The development of nations conditions the disease space.
Date: 2019-08-30
Institutions: Joint Research Centre
Subjects: High Representative of the Union for Foreign Affairs and Security Policy, economic development, health policy, illness

The development of nations  
conditions the disease space 

Antonios GARAS,  

Sophie GUTHMULLER, 

Athanasios LAPATINAS 

2019 

JRC Working Papers in Economics and Finance, 2019/9 

 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science 
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the European Commission nor any person acting on behalf of the Commission is responsible for the use that 
might be made of this publication. 

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JRC116399 

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ISBN 978-92-76-02924-3 

ISSN 2467-2203 

doi:10.2760/793876 

Luxembourg: Publications Office of the European Union, 2019 

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How to cite this report: Garas A, Guthmuller S, Lapatinas A, The development of nations conditions the disease 
space, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-02924-3, 
doi:10.2760/793876, JRC116399. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
,

Contents

Acknowledgements .............................................................................................. 1

Abstract ............................................................................................................. 2

1 Introduction ................................................................................................... 3

2 The country-disease network ............................................................................ 5

2.1 Data on diseases and injuries....................................................................... 5

2.2 The country-disease bipartite network ........................................................... 5

3 The disease space and the health complexity index.............................................. 8

3.1 The relative disease disadvantage................................................................. 8

3.2 The disease space ...................................................................................... 8

3.3 Health complexity index .............................................................................. 9

4 The geography of complex diseases ................................................................... 11

5 The effect of economic development on health complexity .................................... 11

5.1 Regression analysis .................................................................................... 11

5.1.1 Control Variables ................................................................................... 12

5.1.2 Instrumental variables ........................................................................... 14

5.2 Regression results ...................................................................................... 14

6 Economic development and disease complexity ................................................... 22

6.1 The disease complexity index ....................................................................... 22

6.2 The disease-income complexity index............................................................ 23

6.3 Linking disease complexity and economic development.................................... 24

7 Conclusions .................................................................................................... 25

References ......................................................................................................... 26

List of figures...................................................................................................... 43

List of tables ....................................................................................................... 44

Annexes ............................................................................................................. 45

Annex 1. List of countries .................................................................................. 45

Annex 2. List of diseases and injuries .................................................................. 46

i

Acknowledgements

The authors are grateful to Tor Iversen, Lisa Cameron, Enrica Croda for useful comments.
They also thank all participants of the 10th Australasian Workshop on Econometrics and
Health Economics (April 2019), and all participants of the 4th Edition of the European-
American Health Economics Study Group (July 2019) for helpful comments and suggestions.
All remaining errors are their own. This research was conducted while Sophie Guthmuller
and Athanasios Lapatinas were in service at the European Commission's Joint Research
Centre. The scientific output expressed does not imply a European Commission policy posi-
tion. Neither the European Commission nor any person acting on behalf of the Commission
is responsible for any use that might be made of this publication.

Authors
Antonios Garas1, Sophie Guthmuller2, and Athanasios Lapatinas2
(Corresponding author athanasios.lapatinas@ec.europa.eu)
1ETH Zurich, Chair of Systems Design,
Weinbergstrasse 56/58, 8092 Zurich, Switzerland
2European Commission, Joint Research Centre,
Via E. Fermi 2749, TP 361, Ispra (VA), I-21027, Italy

1

Abstract

Using the economic complexity methodology on data for disease prevalence in 195 coun-
tries during the period of 1990-2016, we propose two new metrics for quantifying the
relatedness between diseases, or the `disease space' of countries. With these metrics, we
analyze the geography of diseases and empirically investigate the effect of economic de-
velopment on the health complexity of countries.
We show that a higher income per capita increases the complexity of countries' diseases.
Furthermore, we build a disease-level index that links a disease to the average level of GDP
per capita of the countries for which they have a relative disease disadvantage. With this
index, we highlight the link between economic development and the complexity of diseases
and illustrate, at the disease-level, how increases in income per capita are associated with
more complex diseases.

Keywords: health complexity, disease complexity, economic development

2

1 Introduction

Popular belief holds that the European conquest of America was accomplished with guns
and soldiers. However, (Bianchine and Russo, 1992) show that new illnesses brought
from the Old World by European conquistadors, which resulted in devastating epidemics
throughout the New World, were the major forces behind the aboriginal depopulation of
the Americas. Our history, geography, culture, religion, and language have often been
influenced by infections that have plagued humankind and shaped important events. Ex-
amples include the plague in fourteenth century Europe, how the yellow fever increased
the importation of African slaves in the sixteenth century due to shortage of indigenous
workers and the relative resistance of Africans to the disease, as well as the typhus deaths
of the Napoleon's army during the 1812 Russian campaign, and Franklin D. Roosevelt's
hypertension and heart failure, which worsened during his February 1945 dealings with
Joseph Stalin in Malta (Post and Robins, 1995, Sherman, 2007, Taylor, 2016).
Furthermore, there is strong historical evidence that the wealth of nations is positively
linked to the health of their populations. Since the eighteenth century, economic de-
velopment associated with improvements in nutrition, access to sanitation, public health
interventions, and medical innovations such as vaccination, have contributed to the re-
duction of major infectious diseases, the decline of premature death rates, and a longer
life expectancy for children and adults in both developed and developing countries (Fogel,
1990, Fogel, 2004, Birchenall, 2007).
Nevertheless, many significant health problems have emerged in concert with economic
development and technological modernization. Among them, stress, anxiety, sleep de-
privation, and depression are mental disorders that are more prevalent in high-income
countries. While they account for only 9% of the burden in low-income countries, this fig-
ure is 18% in middle-income and 27% in high-income countries (Prince, 2011). In OECD
countries, a longer life expectancy is coupled with a higher rate of chronic and long-term
illnesses in older populations (GBD2016 Dementia Collaborators, 2018). Industrialization
has expanded the reach of existing food-related diseases and created new disorders and
addictions (GBD 2015 Obesity Collaborators, 2017). Industrialization also stimulates ur-
banization, the process of population migration from rural areas to cities. This makes
urban areas focal points for many emerging environmental and health hazards. Accord-
ing to the World Health Organization (WHO), (cid:16)as urban populations grow, the quality of
global and local ecosystems, and the urban environment, will play an increasingly impor-
tant role in public health with respect to issues ranging from solid waste disposal, provi-
sion of safe water and sanitation, and injury prevention, to the interface between urban
poverty, environment and health.(cid:17).1 Industrialization is also linked to occupational acci-
dents and work-related diseases (e.g., work-related cancers, musculoskeletal disorders,
respiratory diseases, psycho-social problems, and circulatory diseases), which are world-
wide problems resulting in important losses for individuals, organizations and societies
(Driscoll et al., 2005,Nelson et al., 2005,Steenland et al., 2003,Park et al., 2002,Zahm and
Blair, 2003, Hansen, 2001, Hoy et al., 2014, Punnett et al., 2005, Hoy et al., 2010).
From the above discussion, it becomes clear that economic development can affect popu-
lation health in a number of ways, both positive and negative. To gain more insights on the
link between economic development and countries' health status, we follow an innovative
and holistic approach and study the country disease network and how countries are com-
parable or diverse in terms of diseases by building the disease space of countries, i.e., the
network representation of the relatedness and proximity between diseases worldwide.
In fact, the country-disease network and the disease space reveal information about the
health-related habits of populations, such as, lifestyle and dietary habits. There are also
multiple reasons to expect the disease structures to be associated with their `structural
transformations' (i.e., the industrialization process by which economies diversify from agri-
culture to manufacturing and services (Hausmann and Klinger, 2006, Ngai and Pissarides,
2007, Gollin et al., 2002, Herrendorf et al., 2014, Laitner, 2000)), with their environmental
performance (Künzli et al., 2000, Holgate et al., 1999, Chay and Greenstone, 2003, Kampa
and Castanas, 2008), or with their adopted health-related policies (Glasgow et al., 1999,
Whitlock et al., 2002,McKenzie et al., 2005,Drummond et al., 2015,Folland et al., 2007), as
these contribute to their health status and living standards (Mankiw et al., 1992, Friedman,
2006). When a country is affected by a disease located in the core of the disease space,

1WHO, `Urbanization and health' https://www.who.int/globalchange/ecosystems/urbanization/en/

3

many other countries are also affected by this disease. These countries might share the
same type of risks factors, population characteristics or life styles, and similar health care
systems. However, this does not hold for diseases lying at the periphery of the disease
space. Studying the structure of the disease space and knowing where a country is located
in the country-disease network is therefore a useful tool to monitor the evolution of dis-
eases and to plan global health policies.
We develop a new metric called the Health Complexity Index (HCI), that quantifies the dis-
ease space and encapsulates this information by assigning lower values to countries with
diseases located at the periphery of the disease space and higher value to countries with
diseases located at the centre of the disease space. This complexity index measures the
composition of a country's pool of diseases by combining information on the diversity of
diseases in the country and the ubiquity of its diseases (the number of other countries in
which the disease exists). The intuition is that relatively high scores on the health complex-
ity index indicate populations that are diverse and have diseases that, on average, have
low ubiquity, i.e., these diseases are present in only a few other countries. In this view, the
health complexity index does not refer to a complex treatment or to complex causes of a
disease, but measures instead whether a disease is located in the densely connected core
of the disease space i.e., whether many other related diseases are present in many other
countries.
To compute the HCI, we follow the economic complexity methodology, which was initially
applied to trade micro-data, measuring the amount of knowledge materialized in a country's
productive structure (Hidalgo et al., 2007). Based on the ECI methodology, a number of
recent contributions explain economic development and growth as a process of information
development and of learning how to produce and export more diversified products (Abdon
and Felipe, 2011, Bustos et al., 2012, Caldarelli et al., 2012, Cristelli et al., 2013, Felipe,
2012, Hausmann et al., 2007, Hausmann et al., 2014, Hidalgo and Hausmann, 2009, Hi-
dalgo et al., 2007, Rodrik, 2006a, Tacchella et al., 2013, Albeaik et al., 2017b, Saviotti and
Frenken, 2008, Cristelli et al., 2015, Hausmann and Hidalgo, 2011). Furthermore, (Hart-
mann et al., 2017) have recently shown that countries exporting complex products tend
to be more inclusive and have lower levels of income inequality than countries export-
ing simpler products.
In addition, (Lapatinas and Litina, 2018) find that countries with
high intellectual quotient (IQ) populations produce and export more sophisticated/complex
products, while (Lapatinas, 2019) shows that the Internet has a positive effect on eco-
nomic complexity. Adopting the economic complexity methodology, (Balland and Rigby,
2017) compute a knowledge complexity index with more than two million patent records
for US metropolitan areas between 1975-2010. They analyze the geography and evolution
of knowledge complexity in US cities and show that the most complex cities in terms of
patents are not always those with the highest rates of patenting. In addition, using citation
data, they show that more complex patents are less likely to be cited than simpler patents
when the citing and cited patents are located in different metropolitan areas.
The aim of this paper is fourfold: (i) to build two new metrics that quantify the disease
space, following the economic complexity methodology; (ii) to estimate the effect of eco-
nomic development on countries' health complexity using the new metrics and following
dynamic panel data econometric techniques; (iii) to develop a disease-level index that
links a disease to the average level of GDP per capita of the countries with a relative dis-
ease disadvantage; (iv) to illustrate how a country's economic development is associated
with changes in its disease composition and verify the relationship between economic de-
velopment and health complexity at the disease level.
The remainder of the paper is structured as follows. Section 2 describes the construction
of the country-disease network and the the data on disease prevalence. Section 3 presents
the methodology for developing the Health Complexity Index (HCI): the relative disease
disadvantage and the disease space which form the analytic backbone of our study. Section
4 describes the geography of complex diseases, the link between ubiquity, diversity and the
(HCI) with a particular focus on countries and regions. Section 5 empirically investigates
the effect of economic development on health complexity using the HCI, data on GDP per
capita and potential covariates. Section 6 introduces an index that decomposes economic
performance at the disease level. Using this index, we highlight the link between disease
complexity and economic development. We demonstrate, at the disease level, that better
economic performance is associated with more complex diseases. Finally, in section 7, we
offer some concluding remarks.

