Trends in Lipid-Modifying Agent Use in 83 Countries

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medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Trends in lipid-modifying agent use in 83 countries

Authors (ORCID)

Joseph E Blais (0000-0001-7895-198X)1; Yue Wei (NA)1; Kevin KW Yap (NA)2;

Hassan Alwafi (NA)3; Tian-Tian Ma (0000-0003-1361-4055)3; Ruth Brauer (0000-

0001-8934-347X)3; Wallis CY Lau (0000-0003-2320-0470)3; Kenneth KC Man (0000-

0001-8645-1942)3; Chung Wah Siu (0000-0002-5570-983X)4; Kathryn C Tan (0000-

0001-9037-0416)5; Ian CK Wong (0000-0001-8242-0014)1,3; Li Wei (0000-0001-

8840-7267)3; Esther W Chan (0000-0002-7602-9470)1

Affiliations

1 Centre for Safe Medication Practice and Research, Department of Pharmacology

and Pharmacy, LKS Faculty of Medicine, University of Hong Kong, Hong Kong

Special Administrative Region, China

2 Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University

of Hong Kong, Hong Kong Special Administrative Region, China

3 Research Department of Practice and Policy, UCL School of Pharmacy, London,

4 Division of Cardiology, Department of Medicine, University of Hong Kong, Hong

Kong Special Administrative Region, China

5 Department of Medicine, University of Hong Kong, Hong Kong Special

Administrative Region, China

Correspondence

Esther W Chan, PhD

Centre for Safe Medication Practice and Research

Department of Pharmacology and Pharmacy NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

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General Office, L02-56 2/F

Laboratory Block LKS Faculty of Medicine

The University of Hong Kong

21 Sassoon Road, Pokfulam, Hong Kong SAR, China

Tel: +852 2831 5110

Fax: +852 2817 0859

Email: [email protected]

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ABSTRACT

Aims

Lipid-modifying agents (LMAs) are increasingly used to reduce lipid levels and

prevent cardiovascular events, but the magnitude of their consumption in different

world regions is unknown. We aimed to describe recent global trends in LMA

consumption and to explore the relationship between country-level LMA

consumption and cholesterol concentrations.

Methods and results

This cross-sectional and ecological study used monthly pharmaceutical sales data

from January 2008 to December 2018, for 83 countries from the IQVIA Multinational

Integrated Data Analysis System, and total and non–high-density lipoprotein (non–

HDL) cholesterol concentrations from the NCD Risk Factor Collaboration. The

compound annual growth rate (CAGR) was used to assess changes in LMA

consumption over time. From 2008 to 2018, the use of LMAs increased from 7,468

to 11,197 standard units per 1000 inhabitants per year (CAGR 4.13%). Statins were

the most used class of LMA and their market share increased in 75% of countries

between 2008 and 2018. Fibrates were the most consumed nonstatin drugs. From

2013 to 2018, consumption of low-density lipoprotein lowering therapies increased

(statins 3.99%; ezetimibe 4.01%; proprotein convertase subtilisin/kexin type 9

(PCSK9) inhibitors 104.47%). Limited evidence supports a clear relationship

between country-level changes in LMA consumption and total and non–HDL

cholesterol concentrations in 2008 versus 2018.

3 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Conclusion

Since 2008, global access to LMAs, especially statins, has improved. In line with

international lipid guideline recommendations, recent trends indicate growth in the

use of statins, ezetimibe, and PCSK9 inhibitors. Country-level patterns of LMA use

and total and non–HDL cholesterol varied considerably.

Keywords

lipid regulating agents; drug utilization; global health; sales; cholesterol; statins

4 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

INTRODUCTION

Lipid-modifying agents are widely recommended to reduce atherogenic lipid levels

and to prevent atherosclerotic cardiovascular disease.(1–3) Over the last few

decades, emerging evidence of the benefits and risks of different classes of lipid-

modifying agents has informed updates to cholesterol guidelines and influenced

treatment choices. Recommendations for the broader use of statins in the 2013

American College of Cardiology/American Heart Association (ACC/AHA) cholesterol

guideline(4) raised concerns about extrapolating the available evidence, and

subsequent worldwide ‘statinization’.(5) It is unknown whether overall lipid-modifying

agent use has continued to increase at the global level. Given the robust evidence of

benefit for statins, their share of lipid-modifying agent use may have evolved over

time.

Several developing regions now face a greater burden from elevated cholesterol

levels, yet they have lower consumption of lipid-modifying agents as compared to

high-income regions.(6,7) Since 1980, parts of Africa and East and Southeast Asia

have experienced increases in total and non–HDL cholesterol, while high-income

Western countries have experienced decreases.(6) It is unknown whether recent

consumption trends of lipid-modifying agents correspond with the observed changes

in cholesterol levels. To date, there is also limited data on the use of lipid-modifying

agents for many countries outside of North America and Europe.

In this study, we analyzed pharmaceutical market intelligence data from the IQVIA

Multinational Integrated Data Analysis System (MIDAS) from 2008 to 2018. We

aimed to describe contemporary trends in lipid-modifying agent use and to explore

the relationship between the use of lipid-modifying agents and average total and

5 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

non–HDL cholesterol concentrations. Regional and cross-country comparisons on

trends of lipid-modifying agent consumption can be used to monitor progress on

access to lipid-modifying treatment, to explore reasons for different patterns of use,

and to connect information about an important cause of cardiovascular disease and

its pharmacological treatment.

6 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

METHODS

Study Design and Data Sources

We conducted a cross-sectional and ecological study using monthly pharmaceutical

sales data collected by IQVIA in 83 countries from January 2008 to December 2018.

