You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

Mortality in HIV-positive individuals continues to decrease due to improvements in combination antiretroviral therapy (cART) and its availability, although it remains higher than in the general population [@pone.0041673-AntiretroviralTherapyCohort1]. A high proportion of the mortality observed in HIV-positive individuals is now due to non-AIDS related causes [@pone.0041673-Lewden1], [@pone.0041673-Mocroft1]. However, the incidence of non-AIDS related mortality, from causes such as cardiovascular disease (CVD), liver disease, and cancer, has also declined [@pone.0041673-Mocroft1]. Drug specific toxicity, lifestyle factors (smoking drug and alcohol misuse), co-infection with other viruses (hepatitis B and C, human papillomavirus) and residual immunodeficiency are all thought to contribute to the higher relative risk of non-AIDS related mortality observed in HIV-positive individuals compared to the general population [@pone.0041673-Deeks1].

Research investigating regional differences across Europe, particularly allowing a comparison with East Europe, is limited. In Western and Central Europe, widespread access to antiretroviral therapy has led to considerable drops in AIDS related mortality [@pone.0041673-Joint1]. However, in Eastern Europe, a region with one of the fastest growing HIV epidemics worldwide [@pone.0041673-UNAIDSWorldHealth1], the uptake of cART remains low and previous studies have reported substantial differences in access to care and treatment compared to the rest of Europe [@pone.0041673-Podlekareva1], [@pone.0041673-Podlekareva2]. Shortly after the introduction of cART, the EuroSIDA study reported that the all-cause mortality rate in Western Central Europe was significantly lower than in North and South Europe [@pone.0041673-Chiesi1]. Since then, the study has grown substantially, particularly in Eastern Europe where few cohort studies are established. Additionally, to allow for comparisons outside of Europe the study includes data from Argentina, where the uptake of cART is high, with more than 95% of those in need of cART (CD4 count \<350 cells/mm^3^) receiving it [@pone.0041673-UNAIDS1]. The study provides a unique opportunity to establish whether, in the current treatment era, regional differences in the mortality rate of HIV-positive individuals remain.

Methods {#s2}
=======

Ethics Statement {#s2a}
----------------

Informed consent and ethics committee approval were obtained in each participating centre according to national guidelines. Full study details are available at [www.cphiv.dk](http://www.cphiv.dk).

Inclusion Criteria {#s2b}
------------------

The EuroSIDA study is a prospective study of individuals with HIV-1 in more than 100 centres across Europe, Israel and Argentina. To be eligible for inclusion into EuroSIDA, individuals must be aged 16 or older and have a pre-booked outpatient appointment at the centre. Starting in May 1994, there have been eight different periods of enrolment in EuroSIDA (cohorts I--VIII). Currently, 16,597 individuals are enrolled in EuroSIDA. The study has expanded into Eastern Europe with half of the individuals originating from Eastern European countries in Cohort VIII. Data is collected and updated at 6-monthly intervals on standardised recruitment and follow-up forms. Full study details are available at [www.cphiv.dk](http://www.cphiv.dk). The incidence of LTFU in EuroSIDA has previously been reported to be fairly consistent over time at \<5% per 100 PYFU [@pone.0041673-Mocroft2]. An extensive quality assurance program has been established that includes data checking at the coordinating office, as well as regular monitoring visits with source verification of all new major events, plus a random selection of individuals followed at the clinics.

Statistical Methods {#s2c}
-------------------

Individuals were included from baseline, defined as either 1^st^ January 2002 or enrolment into EuroSIDA, whichever occurred later, this was when EuroSIDA expanded its network in East Europe. Additionally, previous EuroSIDA studies have reported mortality rates in earlier time periods [@pone.0041673-Mocroft1], [@pone.0041673-Mocroft3]. Follow-up was until death, or 6 months after their last recorded visit (to allow for delays in reporting death), whichever occurred first. The incidence of mortality was calculated per 1,000 person years of follow-up (PYFU) and stratified by region. Individuals were divided into 6 regions according to country of residence as follows:

-   South Europe: Greece, Israel, Italy, Portugal, Spain.

-   West Central Europe: Austria, Belgium, France, Germany, Luxembourg, Switzerland.

-   North Europe: Denmark, Finland, Ireland, the Netherlands, Norway, Sweden, the United Kingdom

-   East Central Europe: Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Serbia, Slovakia

-   Eastern Europe: Belarus, Estonia, Latvia, Lithuania, the Russian Federation, Ukraine

-   Argentina

In order to compare the regions of Europe and Argentina, South Europe was chosen as the reference group as this group contained the largest number of individuals.

For individuals who died, date and cause of death are reported by the site investigator and, since 2004, a Coding Causes of Death in HIV (CoDe) case report form is additionally completed for each fatal case and CoDe methods used to determine the underlying cause of death [@pone.0041673-Kowalska1]. Deaths reported without a known cause were classified as either unknown AIDS or unknown non-AIDS mortality according to earlier developed methods [@pone.0041673-Kowalska2].

Poisson regression analyses were used to investigate factors associated with the incidence of mortality. Variables investigated were age, gender, ethnic origin, smoking status, anaemia, hypertension, diabetes, hepatitis B and C status, HIV transmission group, CD4 count, any previous AIDS defining illnesses and year of follow-up. Age and year of follow-up were treated as continuous variables. CD4 count was categorised (\<200, 200--349, 350--500, ≥500 cell/mm^3^, missing) to allow for patients with missing values to remain in the analysis. Further treatment for HIV was split into 4 categories, not on cART, on cART with a viral load \<500 copies/ml, on cART with a viral load ≥500 copies/ml, or on cART with no viral load measurement available. cART was defined as receiving ≥3 antiretrovirals. A cut-off of 500 copies/ml was chosen as this was lower limit of detection of the viral load assay for some individuals. Both current (time-updated) and baseline values were investigated for variables that could change over time. Factors that were significant in univariate analysis (p\<0.1) were included in multivariate analyses.