4

2 The country-disease network

2.1 Data on diseases and injuries

Information on diseases and injuries comes from the Global Burden of Diseases (GBD)
study by the Institute for Health Metrics and Evaluation (IHME), an independent population
health research centre at UW Medicine (University of Washington) (GBD Study 2016, 2016)
that produces comparable estimates of prevalence of diseases and injuries across the world
for 195 countries and over 300 diseases and injuries (http://www.healthdata.org/).
We use data from the 2016 version of the GBD study in which data are estimated based on
various sources (input data come from administrative records, surveys, census, scientific
literature, etc) with different methods to reflect the available data and the specific epi-
demiology of each disease from 1990 to 2016. Computational methods are detailed in the
appendix of (GBD Study 2016, 2016).
Diseases and injuries are grouped by causes. The broader classification of causes (level 1)
includes: (a) communicable, maternal, neonatal, and nutritional diseases such as HIV/AIDS
and sexually transmitted infections, respiratory infections and tuberculosis, enteric infec-
tions (e.g., diarrheal diseases, typhoid fever), neglected tropical diseases (e.g. malaria,
chagas disease) and other infectious diseases (e.g. meningitis and acute hepatitis), ma-
ternal and neonatal disorders (e.g., maternal abortion and miscarriage, ectopic pregnancy,
maternal obstructed labor and uterine rupture), nutritional deficiencies (e.g., protein-energy
malnutrition, vitamin A, iron, iodine deficiencies); (b) non-communicable diseases such as
cancers, cardiovascular diseases, chronic respiratory diseases, digestive diseases (e.g.,
cirrhosis, gastritis, pancreatitis), neurological disorders (e.g., multiple sclerosis, epilepsy,
Parkinson's and Alzheimer's diseases, migraine), mental disorders (e.g., schizophrenia,
anorexia nervosa and bulimia nervosa, conduct and hyperactivity disorders), substance
use disorders (e.g., alcohol and drug use disorders), diabetes, kidney diseases, skin dis-
eases (e.g., dermatitis, bacterial skin diseases), sense organ diseases (e.g., glaucoma,
cataract, vision loss), musculoskeletal disorders (e.g., osteoarthritis, rheumatoid arthritis);
(c) injuries such as transport injuries (e.g., pedestrian road injuries, cyclist and motorcy-
clist road injuries), unintentional injuries (e.g., falls, poisonings, exposure to mechanical
forces), self-harm and interpersonal violence (e.g., sexual violence, conflict and terrorism,
executions).2
We use information for the most detailed level of causes in the GBD taxonomy (i.e., level
4, and when there is no level 4 classification, we use level 3). For example, among the
non-communicable diseases (level 1), neoplasms (level 2) include the following level 3 cat-
egories: lip and oral cavity cancer, nasopharynx cancer, other pharynx cancer, esophageal
cancer, stomach cancer, colon and rectal cancer, liver cancer, gallbladder and biliary tract
cancer, pancreatic cancer, larynx cancer, etc. Then, liver cancer includes the following level
4 subcategories: liver cancer due to hepatitis B, liver cancer due to hepatitis C, liver cancer
due to alcohol use, liver cancer due to non-alcoholic steatohepatitis (NASH), liver cancer
due to other causes.
Two measures of disease prevalence are exploited: the rate of prevalence (number of
cases per 100,000 population) for all ages, and the age-standardized rate of prevalence
to account for the differences in age structures across countries. This is useful because
relative over- or under-representation of different age groups can obscure comparisons of
age-dependent diseases (e.g., ischemic heart disease or malaria) across populations.
The list of countries and diseases are detailed in Annexes.

2.2 The country-disease bipartite network

Instrumental to our analysis is the bipartite network mapping of countries and diseases.
Bipartite, or bi-modal networks are abundant in the scientific literature, with examples
including the city-tech knowledge network (Balland and Rigby, 2017), the city-firm net-
work (Garas et al., 2019), firm-projects networks (Balland, 2012), predator-prey net-
works (Allesina and Tang, 2012), plants-pollinator networks (Bascompte et al., 2003) etc.
Here, based on data from the GBD study 2016 that assessed the disease burden of coun-
tries in the period of 1990 to 2016, we generate an l × k country-diseases matrix E, were

2In the remainder of the paper we use the word `disease' to refer to all diseases and injuries classified in the GBD
study.

5

the matrix element Ecd represents the number of cases of disease d per 100,000 population
in country c.
The aforementioned matrix allows for the construction of an undirected, weighted country-
disease network by linking each disease to the countries with disease cases. These net-
works are very dense, and in order to visually explore their structure, we apply the Dijkstra
algorithm (Dijkstra, 1959) to extract a Maximum Spanning Tree (MST) that summarizes
their structures. More precisely, the MST, which is usually considered as the backbone of
the network, is a connected subgraph having l + k − 1 edges with the maximum total weight
and without forming any loops.
In Figure 1 we illustrate the country-disease MST for 2016. From this MST, we can easily
identify clusters of countries that are linked to specific types of diseases. The main node of
the network is caries in permanent teeth (disease cause number 682, Appendix 2 reports
the correspondence table between each disease number and its name.) This disease is the
most common disease across the world, as it is present in the majority of countries. It is
also the disease with the highest prevalence worldwide (2.44 billion cases in 2016 (GBD
Study 2016, 2016))

6

Figure 1: Maximum Spanning Tree of the country-disease bipartite network. Coun-
tries are represented by yellow nodes, and diseases cover the following categories: [A. `Communicable, maternal,
neonatal, and nutritional diseases'] A.1 `HIV/AIDS and tuberculosis', A.2 `Diarrhea, lower respiratory, and other
common infectious diseases', A.3 `Neglected tropical diseases and malaria', A.4 `Maternal disorders', A.5 `Neonatal
disorders', A.6 `Nutritional deficiencies', A.7 `Other communicable, maternal, neonatal, and nutritional diseases';
[B. `Non-communicable diseases'] B.1 `Neoplasms', B.2 `Cardiovascular diseases', B.3 `Chronic respiratory dis-
eases', B.4 `Cirrhosis and other chronic liver diseases', B.5 `Digestive diseases', B.6 `Neurological disorders', B.7
`Mental and substance use disorders', B.8 `Diabetes, urogenital, blood, and endocrine diseases', B.9 `Musculoskele-
tal disorders'; [C. `Injuries'] C.1 `Transport injuries', C.2 `Unintentional injuries', C.3 `Self-harm and interpersonal
violence', C.4 `Forces of nature, conflict and terrorism, and executions and police conflict'. Data for 2016. Annexe
2 reports the correspondence table between the disease number and the disease name.

7

698419503456453426Japan663675414715387341357South Sudan363322587657333Swaziland300949388348946334405371394533530544554585591679668411543723432659665660652319710Canada547606672474523711Guinea345320Burkina Faso615616582843Sierra Leone838338839Somalia389TogoCentral African Republic350Malawi399Chad359355LiberiaKenya936349353643Afghanistan579531648CongoEquatorial GuineaSao Tome and PrincipeMalaysiaGabon361500498511507534654631685700328367716Latvia435607726651854480496Bulgaria468578Sweden627721674608650593471604495557628699485358ChileMontenegro590IcelandGermany671644706384516568381837IraqJordanMozambiqueMadagascarMoroccoSaint LuciaTurkmenistanMaldivesLaosGreeceGuamRomaniaMoldovaFrance524484BoliviaSwitzerlandAustria525658588337950423493Italy398Bangladesh645Ukraine346Lithuania509382647AlbaniaKazakhstanFijiBarbadosIsraelUzbekistanDemocratic Republic of theCongoThe BahamasSamoaNorthern Mariana IslandsGrenadaEl SalvadorFederated States of MicronesiaParaguayTanzaniaJamaicaNorth KoreaMaltaBotswanaEthiopiaIrelandZambiaCubaGuinea-BissauCyprusGuatemalaLibyaCosta RicaIndonesiaMarshall IslandsSri LankaUnited Arab Emirates713619592597612842GuyanaTunisiaIranDominican RepublicMyanmarAlgeriaRwandaTimor-LesteBurundiAngolaMexicoMacedoniaPeruUruguayBosnia and HerzegovinaTurkeyZimbabwePuerto RicoKuwaitPanamaAntigua and BarbudaArgentinaBelarusEritreaOman682SlovakiaPortugalSolomon IslandsBelizeVanuatuKiribatiAndorraAzerbaijanHondurasNicaraguaSaudi ArabiaDominicaSpainCambodiaDjiboutiChinaArmeniaSaint Vincent and theGrenadinesSyriaGeorgiaColombiaAmerican SamoaYemen618408335Niger402365681340Mauritania390SenegalIndiaNigeriaMali404351364369522Ivory CoastBenin528MauritiusBhutanThailandCameroonBahrainGhanaThe GambiaNamibiaUganda954302564849441Australia566639935488670486438546Virgin Islands, U.S.695New Zealand403465Estonia401596EgyptPakistanNepal632BermudaBelgiumSurinameTrinidad and TobagoSingapore548Netherlands571515429329Sudan847943684GreenlandSouth KoreaBrunei447661845459570850848576SeychellesVenezuela948362Comoros370Lesotho725598Cape VerdeSouth Africa396614545577Qatar692529514VietnamTaiwan694941418342352395421444397559483513Papua New Guinea595339609942Czech Republic356707673690944693642368492664947945Brazil646560569Russian Federation714727LebanonFinlandKyrgyzstanSerbiaTongaCroatiaPolandEcuadorUnited KingdomTajikistanPalestine383LuxembourgDenmark336938354705649535512563562708502573477574630683HungaryPhilippinesMongoliaNorwaySloveniaHaiti846691934655527450420697729United States565532656605662A.5B.1B.4A.7B.3B.2B.7B.5B.6A.1A.4A.3A.6A.2C.2B.8C.1B.9C.4C.33 The disease space and the health complexity index

To calculate health complexity, we combine information on diseases prevalence and how
common these diseases are across countries, following the economic complexity method-
ology, i.e., the formulas in the pioneering work of (Hidalgo and Hausmann, 2009).

3.1 The relative disease disadvantage

In short, let us assume that we have disease information for l number of countries and
k diseases. With this information, we can fill an l × k diseases matrix E, so that matrix
element Ecd is country c's information for disease d. If there is no information for disease d
in country c, then Ecd = 0.
From this matrix, it is easy to calculate the following ratio:

RDDcd =

(cid:80)

Xcd/(cid:80)
c(cid:48) Xc(cid:48) d/(cid:80)

d(cid:48) Xcd(cid:48)

c(cid:48)d(cid:48) Xc(cid:48) d(cid:48)

,

(1)

where Xcd is the number of disease cases d per 100,000 population in country c.
Similar to the economic complexity methodology and the discussion in (Hidalgo and Haus-
mann, 2009, Caldarelli et al., 2012, Hartmann et al., 2017), we claim that a country has a
relative disease disadvantage in a disease when RDDcd ≥ 1. In other words, a country c has
a RDD in disease d if the proportion of disease case d in the country's pool of all cases of
diseases is higher than the proportion of disease case d in the world's pool of all diseases
cases.
Using this threshold value, we obtain the l × k matrix M, with matrix elements Mcd = 1 if
country c has a RDD in disease d, and zero otherwise. A visualization of the matrix M for
this dataset is shown in Figure 2, where a dark point indicates that country c has a RDD in
a given disease d.
The matrix is sorted using the NODF algorithm (Almeida-Neto et al., 2008), which highlights
the existence of countries that are very well diversified and countries that have a relative
disease disadvantage only in a small set of diseases.

Figure 2: Matrix representation of the links between countries and diseases. A
visualization of this matrix for the year 2016, where a dark point indicates that country c has a RDD in a given
disease d. The matrix is sorted using the NODF algorithm (Almeida-Neto et al., 2008), which highlights the
existence of countries that are very well diversified and countries that have a relative disease disadvantage only
in a small set of diseases.