Country-level data is collected from multiple distribution channels, such as

wholesalers, direct distribution from manufacturers, retail, and hospital pharmacies

and differs by country and data type. If required, the sampled data is then projected

to represent 100% of the total sales channel in each country. Product information in

MIDAS is classified according to Anatomic Therapeutic Chemical (ATC) codes, and

undergoes various quality-control checks. The available data for this study provides

an international view of medication use across 39 high-, 24 upper-middle, 13 lower-

middle, and 7 low-income economies included in 67 MIDAS country panels

(Supplementary Table 1). MIDAS countries can be defined as individual countries,

regions or territories (e.g., Puerto Rico), or groups of countries (i.e., Central America

and French West Africa). IQVIA annually validates its data using manufacturer sales

to estimate the accuracy of its data, with 95% global precision in most years.(8,9)

Data from MIDAS has been used to assess global patterns in medicine

consumption.(7,10) No information about patients or prescribers was available in our

data; therefore, this study was exempt from ethical approval by the Institutional

Review Board of the University of Hong Kong/Hospital Authority Hong Kong West

Cluster.

We also used the mid-year population estimates from the United Nations (UN) World

Population Prospects 2019 report.(11) The population of the constituent countries for

French West Africa and Central America were summed for each year. Countries

7 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

were then classified according to the UN geographical region (i.e. Africa, Latin

America and the Caribbean, Oceania, Europe, Northern America, and Asia) and

subregions. Country-level estimates for mean total and non–HDL cholesterol were

obtained from the NCD Risk Factor Collaboration.(6) The NCD Risk Factor

Collaboration used publicly available data from population-based surveys, and

estimated age-standardized total and non-HDL cholesterol for 200 countries, from

1980 to 2018, using a Bayesian model with the Markov chain Monte Carlo algorithm.

Lipid-modifying Agent Categories

The available data included sales of brand, generic, and combination lipid-modifying

agents. Because of limited coverage by the WHO, IQVIA does not assign ATC codes

or defined daily doses to combination products (e.g. C10B Lipid modifying agents,

combinations).(12) In addition, because the WHO ATC system groups ezetimibe,

PCSK9 inhibitors, and omega-3 fatty acids, under the C10AX Other lipid modifying

agents category, we classified lipid-modifying agents into the following therapeutic

categories: statins, fibrates, bile acid sequestrants, niacin, ezetimibe, omega-3 fatty

acids, PCSK9 inhibitors, and others (Supplementary Table 2).

Consumption Measurements

The primary variables of interest were the monthly number of standard units, and

consumption standardized for population, defined as standard units per 1000

inhabitants, either per quarter or per year. The measure of standard units captures

the entire market of products in MIDAS and includes combination products. IQVIA

defines a standard unit as the smallest common dose which allows for comparisons

between products of different dosage forms. For example, one standard unit equals

a tablet, a capsule, or a pre-filled syringe (for PCSK9 inhibitors, for example).

8 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Data Analysis

We reported the total number of standard units sold per month for all lipid-modifying

agents and by each drug class. For each calendar year from 2008 to 2018, monthly

sales data were aggregated per quarter (Q1 = Jan, Feb, March; Q2 = April, May,

June; Q3 = July, August, September; Q4 = October, November, December) and per

year (January to December), and standardized per 1000 inhabitants. Consumption of

all lipid-modifying agents by region and subregion was plotted. For all lipid-modifying

agents and each class, we assessed consumption trends using the compound

annual growth rate (CAGR). CAGR is the geometric mean of annual growth rates

and is used to compare growth trends over several years and is defined as =

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 1 × 100%.(13) Because CAGR is sensitive to 𝑦𝑦𝑦𝑦𝑦𝑦𝑟𝑟 −𝑦𝑦𝑦𝑦𝑦𝑦𝑟𝑟 2 1 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑦𝑦𝑦𝑦𝑦𝑦𝑟𝑟2 � �𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑦𝑦𝑦𝑦𝑦𝑦𝑟𝑟1 − � the start and end years of the time period, we calculated it both for the overall study

period (2018 vs 2008; or the earliest year of available data), and for the last five

years of the study (2018 vs 2013). The latter five-year period is when most MIDAS

country panel additions were completed and can capture consumption trends since

the publication of the 2013 American College of Cardiology/American Heart

Association cholesterol guideline (Supplementary Table 1).(4) We estimated CAGR

for PCSK9 inhibitors by comparing the annual sales in 2016 and 2018, since

evolocumab and alirocumab were first approved in July 2015, and thus did not have

a 12 months of sales data in 2015.(14,15)

Statin market share was estimated as the percentage of statin standard units sold

relative to all lipid-modifying agent standard units in 2008 versus 2018. We plotted

lipid-modifying agent consumption versus sex-specific cholesterol concentrations in

2018 and assessed their correlation using Spearman’s . Furthermore, we plotted

𝜌𝜌 9 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

the change in annual lipid-modifying agent consumption and mean concentrations of

total cholesterol and non–HDL cholesterol for 2008 to 2018. For these plots, we

excluded countries with no consumption data in 2008 (i.e. Thailand, Bosnia,

Kazakhstan, Serbia, and Netherlands). Furthermore, for the MIDAS countries of

French West Africa and Central America, we selected the mean cholesterol

concentrations for Togo and El Salvador, respectively, to represent changes for

these MIDAS countries. These countries were arbitrarily selected since their

cholesterol concentrations were approximately in the middle of the plots for average

cholesterol in their respective MIDAS country. Two authors (JEB and YW)

independently accessed the data and conducted the analysis using R software

version 3.6.1 (R Core Team; Vienna, Austria).

