Infant mortality rates in the United States reveal significant disparities among racial and ethnic groups. In 2023, Black mothers faced the highest rate at nearly 11 deaths per 1,000 live births, more than double the rate for white mothers. This stark contrast persists despite overall improvements in healthcare and highlights the need for targeted interventions to address these inequalities. Birth rates and fertility trends While infant mortality rates vary, birth rates also differ across ethnicities. Native Hawaiian and Pacific Islander women had the highest fertility rate in 2022, with about 2,237.5 births per 1,000 women, far exceeding the national average of 1,656.5. In 2023, this group maintained the highest birth rate at 79 births per 1,000 women. Asian women, by contrast, had a much lower birth rate of around 50 per thousand women. These differences in fertility rates can impact overall population growth and demographic shifts within the United States. Hispanic birth trends and fertility decline The Hispanic population in the United States has experienced significant changes in birth trends over recent decades. In 2021, 885,916 babies were born to Hispanic mothers, with a birth rate of 14.1 per 1,000 of the Hispanic population. This represents a slight increase from the previous year. However, the fertility rate among Hispanic women has declined dramatically since 1990, dropping from 108 children per 1,000 women aged 15-44 to 63.4 in 2021. This decline aligns with broader trends of decreasing fertility rates in more industrialized nations.

The total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

Infants born before completing 26 weeks of gestation account for less than 1% of live births in the United States but more than 40% of infant deaths. The rate of these “periviable†births among non-Hispanic (NH) Blacks exceeds 4 times that among NH Whites. Among periviable infants, small males die most frequently. The mean birthweight of NH White periviable male singletons persistently exceeds that of their NH Black counterparts. The scientific literature includes no explorations of mechanisms that could explain this disparity in birth weight. We offer, and test, the argument that survivors of the vanishing twin syndrome, a phenomenon in which the slower-growing fetus of a twin pair dies in utero, contribute to the disparity. Among male periviable singleton births from 288 monthly conception cohorts (1/95 through 12/2018), we found an average NH White advantage of 30 grams (759g versus 729g). Consistent with our argument, however, cohorts with relatively few survivors of the vanishing t..., , , # Vanishing twins, spared cohorts, and the difference in birthweight between the frailest White and Black infants in the United States

https://doi.org/10.5061/dryad.xksn02vp9

Description of the data and file structure

The data are time series for 288 months beginning January 1995 and ending December 2018.

The variable dictionary is as follows.

Variable Dictionary

tperimwN=count of periviable male Nhwhite twins

tperimwN=count of periviable male NHwhite twins

tperifbN=count of periviable female NHblack twins

tperifbN=count of periviable female NHblack twins

 _

spmumwbw=mean birthweight for singleton nhwhite males

spmdmwbw=median birthweight for singleton nhwhite males

spp1mwbw= first percentile birthweight for singleton nhwhite males

spp5mwbw =fifth percentile birthweight for singleton nhwhite males

spmufwbw=mean birthweight for singleton nhwhite females

spmdfwbw=median birthweight for singleton nhwhite females

spp1fwbw...

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

BackgroundIn the US, non-Hispanic (NH) Black birthing persons show a two-fold greater risk of fetal death relative to NH white birthing persons. Since males more than females show a greater risk of fetal death, such loss in utero may affect the sex composition of live births born preterm (PTB;

Teen Birth Rate (births per 1,000 females ages 1519) is the number of births to teenagers between ages 15 and 19 per 1,000 females in this age group. Data reflect the mothers place of residence, rather than the place of the birth. SOURCES: * Birth Statistics: U.S. Centers for Disease Control and Prevention, National Center for Health Statistics. * Population Statistics: U.S. Census Bureau.

Despite recent decreases in Black infant mortality, racial disparities persist, motivating continued research into factors related to these inequalities. While the inverse association between education and infant mortality has been documented across races, less is known about its geographic heterogeneity. Using vital statistics from the National Center for Health Statistics, this study considers Black-white disparities in infant mortality for births occurring between 2011 and 2015 across regions and metropolitan status of maternal residence. With logistic regressions, we investigate heterogeneity in maternal educational gradients of infant mortality by geographic residence both within and between races. Beyond confirming the well-known relationship between education and infant mortality, our findings document a slight metropolitan advantage for infants born to white mothers as well as lower returns to education for infants born to Black mothers residing in nonmetropolitan counties. We observe a metropolitan advantage for infants born to Black mothers with at least a bachelor's degree, but a metropolitan disadvantage for infants born to Black mothers with less than a high school degree. The South is driving this divergence, pointing to particular mechanisms limiting returns to education for Southern Black mothers in nonmetropolitan areas. This paper's geographic perspective emphasizes that racial infant health disparities are not uniform across the country and cannot be fully understood through individual and household characteristics.