3.2 The disease space

To explore this further, we construct the disease space, similar to the product-space intro-
duced by (Hidalgo et al., 2007).

8

DiseasesCountriesCalculating the RDD for all country-disease pairs allows us to derive a matrix Φ, whose
elements Φi,j define a proximity measure between all pairs of diseases. This proximity
measure reveals diseases that are present in tandem, or in other words, with Φ, we mea-
sure the probability that a country c, which has a relative disease disadvantage in disease
i, also has a relative disease disadvantage in disease j. The proximity measure is defined
as:

(2)

Φi,j = min{Pr(RDDi ≥ 1 | RDDj ≥ 1), Pr(RDDj ≥ 1 | RDDi ≥ 1)},
where Pr(RDDi ≥ 1 | RDDj ≥ 1) is the conditional probability of having a relative disease
disadvantage in disease i if you have a relative disease disadvantage in disease j. Using the
minimum of both conditional probabilities, we avoid issues of a rare disease being present
in only one country. Additionally, we make the resulting matrix Φ symmetric (see Figure
3). The proximity matrix is highly modular and its block structure reveals the presence of
`communities', i.e., groups of diseases that are expected to occur together.
Next, we map this matrix onto a network, where each disease is represented by a node and
every matrix element represents a weighted and undirected link. Similar to Figure 1, we
start by applying Dijkstra's algorithm on matrix Φ which calculates the MST of the network.
Following the rationale of (Hidalgo et al., 2007), we start from the strongest links that are
not part of the MST and keep adding links to the network until the average degree is four.
The resulting network is a visual representation of the disease space, which is shown in
Figure 3.

From Figure 3, it is evident that in the disease space network, different disease cat-
egories are clustered together and the network is heterogeneous and follows a core-
periphery structure. The external part of the network (the periphery) is mostly dominated
by `communicable, maternal, neonatal, and nutritional diseases'. On the other hand, the
core of the network is dominated by `non-communicable diseases'.

Figure 4 shows the patterns of disease localisation in the world's economies, classified
by the World Bank into four income groups - `high', `upper-middle', `lower-middle', and
`low'. Diseases in a region where more than half of its countries have a RDD > 1 are shown
with black nodes. It seems that high-income countries occupy the core, composed of `non-
communicable diseases' such as `pancreatic cancer', `Parkinson disease', `ischemic stroke'
and injuries such as `falls', `poisonings` and `other exposure to mechanical forces'. On the
other side of the spectrum, low-income countries tend to have a RDD in `communicable,
neonatal, maternal and nutritional diseases' that lie in the periphery of the disease space
such as `diarrheal diseases', 'encephalitis' and `malaria'. Most of the communicable diseases
for which low-income countries have a RDD > 1 also appear in the periphery (for example,
`Turner syndrome', `neural tube defects' and `pyoderma'). Examples of injuries for which
low-income countries have a RDD include `venomous animal contact' and `sexual violence',
which again appear in the periphery of the disease space.

3.3 Health complexity index

From matrix M, similar to (Hidalgo and Hausmann, 2009), we introduce the health com-
plexity index (HCI) as a measure of countries' disease structures. To obtain the HCI, we
first calculate the l × l square matrix (cid:3)M. In short, matrix (cid:3)M provides information about links
connecting two countries c and c(cid:48), based on the prevalent cases of diseases in both. The
matrix elements ˜Mcc(cid:48) are computed as

˜Mcc(cid:48) =

1
kc,0

(cid:88)

d

McdMc(cid:48)d
kd,0

,

(3)

where kc,0 = (cid:80)
d Mcd measures the diversification of country c in terms of its different dis-
eases, and kd,0 = (cid:80)
c Mcd measures the number of countries with disease cases d.
If K
is the eigenvector of (cid:3)M associated with the second largest eigenvalue, then according to
(Hausmann et al., 2014), the HCI is calculated as

HCI =

K − (cid:104)K(cid:105)
std(K)

.

(4)

The HCI reflects the disease-composition of a country's pool of diseases, taking into account
the composition of the pools of all other countries. Populations with diseases that occur

9

Figure 3: The proximity matrix and the resulting disease space. The size of the nodes is
proportional to their degree, i.e., the number of links. Disease colors cover the following categories: [A. `Com-
municable, maternal, neonatal, and nutritional diseases'] A.1 `HIV/AIDS and tuberculosis', A.2 `Diarrhea, lower
respiratory, and other common infectious diseases', A.3 `Neglected tropical diseases and malaria', A.4 `Maternal
disorders', A.5 `Neonatal disorders', A.6 `Nutritional deficiencies', A.7 `Other communicable, maternal, neonatal,
and nutritional diseases'; [B. `Non-communicable diseases'] B.1 `Neoplasms', B.2 `Cardiovascular diseases', B.3
`Chronic respiratory diseases', B.4 `Cirrhosis and other chronic liver diseases', B.5 `Digestive diseases', B.6 `Neu-
rological disorders', B.7 `Mental and substance use disorders', B.8 `Diabetes, urogenital, blood, and endocrine
diseases', B.9 `Musculoskeletal disorders'; [C. `Injuries'] C.1 `Transport injuries', C.2 `Unintentional injuries', C.3
`Self-harm and interpersonal violence', C.4 `Forces of nature, conflict and terrorism, and executions and police
conflict'. Data for 2016. Annexe 2 reports the correspondence table between the disease number and the disease
name.

in many other countries have relatively low health complexity scores, while more health-
complex countries have a high prevalence of non-ubiquitous diseases. In other words, a
country has a complex disease composition, i.e., it is health-complex, if its diseases have
high prevalence in only a few other countries. The HCI is higher for countries with diseases
located at the core of the `disease-space' and lower for countries with diseases located at
the periphery of the `disease-space'.
As discussed in Section 2.1, the HCI is computed using in Xcd the number of prevalent cases
of a disease (according to cause levels 3 or 4) per 100,000 population for 195 countries and
for 196 diseases. The time-period covered is from 1990 to 2016. With the age-standardized
data (see the discussion in Section 2.1), we also calculate the age-standardized health
complexity index (AHCI) following the same formulas. We use the two indices as alternative
measures when checking the robustness of our results. It should be noted here that the
computation of the indices is based only on diseases for which a country has a RDD in
terms of disease prevalence (the incidence matrix of the bipartite network linking countries
to diseases, M, reflects whether or not a country has a RDD in a specific disease; see Figure
2).

10

Φ < 0.6Φ < 0.7Φ < 0.8Φ < 0.9Φ < 1.0A.5B.1B.4A.7B.3B.2B.7B.5B.6A.1A.4A.3A.6A.2C.2B.8C.1B.9C.4C.33503553589483458439366146625734835545873489493003494599467293548379389453533353519479508545856065595155715094986316516945115644475695885915936197274936274966285906556656686746796845766716705746506456436985166045226156589543226645798387153683979435245286525687114926183023814323983839415605625145135125957265635235273287215703846615456166593373993955983643469356546605775783578396056127257056759343633943623613593383524044213413204186914534685333404053195306086476578498503704014194203893567134033427086395656445486954888426735574444805256096858455324504854864385038468484715024774414954846326978474565354749444354264114295926826835317145476634654145004235446077235666906935439423333367067073693393873344023826465073676426495466305976485296566925966993713903653293884085823966817007166725347104 The geography of complex diseases

As shown in Figure 4, the localisation of diseases in different income-regions of the world is
also observable in Figure 5, where we map the spatial variation in complex diseases. This
figure shows the repartition of the HCI across countries when taking average values for the
period 1990-2016. We see rather clearly that disease complexity is unevenly distributed in
the world and that the most complex countries in terms of diseases seem to be located in
Europe, North America, and Australia (cid:21) European countries, Australia, the US, and Canada
belong to the set of countries with the highest HCI (>80%). In contrast, most countries in
Africa have much lower HCIs on average.

Regarding the evolution of HCI scores over time, figures 6 and 7 depict how health com-
plexity in our sample of countries has changed from 1990-1996 to 2010-2016. Cambodia,
Myanmar, Nepal, Vietnam, Saint Lucia, and Cameroon have registered significant increases
in the complexity of their diseases. On the other hand, the diseases of countries like Vanu-
atu, Kiribati, Palestine, Tajikistan, and Gabon are now less complex than in the early 1990s.
Figure 7 depicts the same information in a world map. Blue and light blue colors depict
a decrease in HCI score, while orange and red colors denote countries with an increase
in HCI score from 1990-1996 to 2010-2016. From these figures, it can be observed that
changes over time are rather small. Hence, it seems that a country's HCI score tends to
persist through time, which is to be expected for a metric of prevalent cases of diseases
aggregated at the country level. This motivates the inclusion of the lagged value of HCI in
the set of explanatory variables when estimating the effect of economic development on
health complexity in the next section.

5 The effect of economic development on health complexity

We study the effect of economic development on health complexity using data on GDP per
capita (from the World Bank's World Development Indicators) and the HCI (see Section
3). Given the availability of controls, the sample covers a minimum of 168 developed and
developing countries over the period of 1992-2015. 3

5.1 Regression analysis

Previous research shows that there is a strong association between income and indicators
of population health such as life expectancy and child mortality. There are various channels
through which economic development can stimulate health improvements, for example,
via its effect on nutrition (which in turn leads to better resistance to bacterial diseases
and faster recovery from illnesses), as well as through greater labor market participa-
tion, worker productivity, investment in human capital, investment in public and private
health services, savings, fertility, transportation infrastructure, and lifestyle habits. (Pre-
ston, 1975, Smith, 1999, Bloom et al., 1998, Gallup and Sachs, 2001, ?, Alleyne and Cohen,
2002, Bloom et al., 2005, Lorentzen et al., 2008, Easterlin, 1999, Hamoudi et al., 1999).4
The term `diseases of affluence' refers to selected diseases and health conditions that are
more prevalent in wealthy nations. Examples include mostly non-communicable diseases
such as cardiovascular diseases and their nutritional risk factors (overweight and obesity,

3Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbai-
jan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and
Herzegovina, Botswana, Brazil, Brunei, Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia,
Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Rep. of the Congo, Costa
Rica, Cote d'Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador, Arab Rep. of Egypt,
El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Ger-
many, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland,
India, Indonesia, Islamic Rep. of Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya,
Kiribati, Rep. of Korea, Kuwait, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithua-
nia, Luxembourg, Macedonia FYR, Madagascar, Malawi, Malaysia, Maldives, Malta, Mauritania, Mauritius, Mexico,
Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand,
Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines,
Poland, Portugal, Qatar, Russian Federation, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal,
Serbia, Seychelles, Sierra Leone, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, St. Lucia,
St. Vincent and the Grenadines, Sudan, Suriname, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Togo,
Tonga, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, United Arab Emirates, United Kingdom, United
States, Uruguay, Vanuatu, Venezuela RB, Vietnam, Rep. of Yemen, Zambia, Zimbabwe.
4For a review of the empirical evidence see (Lange and Vollmer, 2017).

11

elevated blood pressure and cholesterol). It has been shown that economic development is
a robust predictor of `diseases of affluence' (Ezzati et al., 2005, Gupta, 2006, Murray et al.,
1996, Reddy and Yusuf, 1998, Yusuf et al., 2001). However, there is also a large and grow-
ing literature that investigates the reverse channel, i.e., that better population health leads
to economic development (Acemoglu and Johnson, 2007, Bleakley, 2010). The argument
is that improved health conditions increase population size, which (cid:21) in the medium term
(cid:21) leads to more people entering the labor force, higher capital accumulation, and higher
income per capita.
In order to estimate the effect of economic development on the health complexity of coun-
tries we follow a fixed-effects two-stage least squares/instrumental variables (FE 2SLS/IV)
strategy, complemented with a difference Generalized Method of Moments (diff-GMM) ap-
proach. We regress the baseline specification described by the following equation:

HCI i,t = ρHCI i,t−1 + β1GDP pci,t + βkcontrolsi,t + γi + δt + ui,t.