10 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

RESULTS

Consumption Volume

Use of all lipid-modifying agents increased from a total of 2,904,258,689 standard

units in January 2008 to 5,509,412,107 in December 2018. Statins were the most

commonly used class of lipid-modifying agent (Figure 1A). Fibrates, ezetimibe, and

omega-3 fatty acids were the most consumed nonstatins drugs (Figure 1B). When

accounting for the population of the included countries, the annual use of lipid-

modifying agents increased at a CAGR of 4.13% from 2008 to 2018. In the latter

five-year period (2013-2018), consumption slowed with a CAGR of 3.16% (Table 1).

Statins experienced the largest average growth from 2008 to 2018 (CAGR 5.19%).

Between 2013 to 2018, trends for nonstatins differed. There was increased

consumption of other lipid-modifying agents, ezetimibe, and PCSK9 inhibitors. In

contrast, consumption of fibrates, niacin, omega-3 fatty acids, and bile acid

sequestrants decreased (Table 1).

The use of all lipid-modifying agents over time varied widely according to region,

subregion, and country. The overall use of lipid-modifying agents in Northern

America remained similar in 2018 as compared with 2008, while other regions saw

steady increases in consumption (Figure 2A). In 2018, lipid-modifying agent

consumption in Oceania was nearly 7.3 times greater than Asia, 6.1 times greater

than Latin America and the Caribbean, and 16.1 times greater than Africa. At the

subregion level, Central Asia, Central America, and Western Africa had the lowest

use of lipid-modifying agents (Figure 2B). In 2008, use of lipid-modifying agents in

countries outside Northern America and Europe was low (Figure 3A). By 2018, use

11 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

had increased in most countries (Figure 3B), notably in South America, Southern

Europe, Northern Africa, and in East Asia.

Countries in Northern America and Europe were the greatest consumers of lipid-

modifying agents. In 2008, the countries with the highest consumption of lipid-

modifying agents were the United States (40,902 standard units/1000 inhabitants),

Greece (39,827 standard units/1000 inhabitants), and France (39,751 standard

units/1000 inhabitants). By 2018, Greece, Portugal, and Belgium, were the greatest

consumers of lipid-modifying agents (Supplementary Table 3). Over the most recent

5-year period, 64 (95.5%) of the IQVIA countries had increased use of lipid-

modifying agents (Supplementary Table 3). During this time period, Belgium, Chile,

Ecuador, France, Hungary, Luxembourg, the United States, and Venezuela all

experienced decreased consumption of lipid-modifying agents.

When comparing 2008 to 2018, 17 countries (25%) had decreases in statin market

share (Figure 4), while statin market share increased in the remainder. Large

increases in statin market share occurred in several countries from different regions,

such as the Russian Federation, Venezuela, French West Africa, India, China, and

the United States. In 2018, subregions with the highest proportion of statin use

included Eastern, Northern, and Southern Europe, Western Africa, and Central Asia

(Figure 4B).

Relationship to Cholesterol Concentrations

In 2018, there was a moderate positive correlation between lipid-modifying agent use

and mean total cholesterol concentrations in men (Figure 5A) and women (Figure

5B). However, there was weaker evidence of a correlation for non–HDL cholesterol

concentrations (Figure 5C and Figure 5D). Distinct country-level patterns were

12 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

observed when consumption of lipid-modifying agents and sex-specific total (Figure

6) and non–HDL cholesterol concentrations were compared in 2008 and 2018

(Figure 7). Many countries, such as Korea, Greece, and Puerto Rico, experienced

large changes in consumption and small changes in total and non–HDL cholesterol.

Others, such as French West Africa, China, and the Philippines, saw little change in

the use of lipid-modifying agents, but had increases in cholesterol levels. In contrast,

France, and the United States experienced both reductions in use of lipid-modifying

agents and total and non–HDL cholesterol concentrations.

13 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

DISCUSSION

Our analysis shows a consistent increase in the use of lipid-modifying agents from

2008 to 2018, largely driven by the increased global consumption of statins. In the

latter five-year period, rapid uptake of LDL-C lowering nonstatin treatments–PCSK9

inhibitors and ezetimibe–contrasts with declines in consumption of older nonstatin

treatments (i.e., fibrates, nicotinic acid, and bile acid sequestrants). Distinct country-

level patterns of changes in lipid-modifying agent use and lipid concentrations exist.

Although statins have robust evidence of efficacy and safety from multiple

randomized controlled trials and are a mainstay of lipid-lowering treatment, there

was great diversity in statin market share between countries.

Comparison with Previous Studies

Few data are available on lipid-modifying agent use, and the market share of statins,

in countries outside Northern America and Western Europe. The use of ezetimibe,

fibrates, and niacin increased steadily in the United States and Canada from 2002 to

2009.(16–18) In these studies, the overall use of nonstatins was higher in the United

States than Canada and is reflected in our findings which saw an increased statin

market share in the United States, but a relatively stable market share for statins in

Canada. Increases in statin use and the use of generic statins occurred in the United

States from 2002 to 2013.(19). Previous reports from European countries describe

consistent increases in consumption of lipid-modifying agents.(20–23) Statins were

the predominant lipid-modifying agent although the market share of fibrates

remained strong in France (~25%), Belgium (~25%) and Germany (~10%) in

2003.(20) Sabo et al reported lower use of statins in Serbia compared with

Scandinavian countries.(24) Roth et al reported cross-country comparisons of the

14 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

proportion of the population treated with lipid-modifying agents and included data

from three middle-income countries.(25) While in India, growth in statin prescriptions

and a large proportion of combination lipid-modifying agent product use was reported

between 2006 to 2010.(26) In East Asia, increased use of statins has been

described in Hong Kong and Taiwan.(27,28) Taken together, the global trends in

increased use of lipid-modifying agents continued over the past decade, but appear

to have weakened since 2013. In contrast to earlier reports from North America,

recent trends point to an overall decreased use of fibrates and niacin.

