Associations between rurality and regional differences in sociodemographic factors and the 1918-20 influenza and 2020-21 COVID-19 pandemics in Missouri counties: An ecological study.

This study compares pandemic experiences of Missouri's 115 counties based on rurality and sociodemographic characteristics during the 1918-20 influenza and 2020-21 COVID-19 pandemics. The state's counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.S. census data and used to create clusters of similar counties. Counties were also grouped by rural status (RSU), including fully (100%) rural, semirural (1-49% living in urban areas), and urban (>50% of the population living in urban areas). Deaths from 1918 through 1920 were collated from the Missouri Digital Heritage database and COVID-19 cases and deaths were downloaded from the Missouri COVID-19 dashboard. Results from sociodemographic analyses indicate that, during both time periods, average farm value, proportion White, and literacy were the most important determinants of sociodemographic clusters. Furthermore, the Urban/Central and Southeastern regions experienced higher mortality during both pandemics than did the North and South. Analyses comparing county groups by rurality indicated that throughout the 1918-20 influenza pandemic, urban counties had the highest and rural had the lowest mortality rates. Early in the 2020-21 COVID-19 pandemic, urban counties saw the most extensive epidemic spread and highest mortality, but as the epidemic progressed, cumulative mortality became highest in semirural counties. Additional results highlight the greater effects both pandemics had on county groups with lower rates of education and a lower proportion of Whites in the population. This was especially true for the far southeastern counties of Missouri ("the Bootheel") during the COVID-19 pandemic. These results indicate that rural-urban and socioeconomic differences in health outcomes are long-standing problems that continue to be of significant importance, even though the overall quality of health care is substantially better in the 21st century.

Age-specific mortality and the role of living remotely: The 1918-20 influenza pandemic in Kautokeino and Karasjok, Norway.

The 1918-20 pandemic influenza killed 50-100 million people worldwide, but mortality varied by ethnicity and geography. In Norway, areas dominated by Sámi experienced 3-5 times higher mortality than the country's average. We here use data from burial registers and censuses to calculate all-cause excess mortality by age and wave in two remote Sámi areas of Norway 1918-20. We hypothesise that geographic isolation, less prior exposure to seasonal influenza, and thus less immunity led to higher Indigenous mortality and a different age distribution of mortality (higher mortality for all) than was typical for this pandemic in non-isolated majority populations (higher young adult mortality & sparing of the elderly). Our results show that in the fall of 1918 (Karasjok), winter of 1919 (Kautokeino), and winter of 1920 (Karasjok), young adults had the highest excess mortality, followed by also high excess mortality among the elderly and children. Children did not exhibit excess mortality in the second wave in Karasjok in 1920. It was not the young adults alone who produced the excess mortality in Kautokeino and Karasjok. We conclude that geographic isolation caused higher mortality among the elderly in the first and second waves, and among children in the first wave.

Investigating COVID-19 transmission and mortality differences between indigenous and non-indigenous populations in Mexico.

OBJECTIVES: Indigenous populations have been disproportionately affected during pandemics. We investigated COVID-19 mortality estimates among indigenous and non-indigenous populations at national and sub-national levels in Mexico. METHODS: We obtained data from the Ministry of Health, Mexico, on 2,173,036 laboratory-confirmed RT-PCR positive COVID-19 cases and 238,803 deaths. We estimated mortality per 1000 person-weeks, mortality rate ratio (RR) among indigenous vs. non-indigenous groups, and hazard ratio (HR) for COVID-19 deaths across four waves of the pandemic, from February 2020 to March 2022. We also assessed differences in the reproduction number (Rt). RESULTS: The mortality rate among indigenous populations of Mexico was 68% higher than that of non-indigenous groups. Out of 32 federal entities, 23 exhibited higher mortality rates among indigenous groups (P < 0.05 in 13 entities). The fourth wave showed the highest RR (2.40). The crude HR was 1.67 (95% CI: 1.62, 1.72), which decreased to 1.08 (95% CI: 1.04, 1.11) after controlling for other covariates. During the intense fourth wave, the Rt among the two groups was comparable. CONCLUSION: Indigenous status is a significant risk factor for COVID-19 mortality in Mexico. Our findings may reflect disparities in non-pharmaceutical (e.g., handwashing and using facemasks), and COVID-19 vaccination interventions among indigenous and non-indigenous populations in Mexico.