This dataset includes Table 11 of the Maryland Vital Statistics Annual Report 2005 which includes GENERAL FERTILITY RATES (Total births per 1,000 women ages 15-44.) AND BIRTH RATES (Live births per 1,000 women in specified age group.) BY AGE OF MOTHER, RACE OF MOTHER, REGION, AND POLITICAL SUBDIVISION, MARYLAND, 2005. Rates that are based on fewer than five events in the numerator are not presented and are represented here as -1. Numbers and rates for Baltimore city and Baltimore county are combined.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Infant Mortality Rate (deaths per 1,000 live births) is the number of deaths occurring to infants under 1 year of age per 1,000 live births. The data are reported by place of residence, not place of death. The rate as well as the rank figures are included in this data. SOURCE: * U.S. Centers for Disease Control and Prevention, National Center for Health Statistics.

This dataset includes Table 13, Table 15, Table 19, Table 21. of the Maryland Vital Statistics Annual Report 2005 which include births by live order, births to unmarried women, births to women receiving first trimester prenatal care, and births to women receiving late or no prenatal care, all for 2005 by race and county. Rates that are based on fewer than five events in the numerator are not presented and are represented here as -1. Baltimore County and Baltimore City have been combined.

The dataset, provided both in comma-separated values (.csv) and the more informative Stata (.dta) format, contains place/year demographic data on more than 300 rural Alaska communities annually for 1990 to 2022 -- about 10,000 place/years. For each of the available place/years, the data include population estimates from the Alaska Department of Labor and Workforce Development or (in Census years) from the US Census. For a subset consisting of 104 northern or western Alaska (Arctic/subarctic) towns and villages, the dataset also contains yearly estimates of natural increase (births minus deaths) and net migration (population minus last year's population plus natural increase). Natural increase was calculated from birth and death counts provided confidentially to researchers by the Alaska Health Analytics and Vital Records Section (HAVRS). By agreement with HAVRS, the community-level birth and death counts are not available for publication. Population, natural increase, and net migration estimates reflect mid-year values, or change over the past fiscal rather than calendar year. For example, the natural increase value for a community in 2020 is based on births and deaths of residents from July 1, 2019 to June 31, 2020. We emphasize that all values here are best estimates, based on records of the Alaska government organizations. The dataset contains 19 variables: placename Place name (string) placenum Place name (numeric) placefips Place FIPS code year Year borough Borough name boroughfips Borough FIPS code latitude Latitude (decimal, - denotes S) longitude Longitude (decimal, - denotes W) town Village {0:pop2020<2,000} or town {1:pop2020>2,000} village104 104 selected Arctic/rural communities {0,1} arctic43 43 Arctic communities {0,1}, Hamilton et al. 2016 north37 37 Northern Alaska communities {0,1), Hamilton et al. 2016 pop Population (2022 data) cpopP Change in population, percent natinc Natural increase: births-deaths natincP Natural increase, percent netmig Net migration estimate netmigP Net migration, percent nipop Population without migration Three of these variables flag particular subsets of communities. The first two subsets (43 or 37 places) were analyzed in earlier publications, so the flags might be useful for replications or comparisons. The third subset (104 places) is a newer, expanded group of Arctic/subarctic towns and villages for which natural increase and net migration estimates are now available. The flag variables are: If arctic43 = 1 Subset consisting of 43 Arctic towns and villages, previously studied in three published articles: 1. Hamilton, L.C. & A.M. Mitiguy. 2009. “Visualizing population dynamics of Alaska’s Arctic communities.” Arctic 62(4):393–398. https://doi.org/10.14430/arctic170 2. Hamilton, L.C., D.M. White, R.B. Lammers & G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. https://doi.org/10.1007/s11111-011-0145-1 3. Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers & H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. https://doi.org/10.1007/s11111-016-0259-6 If north37 = 1 Subset consisting of 37 northern Alaska towns and villages, previously analyzed for comparison with Nunavut and Greenland in a paper on demographics of the Inuit Arctic: 4. Hamilton, L.C., J. Wirsing & K. Saito. 2018. “Demographic variation and change in the Inuit Arctic.” Environmental Research Letters 13:11507. https://doi.org/10.1088/1748-9326/aae7ef If village104 = 1 Expanded group consisting of 104 communities, including all those in the arctic43 and north37 subsets. This group includes most rural Arctic/subarctic communities that had reasonably complete, continuous data, and 2018 populations of at least 100 people. These data were developed by updating older work and drawing in 61 additional towns or villages, as part of the NSF-supported Arctic Village Dynamics project (OPP-1822424).