Changes in Consumption Versus Trends in Cholesterol

The epidemiological transition of elevated blood cholesterol in several developing

regions has now been quantified.(6) Rapid growth in the uptake of lipid-modifying

agents has coincided with increases in cholesterol concentrations, particularly in

countries located in East and Southern Asia, and Northern and Western Africa.

However, lipid-modifying agent use in some countries has not kept pace with

increases in total and non–HDL cholesterol. Statin use appears to be responsible for

about one-third of reductions in total cholesterol in the United States and the United

Kingdom.(29,30) For the countries in our analysis, it appears that country-level

changes in non–HDL cholesterol over time do not necessarily coincide with a

corresponding change in lipid-modifying agent consumption. Several countries had

reductions in non–HDL cholesterol and had similar or reduced use of lipid-modifying

agents from 2008 to 2018. The patterns observed in this study, support the assertion

that declines in non–HDL cholesterol are mostly due to dietary changes, and not

increased national use of lipid-modifying agents.(30–33)

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Cholesterol Guidelines and Trials

The timing of publication of key cholesterol guidelines and clinical trials aligns with

the observed consumption trends for niacin, ezetimibe, and PCSK9 inhibitors.

Evidence of the futility of adding niacin to statin therapy, led to the early stopping of

the AIM-HIGH study (published December 2011).(34) In our study, the use of niacin

gradually increased and peaked in 2010-2011. Declining use of niacin further

accelerated (2013-2018 CAGR -6.92%) after niacin-laropiprant failed to reduce the

incidence of major vascular events in the HPS2-THRIVE study (published July

2014).(35) The use of ezetimibe, in contrast, decreased up until 2015, because of

the results of two clinical trials which suggested minimal evidence of benefit when

ezetimibe was added to a statin: ENHANCE (published April 2008),(36) and SEAS

(published September 2008).(37) The resumption of increased use of ezetimibe

since 2015 was likely influenced by the evidence of benefit of ezetimibe-simvastatin

in the IMPROVE-IT study (published June 2015),(38) the introduction of generic

ezetimibe (available in the United States in 2016),(39) and the recommendation to

add ezetimibe to statins in high-risk individuals in the 2016 ESC/EAS cholesterol

guideline.(40) Interestingly, ezetimibe’s U shaped trajectory means that consumption

in 2018 was nearly unchanged from that in 2008. Use of PCSK9 inhibitors increased

rapidly after their introduction in 2015, and their use was further supported by

evidence of benefit from the FOURIER (published May 2017)(41) and the ODYSSEY

OUTCOMES (published November 2018) trials.(42) Consumption of PCSK9

inhibitors will likely continue to increase beyond 2018 given the emphasis on lower

LDL cholesterol goals, particularly in very high-risk patients in both the ACC/AHA

2018(2) and EAS/ESC 2019(1) cholesterol guidelines, and potential further price

reductions.(43)

16 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Strengths and Limitations

We have identified diverse country-level trajectories by linking data on national lipid

concentrations and lipid-modifying agent consumption. Another strength of our study

is that it includes recent data from 83 countries, from different regions and with

different income levels. To our knowledge, IQVIA MIDAS is the only platform which

permits timely global and cross-country comparisons of medication consumption

using locally collected data. Previous studies of lipid-modifying agents have tended

to focus only on statin consumption, but we report consumption of all major classes

of statin and nonstatin lipid-modifying agents. In particular, a current assessment of

the recent trends in use of ezetimibe, PCSK9 inhibitors, and omega-3 fatty acids is

needed since emerging evidence and clinical guidelines now suggest their use in

select high-risk patients. Our study also has limitations. Comparisons between

regions, subregions, and countries should be interpreted in the context of the

available data and total market coverage of the included countries. Less than 100%

market coverage underestimates the true consumption of lipid-modifying agents

(e.g. Korea lacks hospital data). However, total pharmaceutical market coverage in

most countries was greater than 80%. Lipid-modifying agents are taken on a chronic

basis and are generally obtained from retail stores in countries that are not centered

on the delivery of healthcare in hospitals; retail consumption data was available for

all countries except for Taiwan and Thailand. Conclusions from our ecological study

should not be interpreted as being applicable to individual inhabitants of each

country. Furthermore, relationships between changes in lipid-modifying agent

consumption and total and non–HDL cholesterol are not causal. We did not adjust

for other factors that may influence lipid-modifying agent consumption such as

indication, access to healthcare services, affordability of medications, and dietary

17 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

patterns. However, distinct country-level patterns are useful for generating

hypotheses for further research.

Conclusion

Growth in the use of lipid-modifying agents has continued globally and statins are the

dominant class of lipid-modifying agent. However, three decades since the

introduction of the first statin, their adoption in Africa, Asia, and Latin America and

the Caribbean remain markedly lower than in Northern America and Europe, but is

growing rapidly.

Competing Interests

Dr Man reports grants from C W Maplethorpe Fellowship, grants from National

Institute of Health Research, UK, grants from European Commission H2020,

personal fees from IQVIA, outside the submitted work. Prof Wong reports personal

fees and non-financial support from IQVIA, outside the submitted work. Dr Chan

reports honorarium from the Hospital Authority (Hong Kong), grants from Research

Grants Council (RGC, Hong Kong), Research Fund Secretariat of the Food and

Health Bureau, National Natural Science Fund of China, Wellcome Trust, Bayer,

Bristol-Myers Squibb, Pfizer, Janssen, Amgen, Takeda, and the Narcotics Division of

the Security Bureau of the Hong Kong SAR, outside the submitted work. All other

authors declare no competing interests.

