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15.2: Tracking Infectious Diseases - Biology

15.2: Tracking Infectious Diseases - Biology


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skills to develop

  • Explain the research approaches used by the pioneers of epidemiology
  • Explain how descriptive, analytical, and experimental epidemiological studies go about determining the cause of morbidity and mortality

Epidemiology has its roots in the work of physicians who looked for patterns in disease occurrence as a way to understand how to prevent it. The idea that disease could be transmitted was an important precursor to making sense of some of the patterns. In 1546, Girolamo Fracastoro first proposed the germ theory of disease in his essay De Contagione et Contagiosis Morbis, but this theory remained in competition with other theories, such as the miasma hypothesis, for many years (see What Our Ancestors Knew). Uncertainty about the cause of disease was not an absolute barrier to obtaining useful knowledge from patterns of disease. Some important researchers, such as Florence Nightingale, subscribed to the miasma hypothesis. The transition to acceptance of the germ theory during the 19th century provided a solid mechanistic grounding to the study of disease patterns. The studies of 19th century physicians and researchers such as John Snow, Florence Nightingale, Ignaz Semmelweis, Joseph Lister, Robert Koch, Louis Pasteur, and others sowed the seeds of modern epidemiology.

Pioneers of Epidemiology

John Snow (Figure (PageIndex{1})) was a British physician known as the father of epidemiology for determining the source of the 1854 Broad Street cholera epidemic in London. Based on observations he had made during an earlier cholera outbreak (1848–1849), Snow proposed that cholera was spread through a fecal-oral route of transmission and that a microbe was the infectious agent. He investigated the 1854 cholera epidemic in two ways. First, suspecting that contaminated water was the source of the epidemic, Snow identified the source of water for those infected. He found a high frequency of cholera cases among individuals who obtained their water from the River Thames downstream from London. This water contained the refuse and sewage from London and settlements upstream. He also noted that brewery workers did not contract cholera and on investigation found the owners provided the workers with beer to drink and stated that they likely did not drink water.1 Second, he also painstakingly mapped the incidence of cholera and found a high frequency among those individuals using a particular water pump located on Broad Street. In response to Snow’s advice, local officials removed the pump’s handle,2 resulting in the containment of the Broad Street cholera epidemic.

Snow’s work represents an early epidemiological study and it resulted in the first known public health response to an epidemic. Snow’s meticulous case-tracking methods are now common practice in studying disease outbreaks and in associating new diseases with their causes. His work further shed light on unsanitary sewage practices and the effects of waste dumping in the Thames. Additionally, his work supported the germ theory of disease, which argued disease could be transmitted through contaminated items, including water contaminated with fecal matter.

Snow’s work illustrated what is referred to today as a common source spread of infectious disease, in which there is a single source for all of the individuals infected. In this case, the single source was the contaminated well below the Broad Street pump. Types of common source spread include point source spread, continuous common source spread, and intermittent common source spread. In point source spread of infectious disease, the common source operates for a short time period—less than the incubation period of the pathogen. An example of point source spread is a single contaminated potato salad at a group picnic. In continuous common source spread, the infection occurs for an extended period of time, longer than the incubation period. An example of continuous common source spread would be the source of London water taken downstream of the city, which was continuously contaminated with sewage from upstream. Finally, with intermittent common source spread, infections occur for a period, stop, and then begin again. This might be seen in infections from a well that was contaminated only after large rainfalls and that cleared itself of contamination after a short period.

In contrast to common source spread, propagated spread occurs through direct or indirect person-to-person contact. With propagated spread, there is no single source for infection; each infected individual becomes a source for one or more subsequent infections. With propagated spread, unless the spread is stopped immediately, infections occur for longer than the incubation period. Although point sources often lead to large-scale but localized outbreaks of short duration, propagated spread typically results in longer duration outbreaks that can vary from small to large, depending on the population and the disease (Figure (PageIndex{1})). In addition, because of person-to-person transmission, propagated spread cannot be easily stopped at a single source like point source spread.

Figure (PageIndex{1}): (a) John Snow (1813–1858), British physician and father of epidemiology. (b) Snow’s detailed mapping of cholera incidence led to the discovery of the contaminated water pump on Broad street (red square) responsible for the 1854 cholera epidemic. (credit a: modification of work by “Rsabbatini”/Wikimedia Commons)

Figure (PageIndex{2}): (a) Outbreaks that can be attributed to point source spread often have a short duration. (b) Outbreaks attributed to propagated spread can have a more extended duration. (credit a, b: modification of work by Centers for Disease Control and Prevention)

Florence Nightingale’s work is another example of an early epidemiological study. In 1854, Nightingale was part of a contingent of nurses dispatched by the British military to care for wounded soldiers during the Crimean War. Nightingale kept meticulous records regarding the causes of illness and death during the war. Her recordkeeping was a fundamental task of what would later become the science of epidemiology. Her analysis of the data she collected was published in 1858. In this book, she presented monthly frequency data on causes of death in a wedge chart histogram (Figure (PageIndex{3})). This graphical presentation of data, unusual at the time, powerfully illustrated that the vast majority of casualties during the war occurred not due to wounds sustained in action but to what Nightingale deemed preventable infectious diseases. Often these diseases occurred because of poor sanitation and lack of access to hospital facilities. Nightingale’s findings led to many reforms in the British military’s system of medical care.

Joseph Lister provided early epidemiological evidence leading to good public health practices in clinics and hospitals. These settings were notorious in the mid-1800s for fatal infections of surgical wounds at a time when the germ theory of disease was not yet widely accepted (see Foundations of Modern Cell Theory). Most physicians did not wash their hands between patient visits or clean and sterilize their surgical tools. Lister, however, discovered the disinfecting properties of carbolic acid, also known as phenol (see Using Chemicals to Control Microorganisms). He introduced several disinfection protocols that dramatically lowered post-surgical infection rates.3 He demanded that surgeons who worked for him use a 5% carbolic acid solution to clean their surgical tools between patients, and even went so far as to spray the solution onto bandages and over the surgical site during operations (Figure (PageIndex{4})). He also took precautions not to introduce sources of infection from his skin or clothing by removing his coat, rolling up his sleeves, and washing his hands in a dilute solution of carbolic acid before and during the surgery.

Figure (PageIndex{3}): (a) Florence Nightingale reported on the data she collected as a nurse in the Crimean War. (b) Nightingale’s diagram shows the number of fatalities in soldiers by month of the conflict from various causes. The total number dead in a particular month is equal to the area of the wedge for that month. The colored sections of the wedge represent different causes of death: wounds (pink), preventable infectious diseases (gray), and all other causes (brown).

Figure (PageIndex{4}): Joseph Lister initiated the use of a carbolic acid (phenol) during surgeries. This illustration of a surgery shows a pressurized canister of carbolic acid being sprayed over the surgical site.

Visit the website for The Ghost Map, a book about Snow’s work related to the Broad Street pump cholera outbreak.

John Snow’s own account of his work has additional links and information.

This CDC resource further breaks down the pattern expected from a point-source outbreak.

Learn more about Nightingale’s wedge chart here.

Exercise (PageIndex{1})

  1. Explain the difference between common source spread and propagated spread of disease.
  2. Describe how the observations of John Snow, Florence Nightingale, and Joseph Lister led to improvements in public health.

Types of Epidemiological Studies

Today, epidemiologists make use of study designs, the manner in which data are gathered to test a hypothesis, similar to those of researchers studying other phenomena that occur in populations. These approaches can be divided into observational studies (in which subjects are not manipulated) and experimental studies (in which subjects are manipulated). Collectively, these studies give modern-day epidemiologists multiple tools for exploring the connections between infectious diseases and the populations of susceptible individuals they might infect.

Observational Studies

In an observational study, data are gathered from study participants through measurements (such as physiological variables like white blood cell count), or answers to questions in interviews (such as recent travel or exercise frequency). The subjects in an observational study are typically chosen at random from a population of affected or unaffected individuals. However, the subjects in an observational study are in no way manipulated by the researcher. Observational studies are typically easier to carry out than experimental studies, and in certain situations they may be the only studies possible for ethical reasons.

Observational studies are only able to measure associations between disease occurrence and possible causative agents; they do not necessarily prove a causal relationship. For example, suppose a study finds an association between heavy coffee drinking and lower incidence of skin cancer. This might suggest that coffee prevents skin cancer, but there may be another unmeasured factor involved, such as the amount of sun exposure the participants receive. If it turns out that coffee drinkers work more in offices and spend less time outside in the sun than those who drink less coffee, then it may be possible that the lower rate of skin cancer is due to less sun exposure, not to coffee consumption. The observational study cannot distinguish between these two potential causes.

There are several useful approaches in observational studies. These include methods classified as descriptive epidemiology and analytical epidemiology. Descriptive epidemiology gathers information about a disease outbreak, the affected individuals, and how the disease has spread over time in an exploratory stage of study. This type of study will involve interviews with patients, their contacts, and their family members; examination of samples and medical records; and even histories of food and beverages consumed. Such a study might be conducted while the outbreak is still occurring. Descriptive studies might form the basis for developing a hypothesis of causation that could be tested by more rigorous observational and experimental studies.

Analytical epidemiology employs carefully selected groups of individuals in an attempt to more convincingly evaluate hypotheses about potential causes for a disease outbreak. The selection of cases is generally made at random, so the results are not biased because of some common characteristic of the study participants. Analytical studies may gather their data by going back in time (retrospective studies), or as events unfold forward in time (prospective studies).

Retrospective studies gather data from the past on present-day cases. Data can include things like the medical history, age, gender, or occupational history of the affected individuals. This type of study examines associations between factors chosen or available to the researcher and disease occurrence.

Prospective studies follow individuals and monitor their disease state during the course of the study. Data on the characteristics of the study subjects and their environments are gathered at the beginning and during the study so that subjects who become ill may be compared with those who do not. Again, the researchers can look for associations between the disease state and variables that were measured during the study to shed light on possible causes.

Analytical studies incorporate groups into their designs to assist in teasing out associations with disease. Approaches to group-based analytical studies include cohort studies, case-control studies, and cross-sectional studies. The cohort method examines groups of individuals (called cohorts) who share a particular characteristic. For example, a cohort might consist of individuals born in the same year and the same place; or it might consist of people who practice or avoid a particular behavior, e.g., smokers or nonsmokers. In a cohort study, cohorts can be followed prospectively or studied retrospectively. If only a single cohort is followed, then the affected individuals are compared with the unaffected individuals in the same group. Disease outcomes are recorded and analyzed to try to identify correlations between characteristics of individuals in the cohort and disease incidence. Cohort studies are a useful way to determine the causes of a condition without violating the ethical prohibition of exposing subjects to a risk factor. Cohorts are typically identified and defined based on suspected risk factors to which individuals have already been exposed through their own choices or circumstances.

Case-control studies are typically retrospective and compare a group of individuals with a disease to a similar group of individuals without the disease. Case-control studies are far more efficient than cohort studies because researchers can deliberately select subjects who are already affected with the disease as opposed to waiting to see which subjects from a random sample will develop a disease.

