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This is a calendar in English of the coroners' rolls for c. 1265-1317 and 1378-1380, held respectively in The National Archives and Gonville and Caius College Cambridge. The introduction explains coroners' duties (which could be varied) and court procedure before focusing on the work of the Bedfordshire coroners. Included are lists of the medieval coroners for Bedfordshire (1230-1478), Bedford (1240-1456), Dunstable (1228-1411) and the Bedfordshire liberties of the Abbot of St. Albans (1287-1326) and Eton College (1451). The rolls contain cases of murder, assaults and thefts and tragic accidents such as that of five-year-old Joan in 1274 who 'went through Riseley to beg for bread, came to a bridge called Fordebrugge and, as she tried to cross it, fell into the water and drowned.' Aside from crime, the rolls reveal the conditions of daily life at the poorer level of society, agriculture and the countryside.

For many Americans, the birth certificate is a mundane piece of paper, unearthed from deep storage when applying for a driver's license, verifying information for new employers, or claiming state and federal benefits. Yet as Donald Trump and his fellow \"birthers\" reminded us when they claimed that Barack Obama wasn't an American citizen, it plays a central role in determining identity and citizenship. In The Birth Certificate: An American History , award-winning historian Susan J. Pearson traces the document's two-hundred-year history to explain when, how, and why birth certificates came to matter so much in the United States. Deftly weaving together social, political, and legal history, The Birth Certificate is a fascinating biography of a piece of paper that grounds our understanding of how those who live in the United States are considered Americans.

The global doubling of human life expectancy between 1850 and 1950 is arguably one of the most consequential developments in human history, undergirding massive improvements in human life and lifestyles. In 1850, Americans died at an average age of 30. Today, the average is almost 80. This story is typically told as a series of medical breakthroughs-Jenner and vaccination, Lister and antisepsis, Snow and germ theory, Fleming and penicillin-but the lion's share of the credit belongs to the men and women who dedicated their lives to collecting good data. Examining the development of death registration systems in the United States-from the first mortality census in 1850 to the development of the death certificate at the turn of the century- Count the Dead argues that mortality data transformed life on Earth, proving critical to the systemization of public health, casualty reporting, and human rights. Stephen Berry shows how a network of coroners, court officials, and state and federal authorities developed methods to track and reveal patterns of dying. These officials harnessed these records to turn the collective dead into informants and in so doing allowed the dead to shape life and death as we know it today.

We examined whether and how birth outcomes and prenatal care utilization among Latina mothers changed over time across years associated with the Trump sociopolitical environment, using restricted-use birth records from the National Center for Health Statistics (NCHS). To assess potential variation among subpopulations, we disaggregated the analyses by maternal nativity and country/region of origin. Our results indicate that both US- and foreign-born Latina mothers experienced increasingly higher risks of delivering low birthweight (LBW) and preterm birth (PTB) infants over the years associated with Trump’s political career. Among foreign-born Latinas, adverse birth outcomes increased significantly among mothers from Mexico and Central America but not among mothers from Puerto Rico, Cuba, and South America. Levels of inadequate prenatal care utilization remained largely unchanged among groups who saw increases in LBW and PTB, suggesting that changes in prenatal care did not generally explain the observed worsening of birth outcomes among Latina mothers during the Trump era. Results from this study draw attention to the possibility that the Trump era may have represented a source of chronic stress among the Latinx population in the US and add to the growing body of literature linking racism and xenophobia in the sociopolitical environment to declines in health among Latinx people, especially among targeted groups from Mexico and Central America.

abstract The American Rescue Plan Act of 2021 enables states to lengthen eligibility for pregnancy-related Medicaid coverage from the current sixty days after birth to up to one year, a time when mothers remain at elevated pregnancy-related health risk. Using linked birth records, income, and all-payer claims data for Medicaid-paid births in Colorado during the period 2014-19, we compared continuity of coverage during one year postpartum among people eligible for low-income adult Medicaid (with incomes of 138 percent of the federal poverty level or lower) versus those ineligible for Medicaid by any pathway (with incomes of 139 percent of poverty or higher). We found that retention of Medicaid coverage as a low-income adult was associated with 1.5 additional months of postpartum insurance enrollment and a 12-percentage-point increase in the probability of continuous insurance coverage during the first year after birth. Our findings suggest that states that adopt the American Rescue Plan Act option to provide eligibility for pregnancy-related benefits for a full year after birth are likely to improve continuity of postpartum insurance coverage.