Author Contributions

Conceptualization: Blais, Yap, Chan

Formal Analysis: Blais, Y. Wei

Funding acquisition: Chan, Wong

Resources: Chan, Wong, L. Wei

18 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Visualization: Yap, Blais

Supervision: Chan, Wong

Writing - original draft: Blais, Yap

Writing - review and editing: All authors

Funding

This work was supported by internal seed funding from the Li Ka Shing Faculty of

Medicine, The University of Hong Kong. The funder had no role in the study design,

analysis, or decision to publish the results. Mr Blais is supported by the Hong Kong

Research Grants Council as a recipient of the Hong Kong PhD Fellowship Scheme.

Data Availability

The underlying MIDAS data were provided by IQVIA under license. The terms of our

agreement do not permit disclosure, sublicensing, or sharing of IQVIA MIDAS data.

The underlying cholesterol data are available from the NCD Risk Factor

Collaboration: http://www.ncdrisc.org/data-downloads-cholesterol.html.

19 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

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TABLES

Table 1. Use and Growth Rate of Lipid-Modifying Agents by Category in 83 Countries Between 2008 to 2018

Consumption (standard units per 1000 inhabitants) CAGR (%)

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2008-2018 2013-2018 All lipid-modifying agents 7,467.86 8,089.53 8,694.76 9,149.65 9,289.57 9,585.08 9,839.76 10,064.89 10,464.85 10,767.08 11,197.07 4.13 3.16 Statins 5,706.76 6,261.74 6,761.64 7,223.41 7,434.18 7,780.26 8,088.59 8,367.84 8,777.62 9,071.05 9,463.05 5.19 3.99 Fibrates 710.33 755.87 825.22 824.99 815.49 821.09 823.63 812.87 800.15 781.88 774.58 0.87 -1.16 Ezetimibe 482.86 439.23 422.83 411.14 402.27 400.70 390.03 386.46 410.38 441.26 487.67 0.10 4.01 Omega-3 fatty acids 237.89 282.92 311.55 320.67 309.58 276.16 256.37 245.67 236.51 237.44 241.83 0.16 -2.62 Niacin 210.98 221.82 243.05 241.01 205.69 180.94 159.89 135.73 128.18 128.16 126.45 -4.99 -6.92 Bile acid sequestrants 103.72 112.50 115.15 112.17 105.27 107.34 100.13 93.84 87.03 80.56 79.69 -2.60 -5.78 PCSK9 inhibitors 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.13 0.32 0.56 NA 104.47a Others 15.32 15.45 15.33 16.26 17.09 18.58 21.12 22.47 24.85 26.41 23.23 4.25 4.57 aCAGR calculated from 2016-2018. Abbreviations: CAGR, compound annual growth rate; NA, not applicable.

26 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

FIGURES

Figure 1. Monthly Number of Lipid-Modifying Agent Standard Units Used in 83

Countries from 2008 to 2018

Monthly sales data are from IQVIA MIDAS. Lipid-modifying agents are categorized

according to therapeutic categories (see Supplementary Table 2). A, number of

standard units (billions) sold of all lipid-modifying agents and according to

therapeutic category. B, number of standard units (millions) sold of fibrates,

ezetimibe, omega-3 fatty acids, niacin, bile acid sequestrants, other lipid-modifying

agents, and PCSK9 inhibitors.

27 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 2. Quarterly Use of Lipid-Modifying Agents by Region from 2008 to 2018

Monthly sales data were aggregated on a quarterly basis. Individual countries were

grouped according to UN geographical regions and subregions (see Supplementary

Table 1).

28 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 3. Use of Lipid-Modifying Agents by Country in 2008 and 2018

A, consumption of lipid-modifying agents in 2008. B, consumption of lipid-modifying

agents in 2018.

29 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 4. Market Share of Statins by Country in 2008 and 2018

A, market share of statins in 2008. B, market share of statins in 2018. Market share

was calculated as the proportion of statin standard units to all lipid-modifying agent

standard units.

30 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 5. Relationship Between Consumption of Lipid-Modifying Agents and Mean

Concentrations of Total Cholesterol and non–HDL Cholesterol in 2018

A, consumption of lipid-modifying agents vs age-standardized mean total cholesterol

for men. B, consumption of lipid-modifying agents vs age-standardized mean total

cholesterol for women. C, consumption of lipid-modifying agents vs age-

standardized non–HDL cholesterol for men. D, consumption of lipid-modifying agents

vs age-standardized mean non–HDL cholesterol for women. Cholesterol data are

from the NCD Risk Factor Collaboration.(6) indicates Spearman’s correlation

coefficient. 𝜌𝜌

31 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 6. Changes in Lipid-modifying Agent Consumption and Mean Total

Cholesterol Concentrations from 2008 to 2018

The start of the arrow (black point) indicates 2008 and the head of the arrow

indicates 2018.

A, consumption of lipid-modifying agents vs age-standardized mean total cholesterol

for men. B, consumption of lipid-modifying agents vs age-standardized mean total

cholesterol for women. Cholesterol data are from NCD Risk Factor Collaboration.(6)

Bosnia, Kazakhstan, Netherlands, Serbia, and Thailand do not have MIDAS data for

2008 and were excluded.

