Random Analytica

The Avian Influenza A(H7N9) continues its steady attrition. According to CIDRAP there have been 277-cases of H7N9 with the fatality count standing unofficially at 61 (a Case Fatality Rate of 22%).

Earlier in the week I posted some analytics looking at the case list employment data. Subsequently I’ve been involved in a rolling tweet-up with Ian Mackay, A biologist and Potrblog.com on some of those findings. Some of that discussion has caused me to further reflect on the data I presented.

Reflection then turned into action (and some updated/revised charts plus one new one!).

The first chart is very similar to the H7N9 incidences by Job Title previously released with the exception that I have updated a number of Job Titles to align with the Chinese data (i.e. amended Maid (Expat) to Domestic Helper) but also to better reflect actual real world situations. Thus School Age (5-17) has been split into both Primary and High School age groups.

The current chart reflects Job Title data in 204 of 277 cases (73.7% of all data inputs). The two predominant employment types continue to be either a Farmer (33.8%) or Retired (27.5%). Farming job titles are up slightly and Retired job titles are down slightly from data released earlier in the week.

Some further points of interest and conjecture:

• In Wave 1 (to case #136) Farmers represented 28 out of all 136 of all cases (20.6%). Currently in Wave 2 the following 141 cases had 41 Farmers (29.1%);
• The current average age of the H7N9 impacted farmer is 61.9-years. More than 5.1-years over the average age of all those impacted by the virus, which probably demonstrates an ageing issue for Chinese agriculture; and
• The average age of the H7N9 retiree is 71.3. How good is the Chinese economy, its medical system and its infrastructure compared to barely three decades ago?

Before I go on to my next three charts I want to discuss the importance of job titles. During my tweet discussions this week I brought up the issue of the differentials between a small cropping and a pig farmer. Everyone agreed with the issue but, by chance, I found a great example as I was completing my data updates today.

Via CIDRAP reported (29 Jan 2014). Seven new H7N9 cases, plus family cluster, reported. Detail:

The family cluster reported today involves three people from Zhejiang province, a 49-year-old man, his wife, and their 23-year-old daughter, according to a report from Xinhua, China’s state news agency. All three cases were previously reported. The man’s infection, which ultimately proved fatal, was confirmed on Jan 20. His daughter got sick 3 days after taking her father to the hospital, and she is in serious condition. The man’s wife’s infection was confirmed on Jan 27, and her illness is mild, according to Xinhua. Media reports in China yesterday, citing officials from China’s Center for Disease Control and Prevention, said the parents are from Xiaoshan and worked as vegetable dealers in a live-bird market before they got sick and that their daughter had worked at the market for a short time, the South China Morning Post, an English-language newspaper based in Hong Kong, reported today.

That detail might have made me change my job title for the parents to a Market Vendor, yet I suspect they are a small cropping family (who as first reported are ‘Farmers’) who also ran a small vegetable stall in a local poultry market (thus a secondary occupation of ‘Market Vendor’/’Vegetable Dealers’). Their daughter who also became ill was first reported as ‘Staff’ potentially equating to her role as running their market stall.

For all the conjecture that I put forward they might have caught H7N9 from wild birds at their vegetable farm, rather than the poultry market.

The second chart looks at employment by Job Family (see previous H7N9 employment related blog for methodology). Unlike the previous post about Job Families I thought it important to include the unknown data inputs which have been relatively unchanged since the commencement of the outbreak in February 2013. The largest groups are represented by Non-Participatory (27.4% comprising children, retirees, students and the unemployed), followed by unknown employments (26.4% or more than one in four) finally followed by Farming, Fishing & Forestry (25.6%). After those two groups Food Preparation & Serving (6.9% including catering, chef/cook, food sales, live poultry trade, market vendor) and Production, Factory & Food Processing (2.5% comprising butcher, factory worker, poultry abattoir, sheet-metal worker and stone processor). Those five groups equate to 88.8% of all cases.

The final chart asks the question. Has the recent spike in H7N9 cases been over represented by farmers?

Short answer is No.

The above chart displays acquisition by employment type (at onset) with four main groups represented: Farmer, Retired, All Other Known Employments and those that are currently unknown.

