Random Analytics

Charts, Infographics & Analytics. No Spinning the Data. No Juking the Stats

Month: May, 2014

Random Analytics: MERS-CoV in the Middle East (to 26 May 2014)

1 - MERSinMidEast_Infographic_140527

***** Please note that this infographic of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was updated with public source information to 1300hrs 27 May 2014 EST *****

MERS-CoV has just hit Iran for the first time so I was trying to get the most up-to-date information on the virus spread but all the numbers are a little dated or tainted at the moment so I thought to make sense of it myself.

The above infographic is a look into the MERS-CoV with specific emphasis on its cases within the Middle East. The data is taken primarily from the latest ECDC update along with the current update from the Saudi Arabian MoH (to 26 May 2014) plus a little guesswork from myself (see appended). Unfortunately, given the five day delay in the most recent ECDC update (and errors within that update including an incorrect total of deaths in KSA) I wasn’t able to match the 680-cases (as per FluTrackers) to the public sourced data.

Here is my best guess today:

Middle East:

  • Saudi Arabia: 562 cases/179 deaths (official KSA MoH total)
  • United Arab Emirates: 67 cases/9 deaths
  • Jordan: 17 cases/5 deaths (+8 cases since 16 May ECDC update & 1-fatality)
  • Qatar: 7 cases/4 deaths
  • Kuwait: 3 cases/1 death
  • Oman: 2 cases/2 deaths
  • Iran: 2 cases/0 deaths (+2 cases since 16 May ECDC update)
  • Egypt: 1 case/0 deaths
  • Yemen: 1 case/1 death
  • Lebanon: 1 case/0 deaths

Europe:

  • UK: 4 cases/3 deaths
  • Germany: 2 cases/1 death
  • France: 2 cases/1 death
  • Italy: 1 case/0 deaths
  • Greece: 1 case/0 deaths
  • Netherlands: 2 cases/0 deaths

Africa:

  • Tunisia: 3 cases/1 death

Asia:

  • Malaysia: 1 case/1 death
  • Philippines: 1 case/0 deaths

Americas:

  • United States of America: 2 cases/0 deaths

The ECDC notes in its 18-24 May Update that:

Nineteen cases have been reported from outside the Middle East: the UK (4), France (2), Tunisia (3), Germany (2), USA (2), Italy (1), Malaysia (1), Philippines (1), Greece (1) and Netherlands (2). In France, Tunisia and the UK, there has been local transmission among patients who had not been to the Middle East, but had been in close contact with laboratory-confirmed or probable cases. Person-to-person transmission has occurred both among close contacts and in healthcare facilities.

No one’s numbers agree so I’m looking forward to the next ECDC update so I can work out the anomaly. That aside, given the newly reported cases in Iran I felt the infographic needed to be updated just to highlight its continuing international spread.

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Random Analytics: MERS by Key Occupation (to +300)

After some great suggestions by Anil Adisesh I have found a way to include three levels of complexity into my epidemiological database while only presenting two levels of charts. More work for me but I needed the push from the medical community to make it happen.

For those who have previously looked at my epidemiological/occupational charts (mainly H7N9/MERS) they will see a big uptick in numbers as I add Retirees (Provisional) to my count. The detail will be in my notes but I am now treating those over a certain age (dependent on national legislation) as retirees unless the State in question gives me some the World Health Organisation suggested occupational details.

Presented are the updated Middle Eastern Respiratory Syndrome – Corona Virus (MERS-CoV) by Occupation charts:

***** Please note that all infographics for this MERS-CoV article are using publically sourced information to 1800hrs 21 May 2014 (EST) with n=652 *****

01 - MERSbyJobFunction_140521

This infographic looks at those infected with MERS-CoV by Job Title. Some job titles do ‘roll-up’ in terms of function, thus a Nurse and a Nurse (ICU) are shown under the same horizontal data-point (see Key Notes).

Key Notes:

  • Retirees (1) there are 137-retirees represented in this chart but only 1/137 can be confirmed (less than 1%). According to the Saudi Arabian Shoura Council the official retirement age for citizens is now extended from 60 to 62 (effective 21 May 2014) but this does not include female citizens (55?) or residents (reducing from 60). The other country of interest is the United Arab Emirates where the retirement age is 65. In terms of my occupational data anyone over the age of 63 (KSA) and 66 (UAE) is considered retired unless their occupation data is known.
  • Farm Owners (*2*) includes Camel Breeders & Farm Owners (Camels). Doctor (*3*) includes Doctor (ICU). Farm Employee (*4*) includes Shepherd. Nurse (*5*) includes Nurse (ICU). Hospital Employee (*6*) includes Hospital Domain Worker.
  • Health Care Workers (*7*) excludes Paramedics (6), Dermatologists (1) and Pharmacists (1).***** Please note that this infographic of MERS was updated with public source information to 1800hrs 21 May 2014 (EST) with n= 652 *****Key Notes:
  • This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Next chart, Job Families:

02 - MERSbyJobFamily_140521 

This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Key Notes:

  • The most represented by the data are Non-Participatory. It should be noted that I don’t believe that this data is a true representation and there is a lot of ‘hidden’ Pilgrims, Employees (unspecified) and Tourists in this data;
  • Healthcare Prac/Tech Ops are overrepresented in the known data, due to significant outbreaks in hospitals across Saudi Arabia. This is now (in my mind) a proven data point as the new Health Minister, Adel M. Fakieh, has effectively suppressed any new data on HCW infections since he took over the job but they still represent 16.7% of my data;
  • Healthcare Support gets a mention (finally). At less than 1% (confirmed) I think the actual number is much higher given the good data comes out of the UAE. The Saudi’s (again) are not discussing HCW but having worked in an Operating Theatre myself as a non-HCW (I was an Anaesthetic Secretary) it is easy to see how these workers become infected;
  • Paediatric(s) only make up 2.8% of the data inputs. Seems low, especially compared against H7N9 but I’m no virologist, just an amateur flublogist.

Last chart.

02 - MERSbyMainJobTitle_140521

The last chart looks at those overall main job families that are most impacted by MERS-CoV (specifically Farmers, Travellers, Paediatrics, Retired, HCW’s, Other and Unknown).

Key Notes:

  • Farmer (2.0%): It is thought that MERS-CoV is initially spread by the handling of camels. Not represented by the known data so probably underrepresented or the wrong narrative.
  • Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Again, not strongly represented by the data. I suspect it is suppressed data.
  • Paediatrics (2.8%): Any children reported between 0 – 14 years of age are here. Data doesn’t seem to show strong family clusters but I wonder given the P2P data we do know (amongst HCW’s) seem to show strong secondary infections amongst close working colleagues.
  • HCW (16.9%): Health Care Workers of any description but doesn’t include Health Support workers. Often a good indicator of secondary infections potency. Subject Matter Expert(s) on HCW infections are m’coll’s Ian Mackay and Maia Majumder.
  • Other (3.1%): All other occupations that have been publically released.
  • Unknown (53.2%): Unknown occupations. It should be noted that I have ‘guessed’ 136 occupations as ‘Retired’.

Final Thoughts

I now call on Professor Crawford Kilian to add Adel M. Fakieh to the Supari Prize list along with the previous Saudi Health Minister. As many have noted although we saw an initial uptick in publically sourced data from the Saudi MoH the data has now precluded data around occupation (HCW specifically) and expatriate status. This is now pure suppression of data and will no doubt lead to more cases ‘exporting’ from the Kingdom to other countries, including the United States which has now experienced two exported cases and one secondary infection.

Random Analytics: Tony, Please Stop Pretending You Share My Budget Pain

Peter Whiteford did an excellent article for The Guardian earlier this week which highlighted the fact that the poor will suffer the most through to 2017. Given that the Treasurer (Joe Hockey) and the Prime Minister seemed convinced that the budget pain is being shared across the board I thought I might utilise some of the research data that Professor Peter Whiteford and PhD candidate Daniel Nethery have come up with for an infographic.

1 - BudgetPain_Infographic_140520

Technical notes:

  • I have chosen to highlight the differences in categories using more direct examples (i.e. a university student going on to Newstart rather than just a ‘Single Person on Newstart, aged 23-years’);
  • The research uses Average Weekly Earnings (AWE) rather than an annual income (which most non-economists recognize more readily). I have averaged the AWE using the most recent Australian Bureau of Statistics data;
  • Any mistakes are unintentional and are of my own making, thus not reflecting the excellent work of Peter and Daniel.

The Guardian piece can be found in full (including the table) here. Working-age poor will suffer most pain from Australian budget.

 

Image: Tony and Margie Abbott photo care of the ABC.

Random Analytics: Bronwyn Bishop and Standing Order 94(a) Ejections (to 15 May 2014)

Fran Kelly’s final point on Insiders (18th May 2014) was that the ejection of Terri Butler (Member for Griffith) on the previous Wednesday under Standing Orders 94(a) brought the count to 100 for Labor and zero for the coalition (and minor parties).

For those in the dark, according to the Australian Parliament House (APH): “On 21 February 1994, a new Standing Order (304A, later 94a) came into effect. This allows the Speaker to order the withdrawal of a member from the Chamber for one hour (‘sin bin’) for disorderly conduct without a question having to be put to the House. Wilson Tuckey (LP, O’Connor, WA) was the first member to be asked to withdraw from the Chamber under this standing order on 24 February 1994, just three days after it came into effect.”