Individuals were classed as Hepatitis B positive if they had a positive HBV surface antigen test recorded and Hepatitis C positive if they had a positive HCV antibody test. Hypertension was defined as a diastolic blood pressure ≥90 or a systolic blood pressure ≥140 (mmHg) or receiving anti-hypertensive medication. If a diagnosis of insulin dependent diabetes was reported by the investigating centre or if an individual was receiving anti-diabetic medication they were classed as diabetic. Anaemia was defined as a haemoglobin level ≤12 or ≤14 (mg/dl) for females and males respectively [@pone.0041673-Mocroft4]. Smoking status was defined as either never, current, previous or unknown.

All analysis was performed using SAS 9.1 (SAS institute, Cary North Carolina, USA).

Results {#s3}
=======

13,310 HIV-positive individuals were included in the analysis. There were clear differences in demographics across the regions ([table 1](#pone-0041673-t001){ref-type="table"}). In East Europe, individuals were more likely to be younger (median age 30), female (42%) and infected via intravenous drug use \[IDU\] (49%) or heterosexual sex (41%). In contrast the majority of the individuals in West Central (45%), and North Europe (59%) were infected through homosexual sex. Similar baseline CD4 counts were observed across the regions. In South, West Central, North, East Central Europe and Argentina around 75--80% were on cART at baseline compared to only 30% in East Europe. In these individuals on cART, 62% in East Europe had a viral load \<500 copies/ml compared to 70--85% in the other regions.

10.1371/journal.pone.0041673.t001

###### Baseline characteristics (baseline was defined as either 1^st^ January 2002 or enrolment into EuroSIDA which ever occurred later).

![](pone.0041673.t001){#pone-0041673-t001-1}

                                                                              South Europe   West centralEurope   North Europe   East Central Europe   East Europe   Argentina                                                             
  ----------------------------------------------------- -------------------- -------------- -------------------- -------------- --------------------- ------------- ----------- ------------- -------- ------------ -------- ------------- --------
  Total (N,% of total)                                                            3246             24.4%              2920              21.9%             2866         21.5%        1700       12.8%       2082      15.6%        496        3.7%
  Gender (N.%)                                                  Male              2391             73.7%              2246              76.9%             2332         81.4%        1221       71.8%       1171      57.7%        308       62.1%
  Race (N.%)                                                   White              2971             91.5%              2102              72.0%             2426         84.6%        1674       98.5%       2074      102.3%       489       98.6%
  Exposure Group                                             Homosexual           1101             33.9%              1307              44.8%             1693         59.1%         627       36.9%       154        7.6%        126       25.4%
  (N.%)                                                  Injection DrugUser       949              29.2%              406               13.9%              317         11.1%         449       26.4%       996       49.1%        62        12.5%
                                                            Heterosexual          988              30.4%              849               29.1%              684         23.9%         490       28.8%       830       40.9%        292       58.9%
  Prior AIDS (N.%)                                                                853              26.3%              951               32.6%              915         31.9%         424       24.9%       402       19.8%        161       32.5%
  Hepatitis B (N.%)                                           Negative            2186             67.3%              2308              79.0%             2339         81.6%        1408       82.8%       1653      81.5%        357       72.0%
                                                              Positive            162               5.0%              202               6.9%               206         7.2%          81         4.8%       112        5.5%        26         5.2%
                                                              Unknown             898              27.7%              410               14.0%              321         11.2%         211       12.4%       317       15.6%        113       22.8%
  Hepatitis C (N.%)                                           Negative            1595             49.1%              1982              67.9%             1839         64.2%         995       58.5%       699       34.5%        295       59.5%
                                                              Positive            755              23.3%              460               15.8%              329         11.5%         480       28.2%       1014      50.0%        91        18.3%
                                                              Unknown             896              27.6%              478               16.4%              698         24.4%         225       13.2%       369       18.2%        110       22.2%
  Hypertension                                                   No               1629             50.2%              1136              38.9%              862         30.1%        1045       61.5%       1295      63.9%        292       58.9%
                                                                Yes               488              15.0%              538               18.4%              505         17.6%         327       19.2%       119        5.9%        42         8.5%
                                                              Unknown             1129             34.8%              1246              42.7%             1499         52.3%         328       19.3%       668       32.%9        162       32.7%
  Diabetes                                                       No               2904             89.5%              2012              68.9%             2614         91.2%        1609       94.6%       2061      101.6%       489       98.6%
                                                                Yes               180               5.5%              148               5.1%               93          3.2%          44         2.6%        5         0.2%         5         1.0%
                                                              Unknown             162               5.0%              760               26.0%              159         5.5%          47         2.8%        16        0.8%         2         0.4%
  Anaemia                                                        No               2194             67.6%              1904              65.2%             1642         57.3%         776       45.6%       197        9.7%        187       37.7%
                                                                Yes               782              24.1%              747               25.6%              911         31.8%         295       17.4%       110        5.4%        205       41.3%
                                                              Unknown             270               8.3%              269               9.2%               313         10.9%         629       37.0%       1775      87.5%        104       21.0%
  Smoking                                                      Never              706              21.7%              765               26.2%              576         20.1%         416       24.5%       387       19.1%        146       29.4%
                                                              Current             1495             46.1%              1179              40.4%             1156         40.3%         872       51.3%       1142      56.3%        169       34.1%
                                                              Previous            623              19.2%              669               22.9%              643         22.4%         306       18.0%       369       18.2%        146       29.4%
                                                              Unknown             422              13.0%              307               10.5%              491         17.1%         106        6.2%       184        9.1%        35         7.1%
  On cART (N.%)                                                 2558             78.8%              2170             74.3%              2286              79.8%        1269         74.6%       603       29.7%       367        74.0%     
  On cART with viral load\<500copies/ml(N, % on cART)           1795             70.2%              1732             79.3%              1927              84.3%         978         77.0%       371       61.5%       253        68.9%     
  Missing viral load on cART (N, % on cART)                      16               0.6%               5                0.2%               11               0.5%          20          1.6%         40        6.6%        9         2.5%      
  Age (median, IQR)                                                                40              35--46              42              37--49              43         37--50         35        30--42       30       25--36       37        31--43
  CD4 count (median, IQR)                                       450             291--650            432             286--621             420            279--598        373       242--543      407      259--575     342      201--510    
  Missing CD4 count (N, %)                                       20               0.6%               10               0.3%               15               0.5%           6          0.4%         96        4.6%        4         0.8%      
  Baseline date (median, IQR)                                   1/02          1/02--12/03           1/02          1/02--12/05           1/02           1/02--12/03     1/04      1/02--12/08    2/06    4/04--6/08    2/06    11/03--6/06  

