11 – Occupational Lung Diseases

Occupational Lung Diseases

Video Link: http://youtu.be/U0u65e8Qm6k

My mapping of occupational lung diseases was one of my first major projects in the spatial analysis of disease patterns, and is a topic I have returned to repeated since the early 1990s.

This work began with my interest in several occupational lung diseases fairly prevalent in the Pacific Northwest–the spore-induced mushroom grower’s lung which occurs due to the local Pleurotus and Shitaki industries and the frullania-powder induced allergenic hypersensitivity-induced alveolitis induced in tree scalers in the local old-growth forest lumber industries.  My lab was responsible for researching and querying into the frullania induced alveolitis cases during the late 1980s and early 1990s, followed by a similar need in the mid 1990s due to the development of a new bioengineering (sporulated hay-blocks) method for producing home-grown edible and medicinal fungi in small building settings.

Two series of national maps on lung disease were developed for a review of this class of diseases or conditions.   The first series used 3D point imagery to depict distributions, in which I tested various color-coding techniques.  The second series utilized vertical 3D bar mapping to depict the distributions on a US map.

They are as follows (no links yet assigned):

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OccupationalLungDisx_503-504

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OccupationalLungDisx_500_CoalMinersLung_OU

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OccupationalLungDisx_500_CoalMinersLung

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Occupational Lung Diseases (Hypersensitivity-induced Alveolitis) (Cases)

Video Link for a 1 min. rotating 3D map (ancient video): http://youtu.be/U0u65e8Qm6k

OccupationalLungDisx_BirdsLungMapleBarkFarmersMaltMakers.png

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Cases versus Independent Prevalence (IP)

The mapping of cases depicts the numbers of cases in a given research area.  The mapping of IP depicts frequencies or rates, which typically are adjusted rated.

Both of these methods tell us something.

Bagassosis_N-IP_Pyramid

Case mapping depicts the absolute truth of where the problem lies.  In cases where environment is the major concern, such as an environmental disease pattern, cases tell us more than Incidence maps.

Incidence maps tell us the frequency at which a disease occurs.  Incidence is important because an area with thousands of disease cases may appear to be of higher risk, when in fact the number of people residing there and their age-gender distribution really tells us more about the actual risk to expect.  For example, 1,000 people with a disease in an area where 10,000,000 reside represent very little in terms of risk, but still present their health care givers, insurers and communities a certain cost expectation.  In a community where 10 people are infected within a population of 1,000, present ten times more likelihood in theory than the previous group.  One thousand people out of 10 million represents a percentage of 0.1% of the people; 10 out of 1,000 represents 1% (all of this of course assuming age-gender adjustments).

CorkHandlersSuberosis_n-IP

There is a hybridized method of Case-Incidence mapping I developed in which I assign value or priorities to certain subsets of cases, based upon case types, gender-age-ethnicity, urban vs. rural region, part of the country, etc.  The reason to employ this is to assign value and meaning to both cost and patient.  High numbers of cases incurs high cost, and can often be made more efficient.  High prevalence infers high demand and cost should population density increase.  Both are stressors to the system.  With an expensive disease like a rare hemophilia (rx of up to $100,000/dose, $1-$5M/year, n ~ 1-5/40M), even a few cases matter.  With a high count of a low cost service (100,000 pts x $50 ea. or $5M/year, for screening requiring a follow-up), financial waste becomes an issue (over screening), as well as lack of follow-up in treatment and lack of compliance.  

The following is an example of a hybridization of this process, but as a theoretical model, not an actual model based on case data:

NGMAlgorithmBasics

Weights are used to assign a value to each metric.  Numerous metrics can be used to interpret the raw data.  Age, Gender, ethnic group, family history, place of residence, occupation, income range, mental health evaluation results, results of a health survey, severity or level of metabolic syndrome, etc. can all be added to the equation, resulting in a weighted hybrid result.  This mimics the same methods used to define disease niduses and high risk cultural centers for culturally-linked disease patterns using GIS.

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