The Speaker for the 44th Parliament is renowned Conservative Bronwyn Bishop and it looks like she is taking no prisoners.

1-BronwynBishop_viaABC

After a detailed look online for the data to support Labor’s claim of 100-0 I could find nothing outside of the claim so I spent a number of hours trawling through both Hansard and Judith Ireland’s excellent Politics Live Blog and put together the figures myself.

2 - 94aEjectionsbyPerson_140519

***** Please note that ALL charts of Standing Order 94(a) ejections during the 44th Parliament were updated with public source information to the end of the ninth sitting week (15 May 2014) ***** The first chart details the list of those ejected from the house. Early money was on the Member for Wakefield to take out the prize for most ejections and he remains the favourite.

To the 15th May 102 Labor members were ejected. The current Top 3 are:

3-NickChampion_viaAPH

Nicholas David “Nick” Champion (15-ejections) is the ALP Member of the Australian House of Representatives representing the electorate of Wakefield (SA). He won the seat at the 2007 Australian federal election.

4-MarkDreyfus_viaAPH

Mark Alfred Dreyfus QC MP (11-ejections) is the ALP Member of the Australian House of Representatives representing the electorate of Issacs (WA). He won the seat at the 2007 Australian federal election. He is the former Attorney-General, Minister for the Public Service and Integrity, Minister for Emergency Management, and Special Minister of State in the Second Rudd Ministry.

5-PatConroy_viaAPH     6-GrahamPerrett_viaAPH

Pat Conroy and Graham Perrett (both 8-ejections). Pat Conroy is the Member for Charlton (NSW) and Graham Perrett is the Member for Moreton (QLD). Now a final look at the Speakers ejections by Sitting Day.

7 - 94aEjectionsbySittingDay_140519

According to the APH the 10-ejections which occurred on the 11th December 2013 are not the most recorded but at 102 ejections over 37-sitting days the average, which currently stands at 2.8 might well be worth consideration.

When the tally hits 200 it might be worth looking into.

 

Image Sources: Bronwyn Bishop’s image care of the ABC while photos of individual members are taken from their official Australian Parliament House photo.

Random Analytics: MERS-CoV in the Middle East (to 16 May 2014)

1 - MERSinMidEast_Infographic_140517

***** Please note that this infographic of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was updated with public source information to 2100hrs 17 May 2014 EST *****

The above infographic is a look into the MERS-CoV with specific emphasis on its cases within the Middle East. The data is taken primarily from the latest ECDC update (see appended) plus the most recent update from the Saudi Arabian MoH (care of Al Jazeera). Since I last updated this infographic back in November 2013 the cases have exploded, especially in Saudi Arabia and the United Arab Emirates. New Middle East countries have also been added since November including Egypt, Lebanon and Yemen.

The European Centre for Disease Prevention and Control has released its latest update on the MERS-CoV. Epidemiological update: Middle East respiratory syndrome coronavirus (MERS-CoV) for 16 May 2014. Excerpt:

ECDC notes the decision of Margaret Chan, the Director General of WHO, on 14 May 2014 not to call the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak a Public Health Emergency of International Concern (PHEIC) as the conditions have not been met yet. This decision was based on the advice of the WHO Emergency Committee under the IHR on MERS-CoV. However the committee indicated that, based on current information, “the seriousness of the situation had increased in terms of public health impact, but that there is no evidence of sustained human-to-human transmission.”

Since April 2012 and as of 16 May 2014, 621 cases of MERS-CoV infection have been reported globally, including 188 deaths.

On 11 May 2014 a second imported case of MERS-CoV was confirmed by the United States’ Centers for Disease Control and Prevention.

On 13 May 2014, National Institute for Public Health and the Environment (RIVM) in the Netherlands reported the first imported case of MERS-CoV in the country. On 15 May 2014 a second case, who travelled with the first case, was reported.

Confirmed cases and deaths by region:

Middle East:

  • Saudi Arabia: 511 cases/160 deaths
  • United Arab Emirates: 67 cases/9 deaths
  • Qatar: 7 cases/4 deaths
  • Jordan: 9 cases/4 deaths
  • Oman: 2 cases/2 deaths
  • Kuwait: 3 cases/1 death
  • Egypt: 1 case/0 deaths
  • Yemen: 1 case/1 death
  • Lebanon: 1 case/0 deaths

Europe:

  • UK: 4 cases/3 deaths
  • Germany: 2 cases/1 death
  • France: 2 cases/1 death
  • Italy: 1 case/0 deaths
  • Greece: 1 case/0 deaths
  • Netherlands: 2 cases/0 deaths

Africa:

  • Tunisia: 3 cases/1 death

Asia:

  • Malaysia: 1 case/1 death
  • Philippines: 1 case/0 deaths

Americas:

  • United States of America: 2 cases/0 deaths

Most cases have either occurred in the Middle East or have direct links to a primary case infected in the Middle East. Local secondary transmission following importation was reported from the United Kingdom, France, and Tunisia.