From 2002 until a median last follow-up date of September 2011 (interquartile range \[IQR\] April 09-December 2011), individuals were followed for a total of 82,212 PYFU. During this time 1,147 patients died, the crude mortality rate was 14.0 per 1,000 PYFU (95% confidence interval \[CI\] 13.1--14.8). The majority (837, 73%) were attributed to non-AIDS related causes, the crude mortality rate was 10.2 non-AIDS related deaths per 1,000 PYFU (95% CI 9.5--10.9) compared to 3.8 AIDS related deaths per 1,000 PYFU (95% CI 3.4--4.2). [Figure 1](#pone-0041673-g001){ref-type="fig"} shows the crude mortality rate for all cause, AIDS and non-AIDS related causes by region. Significant differences in mortality were seen across the 6 different regions (global p\<0.0001 for all three endpoints). Higher all-cause mortality rates were observed in North and East Europe compared to the other regions. In East Europe a higher rate of AIDS related mortality was observed, the crude mortality rate due to AIDS related causes in East Europe was over three times higher than in South Europe (incidence rate ratio \[IRR\] 3.41, 95%CI 2.48--4.68, p\<0001). In contrast, a higher rate of non-AIDS related mortality was observed in North Europe, compared to South Europe, the crude rate of non-AIDS related mortality in North Europe was 1.4 times higher (IRR 1.41, 95% CI 1.18--1.69, p\<0001).

![Crude mortality rate across the six different regions.\
Cause of death are reported by the site investigator and, since 2004, a Coding of Death in HIV (CoDe) case report form is additionally completed for each fatal case and CoDe methods used to determine the underlying cause of death.](pone.0041673.g001){#pone-0041673-g001}

[Table 2](#pone-0041673-t002){ref-type="table"} shows the characteristics of the individuals who died at the time of their death in each of the 6 regions. Those in East Europe were younger at the time of their death and had lower CD4 counts. A higher proportion on individuals in East Europe (66%) and Argentina (68%) had experienced an AIDS event prior to their death than in the other regions. In contrast, in those dying in South, West Central, North, East Central Europe and Argentina around 30--40% had been diagnosed with a non-AIDS event prior to their death, whereas only 17% had been diagnosed in East Europe.