A cross-sectional study analyzes randomly selected individuals in a population and compares individuals affected by a disease or condition to those unaffected at a single point in time. Subjects are compared to look for associations between certain measurable variables and the disease or condition. Cross-sectional studies are also used to determine the prevalence of a condition.

Experimental Studies

Experimental epidemiology uses laboratory or clinical studies in which the investigator manipulates the study subjects to study the connections between diseases and potential causative agents or to assess treatments. Examples of treatments might be the administration of a drug, the inclusion or exclusion of different dietary items, physical exercise, or a particular surgical procedure. Animals or humans are used as test subjects. Because experimental studies involve manipulation of subjects, they are typically more difficult and sometimes impossible for ethical reasons.

Koch’s postulates require experimental interventions to determine the causative agent for a disease. Unlike observational studies, experimental studies can provide strong evidence supporting cause because other factors are typically held constant when the researcher manipulates the subject. The outcomes for one group receiving the treatment are compared to outcomes for a group that does not receive the treatment but is treated the same in every other way. For example, one group might receive a regimen of a drug administered as a pill, while the untreated group receives a placebo (a pill that looks the same but has no active ingredient). Both groups are treated as similarly as possible except for the administration of the drug. Because other variables are held constant in both the treated and the untreated groups, the researcher is more certain that any change in the treated group is a result of the specific manipulation.

Experimental studies provide the strongest evidence for the etiology of disease, but they must also be designed carefully to eliminate subtle effects of bias. Typically, experimental studies with humans are conducted as double-blind studies, meaning neither the subjects nor the researchers know who is a treatment case and who is not. This design removes a well-known cause of bias in research called the placebo effect, in which knowledge of the treatment by either the subject or the researcher can influence the outcomes.

Exercise (PageIndex{2})

  1. Describe the advantages and disadvantages of observational studies and experimental studies.
  2. Explain the ways that groups of subjects can be selected for analytical studies.

part 3

Since laboratory tests had confirmed Salmonella, a common foodborne pathogen, as the etiologic agent, epidemiologists suspected that the outbreak was caused by contamination at a food processing facility serving the region. Interviews with patients focused on food consumption during and after the Thanksgiving holiday, corresponding with the timing of the outbreak. During the interviews, patients were asked to list items consumed at holiday gatherings and describe how widely each item was consumed among family members and relatives. They were also asked about the sources of food items (e.g., brand, location of purchase, date of purchase). By asking such questions, health officials hoped to identify patterns that would lead back to the source of the outbreak.

Analysis of the interview responses eventually linked almost all of the cases to consumption of a holiday dish known as the turducken—a chicken stuffed inside a duck stuffed inside a turkey. Turducken is a dish not generally consumed year-round, which would explain the spike in cases just after the Thanksgiving holiday. Additional analysis revealed that the turduckens consumed by the affected patients were purchased already stuffed and ready to be cooked. Moreover, the pre-stuffed turduckens were all sold at the same regional grocery chain under two different brand names. Upon further investigation, officials traced both brands to a single processing plant that supplied stores throughout the Florida panhandle.

Exercise (PageIndex{3})

  1. Is this an example of common source spread or propagated spread?
  2. What next steps would the public health office likely take after identifying the source of the outbreak?
  • Early pioneers of epidemiology such as John Snow, Florence Nightingale, and Joseph Lister, studied disease at the population level and used data to disrupt disease transmission.
  • Descriptive epidemiology studies rely on case analysis and patient histories to gain information about outbreaks, frequently while they are still occurring.
  • Retrospective epidemiology studies use historical data to identify associations with the disease state of present cases. Prospective epidemiology studies gather data and follow cases to find associations with future disease states.
  • Analytical epidemiology studies are observational studies that are carefully designed to compare groups and uncover associations between environmental or genetic factors and disease.
  • Experimental epidemiology studies generate strong evidence of causation in disease or treatment by manipulating subjects and comparing them with control subjects.

Matching

Match each type of epidemiology study with its description.

___experimentalA. examination of past case histories and medical test results conducted on patients in an outbreak
___analyticalB. examination of current case histories, interviews with patients and their contacts, interpretation of medical test results; frequently conducted while outbreak is still in progress
___prospectiveC. use of a set of test subjects (human or animal) and control subjects that are treated the same as the test subjects except for the specific treatment being studied
___descriptiveD. observing groups of individuals to look for associations with disease
___retrospectiveE. a comparison of a cohort of individuals through the course of the study

C, D, E, B, A

Match each pioneer of epidemiology with his or her contribution.

___Florence NightingaleA. determined the source of a cholera outbreak in London
___Robert KochB. showed that surgical wound infection rates could be dramatically reduced by using carbolic acid to disinfect surgical tools, bandages, and surgical sites
___Joseph ListerC. compiled data on causes of mortality in soldiers, leading to innovations in military medical care
___John SnowD. developed a methodology for conclusively determining the etiology of disease

C, D, B, A

Fill in the Blank

________occurs when an infected individual passes the infection on to other individuals, who pass it on to still others, increasing the penetration of the infection into the susceptible population.

Propagated spread

A batch of food contaminated with botulism exotoxin, consumed at a family reunion by most of the members of a family, would be an example of a ________ outbreak.

point source

Short Answer

What activity did John Snow conduct, other than mapping, that contemporary epidemiologists also use when trying to understand how to control a disease?

Contributor

  • Nina Parker, (Shenandoah University), Mark Schneegurt (Wichita State University), Anh-Hue Thi Tu (Georgia Southwestern State University), Philip Lister (Central New Mexico Community College), and Brian M. Forster (Saint Joseph’s University) with many contributing authors. Original content via Openstax (CC BY 4.0; Access for free at https://openstax.org/books/microbiology/pages/1-introduction)


Portable epidemiology

Smartphone science didn’t start with COVID-19. But the pandemic has spurred researchers to fast-track citizen-science efforts that use smartphones to gather information about the disease. Volunteers can regularly log details about their symptoms, testing status and location through apps or websites. For instance, data from 5 million users of Brownstein’s crowdsourced tracker for influenza and COVID-19 — called Outbreaks Near Me — provided early evidence of the benefits of masking 2 . “You get these impressive insights that would be harder to collect from traditional health care quickly,” he says.

Coronavirus contact-tracing apps: can they slow the spread of COVID-19?

Such projects can be rapidly deployed, an advantage in a fast-moving public-health crisis. Cardiologist Gregory Marcus at the University of California, San Francisco, was able to take his team’s COVID-19 Citizen Science app from concept to 50,000 participants in under a year. “That would be impossible in a conventional research study,” he says. And because users enrol by downloading an app, such studies are easy to scale up and tweak: researchers can add questions about new vaccines or virus variants, for instance.

Claire Steves, who studies ageing at King’s College London, analysed data from the United Kingdom’s COVID Symptom Study app, which has been downloaded by some 4.5 million people. She and her team used data from the app to develop a predictive model that found that a loss of the sense of smell (known as anosmia) was a predictor of testing positive for COVID-19 3 . The team also used data from the app to identify COVID-19 hotspots in the United Kingdom 4 and to establish that long COVID, in which people experience persistent symptoms, was more likely in app users who had reported experiencing more than five COVID-19 symptoms in the first week of illness 5 .

Young children and older adults, who often don’t have smartphones, are under-represented in the COVID Symptom Study, Steves and her colleagues found. But thoughtful outreach, statistical analyses and cross-validation against other research can blunt those biases, she says. By comparing their results to the findings of UK COVID-19 tracking studies that used conventional designs, the team was able to validate its models 4 . “We’ve been able to show that our data very much reflect the data from [those] big population studies.”

Smartphones can track other diseases, too, such as malaria, Zika and dengue, which are spread through mosquitoes. Instead of sending technicians to trap and collect mosquitoes, Craig Williams, a public- and environmental-health scientist at the University of South Australia in Adelaide, posted traps to 126 volunteers in southern Australia and asked them to e-mail him smartphone photos of the trapped insects.

Contact-tracing apps help reduce COVID infections, data suggest

The project, called Mozzie Monitors, provided large-scale mosquito surveillance at 20% of the cost of a comparable professional surveillance programme 6 . “It has been surprising that citizen scientists were able to collect a higher abundance of mosquitoes than professional programmes in the same period, through a low-cost project from their backyards,” says Larissa Braz Sousa, a graduate student on Williams’s team who works on the project.

Williams has since added the option for volunteers in Australia to eschew traps and instead use a third-party app, iNaturalist, to photograph and identify the insects. This led to a nationwide trial in February, dubbed Mozzie Month. “We’re hoping to have the first national citizen-science mosquito-surveillance programme,” he says. “The smartphone is at the centre of that.”


Désirée Lie, MD, MSEd, Clinical Professor, Family Medicine, University of California, Orange Director, Division of Faculty Development, UCI Medical Center, Orange, California. Disclosure: Désirée Lie, MD, MSEd, has disclosed no relevant financial relationships.

Disclosure: Simon Cauchemez, PhD Dominic E. Dwyer, BSc(Med), MBBS, FRACP, FRCPA, MD Holly Seale, BSc, PhD Pamela Cheung, RN Gary Browne, MBBS James Wood, BSc, PhD and Zhanhai Gao, BSc, MSc, PhD, have disclosed no relevant financial relationships. C. Raina MacIntyre, MBBS, FRACP, FAFPHM, M App Epid, PhD, has disclosed that she has received grants for clinical research from 3M. Michael Fasher, MBBS, PhD, has disclosed that he has received grants for educational activities from and has served as an advisor or consultant to GlaxoSmithKline. Robert Booy, MBBS, FRACP, FRCPCH, MSc, MD, has disclosed that he has received grants for clinical research and educational activities from, and has served as an advisor or consultant to, CSL, Roche, Sanofi, GlaxoSmithKline, and Wyeth. All funding received is directed to a research account at The Children&rsquos Hospital at Westmead, Sydney, Australia, and is not personally accepted by Dr. Booy. Neil Ferguson, FmedSci, DPhi, has disclosed that he has served as an advisor or consultant to Crucell Inc.

Abstract

Many countries are stockpiling face masks for use as a nonpharmaceutical intervention to control virus transmission during an influenza pandemic. We conducted a prospective cluster-randomized trial comparing surgical masks, non–fit-tested P2 masks, and no masks in prevention of influenza-like illness (ILI) in households. Mask use adherence was self-reported. During the 2006 and 2007 winter seasons, 286 exposed adults from 143 households who had been exposed to a child with clinical respiratory illness were recruited. We found that adherence to mask use significantly reduced the risk for ILI-associated infection, but <50% of participants wore masks most of the time. We concluded that household use of face masks is associated with low adherence and is ineffective for controlling seasonal respiratory disease. However, during a severe pandemic when use of face masks might be greater, pandemic transmission in households could be reduced. Many countries are stockpiling face masks for use as nonpharmaceutical interventions to reduce viral transmission during an influenza pandemic. We conducted a prospective cluster-randomized trial comparing surgical masks, non–fit-tested P2 masks, and no masks in prevention of influenza-like illness (ILI) in households. During the 2006 and 2007 winter seasons, 286 exposed adults from 143 households who had been exposed to a child with clinical respiratory illness were recruited. Intent-to-treat analysis showed no significant difference in the relative risk of ILI in the mask use groups compared with the control group however, <50% of those in the mask use groups reported wearing masks most of the time. Adherence to mask use was associated with a significantly reduced risk of ILI-associated infection. We concluded that household use of masks is associated with low adherence and is ineffective in controlling seasonal ILI. If adherence were greater, mask use might reduce transmission during a severe influenza pandemic.