Random Analytics: MERS by Occupation (to 150 confirmed)

According to Flutrackers there have been 580-cases of MERS-CoV that have been clinically diagnosed. The fatality count is a little harder to gauge given the poor data that comes out of the Middle East but Dr Ian Mackay via his blog Virology Down Under believes the number to be 161 out of 571-cases (a Case Fatality Rate of 28.2%). His total does not include the three deaths that were announced by the Saudi Ministry of Health (MoH) overnight.

In the past 24-hours the Centers for Disease Control and Prevention (CDC) have announced the second imported case of the Middle Eastern Respiratory Syndrome (MERS-CoV). All the details have not yet been confirmed but the second case, like the first was in a returning Health Care Worker.

Since the 28th April the Saudi MoH has cut all details about cases involving Health Care Workers and resident/citizen status. Even so, I have been able to detail 151-job titles including the most recent Health Care Worker who travelled from Saudi Arabia to the United States and was hospitalised in Florida.

1 - JobTitle_MERS_Top20_140513

 

Looking at the Job Titles the stand out point is that 47.7% of the data is associated with the occupation of ‘Health Care Worker’. This could be anyone occupied as an anaesthetist, dentist, doctor, lab-tech, midwife, nurse, optometrist, pharmacist, surgeon or even veterinarian. One of my chief complaints about occupation data being given (outside of outright suppression) is that the term ‘Health Care Worker’ lacks detail and is little more than a ‘throw-away line’.

That aside the leading occupations are Health Care Worker (47.7%), Nurse (13.9%), High School (12-14) at (4.6%), Primary School (5-11) and Pilgrims (both at 3.3%).

Points of interest:

  • There are currently 9 different Health Care Workers job titles that have confirmed… They include Nurse (21); Paramedic (6 – currently counted as a HCW), Doctor (4), Nurse (ER – 1), Health Domain Worker (1), Doctor (ICU), Pharmacist (1 – currently counted as a HCW), Respiratory Therapist (1) and a Surgeon (1).
  • The current average age of the 22 confirmed Nurses is 37-years;
  • Of the 22 confirmed Nurses, 17 are expatriate workers and five have not been confirmed by the relevant Ministry as either a resident or citizen;
  • Nine Farm Owners have been confirmed but only two Farm Employees which might suggest that Farm Owners (citizens) are being tested but their Farm Employees (often poor residents) have not been tested or their details are being suppressed.

2 - JobFamily_MERS_140513

The key data-point in the Job Family chart is that 74% of the occupation data is unknown. When we roll up all the Job Titles that we do know into their relevant Job Families the top-3 groups are:

  • Healthcare Prac/Tech Ops (67.7%) comprising Doctors, HCW, Nurses, Specialists and Surgeons);
  • Paediatric (10.6%) a new group that I have created recently to split apart Non-Participatory into more useful datasets. The Paediatric groups comprises Child (0-4), Primary School (5-11) and High School (12-14);
  • Farming, Forestry and Fishing (7.3%) comprising Camel Breeder, Farm Employee, Farm Owner and a Shepherd.

Of interest:

  • I just cannot believe that only four Pilgrims and four Tourists have become infected. I think this is a key area of data suppression and probably due to the fact that the Hajj and Umrah bring in huge tourism revenues into the Kingdom. According to Albawaba the Hajj alone added 3% to Gross Domestic Product in 2012 (or roughly $16.5-billion);
  • Paediatric cases only make up 2.8% of the data. I don’t see a lot of virology commentary on this but it seems low given that MERS seems to be able to spread within family and hospital clusters.
  • The MERS data has thrown up the first Healthcare Support worker, a Hospital Receptionist who was diagnosed in Jeddah, KSA. I also suspect the Health Domain Worker to be in this category but without specific detail I left it in the Healthcare Prac/Tech Ops Family.

FINAL THOUGHTS

With just one in four cases being confirmed for occupation I’m not sure that any reporting on the subject adds much to the level of public knowledge about the causes and progress of MERS outside of the fact that the spike in HCW in April points to a lack of infection control in Saudi hospitals.

Suppression is a strong term but as noted in the opening paragraph the Saudi’s have not provided any detail into occupation or resident/citizen states since the 28th April.

The Saudi’s have their reasons for this (and they cannot be good). Sometimes a lack of data can be just as enlightening as lots of it.

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.