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The National Center for Health Statistics’ (NCHS’s) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519 )

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a method to identify topics in a corpus and represent documents as topic sequences. Discourse atom topic modeling (DATM) draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on their distinct capabilities. We first identify a set of vectors (“discourse atoms”) that provide a sparse representation of an embedding space. Discourse atoms can be interpreted as latent topics; through a generative model, atoms map onto distributions over words. We can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the US National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.

Background The long‐term trend in cancer death in a rapidly developing country provides information for cancer prophylaxis. Here, we aimed to identify the trends in cancer mortality in China during the 2004‐2018 period. Methods Using raw data from the national mortality surveillance system of China, we assessed the mortalities of all cancer and site‐specific cancers during the 2004‐2018 period. The participants were divided into three age groups: ≥65 years, 40‐64 years, and ≤39 years. Changing trends in cancer death by gender, residency, and tumor location were estimated using fitting joinpoint models to log‐transformed crude mortality rates (CMRs) and age‐standardized mortality rates (ASMRs). Results Cancer death accounted for 24% of all‐cause of death in China during 2014‐2018. The CMR of all cancer was 150.0 per 100,000 persons. Cancer was the leading cause of death in the population <65 years. The six major cancer types (lung/bronchus cancer, liver cancer, stomach cancer, esophagus cancer, colorectal cancer, and pancreas cancer) accounted for 75.85% of all cancer deaths. The CMR of all cancer increased while the ASMR decreased during 2014‐2018 (P < 0.001). Lung/bronchus cancer and liver cancer were the leading causes of cancer death in the population <65 years, accounting for 45.31% (CMR) and 44.35% (ASMR) of all cancer death, respectively. The ASMR of liver cancer was higher in the 40‐64 years population than in the ≥65 years population, in contrast to the other five major cancers. The ASMRs of liver cancer, stomach cancer, and esophagus cancer decreased although they were higher in rural residents than in urban residents; the ASMRs of lung/bronchus cancer, colorectal cancer, and pancreas cancer increased in rural residents although they were higher in urban residents than in rural residents during 2014‐2018. Conclusion Although the ASMR of all cancer decreased in China during 2004‐2018, lung/bronchus cancer and liver cancer remained the leading causes of cancer‐related premature death. Lung/bronchus cancer, colorectal cancer, and pancreas cancer increased in rural residents. This 15‐year longitudinal study described cancer burden of a rapid changing country with significant regional and urban‐rural disparities, which is important in evaluating the effect of population ageing, risk factor exposure, and public health efforts on cancer mortality. Lung and liver cancers were the 1st leading cause of immature death in women and men, respectively. Lung, colorectal, and pancreatic cancers kept increasing in rural areas. These findings are references for policy making to control cancer.

Background Injury mortality surveillance systems are critical to monitor changes in a population’s injury outcomes so that relevant injury prevention responses may be adopted. This is particularly the case in South Africa, where the injury burden is nearly twice the global rate. Regular evaluations of surveillance systems are pivotal to strengthening surveillance capacity, performance, and cost effectiveness. The National Injury Mortality Surveillance System (NIMSS) is an injury mortality surveillance system that is currently focused in Mpumalanga and utilises manual and electronic web-based systems for data collection. This study explored Forensic Pathology Service (FPS) staff perceptions of the implementation barriers and facilitators of manual- and electronic injury mortality surveillance system methods. Methods A qualitative study was employed using purposive sampling. Forty-seven participants, aged 29 to 59 years comprising 31 males and 16 females were recruited across 21 FPS facilities that serve the province. The formative evaluation occurred over the November 2019 to November 2022 period. Twelve focus group discussions were thematically analysed to determine emerging themes and patterns related to the use of the system using the WHO surveillance system guidelines as a framework. Results The key themes concerning the barriers and facilitators were located along WHO attributes of simplicity, acceptability, timeliness, flexibility, data quality and stability. Distinctions between the manual and e-surveillance systems were drawn upon across the attributes highlighting their experience with the system, user preference, and its contextual relevance. With Mpumalanga predominantly rural, internet connectivity was a common issue, with most participants consequently showing a preference for the manual system, even though the electronic system’s automated internal validation process was of benefit. The data quality however remained similar for both methods. With program stability and flexibility, the manual system proved more beneficial as the dataset was reported to be easily transferrable across computer devices. Conclusion Obtaining FPS perceptions of their experiences with the system methodologies are pertinent for the enhancement of injury surveillance systems so to improve prospective engagements with the systems. This will facilitate timely and accurate injury mortality information which is vital to inform public policy, and injury control and prevention responses.

