Random Analytica

Charts, Infographics & Analysis without the spin

Tag: H7N9

Random Analytics: Comparing H7N9 and MERS by Key Occupations

“Theatre Staff Nurse | King Faisal Specialist Hospital, Riyadh! Excellent tax-free income | 54 days annual leave | Free & secure furnished accommodation | Free medical insurance & emergency dental | and much more!” Online advertisement (7 May 2014).

There has been volumes written recently about the amount of Health Care Workers who been impacted by the Middle Eastern Respiratory Syndrome (MERS-CoV) in the past month after a surge of Saudi Arabian cases in April. Given the discussion I thought I might add my two cents worth.

Here are two charts, focusing on the H7N9 outbreak in China and one for MERS:

01 - H7N9_MainOccupations_140507

***** Please note that this infographic of H7N9 was updated with public source information to 1800hrs 7 May 2014 (EST) with n=435 *****

01 - MERS_MainOccupations_140507

***** Please note that this infographic of MERS was updated with public source information to 1800hrs 7 May 2014 (EST) with n=506 *****

H7N9 & MERS by Key Occupation

The first chart displays three key occupations, an age cohort and two other groups for H7N9 (China). The groups include:

  • Farmer (21.4%): It is thought that H7N9 is spread primarily by the handling and eating of incorrectly cooked poultry so the publically sourced information on occupation type has focussed on farming as a key vector for H7N9.
  • Retired (14.3%): Chinese retirees have been adversely impacted by this disease, it is thought because they take on significant family responsibilities such as shopping (at live markets) and cooking.
  • Paediatrics (6%): Any children reported between 0 – 14 years of age.
  • HCW (0.5%): Health Care Workers of any description. Often a good indicator of secondary infections potency.
  • Other (18.6%): All other occupations that have been publically released.
  • Unknown (39.3%): Unknown occupations.

The second chart displays three key occupations, an age cohort and two other groups for MERS-CoV (Middle East). The groups include:

  • Farmer (1.4%): It is thought that MERS-CoV is initially spread by the handling of camels.
  • Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Although not a primary occupation while on pilgrimage or holiday you are not working so created a new non-employment type for this activity.
  • Paediatrics (2.8%): Any children reported between 0 – 14 years of age.
  • HCW (20.4%): Health Care Workers of any description. Often a good indicator of secondary infections potency.
  • Other (1.6%): All other occupations that have been publically released.
  • Unknown (72.9%): Unknown occupations.

Looking in the Wrong Direction

To the best of the Flublogia community’s knowledge, the Chinese medical authorities have worked very diligently on updating the World Health Organisation on key information. Many WHO updates have included an update on individual cases exposure to chickens and if there occupation was farming. Some provincial governments also release more detailed information which includes the person’s occupation. One of my issues with the Chinese occupational data is the level of detail, especially around farming (my whinge can be found here).

As for the MERS occupational data the first point that needs to be made is there is not enough data (publically available occupation data for MERS sits at just 27%). What data that is there is dominated by Health Care Workers who represent 74% of the known occupations (at least according to my Excel and I dig a little harder for this stuff than most other flublogists that concentrate on the medical side). Two points to be made here:

  1. The fact that Health Care Workers dominate the occupational space in MERS-CoV is important but given that HCW are usually secondary infections it is not as important as finding a possible primary occupational vector. An example of a primary occupation might include farming or racing, especially those that might relate to the camel industry.
  2. If you are going to explore the surge in Health Care Worker cases then start to limit the use of the term ‘Health Care Worker’. Health Care Workers are legion in titles, roles, functions and job families. Without going into detail about grades a great example would be a Nurse with specialities in A&E, Aged Care, Child Health, Community, ICU, General, Midwifery, Neo-Natal, Paediatric, Psych and Theatre. I commenced this blog with a live job ad for King Faisal Hospital in Riyadh looking for Australian and New Zealand Theatre nurses (Registered, two years minimum Theatre experience).

To sum up, the attention given to Health Care Worker cases in the current MERS-CoV outbreak and the almost 7 out of 10 lack of detail on other occupations might make for scary charts but it is not the main game. Stop MERS in the field and you stop MERS in hospitals.

Upcoming Occupational Data Issues

I also wanted to list some other ongoing concerns I have with the level of information coming out about H7N9 and MERS-COV as it relates to occupation data.

H7N9

  1. The level of occupation data supplied by China has dropped significantly in 2014. I note that to the end of 2013 that information was publically available for 75% of all cases but over the past four months that figure has dropped to 60%. Of the past 50-cases occupation have only been supplied on 5 (just 10%).