Highly pathogenic avian influenza virus A (H5N1) continues to spread globally, posing a serious human pandemic threat. In the event of an influenza pandemic or other emerging respiratory disease such as severe acute respiratory syndrome (SARS), it is likely that antiviral drugs and vaccines will be in short supply or that delivery could be delayed. Therefore, nonpharmaceutical interventions such as mask use, handwashing, and other hygiene measures or school closure might be effective early control strategies. In contrast to pharmaceutical interventions, little is known about the effectiveness of nonpharmaceutical interventions in the community. A recent analysis gives estimates of the effect of school closure (1), and several prospective, randomized controlled trials of handwashing have been published (211). However, clinical trial data on the ability of face masks to reduce respiratory virus transmission in the community are limited to 1 published prospective trial, which showed lack of efficacy (12). In addition, adverse effects of wearing masks (particularly respirators) may affect compliance and effectiveness (1315). Despite the lack of quantitative evidence, many countries have included recommendations in their pandemic plans on the use of face masks (1618). We present the results of a cluster-randomized household study of the effectiveness of using face masks to prevent or reduce transmission of influenza-like illness (ILI).

Methods

A prospective, cluster-randomized trial of mask use in households was conducted during the 2 winter seasons of 2006 and 2007 (August to the end of October 2006 and June to the end of October 2007) in Sydney, Australia. Enrollment in the study was restricted to households with > 2 healthy adults > 16 years of age the adults had known exposure within the household to a child with fever and respiratory symptoms. Suitable households were identified at a pediatric health service comprising the emergency department of a pediatric hospital and a pediatric primary care practice in Sydney, New South Wales, Australia. The study protocol was approved by the local institutional review board.

Randomization and Intervention

Figure 1. . . Flow diagram of recruitment for the prospective cluster-randomized trial, Sydney, New South Wales, Australia, 2006 and 2007 winter influenza seasons.

Participating households were randomized to 1 of 3 arms by a secure computerized randomization process: 1) surgical masks (3M surgical mask, catalogue no. 1820 St. Paul, MN, USA) for 2 adults, to be worn at all times when in the same room as the index child, regardless of the distance from the child 2) P2 masks (3M flat-fold P2 mask, catalogue no. 9320 Bracknell, Berkshire, UK), for 2 adults, to be worn at all times when in the same room as the index child, regardless of the distance from the child and 3) a control group (no masks used). The P2 masks used have an almost identical specification as N95 masks used in the United States (19). According to New South Wales Health guidelines, pamphlets about infection control were provided to participants in all arms. Study participants and trial staff were not blinded, as it is not technically possible to blind the mask type to which participants were randomized. However, laboratory staff were blinded to the arm of randomization. Figure 1 shows the flow diagram for the trial as suggested by CONSORT guidelines (20).

Recruitment and Follow-up

Children 0–15 years of age seeking treatment at pediatric health services with fever (temperature >37.8 o C) and either cough or sore throat were identified by an electronic triage system. Parents or primary caregivers were approached in the waiting room, and that household was invited to join the study if all of the following criteria were satisfied: 1) the household contained > 2 adults > 16 years of age and 1 child 0–15 years of age 2) the index child had fever (temperature >37.8 o C) and either a cough or sore throat 3) the child was the first and only person to become ill in the family in the previous 2 weeks 4) adult caregivers consented to participate in the study and 5) the index child was not admitted to the hospital.

If eligibility criteria were satisfied, adults from the household were enrolled in the study. Enrolled adults and any siblings of the index child were then evaluated for respiratory symptoms and signs (fever, history of fever or feeling feverish in the past week, myalgia, arthralgia, sore throat, cough, sneezing, runny nose, nasal congestion, headache). If any of these symptoms were present, the family and household were excluded. Sociodemographic and medical information including influenza vaccination history (both the index child and participating adults) was obtained using a researcher-administered questionnaire. Medication use was also recorded. The index case-patient had combined nasal (each nostril) and throat swabs collected for multiplex reverse transcription–PCR (RT-PCR) testing. The household was randomized to 1 of the 3 arms, allocated the appropriate mask type, and educated about infection prevention. Formal fit testing of the P2 masks was not performed, but information pertaining to the correct method for fitting and disposing of the masks was provided. Over the next week, participants were contacted by telephone daily to determine if symptoms had developed and to record adherence to mask use throughout the day.

Each household was supplied with a thermometer to measure the temperature of symptomatic adult participants twice daily. If study staff determined that a participant had developed respiratory disease symptoms at follow-up, a home visit was conducted on the same day and the participant was swabbed and tested for respiratory viruses (see methods described below). Symptomatic participants were then followed up daily for 2 weeks.

Because all respiratory pathogens share similar transmission mechanisms—aerosol, droplet, and fomite spread (although the relative role of these factors may vary among different viruses and in different clinical situations)—we deliberately considered a broad definition of clinical cases consistent with a wide range of common respiratory viruses. Respiratory viruses detected in the study included influenza A and B, respiratory syncytial virus (RSV), adenovirus, parainfluenza viruses (PIV) types 1–3, coronaviruses 229E and OC43, human metapneumovirus (hMPV), enteroviruses, and rhinoviruses.

Adherence to face mask use was specifically monitored during each household follow-up. Measuring adherence and reasons for nonadherence is critical for evaluating the efficacy of mask use for reducing treatment and for providing practical advice on future use of face masks. Exit interviews with participants in the surgical mask and the P2 mask arms were conducted to gain further insights into adherence.

Sample Collection and Laboratory Testing

Rayon-tipped, plastic-shafted swabs were inserted separately into each participant’s nostrils and pharynx, placed into viral transport media, and transported immediately to the laboratory or stored at 4 o C if transport was delayed. Nose and throat swabs of index children and adult participants with symptoms of respiratory illness were tested by using nucleic acid and a series of multiplex RT-PCR tests (21) to detect influenza A and B and RSV, PIV types 1–3, picornaviruses (enteroviruses or rhinoviruses), adenoviruses, coronaviruses 229E and OC43, and hMPV.

Case Definition

To include the broadest possible spectrum of clinical syndromes occurring among enrolled adults (22), during follow-up we defined ILI by the presence of fever (temperature >37.8°C), feeling feverish or a history of fever, > 2 symptoms (sore throat, cough, sneezing, runny nose, nasal congestion, headache), or 1 of the symptoms listed plus laboratory confirmation of respiratory viral infection. The choice of a relatively broad clinical case definition was dictated by our interest in interrupting transmission of a broad range of respiratory viruses. Laboratory-confirmed cases during the follow-up were defined by the presence of > 1 of the symptoms listed above plus laboratory detection of a respiratory virus.

Study Outcomes and Analysis

The primary study outcomes in enrolled adults were the presence of ILI or a laboratory diagnosis of respiratory virus infection within 1 week of enrollment. Given that we demonstrated some dual infections and that there may be a variable sensitivity of RT-PCR for different respiratory viruses, we included all incident infections in adults (by clinical case definition and laboratory testing) in the analysis. We also measured the time from recruitment to infection. Causal linking of the outcomes of ILI and adherence to use of face masks required consideration of the timing of both.

Analysis of primary outcomes was by intention to treat. We performed a multivariate Cox proportional-hazards survival analysis to study secondary outcomes and determine how time lag from recruitment to infection of a secondary case-patient was affected by explanatory covariates (23). Gaussian random effects were incorporated in the model to account for the natural clustering of persons in households (24). The day of infection was reconstructed from the day of symptom onset under the assumption that the incubation period was 1–2 days. To account for exposures that occurred before recruitment, the time when survival analysis started was defined as the maximum value between the day of recruitment minus the incubation period and the start of illness in the index case. (For example, assume a household recruited on day 0 and an incubation period of 2 days. If illness in the index case began on day –3, then the survival analysis began on day –2 if illness in the index case began on day –1, then the survival analysis began the same day.)

The following variables were included in the models: daily adherence to use of P2 or surgical masks, number of adults in the household, number of siblings in the household, and index case < 5 years of age. This analysis was performed using the survival package of the statistical software R (www.r-project.org). Comparisons among groups were made with the Fisher exact test for categorical variables. A 2-sided p value < 0.05 was considered significant.

Power Analysis

Assuming a secondary attack rate in exposed adults of 20% and an intraclass correlation coefficient of 30%, we estimated that 94 adults would be needed in each arm of the study to show efficacy of > 75% of P2 or surgical masks at 80% power and with a p value of 0.05. Our efficacy estimate was a conservative assumption based on observational data for the combined effects of all mask types during the SARS epidemic in Hong Kong (25).

Results

Study Population

We recruited 290 adults from 145 families 47 households (94 enrolled adults and 180 children) were randomized to the surgical mask group, 46 (92 enrolled adults and 172 children) to the P2 mask group, and 52 (104 enrolled adults and 192 children) to the no-mask (control) group. Two families in the control group were lost to follow-up during the study. Characteristics of the families who participated are shown in Table 1, with no significant differences noted among the 3 arms.

Samples were collected from 141 children respiratory viruses were detected in 90 (63.8%) children. In 79 (56.0%) of 141 cases, a single pathogen was detected: influenza A in 19/141 (13.5%) influenza B in 7/141 (4.9%) adenoviruses in 7/141 (4.9%) RSV in 5/141 (3.5%) PIV in 8/141 (5.5%) (PIV-1 in 1/141 [0.70%] PIV-2 in 2/141 [1.4%] PIV-3 in 5/141 [3.5%]) hMPV in 8/141 (5.7%) and coronavirus OC43 in 3/141 (2.1%). Other viruses detected included picornaviruses in 22/141 (15.6%): rhinoviruses in 11/22 (50.0%) enteroviruses in 5/22 (22.7%) (enterovirus 68 in 1/5 [20.0%] and others in 4/5 [80.0%]) and uncharacterized nonsequenced picornaviruses in 6/22 (27.0%). An additional 11 children (7.8%) had dual or co-infection: 4 (2.8%) with adenovirus and rhinovirus, 2 (1.4%) with rhinovirus and coronavirus and 1 each with influenza A and enterovirus, influenza A and PIV-2, influenza A and rhinovirus, RSV and enterovirus, and adenovirus and hMPV.

Adherence

Figure 2. . . Compliance with mask use by day over 5 consecutive days during the study, Sydney, New South Wales, Australia, 2006 and 2007 winter influenza seasons.

Characteristics of the adherent versus nonadherent participants who were recruited are shown in Table 2 no significant differences were noted between the 2 groups except for the presence of > 3 adults in the household. On day 1 of mask use, 36 (38%) of the 94 surgical mask users and 42 (46%) of the 92 P2 mask users stated that they were wearing the mask “most or all” of the time. Other participants were wearing face masks rarely or never. The difference between the groups was not significant (p = 0.37). Adherence dropped to 29/94 (31%) and 23/92 (25%), respectively, by day 5 of mask use (Figure 2).