(5)

Here, the health complexity of country i in period t (HCIi,t) depends on the country's
level of economic development in per capita terms (in logs), GDP pci,t. The lagged value of
the dependent variable on the right-hand side is included to capture persistence in health
complexity. The main variable of interest is GDP pc. The parameter β1 therefore measures
the effect of income per capita on health complexity.5 Additional potential covariates are
included in the vector controlsi,t. The γi's denote a full set of country dummies and the
δt's denote a full set of time effects that capture common shocks to the health complexity
scores of all countries. The error term ui,t captures all other omitted factors, with E(ui,t) = 0
for all i and t. To examine the robustness of our results and to generalize our findings,
we replicate our analysis for additional/alternative control variables and substitute the HCI
with the AHCI, finding qualitatively similar results (see subsection 5.2).

5.1.1 Control Variables

We include in the estimated equation a number of control variables that are likely related
to health complexity.
The proportion of nations' populations over the age of 65 has been increasing in recent
years and will continue to rise in future as a result of longer life expectancy. Population
age-structure is a significant determinant of a nation's health status, due to age-related
diseases (i.e., illnesses and conditions that occur more frequently in people as they get
older). Examples of age-related diseases include cardiovascular and cerebrovascular dis-
eases, hypertension, cancer, Parkinson's disease, Alzheimer's disease, osteoarthritis and
osteoporosis. Demographic factors such as age and sex are considered key covariates in
the study of human health and well-being, hence the percentage of old population (aged 65
and above, in logs) and the percentage of f emale population (in logs) are included in the
set of control variables.
It has previously been shown that sex interacts with social, economic and biological deter-
minants to create different health outcomes for males and females. For example, (Vlassoff,
2007) reviews a large number of studies on the interaction between sex and the deter-
minants and consequences of chronic diseases, showing how these interactions result in
different approaches to prevention, treatment, and coping with illness.
In our analysis, we also control for the (log) percentage of urban population, urban. Ac-
cording to the World Health Organization (WHO), a large proportion of non-communicable
diseases is linked to risks related to the urban environment, such as physical inactivity
and obesity, cardiovascular and pulmonary diseases from transport-generated urban air
pollution, ischemic heart disease and cancers from household biomass energy use, asthma
from indoor air pollution, and heat-related strokes and illnesses. In addition, communicable
diseases such as tuberculosis, dengue fever, and many respiratory and diarrheal diseases
result from unhealthy urban environments (e.g. lack of adequate ventilation, unsafe water
storage and poor waste management, indoor air pollution, moldy housing interiors, poor
sanitation).6

5In order to account for possible changes in the relation between economic development and health complexity
over the process of economic development, we have experimented with the inclusion of the quadratic specification
of GDP per capita in the estimated equation. Our baseline results (which are available upon request) do not
confirm a U-shaped relationship between economic development and health complexity.
6See WHO, Health and sustainable development:
About health risks in cities,
sustainable-development/cities/health-risks/about/en/

https://www.who.int/

12

Table 1: Variable definitions, sources and summary statistics

Variable

Definition

Source

Mean Std. Dev.

HCI

HCI+

GDPpc

old

Health Complexity Index.

Authors' calculations

-0.006

1.004

age-standardized Health Complexity Index.

Authors' calculations

-0.008

1.008

(log) GDP per capita, PPP (constant 2011 in-
ternational $)

(log) Population aged 65 and above (% of
total)

World Development Indicators

8.342

1.521

World Development Indicators

1.764

0.667

female

(log) Female population (% of total)

World Development Indicators

3.912

0.067

urban

(log) Urban population (% of total)

World Development Indicators

3.871

0.519

agriculture

(log) Agriculture, value added (% of GDP)

World Development Indicators

2.054

1.228

manufacturing(log) Manufacturing, value added (% of GDP)

World Development Indicators

2.407

0.591

education

(log) Enrollment
both sexes (total)

in secondary education,

World Development Indicators

4.229

0.587

population
density

(log) Population density (people per sq. km
of land area)

CO2

(log) CO2 emissions (kg per 2010 $US of
GDP)

health ex-
penditure

(log) Total health spending, PPP (thousands
of 2017 $US)

economic
globaliza-
tion

Actual flows (trade,
foreign direct invest-
ment, stocks, portfolio investment, income
payments to foreign nationals), restrictions
(hidden import barriers, mean tariff rate,
taxes on international trade, capital account
restrictions). Higher values reflect greater
economic globalization.

political
globaliza-
tion

Embassies in country, membership in in-
ternational organizations, participation in
UN security council missions, international
treaties. Higher values reflect greater po-
litical globalization.

World Development Indicators

4.123

1.398

World Bank

-0.930

0.704

Global Health Spending 1995-2015, IHME 15.071

2.239

KOF Index of Globalization

54.462

16.234

KOF Index of Globalization

62.283

21.985

13

Industrialization, i.e., the structural transformation from agricultural to industrial production
also has a range of significant health implications (McNeill, 1998, Chaudhuri, 1985, Steckel
and Rose, 2002, Szreter, 2004, Steckel, 1999). We capture these implications in our analy-
sis by including the (log) value added of agriculture (% of GDP) and the (log) value added
of manuf acturing (% of GDP) in the estimated equation.
To check the robustness of our baseline results, we replicate our analysis controlling also
for the human capital of the population by utilizing total enrolment in secondary education
(in logs). It is well established in the relevant literature that through education, people
gain the ability to be effective in their lives. They adopt healthier lifestyles and inspire their
offspring to do as well (Mirowsky and Ross, 1998). Individuals with higher levels of educa-
tion also tend to have better socioeconomic resources for a healthy lifestyle and a higher
probability of living and working in healthy environments (Adler and Newman, 2002,Brave-
man et al., 2010). In addition, educated individuals tend to have lower exposure to chronic
stress (Pampel et al., 2010). Low educational attainment, on the other hand, is associated
with a shorter life expectancy, poor self-reported health, and a high prevalence of infectious
and chronic non-infectious diseases (Feldman et al., 1989, Guralnik and Kaplan, 1989, Liu
et al., 1982, Woodward et al., 1992).7 Furthermore, we re-estimate the baseline model by
substituting urban with population density (people per square km of land, in logs).

The variable CO2 (log of CO2 emissions in kg per 2010 $US of GDP) captures the effect
of air pollution on health, which has been the subject of numerous studies in recent years
(for an extensive review, see (Brunekreef and Holgate, 2002)).
Finally, health expenditure (log of total health spending in thousands of purchasing power
parity (PPP)-adjusted 2017 $US) is also included in the set of explanatory variables con-
trolling for the association between healthcare spending and health outcomes (Werblow
et al., 2007, Barlow and Vissandjee, 1999, Crémieux et al., 1999, Wolfe, 1986, Mackenbach,
1991, Seshamani and Gray, 2004, Zweifel et al., 1999, Nixon and Ulmann, 2006)
Data definitions, sources and summary statistics for the variables included in the analysis
are given in Table 1.

5.1.2 Instrumental variables

We estimate equation (5) using different econometric methods. First, we use fixed-effects
OLS. However, fixed effects estimators do not necessarily identify the effect of economic
development on health complexity. The estimation of causal effects requires exogenous
sources of variation. While we do not have an ideal source of exogenous variation rec-
ognized by previous studies, there are two promising potential instruments of economic
development that we adopt in our fixed-effects 2SLS/IV and diff-GMM analyses.
First, we use the KOF Swiss Economic Institute's economic globalization index, character-
ized as the flows of goods, capital, and services, as well as information and perceptions
that accompany market exchanges (Dreher, 2006). Higher values reflect greater economic
globalization.
The second instrument considered is the KOF Swiss Economic Institute's political globalization
index, characterized by the number of embassies in a country, its membership in inter-
national organizations, its participation in UN security council missions and international
treaties. Higher values reflect greater political globalization.
There is extensive research documenting the positive relationship between globalization
and economic development and growth (Dreher, 2006, Gözgör and Can, 2017, Duttagupta
et al., 2018, Gurgul and Lach, 2014, Chang and Lee, 2010). While we do not have a precise
theory to support the prediction, it is expected that changes in the economic globalization
and political globalization indices have no direct effect on a country's disease structure and
impact health complexity only indirectly, through the channel of economic development.
The tests performed and described in the next section (Section 5.2) strongly confirm the
relevance of the use of these instruments.

5.2 Regression results

In this section, we discuss the results of estimating equation (5) with different econometric
techniques. Table 2 reports the results of fixed-effects ordinary least squares (FE-OLS)

7(Grossman, 2006) and (Ross and Wu, 1995) review the relationships between education and a wide variety of
health measures.

14

Table 2: The effect of economic development on health complexity: Fixed-effects OLS

(1)

(2)

(3)

(4)

(5)

(6)

HCIt−1

GDP pc

old

f emale

urban

agriculture

manuf acturing

(0.009)

(0.010)

0.938*** 0.938*** 0.938*** 0.933***
(0.009)
(0.010)
0.013*** 0.013*** 0.013*** 0.012***
(0.003)
(0.003)
0.002
(0.006)
-0.013
(0.024)
0.020**
(0.008)

(0.003)
-0.001
(0.006)
-0.014
(0.024)

(0.003)
-0.001
(0.006)

0.929***
(0.010)
0.009**
(0.003)
0.000
(0.007)
0.002
(0.023)
0.025***
(0.008)
-0.006***
(0.002)

Observations
Countries
R-squared
Note: Dependent variable: Health Complexity Index (HCI). Main independent variable: GDP per capita
in logs (GDP pc). Country and time fixed-effects are included in all regressions. Robust standard errors
are in parentheses. * p<0.10, ** p<0.05, *** p<0.01

4,593
183
0.874

4,593
183
0.875

4,377
180
0.872

4,750
190
0.873

4,593
183
0.874

0.929***
(0.010)
0.011***
(0.004)
0.002
(0.007)
-0.006
(0.024)
0.021**
(0.009)
-0.006***
(0.002)
0.001
(0.002)
4,181
177
0.872

with time dummies, adding an additional variable from the set of controls in each step (col-
umn). In all specifications, economic development has a positive relationship with health
complexity, and the control variables enter with the expected sign. The agriculture coeffi-
cient is negative, and countries with a higher proportion of urban population exhibit greater
health complexity.
The results depicted in Table 2 could only be interpreted as correlations. For a robust analy-
sis accounting for the potential endogeneity problem in the relationship under consideration
(discussed above), we use fixed-effects 2SLS/IV estimation techniques complemented with
diff-GMM estimations à la Arellano-Bond (Arellano and Bond, 1991). Table 3 presents our
baseline results.
In columns (1)-(8), we estimate equation (5) with FE 2SLS/IV regressions. We use time
dummies and robust standard errors (in parentheses). In all cases, GDP pc is a positive
and statistically significant predictor of health complexity.
In fact, we find that an in-
crease in GDP per capita of 10% is associated with an improvement of about 0.003 in the
HCI (standard deviation: 1.004). This positive impact of economic development on health
complexity is robust to the inclusion of control measures discussed in subsection 5.1.1. The
statistically significant urban coefficient implies that a higher proportion of urban population
is associated with more complex diseases.
In the fixed effects 2SLS/IV estimations we report: (a) the F -test for the joint significance of
the instruments in the first stage: the rule of thumb is to exceed 10, hence the test implies
no weak significance (Stock and Yogo, 2005); (b) the Durbin-Wu-Hausmann (DW H) test
for the endogeneity of regressors: the null hypothesis that the IV regression is not required
is rejected; (c) the Cragg-Donald F-statistic (W eak-id), testing the relevance of the instru-
ments in the first-stage regression: no evidence of a low correlation between instruments
and the endogenous regressor is found after controlling for the exogenous regressors; (d)
the Kleibergen-Paap Wald test (LM -weakid) of weak identification: the null hypothesis that
the model is weakly identified is rejected; (e) the p-value for Hansen's test of overidentifi-
cation: the acceptance of the null indicates that the overidentifying restrictions cannot be
rejected.
In column (9) of Table 3 we report the diff-GMM estimations including year fixed effects
and robust standard errors. The results verify the previous findings both qualitatively and
quantitatively, i.e., the estimated coefficient of GDP pc implies an improvement of 0.003
in the HCI with a GDP per capita increase of 10%. Among the control variables, only the
urban variable has a statistically significant and positive sign. The values reported for AR(1)
and AR(2) are the p-values for first- and second-order autocorrelated disturbances. As ex-
pected, there is high first-order autocorrelation and no evidence for significant second-order
autocorrelation. Hence, our test statistics hint at a proper specification.

