 weeks, parity 2-3, more common in mothers who have never had a history of abortion, and is currently experiencing the puerperium. This maternal death occurred in 28 of the 30 Puskesmas in Grobogan.

Indeterminacy of the Diagnosis of Sudden Infant Death Syndrome Leading to Problems with the Validity of Data.

The validity of infant mortality data is essential in assessing health care quality and in the setting of preventive measures. This study explores different diagnostic procedures used to determine the cause of death across forensic settings and thus the issue of the reduced validity of data. All records from three forensic medical departments that conducted autopsies on children aged 12 months or younger (n = 204) who died during the years 2007–2016 in Moravia were included. Differences in diagnostic procedures were found to be statistically significant. Each department works with a different set of risk factors and places different emphasis on different types of examination. The most significant differences could be observed in sudden infant death syndrome and suffocation diagnosis frequency. The validity of statistical data on the causes of infant mortality is thus significantly reduced. Therefore, the possibilities of public health and social policy interventions toward preventing sudden and unexpected infant death are extraordinarily complicated by this lack of data validity.

Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur

Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019 indicated overdispersion. Infant mortality is an indicator that can measure the progress of development outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it. The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect infant mortality in East Kalimantan in 2019.

SutteARIMA: A Novel Method for Forecasting the Infant Mortality Rate in Indonesia

This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083.

Levels, Trends and Determinants of Infant Mortality in Nigeria: An Analysis using the Logistic Regression Model

This paper presents a statistical analysis of the levels, trends and determinants of infant mortality in Nigeria using the logistic regression model. Infant mortality data for each of the five years preceding the 2003, 2008, 2013 and 2018 Nigeria Demographic Health Survey (NDHS) was retrieved and used for the analysis. Findings from the study revealed that infant mortality rates decline have stagnated in the five year period prior to the 2018 survey with an Annual Rate of Reduction (ARR) of 0% relative to an initial ARR of 5.7% between 2003 and 2008. The ARR of 2.039% over the 15 year period spanning 2003 to 2018 suggests that the rate of infant mortality reduction is slow. This study also showed that maternal characteristics such as age and educational levels as well as cultural practises like use of clean water and toilet facilities were statistically significant determinants of infant mortality in Nigeria with P-values &lt; 0.05 across each of the survey years.

A control chart based on Pearson residuals for a negative binomial regression: application to infant mortality data

PurposeThe study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.Design/methodology/approachIn order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.FindingsWhen the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.Originality/valueThe proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

Air Pollution From Forest and Vegetation Fires in Southeast Asia Disproportionately Impacts the Poor.

Forest and vegetation fires, used as tools for agriculture and deforestation, are a major source of air pollutants and can cause serious air quality issues in many parts of Asia. Actions to reduce fire may offer considerable, yet largely unrecognized, options for rapid improvements in air quality. In this study, we used a combination of regional and global air quality models and observations to examine the impact of forest and vegetation fires on air quality degradation and public health in Southeast Asia (including Mainland Southeast Asia and south‐eastern China). We found that eliminating fire could substantially improve regional air quality across Southeast Asia by reducing the population exposure to fine particulate matter (PM2.5) concentrations by 7% and surface ozone concentrations by 5%. These reductions in PM2.5 exposures would yield a considerable public health benefit across the region; averting 59,000 (95% uncertainty interval (95UI): 55,200–62,900) premature deaths annually. Analysis of subnational infant mortality rate data and PM2.5 exposure suggested that PM2.5 from fires disproportionately impacts poorer populations across Southeast Asia. We identified two key regions in northern Laos and western Myanmar where particularly high levels of poverty coincide with exposure to relatively high levels of PM2.5 from fires. Our results show that reducing forest and vegetation fires should be a public health priority for the Southeast Asia region.

Infant Mortality Based on Causes, Age, Place and Referral Case High Risk in East Sumba District

Maternal mortality can almost certainly also mean death in infants who are conceived or born. Reportedly, 75% of babies born who lost their mothers will die before celebrating their first birthday. East Nusa Tenggara (NTT) is one of the regions with the highest infant / neonatal mortality rate in Indonesia, Infant mortality rate in East Sumba is one of the highest in East Nusa Tenggara (NTT) province and continues to fluctuate in the last five years from 2011 to 2015 and the number is 61 - 99 - 122 - 108 and 99. The research objective was to identify the cause, age, place of death of infants and referral cases in 2011 – 2015. This type of research was descriptive, cross sectional method, the total population of infant mortality cases in 2011 - 2015 was 30 cases. The sample selection used was simple random considering the time and funds as well as access to the respondent's location so that the latest infant mortality data in 2015, for which data exists, parents still remember the incident. Collection using a questionnaire for mothers / fathers / families with midwives in the village and coordinating midwives who handle ANC and case referrals. The data were processed and analyzed descriptively. The cause of infant mortality over the last 5 years in East Sumba district was 66.7% Pneumonia, as many as 46.7 percent of infants died at the age of 1 month. Based on the place of infant mortality, 80 percent of infants died at home because they were not referred and 20 percent died in PHC or Hospital for late referral. Keywords: infant mortality; risk factors; East Sumba

Infant mortality in the Metropolitan Region of São Paulo: an ecological study.

ABSTRACTObjective To determine the impact of risk factors on infant mortality in the Metropolitan Region of São Paulo according to maternal and neonate characteristics, as well as mode of delivery.Methods An ecological, quantitative study based on secondary data retrieved from infant mortality and live birth data systems. Data from 39 municipalities located in the Metropolitan Region of São Paulo were analyzed. Newborn and maternal variables were extracted from the Information Technology Department of the Unified Health System. Absolute and relative frequencies were presented, as well as linear regression and Pearson´s correlation coefficient.Results The following maternal profile prevailed from 2006 to 2016: 8 to 11 years of education (β=73.58; p=0.023), age between 30 and 34 years (β=19.04; p=0.015) and delivery by cesarean section (β=39.59; p=0.009) after full-term pregnancy (β=-14.20; p=0.324). Mortality rates decreased in neonates compared to other age groups (β=-25.30; p<0.001). Infant mortality rates tended to be higher among women experiencing pre-term (r=0.86; p<0.001) or post-term (r=0.95; p<0.001) gestation.Conclusion Maternal age and level of education increased among women giving birth in the Metropolitan Region of São Paulo from 2006 to 2016. These were relevant factors for infant mortality rate reduction.

Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data

Bivariate generalized Poisson regression (BGPR) is an extension of bivariate Poisson regression which deals overdipersion or underdispersion problem. This model gives global regression coefficients for all observations (locations) in the analysis. The BGPR model is then extended to take into account spatial heterogeneity, called geographically weighted bivariate generalized Poisson regression model, that yields varying regression coefficients locally. The regression model is applied to analyse factors affecting number of infant and maternal mortality in East Java, Indonesia.