Table 3 shows reported problems with mask use. There were no significant differences in difficulties with mask use between the P2 and surgical mask groups, but > 50% reported concerns, the main one being that wearing a face mask was uncomfortable. Other concerns were that the child did not want the parent wearing a mask and the parent forgot to wear the mask. Additional comments made by some included that the mask did not fit well and that it was not practical to wear at meal time or while asleep. Some adults wore the mask during the day but not at night, even though the sick child was sleeping beside them in their bed.

Intention-to-Treat Analysis

ILI was reported in 21/94 (22.3%) in the surgical group, 14/92 (15.2%) in the P2 group, and 16/100 (16.0%) in the control group, respectively. Samples were collected from 43/51 (84%) sick adults, with respiratory viruses isolated in 17/43 (40%) sick adults. Viral pathogens were isolated from 6/94 (6.4%) in the surgical mask group, 8/92 (8.7%) in the P2 group, and 3/100 (3.0%) in the control group. In 10/17 laboratory-positive cases, the same respiratory virus was isolated in the adult and the child (surgical, 3/94 P2 group, 5/92 and control, 2/100). In 2 cases, the adult was the only person with a laboratory-confirmed virus (1 each from the P2 and surgical groups) in the remaining 5 adults, the virus detected in the child differed from that in the adult (surgical, 2 P2 group, 2 and control group, 1). No dual infections were detected in the adults. Intention-to-treat analysis by households and by participants showed no significant difference between the groups (Table 4).

Risk Factors for ILI

Under the assumption that the incubation period is equal to 1 day (the most probable value for the 2 most common viruses isolated, influenza [21] and rhinovirus [26]), adherent use of P2 or surgical masks significantly reduces the risk for ILI infection, with a hazard ratio equal to 0.26 (95% CI [confidence interval] 0.09–0.77 p = 0.015). No other covariate was significant. Under the less likely assumption that the incubation period is equal to 2 days, the quantified effect of complying with P2 or surgical mask use remains strong, although borderline significant hazard ratio was 0.32 (95% CI 0.11–0.98 p = 0.046). The study was underpowered to determine if there was a difference in efficacy between P2 and surgical masks (Table 5).

Discussion

We present the results of a prospective clinical trial of face mask use conducted in response to an urgent need to clarify the clinical benefit of using masks. The key findings are that < 50% of participants were adherent with mask use and that the intention-to-treat analysis showed no difference between arms. Although our study suggests that community use of face masks is unlikely to be an effective control policy for seasonal respiratory diseases, adherent mask users had a significant reduction in the risk for clinical infection. Another recent study that examined the use of surgical masks and handwashing for the prevention of influenza transmission also found no significant difference between the intervention arms (12).

Our study found that only 21% of household contacts in the face mask arms reported wearing the mask often or always during the follow-up period. Adherence with treatments and preventive measures is well known to vary depending on perception of risk (27) and would be expected to increase during an influenza pandemic. During the height of the SARS epidemic of April and May 2003 in Hong Kong, adherence to infection control measures was high 76% of the population wore a face mask, 65% washed their hands after relevant contact, and 78% covered their mouths when sneezing or coughing (28). In addition, adherence may vary depending on cultural context Asian cultures are more accepting of mask use (29). Therefore, although we found that distributing masks during seasonal winter influenza outbreaks is an ineffective control measure characterized by low adherence, results indicate the potential efficacy of masks in contexts where a larger adherence may be expected, such as during a severe influenza pandemic or other emerging infection.

We estimated that, irrespective of the assumed value for the incubation period (1 or 2 days), the relative reduction in the daily risk of acquiring a respiratory infection associated with adherent mask use (P2 or surgical) was in the range of 60%–80%. Those results are consistent with those of a simpler analysis in which persons were stratified according to adherence (Technical Appendix). We emphasize that this level of risk reduction is dependent on the context, namely, adults in the household caring for a sick child after exposure to a single index case. We urge caution in extrapolating our results to school, workplace, or community contexts, or where multiple, repeated exposures may occur, such as in healthcare settings. The exact mechanism of potential clinical effectiveness of face mask use may be the prevention of inhalation of respiratory pathogens but may also be a reduction in hand-to-face contact. Our study could not determine the relative contributions of these mechanisms. In this study, it is only possible to talk about a statistical association between adherent mask use and reduction in the risk of ILI-infection. The causal link cannot be demonstrated because adherence was not randomized in the trial. Although we found no significant difference in handwashing practices between adherent and non-adherent mask users, it is possible that adherent mask use is correlated with other, unobserved variables that reduce the risk of infection. Further work will therefore be needed to definitively demonstrate that adherent mask use reduces the risk of ILI-infection.

In our study, fit testing for P2 masks was not conducted because this is unlikely to be feasible in the general community during a pandemic. As such, we felt it was more appropriate to determine the efficacy of non–fit-tested masks. We found no difference in adherence between P2 and surgical masks, an important finding, as there is a common belief among healthcare workers that P2 masks are less comfortable. The size of the study did not permit conclusive comparison of the relative efficacy of P2 masks and surgical masks. Given the 5- to 10-fold cost difference between the 2 mask types, quantifying any difference in efficacy between surgical masks and particulate respirators remains a priority that needs to be addressed by a larger trial.

A possible limitation of the study is that some adults may have been incubating infection at the time of enrollment. However, this effect would have biased the results toward the null in the intention-to-treat analysis. The survival analysis explicitly accounted for the existence of a fixed incubation period and incubating infections at the time of enrollment. A potential alternative study design would be to enroll participants from asymptomatic households, do follow-up for development of infection, and then immediately intervene with masks. For such a design, given that only 15%–20% of closely exposed adults will develop illness after exposure to an ill child, thousands of households (rather than hundreds) would be required to afford the same study power. In addition, such a design would have been fraught with underascertainment of incident infections and delayed implementation of mask intervention. We believe ours is a more efficient design. A further limitation is that some parents may have acquired infection outside the home. We identified 5 child–parent pairs with discordant viral infections. The randomization process should have ensured that outside exposure was equally distributed between arms, and this effect would have biased the results toward the null.

In retrospect, relying on laboratory-confirmed cases as the primary outcome may have been unrealistic for a study of this size. ILI in enrolled adults was 17.1%, but laboratory confirmation was modest the virus was identified in only 34.7% of adult ILI cases (the rate of laboratory diagnosis in children was high at 63.8%). However, even intention-to-treat analysis using ILI outcome shows no significant difference between the groups. We used self-reporting to determine adherence previous research indicates that patient self-reporting is more reliable than judgments by doctors or nurses when compared against urine drug levels (30). In addition, the significant association between adherence and clinical protection provides internal validation of self-reporting as a measure.

An important aspect of this study is that we included respiratory viruses other than influenza. Although these viruses may differ in their relative dependence (accurate quantitation of this relativity is uncertain for the various viruses) on different transmission mechanisms (i.e., large droplet, aerosol, or fomite), all are transmitted by the respiratory route. Therefore, face mask use should have some effect on virus transmission (e.g., interference with hand-nose contact), given that participants in all arms of the study received the same infection control advice. In addition, we argue that assessing multiple respiratory viruses allows our results to be generalized more broadly to other infections, including new respiratory viruses that may emerge in the future. Conversely, the low rate of confirmed influenza A or B infection (18.4%) in the study could mean that our findings are not directly applicable to a scenario in which influenza predominates. If influenza is more likely than the other viruses in our study to be transmitted by the respiratory route, the prevalence of mixed infections would tend to bias our results toward the null. However, it is possible that a pandemic strain may have different transmission characteristics than seasonal strains as demonstrated by attack rates in different age groups in pandemics compared with seasonal outbreaks and by the detection of influenza virus in different clinical samples in human influenza virus A (H5N1) cases.

Results of our study have global relevance to respiratory disease control planning, especially with regard to home care. During an influenza pandemic, supplies of antiviral drugs may be limited, and there will be unavoidable delays in the production of a matched pandemic vaccine (31). For new or emerging respiratory virus infections, no pharmaceutical interventions may be available. Even with seasonal influenza, widespread oseltamivir resistance in influenza virus A (H1N1) strains have recently been reported (32). Masks may therefore play an important role in reducing transmission.

Dr MacIntyre is head of the School of Public Health and Community Medicine at the University of New South Wales, Australia, and professor of Infectious Diseases Epidemiology. Her research interests include the detailed understanding of the transmission dynamics and prevention of infectious diseases, particularly respiratory pathogens such as influenza, tuberculosis, and other vaccine-preventable infections.

Acknowledgments

We thank John Horvath, Chief Medical Officer of Australia, for providing us with the opportunity to respond to an urgent policy need for obtaining evidence on the efficacy of using face masks. Thanks also to Noemie Ovdin, Linda Donovan, Sophie Branch, Ken McPhie, and Mala Ratnamohan for laboratory testing Terence Campbell for comments on the manuscript and the staff of the Emergency Department at the Children’s Hospital Westmead and of the primary care practice of Michael Fasher for assisting with the recruitment of study participants.

The Office of Health Protection, Department of Health and Ageing, Australia, 3M Australia, and Medical Research Council (UK) provided funding for this trial. The National Health and Medical Research Council and the School of Pediatrics and Child Health, University of Sydney provided salary support.

References

Figures
Tables

Follow Up


15.2 How Pathogens Cause Disease

For most infectious diseases, the ability to accurately identify the causative pathogen is a critical step in finding or prescribing effective treatments. Today’s physicians, patients, and researchers owe a sizable debt to the physician Robert Koch (1843–1910), who devised a systematic approach for confirming causative relationships between diseases and specific pathogens.

Koch’s Postulates

In 1884, Koch published four postulates (Table 15.3) that summarized his method for determining whether a particular microorganism was the cause of a particular disease. Each of Koch’s postulates represents a criterion that must be met before a disease can be positively linked with a pathogen. In order to determine whether the criteria are met, tests are performed on laboratory animals and cultures from healthy and diseased animals are compared (Figure 15.4).

Koch’s Postulates
(1) The suspected pathogen must be found in every case of disease and not be found in healthy individuals.
(2) The suspected pathogen can be isolated and grown in pure culture.
(3) A healthy test subject infected with the suspected pathogen must develop the same signs and symptoms of disease as seen in postulate 1.
(4) The pathogen must be re-isolated from the new host and must be identical to the pathogen from postulate 2.

In many ways, Koch’s postulates are still central to our current understanding of the causes of disease. However, advances in microbiology have revealed some important limitations in Koch’s criteria. Koch made several assumptions that we now know are untrue in many cases. The first relates to postulate 1, which assumes that pathogens are only found in diseased, not healthy, individuals. This is not true for many pathogens. For example, H. pylori, described earlier in this chapter as a pathogen causing chronic gastritis, is also part of the normal microbiota of the stomach in many healthy humans who never develop gastritis. It is estimated that upwards of 50% of the human population acquires H. pylori early in life, with most maintaining it as part of the normal microbiota for the rest of their life without ever developing disease.

