Estimating 1a0 and 4a1 in a Life Table: A Model Approach Based on Newly Collected Data.

The average age of infant deaths, a10, and the average number of years lived-in the age interval-by those dying between ages 1 and 5, a41, are important quantities allowing the construction of any life table including these ages. In many applications, the direct calculation of these parameters is not possible, so they are estimated using the infant mortality rate-or the death rate from 0 to 1-as a predictor. Existing methods are general approximations that do not consider the full variability in the age patterns of mortality below the age of 5. However, at the same level of mortality, under-five deaths can be more or less concentrated during the first weeks and months of life, thus resulting in very different values of a10 and a41. This article proposes an indirect estimation of these parameters by using a recently developed model of under-five mortality and taking advantage of a new, comprehensive database by detailed age-which is used for validation. The model adapts to a variety of inputs (e.g., rates, probabilities, or the proportion of deaths by sex or for both sexes combined), providing more flexibility for the users and increasing the precision of the estimates. This fresh perspective consolidates a new method that outperforms all previous approaches.

Comparison of pregnancy and neonatal outcomes in a retrospective full pregnancy history survey versus population-based prospective records: a validation study in rural Sarlahi District, Nepal.

INTRODUCTION: Countries without complete civil registration and vital statistics systems rely on retrospective full pregnancy history surveys (FPH) to estimate incidence of pregnancy and mortality outcomes, including stillbirth and neonatal death. Yet surveys are subject to biases that impact demographic estimates, and few studies have quantified these effects. We compare data from an FPH vs. prospective records from a population-based cohort to estimate validity for maternal recall of live births, stillbirths, and neonatal deaths in a rural population in Sarlahi District, Nepal. METHODS: We used prospective data, collected through frequent visits of women from early pregnancy through the neonatal period, from a population-based randomized trial spanning 2010-2017. We randomly selected 76 trial participants from three pregnancy outcome groups: live birth (n = 26), stillbirth (n = 25), or neonatal death (n = 25). Data collectors administered the Nepal 2016 Demographic and Health Surveys (DHS)-VII pregnancy history survey between October 22, 2021, and November 18, 2021. We compared total pregnancy outcomes and numbers of pregnancy and neonatal outcomes between the two data sources. We matched pregnancy outcomes dates in the two sources within ± 30 days and calculated measures of validity for adverse outcomes. RESULTS: Among 76 participants, we recorded 122 pregnancy outcomes in the prospective data and 104 outcomes in the FPH within ± 30 days of each woman's total observation period in the trial. Among 226 outcomes, we observed 65 live births that survived to 28 days, 25 stillbirths, and 32 live births followed by neonatal death in the prospective data and participants reported 63 live births that survived to 28 days, 15 stillbirths, and 26 live births followed by neonatal death in the pregnancy history survey. Sixty-two FPH outcomes were matched by date within ± 30 days to an outcome in prospective data. Stillbirth, neonatal death, higher parity, and delivery at a health facility were associated with likelihood of a non-matched pregnancy outcome. CONCLUSIONS: Stillbirth and neonatal deaths were underestimated overall by the FPH, potentially underestimating the burden of mortality in this population. There is a need to develop tools to reduce or adjust for biases and errors in retrospective surveys to improve reporting of pregnancy and mortality outcomes.

Quality of vital event data for infant mortality estimation in prospective, population-based studies: an analysis of secondary data from Asia, Africa, and Latin America.

INTRODUCTION: Infant and neonatal mortality estimates are typically derived from retrospective birth histories collected through surveys in countries with unreliable civil registration and vital statistics systems. Yet such data are subject to biases, including under-reporting of deaths and age misreporting, which impact mortality estimates. Prospective population-based cohort studies are an underutilized data source for mortality estimation that may offer strengths that avoid biases. METHODS: We conducted a secondary analysis of data from the Child Health Epidemiology Reference Group, including 11 population-based pregnancy or birth cohort studies, to evaluate the appropriateness of vital event data for mortality estimation. Analyses were descriptive, summarizing study designs, populations, protocols, and internal checks to assess their impact on data quality. We calculated infant and neonatal morality rates and compared patterns with Demographic and Health Survey (DHS) data. RESULTS: Studies yielded 71,760 pregnant women and 85,095 live births. Specific field protocols, especially pregnancy enrollment, limited exclusion criteria, and frequent follow-up visits after delivery, led to higher birth outcome ascertainment and fewer missing deaths. Most studies had low follow-up loss in pregnancy and the first month with little evidence of date heaping. Among studies in Asia and Latin America, neonatal mortality rates (NMR) were similar to DHS, while several studies in Sub-Saharan Africa had lower NMRs than DHS. Infant mortality varied by study and region between sources. CONCLUSIONS: Prospective, population-based cohort studies following rigorous protocols can yield high-quality vital event data to improve characterization of detailed mortality patterns of infants in low- and middle-income countries, especially in the early neonatal period where mortality risk is highest and changes rapidly.

Divergent age patterns of under-5 mortality in south Asia and sub-Saharan Africa: a modelling study.