10.1371/journal.pone.0041673.t002

###### Characteristics at time of death of 1147 patients who died between 2002--2011 across Europe and Argentina.

![](pone.0041673.t002){#pone-0041673-t002-2}

                                                                                      Region                                                                      
  --------------------------------------------------------------- --------------- --------------- ---------------- --------------- --------------- -------------- ---------
  N died (% of total in each region)                                273 (8.4%)      208 (7.1%)      337 (11.8%)      126 (7.4%)      178 (8.6%)      25 (5.0%)     \<.0001
  Age (median, IQR)                                                 47 (42--57)     48 (43--59)     50 (43--59)      42 (36--54)     35 (30--41)    41 (34--48)    \<.0001
  CD4 count, cells/mm^3^ (median, IQR)                             208 (78--392)   249 (85--450)   260 (113--452)   236 (90--416)   156 (45--296)   58 (24--136)   0.0002
  Missing CD4 count (N, %)                                          56 (20.5%)      39 (18.8%)       78 (23.2%)      23 (18.3%)      82 (46.1%)      11 (44.0%)   
  Ever started cART (N, %)                                          261 (95.6%)     195 (93.8%)     325 (96.4%)      112 (89.9%)     89 (50.0%)      24 (96.0%)    \<.0001
  HIV viral load \<500 copies/ml (N,% with viral load measured)     128 (63.1%)     101 (61.6%)     187 (71.4%)      48 (58.5%)      17 (37.0%)      6 (60.0%)     0.0005
  Missing viral load (N,%)                                          70 (25.6%)       44(21.2%)       75 (22.3%)      44 (34.9%)      132 (74.2%)     15 (60.0%)   
  AIDS reported prior to death (N, %)                               159 (58.2%)     123 (59.1%)     168 (49.9%)      75 (59.5%)      118 (66.3%)     17 (68.0%)    \<.0001
  Most recent AIDS diagnosis (N, % of AIDS diagnosis)                                                                                                             
  Oesophageal candidiasis                                            12 (7.6%)      23 (18.7%)       32 (19.1%)       7 (9.3%)       26 (22.0%)      2 (11.8%)    
  PCP                                                                14 (8.8%)       9 (7.3%)        25 (14.9%)       7 (9.3%)        8 (6.8%)       2 (11.8%)    
  HIV wasting syndrome                                               8 (5.0%)        12 (9.8%)       10 (6.0%)        6 (8.0%)       18 (15.3%)       1 (5.9%)    
  Pulmonary TB                                                      18 (11.3%)       5 (4.1%)         6 (3.6%)        7 (9.3%)        21 (17.8%       1 (5.9%)    
  NHL                                                               24 (15.1%)       10 (8.1%)       20 (11.9%)       8 (10.7%)       2 (1.7%)        1 (5.9%)    
  Non-AIDS event prior to death (N,%)                               109 (39.9%)     63 (30.3%)      107 (31.8%)      45 (35.7%)      31 (17.4%)      8 (32.0%)     \<.0001
  Non-AIDS events reported (N,%)                                                                                                                                  
  NADM                                                              49 (17.9%)      24 (11.5%)       45 (13.3%)      18 (14.3%)       4 (2.2%)        4 (16%)     
  Cardiovascular disease                                            32 (11.7%)      30 (14.4%)       52 (15.4%)      19 (15.1%)       3 (1.7%)         1 (4%)     
  Pancreatitis                                                       10 (3.7%)       7 (3.4%)         7 (2.1%)        4 93.2%)        10 (5.6%)        0 (0%)     
  Liver related event                                                25 (9.2%)       9 (4.3%)        11 (3.3%)        10 (7.9%)       17 (9.6%)       4 (16%)     
  End-stage renal disease                                            10 (3.7%)       7 (3.4%)         5 (1.5%)         0 (0%)         1 (0.6%)         0(0%)      

Chi-squared test for categorical variables and Kruskal-Wallis for continuous variables. IQR; Inter-quartile range, PCP: Pneumocystis jiroveci pneumonia, TB: mycobacterium tuberculosis, NHL: non-Hodgkin's lymphoma, NADM: non-AIDS defining malignancy.

[Table 3](#pone-0041673-t003){ref-type="table"} gives the results of the Poisson regression analysis and presents the incidence rate ratios for all-cause, AIDS and non-AIDS related mortality for the five regions compared to South Europe. Adjusting for gender, age, HIV exposure group, AIDS diagnosis prior to baseline, non-AIDS event prior to baseline, hepatitis B and C status, hypertension, anaemia, baseline CD4 count, baseline date and baseline on cART/viral load status accounted for some of the higher rate of AIDS-related mortality observed in East Europe, though the rate of AIDS-related mortality was still more than double the rate in South Europe (IRR 2.27, 95%CI 1.42--3.46, p = 0.0006). Further, modelling age, hepatitis B and C status, hypertension, anaemia, CD4 count, year of follow-up and on cART viral load as time updated covariates the rate of AIDS related mortality was more than three times higher in East Europe compared to South.

10.1371/journal.pone.0041673.t003

###### Results from univariate and multivariate Poisson regression analysis investigating mortality rates by region (South is the reference region).

![](pone.0041673.t003){#pone-0041673-t003-3}

  All-cause mortality                 Univariate       Multivariate[a](#nt102){ref-type="table-fn"}        Multivariate[b](#nt103){ref-type="table-fn"}                                                                 
  -------------------------------- ---------------- --------------------------------------------------- --------------------------------------------------- --------- ------------ ------------- --------- ------------ -------------
  South Europe                           1.00                               --                                                  --                            1.00         --           --         1.00         --           --
  West Central Europe                    0.83                           0.69--0.99                                             0.04                           0.99     0.82--1.20      0.90        0.85     0.70--1.03      0.09
  North Europe                           1.30                           1.11--1.53                                             0.001                          1.50     1.26--1.78     \<.0001      1.37     1.15--1.62     0.0003
  East Central Europe                    0.96                           0.77--1.18                                             0.67                           1.31     1.05--1.64      0.01        1.28     1.03--1.59      0.02
  East Europe                            1.65                           1.36--1.99                                            \<.0001                         2.07     1.57--2.73     \<.0001      1.77     1.42--2.20     \<.0001
  Argentina                              0.68                           0.45--1.03                                             0.06                           0.99     0.65--1.52      0.97        0.77     0.50--1.16      0.21
  **AIDS related mortality**        **Univariate**   **Multivariate** [c](#nt104){ref-type="table-fn"}   **Multivariate** [d](#nt105){ref-type="table-fn"}                                                              
  **Region**                           **IRR**                          **95% CI**                                          **p-value**                      **IRR**   **95% CI**   **p-value**   **IRR**   **95% CI**   **p-value**
  South Europe                           1.00                               --                                                  --                            1.00         --           --         1.00         --           --
  West Central Europe                    0.77                           0.53--1.13                                             0.18                           0.84     0.57--1.23      0.36        0.72     0.49--1.06      0.09
  North Europe                           0.94                           0.66--1.34                                             0.73                           1.09     0.75--1.58      0.66        1.01     0.70--1.46      0.95
  East Central Europe                    1.16                           0.78--1.74                                             0.46                           1.29     0.84--1.97      0.24        1.43     0.93--2.19      0.10
  East Europe                            3.41                           2.48--4.68                                            \<.0001                         2.27     1.42--3.64     0.0006       2.90     1.97--4.28     \<.0001
  Argentina                              1.36                           0.73--2.51                                             0.33                           1.03     0.54--1.95      0.93        1.01     0.53--1.90      0.98
  **Non-AIDS related mortality**    **Univariate**   **Multivariate** [a](#nt102){ref-type="table-fn"}   **Multivariate** [b](#nt103){ref-type="table-fn"}                                                              
  **Region**                           **IRR**                          **95% CI**                                          **p-value**                      **IRR**   **95% CI**   **p-value**   **IRR**   **95% CI**   **p-value**
  South Europe                           1.00                               --                                                  --                            1.00         --           --         1.00         --           --
  West Central Europe                    0.85                           0.69--1.04                                             0.11                           1.01     0.81--1.26      0.92        0.88     0.70--1.10      0.27
  North Europe                           1.41                           1.18--1.69                                            0.0002                          1.63     1.34--1.99     \<.0001      1.51     1.24--1.82     \<.0001
  East Central Europe                    0.89                           0.69--1.14                                             0.35                           1.31     1.01--1.70      0.04        1.22     0.95--1.58      0.11
  East Europe                            1.09                           0.85--1.39                                             0.51                           1.81     1.27--2.57      0.001       1.29     0.98--1.71      0.07
  Argentina                              0.47                           0.27--0.82                                             0.008                          0.87     0.49--1.54      0.62        0.61     0.35--1.09      0.09