Koch’s second faulty assumption was that all healthy test subjects are equally susceptible to disease. We now know that individuals are not equally susceptible to disease. Individuals are unique in terms of their microbiota and the state of their immune system at any given time. The makeup of the resident microbiota can influence an individual’s susceptibility to an infection. Members of the normal microbiota play an important role in immunity by inhibiting the growth of transient pathogens. In some cases, the microbiota may prevent a pathogen from establishing an infection in others, it may not prevent an infection altogether but may influence the severity or type of signs and symptoms. As a result, two individuals with the same disease may not always present with the same signs and symptoms. In addition, some individuals have stronger immune systems than others. Individuals with immune systems weakened by age or an unrelated illness are much more susceptible to certain infections than individuals with strong immune systems.

Koch also assumed that all pathogens are microorganisms that can be grown in pure culture (postulate 2) and that animals could serve as reliable models for human disease. However, we now know that not all pathogens can be grown in pure culture, and many human diseases cannot be reliably replicated in animal hosts. Viruses and certain bacteria, including Rickettsia and Chlamydia , are obligate intracellular pathogens that can grow only when inside a host cell. If a microbe cannot be cultured, a researcher cannot move past postulate 2. Likewise, without a suitable nonhuman host, a researcher cannot evaluate postulate 2 without deliberately infecting humans, which presents obvious ethical concerns. AIDS is an example of such a disease because the human immunodeficiency virus (HIV) only causes disease in humans.

Check Your Understanding

Molecular Koch’s Postulates

In 1988, Stanley Falkow (1934–) proposed a revised form of Koch’s postulates known as molecular Koch’s postulates . These are listed in the left column of Table 15.4. The premise for molecular Koch’s postulates is not in the ability to isolate a particular pathogen but rather to identify a gene that may cause the organism to be pathogenic.

Falkow’s modifications to Koch’s original postulates explain not only infections caused by intracellular pathogens but also the existence of pathogenic strains of organisms that are usually nonpathogenic. For example, the predominant form of the bacterium Escherichia coli is a member of the normal microbiota of the human intestine and is generally considered harmless. However, there are pathogenic strains of E. coli such as enterotoxigenic E. coli ( ETEC ) and enterohemorrhagic E. coli (O157:H7) (EHEC). We now know ETEC and EHEC exist because of the acquisition of new genes by the once-harmless E. coli, which, in the form of these pathogenic strains, is now capable of producing toxins and causing illness. The pathogenic forms resulted from minor genetic changes. The right-side column of Table 15.4 illustrates how molecular Koch’s postulates can be applied to identify EHEC as a pathogenic bacterium.

Molecular Koch’s Postulates Applied to EHEC
Molecular Koch’s Postulates Application to EHEC
(1) The phenotype (sign or symptom of disease) should be associated only with pathogenic strains of a species. EHEC causes intestinal inflammation and diarrhea, whereas nonpathogenic strains of E. coli do not.
(2) Inactivation of the suspected gene(s) associated with pathogenicity should result in a measurable loss of pathogenicity. One of the genes in EHEC encodes for Shiga toxin, a bacterial toxin (poison) that inhibits protein synthesis. Inactivating this gene reduces the bacteria’s ability to cause disease.
(3) Reversion of the inactive gene should restore the disease phenotype. By adding the gene that encodes the toxin back into the genome (e.g., with a phage or plasmid), EHEC’s ability to cause disease is restored.

As with Koch’s original postulates, the molecular Koch’s postulates have limitations. For example, genetic manipulation of some pathogens is not possible using current methods of molecular genetics. In a similar vein, some diseases do not have suitable animal models, which limits the utility of both the original and molecular postulates.

Check Your Understanding

Pathogenicity and Virulence

The ability of a microbial agent to cause disease is called pathogenicity , and the degree to which an organism is pathogenic is called virulence . Virulence is a continuum. On one end of the spectrum are organisms that are avirulent (not harmful) and on the other are organisms that are highly virulent. Highly virulent pathogens will almost always lead to a disease state when introduced to the body, and some may even cause multi-organ and body system failure in healthy individuals. Less virulent pathogens may cause an initial infection, but may not always cause severe illness. Pathogens with low virulence would more likely result in mild signs and symptoms of disease, such as low-grade fever, headache, or muscle aches. Some individuals might even be asymptomatic.

An example of a highly virulent microorganism is Bacillus anthracis , the pathogen responsible for anthrax . B. anthracis can produce different forms of disease, depending on the route of transmission (e.g., cutaneous injection, inhalation, ingestion). The most serious form of anthrax is inhalation anthrax. After B. anthracis spores are inhaled, they germinate. An active infection develops and the bacteria release potent toxins that cause edema (fluid buildup in tissues), hypoxia (a condition preventing oxygen from reaching tissues), and necrosis (cell death and inflammation). Signs and symptoms of inhalation anthrax include high fever, difficulty breathing, vomiting and coughing up blood, and severe chest pains suggestive of a heart attack. With inhalation anthrax, the toxins and bacteria enter the bloodstream, which can lead to multi-organ failure and death of the patient. If a gene (or genes) involved in pathogenesis is inactivated, the bacteria become less virulent or nonpathogenic.

Virulence of a pathogen can be quantified using controlled experiments with laboratory animals. Two important indicators of virulence are the median infectious dose (ID50) and the median lethal dose (LD50) , both of which are typically determined experimentally using animal models. The ID50 is the number of pathogen cells or virions required to cause active infection in 50% of inoculated animals. The LD50 is the number of pathogenic cells, virions, or amount of toxin required to kill 50% of infected animals. To calculate these values, each group of animals is inoculated with one of a range of known numbers of pathogen cells or virions. In graphs like the one shown in Figure 15.5, the percentage of animals that have been infected (for ID50) or killed (for LD50) is plotted against the concentration of pathogen inoculated. Figure 15.5 represents data graphed from a hypothetical experiment measuring the LD50 of a pathogen. Interpretation of the data from this graph indicates that the LD50 of the pathogen for the test animals is 10 4 pathogen cells or virions (depending upon the pathogen studied).

Table 15.5 lists selected foodborne pathogens and their ID50 values in humans (as determined from epidemiologic data and studies on human volunteers). Keep in mind that these are median values. The actual infective dose for an individual can vary widely, depending on factors such as route of entry the age, health, and immune status of the host and environmental and pathogen-specific factors such as susceptibility to the acidic pH of the stomach. It is also important to note that a pathogen’s infective dose does not necessarily correlate with disease severity. For example, just a single cell of Salmonella enterica serotype Typhimurium can result in an active infection. The resultant disease, Salmonella gastroenteritis or salmonellosis , can cause nausea, vomiting, and diarrhea, but has a mortality rate of less than 1% in healthy adults. In contrast, S. enterica serotype Typhi has a much higher ID50, typically requiring as many as 1,000 cells to produce infection. However, this serotype causes typhoid fever , a much more systemic and severe disease that has a mortality rate as high as 10% in untreated individuals.

ID50 for Selected Foodborne Diseases 4
Pathogen ID50
Viruses
Hepatitis A virus 10–100
Norovirus 1–10
Rotavirus 10–100
Bacteria
Escherichia coli, enterohemorrhagic (EHEC, serotype O157) 10–100
E. coli, enteroinvasive (EIEC) 200–5,000
E. coli, enteropathogenic (EPEC) 10,000,000–10,000,000,000
E. coli, enterotoxigenic (ETEC) 10,000,000–10,000,000,000
Salmonella enterica serovar Typhi <1,000
S. enterica serovar Typhimurium ≥1
Shigella dysenteriae 10–200
Vibrio cholerae (serotypes O139, O1) 1,000,000
V. parahemolyticus 100,000,000
Protozoa
Giardia lamblia 1
Cryptosporidium parvum 10–100

Check Your Understanding

  • What is the difference between a pathogen’s infective dose and lethal dose?
  • Which is more closely related to the severity of a disease?

Primary Pathogens versus Opportunistic Pathogens

Pathogens can be classified as either primary pathogens or opportunistic pathogens. A primary pathogen can cause disease in a host regardless of the host’s resident microbiota or immune system. An opportunistic pathogen , by contrast, can only cause disease in situations that compromise the host’s defenses, such as the body’s protective barriers, immune system, or normal microbiota. Individuals susceptible to opportunistic infections include the very young, the elderly, women who are pregnant, patients undergoing chemotherapy, people with immunodeficiencies (such as acquired immunodeficiency syndrome [AIDS]), patients who are recovering from surgery, and those who have had a breach of protective barriers (such as a severe wound or burn).

An example of a primary pathogen is enterohemorrhagic E. coli ( EHEC ), which produces a virulence factor known as Shiga toxin . This toxin inhibits protein synthesis, leading to severe and bloody diarrhea, inflammation, and renal failure, even in patients with healthy immune systems. Staphylococcus epidermidis , on the other hand, is an opportunistic pathogen that is among the most frequent causes of nosocomial disease. 5 S. epidermidis is a member of the normal microbiota of the skin, where it is generally avirulent. However, in hospitals, it can also grow in biofilms that form on catheters, implants, or other devices that are inserted into the body during surgical procedures. Once inside the body, S. epidermidis can cause serious infections such as endocarditis, and it produces virulence factors that promote the persistence of such infections.

Other members of the normal microbiota can also cause opportunistic infections under certain conditions. This often occurs when microbes that reside harmlessly in one body location end up in a different body system, where they cause disease. For example, E. coli normally found in the large intestine can cause a urinary tract infection if it enters the bladder. This is the leading cause of urinary tract infections among women.

Members of the normal microbiota may also cause disease when a shift in the environment of the body leads to overgrowth of a particular microorganism. For example, the yeast Candida is part of the normal microbiota of the skin, mouth, intestine, and vagina, but its population is kept in check by other organisms of the microbiota. If an individual is taking antibacterial medications, however, bacteria that would normally inhibit the growth of Candida can be killed off, leading to a sudden growth in the population of Candida, which is not affected by antibacterial medications because it is a fungus. An overgrowth of Candida can manifest as oral thrush (growth on mouth, throat, and tongue), a vaginal yeast infection , or cutaneous candidiasis . Other scenarios can also provide opportunities for Candida infections. Untreated diabetes can result in a high concentration of glucose in the saliva, which provides an optimal environment for the growth of Candida, resulting in thrush. Immunodeficiencies such as those seen in patients with HIV, AIDS, and cancer also lead to higher incidence of thrush. Vaginal yeast infections can result from decreases in estrogen levels during the menstruation or menopause. The amount of glycogen available to lactobacilli in the vagina is controlled by levels of estrogen when estrogen levels are low, lactobacilli produce less lactic acid. The resultant increase in vaginal pH allows overgrowth of Candida in the vagina.

Check Your Understanding

  • Explain the difference between a primary pathogen and an opportunistic pathogen.
  • Describe some conditions under which an opportunistic infection can occur.

Stages of Pathogenesis

To cause disease, a pathogen must successfully achieve four steps or stages of pathogenesis : exposure (contact), adhesion (colonization), invasion, and infection. The pathogen must be able to gain entry to the host, travel to the location where it can establish an infection, evade or overcome the host’s immune response, and cause damage (i.e., disease) to the host. In many cases, the cycle is completed when the pathogen exits the host and is transmitted to a new host.

