March 14, 2011
Last week, we officially launched the Pneumococcal Modeling (PneuMOD) Project on the CDDEP website. Publications and tools will be continually added as the project proceeds, but to kick things off we would like to provide the necessary background for the project and examine the global burden and epidemiology of pneumococcal diseases.
According to the World Health Organization, pneumococcus is the number one infectious killer in the world. Pneumococcal pneumonia, bacteremia, meningitis, and other pneumococcal diseases pose the highest risk for infants and children under the age of 5 and for those over the age of 65. Analysis is a challenging endeavor because the culprit bacteria, Streptococcus pneumoniae (SP), is not the cause of a unique disease, nor is it the unique pathogen leading to the diseases associated with it. The spread of the S. pneumoniae bacteria to the lungs causes pneumonia. But while SP is the most common cause, invasion of the lungs by viruses, fungi, parasites, or other bacteria also results in pneumonia. In fact, SP pneumonia was confused with other types of pneumonia until the discovery of Gram Stain in 1884. Similarly, when SP spreads to the protective membranes covering the brain and spinal cord, it culminates in meningitis. But again, viruses, fungi, parasites, and other bacteria may be the cause of a meningitis infection. The case is similar with other diseases and infections resulting from the spread of SP to normally sterile areas of the body.
Despite the challenges involved in dissecting the fraction of SP as a cause of infection, there have been a few recent studies that estimate pneumococcal disease incidence and related deaths. Based on data from 2000, there are between 11 and 18 million under 5-year-old pneumococcal cases each year and approximately 826,000 deaths globally. These estimates attribute around 11% of all under-5 deaths to pneumococci. Less data is available on other lower risk age groups.
In addition to variation across age groups, there is a significant geographic disparity in the pathogen’s epidemiology. The developing world is under the greatest strain, and economics literature has found a cyclical relationship between health and productivity – health raises productivity, in turn raising health by making nutritious foods and health services more affordable. In addition to high mortality and direct treatment costs, individuals and societies are hampered by lower productivity due to lost work time for patients and those who take care of them, and lost future earnings. Moreover, when pneumonia occurs along with meningitis, the risk of neuropsychological sequelae surges drastically.
Asymptomatic carriage (colonization) in the nasopharynx is generally two to three times higher in developing countries, reaching prevalence as high as 80% for under 5-year-olds in some countries. While colonized individuals often never acquire an invasive pneumococcal disease (IPD), they are the reservoir of pneumococcus and are a major factor in its spread. The incidence rate in Africa was estimated at 3,627 per 100,000 under 5-year-olds and in South East Asia at 2,991. In contrast, in Europe in 2000 prior to release of the 7-valent pneumococcal conjugate vaccine (PCV7), the incidence rate was 504 per 100,000 in the same age group.
While the highest pneumococcal burden is in Africa, we will take a quick look at Asia, where the Asian Strategic Alliance for Pneumococcal Disease Prevention (ASAP), established in 2007, has begun collecting data. Disease incidence and mortality rates vary across the region, PCV7 covers between 56.9% of disease serotypes in a study of under 5-year-olds in India, and 89.8% of the same age group according to a study in Honk Kong. Resistance to penicillin, a common antibiotic used to treat the pneumococcal infections, ranges from 1% in a Philippine study to 55.4% in a South Korea study.
Such variation is partly caused due to study designs. However, differences may derive from genetic factors, climate, altitude, health care access, prevalence of health care conditions (such as HIV), differences in antibiotic resistance levels, socio-economic conditions, population density, and population structure. In turn, these factors could have important implications for herd immunity, serotype replacement (which we will discuss further in a forthcoming blog post), the spread of pneumococcus, and generally for how to tackle the disease burden. Moreover, they make it very difficult to extrapolate from setting to setting and define consistent policy.
This is where PneuMOD comes in. While more data from across the globe would be helpful, there are several obstacles to both financing and study design. For example, conducting a longitudinal study could be difficult due to highly mobile populations with unpredictable patterns. Although the models are data hungry, PneuMOD can circumvent many of the challenges to data collection by modeling various interventions to reduce the pneumococcal burden in different settings with different parameters. PneuMOD can also help identify the important factors in disease transmission, answer interesting epidemiological questions and, at the same time, provide important context to develop intervention policy recommendations across scenarios and geographies.
Image credit: Flikr: cambodia4kidsorg