"We didn't get much schooling because we were fishing all the time": Potential impacts of irregular school attendance on the spread of epidemics.

OBJECTIVES: Especially in traditional, rural, and low-income areas, children attend school irregularly. School-based interventions are common mitigation strategies for infectious disease epidemics, but if daily attendance is not the norm, the impact of schools on disease spread might be overestimated. METHODS: We use an agent-based model of an early 20th century Newfoundland community to compare epidemic size and duration in three scenarios: (1) all school-aged children attend school each weekday, (2) students aged 10-15 have a chance of engaging in adult activities each day, and (3) students aged 10-15 have a chance of being reassigned to adult roles at the start of each simulation and thus never attend school. RESULTS: As the probability of not attending school increases, epidemics become smaller and peak earlier. The change in final size is larger with permanent reassignment (35% at baseline, 18% at maximum reassignment) than with daily nonattendance (35% vs. 22%). For both scenarios, the peak occurs 3 days earlier with maximum absence compared to the baseline. Benefits extend beyond the reassigned agents, as all school-aged agents are more likely to escape infection with increasing reassignment, and on average, 3-6 additional agents (2.6%-5.3%) escape infection compared to the baseline. CONCLUSIONS: This study demonstrates that absenteeism can have important impacts on epidemic outcomes. Thus, socioeconomic and other reasons for nonattendance of school, as well as how rates vary in different contexts, must be considered in models predicting epidemic outcomes or evaluating public health interventions in the face of major pandemics.

Thinking clearly about social aspects of infectious disease transmission.

Social and cultural forces shape almost every aspect of infectious disease transmission in human populations, as well as our ability to measure, understand, and respond to epidemics. For directly transmitted infections, pathogen transmission relies on human-to-human contact, with kinship, household, and societal structures shaping contact patterns that in turn determine epidemic dynamics. Social, economic, and cultural forces also shape patterns of exposure, health-seeking behaviour, infection outcomes, the likelihood of diagnosis and reporting of cases, and the uptake of interventions. Although these social aspects of epidemiology are hard to quantify and have limited the generalizability of modelling frameworks in a policy context, new sources of data on relevant aspects of human behaviour are increasingly available. Researchers have begun to embrace data from mobile devices and other technologies as useful proxies for behavioural drivers of disease transmission, but there is much work to be done to measure and validate these approaches, particularly for policy-making. Here we discuss how integrating local knowledge in the design of model frameworks and the interpretation of new data streams offers the possibility of policy-relevant models for public health decision-making as well as the development of robust, generalizable theories about human behaviour in relation to infectious diseases.

The 1918 influenza pandemic did not accelerate tuberculosis mortality decline in early-20th century Newfoundland: Investigating historical and social explanations.

OBJECTIVES: The selective mortality hypothesis of tuberculosis after the 1918 influenza pandemic, laid out by Noymer and colleagues, suggests that acute exposure or pre-existing infection with tuberculosis (TB) increased the probability of pneumonia and influenza (P&I) mortality during the 1918 influenza pandemic, leading to a hastened decline of TB mortality in post-pandemic years. This study describes cultural determinants of the post-pandemic TB mortality patterns in Newfoundland and evaluates whether there is support for this observation. MATERIALS AND METHODS: Death records and historical documents from the Provincial Archives of Newfoundland and Labrador were used to calculate age-standardized island-wide and sex-based TB mortality, as well as region-level TB mortality, for 1900-1939. The Joinpoint Regression Program (version 4.8.0.1) was used to estimate statistically significant changes in mortality rates. RESULTS: Island-wide, females had consistently higher TB mortality for the duration of the study period and a significant shift to lower TB mortality beginning in 1928. There was no similar predicted significant decline for males. On the regional level, no models predicted a significant decline after the 1918 influenza pandemic, except for the West, where significant decline was predicted in the late-1930s. DISCUSSION: Although there was no significant decline in TB mortality observed immediately post-pandemic, as has been shown for other Western nations, the female post-pandemic pattern suggests a decline much later. The general lack of significant decrease in TB mortality rate is likely due to Newfoundland's poor nutrition and lack of centralized healthcare rather than a biological interaction between P&I and TB.