Exposure

An encounter with a potential pathogen is known as exposure or contact . The food we eat and the objects we handle are all ways that we can come into contact with potential pathogens. Yet, not all contacts result in infection and disease. For a pathogen to cause disease, it needs to be able to gain access into host tissue. An anatomic site through which pathogens can pass into host tissue is called a portal of entry . These are locations where the host cells are in direct contact with the external environment. Major portals of entry are identified in Figure 15.6 and include the skin, mucous membranes, and parenteral routes.

Mucosal surfaces are the most important portals of entry for microbes these include the mucous membranes of the respiratory tract, the gastrointestinal tract, and the genitourinary tract. Although most mucosal surfaces are in the interior of the body, some are contiguous with the external skin at various body openings, including the eyes, nose, mouth, urethra, and anus.

Most pathogens are suited to a particular portal of entry. A pathogen’s portal specificity is determined by the organism’s environmental adaptions and by the enzymes and toxins they secrete. The respiratory and gastrointestinal tracts are particularly vulnerable portals of entry because particles that include microorganisms are constantly inhaled or ingested, respectively.

Pathogens can also enter through a breach in the protective barriers of the skin and mucous membranes. Pathogens that enter the body in this way are said to enter by the parenteral route . For example, the skin is a good natural barrier to pathogens, but breaks in the skin (e.g., wounds, insect bites, animal bites, needle pricks) can provide a parenteral portal of entry for microorganisms.

In pregnant women, the placenta normally prevents microorganisms from passing from the mother to the fetus. However, a few pathogens are capable of crossing the blood-placental barrier. The gram-positive bacterium Listeria monocytogenes , which causes the foodborne disease listeriosis, is one example that poses a serious risk to the fetus and can sometimes lead to spontaneous abortion. Other pathogens that can pass the placental barrier to infect the fetus are known collectively by the acronym TORCH (Table 15.6).

Transmission of infectious diseases from mother to baby is also a concern at the time of birth when the baby passes through the birth canal. Babies whose mothers have active chlamydia or gonorrhea infections may be exposed to the causative pathogens in the vagina, which can result in eye infections that lead to blindness. To prevent this, it is standard practice to administer antibiotic drops to infants’ eyes shortly after birth.

Pathogens Capable of Crossing the Placental Barrier (TORCH Infections)
Disease Pathogen
T Toxoplasmosis Toxoplasma gondii (protozoan)
O 6 Syphilis
Chickenpox
Hepatitis B
HIV
Fifth disease (erythema infectiosum)
Treponema pallidum (bacterium)
Varicella-zoster virus (human herpesvirus 3)
Hepatitis B virus (hepadnavirus)
Retrovirus
Parvovirus B19
R Rubella (German measles) Togavirus
C Cytomegalovirus Human herpesvirus 5
H Herpes Herpes simplex viruses (HSV) 1 and 2

Clinical Focus

Part 2

At the clinic, a physician takes down Michael’s medical history and asks about his activities and diet over the past week. Upon learning that Michael became sick the day after the party, the physician orders a blood test to check for pathogens associated with foodborne diseases. After tests confirm that presence of a gram-positive rod in Michael’s blood, he is given an injection of a broad-spectrum antibiotic and sent to a nearby hospital, where he is admitted as a patient. There he is to receive additional intravenous antibiotic therapy and fluids.

  • Is this bacterium in Michael’s blood part of normal microbiota?
  • What portal of entry did the bacteria use to cause this infection?

Jump to the next Clinical Focus box. Go back to the previous Clinical Focus box.

Adhesion

Following the initial exposure, the pathogen adheres at the portal of entry. The term adhesion refers to the capability of pathogenic microbes to attach to the cells of the body using adhesion factors , and different pathogens use various mechanisms to adhere to the cells of host tissues.

Molecules (either proteins or carbohydrates) called adhesins are found on the surface of certain pathogens and bind to specific receptors (glycoproteins) on host cells. Adhesins are present on the fimbriae and flagella of bacteria, the cilia of protozoa, and the capsids or membranes of viruses. Protozoans can also use hooks and barbs for adhesion spike proteins on viruses also enhance viral adhesion. The production of glycocalyces (slime layers and capsules) (Figure 15.7), with their high sugar and protein content, can also allow certain bacterial pathogens to attach to cells.

Biofilm growth can also act as an adhesion factor. A biofilm is a community of bacteria that produce a glycocalyx, known as extrapolymeric substance (EPS) , that allows the biofilm to attach to a surface. Persistent Pseudomonas aeruginosa infections are common in patients suffering from cystic fibrosis, burn wounds, and middle-ear infections (otitis media) because P. aeruginosa produces a biofilm. The EPS allows the bacteria to adhere to the host cells and makes it harder for the host to physically remove the pathogen. The EPS not only allows for attachment but provides protection against the immune system and antibiotic treatments, preventing antibiotics from reaching the bacterial cells within the biofilm. In addition, not all bacteria in a biofilm are rapidly growing some are in stationary phase. Since antibiotics are most effective against rapidly growing bacteria, portions of bacteria in a biofilm are protected against antibiotics. 7

Invasion

Once adhesion is successful, invasion can proceed. Invasion involves the dissemination of a pathogen throughout local tissues or the body. Pathogens may produce exoenzymes or toxins, which serve as virulence factors that allow them to colonize and damage host tissues as they spread deeper into the body. Pathogens may also produce virulence factors that protect them against immune system defenses. A pathogen’s specific virulence factors determine the degree of tissue damage that occurs. Figure 15.8 shows the invasion of H. pylori into the tissues of the stomach, causing damage as it progresses.

Intracellular pathogens achieve invasion by entering the host’s cells and reproducing. Some are obligate intracellular pathogens (meaning they can only reproduce inside of host cells) and others are facultative intracellular pathogens (meaning they can reproduce either inside or outside of host cells). By entering the host cells, intracellular pathogens are able to evade some mechanisms of the immune system while also exploiting the nutrients in the host cell.

Entry to a cell can occur by endocytosis . For most kinds of host cells, pathogens use one of two different mechanisms for endocytosis and entry. One mechanism relies on effector proteins secreted by the pathogen these effector proteins trigger entry into the host cell. This is the method that Salmonella and Shigella use when invading intestinal epithelial cells. When these pathogens come in contact with epithelial cells in the intestine, they secrete effector molecules that cause protrusions of membrane ruffles that bring the bacterial cell in. This process is called membrane ruffling . The second mechanism relies on surface proteins expressed on the pathogen that bind to receptors on the host cell, resulting in entry. For example, Yersinia pseudotuberculosis produces a surface protein known as invasin that binds to beta-1 integrins expressed on the surface of host cells.

Some host cells, such as white blood cells and other phagocytes of the immune system, actively endocytose pathogens in a process called phagocytosis. Although phagocytosis allows the pathogen to gain entry to the host cell, in most cases, the host cell kills and degrades the pathogen by using digestive enzymes. Normally, when a pathogen is ingested by a phagocyte, it is enclosed within a phagosome in the cytoplasm the phagosome fuses with a lysosome to form a phagolysosome, where digestive enzymes kill the pathogen (see Pathogen Recognition and Phagocytosis). However, some intracellular pathogens have the ability to survive and multiply within phagocytes. Examples include Listeria monocytogenes and Shigella these bacteria produce proteins that lyse the phagosome before it fuses with the lysosome, allowing the bacteria to escape into the phagocyte’s cytoplasm where they can multiply. Bacteria such as Mycobacterium tuberculosis , Legionella pneumophila , and Salmonella species use a slightly different mechanism to evade being digested by the phagocyte. These bacteria prevent the fusion of the phagosome with the lysosome, thus remaining alive and dividing within the phagosome.

Infection

Following invasion, successful multiplication of the pathogen leads to infection. Infections can be described as local, focal, or systemic, depending on the extent of the infection. A local infection is confined to a small area of the body, typically near the portal of entry. For example, a hair follicle infected by Staphylococcus aureus infection may result in a boil around the site of infection, but the bacterium is largely contained to this small location. Other examples of local infections that involve more extensive tissue involvement include urinary tract infections confined to the bladder or pneumonia confined to the lungs.

In a focal infection , a localized pathogen, or the toxins it produces, can spread to a secondary location. For example, a dental hygienist nicking the gum with a sharp tool can lead to a local infection in the gum by Streptococcus bacteria of the normal oral microbiota. These Streptococcus spp. may then gain access to the bloodstream and make their way to other locations in the body, resulting in a secondary infection.

When an infection becomes disseminated throughout the body, we call it a systemic infection . For example, infection by the varicella-zoster virus typically gains entry through a mucous membrane of the upper respiratory system. It then spreads throughout the body, resulting in the classic red skin lesions associated with chickenpox . Since these lesions are not sites of initial infection, they are signs of a systemic infection.

Sometimes a primary infection , the initial infection caused by one pathogen, can lead to a secondary infection by another pathogen. For example, the immune system of a patient with a primary infection by HIV becomes compromised, making the patient more susceptible to secondary diseases like oral thrush and others caused by opportunistic pathogens. Similarly, a primary infection by Influenzavirus damages and decreases the defense mechanisms of the lungs, making patients more susceptible to a secondary pneumonia by a bacterial pathogen like Haemophilus influenzae or Streptococcus pneumoniae . Some secondary infections can even develop as a result of treatment for a primary infection. Antibiotic therapy targeting the primary pathogen can cause collateral damage to the normal microbiota, creating an opening for opportunistic pathogens (see Case in Point: A Secondary Yeast Infection).

Case in Point

A Secondary Yeast Infection

Anita, a 36-year-old mother of three, goes to an urgent care center complaining of pelvic pressure, frequent and painful urination, abdominal cramps, and occasional blood-tinged urine. Suspecting a urinary tract infection (UTI) , the physician requests a urine sample and sends it to the lab for a urinalysis. Since it will take approximately 24 hours to get the results of the culturing, the physician immediately starts Anita on the antibiotic ciprofloxacin. The next day, the microbiology lab confirms the presence of E. coli in Anita’s urine, which is consistent with the presumptive diagnosis. However, the antimicrobial susceptibility test indicates that ciprofloxacin would not effectively treat Anita’s UTI, so the physician prescribes a different antibiotic.

After taking her antibiotics for 1 week, Anita returns to the clinic complaining that the prescription is not working. Although the painful urination has subsided, she is now experiencing vaginal itching, burning, and discharge. After a brief examination, the physician explains to Anita that the antibiotics were likely successful in killing the E. coli responsible for her UTI however, in the process, they also wiped out many of the “good” bacteria in Anita’s normal microbiota. The new symptoms that Anita has reported are consistent with a secondary yeast infection by Candida albicans, an opportunistic fungus that normally resides in the vagina but is inhibited by the bacteria that normally reside in the same environment.

To confirm this diagnosis, a microscope slide of a direct vaginal smear is prepared from the discharge to check for the presence of yeast. A sample of the discharge accompanies this slide to the microbiology lab to determine if there has been an increase in the population of yeast causing vaginitis. After the microbiology lab confirms the diagnosis, the physician prescribes an antifungal drug for Anita to use to eliminate her secondary yeast infection.