15

Figure 4: Localisation of diseases for different income-regions of the world. A: High
income, B: Upper-middle income, C: Lower-middle income, D: Low income. Diseases in an income-region where
more than half of the countries belonging to this region have a RDD>1 are shown with black nodes in the disease
space network. Data for 2016.

16

335351350345353355936948947950361320405608362319363394530934843949945854348349358300421340341468418533471691632477441453847849697850352356404338359401370389946419846420450354532503938711302578579492322839381664954534596568692715716546630607723707690566942545562512577700616364598395399337346659935646657642656647648649507529710582367408396388838357658615383941334333402369339382387336706397368618398432652329390371365681524498523627684674645943574605571612650675628590585631543655668509665496679694514699597528513660493672661693726570670328576384671527721516619588511564447604522569698705559643515725606651727654595560563685414465444845495426435429502423500544644565403531342683708411535486438474456485484944713525480639609548673842695729587614837663592591682547557593714459573483662488554848615383652838618941658334382339507387402369646371396388398681368397432399346364598395659365367329390408582302322568664492381954711579710642649529648656357337578935839616692942715534690546716596356503450419359389370420532341362363361340405530319320394608338691421418468533453697477723707607850849471632647706934333336657950947401946352854404335348351948349353949345945300358843354938936350355512545700566577562630660597493514528672726513693699661705725643675612574605650523684674679668590588569522698447511564604694571631585509498645628627524665496543943655516606727559619515651570671670576384721527328654595560563713837403639587708695565842342644663557548685609429444944426474435411480682531591592683547714593438488486525484673535441456846847459848485414845465502500423495544483554614729573662715568534492716710390618365346935598364395337839579399322545700577562512408388648642582396529367838615941658954383357381302656649546692371368681398397652432329507369336382402387646339334616664711659578566723596690630607942348949936843948358300421849418608420419354697532691468632453503938533647850934657706707333355353350351345389356359404401946352370341340338530405320361363362394319349945854947950335557592444714682845837587403729673708614713695342593547500495685423465663591414639644842565525609548683531480429488535944438484486435411544477441502456471474426662483573554846847848485459450627684674672493645524523571943698651516726661514528660513655543585588631693590511670671721576570384725574675612605705604650643522597679628668699665496564606569515619559498727509694447595654560527563328353350351345354335355370348843945358300948936949349356404352320340401946950854947419420389421338468418359341532697849453471477691632850846533441503450847938530362361363608319405394934578839598395616399364659346337935534715579711568664716607566707723546630545562512577700838381322615357658954656596507690692942329388367396365371390408582681339382387402369334646647333657336706397383432368302652492618398941649648642529710493645627528684524672674523943725606651559515643705727511588593585591592619597665496679668509498694699660661513514726574570671670576384721605612650571675522516569447564604698500465543655631590628693654328595560563527429423663557444685414845495456474426502544644403683682342547708565531714488486662573438535435554411944729587614695713673837609639480525548842459483485484848ABCDFigure 5: Health complexity index across the world. Mean values for the period 1990-2016:
percentile repartition. Countries depicted in dark red have a mean value above the 80th percentile.

Figure 6: Evolution of the health complexity index over time.
Changes in the health
complexity index from 1990-1996 to 2010-2016. The labelled black dots are the 10 countries with the largest
changes over time.

17

20%40%60%80%Saint LuciaVietnamNepalMyanmarCambodiaCameroonVanuatuTajikistanPalestineGabonKiribati-2-1.5-1-.50.511.5HCI (1990-1996)-2-1.5-1-.50.511.5HCI (2010-2016)Figure 7: Changes in the health complexity index across the world.
Changes in HCI
scores (percentile repartition) from the period 1990-1996 to 2010-2016. Countries depicted in light blue and blue
(orange and red) have experienced a decrease (increase) in HCI score below and above the median decrease
(increase), respectively.

18

<50% (-)>50% (-)<50% (+)>50% (+)Table 3: The effect of economic development on health complexity: baseline results

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV

HCIt−1

GDP pc

old

f emale

urban

agriculture

manuf acturing

Fist-stage results
economic globalization

political globalization

0.926***
(0.010)
0.031***
(0.011)

0.927***
(0.010)
0.031***
(0.011)
-0.003
(0.005)

0.927***
(0.010)
0.031***
(0.011)
-0.002
(0.005)
-0.015
(0.023)

0.923***
(0.010)
0.031***
(0.011)
-0.001
(0.005)
-0.015
(0.023)
0.013
(0.008)

0.923***
(0.010)
0.026**
(0.011)
-0.003
(0.006)
-0.019
(0.027)
0.020***
(0.008)
-0.003
(0.003)

0.003***
(0.001)
0.004***
(0.001)

0.003***
(0.001)
0.004***
(0.001)

0.003***
(0.001)
0.004***
(0.001)

0.003***
(0.001)
0.004***
(0.001)

0.004***
(0.000)
0.004***
(0.000)

0.922***
(0.010)
0.029***
(0.011)
-0.001
(0.006)
-0.029
(0.028)
0.017**
(0.008)
-0.003
(0.003)
0.002
(0.002)

0.003***
(0.000)
0.005***
(0.000)

0.922***
(0.011)
0.030*
(0.017)
-0.002
(0.006)
-0.031
(0.033)
0.017**
(0.009)
-0.002
(0.004)
0.002
(0.002)

0.005***
(0.000)

0.921***
(0.010)
0.027**
(0.013)
-0.001
(0.006)
-0.026
(0.029)
0.017**
(0.008)
-0.003
(0.003)
0.002
(0.002)

0.006***
(0.000)

(9)
diff-GMM

0.814***
(0.061)
0.032**
(0.013)
-0.005
(0.017)
0.029
(0.044)
0.031*
(0.018)
-0.002
(0.008)
-0.003
(0.012)

4,168
171
37.24
2.978
59.73
71.12
0.590

4,143
170
37.05
2.911
54.25
70.95
0.699

Observations
Countries
F-test
DWH-test
Weak-id
LM-weakid
Hansen (p-value)
AR(1)
AR(2)
Note: Dependent variable: Health Complexity Index (HCI). Main independent variable: GDP per capita in logs (GDP pc). Columns (1)-(8): Fixed-effects 2SLS/IV; HCI is instrumented:
To save space, we only include the first-stage estimated coefficients of the instruments. The results for the rest of the variables are available upon request. Column (9): One-step
diff-GMM. All regressions include time dummies. Robust standard errors are in parentheses. F-test gives the F-statistic for the joint significance of the instruments in the first stage.
DWH-test is the Durbin-Wu-Hausman test of endogeneity of the regressors. Weak-id gives the Cragg-Donald F-statistic for weak identification. LM-weakid gives the Kleibergen-Paap
Wald test of weak identification. Hansen (p-value) gives the p-value of the Hansen test of overidentification. AR(1) and AR(2) are the p-values for first- and second-order autocorrelated
disturbances. * p<0.10, ** p<0.05, *** p<0.01

3,976
170
56.26
2.500
97.43
109.7
1.000

3,826
168
61.41
2.646
107.0
116.3
0.904

4,143
170
36.31
2.785
54.88
69.68
0.676

4,143
170
39.93
3.073
56.82
75.01
0.897

3,826
168
79.18
1.304
104.3
72.90

3,926
173
76.56
1.523
152.6
73.98

0.000
0.475

3,652
168

1
9

Table 4: The effect of economic development on health complexity: AHCI

(1)

(2)
FE 2SLS/IV FE 2SLS/IV FE 2SLS/IV

(3)

AHCIt−1

GDP pc

f emale

urban

agriculture

manuf acturing

Fist-stage results
economic globalization

political globalization

0.923***
(0.011)
0.037***
(0.011)
-0.028
(0.027)
0.009
(0.007)
0.002
(0.003)
0.002
(0.002)

0.004***
(0.000)
0.004***
(0.000)

0.925***
(0.012)
0.033**
(0.015)
-0.021
(0.034)
0.010
(0.008)
0.001
(0.004)
0.002
(0.002)

0.005***
(0.000)

0.919***
(0.011)
0.041***
(0.014)
-0.037
(0.031)
0.007
(0.008)
0.003
(0.004)
0.003
(0.002)

0.005***
(0.000)

(4)
diff-GMM

0.828***
(0.114)
0.024*
(0.013)
0.030
(0.040)
0.011
(0.023)
0.008
(0.010)
-0.013
(0.011)

3,652
168

3,826
168
101.5
2.200
136.4
92.43

3926
173
71.22
4.548
138.9
67.20

3,826
168
67.39
6.055
116.2
125.8
0.681

Observations
Countries
F-test
DWH-test
Weak-id
LM-weakid
Hansen (p-value)
AR(1)
AR(2)
Note: Dependent variable: Age-standardized Health Complexity Index (AHCI). Main indepen-
dent variable: GDP per capita in logs (GDP pc). Columns (1)-(3): Fixed-effects 2SLS/IV; AHCI
is instrumented: To save space, we only include the first-stage estimated coefficients of the
instruments. The results for the rest of the variables are available upon request. Column (4):
One-step diff-GMM. All regressions include time dummies. Robust standard errors are in paren-
theses. F-test gives the F-statistic for the joint significance of the instruments in the first stage.
DWH-test is the Durbin-Wu-Hausman test of endogeneity of the regressors. Weak-id gives the
Cragg-Donald F-statistic for weak identification. LM-weakid gives the Kleibergen-Paap Wald test
of weak identification. Hansen (p-value) gives the p-value of the Hansen test of overidentifica-
tion. AR(1) and AR(2) are the p-values for first- and second-order autocorrelated disturbances.
* p<0.10, ** p<0.05, *** p<0.01

0.000
0.158

20

Table 5: The effect of economic development on health complexity: robustness checks

HCIt−1

GDP pc

old

f emale

urban

agriculture

manuf acturing

education

population density

CO2

health expenditure

(1)

(2)

(3)

(4)

diff-GMM diff-GMM diff-GMM diff-GMM

(0.046)
0.031***
(0.011)
-0.010
(0.017)
0.018
(0.051)

0.752*** 0.871*** 0.812*** 0.920***
(0.040)
(0.059)
0.042***
0.031*
(0.016)
(0.016)
-0.002
-0.022
(0.016)
(0.024)
-0.068
0.053
(0.047)
(0.050)
0.022
0.055
(0.023)
(0.035)
0.001
0.007
(0.008)
(0.009)
0.014
0.008
(0.012)
(0.011)
-0.003
(0.011)

(0.063)
0.024*
(0.013)
0.004
(0.018)
0.053
(0.045)
0.013
(0.017)
0.003
(0.009)
-0.015
(0.010)

-0.001
(0.006)
-0.003
(0.011)

0.000
(0.016)

0.013*
(0.007)

-0.006
(0.012)

(5)
diff-GMM

0.666***
(0.062)
0.038*
(0.020)
-0.005
(0.029)
-0.170
(0.124)

0.015
(0.011)
0.020
(0.014)
0.026
(0.017)
-0.073
(0.049)
-0.003
(0.015)
-0.012
(0.018)

2,347
161
0.000
0.395

Observations
Countries
AR(1)
AR(2)
Note: Dependent variable: Health Complexity Index (HCI). Main independent variable: GDP per capita
in logs (GDP pc). Regression analysis: One-step diff-GMM. All regressions include time dummies.
Robust standard errors are in parentheses. AR(1) and AR(2) are the p-values for first- and second-
order autocorrelated disturbances. * p<0.10, ** p<0.05, *** p<0.01

3,642
168
0.000
0.505

3,470
168
0.000
0.621

3,158
168
0.000
0.525

1,936
158
0.000
0.274

In Tables 4 and 5, we investigate the robustness of our baseline findings. First, we
substitute the HCI with the age-standardized health complexity index (AHCI) maintaining
the same set of controls (and time dummies) as in the baseline specification. Second,
we investigate whether the positive impact of economic development on health complexity
persists under additional and/or alternative control measures (including time dummies). In
all cases, the baseline results remain qualitatively intact. In particular, the coefficient of
GDP pc is positive and statistically significant in the instrumented regressions (see Table 4;
to save space, we only include the first-stage estimated coefficients of the instruments (cid:21)
the results for the rest of the variables are available upon request).
Table 5 starts from the baseline specification with the full set of controls [column (9) in Table
3] and introduces additional variables or alternative measures for some of the previous
controls. Specifically, in column (1), we add education (enrolment in secondary education
in logs). In column (2), we substitute the urban population variable with population density
(people per sq. km of land in logs). In columns (3) and (4), we employ (log) CO2 emissions
(CO2 emissions, kg per 2010 $US of GDP) and (log) health expenditure (total health spending,
thousands of 2017 PPP adjusted $US), respectively. Finally, in column (5), we consider all
of the above variables together. Adding these controls in our estimations leaves the findings
qualitatively and quantitatively intact.
The above analysis suggests that economically developed countries tend to exhibit more
complex disease structures. Furthermore, exploiting the temporal variation in the data,
the fixed-effects 2SLS/IV analysis and the difference GMM estimators reveal a positive,
statistically significant, and robust impact of economic development on health complexity.