32 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Figure 7. Changes in Lipid-modifying Agent Consumption and Mean non–HDL

Cholesterol Concentrations from 2008 to 2018

The start of the arrow (black point) indicates 2008 and the head of the arrow

indicates 2018.

A, consumption of lipid-modifying agents vs age-standardized mean non–HDL

cholesterol for men. B, consumption of lipid-modifying agents vs age-standardized

mean non–HDL cholesterol for women. Cholesterol data are from NCD Risk Factor

Collaboration.(6) Bosnia, Kazakhstan, Netherlands, Serbia, and Thailand do not

have MIDAS data for 2008 and were excluded.

33 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

SUPPLEMENTARY MATERIAL

Supplementary Table 1. Geographic Classification, Income Category, and Total Pharmaceutical Market Data Coverage of the included IQVIA MIDAS Countries Channels (retail and hospital) in each country that do not have 100% audit coverage are projected to 100% of each channel by IQVIA. Coverage of MIDAS country panels indicates data were available for analysis in this study. Figures in brackets indicate the number of countries included in each region, subregion, or income category. Income category was defined according to the World Bank Fiscal Year 2020 (calendar year 2018).

Coverage MIDAS Income Retail Hospital Region Subregion (% of Notes country category sector sector total market) Upper Algeria Yes No 80 middle

Lower Egypt Yes No 75 middle Northern Africa (4) Lower Morocco Yes No 88 middle

Hospital data Lower Tunisia Yes Yes 100 available from middle January 2011

Southern Upper South Africa Yes No 62 Africa (17) Africa (1) middle Consists of 12 countries: Benin, Burkina Faso, Low (7) Cameroon, Chad, Lower Côte d'Ivoire, middle Gabon, Guinea, Western French West (4) Yes No 86 République du Africa (12) Africa Congo, Mali, Niger, Upper Senegal, and middle Togo. (1) Chad and Niger were included in January 2011.

Retail data available from Central Asia Upper January 2011, Kazakhstan Yes Yes 100 (1) middle hospital data available from January 2013.

Retail data Upper China Yes Yes 72 available from middle October 2011

Asia (15) Eastern Asia Japan High Yes Yes 100 (4) Korea High Yes No 65

Taiwan High No Yes 83

Lower India Yes Yes 95 Southern middle Asia (2) Lower Pakistan Yes No 85 middle

34 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Coverage MIDAS Income Retail Hospital Region Subregion (% of Notes country category sector sector total market) Lower Philippines Yes Yes 100 South- middle Eastern Asia Hospital data (2) Upper Thailand No Yes 71 available from April middle 2010

Upper Jordan Yes No 71 middle

Kuwait High Yes No 35

Upper Lebanon Yes No 77 Western Asia middle (6) Saudi Arabia High Yes No 45

Upper Turkey Yes Yes 100 middle

United Arab High Yes No 45 Emirates

Upper Belarus Yes Yes 100 middle

Upper Bulgaria Yes Yes 98 middle

Czech High Yes Yes 95 Republic

Hungary High Yes Yes 100 Eastern Europe (8) Poland High Yes Yes 100

Upper Romania Yes Yes 100 middle

Hospital data Russia Upper Yes Yes 97 available from Federation middle January 2010

Slovakia High Yes Yes 97

Europe Estonia High Yes No 88 (31) Finland High Yes Yes 100

Ireland High Yes Yes 100

Latvia High Yes Yes 100 Northern Europe (8) Lithuania High Yes Yes 99

Norway High Yes Yes 100

Sweden High Yes Yes 100

United High Yes Yes 89 Kingdom

Upper Data available Bosnia Yes Yes 95 middle from January 2011 Southern Europe (8) Croatia High Yes Yes 98

Greece High Yes No 60

35 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Coverage MIDAS Income Retail Hospital Region Subregion (% of Notes country category sector sector total market) Italy High Yes Yes 99

Hospital data Portugal High Yes Yes 100 available from January 2010

Upper Data available Serbia Yes Yes 93 middle from January 2011

Slovenia High Yes Yes 98

Spain High Yes Yes 99

Austria High Yes Yes 100

Belgium High Yes Yes 99

France High Yes Yes 100

Western Germany High Yes Yes 100 Europe (7) Luxembourg High Yes No 98

Hospital panel data Netherlands High Yes Yes 91 available from January 2011

Switzerland High Yes Yes 100

Caribbean (1) Puerto Rico High Yes Yes 93

Panel of Lower pharmacies middle located in 6 (3) countries: Costa Central Upper Yes No 77 Rica, El Salvador, America Central middle Guatemala, America (7) (2) Honduras, High (1) Nicaragua and Panama.

Upper Mexico Yes No 61 middle

Upper Latin Argentina Yes No 73 American middle and the Upper Caribbean Brazil Yes No 97 middle (16) Chile High Yes No 71

Upper Colombia Yes No 71 South middle America (8) Upper Ecuador Yes No 80 middle

Upper Peru Yes No 67 middle

Uruguay High Yes No 71

Upper Venezuela Yes No 78 middle

Australia High Yes Yes 90

36 medRxiv preprint doi: https://doi.org/10.1101/2021.01.10.21249523; this version posted January 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Coverage MIDAS Income Retail Hospital Region Subregion (% of Notes country category sector sector total market) Oceania Australia/New New Zealand High Yes Yes 97 (2) Zealand (2)

Northern Canada High Yes Yes 100 Northern America America (2) United States (2) High Yes Yes 89 of America