Check Your Understanding

Transmission of Disease

For a pathogen to persist, it must put itself in a position to be transmitted to a new host, leaving the infected host through a portal of exit (Figure 15.9). As with portals of entry, many pathogens are adapted to use a particular portal of exit. Similar to portals of entry, the most common portals of exit include the skin and the respiratory, urogenital, and gastrointestinal tracts. Coughing and sneezing can expel pathogens from the respiratory tract. A single sneeze can send thousands of virus particles into the air. Secretions and excretions can transport pathogens out of other portals of exit. Feces, urine, semen, vaginal secretions, tears, sweat, and shed skin cells can all serve as vehicles for a pathogen to leave the body. Pathogens that rely on insect vectors for transmission exit the body in the blood extracted by a biting insect. Similarly, some pathogens exit the body in blood extracted by needles.


22.4 Bacterial Diseases in Humans

By the end of this section, you will be able to do the following:

  • Identify bacterial diseases that caused historically important plagues and epidemics
  • Describe the link between biofilms and foodborne diseases
  • Explain how overuse of antibiotics may be creating “super bugs”
  • Explain the importance of MRSA with respect to the problems of antibiotic resistance

To a prokaryote, humans may be just another housing opportunity. Unfortunately, the tenancy of some species can have harmful effects and cause disease. Bacteria or other infectious agents that cause harm to their human hosts are called pathogens . Devastating pathogen-borne diseases and plagues, both viral and bacterial in nature, have affected humans and their ancestors for millions of years. The true cause of these diseases was not understood until modern scientific thought developed, and many people thought that diseases were a “spiritual punishment.” Only within the past several centuries have people understood that staying away from afflicted persons, disposing of the corpses and personal belongings of victims of illness, and sanitation practices reduced their own chances of getting sick.

Epidemiologists study how diseases are transmitted and how they affect a population. Often, they must following the course of an epidemic —a disease that occurs in an unusually high number of individuals in a population at the same time. In contrast, a pandemic is a widespread, and usually worldwide, epidemic. An endemic disease is a disease that is always present, usually at low incidence, in a population.

Long History of Bacterial Disease

There are records about infectious diseases as far back as 3000 B.C. A number of significant pandemics caused by bacteria have been documented over several hundred years. Some of the most memorable pandemics led to the decline of cities and entire nations.

In the 21 st century, infectious diseases remain among the leading causes of death worldwide, despite advances made in medical research and treatments in recent decades. A disease spreads when the pathogen that causes it is passed from one person to another. For a pathogen to cause disease, it must be able to reproduce in the host’s body and damage the host in some way.

The Plague of Athens

In 430 B.C., the Plague of Athens killed one-quarter of the Athenian troops who were fighting in the great Peloponnesian War and weakened Athens’s dominance and power. The plague impacted people living in overcrowded Athens as well as troops aboard ships that had to return to Athens. The source of the plague may have been identified recently when researchers from the University of Athens were able to use DNA from teeth recovered from a mass grave. The scientists identified nucleotide sequences from a pathogenic bacterium, Salmonella enterica serovar Typhi (Figure 22.20), which causes typhoid fever. 3 This disease is commonly seen in overcrowded areas and has caused epidemics throughout recorded history.

Bubonic Plagues

From 541 to 750, the Plague of Justinian, an outbreak of what was likely bubonic plague, eliminated one-quarter to one-half of the human population in the eastern Mediterranean region. The population in Europe dropped by 50 percent during this outbreak. Astoundingly, bubonic plague would strike Europe more than once!

Bubonic plague is caused by the bacterium Yersinia pestis. One of the most devastating pandemics attributed to bubonic plague was the Black Death (1346 to 1361). It is thought to have originated in China and spread along the Silk Road, a network of land and sea trade routes, to the Mediterranean region and Europe, carried by fleas living on black rats that were always present on ships. The Black Death was probably named for the tissue necrosis (Figure 22.21c) that can be one of the symptoms. The "buboes" of bubonic plague were painfully swollen areas of lymphatic tissue. A pneumonic form of the plague, spread by the coughing and sneezing of infected individuals, spreads directly from human to human and can cause death within a week. The pneumonic form was responsible for the rapid spread of the Black Death in Europe. The Black Death reduced the world’s population from an estimated 450 million to about 350 to 375 million. Bubonic plague struck London yet again in the mid-1600s (Figure 22.21). In modern times, approximately 1,000 to 3,000 cases of plague arise globally each year, and a “sylvatic” form of plague, carried by fleas living on rodents such as prairie dogs and black footed ferrets, infects 10 to 20 people annually in the American Southwest. Although contracting bubonic plague before antibiotics meant almost certain death, the bacterium responds to several types of modern antibiotics, and mortality rates from plague are now very low.

Link to Learning

Watch a video on the modern understanding of the Black Death—bubonic plague in Europe during the 14 th century.

Migration of Diseases to New Populations

One of the negative consequences of human exploration was the accidental “biological warfare” that resulted from the transport of a pathogen into a population that had not previously been exposed to it. Over the centuries, Europeans tended to develop genetic immunity to endemic infectious diseases, but when European conquerors reached the western hemisphere, they brought with them disease-causing bacteria and viruses, which triggered epidemics that completely devastated many diverse populations of Native Americans, who had no natural resistance to many European diseases. It has been estimated that up to 90 percent of Native Americans died from infectious diseases after the arrival of Europeans, making conquest of the New World a foregone conclusion.

Emerging and Re-emerging Diseases

The distribution of a particular disease is dynamic. Changes in the environment, the pathogen, or the host population can dramatically impact the spread of a disease. According to the World Health Organization (WHO), an emerging disease (Figure 22.22) is one that has appeared in a population for the first time, or that may have existed previously but is rapidly increasing in incidence or geographic range. This definition also includes re-emerging diseases that were previously under control. Approximately 75 percent of recently emerging infectious diseases affecting humans are zoonotic diseases. Zoonoses are diseases that primarily infect animals but can be transmitted to humans some are of viral origin and some are of bacterial origin. Brucellosis is an example of a prokaryotic zoonosis that is re-emerging in some regions, and necrotizing fasciitis (commonly known as flesh-eating bacteria) has been increasing in virulence for the last 80 years for unknown reasons.

Some of the present emerging diseases are not actually new, but are diseases that were catastrophic in the past (Figure 22.23). They devastated populations and became dormant for a while, just to come back, sometimes more virulent than before, as was the case with bubonic plague. Other diseases, like tuberculosis, were never eradicated but were under control in some regions of the world until coming back, mostly in urban centers with high concentrations of immunocompromised people. WHO has identified certain diseases whose worldwide re-emergence should be monitored. Among these are three viral diseases (dengue fever, yellow fever, and zika), and three bacterial diseases (diphtheria, cholera, and bubonic plague). The war against infectious diseases has no foreseeable end.

Foodborne Diseases

Prokaryotes are everywhere: They readily colonize the surface of any type of material, and food is not an exception. Most of the time, prokaryotes colonize food and food-processing equipment in the form of a biofilm, as we have discussed earlier. Outbreaks of bacterial infection related to food consumption are common. A foodborne disease (commonly called “food poisoning”) is an illness resulting from the consumption the pathogenic bacteria, viruses, or other parasites that contaminate food. Although the United States has one of the safest food supplies in the world, the U.S. Centers for Disease Control and Prevention (CDC) has reported that “76 million people get sick, more than 300,000 are hospitalized, and 5,000 Americans die each year from foodborne illness.”

The characteristics of foodborne illnesses have changed over time. In the past, it was relatively common to hear about sporadic cases of botulism, the potentially fatal disease produced by a toxin from the anaerobic bacterium Clostridium botulinum. Some of the most common sources for this bacterium were non-acidic canned foods, homemade pickles, and processed meat and sausages. The can, jar, or package created a suitable anaerobic environment where Clostridium could grow. Proper sterilization and canning procedures have reduced the incidence of this disease.

While people may tend to think of foodborne illnesses as associated with animal-based foods, most cases are now linked to produce. There have been serious, produce-related outbreaks associated with raw spinach in the United States and with vegetable sprouts in Germany, and these types of outbreaks have become more common. The raw spinach outbreak in 2006 was produced by the bacterium E. coli serotype O157:H7. A serotype is a strain of bacteria that carries a set of similar antigens on its cell surface, and there are often many different serotypes of a bacterial species. Most E. coli are not particularly dangerous to humans, but serotype O157:H7 can cause bloody diarrhea and is potentially fatal.

All types of food can potentially be contaminated with bacteria. Recent outbreaks of Salmonella reported by the CDC occurred in foods as diverse as peanut butter, alfalfa sprouts, and eggs. A deadly outbreak in Germany in 2010 was caused by E. coli contamination of vegetable sprouts (Figure 22.24). The strain that caused the outbreak was found to be a new serotype not previously involved in other outbreaks, which indicates that E. coli is continuously evolving. Outbreaks of listeriosis, due to contamination of meats, raw cheeses, and frozen or fresh vegetables with Listeria monocytogenes, are becoming more frequent.

Biofilms and Disease

Recall that biofilms are microbial communities that are very difficult to destroy. They are responsible for diseases such as Legionnaires’ disease, otitis media (ear infections), and various infections in patients with cystic fibrosis. They produce dental plaque and colonize catheters, prostheses, transcutaneous and orthopedic devices, contact lenses, and internal devices such as pacemakers. They also form in open wounds and burned tissue. In healthcare environments, biofilms grow on hemodialysis machines, mechanical ventilators, shunts, and other medical equipment. In fact, 65 percent of all infections acquired in the hospital (nosocomial infections) are attributed to biofilms. Biofilms are also related to diseases contracted from food because they colonize the surfaces of vegetable leaves and meat, as well as food-processing equipment that isn’t adequately cleaned.

Biofilm infections develop gradually and may not cause immediate symptoms. They are rarely resolved by host defense mechanisms. Once an infection by a biofilm is established, it is very difficult to eradicate, because biofilms tend to be resistant to most methods used to control microbial growth, including antibiotics. The matrix that attaches the cells to a substrate and to other another protects the cells from antibiotics or drugs. In addition, since biofilms grow slowly, they are less responsive to agents that interfere with cell growth. It has been reported that biofilms can resist up to 1,000 times the antibiotic concentrations used to kill the same bacteria when they are free-living or planktonic. An antibiotic dose that large would harm the patient therefore, scientists are working on new ways to get rid of biofilms.

Antibiotics: Are We Facing a Crisis?

The word antibiotic comes from the Greek anti meaning “against” and bios meaning “life.” An antibiotic is a chemical, produced either by microbes or synthetically, that is hostile to or prevents the growth of other organisms. Today’s media often address concerns about an antibiotic crisis. Are the antibiotics that easily treated bacterial infections in the past becoming obsolete? Are there new “superbugs”—bacteria that have evolved to become more resistant to our arsenal of antibiotics? Is this the beginning of the end of antibiotics? All these questions challenge the healthcare community.

