This week The Economist has an article about Asthmapolis in the science section.
In an interview with the BBC about an article published by his group in the Lancet, Fernando Martinez, from the University of Arizona, said: “If you have a daily drug and a very significant number are not taking it, then that tells you it’s a losing strategy.”
The BBC summarizes: “Researchers at the University of Arizona believe there is a problem with the way the disease is managed.”
I think Dr. Martinez has framed lack of adherence in a different and potentially productive way.
The stabilizing prevalence of asthma and the origins of the disease
Statistics Canada recently reported that the prevalence of asthma among 2-7 year old children had declined to its lowest level in more than 10 years, from 13.2 percent in 2000-01 to 9.8 percent in 2008-09, the most recent year for which data are available.
The findings, drawn from the National Longitudinal Survey of Children and Youth, echo many others that have previously pointed to a plateau and decline in asthma prevalence in the last decade in many high-income countries. Reports in respiratory journals began to appear as early as 2000 suggesting that the number of cases of asthma appeared to be stabilizing in some countries and even subsiding in others. These data imply that in Canada, exposure to the cause(s) of the epidemic is no longer increasing, and could even be declining.
The changing trends in asthma prevalence highlight fundamental shortcomings in our current epidemiological theories. Just when it appeared that we finally had the evidence and theoretical framework to explain the increasing prevalence of asthma worldwide, we now rather suddenly have to account for an inexplicable and unanticipated decline.
The hygiene hypothesis, for example, emerged as a predominant explanation for the epidemic of asthma. It suggested that lifestyle changes accompanying westernization and modernization had reduced exposure to microbes that had previously played a valuable role in training the immune system during childhood. It proved to be a useful explanation that made sense of important findings of lower rates of asthma among children raised on farms, for example. But the hygiene hypotheses could never really adequately account for rising rates of asthma in inner cities, and now, of course, it lacks the power to explain the decline in asthma.
Official reports announcing the declining prevalence of asthma in Canada pin it on reductions in the rates of tobacco smoking and improvements in other environmental exposures. In the report, Eleanor Thomas from Statistics Canada writes:
“A number of environmental factors may be related to the recent declines in childhood…asthma: changes in the population structure; changes in diagnostic practices; decreases in the prevalence of respiratory allergies; improvements in air quality; changes in hygiene practices (particularly, in child care settings); and reductions in children’s exposure to cigarette smoke at home….[R]educed exposure to tobacco smoke may be contributing to the decreased prevalence of…asthma among young children.”
But the situation in asthma epidemiology is actually quite a bit more complex. Statistics Canada has it mostly wrong about the environmental exposures it scrutinizes. And they have it wrong in a way that productively highlights an important difference between public health and population health.
Primary and secondary risk factors in asthma epidemiology
The key difference hinges on the distinction between primary and secondary risk factors. Statistics Canada, and much of asthma related public health at the moment, is focused on tracking secondary risk factors for asthma. Secondary risk factors are those inputs that influence which individuals in a population will develop asthma. By contrast, primary risk factors are those that determine the overall level of asthma in the population.
Tobacco smoke exposure offers a useful example. Over the years, research has repeatedly established that tobacco smoke exposure increases the odds that a given child will develop asthma. However, we also know that tobacco smoke exposure cannot, in and of itself, be responsible for the increase in asthma cases in the latter half of the twentieth century. The asthma epidemic occurred in populations at a time when rates of tobacco smoking were declining. Tobacco smoke exposure and asthma prevalence, in other words, demonstrate counter trends at the population level.
Statistics Canada’s own evidence on tobacco use bears this out. Here is a figure showing the prevalence of cigarette smoking between 1985 and 2001.
Cigarette smoking (above) declined from 35.1% in 1985 to 21.7% in 2001 while asthma prevalence (below) rose from approximately 3% in 1984 to more than 13% in 2000-01.
In addition to tobacco smoking, Statistics Canada highlighted improving environmental factors — improvements in air quality specifically — as potential contributors to the recent decline in asthma rates. But again we have evidence that atmospheric pollutants cannot be responsible for the epidemic of asthma because exposure to these risk factors has been reduced at the population level over the same time period. The following figure illustrates changes in the mean concentration of particulate matter (2.5 microns) as measured by Canadian National Air Pollution Surveillance network between 1990 and 2001.