BackgroundMigrant and ethnic minority groups are often assumed to have poor health relative to the majority population. Few countries have the capacity to study a key indicator, mortality, by ethnicity and country of birth. We hypothesized at least 10% differences in mortality by ethnic group in Scotland that would not be wholly attenuated by adjustment for socio-economic factors or country of birth.Methods and findingsWe linked the Scottish 2001 Census to mortality data (2001–2013) in 4.62 million people (91% of estimated population), calculating age-adjusted mortality rate ratios (RRs; multiplied by 100 as percentages) with 95% confidence intervals (CIs) for 13 ethnic groups, with the White Scottish group as reference (ethnic group classification follows the Scottish 2001 Census). The Scottish Index of Multiple Deprivation, education status, and household tenure were socio-economic status (SES) confounding variables and born in the UK or Republic of Ireland (UK/RoI) an interacting and confounding variable. Smoking and diabetes data were from a primary care sub-sample (about 53,000 people). Males and females in most minority groups had lower age-adjusted mortality RRs than the White Scottish group. The 95% CIs provided good evidence that the RR was more than 10% lower in the following ethnic groups: Other White British (72.3 [95% CI 64.2, 81.3] in males and 75.2 [68.0, 83.2] in females); Other White (80.8 [72.8, 89.8] in males and 76.2 [68.6, 84.7] in females); Indian (62.6 [51.6, 76.0] in males and 60.7 [50.4, 73.1] in females); Pakistani (66.1 [57.4, 76.2] in males and 73.8 [63.7, 85.5] in females); Bangladeshi males (50.7 [32.5, 79.1]); Caribbean females (57.5 [38.5, 85.9]); and Chinese (52.2 [43.7, 62.5] in males and 65.8 [55.3, 78.2] in females). The differences were diminished but not eliminated after adjusting for UK/RoI birth and SES variables. A mortality advantage was evident in all 12 minority groups for those born abroad, but in only 6/12 male groups and 5/12 female groups of those born in the UK/RoI. In the primary care sub-sample, after adjustment for age, UK/RoI born, SES, smoking, and diabetes, the RR was not lower in Indian males (114.7 [95% CI 78.3, 167.9]) and Pakistani females (103.9 [73.9, 145.9]) than in White Scottish males and females, respectively. The main limitations were the inability to include deaths abroad and the small number of deaths in some ethnic minority groups, especially for people born in the UK/RoI.ConclusionsThere was relatively low mortality for many ethnic minority groups compared to the White Scottish majority. The mortality advantage was less clear in UK/RoI-born minority group offspring than in immigrants. These differences need explaining, and health-related behaviours seem important. Similar analyses are required internationally to fulfil agreed goals for monitoring, understanding, and improving health in ethnically diverse societies and to apply to health policy, especially on health inequalities and inequities.

Arctic demography is commonly viewed on a large scale, across entire regions such as states, counties or boroughs. The data archived here contain annual time series for each of 43 Arctic Alaska towns and villages. Variables include annual estimates of population, natural increase (births minus deaths), and net migration (inmigration minus outmigration) for each place. Graphics depicting community population dynamics from 1990 to 2016 have been published online in connection with this research, and show that seemingly comparable places even within one borough can take widely divergent paths. Birth rates generally exceed death rates, although both are high. Year-to-year and place-to-place variations are dominated not by natural increase, but by differences in net migration. Population changes influence demand for resources such as water, electricity, fuel, and capital improvements, and probably for subsistence resources as well. Migration rates provide sensitive indicators that integrate diverse internal and external pressures. Recent analyses used these data to test for evidence of "climigration," or enhanced outmigration from places facing serious threats from climate-linked erosion. The data also provide information for comparative studies involving other far Northern regions; and for detecting possible impacts from economic events such as the 2008 recession. Example publications showing use of these data, with more background and sources, include: Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers &amp;amp;amp;amp;amp; H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. doi: 10.1007/s11111-016-0259-6 Hamilton, L.C., D.M. White, R.B. Lammers &amp;amp;amp;amp;amp; G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. doi: 10.1007/s11111-011-0145-1