One of the main causes of antibiotic resistance in bacteria is overexposure to antibiotics. The imprudent and excessive use of antibiotics has resulted in the natural selection of resistant forms of bacteria. The antibiotic kills most of the infecting bacteria, and therefore only the resistant forms remain. These resistant forms reproduce, resulting in an increase in the proportion of resistant forms over non-resistant ones. In addition to transmission of resistance genes to progeny, lateral transfer of resistance genes on plasmids can rapidly spread these genes through a bacterial population. A major misuse of antibiotics is in patients with viral infections like colds or the flu, against which antibiotics are useless. Another problem is the excessive use of antibiotics in livestock. The routine use of antibiotics in animal feed promotes bacterial resistance as well. In the United States, 70 percent of the antibiotics produced are fed to animals. These antibiotics are given to livestock in low doses, which maximize the probability of resistance developing, and these resistant bacteria are readily transferred to humans.

Link to Learning

Watch a recent news report on the problem of routine antibiotic administration to livestock and antibiotic-resistant bacteria.

One of the Superbugs: MRSA

The imprudent use of antibiotics has paved the way for the expansion of resistant bacterial populations. For example, Staphylococcus aureus, often called “staph,” is a common bacterium that can live in the human body and is usually easily treated with antibiotics. However, a very dangerous strain, methicillin-resistant Staphylococcus aureus (MRSA) has made the news over the past few years (Figure 22.25). This strain is resistant to many commonly used antibiotics, including methicillin, amoxicillin, penicillin, and oxacillin. MRSA can cause infections of the skin, but it can also infect the bloodstream, lungs, urinary tract, or sites of injury. While MRSA infections are common among people in healthcare facilities, they have also appeared in healthy people who haven’t been hospitalized, but who live or work in tight populations (like military personnel and prisoners). Researchers have expressed concern about the way this latter source of MRSA targets a much younger population than those residing in care facilities. The Journal of the American Medical Association reported that, among MRSA-afflicted persons in healthcare facilities, the average age is 68, whereas people with “community-associated MRSA” ( CA-MRSA ) have an average age of 23. 4

In summary, the medical community is facing an antibiotic crisis. Some scientists believe that after years of being protected from bacterial infections by antibiotics, we may be returning to a time in which a simple bacterial infection could again devastate the human population. Researchers are developing new antibiotics, but it takes many years of research and clinical trials, plus financial investments in the millions of dollars, to generate an effective and approved drug.

Career Connection

Epidemiologist

Epidemiology is the study of the occurrence, distribution, and determinants of health and disease in a population. It is, therefore, part of public health. An epidemiologist studies the frequency and distribution of diseases within human populations and environments.

Epidemiologists collect data about a particular disease and track its spread to identify the original mode of transmission. They sometimes work in close collaboration with historians to try to understand the way a disease evolved geographically and over time, tracking the natural history of pathogens. They gather information from clinical records, patient interviews, surveillance, and any other available means. That information is used to develop strategies, such as vaccinations (Figure 22.26), and design public health policies to reduce the incidence of a disease or to prevent its spread. Epidemiologists also conduct rapid investigations in case of an outbreak to recommend immediate measures to control it.


Introduction

With a new outbreak of infectious disease erupting every 12 to 18 months somewhere in the world, it is of utmost importance that the public have an understanding of infectious diseases, and the skills and vocabulary needed to find and apply the information in order to respond appropriately.

This site provides an engaging and scientifically accurate set of instructional materials and resources aimed at deepening high school students’ understanding of infectious diseases and enhancing students’ skills in seeking out additional information they need to make informed decisions, and influence their behaviors in response to future outbreaks and epidemics that undoubtedly will occur

Using an overarching storyline of the recent Ebola epidemic and measles outbreak, as well as some emerging material on COVID-19, the resources are organized into four modules that can be used as a coherent unit of instruction or individually as separate topics.


Tracking Infectious Diseases

In 1976, the World Health Organization (WHO) used an System/370 at the United Nations’ International Computing Center in Geneva to precisely map trends and outbreaks of smallpox so it could best allocate its limited personnel and resources to the locations most in need. This effort contributed to the eventual eradication of the disease in the general population a few years later.

From the IBM archives

Images from the sales brochure for the System/370, which debuted in June 1970.

Since that time, IBM has evolved from enabling such efforts to becoming a leader in applying technology to improve healthcare—from identifying trends and demographics to help halt the spread of disease to increasing the ability of government institutions to deal with epidemics and pandemics. IBM has ushered in a new age of healthcare and bioinformatics by funding diverse, cross-disciplinary research, which—combined with an institutional strength in systems thinking and analysis—helps researchers better understand the organic, emergent nature of information systems, whether in businesses, cities or diseases.

Today, IBM researchers are partnering with the EuResist Network—a scientific collaboration led by universities from Germany, Italy and Sweden—focused on creating prediction models for HIV combination drug therapy for specific patients with specific viral profiles. The web-based analytical tool allows doctors to input patient data and receive a recommendation for the optimal drug cocktail. Recommendations have proven to be more than 76 percent accurate in their predictions, beating human experts 9 out of 10 times.

In 2008, IBM joined with the Nuclear Threat Initiative's Global Health and Security Initiative and the Middle East Consortium on Infectious Disease Surveillance to create a unique technology that standardizes the method of sharing health information and automates the analysis of infectious disease outbreaks, in order to help contain diseases and minimize their impact. The web-based portal system, the Public Health Information Affinity Domain (PHIAD), provides public health organizations with the right decision-making tools to implement a fast, effective response to infectious disease outbreaks—even across geographic and political boundaries. PHIAD uses near-real-time information to facilitate fast response, and helps enable the secure exchange of data on both national and international levels with appropriate protection of privacy at all levels.

IBM also created the World Community Grid, a philanthropic venture that allows volunteer participants from around the world to donate their computer processing power to help solve large-scale analytical challenges. IBM provides free hosting, maintenance and support for the grid, as well as the hardware, software, technical services and infrastructure expertise. To date, World Community Grid has tackled the development of HIV/AIDS medication, muscular dystrophy and cancer research, human genome and proteome folding, clean energy research, and a project dedicated to improving the availability of nutritious rice crops globally.

In 2006, IBM and more than 20 major worldwide public health institutions, including the WHO and the US Centers for Disease Control and Prevention, announced the Global Pandemic Initiative, a collaborative effort to help stem the spread of infectious diseases. IBM scientists formed healthcare "Innovation Centers" at the company's worldwide research laboratories to work with the global healthcare community in this collaborative effort.

Following the development of the IBM Spatiotemporal Epidemiological Modeler (STEM) tool, designed to help forecast the spread of infectious diseases, IBM donated STEM as an open source tool to the Eclipse Project. Eclipse was created by IBM in November 2001 and is supported by a consortium of software vendors in a vendor-neutral and open, transparent community.

STEM is a platform-agnostic application that allows users to create models of emerging infectious diseases. With STEM, researchers and public health officials can understand the spread of a disease over time and can put in place preventive measures to help halt the spread of the disease.

In 2009 through 2010, Mexico City found itself as an epicenter of the H1N1 flu pandemic. With about half of Mexico’s entire population living within the city, officials from the Mexico City government feared a pandemic like the 1918 Spanish Flu that killed between 50 and 100 million people.

With 6000 to 7000 confirmed cases, the government approached IBM in the hopes that STEM could help them calculate the spread of the disease. In a no-cost collaborative effort with the Government of the Federal District (GDF), IBM led workshops for the GDF on STEM and PHIAD, an on-demand public health industry system for sharing clinical and public health data.

After officials took preventive steps to close schools and restaurants for 7 to 9 days, IBM worked with the GDF using STEM to measure the impact. The study showed that their policies helped lower transmission by 22 percent.

STEM is in use today in a variety of applications. IBM worked with the Israeli Center for Disease Control to predict and track the spread of seasonal influenza. A zoonotic (animal disease) researcher visiting the United States from Thailand heard about STEM and is exploring using the tool to create global models of mosquito densities.

In Vermont, IBM researchers are using STEM to model disease outbreaks in the state. They have created a model at the town/city level and are using transportation corridors—interstates and highways—as the pathways for disease spread. They are investigating the potential spread of pandemic influenza in a variety of scenarios and examining how various interventions might mitigate the spread of the disease.

From world-leading research to international collaborations, IBM continues to shape the way the world can use technology to control outbreaks, improve healthcare and save lives.


NIAID, WCS Scientists Show Feasibility of Tracking Bats, Ebola With GPS Collars

GPS tracking devices placed on hammer-headed bats in the Republic of the Congo have given NIAID scientists and colleagues the first detailed look at the daily routines of these suspected Ebola virus reservoirs.

The work is important: No researcher has isolated live Ebola virus in any bat species—though antibodies against Ebola virus proteins and fragments of the Ebola virus genome have been found in the hammer-headed bat. Those findings make scientists believe that hammer-headed bats could maintain Ebola virus in nature.

Tracking bat movements in the northern Republic of the Congo, near the village of Libonga, allows researchers to better understand bat patterns, including how often and when they visit villages. Future work, in a collaboration involving NIAID and the Wildlife Conservation Society (WCS), will involve studying how bats respond to stresses across seasons. Scientists will analyze stress data with Ebola virus surveillance, movement, demographic, and environmental data to help understand when bats are most likely to shed virus and come in contact with people. The project is part of a larger study to learn whether environmental factors—such as the length of the rainy season or high temperatures during the dry season—affect the behavior of bats and other wildlife. Scientists hope the results can help detect or predict viral disease outbreaks.

Since 2011, a NIAID research group that studies the relationship between ecology and viruses has trapped, sampled, released and watched bats near Libonga. In coordination with scientists from WCS, in December 2017 the groups began a pilot project using radio collars to track 10 bats for just over one week. In April 2018, they started a second pilot project using solar-powered GPS trackers placed on 11 bats, four females and seven males. The group tracked one female bat for about one year.

PLOS One published the results of the two pilot projects on Oct. 1. The study highlights a “lek”—a collective mating site for bats—that the researchers discovered. They also learned that female bats fly much further from the lek than males for a total of about 10 kilometers each day. Males tend to stay within about 1 kilometer of the lek, visiting the site frequently but otherwise having little daily movement. The scientists concluded that females visit the lek exclusively for mating.

The scientists say the study information will improve GPS-based bat tracking, in terms of technology and data-gathering choices, but also knowing when and where to place staff in relation to a lek and daily roosting sites. Ultimately, they believe that protecting human health in the region is connected to learning more about the relationship between bats and viruses.


With Mental Health Awareness Month in May, the BMC Series presents a focus issue on ‘Understanding Mental Health’. We have brought together content to highlight research into understanding and improving mental health.

The 2020 Nobel Prize for Physiology or Medicine was awarded to Harvey J. Alter, Michael Houghton and Charles M. Rice for their discovery of Hepatitis C virus. In celebration of what they started and showcasing areas of research that have since emerged, we here present a collection of articles from the BMC Biology, BMC Medicine, Genome Medicine and the BMC Series Journals on biology, diagnosis and treatment and wider public health and medical outcomes of the Hepatitis C virus and its infection.


Watch the video: Stats in Action: Tracking Infectious Diseases (December 2022).