MERS-CoV

  1. The Kingdom has buried any mention of pilgrims catching the disease and the only real confirmations come from those who travelled to or through Saudi Arabia and returned to Europe, Tunisia or Malaysia. 1.2% for pilgrims and tourists is way to low and I would expect that a fair section of the unknown(s) is in this category. I’ll continue to chase up.
  2. I noted that I could account for five ‘Farm Owners’ but of all the cases I can only identify one ‘Shepherd’. Where are all the farm employees on this list? (My guess is they could lack access to medical facilities as they are more likely to come from poorer migration countries).
  3. On that note, The Guardian recently released a story detailing the almost 1,000 construction workers who have already died building the FIFA World Cup facilities in Qatar. One of the main causes of death listed is a heart attack. Many of these workers live in cramped and neglected accommodation (if it can spread in hospitals…) As yet I haven’t seen any construction occupations included in the publically available information. Could there be Qatari migrant workers dying of MERS and not being investigated.
  4. Although the previous KSA Ministry of Health news releases were noted for their lack of content one change that should be noted is that under the new system the difference between citizen and resident cases is no longer being noted. In a country that is made up of 20% foreign workers linking this data (along with expatriate country) can be critical in seeing patterns, such as issues with secondary hospital infections.

There is more but that’s enough for now.

Random Analytics: H7N9 by Employment (to 250 confirmed)

The Avian Influenza A(H7N9) continues its steady attrition.  According to Flutrackers there have been 358-cases of H7N9. With Wave 1 (45) and Wave 2 (32) fatality counts as confirmed by Xinhua my unofficial fatality total stands at 77 (a Case Fatality Rate of 21.5%).

While updating the most recent case details to my personal H7N9 Db today, a 29-year old female from Changsha, Hunan I noticed that we had reached an interesting milestone. Of the 358-cases thus far I have now been able to confirm 250 of their job titles.

Let’s look at the data to data.

1 - JobTitle_H7N9Top20_140218

Looking at the Job Titles we still find that the leading data item (occupation) is Farmer (35.6%), then Retired (24.4%) then the two paediatric titles of Primary School (5-12) and Child (0-4) with a combined total of 21 (8.4%). I’ve now been able to record 40 different titles with the top half accounting for 61.2% of the entire data, the bottom half just 8.6% and unknown 30.2%.

Some further points of interest:

  • In Wave 1 (to case #136) Farmers represented 28/136 of all cases (20.6%). Currently in Wave 2 there have been 222 cases of which 61 were Farmers (27.5%);
  • The current average age of the H7N9 impacted Farmer is 62-years while the average age of all H7N9 victim is 54.5-years;
  • The average age of a H7N9 Retiree is 70.4;
  • In Wave 1 Paediatric cases (0-15) represented 7/136 of all cases (5.1%). Currently in Wave 2 there have been 15-cases (6.8%) which shows a slight increase;

2 - JobFamily_H7N9_140218

When we role all the Job Titles into a Job Family the top-3 groups are Non-Participatory (26.5% comprising children, retirees and the unemployed), Farming, Fishing & Forestry (25.4%) and then Food Preparation & Serving (5.3% including catering, chef/cook, food sales, live poultry trade & market vendor).

Of interest:

  • The first two groups have remained largely unchanged in 2014 but the Food Preparation & Servicing group has been steadily declining in recent weeks (down from 6.9% recorded on the 1st February);
  • Only one Healthcare Practitioner (an ER Surgeon from Shanghai) has been recorded;
  • Along with the single Healthcare Practitioner recorded, no Healthcare Support (Enrolled Nurses, Vet Assistants or Orderlies) or Protective Services (Police, Ambulance, Fire & Wildlife Rangers) have yet been recorded equating to just 0.3% of all cases. A marked contrast to MERS which as Ian M. Mackay noted on 5 February 2014 Health Care Workers accounted for 18% of all cases and 2.7% of all deaths;
  • The average age of all H7N9 victims without a job title is 57.

3 - MainJobs_H7N9_140218

Last chart is a look at some main Job Titles in a running total. I’ve included child cases up to the age of 15-years in response to some of Ian M. Mackay’s concerns about an increasing paediatric count.

Given that those unknown job titles cases have an average of 57-years I believe that Retirees are somewhat underreported but given the older age of Chinese farmers it’s hard to estimate a breakdown without some local knowledge (of which I don’t possess).