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

BackgroundSuboptimal weight gain during pregnancy is a potentially modifiable risk factor. We aimed to investigate the association between suboptimal gestational weight gain and severe adverse birth outcomes by pre-pregnancy body mass index (BMI) categories, including obesity class I to III.Methods and findingsWe conducted a population-based study of pregnant women with singleton hospital births in Washington State, US, between 2004 and 2013. Optimal, low, and excess weight gain in each BMI category was calculated based on weight gain by gestational age as recommended by the American College of Obstetricians and Gynecologists and the Institute of Medicine. Primary composite outcomes were (1) maternal death and/or severe maternal morbidity (SMM) and (2) perinatal death and/or severe neonatal morbidity. Logistic regression was used to obtain adjusted odds ratios (AORs) and 95% confidence intervals. Overall, 722,839 women with information on pre-pregnancy BMI were included. Of these, 3.1% of women were underweight, 48.1% had normal pre-pregnancy BMI, 25.8% were overweight, and 23.0% were obese. Only 31.5% of women achieved optimal gestational weight gain. Women who had low weight gain were more likely to be African American and have Medicaid health insurance, while women with excess weight gain were more likely to be non-Hispanic white and younger than women with optimal weight gain in each pre-pregnancy BMI category. Compared with women who had optimal weight gain, those with low gestational weight gain had a higher rate of maternal death, 7.97 versus 2.63 per 100,000 (p = 0.027). In addition, low weight gain was associated with the composite adverse maternal outcome (death/SMM) in women with normal pre-pregnancy BMI and in overweight women (AOR 1.12, 95% CI 1.04–1.21, p = 0.004, and AOR 1.17, 95% CI 1.04–1.32, p = 0.009, respectively) compared to women in the same pre-pregnancy BMI category who had optimal weight gain. Similarly, excess gestational weight gain was associated with increased rates of death/SMM among women with normal pre-pregnancy BMI (AOR 1.20, 95% CI 1.12–1.28, p < 0.001) and obese women (AOR 1.12, 95% CI 1.01–1.23, p = 0.019). Low gestational weight gain was associated with perinatal death and severe neonatal morbidity regardless of pre-pregnancy BMI, including obesity classes I, II, and III, while excess weight gain was associated with severe neonatal morbidity only in women who were underweight or had normal BMI prior to pregnancy. Study limitations include the ascertainment of pre-pregnancy BMI using self-report, and lack of data availability for the most recent years.ConclusionsIn this study, we found that most women do not achieve optimal weight gain during pregnancy. Low weight gain was associated with increased risk of severe adverse birth outcomes, and in particular with maternal death and perinatal death. Excess gestational weight gain was associated with severe adverse birth outcomes, except for women who were overweight prior to pregnancy. Weight gain recommendations for this group may need to be reassessed. It is important to counsel women during pregnancy about specific risks associated with both low and excess weight gain.

Percent Low-Birthweight Babies is the percentage of live births weighing less than 2,500 grams (5.5 pounds). The data are reported by place of mothers residence, not place of birth. This data is available on a state level as a percentage and also includes the rank for each year. Data is available from 1990 - 2004.

The latest population figures produced by the Office for National Statistics (ONS) on 28 June 2018 show that an estimated 534,800 people live in Bradford District – an increase of 2,300 people (0.4%) since the previous year.

Bradford District is the fifth largest metropolitan district (in terms of population) in England, after Birmingham, Leeds, Sheffield and Manchester although the District’s population growth is lower than other major cities.

The increase in the District’s population is largely due to “natural change”- there have been around 3,300 more births than deaths, although this has been balanced by a larger number of people leaving Bradford to live in other parts of the UK than coming to live here and a lower number of international migrants. In 2016/17 the net internal migration was -2,700 and the net international migration was 1,700.

A large proportion of Bradford’s population is dominated by the younger age groups. More than one-quarter (29%) of the District’s population is aged less than 20 and nearly seven in ten people are aged less than 50. Bradford has the highest percentage of the under 16 population in England after the London Borough of Barking and Dagenham, Slough Borough Council and Luton Borough Council.

The population of Bradford is ethnically diverse. The largest proportion of the district’s population (63.9%) identifies themselves as White British. The district has the largest proportion of people of Pakistani ethnic origin (20.3%) in England.

The largest religious group in Bradford is Christian (45.9% of the population). Nearly one quarter of the population (24.7%) are Muslim. Just over one fifth of the district’s population (20.7%) stated that they had no religion.

There are 216,813 households in the Bradford district. Most households own their own home (29.3% outright and 35.7% with a mortgage). The percentage of privately rented households is 18.1%. 29.6% of households were single person households.

Information from the Annual Population Survey in December 2017 found that Bradford has 228,100 people aged 16-64 in employment. At 68% this is significantly lower than the national rate (74.9%). 91,100 (around 1 in 3 people) aged 16-64, are not in work. The claimant count rate is 2.9% which is higher than the regional and national averages.