Objectives. To analyze births and birth intendedness after long-acting reversible contraception (LARC) removal among Medicaid-insured women. Methods. We linked all Delaware women with a Medicaid-covered LARC removal in 2012 to 2020 (n = 8047) to birth records and to Pregnancy Risk Assessment Monitoring System (PRAMS) pregnancy intendedness survey responses (n = 241). Results. Births within 3 years of a Medicaid-covered LARC removal were much more likely to be to women in their 20s compared with all Medicaid births (63.5% vs 53.4%; P < .001). The intended proportion for births within 3 years of Medicaid-covered LARC removal (65.2%) was higher than for all Medicaid-covered births (58.8%; P = .08) and was consistently above 60% across all age groups younger than 30 years. Conclusions. A state Medicaid-insured population’s use of highly effective reversible contraception was associated with births being concentrated among women in their 20s and with consistently high fractions of intended births across younger ages at birth. Public Health Implications. Programs and policies may consider LARC access for its potential to increase low-income women’s reproductive autonomy by enhancing their ability to achieve births at the age of their choosing. ( Am J Public Health. 2025;115(1):95–102. https://doi.org/10.2105/AJPH.2024.307844 )

Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models' actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. This study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. Our large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. We found that extreme gradient boosting achieved the highest recall score-0.70-using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. Our findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.

This paper studies the effect of improved early life health care on mortality and long-run academic achievement in school. We use the idea that medical treatments often follow rules of thumb for assigning care to patients, such as the classification of Very Low Birth Weight (VLBW), which assigns infants special care at a specific birth weight cutoff. Using detailed administrative data on schooling and birth records from Chile and Norway, we establish that children who receive extra medical care at birth have lower mortality rates and higher test scores and grades in school. These gains are in the order of 0.15-0.22 standard deviations.

Objectives. To measure neighborhood exposure to proactive policing as a manifestation of structural racism and its association with preterm birth. Methods. We linked all birth records in New Orleans, Louisiana (n = 9102), with annual census tract rates of proactive police stops using data from the New Orleans Police Department (2018–2019). We fit multilevel Poisson models predicting preterm birth across quintiles of stop rates, controlling for several individual- and tract-level covariates. Results. Nearly 20% of Black versus 8% of White birthing people lived in neighborhoods with the highest rates of proactive police stops. Fully adjusted models among Black birthing people suggest the prevalence of preterm birth in the neighborhoods with the highest proactive policing rates was 1.41 times that of neighborhoods with the lowest rates (95% confidence interval = 1.04, 1.93), but associations among White birthing people were not statistically significant. Conclusions. Taken together with previous research, high rates of proactive policing likely contribute to Black‒White inequities in reproductive health. Public Health Implications. Proactive policing is widely implemented to deter violence, but alternative strategies without police should be considered to prevent potential adverse health consequences. (Am J Public Health. 2023;113(S1):S21–S28. https://doi.org/10.2105/AJPH.2022.307079 )

The COVID-19 pandemic has taken a large toll on population health and well-being. We examine the consequences of prenatal exposure for infant health, through which the pandemic may have lasting intergenerational effects. We examine multiple pathways by which the pandemic shaped birth outcomes and socioeconomic disparities in these consequences. Analysis of more than 3.5 million birth records in California with universal information on COVID infection among persons giving birth at the time of delivery reveals deep inequalities in infection by education, race/ethnicity, and place-based socioeconomic disadvantage. COVID infection during pregnancy, in turn, predicts a large increase in the probability of preterm birth, by approximately one third. At the population level, a surprising reduction in preterm births during the first months of the pandemic was followed by an increase in preterm births during the surge in COVID infections in the winter of 2021. Whereas the early-pandemic reduction in preterm births benefited primarily highly educated mothers, the increase in preterm births during the winter infection surge was entirely concentrated among mothers with low levels of schooling. The COVID-19 pandemic is expected to exacerbate U.S. inequality in multiple ways. Our findings highlight a particularly enduring pathway: the long-term legacy of prenatal exposure to an unequal pandemic environment.