The story is the same whether you look at mean annual concentrations of particulate matter (PM2.5 concentration in 2001 was 27% lower than in 1990, while the PM10 level was 34% lower), carbon monoxide (34% lower in 2001) sulfur dioxide (32% lower in 2001), or nitrogen oxides (NO in 2001 21% lower, while NO2 concentration15% lower). [For the full report see Environmental Protection Series National Air Pollution Surveillance (NAPS) Network. Air Quality in Canada: 2001 Summary and 1990-2001 Trend Analysis. Report EPS 7/AP/36 May 2004.]
In short, air quality in Canada improved in many places during the time period when there was a rather uniformly increasing trend in the prevalence of asthma, and continued to do so when the rates of asthma stabilized and began to decline. One cannot attribute only the recent decline in asthma prevalence to improving air quality.
What we see in these explanations is that many clinicians, researchers and epidemiologists and public health agencies tend to focus on associations and explanations involving secondary risk factors, such as tobacco smoke or air pollution, and to develop and field interventions that address them. There are certainly valuable reasons for doing so. These kinds of exposures are more ascertainable in clinical encounters, they are often modifiable by patients, and their control and mitigation may have an important positive effect on the individual and the day-to-day manifestation of their asthma. Indeed, the evidence from Statistics Canada suggests exactly that:
“A key finding is that the percentage of children with asthma who reported an asthma attack in the past 12 months fell steadily from 53 per cent in the mid-1990s to 36 per cent last year.”
In short, children with asthma in Canada are being less frequently exposed to asthma triggers and, as a result, reporting fewer asthma attacks. Good news. Reduced tobacco smoke exposure certainly deserves credit for contributing to this decline.
It should be evident, then, that there are clear, important risk factors for asthma that play a significant role in the natural history of the disease in a given patient, but which, all the same, cannot be responsible for the epidemic.
These secondary risk factors are not (necessarily) the same things that we would choose to focus on if we were trying to change the overall rate of asthma in the population. As Jeroen Douwes and Neil Pearce, two international asthma experts, wrote in a 2006 editorial:
“Thus, the ‘established’ risk factors for asthma do not appear to explain the global prevalence patterns and time trends. These risk factors were ‘discovered’ primarily on the basis of clinical studies and case reports of exacerbations in asthma patients. It is natural for physicians and patients to assume that the factors involved in secondary causation may also be important for primary causation. However, for most of the ‘established’ risk factors, the evidence of primary causation is relatively weak, and risk factors such as allergen exposure do not appear to explain the prevalence patterns and time trends.”
The population health possibilities
In his 1985 article “Sick Individuals and Sick Populations,” Geoffrey Rose made the observation that small changes in the risk of a disease, when summed across a population, could result in dramatic shifts in the prevalence of a disease. Although he didn’t consider the concept of primary and secondary risk factors, his work demonstrated how risk factors insignificant to an individual could be meaningful to the population, and prompted a reevaluation of public health preventive strategies.
The growing evidence of a plateau and decline in asthma should have had similarly major implications for our view of the (partially understood) potential etiologic mechanisms underlying the asthma epidemic and the origins of asthma. So far, however, the signs of shift to a population health perspective have been minimal. In part, our ability to think about and understand the complex causes of the long increase in asthma and the plateau and decline has been hampered by the continuing orientation of epidemiology and public health to surveillance and analysis of secondary risk factors.
In the case of asthma, it remains a struggle to identify primary risk factors from available public health data. We are limited to retrospective and outdated data on only the most severe exacerbations (those that result in hospitalizations and, in some states, emergency room visits). Altogether, that amounts to just a tiny fraction of the daily burden of asthma morbidity.
Moreover, our surveillance system is designed to ignore potentially valuable information about where and when the attack began, whether that is at home, at school, at work, or out in the community. Typically the only geographic information available is the billing address. As a result, we cannot understand the small area variation in rates of asthma that we know exists.