21

6 Economic development and disease complexity

The economic complexity methodology provides a useful toolbox that allows us to compute
indices that quantify the complexity of both countries and diseases. For example, using the
same methodology that computes the HCI, we can calculate the DCI (see Section 3). This
index quantifies the complexity of countries' diseases according to their prevalent cases
worldwide.

6.1 The disease complexity index

In a similar manner, but placing the spotlight on diseases rather than countries, we calculate
the Disease Complexity Index (DCI) in which the k × k matrix (cid:3)M provides information about
links connecting two diseases d and d(cid:48), based on the number of countries in which both
diseases are present 8. Therefore, the matrix elements ˜Mdd(cid:48) are computed as

˜Mdd(cid:48) =

1
kd,0

(cid:88)

c

McdMcd(cid:48)
kc,0

,

and if Q is the eigenvector of (cid:3)M associated with the second largest eigenvalue,

DCI =

Q − (cid:104)Q(cid:105)
std(Q)

.

(6)

(7)

Table 6 lists the five diseases with the highest and lowest DCI scores averaged over the

period of 1990-2016.

Table 6: List of the five diseases with the highest and lowest DCI values during the period of 1990-
2016

Code

Disease name

Disease section

Highest DCI
459
441
502
456
533

Lowest DCI
345
350
370
358
340

Malignant skin melanoma
Colon and rectal cancer
Peripheral artery disease
Pancreatic cancer
Vascular intestinal disorders

Neoplasms
Neoplasms
Cardiovascular diseases
Neoplasms
Digestive diseases

Malaria
African trypanosomiasis
Maternal obstructed labor and uterine rupture Maternal and neonatal disorders
Yellow fever
Tetanus

Neglected tropical diseases and malaria
Neglected tropical diseases and malaria

Neglected tropical diseases and malaria
Other infectious diseases

DCI

1.210
1.170
1.169
1.159
1.152

-2.045
-1.978
-1.957
-1.893
-1.883

Notes: DCI: Disease Complexity Index; Average values for 1990-2016.

8As discussed in 2.1, the DCI is computed using in Xcd the number of prevalent cases of a disease (according to
cause levels 3 or 4) per 100,000 population for 195 countries and for 196 diseases. The time-period covered is
from 1990 to 2016. With the age-standardized data (see the discussion in Section 2.1), we also calculate the
age-standardized disease complexity index (ADCI) following the same formulas.

22

Figure 8: DICI against DCI. The solid line represents the fit of a linear model and the dashed line a 95%
prediction interval based on the fitted linear model.

6.2 The disease-income complexity index

Using the economic complexity methodology, (Hartmann et al., 2017) recently introduced
a measure that associates products with income inequality and showed how the develop-
ment of new products is associated with changes in income inequality. Here, to decompose
economic development at the disease level, we introduce a measure that links a disease to
the average income per capita of the countries for which they have a relative disease dis-
advantage i.e., an estimate of the expected income per capita related to different diseases.
In this way, we illustrate how disease complexity is being affected by the level of economic
development and quantify the relationship between countries' income per capita and the
complexity of their diseases.
Following the methodology in (Hartmann et al., 2017), we define the Disease-Income Com-
plexity Index (DICI), and decompose the relationship between the DCI and the DICI for
the diseases in our sample of countries.9
Assuming that we have information for l countries and k diseases, we can fill the (l × k)
matrix M so that its matrix element Mcd = 1 if country c has a RDD in disease d, and zero
otherwise (see Section 3.1). Our dataset contains information for 195 developed and de-
veloping countries and for 196 diseases from 1990 to 2016. A visualization of the matrix
M that is used to calculate the HCI and the DCI for this dataset is shown in Figure 2.
Every disease d can have prevalent cases in a country c. For every disease d, we can
calculate the fraction scd:

scd =

(cid:80)

Xcd
d(cid:48) Xcd(cid:48)

,

(8)

where Xcd is the number of prevalent cases per 100,000 population for disease d in country
c, while (cid:80)
d(cid:48) Xcd(cid:48) is the number of prevalent cases of all diseases in country c. If GDPc is
the (log) GDP per capita of country c, we can calculate the DICId for every disease d as:

DICId =

1
Nd

(cid:88)

c

McdscdGDPc,

(9)

where Nd = (cid:80)

c Mcdscd is a normalization factor.

The DICI is defined at the disease level as the average level of (log) GDP per capita
of the countries that have a RDD in disease d, weighted by the disease's importance in
each country's pool of diseases. Utilizing the (log) PPP GDP per capita (constant 2011
international $) from the World Bank's World Development Indicators for the countries in
our sample, we calculate the above index for every year in the period of 1990-2016.

Table 7 lists the five diseases with the highest and lowest average DICI values during
the period of 1990-2016. It is evident that higher economic development is associated with
more complex diseases such as motor neuron disease and malignant skin melanoma. At
the other end of the spectrum, less complex diseases such as acute hepatitis E and malaria
are associated with low levels of income per capita.

9We also computed the ADICI and investigated its relationship with the ADCI, finding similar results.

23

−2.0−1.00.00.51.03.03.54.04.5<DCI>p<DICI>pTable 7: List of the five diseases with the highest and lowest DICI values during the period of
1990-2016

code

disease name

disease section

DICI

DCI

Highest DICI
554
459
573
483
485

Lowest DICI
404
345
353
370
359

Motor neuron disease
Malignant skin melanoma
Anorexia nervosa
Mesothelioma
Non-Hodgkin's lymphoma

Neurological disorders
Neoplasms
Mental disorders
Neoplasms
Neoplasms

Acute hepatitis E
Malaria
Cystic echinococcosis
Maternal obstructed labor and uterine rupture Maternal and neonatal disorders
Rabies

Other infectious diseases
Neglected tropical diseases and malaria
Neglected tropical diseases and malaria

Neglected tropical diseases and malaria

4.55
4.52
4.49
4.49
4.48

2.89
2.86
2.84
2.80
2.76

1.025
1.210
0.818
1.041
1.057

-1.707
-2.045
-1.383
-1.957
-1.840

Notes: DICI: Disease-Income Complexity Index; DCI: Disease Complexity Index; Average values for 1990-2016

6.3 Linking disease complexity and economic development

In this subsection, we test the existence of a bivariate relationship between the DCI and
the DICI. Thus, we calculate Pearson's correlation coefficient for DICI against DCI. If such
an association exists, it should allow us to derive expectations about whether disease com-
plexity can be associated with economic development and verify, with disease-level data,
the statistically significant and positive relationship between health complexity and eco-
nomic development that we found above (Section 5.2). The correlation coefficient for the
relationship between the average values of the DICI and the DCI for the period of 1990-
2016 is ρ = 0.96 ± 0.01 with a p-value < 2.2 × 10−16. In Figure 8, we present the scatter-plot of
the relationship between the DICI and the DCI for the 196 diseases in our dataset (average
values for 1990-2016), together with the fitted linear model. The slope of the linear fit is
the corresponding correlation coefficient.
The statistically significant positive correlation between the DICI and the DCI indicates that
more complex diseases are associated with more developed countries, as measured by the
(log) GDP per capita. This allows us to understand which sets of diseases are linked to
better overall economic performance, based on their complexity.
In Table 8, we run panel regressions between the DCI and DICI. The results show that
the relationship between the DCI and the DICI is the outcome of the correlations both be-
tween diseases (regression on group means) and within diseases (fixed-effects regression
with time dummies and standard errors adjusted for disease clusters). This suggests that
the positive effect of economic development on the complexity of diseases is due to both
changes in the structure of the disease space towards more complex diseases and increases
in the complexity of existing diseases.

Table 8: Disease-income complexity index and the complexity of diseases

(1)
DCI
Within Estimation

(2)
DCI
Between Estimation

DICI

Observations
Diseases
R-square

0.479***
(0.149)
5,211
193
0.90

2.041***
(0.045)
5,211
193
0.88

Notes: DICI: Disease-Income Complexity Index; DCI: Disease Complexity Index. Time dummies are included in
the within regression. Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.01

24

7 Conclusions

Our analysis illustrates that a country's level of development determines the structure of
its disease space. Following the economic complexity methodology, we developed the HCI,
which quantifies the network representation of the relatedness and proximity of diseases. In
a dynamic panel data setting, we showed that there is a robust positive effect of a country's
economic development, measured by GDP per capita, on its level of health complexity, i.e.,
on the `structural' composition of its pool of diseases. The evidence presented here suggests
that the economic development of nations conditions the disease space. Specifically, more
complex diseases tend to be relatively more prevalent in populations with a higher income
per capita. Explicitly, it seems that when an economy accelerates, the impact on health
complexity is positive.
In addition, we build the DICI, which links a disease to the average level of income per
capita of the countries in which they have a relative disease disadvantage and illustrate
how disease complexity is related to economic development. Specifically, we show how
changes in GDP per capita are associated with more complex diseases. The temporal
variation of the above indices is important from a policy perspective. Using the HCI and
DCI, it is possible to design policies aimed at improving the recognition, visibility, and
traceability of complex diseases across the globe and through time (e.g., by developing a
classification system for all health information systems). These indices can also be used
as tools for the development of national plans for complex diseases and the establishment
of knowledge networks on complex diseases, so as to improve their diagnosis, treatment,
and cure. Furthermore, the DICI could be used to design a health expenditure reallocation
policy promoting health activities and services associated with the prevention of complex
diseases.
This study employs the economic complexity methodology to compute two new metrics
that quantify the disease space of countries. These can be valuable tools for estimating
the effect of economic development on the health status of nations. The topic of economic
complexity is a rather new one, and its use in economics is rather limited so far. By focusing
on the topic of disease complexity, our contribution lies in bridging the health economics
literature with the literature that highlights economic complexity as a powerful paradigm
in understanding key issues in economics, geography, innovation studies, and other social
sciences.

25

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42

List of figures

Figure 1. Maximum Spanning Tree of the country-disease bipartite network . . . . .
Figure 2. Matrix representation of the links between countries and diseases . . . . .
Figure 3. The proximity matrix and the resulting disease space . . . . . . . . . . . .
Figure 4. Localisation of diseases for different income-regions of the world . . . . .
Figure 5. Health complexity index across the world . . . . . . . . . . . . . . . . . . .
Figure 6. Evolution of the health complexity index over time . . . . . . . . . . . . . .
Figure 7. Changes in the health complexity index across the world . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 8. DICI against DCI

7
8
10
16
17
17
18
23

43

List of tables

. . . . . . . . . . . . .

