Demographics and Infectious Disease

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Demographics and Infectious Disease

Introduction

Demographic trends (trends in a population's vital statistics) within nations and across national boundaries have a profound effect on the distribution of infectious disease worldwide. Gender, age, the movement of populations due to economic opportunity or to escape conflict, and the sheer density of population relative to the capacity of local ecosystems, civic infrastructure, and public health resources all influence the infectivity and virulence (degree of ability to cause disease) of infectious diseases. Close quartering of a population such as in refugee camps, prisons, or schools, can also affect the outbreak and spread of infectious disease.

History and Scientific Foundations

One clear example of how many of these factors came together at once was the mass exodus of more than a million Kurds from Iraq as they fled their villages under attack from the Iraqi army after the end of the first Gulf War in 1991. With the absence of sanitary facilities and the crowding together of so many people in a weakened condition, contaminated water supplies from human waste quickly gave rise to an epidemic of cholera, as well as other communicable diseases. More recently, conflict in the Darfur region of Sudan has resulted in the movement of more than one million internally displaced persons who now live in crowded refugee camps where epidemics of typhoid, hepatitis E, cholera, and meningitis have taken hold.

Movement of populations such as migrants or refugees affects the population itself, the populations encountered, and the ecosystem. Each translocated person carries cultural practices, genetic vulnerabilities and resistances to infections, and organisms that have been held at bay by the individual's immunity, but lie dormant and are potentially dangerous to previously unexposed persons. In addition, the moving populations unwittingly transport microbes, animals that are disease vectors (transmitters), and other flora and fauna (plants and animals) that are foreign to the destination ecosystems.

Even when no mass population movements are taking place, the changing age and sex mix of stable populations over time will impact the spread of infectious disease. In other words, the population of potential disease hosts changes rather than remains stable. These changes affect the patterns of communicable diseases, particularly diseases that are sexually transmitted and that give rise to symptoms in one gender or the other (such as cervical cancer caused by human papilloma virus) or which attack people differentially in different age brackets, such as seasonal influenza, which usually infects older people more often than younger people.

Demographic factors in models of infectious disease

Demographics such as the population density of various age, sex, and ethnic subgroups, along with other statistics that affect patterns of disease can be made into mathematical models that help scientists map and predict infectious disease trends. These models involve various assumptions based on whether people can recover from infections, the rate of disease-related deaths, the development of immunity, and the duration of immunity (whether it is temporary or permanent). These models can also predict infectious disease catastrophes by location. For example, recent models have shown that the persistence (duration) of the AIDS epidemic in many rural African communities has reduced the population size to levels below those that are necessary to maintain the local population of the community. The models showed that AIDS was eliminating adults of reproducing age at a rapid rate.

The simplest models often assume that the total population size is constant. For short-term outbreaks of a disease, simple disease models used to predict the course of an epidemic assume that the population is fixed and closed, and depend only on the disease incidence and prevalence rates, disease duration (persistence), disease death rates, and occurrence of immunity. Models for an endemic disease (one that is naturally occurring in a region such as tuberculosis or malaria) usually assume that births and deaths balance each other so the population size remains unchanged. However, when the disease causes a significant number of deaths, as in the case of AIDS, this assumption is not realistic and more complicated models assuming variable population are needed to predict the course of the epidemic. These sophisticated models incorporate assumptions about both birth and death rates, which can be impacted by the incidence and prevalence of disease as well as other factors. By the same token, population size influences the rapidity with which a disease is spread, with large, dense populations promoting the rapid spread of disease, and small, dispersed populations inhibiting such spread.

Applications and Research

Demographics, seasonality, and infectious disease

Seasonality is an important factor in the in the spread of common infectious diseases that most affect the youngest and oldest demographic groups (school children and the elderly). Illnesses such as influenza, measles, chickenpox, and pertussis (whooping cough) are all more prevalent at certain times of the year. Seasonality is a particularly important factor in models that predict whether these recurrent infectious diseases will occur in a given year or skip a year. Seasonal changes in disease transmission patterns and the susceptibility of a population susceptibility to a disease (such as attending school or staying inside in close quarters during the winter) can prevent late-peaking diseases (disease epidemics that take a long time to reach peak infectivity) from spreading widely. When this happens, the remaining population is more susceptible to future epidemics because of a lack of herd immunity (when the majority of immunized people in a group give some protection to those that are not immunized).

WORDS TO KNOW

CLUSTER: In epidemiology, cluster refers to a grouping of an infectious disease or foodborn illness that occurs very close in time or place.

DEMOGRAPHICS: The characteristics of human populations or specific parts of human populations, most often reported through statistics.

EPIDEMIOLOGY: Epidemiology is the study of various factors that influence the occurrence, distribution, prevention, and control of disease, injury, and other health-related events in a defined human population. By the application of various analytical techniques including mathematical analysis of the data, the probable cause of an infectious outbreak can be pinpointed.

HERD IMMUNITY: Herd immunity is a resistance to disease that occurs in a population when a proportion of them have been immunized against it. The theory is that it is less likely that an infectious disease will spread in a group where some individuals are less likely to contract it.