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Supplementary Table 2. WHO Anatomic Therapeutic Chemical Groups, Codes, Agent Classes, and Medicines Included in this Study WHO ATC group WHO ATC code Drug class Molecule Atorvastatin Fluvastatin Lovastatin HMG CoA reductase inhibitors C10AA Statins Pitavastatin Pravastatin Rosuvastatin Simvastatin Bezafibrate Choline Fenofibrate Ciprofibrate Clinofibrate Clofibrate Fibrates C10AB Fibrates Etofibrate Etofylline Clofibrate Fenofibrate Fenofibric Acid Gemfibrozil Pemafibrate Colesevelam Bile acid Colestipol Bile acid sequestrants C10AC sequestrants Colestyramine Colextran Acipimox Inositol Nicotinate Niceritrol Nicotinic acid and derivatives C10AD Niacin Nicomol Nicotinic Acid Nicotinyl Alcohol Ezetimibe Ezetimibe Ethyl-eicosapent Omega-3 fatty acids Icosapent Alirocumab PCSK9 inhibitors Evolocumab Doconexent Other lipid modifying agents C10AX Lomitapide Magnesium Pyridoxal 5- phosphate Glutamate Others Mipomersen Policosanol Probucol Tiadenol

Supplementary Table 3. Annual Use of All Lipid-Modifying Agents in Each MIDAS Country from 2008 to 2018 Consumption (standard units per 1000 inhabitants) CAGR (%) 2008- 2013- Region Subregion Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2018 2018 Algeria 2,367 3,009 3,650 4,185 4,517 4,894 6,069 5,881 6,119 6,425 7,925 12.84 10.12 Egypt 558 716 841 977 1,154 1,365 1,567 1,898 2,327 2,457 3,268 19.33 19.08 Northern Africa Morocco 609 784 929 1,043 1,200 1,262 1,399 1,529 1,456 1,422 1,468 9.20 3.07 Africa Tunisia 1,977 2,562 3,116 5,633 6,203 7,161 7,323 7,961 8,175 7,205 9,943 17.53 6.79 Southern Africa South Africa 2,835 3,012 3,246 3,581 3,896 4,143 4,279 4,329 4,414 4,597 4,616 5.00 2.18 Western Africa Fr. W. Africa 19 27 36 37 52 71 94 111 132 132 170 24.55 19.23 Central Asia Kazakhstan NA NA NA 757 998 1,241 1,192 1,194 1,183 1,363 2,409 17.99a 14.19 China 321 424 533 737 1,207 1,462 1,776 2,049 2,331 2,706 3,087 25.40 16.12 Japan 27,355 30,237 32,622 34,687 35,335 36,547 36,189 37,260 38,323 39,172 39,554 3.76 1.59 Eastern Asia Korea 8,981 11,422 14,149 15,327 17,539 19,243 21,180 23,369 27,613 31,724 35,549 14.75 13.06 Taiwan 5,783 6,422 7,005 7,298 7,368 8,083 9,285 10,376 11,484 12,392 14,064 9.29 11.71 Philippines 892 1,021 1,195 1,238 1,433 1,529 1,544 1,847 1,825 1,980 2,301 9.94 8.53 South-Eastern Asia b Thailand NA NA 8,973 12,754 13,755 15,084 21,032 18,535 19,117 20,831 22,953 8.76 8.76 Asia India 1,540 1,779 2,077 2,517 2,701 2,964 3,221 3,540 3,727 3,845 4,141 10.40 6.91 Southern Asia Pakistan 550 642 678 763 849 931 1,011 1,113 1,253 1,350 1,495 10.52 9.93 Jordan 1,304 1,390 1,496 1,524 1,629 1,961 1,824 1,970 2,108 2,079 2,058 4.67 0.98 Kuwait 943 996 1,087 1,218 1,494 1,467 1,523 1,344 1,519 1,688 2,162 8.65 8.06 Lebanon 7,405 8,568 9,435 9,700 10,201 10,236 10,516 12,012 12,734 13,133 13,554 6.23 5.78 Western Asia Saudi Arabia 1,945 2,153 2,487 2,941 3,292 3,613 3,917 3,916 4,067 4,227 4,183 7.96 2.97 Turkey 6,349 7,122 8,633 9,364 8,157 6,849 6,535 6,680 6,935 7,440 8,006 2.35 3.17 United Arab Emirates 2,735 3,407 3,883 4,635 5,326 6,137 7,478 8,529 9,002 8,603 9,815 13.63 9.84 Belarus 917 974 1,406 2,205 2,696 3,361 4,155 4,950 5,575 6,513 7,815 23.89 18.38 Bulgaria 5,914 7,782 9,358 10,799 12,363 13,910 15,154 16,082 17,346 18,499 19,501 12.67 6.99 Czech 27,803 31,524 34,351 35,616 37,187 37,551 39,576 40,576 42,212 43,582 44,463 4.81 3.44 Hungary 22,661 24,985 26,689 27,906 28,135 28,534 28,363 28,436 27,955 27,923 28,278 2.24 -0.18 Eastern Europe Poland 19,906 22,711 25,184 27,906 27,539 29,696 32,416 33,004 35,690 36,041 37,066 6.41 4.53 Romania 11,620 10,488 13,491 16,805 17,381 19,355 21,451 22,290 22,840 24,307 26,716 8.68 6.66 Russian Federation 1,924 2,386 3,171 3,801 4,390 5,383 6,108 6,634 7,826 8,767 9,645 17.49 12.37 Slovakia 19,681 22,278 25,589 26,865 26,920 26,764 26,566 27,757 29,417 28,494 28,552 3.79 1.30 Europe Estonia 7,137 7,610 10,891 12,874 15,755 17,314 18,492 20,117 21,142 23,173 25,258 13.47 7.85 Finland 37,053 38,945 40,598 38,318 38,625 38,932 38,798 39,166 38,545 39,973 42,132 1.29 1.59 Ireland 32,192 34,068 34,815 36,730 40,119 40,578 41,090 41,760 42,629 42,921 42,229 2.75 0.80 Latvia 7,433 7,571 9,421 11,391 14,396 17,220 17,120 19,135 19,249 21,803 24,295 12.57 7.13 Northern Europe Lithuania 4,453 4,422 4,970 5,645 5,905 7,041 7,545 7,918 10,007 12,488 14,624 12.63 15.74 Norway 30,195 31,420 33,117 33,858 34,358 34,277 34,622 35,718 36,689 37,352 38,866 2.56 2.54 Sweden 27,475 28,717 29,148 28,860 28,994 29,495 29,971 30,769 31,736 32,850 33,732 2.07 2.72 United Kingdom 37,572 39,446 39,887 41,274 41,856 42,408 43,301 43,239 43,735 43,649 43,166 1.40 0.35 Southern Europe Bosnia NA NA NA 4,935 6,741 7,955 8,954 9,639 10,725 11,212 12,616 14.35a 9.66

