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Int. J. Environ. Res. Public Health 2020, 17, 4139; doi:10.3390/ijerph17114139 www.mdpi.com/journal/ijerph
Article
Physical Rehabilitation Needs in the BRICS Nations
from 1990 to 2017: Cross-National Analyses Using
Data from the Global Burden of Disease Study
Tiago S. Jesus
1,
*, Michel D. Landry
2,3
, Helen Hoenig
4,5
, Yi Zeng
6,7
, Sureshkumar Kamalakannan
8
,
Raquel R. Britto
9
, Nana Pogosova
10
, Olga Sokolova
10
, Karen Grimmer
11
and Quinette A. Louw
11
1
Global Health and Tropical Medicine (GHTM) & WHO Collaborating Centre for Health Workforce Policy
and Planning, Institute of Hygiene and Tropical Medicine - NOVA University of Lisbon (IHMT-UNL), Rua
da Junqueira 100, 1349-008 Lisbon, Portugal
2
School of Medicine, Duke University, Durham, NC 27710, USA; mike.landry@duke.edu
3
Duke Global Health Institute (DGHI), Duke University, Durham, NC 27710, USA
4
Physical Medicine and Rehabilitation Service, Durham Veterans Administration Medical Center,
Durham, NC 27705, USA; helen.hoenig@va.gov
5
Division of Geriatrics, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA
6
Center for Study of Aging and Human Development and Geriatrics Division, School of Medicine, Duke
University, Durham, NC 27710, USA; zengyi@nsd.pku.edu.cn
7
National School of Development and Raissun Institute for Advanced Studies, Peking University,
Beijing 100871, China
8
Public Health Foundation of India (PHFI), South Asia Centre for Disability Inclusive Development and
Research (SACDIR), Indian Institute of Public Health, Hyderabad 500 033, (IIPH-H), India;
Suresh.Kumar@lshtm.ac.uk
9
Rehabilitation Science Post Graduation Programs of Universidade Federal de Minas Gerais and
Universidade Federal de Juiz de Fora, Juiz de Fora 36036-900, Brazil; r3britto@gmail.com
10
National Medical Research Center of Cardiology, Moscow 524901, Russia;
nanapogosova@gmail.com (N.P.); birdname@gmail.com (O.S.)
11
Department of Health and Rehabilitation Sciences, Physiotherapy Division, Stellenbosch University,
Stellenbosch 7505, South Africa; ubiquitous598@hotmail.com (K.G.); qalouw@sun.ac.za (Q.A.L.)
* Correspondence: jesus-ts@outlook.com
Received: 15 May 2020; Accepted: 07 June 2020; Published: 10 June 2020
Abstract: Background: This study analyzes the current and evolving physical rehabilitation needs of
BRICS nations (Brazil, Russian Federation, India, China, South Africa), a coalition of large emergent
economies increasingly important for global health. Methods: Secondary, cross-national analyses of
data on Years Lived with Disability (YLDs) were extracted from the Global Burden of Disease Study
2017. Total physical rehabilitation needs, and those stratified per major condition groups are
analyzed for the year 2017 (current needs), and for every year since 1990 (evolution over time).
ANOVAs are used to detect significant yearly changes. Results: Total physical rehabilitation needs
have increased significantly from 1990 to 2017 in each of the BRICS nations, in every metric analyzed
(YLD Counts, YLDs per 100,000 people, and percentage of YLDs relevant to physical rehabilitation;
all p < 0.01). Musculoskeletal & pain conditions were leading cause of physical rehabilitation needs
across the BRICS nations but to varying degrees: from 36% in South Africa to 60% in Brazil. Country-
specific trends include: 25% of South African needs were from HIV-related conditions (no other
BRICS nation had more than 1%); India had both absolute and relative growths of pediatric
rehabilitation needs (p < 0.01); China had an exponential growth in the per-capita needs from
neurological and neoplastic conditions (p < 0.01; r
2
= 0.97); Brazil had a both absolute and relative
growth of needs coming from musculoskeletal & pain conditions (p < 0.01); and the Russian
Federation had the highest neurological rehabilitation needs per capita in 2017 (over than three
times those of India, South Africa or Brazil). Conclusions: total physical rehabilitation needs have
been increasing in each of the BRICS nations, both in absolute and relative values. Apart from the
Int. J. Environ. Res. Public Health 2020, 17, 4139 2 of 18
common growing trend, each of the BRICS nations had own patterns for the amount, typology, and
evolution of their physical rehabilitation needs, which must be taken into account while planning
for health and physical rehabilitation programs, policies and resources.
Keywords: global burden of disease; global health; health services needs and demand; BRICS;
rehabilitation
1. Introduction
The BRICS countries (i.e., Brazil, Russian Federation–called Russia hereafter, India, China, South
Africa) are increasingly recognized as important players in global health and development [1–9].
Traditionally, the G7 (the group of the seven most powerful world economies) steered major health
initiatives globally, through policies, priorities and developmental aid to support and improve health
in Low-and Middle-Income Countries (LMICs).[1] However, global health attention has been turning
to the strategic role of emerging economies, especially the BRICS nations. These are the five large
emerging economies that formalized a coalition and agenda for economic growth and health gains
apart from the traditional, western global agenda [1,2,4–9].