FINAL THOUGHTS

Without wishing for more H7N9 cases I’ll plan for another employment update as I confirm the first 300 Job Titles.

There is a lot of interesting data in the first 250 Job Titles that I have been able to confirm. I only wish we had some more clarification on the almost 1/3rd of missing data items.

I’ll continue to scrabble for information as it comes in. Public sourced journals with detailed case studies are excellent sources and I am sure we will be seeing some of the Wave 2 case studies in coming weeks and months.

Random Analytics: A H7N9 family cluster in Zhongshan, Guangdong?

Ian M Mackay wrote an update on his Virology Down Under article on Wednesday where he nailed a Wave-2 data-point that I had completely missed. H7N9 snapdate: age with time. Key excerpt:

The interesting line to watch is that of the youngest age group (0-19-years) which has lifted to comprise 50% of cases in the week beginning 27-Jan. Also, the proportion of cases in the oldest age group (70->90-years) has dropped down in the past 2 weeks.

There have been a rash of children in recent announcements; 8 of the last 45 cases have been <10-years of age. For a virus with a median case age sitting at 58-years, this is quite a departure.

 Is this due to an increase in familial clusters? Does it herald a shift in the way the virus is spreading? Interfamilial transmission may provide a hint at increasing transmission efficiency. It might also be a sign of increased testing augmenting clinical observation of close contacts of ill family members.

It was such an interesting thought I started to dig a little deeper into the recent data to see if there were any possible interfamilial patterns that, as yet, might not be confirmed as family clusters but would have a high likelihood of being so.

Consider this.

Looking at the Flutrackers.com case list and case number #285 (37M) and #289 (2F). Data points:

  • Onset within 5-days of each other;
  • Hospitalised 2-days apart;
  • Confirmed one day apart;
  • Both are named Liang, although the original translation was Liang Yijun which might stand for ‘someone Liang’. As Crawford Kilian put it Liang is one of the top 100 Chinese surnames;
  • Both come from Sanjiao Town (original reports had them at Triangle Town but I linked that to Sanjiao Town via local hotel addresses).

The important point to my thinking is that these two cases are the first reported in Zhongshan in both waves. Zhongshan is different from other cities in that it doesn’t have County level administration but rather six inner districts and 18 smaller surrounding towns. Sanjiao Town has a population of just 121K, which by Chinese standards is miniscule.

I don’t believe in coincidences and there is a lot of data which is missing from this picture.

Yet, as we see a lot of P2P denial occurring could we also be seeing the first of many unconfirmed family clusters?

Is this the ‘tip of the iceberg’?

Random Analytics: H7N9 in Hangzhou, Zhejiang (to 4 Feb 2014)

According to the latest updates from Flutrackers.com there have been 299 cases of Avian Influenza A(H7N9) to 1200hrs EST (my time in Brisbane, Queensland) with an unofficial fatality count of 71. The Case Fatality Rate (CFR) plus a comparison between Wave 1 (to case #136) and Wave 2 (from 8 October 2013 to the present) stands at:

Wave 1: 136-cases, 45 known fatalities and a CFR of 33.1%;

Wave 2: 163-cases, 26 known fatalities and a CFR of 16.0%;

Total: 299-cases, 71 known fatalities and a current CFR of 23.7%.

Since mid-January the province of Zhejiang has moved into triple figures for H7N9 cases. At around the same time the provincial capital, Hangzhou became the first city to reach more than 50-cases, surpassing Shanghai as the most impacted city by H7N9.

Given those unfortunate statistics I thought it might be worthwhile to crunch some data on Hangzhou, Zhejiang.

Firstly, let us look at the 119 Zhejiang H7N9 onsets by prefecture level city.

1 - CasesbyCity_Zhejiang_140205

Two points:

Lisa Schnirring from CIDRAP stated in the 3 February H7N9 Update that:

Southern provinces lead second-wave cases

Six of the latest cases are from Guangdong province, continuing a strong second-wave tilt toward the mainland’s southernmost areas. In the first wave, locations north of that area were driving most of the outbreak activity: Shanghai, Jiangsu province, and Zhejiang province.

Not sure I agree with that.

Zhejiang has experienced 73-cases in the second wave which is much higher than Fujian (13) and Guangdong (49) below it. Of the 73-cases, Hangzhou alone had 23.

On the second point the infographic also (interestingly) highlights that 90.8% of Zhejiang’s cases are concentrated in the north of the province, emphasising a north/south provincial divide. I can’t suggest a reason for that outside of population density.