New approaches to chronic disease surveillance and epidemiology are emerging that, if guided by a population health perspective, have enormous potential to advance our understanding of chronic diseases. It is already clear that these systems will be increasingly driven by participatory ethics (see the 2010 article by Freifeld et al. in PLoS Medicine) and will collect and rapidly make sense of valuable new streams of data, drawn from distributed networks of sensors and connected devices. They will bring growing numbers of people (see notes below) and their daily experiences of health, disease, medicine and the environment under increasing statistical scrutiny (cf. RWJF Project HealthDesign), with many potential benefits. It is likely that such approaches will generate new, useful clinical knowledge. A recent analysis of data volunteered by individuals using CureTogether, for example, has identified a new symptom marker that predicts negative response to a specific migraine treatment.
But the materialization of an epidemiological advance from these tools will be more complex and less certain. It will, in fact, turn on our ability to use them to marshal a new hunt for primary causes – that will complement the traditional clinical and public health focus on secondary risk factors that has dominated them so far.
Even then, there may still be very difficult challenges. For example, it is worth considering the possibility that it may be too late to search for the primary cause(s) of a number of chronic diseases in the urban areas of high-income countries. In these settings, exposure to the primary causes of a given disease may already be ubiquitous across the population. Without variability in exposure to the disease’s primary causes, all that would be revealed are the effects of secondary risk factors. Similarly, repeated investigations over short periods will only be informative if they capture a dramatic, and thus unusual, change in lifestyle or environmental exposures.
In other words, our odds of making fundamental discoveries are tied to our ability to develop tools and systems that help us collectively uncover a new list of primary risk factors and hypotheses. To do this might mean we engage communities that display a broad range of prevalence (urban vs. rural areas of low income countries), or populations that abruptly change their environment and lifestyle (immigrants, refugees, adoptees, etc.) Such efforts would have a much greater probability of identifying primary causes of the chronic diseases we’re targeting, especially if the confounding role of secondary causes is also accounted for.
Our new systems should help us explain the overall rates of asthma in populations, its global gradients and time trends, and do so with some evolutionary probability. We need to pause to reflect on the u-turn that the prevalence of asthma has done and re-double our efforts to piece together the rules that underlie the surge, pause and decline in the prevalence of asthma. It will also almost certainly mean evolving our approaches to better sample experience, understand context, lifestyle and environment, and to look for connections where there may not be evidence of an effect on individuals.
In a recent article in Science on open mHealth architecture, Deborah Estrin and Ida Sim noted that if only 1 out of every 250 patients in the US taking antidepressants participated in a collaborative study of their long term efficacy, more than 100,000 patients enrolled would exceed the total number of patients enrolled in all antidepressant studies conducted worldwide since 2005.
This week, the Economist has two brief overviews of low cost medical technology innovation in China and India and the potential implications for health care in these countries and in high income settings. See How China and India can help cut Western bills and Frugal Healing.
The articles offer some interesting insight into how joint ventures are working (Medtronic) and how and why multinationals are attracted to a “culture of frugality,” but two other points caught my attention. One is the noted ability of local firms to overcome skepticism of their lower cost products with incredible amounts of clinical research (the article cites MicroPort – a Chinese heart stent manufacturer). The other is the suggestion by Rachel Lee (of Boston Consulting Group) that the “in-country labs set up by foreign firms are doing better at cutting-edge research than are their local rivals.” I really wanted more details about what kinds of research she is referring to.
Finally, I like the quote attributed to Omar Ishrak of GE that the term “frugal innovation” “understates the revolution under way.” He points to how “firms in emerging markets are leapfrogging the latest technologies, such as miniaturization, mobile communications and advanced materials.” The result – both cheaper and better products.
The Health Interview Survey, conducted by the National Center for Health Statistics, asks respondents:
“Now, I’d like to read you a short list of different kinds of pain. Please say for each one, on roughly how many days–if any–in the last 12 months you have had that type of pain…How many days in the last year have you had headaches?”
The question is then repeated for backaches, stomach pains, joint pains, muscle pains, and dental pains.
I don’t think I could answer that question. Seems to me we’re asking people for things that they can’t tell us.
This is an update and expansion of a post I wrote for the Health 2.0 Developer Challenge earlier this summer. The original is here.