BACKGROUND: Understanding the age pattern of under-5 mortality is essential for identifying the most vulnerable ages and underlying causes of death, and for assessing why the decline in child mortality is slower in some countries and subnational areas than others. The aim of this study is to detect age patterns of under-5 mortality that are specific to low-income and middle-income countries (LMICs). METHODS: In this modelling study, we used data from 277 Demographic and Health Surveys (DHSs), 58 Health and Demographic Surveillance Systems (HDSSs), two cohort studies, and two sample-registration systems. From these sources, we collected child date of birth and date of death (or age at death) from LMICs between 1966 and 2020. We computed 22 deaths rates from each survey with the following age breakdowns: 0, 7, 14, 21, and 28 days; 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, and 21 months; and 2, 3, 4, and 5 years. We assessed how probabilities of dying estimated for the 22 age groups deviated from predictions generated by a vital registration model that reflects the historical mortality of 25 high-income countries. FINDINGS: We calculated mortality rates of 81 LMICs between 1966 and 2020. In contrast with the other regions of the world, we found that under-5 mortality in south Asia and sub-Saharan Africa was characterised by increased mortality at both ends of the age range (ie, younger than 28 days and older than 6 months) at a given level of mortality. Observed mortality in these regions was up to 2 times higher than predicted by the vital registration model for the younger-than-28 days age bracket, and up to 10 times higher than predicted for the older-than-6 months age bracket. This age pattern of under-5 mortality is significant in 17 countries in south Asia and sub-Saharan Africa. Excess mortality in children older than 6 months without excess mortality in children younger than 28 days was found in 38 countries. In south Asia, results were consistent across data sources. In sub-Saharan Africa, excess mortality in children younger than 28 days was found mostly in DHSs; the majority of HDSSs did not show this excess mortality. We have attributed this difference in data sources mainly to omissions of early deaths in HDSSs. INTERPRETATION: In countries with age patterns of under-5 mortality that diverge from predictions, evidence-based public health interventions should focus on the causes of excess of mortality; notably, the effect of fetal growth restriction and infectious diseases. The age pattern of under-5 mortality will be instrumental in assessing progress towards the decline of under-5 mortality and the Sustainable Development Goals. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health.

Does higher early neonatal mortality in boys reverse over the neonatal period? A pooled analysis from three trials of Nepal.

OBJECTIVES: Neonatal mortality is generally 20% higher in boys than girls due to biological phenomena. Only a few studies have examined more finely categorised age patterns of neonatal mortality by sex, especially in the first few days of life. The objective of this study is to examine sex differentials in neonatal mortality by detailed ages in a low-income setting. DESIGN: This is a secondary observational analysis of data. SETTING: Rural Sarlahi district, Nepal. PARTICIPANTS: Neonates born between 1999 and 2017 in three randomised controlled trials. OUTCOME MEASURES: We calculated study-specific and pooled mortality rates for boys and girls by ages (0-1, 1-3, 3-7, 7-14, 14-21 and 21-28 days) and estimated HR using Cox proportional hazards models for male versus female mortality for treatment and control groups together (n=59 729). RESULTS: Neonatal mortality was higher in boys than girls in individual studies: 44.2 vs 39.7 in boys and girls in 1999-2000; 30.0 vs 29.6 in 2002-2006; 33.4 vs 29.4 in 2010-2017; and 33.0 vs 30.2 in the pooled data analysis. Pooled data found that early neonatal mortality (HR=1.17; 95% CI: 1.06 to 1.30) was significantly higher in boys than girls. All individual datasets showed a reversal in mortality by sex after the third week of life. In the fourth week, a reversal was observed, with mortality in girls 2.43 times higher than boys (HR=0.41; 95% CI: 0.31 to 0.79). CONCLUSIONS: Boys had higher mortality in the first week followed by no sex difference in weeks 2 and 3 and a reversal in risk in week 4, with girls dying at more than twice the rate of boys. This may be a result of gender discrimination and social norms in this setting. Interventions to reduce gender discrimination at the household level may reduce female neonatal mortality. TRIAL REGISTRATION NUMBER: NCT00115271, NCT00109616, NCT01177111.

Modeling Age Patterns of Under-5 Mortality: Results From a Log-Quadratic Model Applied to High-Quality Vital Registration Data.

Information about how the risk of death varies with age within the 0-5 age range represents critical evidence for guiding health policy. This study proposes a new model for summarizing regularities about how under-5 mortality is distributed by detailed age. The model is based on a newly compiled database that contains under-5 mortality information by detailed age in countries with high-quality vital registration systems, covering a wide array of mortality levels and patterns. It uses a log-quadratic approach in predicting a full mortality schedule between ages 0 and 5 on the basis of only one or two parameters. With its larger number of age-groups, the proposed model offers greater flexibility than existing models in terms of both entry parameters and model outcomes. We present applications of this model for evaluating and correcting under-5 mortality information by detailed age in countries with problematic mortality data.

Estimating the infant mortality rate from DHS birth histories in the presence of age heaping.

BACKGROUND: The infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias. METHODS AND FINDINGS: Our method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model's coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments. CONCLUSIONS: Age heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality.

Impact of delayed effects on human old-age mortality1.

BACKGROUND: There is growing empirical evidence supporting theories of developmental origins of health and disease (DOHaD). However, the implications of DOHaD conjectures for aggregate population patterns of human disease, disability, mortality and aging are poorly understood. OBJECTIVE: We empirically test two predictions derived from a formal model of aggregate population-level impacts of DOHaD. This model predicts that populations potentially influenced by delayed effects should experience singularities in their adult mortality patterns that can be empirically detected from aggregate data. METHODS: We test predictions using a large mortality database for populations in the Latin American and Caribbean region (LAC) spanning nearly one hundred years of mortality history. RESULTS: Results are consistent. within explicit bounds of uncertainty, with expected patterns. We find that younger cohorts in countries whose mortality decline starts more recently experience deceleration in survival gains at older ages, attenuation of the rate of aging at older ages and a decline in the association between early childhood and adult mortality. CONCLUSIONS: Results point to the importance of adverse early conditions for human longevity. Future research should shed light on the impact on morbidity, disability and healthy life expectancy. CONTRIBUTION: To our knowledge this is the first time that implications of DOHaD conjectures for populations' mortality patterns are formulated precisely and empirically tested with aggregate population data.