13
Table 1. Variable definitions, sources and summary statistics
Table 2. The effect of economic development on health complexity: Fixed-effects OLS 15
19
Table 3. The effect of economic development on health complexity: baseline results
Table 4. The effect of economic development on health complexity: AHCI . . . . . .
20
Table 5. The effect of economic development on health complexity: robustness checks 21
Table 6. List of the five diseases with the highest and lowest DCI values during the
period of 1990-2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 7. List of the five diseases with the highest and lowest DICI values during the
period of 1990-2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 8. Disease-income complexity index and the complexity of diseases . . . . . .
Table 9. List of countries
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 10. List of diseases and injuries . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24
24
45
46

22

44

Annexes

Annex 1. List of countries

Table 9: List of countries

Country id
6
7
8
10
11
12
13
14
15
16
17
18
19
20
22
23
24
25
26
27
28
29
30
33
34
35
36
37
38
39
40
41
43
44
45
46
47
48
49
50
51
52
53
54
55
57
58
59
60
61
62
63
66
67
68
69
71
72
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
97
98
99
101
102
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
121
122
123
125
126
127
128
129
130

Country name
China
North Korea
Taiwan
Cambodia
Indonesia
Laos
Malaysia
Maldives
Myanmar
Philippines
Sri Lanka
Thailand
Timor-Leste
Vietnam
Fiji
Kiribati
Marshall Islands
Federated States of Micronesia
Papua New Guinea
Samoa
Solomon Islands
Tonga
Vanuatu
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyzstan
Mongolia
Tajikistan
Turkmenistan
Uzbekistan
Albania
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Hungary
Macedonia
Montenegro
Poland
Romania
Serbia
Slovakia
Slovenia
Belarus
Estonia
Latvia
Lithuania
Moldova
Russian Federation
Ukraine
Brunei
Japan
South Korea
Singapore
Australia
New Zealand
Andorra
Austria
Belgium
Cyprus
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Israel
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
Argentina
Chile
Uruguay
Canada
United States
Antigua and Barbuda
The Bahamas
Barbados
Belize
Cuba
Dominica
Dominican Republic
Grenada
Guyana
Haiti
Jamaica
Saint Lucia
Saint Vincent and the Grenadines
Suriname
Trinidad and Tobago
Bolivia
Ecuador
Peru
Colombia
Costa Rica
El Salvador
Guatemala
Honduras
Mexico

Region
East Asia
East Asia
East Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Southeast Asia
Oceania
Oceania
Oceania
Oceania
Oceania
Oceania
Oceania
Oceania
Oceania
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Asia
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Central Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
Eastern Europe
High-income Asia Pacific
High-income Asia Pacific
High-income Asia Pacific
High-income Asia Pacific
Australasia
Australasia
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Western Europe
Southern Latin America
Southern Latin America
Southern Latin America
High-income North America
High-income North America
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Caribbean
Andean Latin America
Andean Latin America
Andean Latin America
Central Latin America
Central Latin America
Central Latin America
Central Latin America
Central Latin America
Central Latin America

45

World Bank group
World Bank Upper Middle Income
World Bank Low Income
World Bank High Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank High Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank High Income
World Bank Upper Middle Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank Upper Middle Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Low Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income

Country id
131
132
133
135
136
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
160
161
162
163
164
165
168
169
170
171
172
173
175
176
177
178
179
180
181
182
183
184
185
186
187
189
190
191
193
194
195
196
197
198
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
298
305
349
351
376
385
422
435
522

Country name
Nicaragua
Panama
Venezuela
Brazil
Paraguay
Algeria
Bahrain
Egypt
Iran
Iraq
Jordan
Kuwait
Lebanon
Libya
Morocco
Palestine
Oman
Qatar
Saudi Arabia
Syria
Tunisia
Turkey
United Arab Emirates
Yemen
Afghanistan
Bangladesh
Bhutan
India
Nepal
Pakistan
Angola
Central African Republic
Congo
Democratic Republic of the Congo
Equatorial Guinea
Gabon
Burundi
Comoros
Djibouti
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Mozambique
Rwanda
Seychelles
Somalia
Tanzania
Uganda
Zambia
Botswana
Lesotho
Namibia
South Africa
Swaziland
Zimbabwe
Benin
Burkina Faso
Cameroon
Cape Verde
Chad
Cote d'Ivoire
The Gambia
Ghana
Guinea
Guinea-Bissau
Liberia
Mali
Mauritania
Niger
Nigeria
Sao Tome and Principe
Senegal
Sierra Leone
Togo
American Samoa
Bermuda
Greenland
Guam
Northern Mariana Islands
Puerto Rico
Virgin Islands, U.S.
South Sudan
Sudan

Continuation of Table 10

Region
Central Latin America
Central Latin America
Central Latin America
Tropical Latin America
Tropical Latin America
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
North Africa and Middle East
South Asia
South Asia
South Asia
South Asia
South Asia
Central Sub-Saharan Africa
Central Sub-Saharan Africa
Central Sub-Saharan Africa
Central Sub-Saharan Africa
Central Sub-Saharan Africa
Central Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Southeast Asia
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Southeast Asia
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Eastern Sub-Saharan Africa
Southern Sub-Saharan Africa
Southern Sub-Saharan Africa
Southern Sub-Saharan Africa
Southern Sub-Saharan Africa
Southern Sub-Saharan Africa
Southern Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Western Sub-Saharan Africa
Oceania
Caribbean
High-income North America
Oceania
Oceania
Caribbean
Caribbean
Eastern Sub-Saharan Africa
North Africa and Middle East
Notes: Global Burden of Disease Study 2016

World Bank group
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank High Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank High Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Low Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Low Income
World Bank Upper Middle Income
World Bank Low Income
World Bank Low Income
World Bank High Income
World Bank Low Income
World Bank Low Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Upper Middle Income
World Bank Upper Middle Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Low Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Low Income
World Bank Low Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Lower Middle Income
World Bank Lower Middle Income
World Bank Low Income
World Bank Low Income
World Bank Low Income
World Bank Upper Middle Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank High Income
World Bank Low Income
World Bank Lower Middle Income

Annex 2. List of diseases and injuries

Table 10: List of diseases and injuries

Disease id

Disease name

294
295
296
297
934
946
947
954
298
948
949
950
300

All causes
Communicable, maternal, neonatal, and nutritional diseases
HIV/AIDS and tuberculosis
Tuberculosis
Drug-susceptible tuberculosis
Multidrug-resistant tuberculosis without extensive drug resistance
Extensively drug-resistant tuberculosis
Latent tuberculosis infection
HIV/AIDS
Drug-susceptible HIV/AIDS - Tuberculosis
Multidrug-resistant HIV/AIDS - Tuberculosis without extensive drug resistance
Extensively drug-resistant HIV/AIDS - Tuberculosis
HIV/AIDS resulting in other diseases

Parent dis-
ease
294
294
295
296
297
297
297
297
296
298
298
298
298

Outline

Total
A
A.1
A.1.1
A.1.1.1
A.1.1.2
A.1.1.3
A.1.1.4
A.1.2
A.1.2.1
A.1.2.2
A.1.2.3
A.1.2.4

46

Disease id

Disease name

Continuation of Table 10

301
302
318
319
320
321
322
328
329
332
333
334
335
336
337
338
339
340
341
342
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
405
843
935
936
365
366
367
368
369
370
371
375
376
741
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
408
409
410
444
447
450
411
414
441
417
418
419
420
421
453
456
423
426
459
462
849
850
429
432
435
465
438
468
471
474
477
480
483
484
485
486
487

Diarrhea, lower respiratory, and other common infectious diseases
Diarrheal diseases
Intestinal infectious diseases
Typhoid fever
Paratyphoid fever
Other intestinal infectious diseases
Lower respiratory infections
Upper respiratory infections
Otitis media
Meningitis
Pneumococcal meningitis
H influenzae type B meningitis
Meningococcal meningitis
Other meningitis
Encephalitis
Diphtheria
Whooping cough
Tetanus
Measles
Varicella and herpes zoster
Neglected tropical diseases and malaria
Malaria
Chagas disease
Leishmaniasis
Visceral leishmaniasis
Cutaneous and mucocutaneous leishmaniasis
African trypanosomiasis
Schistosomiasis
Cysticercosis
Cystic echinococcosis
Lymphatic filariasis
Onchocerciasis
Trachoma
Dengue
Yellow fever
Rabies
Intestinal nematode infections
Ascariasis
Trichuriasis
Hookworm disease
Food-borne trematodiases
Leprosy
Ebola
Zika virus
Guinea worm disease
Other neglected tropical diseases
Maternal disorders
Maternal hemorrhage
Maternal sepsis and other maternal infections
Maternal hypertensive disorders
Maternal obstructed labor and uterine rupture
Maternal abortion, miscarriage, and ectopic pregnancy
Indirect maternal deaths
Late maternal deaths
Maternal deaths aggravated by HIV/AIDS
Other maternal disorders
Neonatal disorders
Neonatal preterm birth complications
Neonatal encephalopathy due to birth asphyxia and trauma
Neonatal sepsis and other neonatal infections
Hemolytic disease and other neonatal jaundice
Other neonatal disorders
Nutritional deficiencies
Protein-energy malnutrition
Iodine deficiency
Vitamin A deficiency
Iron-deficiency anemia
Other nutritional deficiencies
Other communicable, maternal, neonatal, and nutritional diseases
Sexually transmitted diseases excluding HIV
Syphilis
Chlamydial infection
Gonococcal infection
Trichomoniasis
Genital herpes
Other sexually transmitted diseases
Hepatitis
Acute hepatitis A
Hepatitis B
Hepatitis C
Acute hepatitis E
Other infectious diseases
Non-communicable diseases
Neoplasms
Lip and oral cavity cancer
Nasopharynx cancer
Other pharynx cancer
Esophageal cancer
Stomach cancer
Colon and rectum cancer
Liver cancer
Liver cancer due to hepatitis B
Liver cancer due to hepatitis C
Liver cancer due to alcohol use
Liver cancer due to other causes
Gallbladder and biliary tract cancer
Pancreatic cancer
Larynx cancer
Tracheal, bronchus, and lung cancer
Malignant skin melanoma
Non-melanoma skin cancer
Non-melanoma skin cancer (squamous-cell carcinoma)
Non-melanoma skin cancer (basal-cell carcinoma)
Breast cancer
Cervical cancer
Uterine cancer
Ovarian cancer
Prostate cancer
Testicular cancer
Kidney cancer
Bladder cancer
Brain and nervous system cancer
Thyroid cancer
Mesothelioma
Hodgkin lymphoma
Non-Hodgkin lymphoma
Multiple myeloma
Leukemia

47

Parent dis-
ease
295
301
301
318
318
318
301
301
301
301
332
332
332
332
301
301
301
301
301
301
295
344
344
344
347
347
344
344
344
344
344
344
344
344
344
344
344
360
360
360
344
344
344
344
344
344
295
366
366
366
366
366
366
366
366
366
295
380
380
380
380
380
295
386
386
386
386
386
295
392
393
393
393
393
393
393
392
400
400
400
400
392
294
409
410
410
410
410
410
410
410
417
417
417
417
410
410
410
410
410
410
462
462
410
410
410
410
410
410
410
410
410
410
410
410
410
410
410