Next, let’s look at cases by month of onset with an emphasis on Hangzhou.

2 - CasesbyMonthofOnset_Hangzhou_140205

During April 2013 (the bulk of first wave cases) there was a significant spike in numbers from Hangzhou (28.9%) as compared to Shanghai for the same month (18-onsets at 18.6%).

As you can see from the provisional data for Hangzhou in January the case load is less both in terms of numbers (25) and as a percentile of total cases (18%), although the overall numbers are greater.

Lastly, a look at the second wave case load within Hangzhou.

3 - CasesbyDistrict_Hangzhou_140205

Here is the biggest surprise (IMO). Although farmers make up 10 of the 23 second wave cases in Hangzhou all of the cases (minus one in Fuyang City and three which are unconfirmed) are not in the outlying cities and districts of Hangzhou but in the more tightly congested metropolitan areas of the prefecture level city. It seems the Chinese peri-urban divide is a significant risk factor in catching H7N9, at least in Hangzhou.

Final Thought

H7N9 has almost been around a year and as we verge on the 300th case I think we have spent more than enough time doing provincial level analytics when we now can and should be spending a little more time getting granular with our analysis.

Random Analytics: H7N9 More Employment Graphs (to 31 Jan 2014)

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!).

1 - JobTitle_H7N9Top20_140201

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.

2 - JobFamily_H7N9_140201

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.

3 - MainJobs_H7N9_140201

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.

Random Analytics: H7N9 by Employment/Zhejiang Age Pyramid (to 26 Jan 2014)

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.

1 - JobTitle_H7N9Top20_140126

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).

2 - JobFamily_H7N9Total_140126_2

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’.

3 - AgePyramid_H7N9Zhejiang_140126

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.

Random Analytics: H7N9 Age Pyramid and Average Age (to 22 Jan 2014)

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).

1 - AgePyramid_H7N9Total_140122

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.

2 - AverageAge_H7N9Total_140122

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.

QuikStats: Avian Influenza A(H7N9) Autumn 2013

H7N9 in China “is still present and there is still a great deal not yet understood about this H7N9 virus. Other influenza viruses that circulate in poultry often decrease dramatically during the summer months, only to reappear later in the year during cold season. Also, many low pathogenic influenza viruses in poultry have transformed into highly pathogenic viruses.” (Hiroyuki Konuma, FAO regional representative for Asia and the Pacific, 18th September 2013)

1 - H7N9_Infographic_131203_Final

***** Please note that this infographic of the Avian Influenza A(H7N9) was updated with public source information to 1200hrs 3 December 2013 EST. There will be no more infographic updates for this post *****

After 80-days in the wilderness and with the world’s pandemic attention more focussed on the Middle East the H7N9 virus has popped up again.

When H7N9 was daily news the eye of the storm was concentrated on the provinces of Zhejiang and Jiangsu and the municipality of Shanghai. As the daily case numbers declined after April the movement of the virus seemed to deploy outward at a snail’s pace. In fact the previous four case onsets to this one were Jiangxi (East China), Beijing (North China), Hebei (North China) and in Huizhou, Guangdong (South Central China) barely a 100-kilometres from Hong Kong.

After two cases were reported in the middle of summer H7N9 has backtracked returning to more familiar hunting grounds in Zhejiang half-way through the northern hemisphere autumn striking down a relatively young man, aged just 35 (according to my data the average age prior to this case was 57).

Cause like all good mysteries just as we think H7N9 is gone, it pops up again.

As my calendar permits I’ll keep the infographic updated as new information comes to hand through the autumn months. Here is a look at the first infographic I produced with the 137th case presenting in Zhejiang on the 8th October 2013.

2 - H7N9_Infographic_131015_Orig

Acknowledgements: Data for this infographic was sourced largely from CIDRAP, H5N1, FluTrackers and the WHO. Background reading supplied mainly via Pandemic Information News, Ian at Virology Down Under and Helen Branswell.

Updates

Random Analytics: H7N9 (August 2013)

1 - H7N9_Infographic_130814

***** Please note that the infographics/charts of the Avian Influenza A(H7N9) virus presented were updated with public source information to 0001hrs 13 August 2013 CET/EST *****

Infographic Details

The recent confirmation of a H7N9 case in Huizhou, Guangdong Province was the inspiration for this month’s infographic.

During the month of July there were two confirmed cases of H7N9. The first case, with a 10 July onset occurred in Langfang with a dispersed population of around 3.9-million located just 60-kilometres from the heart of Beijing and its 20.7-million residents. The more recent case with onset 27 July was in Huizhou, with its 4.6-million citizens and just 100-kilometres from Hong Kong (population 7.1-million).