adjusted for baseline variables gender, age, HIV exposure group, hepatitis B and C status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension, diabetes, anaemia, smoking status, CD4 count, baseline date and on cART viral load.

adjusted as in a but age, hepatitis B and C status, hypertension, diabetes, anaemia, smoking status, CD4 count, year of follow-up and on cART viral load were included as time-updated covariates.

adjusted for baseline variables gender, age, HIV exposure group, hepatitis B and C status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension, diabetes, anaemia, CD4 count, baseline date and on cART viral load.

adjusted as in a but age, hepatitis B and C status, hypertension, diabetes, anaemia, CD4 count, year of follow-up and on cART viral load were included as time-updated covariates.

A higher incidence of non-AIDS related mortality was observed, after adjusting for baseline variables (listed in the footnote of [table 3](#pone-0041673-t003){ref-type="table"}), in North Europe (IRR 1.63, 95%CI 1.34--1.99, p\<0001), East Central Europe (IRR 1.31, 95%CI 1.01--1.70, p = 0.04) and East Europe (IRR 1.81, 95%CI 1.27--2.57, p = 0.001) compared to South Europe ([Table 3](#pone-0041673-t003){ref-type="table"}). After including time-updated covariates in the model the increased rate was no longer significant in East Central Europe (IRR 1.22, 95% CI 0.95--1.58, p = 0.11) or East Europe (IRR 1.29, 95% CI 0.98--1.71, p = 0.07) but there remained a significantly increased rate of mortality in North Europe (IRR 1.51, 95% CI 1.24--1.82, p\<0001).

In the adjusted analyses, increasing year of follow-up was associated with a significantly lower rate of both AIDS (IRR 0.91, 95%CI 0.87--0.95, p\<.0001 and non-AIDS related mortality (IRR 0.90, 95%CI 0.88--0.92, p\<0001). The test for interaction between region and calendar year of follow-up was significant for all-cause (p = 0.02) and AIDS related (p = 0.05) mortality, indicating that the effect of calendar year of follow-up on mortality rate may be different in each region, although it was non-significant for non-AIDS related mortality (p = 0.10). [Figure 2](#pone-0041673-g002){ref-type="fig"} shows the adjusted incidence rate ratio for mortality per calendar year of follow-up stratified by region, from the fully adjusted model. Significant decreases over time in all-cause mortality rates were observed in West Central (IRR 0.90 per calendar year later, 95%CI 0.85--0.95, p = 0.0002), North (IRR 0.85 per calendar year later, 95%CI 0.82--0.89, p\<0001), and East Europe (IRR 0.89 per calendar year later, 95%CI 0.84--0.95, p0.0005). No significant changes over time in all-cause mortality were observed in South (p = 0.32) or East Central Europe (p = 0.37). Argentina was excluded from this analysis due to the limited number of patients. Despite the observed decrease in mortality in East Europe over time, in the most recent period, 2009--2011, there remained a higher rate of all-cause (IRR 1.50, 95%CI 1.50--2.38, p = 0.08) and AIDS-related mortality (IRR 2.41, 95%CI 1.11--5.25, p = 0.02) compared to South Europe in adjusted analysis. However, in 2009--2011, the significantly higher rate in North Europe was no longer observed for all-cause (IRR 0.91, 95%CI 0.58--1.42, p = 0.18) or non-AIDS related mortality (IRR 1.07, 95%CI 0.66--1.75, p = 0.77), suggesting that the differences in North Europe, relative to South, are decreasing over time.