Outline

A.2
A.2.1
A.2.2
A.2.2.1
A.2.2.2
A.2.2.3
A.2.3
A.2.4
A.2.5
A.2.6
A.2.6.1
A.2.6.2
A.2.6.3
A.2.6.4
A.2.7
A.2.8
A.2.9
A.2.10
A.2.11
A.2.12
A.3
A.3.1
A.3.2
A.3.3
A.3.3.1
A.3.3.2
A.3.4
A.3.5
A.3.6
A.3.7
A.3.8
A.3.9
A.3.10
A.3.11
A.3.12
A.3.13
A.3.14
A.3.14.1
A.3.14.2
A.3.14.3
A.3.15
A.3.16
A.3.17
A.3.18
A.3.19
A.3.20
A.4
A.4.1
A.4.2
A.4.3
A.4.4
A.4.5
A.4.6
A.4.7
A.4.8
A.4.9
A.5
A.5.1
A.5.2
A.5.3
A.5.4
A.5.5
A.6
A.6.1
A.6.2
A.6.3
A.6.4
A.6.5
A.7
A.7.1
A.7.1.1
A.7.1.2
A.7.1.3
A.7.1.4
A.7.1.5
A.7.1.6
A.7.2
A.7.2.1
A.7.2.2
A.7.2.3
A.7.2.4
A.7.3
B
B.1
B.1.1
B.1.2
B.1.3
B.1.4
B.1.5
B.1.6
B.1.7
B.1.7.1
B.1.7.2
B.1.7.3
B.1.7.4
B.1.8
B.1.9
B.1.10
B.1.11
B.1.12
B.1.13
B.1.13.1
B.1.13.2
B.1.14
B.1.15
B.1.16
B.1.17
B.1.18
B.1.19
B.1.20
B.1.21
B.1.22
B.1.23
B.1.24
B.1.25
B.1.26
B.1.27
B.1.28

Disease id

Disease name

Continuation of Table 10

845
846
847
848
943
488
491
492
493
494
495
496
498
499
942
938
944
500
501
502
503
507
508
509
510
511
512
513
514
515
516
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
541
542
543
544
545
546
554
547
548
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
582
585
586
587
588
589
590
591
592
593
594
595
596
597
598
602
603
604
605
606
607
608
609
612
613
614
837
615
838
616
839
618
619
626
627
628
629
630
631

Acute lymphoid leukemia
Chronic lymphoid leukemia
Acute myeloid leukemia
Chronic myeloid leukemia
Other leukemia
Other neoplasms
Cardiovascular diseases
Rheumatic heart disease
Ischemic heart disease
Cerebrovascular disease
Ischemic stroke
Hemorrhagic stroke
Hypertensive heart disease
Cardiomyopathy and myocarditis
Myocarditis
Alcoholic cardiomyopathy
Other cardiomyopathy
Atrial fibrillation and flutter
Aortic aneurysm
Peripheral artery disease
Endocarditis
Other cardiovascular and circulatory diseases
Chronic respiratory diseases
Chronic obstructive pulmonary disease
Pneumoconiosis
Silicosis
Asbestosis
Coal workers pneumoconiosis
Other pneumoconiosis
Asthma
Interstitial lung disease and pulmonary sarcoidosis
Other chronic respiratory diseases
Cirrhosis and other chronic liver diseases
Cirrhosis and other chronic liver diseases due to hepatitis B
Cirrhosis and other chronic liver diseases due to hepatitis C
Cirrhosis and other chronic liver diseases due to alcohol use
Cirrhosis and other chronic liver diseases due to other causes
Digestive diseases
Peptic ulcer disease
Gastritis and duodenitis
Appendicitis
Paralytic ileus and intestinal obstruction
Inguinal, femoral, and abdominal hernia
Inflammatory bowel disease
Vascular intestinal disorders
Gallbladder and biliary diseases
Pancreatitis
Other digestive diseases
Neurological disorders
Alzheimer disease and other dementias
Parkinson disease
Epilepsy
Multiple sclerosis
Motor neuron disease
Migraine
Tension-type headache
Other neurological disorders
Mental and substance use disorders
Schizophrenia
Alcohol use disorders
Drug use disorders
Opioid use disorders
Cocaine use disorders
Amphetamine use disorders
Cannabis use disorders
Other drug use disorders
Depressive disorders
Major depressive disorder
Dysthymia
Bipolar disorder
Anxiety disorders
Eating disorders
Anorexia nervosa
Bulimia nervosa
Autistic spectrum disorders
Autism
Asperger syndrome and other autistic spectrum disorders
Attention-deficit/hyperactivity disorder
Conduct disorder
Idiopathic developmental intellectual disability
Other mental and substance use disorders
Diabetes, urogenital, blood, and endocrine diseases
Diabetes mellitus
Acute glomerulonephritis
Chronic kidney disease
Chronic kidney disease due to diabetes mellitus
Chronic kidney disease due to hypertension
Chronic kidney disease due to glomerulonephritis
Chronic kidney disease due to other causes
Urinary diseases and male infertility
Interstitial nephritis and urinary tract infections
Urolithiasis
Benign prostatic hyperplasia
Male infertility
Other urinary diseases
Gynecological diseases
Uterine fibroids
Polycystic ovarian syndrome
Female infertility
Endometriosis
Genital prolapse
Premenstrual syndrome
Other gynecological diseases
Hemoglobinopathies and hemolytic anemias
Thalassemias
Thalassemias trait
Sickle cell disorders
Sickle cell trait
G6PD deficiency
G6PD trait
Other hemoglobinopathies and hemolytic anemias
Endocrine, metabolic, blood, and immune disorders
Musculoskeletal disorders
Rheumatoid arthritis
Osteoarthritis
Low back and neck pain
Low back pain
Neck pain

48

Parent dis-
ease
487
487
487
487
487
410
409
491
491
491
494
494
491
491
499
499
499
491
491
491
491
491
409
508
508
510
510
510
510
508
508
508
409
521
521
521
521
409
526
526
526
526
526
526
526
526
526
526
409
542
542
542
542
542
542
542
542
409
558
558
558
561
561
561
561
561
558
567
567
558
558
558
572
572
558
575
575
558
558
558
558
409
586
586
586
589
589
589
589
586
594
594
594
594
594
586
603
603
603
603
603
603
603
586
613
613
613
613
613
613
613
586
409
626
626
626
629
629

Outline

B.1.28.1
B.1.28.2
B.1.28.3
B.1.28.4
B.1.28.5
B.1.29
B.2
B.2.1
B.2.2
B.2.3
B.2.3.1
B.2.3.2
B.2.4
B.2.5
B.2.5.1
B.2.5.2
B.2.5.3
B.2.6
B.2.7
B.2.8
B.2.9
B.2.10
B.3
B.3.1
B.3.2
B.3.2.1
B.3.2.2
B.3.2.3
B.3.2.4
B.3.3
B.3.4
B.3.5
B.4
B.4.1
B.4.2
B.4.3
B.4.4
B.5
B.5.1
B.5.2
B.5.3
B.5.4
B.5.5
B.5.6
B.5.7
B.5.8
B.5.9
B.5.10
B.6
B.6.1
B.6.2
B.6.3
B.6.4
B.6.5
B.6.6
B.6.7
B.6.8
B.7
B.7.1
B.7.2
B.7.3
B.7.3.1
B.7.3.2
B.7.3.3
B.7.3.4
B.7.3.5
B.7.4
B.7.4.1
B.7.4.2
B.7.5
B.7.6
B.7.7
B.7.7.1
B.7.7.2
B.7.8
B.7.8.1
B.7.8.2
B.7.9
B.7.10
B.7.11
B.7.12
B.8
B.8.1
B.8.2
B.8.3
B.8.3.1
B.8.3.2
B.8.3.3
B.8.3.4
B.8.4
B.8.4.1
B.8.4.2
B.8.4.3
B.8.4.4
B.8.4.5
B.8.5
B.8.5.1
B.8.5.2
B.8.5.3
B.8.5.4
B.8.5.5
B.8.5.6
B.8.5.7
B.8.6
B.8.6.1
B.8.6.2
B.8.6.3
B.8.6.4
B.8.6.5
B.8.6.6
B.8.6.7
B.8.7
B.9
B.9.1
B.9.2
B.9.3
B.9.3.1
B.9.3.2

Disease id

Disease name

Continuation of Table 10

632
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
668
669
670
671
672
673
674
675
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
704
705
706
707
708
709
710
711
712
713
714
715
842
716
717
718
721
723
724
725
726
941
727
728
729
945
854

Gout
Other musculoskeletal disorders
Other non-communicable diseases
Congenital birth defects
Neural tube defects
Congenital heart anomalies
Orofacial clefts
Down syndrome
Turner syndrome
Klinefelter syndrome
Other chromosomal abnormalities
Congenital musculoskeletal and limb anomalies
Urogenital congenital anomalies
Digestive congenital anomalies
Other congenital birth defects
Skin and subcutaneous diseases
Dermatitis
Psoriasis
Cellulitis
Pyoderma
Scabies
Fungal skin diseases
Viral skin diseases
Acne vulgaris
Alopecia areata
Pruritus
Urticaria
Decubitus ulcer
Other skin and subcutaneous diseases
Sense organ diseases
Glaucoma
Cataract
Macular degeneration
Refraction and accommodation disorders
Age-related and other hearing loss
Other vision loss
Other sense organ diseases
Oral disorders
Caries of deciduous teeth
Caries of permanent teeth
Periodontal diseases
Edentulism and severe tooth loss
Other oral disorders
Sudden infant death syndrome
Injuries
Transport injuries
Road injuries
Pedestrian road injuries
Cyclist road injuries
Motorcyclist road injuries
Motor vehicle road injuries
Other road injuries
Other transport injuries
Unintentional injuries
Falls
Drowning
Fire, heat, and hot substances
Poisonings
Exposure to mechanical forces
Unintentional firearm injuries
Unintentional suffocation
Other exposure to mechanical forces
Adverse effects of medical treatment
Animal contact
Venomous animal contact
Non-venomous animal contact
Foreign body
Pulmonary aspiration and foreign body in airway
Foreign body in eyes
Foreign body in other body part
Environmental heat and cold exposure
Other unintentional injuries
Self-harm and interpersonal violence
Self-harm
Self-harm by firearm
Self-harm by other specified means
Interpersonal violence
Physical violence by firearm
Physical violence by sharp object
Sexual violence
Physical violence by other means
Forces of nature, conflict and terrorism, and executions and police conflict
Exposure to forces of nature
Conflict and terrorism
Executions and police conflict

Notes: Global Burden of Disease Study 2016

Parent dis-
ease
626
626
409
640
641
641
641
641
641
641
641
641
641
641
641
640
653
653
653
653
653
653
653
653
653
653
653
653
653
640
669
669
669
669
669
669
669
640
680
680
680
680
680
640
294
687
688
689
689
689
689
689
688
687
696
696
696
696
696
704
704
704
696
696
709
709
696
712
712
712
696
696
687
717
718
718
717
724
724
724
724
687
728
728
728

Outline

B.9.4
B.9.5
B.10
B.10.1
B.10.1.1
B.10.1.2
B.10.1.3
B.10.1.4
B.10.1.5
B.10.1.6
B.10.1.7
B.10.1.8
B.10.1.9
B.10.1.10
B.10.1.11
B.10.2
B.10.2.1
B.10.2.2
B.10.2.3
B.10.2.4
B.10.2.5
B.10.2.6
B.10.2.7
B.10.2.8
B.10.2.9
B.10.2.10
B.10.2.11
B.10.2.12
B.10.2.13
B.10.3
B.10.3.1
B.10.3.2
B.10.3.3
B.10.3.4
B.10.3.5
B.10.3.6
B.10.3.7
B.10.4
B.10.4.1
B.10.4.2
B.10.4.3
B.10.4.4
B.10.4.5
B.10.5
C
C.1
C.1.1
C.1.1.1
C.1.1.2
C.1.1.3
C.1.1.4
C.1.1.5
C.1.2
C.2
C.2.1
C.2.2
C.2.3
C.2.4
C.2.5
C.2.5.1
C.2.5.2
C.2.5.3
C.2.6
C.2.7
C.2.7.1
C.2.7.2
C.2.8
C.2.8.1
C.2.8.2
C.2.8.3
C.2.9
C.2.10
C.3
C.3.1
C.3.1.1
C.3.1.2
C.3.2
C.3.2.1
C.3.2.2
C.3.2.3
C.3.2.4
C.4
C.4.1
C.4.3
C.4.2

49

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doi:10.2760/793876

ISBN 978-92-76-02924-3