Skill levels are improving with 26.5% of 16 to 74 year olds educated to degree level. 18% of the district’s employed residents work in retail/wholesale. The percentage of people working in manufacturing has continued to decrease from 13.4% in 2009 to 11.9% in 2016. This is still higher than the average for Great Britain (8.1%).

A single-centre retrospective case control study was conducted. The protocol defined cases as all neonatal deaths or NICU admissions occurring within an eight-year period from 2012 to 2020, although no neonatal deaths occurred during this period following a vaginal breech birth. Controls were identified as the two vaginal breech births directly prior to the case where no neonatal death nor NICU admission occurred. Two previous births were used to prevent bias on the understanding that an adverse outcome can affect clinical decision-making for subsequent births.12 Any NICU admission was included because this indicates a neonate which requires additional observation, tests and/or intervention. Neonates who are not admitted are deemed as generally well.13 Additionally, separation from the baby was considered an important outcome by our Patient and Public Involvement Group,14 who also requested more information on the timing of cord clamping.The study was conducted within the maternity unit at a London District General Hospital which serves a large population of 176,313 people. Two thirds are of white British ethnicity and one third from Black, Asian and Minority Ethnic (BAME) backgrounds. The community the hospital serves is thought of as affluent, with good employment rates, particularly employment in high-end jobs. The hospital itself serves a wider community than the borough it is situated within and has 5000 births per year. It has a level two NICU situated within the maternity unit. The Algorithm was not in use at the site, and none of the authors were employed by the Trust, during the time period covered by the study. Fifteen cases and thirty controls were identified from routine electronic health records. The Medical Record Numbers were sent to the Health Records Department for the complete files to be retrieved. Data were extracted by the lead researcher from the intrapartum care records and recorded anonymously in a Microsoft Excel spreadsheet.A structured data collection tool was developed based on Reitter et al.13 The data collection tool consisted of information usually recorded in the notes during a breech birth and included: lead professional, type of breech, position, epidural, fetal monitoring, meconium, what emerged first, time each part of the breech born, documented manoeuvres used, time performed and information related to the condition of the neonate at birth.To calculate our sample size, based on the work of Reitter et al,11 we hypothesised that the rate of exposure to a pelvis-to-head interval >3 minutes would be 25% among controls and 75% among cases. Using a case:control ratio of 1:2, we determined that 15 independent cases and 30 controls were required to infer an association between a pelvis-to-head interval >3 minutes and the composite neonatal outcome with a confidence interval of 95% and a power of 80%. First, we calculated the time to event interval for variables of interest. We then reported descriptive statistics for all variables, including means, medians and range for continuous variables. Exposures and confounders were converted into binary variables, reflecting the cut-offs used in the Algorithm. These were then tested against the primary outcome using the non-parametric chi-square, or Fisher’s Exact tests where cell frequencies were too small for the chi-square test. Logistic regression analysis was used to test the predictive values of meeting or exceeding the recommended time limits in the Physiological Breech Birth Algorithm. Further logistic regression analyses were conducted with all variables that showed an association with the composite neonatal outcome to determine their predictive value, and additional variables to explore their potential as confounding factors for investigation in future studies. Finally, a Receiver Operating Characteristics (ROC) curve analysis was conducted to compare the sensitivity and specificity of the 7-5-3 minute time limits. All statistical analyses were performed using IBM SPSS version 26.

In 2022 life expectancy for both males and females at birth fell when compared to 2021. Male life expectancy fell from 78.71 years to 78.57 years, and from 82.68 years to 82.57 years for women. Throughout most of this period, there is a steady rise in life expectancy for both males and females, with improvements in life expectancy beginning to slow in the 2010s and then starting to decline in the 2020s. Life expectancy since the 18th Century Although there has been a recent dip in life expectancy in the UK, long-term improvements to life expectancy stretch back several centuries. In 1765, life expectancy was below 39 years, and only surpassed 40 years in the 1810s, 50 years by the 1910s, 60 years by the 1930s and 70 by the 1960s. While life expectancy has broadly improved since the 1700s, this trajectory was interrupted at various points due to wars and diseases. In the early 1920s, for example, life expectancy suffered a noticeable setback in the aftermath of the First World War and Spanish Flu Epidemic. Impact of COVID-19 While improvements to UK life expectancy stalled during the 2010s, it wasn't until the 2020s that it began to decline. The impact of COVID-19 was one of the primary factors in this respect, with 2020 seeing the most deaths in the UK since 1918. The first wave of the pandemic in Spring of that year was a particularly deadly time, with weekly death figures far higher than usual. A second wave that winter saw a peak of almost 5,700 excess deaths a week in late January 2021, with excess deaths remaining elevated for several years afterward.