![Incidence rate ratio (IRR) for all cause, AIDS and non-AIDS related mortality per calendar year of follow-up stratified by region.\
(Argentina was excluded from this analysis due to limited power). ^a^adjusted for gender, age\*, HIV exposure group, hepatitis B\* and C\* status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension\*, diabetes\*, anaemia\*, smoking status\*, CD4 count\*, baseline date\* and on cART viral load\*. ^b^adjusted for gender, age\*, HIV exposure group, hepatitis B\* and C\* status, prior AIDS diagnosis, prior non-AIDS diagnosis, hypertension\*, diabetes\*, anaemia\*, CD4 count\*, baseline date\* and on cART viral load\*. \*time-updated.](pone.0041673.g002){#pone-0041673-g002}

Discussion {#s4}
==========

EuroSIDA is unique amongst studies of HIV-positive individuals in terms of its coverage across the entire continent of Europe. The results of this study with over 67,000 PYFU found significant differences in mortality rates across regions of Europe and Argentina. In particular, individuals in East Europe were found to have a higher mortality rate from AIDS related causes and individuals in North Europe a higher rate of non-AIDS related mortality. Similar rates of both AIDS and non-AIDS related mortality were seen across the other regions of Europe and Argentina. The mortality rate decreased over time particularly in West Central, North and East Europe.

Differences in the incidence of AIDS related mortality could partly be explained by variations in patient demographics including CD4 count and use of effective cART regimens. The epidemic in Eastern Europe is more recent, from 1995 there has been a rapid increase in HIV infections [@pone.0041673-UNAIDS2]. Since then a number of countries in the region have expanded access to antiretroviral coverage although treatment coverage remains low. UNAIDS estimated that in 2009 19% of those eligible for treatment in East Europe were accessing it, an estimated increase of 34% since the previous year [@pone.0041673-UNAIDS2]. In our study, after adjustment, the rate of AIDS related mortality appeared to be decreasing over time in East Europe, which may reflect improvements in antiretroviral coverage. However, after adjustment for time-updated CD4 count, use of cART, viral load and other factors, regional differences in AIDS related mortality were still significant, indicating that the differences cannot be fully explained by those variables collected and included in our models.

A high proportion of patients in East Europe are infected via IDU [@pone.0041673-UNAIDS3]. This was accounted for in the adjusted analysis but, IDU populations may differ across the regions. Several factors have been identified as contributing to the excess risk of mortality observed in IDUs in the cART era including decreased access and adherence to cART [@pone.0041673-vanAsten1], [@pone.0041673-Wood1], and more comorbidities such as co-infection with hepatitis C [@pone.0041673-Braitstein1], [@pone.0041673-Murray1]. In West Central Europe coverage of HIV services in IDU populations has been reported to be high, particularly access to cART [@pone.0041673-Mathers1]. However in East Europe services are low, there are low levels of needle exchange programmes [@pone.0041673-Sarang1] and very few IDUs have been found to be receiving antiretroviral treatment [@pone.0041673-Mathers1]. Opiate substitution programs are available in most countries but may be very limited in East Europe. Detailed information on opiate substitution programmes or whether individuals were actively injecting drug users was not available in this analysis. However, a sensitivity analysis, excluding those where the reported transmission route was IDU, found a similar increased incidence rate for East compared to South Europe, indicating that the higher prevalence of IDU does not account for the excess risk.

State-of-the-art care of HIV patients requires the utilisation of multiple health care interventions, such as the procurement of antiretroviral drugs, laboratory equipment, proper monitoring of antiretroviral treatment effectiveness and safety, and health care infrastructure in general [@pone.0041673-UNAIDS2]. Not all differences in patient care will have been captured by the use of effective cART, and overall lower levels of patient care in East Europe, may play a significant role in the higher rate of AIDS related mortality. Socio-economic and infrastructure aspects, for which we do not have data [@pone.0041673-Hamers1], may also play a role.

An increased rate of non-AIDS mortality was found in patients in North Europe compared to South Europe, and these differences could not be explained by adjusting for differences in patient demographics, treatment or other risk factors that were measured such as smoking status, co-infection and CVD risk factors, either at baseline or time-updated. However, the rate of non-AIDS related mortality does appears to be decreasing over time in North Europe with no difference observed in the most recent time period. The differences in non-AIDS mortality are probably not driven by poor patient care but by underlying differences in population morbidity and mortality. Smoking and excessive alcohol are found at an increased level in the HIV-positive population compared to the general population [@pone.0041673-FriisMoller1], [@pone.0041673-Lewden2] and could not be fully adjusted for in these analyses. Further, Mediterranean diet has been found to be associated with a lower risk of all-cause mortality, in particular due to CVD and cancer [@pone.0041673-Sofi1]. A recent study found that middle-aged men in North Europe had a 5% and woman 28% higher risk of all-cause mortality compared to South [@pone.0041673-Vollset1]. Although we have accounted for CVD factors such as diabetes and hypertension, these data were not available for all patients. Additionally, data on alcohol use was not available for this analysis although it has recently been added to the EuroSIDA follow-up forms. Furthermore, if HIV infection is accelerating the aging process [@pone.0041673-Deeks2], certain factors in North Europe such as a high intake of animal fat, smoking, and alcohol use, may be acting with this and could partly explain the differences observed, although we found no evidence that the relationship between age and mortality differed between regions (data not shown).

Another possibility is that the higher observed rate in North Europe is due to under ascertainment or incomplete reporting of mortality in other regions. Centres in some countries may be linked to national death registers allowing more complete reporting of mortality or have better methods of finding patients lost to follow-up (LTFU). LTFU in EuroSIDA is low, with individuals in North and West Central Europe having the lowest and those in East Europe the highest rates of LTFU in the study [@pone.0041673-Mocroft2]. To investigate biases caused by under ascertainment of death we varied the right censoring interval from 6 months after last follow-up visit to 3 months and 1 year. The results remained consistent with the main analysis, suggesting that these differences aren't solely due to underreporting in other regions. An analysis comparing the general HIV-negative population in these regions may also help clarify the reasons for these differences.