After a brief sojourn this variant has decided to randomly strike at two locations within a relatively easy drive to two extremely connected and globally linked population centres. Just these four cities alone are more than 50% more populated than the entire land mass of Australia and 1.8-million more than Canada.

The other point that I wanted to make was to highlight the temporal pattern which now has six-months of data confirmed. Since April, where 70.6% of the current onsets were recorded, only four cases (two in May, none in June and two in July) have occurred.

The Northern hemisphere summer has not killed of H7N9 although it is quiet.

The fact that H7N9 has cropped up again near global cities is pure downside risk. The fact that it is occurring during the Northern hemisphere summer is additional risk. The fact that we only have a half year of temporal data available for this emerging disease means we don’t yet have a full picture of what risk we face as we commence the colder seasons.

Autumn is upon us and Winter is Coming.

Cases by Region (including Taiwan)

2 - CasesbyRegion_130814

There have been 135-cases reported in China, 44 of which have resulted in death. Although transported by commercial aeroplane from Jiangsu there is one case reported in Taiwan who has subsequently recovered. For the record my case numbers include the single asymptomatic cases from Beijing. The most recent onset confirmation occurred 27 July in Guangdong, the first known case from that province. The last fatality confirmation via Xinhua was reported on the 12th August.

To date 32.4% of all known cases have been fatal. For context the Case Fatality Rate of SARS was 10.9%.

The World Health Organisation confirmed that to 11 August there were 87 patient discharges (the National Health and Family Planning Commission has been doing monthly updates on the 10th of each month but the August press release is still pending). This equates to a Case Recovery Rate of 64% (with every chance for a slight improvement as there are still four patients receiving treatment). Asymptomatic cases remain at one (0.7%).

Cases by Job Title

3 - CasesbyJobTitle_130812

As a Workforce Planner I am always fascinated by how disease interacts with our employment or our daily activities. This is potentially relevant in understanding how H7N9 transfers as only one case can be scientifically linked to a person-to-person transfer, although there is strong evidence to suggest at least three family clusters.

With 42.2% of cases in the 65+ cohort the greatest job title is that of retirement. Of the 106 confirmed occupations 37-cases (34.9%) are attributed to retirees who are more likely to visit traditional bird markets and potentially are more involved in food preparation at home, both with greater associated risks. I make a small point that food preparation is traditionally more likely to be done by women and there are only seven females (just 18.9%) who are ‘retired’ in my data, thus exposure to bird markets might be a greater factor in exposure.

Farmers account for 27-cases (25.5%). Given that most Chinese agriculture is still small cropping with additional poultry (chicken, ducks, geese etc.) and other livestock the high proportion is not that surprising given that it is an avian influenza.

From there the break-downs by employment don’t offer much in terms of vector assistance, outside those such as market vendors or poultry transporters that have daily exposure to feather and fowl.

It still seems that although your employment might marginally increase your exposure to H7N9 your just as likely to catch the disease by preparing a chook for the table or living within proximity of a bird market.

Recent Health Analytics Blogs: Random Analytics: Hendra! & Random Analytics: Ebola (2013)!

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Acknowledgements: Thanks first and foremost to FluTrackers and the great work you do. For good journalism on this topic I follow Helen Branswell and CIDRAP. If you are interested in getting a daily feed on H7N9 (and other related topics) then I would recommend Crawford Killian’s H5N1 site. If you like more detailed analysis of H7N9 (and other viruses) then I would point you to my fellow Queenslander Dr Ian M Mackay and his recently revamped Virology Down Under blog.

Lastly, thanks to George R.R Martin and his wonderful ‘A Song of Fire and Ice’ epic for the borrowed line (books only, I don’t do ‘A Game of Thrones’ HBO series).

 

Update (14/08/2013)

  • Updated the main infographic and Cases by Region after a Hebei fatality was confirmed. Some minor tweaking of article after a review of the published material confirmed a further 5 patient releases. Added Helen Branswell & CIDRAP to my acknowledgements and can’t say why I didn’t do this in the first case.

Random Analytics: H7N9 Infographics (to 22 Jul 2013)

***** Please note that the infographics/charts of the Avian Influenza A(H7N9) virus presented were updated with public source information to 0001hrs 21 Jul 2013 CET/EST *****

1 - H7N9_Infographic_130721

Infographic Details

There have been 134-cases reported in China, 43 of which have resulted in death. Although originating in Jiangsu there is one case reported in Taiwan without loss of life (my case numbers include known asymptomatic cases). The most recent onset confirmation occurred on 10 July in Hebei Province. The previous onset confirmation was 59-days previously from Beijing. The last fatality confirmation was on the 26 July via Xinhua.