INCIDENCE: The number of new cases of a disease or injury that occur in a population during a specified period of time.

PERSISTENCE: Persistence is the length of time a disease remains in a patient. Disease persistence can vary from a few days to life-long.

PREVALENCE: The actual number of cases of disease (or injury) that exist in a population.

VIRULENCE: Virulence is the ability of a disease organism to cause disease: a more virulent organism is more infective and liable to produce more serious disease.

By analyzing seasonality and how much of the population remains susceptible to a disease, scientists can predict the course of newly emerging and re-emerging diseases, such as West Nile disease, that are brought on by seasonal vectors (transmitters) including mosquitoes or migratory birds.

Population clustering and disease spread

Of course, populations are not distributed uniformly even when they are stable and no significant migration is occurring. Infectious diseases spread in different patterns within a population that is divided into families or other groups, than in a population that consists mostly of people who are living alone. A household constitutes a small population cluster, which is in turn comprised of members that are resistant to the disease, along with members that are susceptible to the disease. An infectious disease spreads quickly and efficiently within the household, but the outbreak lasts longer if it spreads cluster by cluster, or from one household to another.

Impacts and Issues

Infectious disease in the elderly

As discussed above, the proportion of children and the elderly in a population is particularly important in the spread of communicable diseases, particularly because both age groups are more likely than the general population to be in very close quarters for extended periods in schools and in hospitals or nursing homes. In children, immune functioning is still developing and they are constantly being exposed to pathogens (diseasecausing organisms) that are familiar to adults, but new to them. At the other end of the demographic scale, aging is associated with increased incidence and severity of many infectious diseases, including nosocomial infections (infections that originate in hospitals from contaminated equipment or close proximity to other infected people). This increased risk is due to an age-related decline in the body's immune system function. As the average age of the population increases in industrialized nations, the epidemiology (incidence and prevalence), morbidity (proportion of sick people), mortality (death rate), and needs for preventive action against nosocomial infections in the elderly also increase.

Impact of households on vaccination strategies

When an epidemic of a highly infectious disease is spreading in a community of households, the infection of any member of a household generally results in the infection of all susceptible members of that household. The rapidity of disease spread will thus depend on the household size and the variability of the number of susceptible people per household. If the rate of spread of infection from individual to individual within each household and the spread of infection from household to household are calculated, the rate and pattern of spread of the disease can be put into a mathematical model by public health scientists. This model can be used to calculate the levels of immunity that will be needed to prevent major epidemics in the community. It can also be used to evaluate alternative vaccination strategies that could immunize the same number of individuals.

For a community with households of approximately equal size (as seen in many suburban communities in the United States), random vaccination of individuals is better than immunizing all members of a fraction of households that would amount to the same total number of vaccinated people. On the other hand, when households vary widely in size (as seen in many U.S. urban areas) vaccinating all members of large households can slow down the spread of the epidemic more rapidly than would the vaccination of an equal number of randomly selected individuals. This is because disease transmission within these large households is easier than in the general community. Such epidemic spread models can also be used for a community of households with schools or day care centers. Immunizing every child within the school or day care center will be more effective than randomly immunizing an equal number of children in the community because the schools and day care centers are similar to very large households in which disease spread among many susceptible children is made easy by their close quarters.

Demographic characteristics of populations strongly determine the rate and extent of infectious disease distribution and spread. These demographic characteristics are in turn profoundly influenced by the processes of economic development, globalization, migration, and war. Although population demographics and patterns of infectious disease are in continual flux (change), they are rarely susceptible to policy-motivated human intervention. Rather, they are all aspects of the evolution of human cultures, which are intimately interconnected with evolving technology and commerce. The tools of epidemiological models that use demographic factors to help forecast the spread of infectious disease will constantly need to be updated as population characteristics change with increasing velocity in the years and decades ahead.

BIBLIOGRAPHY

Books

Connolly, M.A. Communicable Disease Control in Emergencies: A Field Manual. Geneva: World Health Organization, 2006.

Daly, D.J., and J. Gani. Epidemic Modeling: An Introduction. New York, Cambridge, 2001.

Jamison, Dean T., ed., et al. Disease and Mortality in Sub-Saharan Africa. New York: World Bank Publications, 2006.

Periodicals

Pramodh, Nathaniel. “Limiting the Spread of Communicable Diseases Caused by Human Population Movement.” Journal of Rural and Remote Environmental Health. (2003): 2(1), 23–32.

Stone L., R. Olinky, A. Huppert. “Seasonal Dynamics of Recurrent Epidemics.” Nature. (March 29, 2007): 533–6.

Web Sites

Science Blog. “Web Game Provides Breakthrough in Predicting Spread of Epidemics.” <http://www.scienceblog.com/cms/web_game_provides_breakthrough_in_predicting_spread_of_epidemics_9874> (accessed May 30, 2007).

Kenneth T. LaPensee

Infectious Diseases: In Context