Using cultural, historical, and epidemiological data to inform, calibrate, and verify model structures in agent-based simulations.

Agent-based simulation models are excellent tools for addressing questions about the spread of infectious diseases in human populations because realistic, complex behaviors as well as random factors can readily be incorporated. Agent-based models are flexible and allow for a wide variety of behaviors, time-related variables, and geographies, making the calibration process an extremely important step in model development. Such calibration procedures, including verification and validation, may be complicated, however, and usually require incorporation of substantial empirical data and theoretical knowledge of the populations and processes under study. This paper describes steps taken to build and calibrate an agent-based model of epidemic spread in an early 20th century fishing village in Newfoundland and Labrador, including a description of some of the detailed ethnographic and historical data available. We illustrate how these data were used to develop the structure of specific parts of the model. The resulting model, however, is designed to reflect a generic small community during the early 20th century and the spread of a directly transmitted disease within such a community, not the specific place that provided the data. Following the description of model development, we present the results of a replication study used to confirm the model behaves as intended. This study is also used to identify the number of simulations necessary for high confidence in average model output. We also present selected results from extensive sensitivity analyses to assess the effect that variation in parameter values has on model outcomes. After careful calibration and verification, the model can be used to address specific practical questions of interest. We provide an illustrative example of this process.

Sex- and age-based differences in mortality during the 1918 influenza pandemic on the island of Newfoundland.

OBJECTIVES: Our aim was to understand sex- and age-based differences in mortality during the 1918 influenza pandemic on the island of Newfoundland. The pandemic's impact on different age groups has been the focus of other research, but sex-based differences in mortality are rarely considered. Aspects of social organization, labor patterns, and social behaviors that contribute to mortality between males and females at all ages are used to explain observed mortality patterns. METHODS: Recorded pneumonia and influenza deaths on the island (n = 1871) were used to calculate cause-specific death rates and to evaluate differences in sex-based mortality. Mortality levels in 17 districts and four regions (Avalon, North, South, and West) were compared using standardized mortality ratios (SMRs). A logistic regression model was fit to determine in which regions sex-based mortality could be predicted using age and region as interactive predictors. RESULTS: Differences in sex-based mortality varied across regions; they were not significant for the aggregate population. SMRs were also variable, with no significant sex-based differences. Sex-based differences were highly variable within regions. Results from a logistic regression analysis suggest that females in the South region may have experienced a higher probability of death than other island residents. CONCLUSIONS: Mortality analysis for aggregate populations homogenizes important epidemiologic patterns. Men and women did not experience the 1918 influenza pandemic in the same way, and by analyzing data at the regional and district geographic levels, patterns emerge that can be explained by the economies and social organization of the people who lived there.

The second epidemiologic transition on the brink: What we can learn from the island of Newfoundland during the early 20th century.