Consumption (standard units per 1000 inhabitants) CAGR (%) 2008- 2013- Region Subregion Country 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2018 2018 Croatia 18,581 20,962 22,602 23,198 23,655 23,867 24,937 25,783 26,435 27,487 28,875 4.51 3.88 Greece 39,827 44,072 46,471 50,171 47,904 47,681 47,233 50,322 53,459 56,959 59,839 4.16 4.65 Italy 23,483 26,101 28,825 30,922 32,992 33,786 34,582 35,560 36,841 37,751 39,434 5.32 3.14 Portugal 38,717 42,427 42,588 41,448 44,512 48,430 50,409 51,682 53,987 53,467 54,724 3.52 2.47 Serbia NA NA NA 7,855 9,615 10,378 10,210 12,647 13,666 14,599 16,060 10.76a 9.13 Slovenia 26,516 28,733 30,582 31,991 33,147 34,007 34,272 34,849 35,468 36,378 36,521 3.25 1.44 Spain 28,091 30,517 32,921 35,776 36,328 37,566 39,175 40,230 41,711 42,247 44,281 4.66 3.34 Austria 19,272 20,542 21,743 22,904 23,659 24,098 24,552 24,057 25,447 26,716 27,931 3.78 3.00 Belgium 36,963 39,703 42,094 44,164 46,238 46,290 46,106 45,681 46,044 46,139 46,141 2.24 -0.06 France 39,751 40,914 42,781 43,364 44,417 42,855 42,126 40,696 39,106 38,111 37,776 -0.51 -2.49 Western Europe Germany 23,985 25,707 26,075 27,182 26,948 28,054 28,158 28,899 30,322 31,034 32,018 2.93 2.68 Luxembourg 34,667 37,011 39,807 42,491 45,131 45,288 45,201 45,030 44,247 42,853 41,749 1.88 -1.61 Netherlands NA NA NA 37,080 39,721 40,441 40,799 41,411 42,145 43,513 44,840 2.75a 2.09 Switzerland 23,391 23,774 24,844 25,498 26,613 26,400 26,850 28,162 28,980 29,385 29,825 2.46 2.47 Caribbean Puerto Rico 19,400 23,084 26,856 29,806 32,209 34,898 36,035 39,941 42,118 42,877 45,371 8.87 5.39 C. America 656 712 774 873 909 936 910 969 1,022 1,091 1,113 5.43 3.53 Central America Mexico 1,044 1,065 1,248 1,288 1,363 1,311 1,258 1,363 1,385 1,492 1,560 4.09 3.53 Argentina 6,703 7,625 8,565 9,836 10,803 11,286 11,482 12,139 12,481 12,471 12,286 6.25 1.71 Brazil 2,754 3,165 3,941 5,274 6,776 7,918 9,239 10,256 11,386 12,307 13,143 16.92 10.67 Latin America and the Chile 4,168 4,359 4,466 4,948 5,123 5,506 5,795 5,930 5,725 5,562 5,438 2.69 -0.25 Caribbean Colombia 1,064 1,054 1,136 1,268 1,220 1,155 1,136 1,138 1,051 1,170 1,197 1.18 0.71 South America Ecuador 1,929 2,042 2,239 2,281 2,511 2,628 2,839 2,713 2,456 2,591 2,544 2.81 -0.65 Peru 474 488 567 587 633 620 589 709 689 611 647 3.15 0.86 Uruguay 6,200 7,686 8,551 9,323 11,328 12,454 12,759 12,177 12,905 13,612 12,977 7.67 0.83 Venezuela 4,586 4,782 4,852 4,998 5,733 6,690 7,298 6,782 4,913 2,662 1,546 -10.31 -25.40 Canada 37,288 39,281 41,904 41,433 43,232 44,216 43,941 43,491 44,429 46,195 45,375 1.98 0.52 Northern America Northern America United States 40,902 43,676 45,998 44,945 40,973 40,670 39,008 38,426 39,521 39,536 40,045 -0.21 -0.31 Australia 34,625 36,706 38,003 38,180 39,337 38,640 38,600 39,389 39,838 41,229 42,819 2.15 2.07 Oceania Australia/New Zealand New Zealand 32,113 33,923 35,652 36,518 38,261 38,459 37,126 39,886 38,831 38,482 39,019 1.97 0.29 aCAGR calculated from 2011-2018. bCAGR calculated from 2010-2018. Abbreviations: CAGR, compound annual growth rate; C. America, Central America; Fr. W. Africa, French West Africa; NA, not applicable.