Background Reliable and timely data on maternal and neonatal mortality is required to implement health interventions, monitor progress, and evaluate health programs at national and sub-national levels. In most South Asian countries, including Pakistan, vital civil registration and health information systems are inadequate. The aim of this study is to determine accurate maternal and perinatal mortality through enhanced surveillance of births and deaths, compared with prior routinely collected data. Methods An enhanced surveillance system was established that measured maternal, perinatal and neonatal mortality rates through more complete enumeration of births and deaths in a rural district of Pakistan. Data were collected over a period of 1 year (2015/16) from augmentation of the existing health information system covering public healthcare facilities ( n  = 19), and the community through 273 existing Lady Health Workers; and with the addition of private healthcare facilities ( n  = 10), and 73 additional Community Health Workers to cover a total study population of 368,454 consisting of 51,690 eligible women aged 18 to 49 years with 7580 pregnancies and 7273 live births over 1 year. Maternal, neonatal, perinatal and stillbirth rates and ratios were calculated, with comparisons to routine reporting from the previous period (2014–15). Results Higher maternal mortality, perinatal mortality and neonatal mortality rates were observed through enhanced surveillance compared to mortality rates in the previous 1.5 years from the routine monitoring system from increased completeness and coverage. Maternal mortality was 247 compared to 180 per 100, 000 live births ( p  = 0.36), neonatal mortality 40 compared to 20 per 1, 000 live births ( p  < 0.001), and perinatal mortality 60 compared to 47 per 1000 live births ( p  < 0.001). All the mortality rates were higher than provincial and national estimates proffered by international agencies based on successive Pakistan Demographic and Health Surveys and projections. Conclusion Extension of coverage and improvement in completeness through reconciliation of data from health information systems is possible and required to obtain accurate maternal, perinatal and neonatal mortality for assessment of health service interventions at a local level.

Background The number of COVID-19 deaths reported in Zambia ( N  = 4069) is most likely an underestimate due to limited testing, incomplete death registration and inability to account for indirect deaths due to socioeconomic disruption during the pandemic. We sought to assess excess mortality during the COVID-19 pandemic in Zambia. Methods We conducted a retrospective analysis of monthly-death-counts (2017–2022) and individual-daily-deaths (2020–2022) of all reported health facility and community deaths at district referral health facility mortuaries in 12 districts in Zambia. We defined COVID-19 wave periods based on a sustained nationally reported SARS-CoV-2 test positivity of greater than 5%. Excess mortality was calculated as the difference between observed monthly death counts during the pandemic (2020–2022) and the median monthly death counts from the pre-pandemic period (2017–2019), which served as the expected number of deaths. This calculation was conducted using a Microsoft Excel-based tool. We compared median daily death counts, median age at death, and the proportion of deaths by place of death (health facility vs. community) by wave period using the Mann-Whitney-U test and chi-square test respectively in R. Results A total of 112,768 deaths were reported in the 12 districts between 2020 and 2022, of which 17,111 (15.2%) were excess. Wave periods had higher median daily death counts than non-wave periods (median [IQR], 107 [95–126] versus 96 [85–107], p  < 0.001). The median age at death during wave periods was older than non-wave periods (44.0 [25.0–67.0] versus 41.0 [22.0–63.0] years, p  < 0.001). Approximately half of all reported deaths occurred in the community, with an even greater proportion during wave periods (50.6% versus 53.1%, p  < 0.001), respectively. Conclusion There was excess mortality during the COVID-19 pandemic in Zambia, with more deaths occurring within the community during wave periods. This analysis suggests more COVID-19 deaths likely occurred in Zambia than suggested by officially reported numbers. Mortality surveillance can provide important information to monitor population health and inform public health programming during pandemics.

Background: The Jordan Stillbirth and Neonatal Mortality Surveillance (JSANDS) is an electronic surveillance system that automatically transfers the data on births, stillbirths, and neonatal deaths to the concerned authorities in the Ministry of Health. JSANDS was implemented and tested in 5 maternity hospitals during the period spanning May 2019 through December 2020. Objective: This study aimed to evaluate the usefulness and performance of JSANDS to register births, stillbirths, and neonatal deaths, and determine their causes. Specifically, this study examined the JSANDS attributes of acceptability, simplicity, flexibility, stability, representativeness, sustainability, penetration, data quality, sensitivity, and adoption. Methods: An evaluation study was conducted after 18 months of the JSANDS implementation using the Updated Guidelines for Evaluating Public Health Surveillance Systems. The evaluation focused on how well the system operated to meet its purpose and objectives. The indicators assessing the system attributes were scored on a Likert scale. Each indicator and overall attribute percentage score was represented as score rank and interpreted as excellent (score ≥80%), good (score ≥60 and <80%), average (score ≥40 and <60%), and poor (score <40%). Results: A total of 270 health care professionals participated in this study and evaluated the system performance. The system users rated the usefulness of JSANDS as excellent (percentage score=85.6%). The overall acceptability (percentage score=82.3%), flexibility (percentage score=80.2%), stability (percentage score=80.0%), and representativeness (percentage score=86.6%) were also rated excellent. The overall simplicity was scored good (percentage score=75.4%). All participants were trained on JSANDS and used it in the past 12 months. Of the 270 respondents, 219 (86.2%) reported that they intend to continue using the JSANDS system to register neonatal deaths and stillbirths in the future. All variables in JSANDS had complete data with no missing values. Conclusions: The performance of JSANDS in registering all stillbirths and neonatal deaths as well as their causes was excellent. Almost all attributes and indicators of JSANDS functionality were rated excellent. JSANDS can be scaled up to cover all maternity hospitals in Jordan. The potential for scaling up the system is very high for many reasons, including its usefulness, simplified stillbirth and neonatal death review tools, and ease of the reporting process.