Demographically, individuals enrolled in the EuroSIDA study are fairly representative of each region based on comparisons with data from UNAIDS [@pone.0041673-UNAIDS4]. However, centres included in EuroSIDA are often university-associated clinics in larger cities, thus individuals in these clinics may have better access to care and may not be representative of all clinics in Europe, with potentially better clinical outcomes. This raises most concern in Eastern Europe, where the least favourable outcomes were seen, suggesting that improvements in this region are urgently needed. Further, within each of the regions some countries maybe quite heterogeneous in terms of their political and economic situation, however, the groupings provide a valuable regional overview of the differences in mortality rate across Europe.

In conclusion, differences were observed in the rate of all-cause mortality among HIV-positive individuals across different regions of Europe. Individuals in Eastern Europe had an increased risk of mortality from AIDS related causes in part due to differences in use of effective cART. Individuals in North Europe had the highest rate of non-AIDS related mortality which could not easily be explained but did appear to decrease over time. These findings are important for understanding and reviewing HIV treatment strategies and policies across the European region.

**The multi-centre study group on EuroSIDA (national coordinators in parenthesis).**

**Argentina:** (M Losso), C Elias, Hospital JM Ramos Mejia, Buenos Aires. **Austria:** (N Vetter), Pulmologisches Zentrum der Stadt Wien, Vienna; R Zangerle, Medical University Innsbruck, Innsbruck. **Belarus:** (I Karpov), A Vassilenko, Belarus State Medical University, Minsk, VM Mitsura, Gomel State Medical University, Gomel; O Suetnov, Regional AIDS Centre, Svetlogorsk. **Belgium:** (N Clumeck), S De Wit, M Delforge, Saint-Pierre Hospital, Brussels; R Colebunders, Institute of Tropical Medicine, Antwerp; L Vandekerckhove, University Ziekenhuis Gent, Gent. **Bosnia-Herzegovina:** (V Hadziosmanovic), Klinicki Centar Univerziteta Sarajevo, Sarajevo. **Bulgaria:** (K Kostov), Infectious Diseases Hospital, Sofia. **Croatia:** (J Begovac), University Hospital of Infectious Diseases, Zagreb. **Czech Republic:** (L Machala), D Jilich, Faculty Hospital Bulovka, Prague; D Sedlacek, Charles University Hospital, Plzen. **Denmark:** (J Nielsen), G Kronborg,T Benfield, M Larsen, Hvidovre Hospital, Copenhagen; J Gerstoft, T Katzenstein, A--B E Hansen, P Skinhøj, Rigshospitalet, Copenhagen; C Pedersen, Odense University Hospital, Odense; L Ostergaard, Skejby Hospital, Aarhus. **Estonia:** (K Zilmer), West-Tallinn Central Hospital, Tallinn; Jelena Smidt, Nakkusosakond Siseklinik, Kohtla-Järve. **Finland:** (M Ristola), Helsinki University Central Hospital, Helsinki. **France:** (C Katlama), Hôpital de la Pitié-Salpétière, Paris; J-P Viard, Hôpital Necker-Enfants Malades, Paris; P-M Girard, Hospital Saint-Antoine, Paris; JM Livrozet, Hôpital Edouard Herriot, Lyon; P Vanhems, University Claude Bernard, Lyon; C Pradier, Hôpital de l'Archet, Nice; F Dabis, D Neau, Unité INSERM, Bordeaux. **Germany:** (J Rockstroh), Universitäts Klinik Bonn; R Schmidt, Medizinische Hochschule Hannover; J van Lunzen, O Degen, University Medical Center Hamburg-Eppendorf, Infectious Diseases Unit, Hamburg; HJ Stellbrink, IPM Study Center, Hamburg; S Staszewski, JW Goethe University Hospital, Frankfurt; J Bogner, Medizinische Poliklinik, Munich; G. Fätkenheuer, Universität Köln, Cologne. **Greece:** (J Kosmidis), P Gargalianos, G Xylomenos, J Perdios, Athens General Hospital; G Panos, A Filandras, E Karabatsaki, 1st IKA Hospital; H Sambatakou, Ippokration Genereal Hospital, Athens. **Hungary:** (D Banhegyi), Szent Lásló Hospital, Budapest. **Ireland:** (F Mulcahy), St. James's Hospital, Dublin. **Israel:** (I Yust), D Turner, M Burke, Ichilov Hospital, Tel Aviv; S Pollack, G Hassoun, Rambam Medical Center, Haifa; S Maayan, Hadassah University Hospital, Jerusalem. **Italy:** (S Vella), Istituto Superiore di Sanità, Rome; R Esposito, I Mazeu, C Mussini, Università Modena, Modena; C Arici, Ospedale Riuniti, Bergamo; R Pristera, Ospedale Generale Regionale, Bolzano; F Mazzotta, A Gabbuti, Ospedale S Maria Annunziata, Firenze; V Vullo, M Lichtner, University di Roma la Sapienza, Rome; A Chirianni, E Montesarchio, M Gargiulo, Presidio Ospedaliero AD Cotugno, Monaldi Hospital, Napoli; G Antonucci, A Testa, P Narciso, C Vlassi, M Zaccarelli, Istituto Nazionale Malattie Infettive Lazzaro Spallanzani, Rome; A Lazzarin, A Castagna, N Gianotti, Ospedale San Raffaele, Milan; M Galli, A Ridolfo, Osp. L. Sacco, Milan; A d'Arminio Monforte, Istituto Di Clinica Malattie Infettive e Tropicale, Milan. **Latvia:** (B Rozentale), I Zeltina, Infectology Centre of Latvia, Riga. **Lithuania:** (S Chaplinskas), Lithuanian AIDS Centre, Vilnius. **Luxembourg:** (R Hemmer), T Staub, Centre Hospitalier, Luxembourg. **Netherlands:** (P Reiss), Academisch Medisch Centrum bij de Universiteit van Amsterdam, Amsterdam. **Norway:** (V Ormaasen), A Maeland, J Bruun, Ullevål Hospital, Oslo. **Poland:** (B Knysz) J Gasiorowski, Medical University, Wroclaw; A Horban, E Bakowska, Centrum Diagnostyki i Terapii AIDS, Warsaw; A Grzeszczuk, R Flisiak, Medical University, Bialystok; A Boron-Kaczmarska, M Pynka, M Parczewski, Medical Univesity, Szczecin; M Beniowski, E Mularska, Osrodek Diagnostyki i Terapii AIDS, Chorzow; H Trocha, Medical University, Gdansk; E Jablonowska, E Malolepsza, K Wojcik, Wojewodzki Szpital Specjalistyczny, Lodz. **Portugal:** (F Antunes), M Doroana, L Caldeira, Hospital Santa Maria, Lisbon; K Mansinho, Hospital de Egas Moniz, Lisbon; F Maltez, Hospital Curry Cabral, Lisbon. **Romania:** (D Duiculescu), Spitalul de Boli Infectioase si Tropicale: Dr. Victor Babes, Bucarest. **Russia:** (A Rakhmanova), Medical Academy Botkin Hospital, St Petersburg; N Zakharova, St Petersburg AIDS Centre, St Peterburg; S Buzunova, Novgorod Centre for AIDS, Novgorod. **Serbia:** (D Jevtovic), The Institute for Infectious and Tropical Diseases, Belgrade. **Slovakia:** (M Mokráš), D Staneková, Dérer Hospital, Bratislava. Slovenia: (J Tomazic), University Clinical Centre Ljubljana, Ljubljana. **Spain:** (J González-Lahoz), V Soriano, P Labarga, J Medrano, Hospital Carlos III, Madrid; S Moreno, J. M. Rodriguez, Hospital Ramon y Cajal, Madrid; B Clotet, A Jou, R Paredes, C Tural, J Puig, I Bravo, Hospital Germans Trias i Pujol, Badalona; JM Gatell, JM Miró, Hospital Clinic i Provincial, Barcelona; P Domingo, M Gutierrez, G Mateo, MA Sambeat, Hospital Sant Pau, Barcelona. **Sweden:** (A Karlsson), Venhaelsan-Sodersjukhuset, Stockholm; L Flamholc, Malmö University Hospital, Malmö. **Switzerland:** (B Ledergerber), R Weber, University Hospital, Zürich; P Francioli, M Cavassini, Centre Hospitalier Universitaire Vaudois, Lausanne; B Hirschel, E Boffi, Hospital Cantonal Universitaire de Geneve, Geneve; H Furrer, Inselspital Bern, Bern; M Battegay, L Elzi, University Hospital Basel. **Ukraine:** (E Kravchenko), N Chentsova, Kiev Centre for AIDS, Kiev; V Frolov, G Kutsyna, Luhansk State Medical University; Luhansk; S Servitskiy, Odessa Region AIDS Center, Odessa; M Krasnov, Kharkov State Medical University, Kharkov. **United Kingdom:** (S Barton), St. Stephen's Clinic, Chelsea and Westminster Hospital, London; AM Johnson, D Mercey, Royal Free and University College London Medical School, London (University College Campus); A Phillips, MA Johnson, A Mocroft, Royal Free and University College Medical School, London (Royal Free Campus); M Murphy, Medical College of Saint Bartholomew's Hospital, London; J Weber, G Scullard, Imperial College School of Medicine at St. Mary's, London; M Fisher, Royal Sussex County Hospital, Brighton; C Leen, Western General Hospital, Edinburgh.