To date 31.9% of all known cases have been fatal, close to a 1/3rd of all cases. For context the Case Fatality Rate of SARS was 10.9%.

The Ministry of Health and Chinese media confirmed that to 10 July there were 85 patient discharges which equates to a Case Recovery Rate of 63% (with every chance for a slight improvement). Asymptomatic cases remain at one (0.7%).

2 - CasesbyRegion_130722

Cases by Region (including Taiwan)

The case numbers presented here are correct to 20 July 2013 and include 135 known H7N9 victims. Although there have been 43 confirmed deaths to date I have only been able to verify 31 case fatalities. A recent Jiangsu study noted eight deaths to the 27 May (only four have been confirmed via a case fatality notification). Shanghai’s high Case Fatality Rate includes 16 confirmed case fatalities, the latest update via Xinhua was released 26 June.

Note: This infographic was created using Tableau Public.

Thoughts by Crawford Kilian

It’s one thing to analyse data and to draw a picture from it but you get real impact when you have more than analytical inputs to go by. Crawford Kilian’s comments and local knowledge via his H5N1 blog were just too good to not include in this piece. Here are his thoughts:

“The map in Shane’s post is a reminder that this weekend’s case is an outlier, geographically as well as seasonally. Hebei province almost surrounds Beijing, and if memory serves, that’s where the father of Beijing’s first case purchased the birds he hoped to sell in the capital.

Langfang, the city where the 61-year-old woman contracted H7N9, is no rural backwater. Wikipedia tells us that it has a total population of 3.85 million. An hour’s drive southeast of the Beijing airport, it’s part of the Beijing-Tianjin corridor, with no fewer than 30 universities and an economy based on computer technology. Another city in the corridor is Tangshan, which in 1976 suffered a catastrophic earthquake that effectively ended the Maoist regime and paved the way for over 30 years of explosive economic growth that changed the world.

My point is that the poor woman in critical condition in Chaoyang Hospital is not some faceless nonentity; she’s a real live human being, as real as everyone living between, say, New York and Boston or Seattle and Vancouver, or Riyadh and Jeddah. If she dies it will be a real death, not just a pixel or two winking out on a screen.

She does have some advantages, including a medical system primed and ready for her (imagine the panic if an H7N9 case turned up in Los Angeles or London). But she is still just a real human being. Statistically she may be one of the unluckiest people on the planet, but she’s a real person with a name and a family, and that is why we should all care about her fate.”

Final Thoughts

With infinite patience I’ve been awaiting the final case details of the H7N9 outbreak that commenced in mid-February and looked to have quietly disappeared in late April. Chinese and World Health Organisation media sources, so free with basic information at the start of the outbreak went quiet as the cases of H7N9 decreased. At the same time cases of Middle East Respiratory Syndrome (MERS-COV) increased with a seemingly higher Case Fatality Rate (CFR), multiple source countries and scratchy reporting diluting attention from the much diminished H7N9.

Humanity has a great capacity for curiosity but it also can as easily get side-tracked or bored and lose focus on events if they move back into the shadows.

H7N9 hasn’t gone away and the latest onset, during the Northern Hemisphere Summer is a timely reminder.

Note: Bored with flublogia? Read my updated analysis and analytics of Ebola Random Analytics: Ebola (2013)!

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Note: If you are interested in getting a daily feed on this and other interesting related topics (such as the MERS-COV outbreak) then I would recommend you follow Crawford Kilian or read his H5N1 blog. If you are interested in more detailed analysis of H7N9 (and other viruses) from a medico rather than an analyst then I would recommend my fellow Queenslander Dr Ian M Mackay and his Virology Down Under blog.

Update (28/05/2013)

  • Updated main infographic with four additional recoveries and one confirmed death as reported by Xinhua.

Update (29/05/2013)

  • Updated main infographic and Map with additional Beijing case as reported by Xinhua.

Update (15/06/2013)

  • Updated main infographic with additional two deaths and one recovery as reported by Xinhua.

Update (21/07/2013)

  • Updated main infographic & map with additional case as reported by CHP and H5N1.

Update (22/07/2013)

  • Updated map with additional four verified Jiangsu fatalities via CIDRAP update.