OBJECTIVES: We aim to understand how the second epidemiologic transition unfolded on the island of Newfoundland during the early 20th century. The focus is on changes in predominant causes of mortality throughout this period, urban and rural differences, and comparisons with other Western nations. We explore factors driving these patterns including the economy, nutrition, sanitation, and access to healthcare and discuss their relevance to understanding epidemiologic transitions in other developing regions. METHODS: We examined official provincial death records (n = 65,394) and census materials from 1900 to 1939 for three rural districts (Burgeo-La Poile, Bonavista, and Twillingate) and the large city of St. John's. Life expectancies, infant mortality rates, survival curves, and proportionate mortality from communicable and non-communicable diseases (NCDs) were calculated. RESULTS: In all districts, old age mortality increased while infant and childhood mortality decreased, with corresponding increases in life expectancy. Proportionate mortality from communicable causes decreased while deaths from NCDs increased. These changes occurred earlier in urban St. John's than in outlying districts, suggesting rural-urban differences played a significant role in Newfoundland's second epidemiologic transition. However, the transition was significantly delayed relative to other Western nations. CONCLUSIONS: We suggest that observed mortality patterns were the result of complex interactions between the poor economy, malnutrition, high prevalence of tuberculosis, and limited health and social services. These factors contribute to the delayed onset of the second epidemiologic transition in Newfoundland relative to other Western nations and the earlier onset in St. John's than in rural areas.

Defining epidemics in computer simulation models: How do definitions influence conclusions?

Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.

Mathematical models to characterize early epidemic growth: A review.

There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.

Mortality from contact-related epidemics among indigenous populations in Greater Amazonia.

European expansion and contact with indigenous populations led to catastrophic depopulation primarily through the introduction of novel infectious diseases to which native peoples had limited exposure and immunity. In the Amazon Basin such contacts continue to occur with more than 50 isolated indigenous societies likely to make further contacts with the outside world in the near future. Ethnohistorical accounts are useful for quantifying trends in the severity and frequency of epidemics through time and may provide insight into the likely demographic consequences of future contacts. Here we compile information for 117 epidemics that affected 59 different indigenous societies in Greater Amazonia and caused over 11,000 deaths between 1875 and 2008, mostly (75%) from measles, influenza, and malaria. Results show that mortality rates from epidemics decline exponentially through time and, independently, with time since peaceful contact. The frequency of documented epidemics also decreases with time since contact. While previous work on virgin soil epidemics generally emphasizes the calamity of contacts, we focus instead on improvements through time. The prospects for better survivorship during future contacts are good provided modern health care procedures are implemented immediately.

Regional patterns of mortality during the 1918 influenza pandemic in Newfoundland.

The Spanish Influenza pandemic reached the island of Newfoundland in the summer of 1918 and by the time it disappeared, nearly 2000 of its 250,000 residents died. The pandemic spread in several waves, including a mild outbreak during the summer of 1918 (Wave I), a major, deadly outbreak in the succeeding fall and spring (Wave II), and a small echo wave in 1920. All parts of the island experienced the epidemic, but the effects varied across districts, both in timing and in severity. Overall P&I mortality rates across districts during the entire epidemic (1918-1920) ranged from 28.6 to 109.3 deaths per 10,000 population, with the island as a whole experiencing a mortality rate of 74.5 per 10,000. This island-wide mortality rate was 4.5 times higher than the P&I mortality rate for the 3 years immediately preceding the epidemic. Estimates of the reproduction number, R, range from 1.2 to 2.4 for Wave I and from 2.4 to 9.3 for Wave II. The pandemic experience on Newfoundland illustrates the high degree of regional variability in incidence and severity that epidemics can exhibit. In addition, compared to other world regions, the island's pandemic peaked relatively late and exhibited an unusual bimodal peak during Wave II, emphasizing that local and regional conditions can have major influences on timing, location, and rate of spread. This suggests the need to for greater understanding of how local factors influence epidemic spread so that more effective control strategies can be developed for populations experiencing future influenza pandemics.

Agent-based modeling of the spread of the 1918-1919 flu in three Canadian fur trading communities.