**Steering Committee:** J Gatell, B Gazzard, A Horban, J Lundgren, I Karpov, B Ledergerber, M Losso, A D'Arminio Monforte, C Pedersen, A Phillips, A Rakhmanova, M Ristola, P Reiss, J Rockstroh (Chair), S De Wit (Vice-Chair)

**Coordinating Centre Staff:** O Kirk, A Mocroft, A Cozzi-Lepri, D Grint, M Ellefson, D Podlekareva, J Kjær, L Peters, J Reekie, J Kowalska, J Nielsen, J Tverland, A H Fischer

**EuroSIDA representatives to EuroCoord:** O. Kirk, A. Mocroft, J. Grarup, S. deWitt, P. Reiss, A. Cozzi-Lepri, R. Thiebaut, J. Rockstroh, D. Burger, R. Paredes, J. Kjær

**Competing Interests:**The authors have declared that no competing interests exist.

**Funding:**This work was supported by the European Commission BIOMED 1 (CT94-1637), BIOMED 2 (CT97-2713), the 5th Framework (QLK2-2000-00773) and the 6th Framework (LSHP-CT-2006-018632), and the 7th Framework (FP7/2007-2013, EuroCoord n° 260694) programmes. The participation of centres from Switzerland was supported by The Swiss National Science Foundation (Grant 108787). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[^1]: Conceived and designed the experiments: JDL OK AM. Analyzed the data: JMR JDK. Wrote the paper: JMR. National data coordination: IK JR AK AR AH. Interpretation of results: JMR JDK JDL OK AM. Critically revised manuscript: JDK IK JR AK AR AH JDL OK AM. Approved the final version of the manuscript: JMR JDK IK JR AK AR AH JDL OK AM STUDY PI: JDL OK AM.