With the recent launch of the Community Health Data Initiative (CHDI) and the emergence of a growing number of health apps, the magnitude of health data and the variety and number of tools to explore and analyze that data is expanding quickly. But sitting in the audience on June 2 at the HHS/IOM CHDI forum, watching teams demonstrate search and visualization applications for community health data, I found myself thinking about an unrealized opportunity: The lack of means to pool our capabilities and organize cooperative action around health data and its exploration.
What I envisioned while watching the health app demos that day was the inevitability of redundant searches by visitors to these sites. How many thousands of people would map air pollution against asthma prevalence, or the association between obesity and the distribution of parks or grocery stores. On the whole, such engagement is a sizable positive step, encouraging interest in the epidemiology of health, healthcare and the environment. But so far, health apps stop short of delivering tools to create with the data.
As an epidemiologist, what I’m hoping to see emerge is a way to capture the knowledge created by the data exploration carried out by community members. In short, a way to distill scientific value from widespread interactions with health data – if only to help map out dark corners. In fact, there are many health problems for which the data are (vastly) more abundant than the time to analyze it. In most cases, scientists cannot explore all the plausible associations between a single disease and the hundreds – if not thousands – of potentially relevant variables and other diseases in even one dataset, such as NHANES. There is also a scarcity of perspectives in professional science; many discoveries are made as a result of the variability amateurs bring to the puzzles. The topics of searches themselves will reveal community interests and priorities, and the results of those searches – if captured and analyzed in realtime – could highlight important data gaps or suggest potential, previously unknown relationships worth investigating.
So, while opening up new horizons of health data and providing tools for people to interact with this information, we can also design parallel tools to encourage popular epidemiology. It is easy to envision a health app that stores graphs and visualizations created by individuals, ties them to their constituent datasets, and provides parallel community discussions. Something that aims to capture the emergent, collective analysis of newly available US health data. It should encourage communities, agencies, amateur or citizen scientists, professional scientists, media outlets, and healthcare providers, to cooperate on raising and answering specific questions by using our collective cognitive surplus (see Clay Shirky) to mine the new horizons of data and the possibilities that occur in the minds of individuals from every corner and background.
It could be productively modeled on distributed version control systems, like Git, which enable a diverse group of individuals to participate collaboratively in the creation of software. One can imagine a health app where individuals or groups could create repositories focused on a given health research question or topic. These repositories would contain annotated data, methods, tabular and graphical results, documentation, discussions and supporting code. Most importantly, they would have a trajectory that develops as material and knowledge in the repository accumulates.
For example, a person or group with an interest in examining regional variability in tuberculosis incidence could check out a repository, carry out an analysis, and then write a short note summarizing and committing their results to the pool. Another individual could review or replicate the same work, see the same results, and vote to underscore its accuracy or importance. Subsequent visitors could review the latest information, reach the decision that a new variable, say air quality, should be included in the analysis. At this point, the person could fork the repository, import relevant air quality data and begin a new branch of research.
Open data will be more valuable if we also capture and share open methods. One way to seed such analyses would be to encourage university epidemiology and health sciences faculty (as well as public health practitioners and agencies) to construct curriculum and lesson plans around community health data. In other words, to write and post exercises that teach some aspect of epidemiology by examining the relationship between variables in health data. If students carried out different versions of these exercises each time, the repository of methods and results and interpretations would develop. Similarly, a growing number of projects (such as Educate to Innovate, Teaching Opportunities for Partners in Science; and Retirees Enhancing Science Education through Experiments and Demonstrations; National Lab Day) recruit volunteer scientists and technology educators in an attempt to increase the performance of students in science, technology and engineering and to bring science, statistical and data literacy to local communities. We can start to assemble such a volunteer army for health data, and provide them with the tools to make them successful.
In fact, there is no requirement (other than simplicity) to stop at the analysis of existing data. While rather more complicated, we can imagine (and work towards) health apps that engage communities explicitly in the actual collection of data. Given the increasing difficulties involved in enrolling participants in national population-based surveys, such as NHANES, the time for exploring alternative methods of population-based surveys is in order. The academic health community has for some time been trying with mixed success to bring communities more into the research process. Providing tools to create value from health data – and the guidance of a community of volunteer experts – will complement and speed research and improvements to public health.