OBJECTIVES: Previous attempts to study the 1918-1919 flu in three small communities in central Manitoba have used both three-community population-based and single-community agent-based models. These studies identified critical factors influencing epidemic spread, but they also left important questions unanswered. The objective of this project was to design a more realistic agent-based model that would overcome limitations of earlier models and provide new insights into these outstanding questions. METHODS: The new model extends the previous agent-based model to three communities so that results can be compared to those from the population-based model. Sensitivity testing was conducted, and the new model was used to investigate the influence of seasonal settlement and mobility patterns, the geographic heterogeneity of the observed 1918-1919 epidemic in Manitoba, and other questions addressed previously. RESULTS: Results confirm outcomes from the population-based model that suggest that (a) social organization and mobility strongly influence the timing and severity of epidemics and (b) the impact of the epidemic would have been greater if it had arrived in the summer rather than the winter. New insights from the model suggest that the observed heterogeneity among communities in epidemic impact was not unusual and would have been the expected outcome given settlement structure and levels of interaction among communities. CONCLUSIONS: Application of an agent-based computer simulation has helped to better explain observed patterns of spread of the 1918-1919 flu epidemic in central Manitoba. Contrasts between agent-based and population-based models illustrate the advantages of agent-based models for the study of small populations.

Gleaning signals about the past from cemetery data.

Cemetery headstones provide an easily accessible source of demographic data in human populations. In common with other sources of demographic data, such as skeletal samples, cemetery data may not be representative of the populations from which they were derived. In some circumstances they can be reasonably representative, however, and in such cases they may provide signals about demographic changes in the population that contributed to the cemetery. We present here analyses of burials occurring between 1900 and 1990 at the Columbia Cemetery in Columbia, Missouri. Our analyses, in combination with archival materials relating to infrastructure improvements in Columbia and data on infectious disease mortality in the state of Missouri, show that patterns of death observed in the cemetery data provide evidence for the timing of changes in the health of Columbia's residents. At the time that major improvements in sanitation and hygiene were implemented, burials of individuals dying under age 45 decreased significantly while burials of individuals older than 45 remained relatively high. Furthermore, data on infectious disease mortality indicate significant declines in deaths from water- and milk-borne infections, but no change in mortality from respiratory illnesses. These data also indicate that observed changes occurred about a decade later in Columbia than in large cities and more densely populated states elsewhere in the United States. Thus, this study illustrates the value of cemetery data in helping to fill gaps about how and when different events known to affect patterns of birth and death may have played out across time and space.

The design and use of an agent-based model to simulate the 1918 influenza epidemic at Norway House, Manitoba.

Agent-based modeling provides a new approach to the study of virgin soil epidemics like the 1918 flu. In this bottom-up simulation approach, a landscape can be created and populated with a heterogeneous group of agents who move and interact in ways that more closely resemble human behavior than is usually seen in other modeling techniques. In this project, an agent-based model was constructed to simulate the spread of the 1918 influenza pandemic through the Norway House community in Manitoba, Canada. Archival, ethnographic, epidemiological, and biological information were used to aid in designing the structure of the model and to estimate values for model parameters. During the epidemic, Norway House was a Hudson's Bay Company post and a Swampy Cree-Métis settlement with an economy based on hunting, fishing, and the fur trade. The community followed a traditional, seasonal travel pattern of summer aggregation and winter dispersal. The model was used to examine how seasonal community structures and associated population movement patterns may have influenced disease transmission and epidemic spread. Simulations of the model clearly demonstrate that human behavior can significantly influence epidemic outcomes.

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*)

Social contexts, syndemics, and infectious disease in northern Aboriginal populations.

Until the last half of the 20th century, infectious diseases dominated the health profile of northern North American Aboriginal communities. Research on the 1918 influenza pandemic exemplifies some of the ways in which the social context of European contact and ensuing economic developments affected the nature of infectious disease ecology as well as the frequency and severity of the problem. To understand these impacts it is necessary to consider the web of interactions among multiple pathogens, the biology of the human host, and the social environment in which people lived. At the very least, an understanding of the history of the impact of infectious diseases on northern North American communities requires attention not only to potential interactions among cocirculating pathogens, but their links to key social, historical, and economic factors that exacerbated their adverse effects and contributed to excess mortality.