Two key months dominate. April 2013 and January 2014. By the end of April Farmers represented 19.7% of cases, currently they have increased by more than 5-points to 24.9% while Retired have reduced from 31.8% to 20.2%. Farmers moving from one in five to one in four H7N9 cases is still a reasonable movement but a trend has (not yet) been proven.

Let us give it one or two more months…

Stay safe, stay healthy and continue to make good choices.

This week (ending 26 January 2014) the Avian Influenza A(H7N9) has been busy. According to CIDRAP at least 45-cases have been reported during the past seven days alone, topping the busiest weeks of the disease in its first wave (approximately April 2013). As of today 245-cases of H7N9 have been reported with an unofficial fatality count of 57 (a Case Fatality Rate of 23.3%).

As someone who has practiced Workforce Planning for a decade or more I am always interested in what people do. One item I have noticed over the past ten months of amateur epidemiology is that health researchers are also interested in what you do, especially where your work (or lack thereof) puts you at risk or directly in harms-way of disease or death.

Here are some more H7N9 charts looking first at employment then an age chart concentrating on Zhejiang Province which hit 100-cases as of today.

The first chart looks only at the Job Title announced via a medical facility, Chinese media or via an online journal or study (the latter being my preferred). Over the past ten-months I have been able to populate my Job Title data in 179 out of 245 cases (or 73.1% of all H7N9 cases). The two predominant employment types are either a Farmer (30.7%) or Retired (28.5%). Potentially this proves the theory that exposure to live birds either in a farm setting or purchasing birds from live markets (as many retirees are understood to do) might increase your chances of catching H7N9. After Live Poultry Trade (5.0%) the breakdowns become less than 2.8% shared amongst 35 further job titles. Couple of interesting points:

• The average age of farmers infected by H7N9 is 61.0 whereas the current average age of victims is 55.7;
• Foreign workers (or even tourists) only make up three (1.7%) of the cases. One businessman from Taiwan, one foreign driver and one Indonesian maid based out of Hong Kong;
• There were five unemployed people confirmed in the first wave up to mid-April. The last person confirmed as unemployed was case number #101 with onset 16 April 2013. Does that mean that the unemployed are not catching the disease anymore OR that those without employment are part of the 26.9% of cases without a notifiable job title?
• There has been only one confirmed medical staff employment type to have caught H7N9 and to have subsequently died. The case of Dr. Zhang Xiaodong, a 31-year-old surgeon from Shanghai has raised alarms but reflects 0.6% of known job titles and 0.4% of all H7N9 cases to date. When you compare this against MERS-CoV, especially in Saudi Arabia which has seen multiple cases and deaths amongst its healthcare practitioners you can only but commend the Chinese authorities and medical fraternity;
• Not trying to stir up trouble but I noted that the Japanese Journal of Infectious Diseases refers to Chinese Farmers as ‘Peasants’ (see page 558 Laboratory Diagnosis and Epidemiology of Avian Influenza A (H7N9) Virus Infection in Humans in Nanchang City, China).

The second chart looks at employment by Job Family (see appended note for methodology). In this we see that the largest groups are represented by Non-Participatory (38.0% comprising retirees, children and the unemployed) closely followed by Farming, Fishing & Forestry (31.8%). After those two groups Food Preparation & Serving (9.5% including food sales, catering, market vendor, chef/cook & live poultry trade) and Production, Factory & Food Processing (factory worker, butcher, poultry abattoir, sheet metal worker and stone processor) equate to 82.7% of all cases. As per my comment in the previous chart this aligns reasonably well with the theory that the disease is spread through the contact with poultry.

Note 1: Often Workforce Planners will use a layered methodology of employment groups with job title as the most granular level up to Job Families. The purpose of this is to split the job titles into logical and practical segments to allow deeper workforce analysis to occur. A job family is a grouping of similar jobs at the highest level that usually consists of several job functions. In Australia I would use the Australian and New Zealand Standard Classification of Occupations or ANZSCO but given I have a choice I’ve opted to use the much more logical Bureau of Labor Statistics Occupation Employment Statistics.