The International Journal of Tuberculosis and Lung Disease, the journal of the International Union, just published an article I was really happy to see. It argues that while hypoxemia is a big public health problem in developing countries, a lack of access to oxygen treatment and pulse oximetry gets “little or no attention” in global health circles. From what I can gather, the article is right on target. In the abstract, the authors write:
“Improving access to oxygen and pulse oximetry has demonstrated a reduction in mortality from childhood pneumonia by up to 35% in high-burden child pneumonia settings. The cost-effectiveness of an oxygen systems strategy compares favourably with other higher profile child survival interventions, such as new vaccines. In addition to its use in treating acute respiratory illness, oxygen treatment is required for the optimal management of many other conditions in adults and children, and is essential for safe surgery, anaesthesia and obstetric care.”
Unfortunately for a self-proclaimed “call to international action,” access to the full text of the article costs $29.27. While presumably written to get the attention of the western global health community, it seems that the Union or IJTLD might be better served by seeing to it that these pieces are made freely available. I’m sure that the argument and evidence could be valuable to public health and clinical teams in low and middle income settings working to draw attention to the predicament of oxygen therapy right now. If only they could afford it.
According to the FCC, in the last ten years the number of payphones in the US has declined from more then 2 million to around 700,000. So far this disappearance has occurred almost without capturing any of my attention. Only recently, in traveling across the US, have I started to run across decommisioned pay phones where some or all of the payphone and its enclosure is left to degrade in place (as in this photo from the Midwest).
I use SurveyMonkey and a few other hosted survey platforms for a number of projects. Generally, these are great tools and I recommend them routinely to others.
One significant limitation is that they assume that the group of respondents completing a survey is unrelated to the group completing another survey.
In my work, this is often not true. Instead, it is more typical that we are surveying the same population on multiple occasions over time. For example, an individual may complete an entry survey and then complete additional ones at regular intervals during the project and upon exit.
In these cases we are, among other things, interested in change over time within an individual and among the group, and also in comparison to another group of individuals. Most often, we are examining the effect of some kind of intervention by studying change over time or the effect in an intervention compared to a control group.
However, since we can’t examine an individual’s responses over the course of the surveys, what we end up doing is exporting the discrete result sets from each and then importing them to a database where they are linked by some unique ID. It’s not straightforward but it’s entirely workable.
It would be very helpful to see one of the hosted survey platforms develop the ability to set up surveys appropriate for these kinds of panel studies. This would allow us to quickly review responses from a given person over time. At the group level, we could adjust aspects of the intervention on the fly without having to switch gears and set up the analytical environment.
In addition, it would be helpful to tie the responses of an individual in one survey to the questions or answers presented to the same individual in another survey. In other words, cross-survey logic. Right now, as far as I can tell, it’s missing from all the options I’ve used.
Maybe the most ubiquitous, and slippery, statement about asthma is that it is the primary cause of absence from school.
For example: “Asthma is considered the leading cause of school absence among children 5-17. It accounts for an estimated 14 million missed days of school each year.”
Asthma absences are considered an important surveillance indicator and the majority of states – if not all – who receive federal funding to control asthma are supposed to track them.
But monitoring asthma-related school absences is a challenge. Although schools are paid for attendance, most don’t have good systems for capturing or analyzing the underlying reason for an absence…only to determine whether it is excused or unexcused.
In some cases, schools do attempt to quantify the proportion of absences attributable to a given disease, but the majority of these strategies have major flaws. For example, we’ve heard of several districts where nurses periodically review a list of students absent from school and attribute their absences to the student’s known chronic disease (if any), whether that is the reason for the specific absence or not.
The current school absence system poses trouble for public health surveillance and we’ve recently been thinking about new ways to quickly and reliably capture more accurate and specific data.
One idea we’ve come up with is an automated phone system that would ask the parent reporting the absense whether it is health-related, and if so, to indicate the cause in a series of short voice prompts. Hopefully this would help streamline absence reporting and improve surveillance.
How does your school collect information about asthma-related absences? What would work in your school?