Introduction Spain stands out as one of the countries most affected by rising heat-related mortality in Europe. The MoMo system is a daily all-cause mortality monitoring system that provides estimates of excess mortality attributable to high temperatures in Spain. This study describes the recent evolution of these estimates during the summers of the period 2021–2024. Method We conducted an observational descriptive study using data from MoMo. Population data by province, sex, and age group were obtained from the Spanish National Statistics Institute. Provincial daily maximum and minimum temperature data were provided by the Spanish National Meteorological Agency, following the standard MoMo methodology. Estimates are based on mixed generalised additive models and were described by sex and age groups for the overall country. Geographic patterns were explored by calculating standardized mortality ratios. Results Over the four summers analysed, 11,684 excess deaths attributable to high temperature were estimated by the model, 59.2% occurred in women and 40.8% in men. Excess mortality progressively increased with age, particularly among individuals over 74 years old. The highest number of excess deaths was estimated in 2022. By regions, in 2021, 2022 and 2024 excess of mortality were mainly located in inland provinces, while coastal regions generally showed lower than average risks, except in certain provinces in the East and South. Conclusions MoMo has shown the impact of rising temperatures on excess mortality attributable to high temperatures. It is essential to implement public policies focused on climate change mitigation and adaptation, especially in the most vulnerable populations and in the at-risk areas.

Background There is contradicting information regarding the effect of COVID-19 on mortality in African settings. Knowledge of the complete direct and indirect burden of COVID-19 on mortality is heavily reliant on the availability of a population-based surveillance system. Here we provide robust data on the effect of COVID-19 on mortality trends in a rural, coastal, Kenyan community. Methods A historical cohort study using data from the Kaloleni Rabai Health and Demographic Surveillance System was conducted with special focus on two discernible time periods representing the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods. Mortality rates were estimated as the total number of deaths divided by the person-time (years) at risk, accounting for attrition, and calculated separately for the two periods. A cox proportional hazards model was used to estimate the impact of COVID-19 on mortality. Results 1191 deaths occurred between 2018 and 2021. There was no significant change in overall mortality rates between pre-COVID-19 and COVID-19 periods (3.7 and 3.6 per 1000 person years at risk respectively, p  = 0.74). Older age was significantly associated with mortality (a_HR: 1.05, 95% CI: 1.05–1.06; p  < 0.001). However, an interaction term between age and time-period appeared to reverse this association (a_HR: 0.99, 95% CI: 0.99–1.00; p  < 0.001). Conclusions Our findings suggest that although overall COVID-19 did not directly impact mortality rates within this rural population, the onset of the pandemic did appear to reverse and/or attenuate the impact of several risk factors on mortality. It is possible that COVID-19 brought health and wellness into sharp focus, making people more vigilant about their health, hygiene and associated preventive measures.

Background Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. However, a barrier to its use by national registration systems has been the amount of time and cost needed for data collection. Therefore, a short VA instrument (VAI) is needed. In this paper we describe a shortened version of the VAI developed for the Population Health Metrics Research Consortium (PHMRC) Gold Standard Verbal Autopsy Validation Study using a systematic approach. Methods We used data from the PHMRC validation study. Using the Tariff 2.0 method, we first established a rank order of individual questions in the PHMRC VAI according to their importance in predicting causes of death. Second, we reduced the size of the instrument by dropping questions in reverse order of their importance. We assessed the predictive performance of the instrument as questions were removed at the individual level by calculating chance-corrected concordance and at the population level with cause-specific mortality fraction (CSMF) accuracy. Finally, the optimum size of the shortened instrument was determined using a first derivative analysis of the decline in performance as the size of the VA instrument decreased for adults, children, and neonates. Results The full PHMRC VAI had 183, 127, and 149 questions for adult, child, and neonatal deaths, respectively. The shortened instrument developed had 109, 69, and 67 questions, respectively, representing a decrease in the total number of questions of 40-55 %. The shortened instrument, with text, showed non-significant declines in CSMF accuracy from the full instrument with text of 0.4 %, 0.0 %, and 0.6 % for the adult, child, and neonatal modules, respectively. Conclusions We developed a shortened VAI using a systematic approach, and assessed its performance when administered using hand-held electronic tablets and analyzed using Tariff 2.0. The length of a VA questionnaire was shortened by almost 50 % without a significant drop in performance. The shortened VAI developed reduces the burden of time and resources required for data collection and analysis of cause of death data in civil registration systems.