Note 2: I created two groups outside of the BLS methodology. The first was ‘Non-Participatory’ to align with those people unemployed or no longer participating in the labour market. The second was ‘Other’ which reflects job titles such as ‘Worker’, ‘Company Employee’ or ‘Staff’.

As Ian M. Mackay pointed out in his latest Virology Down Under update Zhejiang H7N9 cases hit 100 today. Last chart is a look at the age pyramid for Zhejiang. A quick comparison shows that of male onsets is slightly lower than the total average (61% for Zhejiang, 70.2% overall) although the average age of 57.5 is slightly higher than the 55.7 H7N9 average. This is reflected in the age pyramid with has no Zhejiang cases in the lowest four cohorts and 59% of cases between the ages of 50 – 74. For a comparison against the first 226-cases see: Random Analytics: H7N9 Age Pyramid and Average Age (to 22 Jan 2014).

As always, stay safe, stay healthy and make good choices.

The very recent death of Dr. Zhang Xiaodong, a 31-year-old surgeon at the Shanghai Pudong New Area People’s Hospital and a number of younger sufferers has raised the spectre that the Avian Influenza A(H7N9) might be morphing into something more deadly in 2014 (as compared to 2013) and only if you listen to mainstream media which is often too quick to push the panic button.

Knowing the go-to people on this subject I thought I’d do some reading.

Via his excellent Virology Down Under blog Ian Mackay, PhD (with the Australian Infectious Diseases Research Centre at the University of Queensland) wrote a piece about this very subject just last week. H7N9 age with time: is a younger adult demographic emerging this time around? Excerpt:

This is a big graphic – sorry for that – but I thought it best to show the distribution of age bands (this is updated from the paper I co-authored recently with Joseph Dudley) alongside the shifting age in total numbers and proportion of cases each week. The data are all publicly sourced and verified against the WHO and scientific literature whenever possible and of course, against FluTrackers excellent case list.

The chart below (click on it to enlarge and see much more clearly) then some comments underneath. Keep the previous sex/week chart in mind (it’s trend has not changed much with the latest cases; these charts also result from a question from CIDRAP’s Lisa Schnirring last Saturday) when looking at this. Is any effect seen below due to the increased female representation?

I’m quite an admirer of Ian’s work, especially those graphs looking at accumulation/epidemiological data. I couldn’t help but notice that his Age Band chart uses a standard 2D column graph rather than a 2-way bar graph as used by demographers. I thought the use of that methodology along with a graph showing the decline in average age since October 2013 might be a better illustration of his very sound reasoning.

So, to add emphasis to Ian’s article I spent last night updating my H7N9 data, untouched since early December and did a couple of new graphs up to and including case number #216 (sourced from FluTrackers).

The first graph is an age pyramid (otherwise known as a beehive graph) commonly used by demographers and health experts to map population and mortality distributions. As you can see by using this methodology I’ve been able to bring the population groupings to just 5-year intervals which highlights the continued concentration of male onsets (70.2%). Of interest also are the aged cohorts with the highest percentile of cases with 55 – 59 (21-male/5-female/12.1%), 65-69 (19-male/5-female/11.2%) and 50-54 (13-male/10-female/10.7%). These three groups alone make up more than a third of all H7N9 onsets to date.

The second chart shows the average age of all onsets since case number #2 through to case number #216 (minus one case which does not come with age data). Interestingly, the first two victims were aged 87 and 27, thus the average age from those two was 57 which is in the variable range of the virus through its entire 11-month history. As you can see from the coloured section which represents all onsets from October 2013 (effectively, the second wave of H7N9) the average (or mean) age has reduced by approximately two-years. According to my data, the average age for onsets for wave 1 was 57.0 while currently for wave 2 it sits at 52.7.

For the record I am by day a post graduate student and a Workforce Planner. In terms of medical knowledge at best I am a keen amateur epidemiologist who gained an interest in the subject having worked in an Operating Room Suites as an Anaesthetic Secretary a decade ago.

I hope this small piece and further blogs during 2014 (time permitting) adds to the H7N9 discussion be it by an additional or improved data point, analytic or infographic.