The BRICS countries, which formalized their coalition in 2006, generate 25% of the world’s gross
national income, have approximately 40% of the world’s population, approximately 50% of the
world’s poor, and represent 40% of the global burden of disease [2]. Through strategic cooperation
and inter-BRICS policies, the BRICS nations increasingly seek to translate their economic growths
into improved population health [7,10,11]. Their health ministries have been met annually to discuss
synergies, priorities and innovations tailored to their resource-constrained settings [7,10]. The BRICS
nations’ agendas have been different than the Western nations, with a particular emphasis on social
justice and equity in health in context of their unique, multifaceted health challenges [7,10]. The
BRICS’ national health challenges include important increases in the prevalence of non-
communicable, chronic diseases[3,12–18] along with a still prevalent burden of communicable
conditions,[19,20] multifaceted social determinants of health, and high inequalities in health and
healthcare access[2,21]. As these challenges are similar to those of other LMICs, advances in the
BRICS health policies, cooperation, and healthcare delivery have been inspiring for other countries
with developing economies [4,6,10]. Finally, the BRICS countries have been providing concrete
assistance to the LMICs; [7–9] for example, through a “South-South Cooperation” over 55 years,
China has dispatched medical teams, constructed facilities, distributed drugs and medical devices,
and has trained local health workers in more than 66 countries in need [9].
Despite their economic developments and coalition, the BRICS countries face themselves a
shortage of key health resources (e.g., health and allied health professionals) for their growing and
complex health needs, in the context of many other societal demands [21,22]. These needs accentuate
the complexity of planning for equitable and effective health and social care amidst rapid
demographic, economic and epidemiological transitioning [3,12–18]. BRICS countries therefore had
to be innovative to re-engineer the health and social care systems challenges (often limited healthcare
finances, workforce, training, service planning and administration to address population healthcare
needs), as well as the growing numbers of persons living with disability [23–26].
Worldwide and especially in emerging economies, increasing numbers of people now live with
functional limitations [1,12,26]. This can be explained from the demographic and epidemiological
transitions with increasing life expectancy, an ageing population and the subsequent burden of
chronic diseases [18,26–31]. Not only are many people now living with chronic communicable
diseases which previously were fatal, but there is also an increasing prevalence of non-communicable
diseases which are lifestyle-related and/or come as complex, multiple co-morbidities resulting in
varying types and degrees of long-term disabilities [1,13,32–41]. Rehabilitation is required to
attenuate the effects of disability and optimize functioning in people with functional limitations from
any health condition [25,42]. Failure to address individuals’ rehabilitation needs impacts on human
Int. J. Environ. Res. Public Health 2020, 17, 4139 3 of 18
functioning, social justice, human rights, productivity, long-term costs of care, and even could impact
countries’ economic growth [42–46].
In line with increasing disability prevalence, a recent study using data from Global Burden of
Disease Study 2017 found a 17% increase in the world’s physical rehabilitation needs per capita since
1990, and an almost twofold greater increase (29.9%) in upper-middle-income countries (UMICs),
which include four of the BRICS countries (except India) [25]. The World Health Organization’s
(WHO) Rehabilitation 2030 initiative advocates for the inclusion of rehabilitation in universal health
coverage, across countries of all income levels [42]. All BRICS countries have now committed to
universal health coverage, although with varied levels of coverage, principles and roll-out over the
next decade [6,11,22,47]. It is therefore timely to determine the need for rehabilitation in BRICS
countries. This information will not only support advocacy and strategic planning for the widespread
inclusion of rehabilitation in the roll-out and expansion of universal health coverage in BRICS
countries, but it will inform improvements in the planning for rehabilitation services in other LMICs.
This paper aims to analyze the current and evolving physical rehabilitation needs of the BRICS
countries. The specific study questions are:
1) How large are the physical rehabilitation needs in 2017 for each BRICS country (e.g., in nominal
values, population-adjusted rates, age-standardized rates), and how have those values evolved
since 1990?
2) Which condition groups (e.g., musculoskeletal, neurological, cardiothoracic) account for the
highest rates of physical rehabilitation need for each of the BRICS countries in 2017, and how
have those values evolved since 1990?
2. Materials and Methods
This paper refers to a secondary, cross-national comparative analysis of global epidemiological
data in the public domain. To estimate the physical rehabilitation needs for each of the BRICS nations,
we use data from the 2017 Global Burden of Disease Study (GBD) [48]. Specifically, we combine the
methods of two recent papers using GBD data to analyzing global physical rehabilitation
needs.[25,49] The first uses GBD data to determining total physical rehabilitation needs, i.e., for all
conditions relevant to physical rehabilitation combined [25]. The second stratifies these needs by
condition type, e.g., musculoskeletal, neurological, cardiothoracic [49]. The use of those standard
methods allows for the new findings for the BRCIS nations to be compared with the existing global
benchmarks, i.e., physical rehabilitation needs for the world and for the groups of countries for all
income levels [25,49].
To determine the physical rehabilitation needs for the BRICS nations, we apply and combine the
abovementioned standard methods as follows: In April 2019, public-domain data from the GBD 2017
were systematically extracted from a freely-available web platform: the Global Health Data Exchange
tool (http://ghdx.healthdata.org/gbd-results-tool).
With the due measures to avoid double counting [25], data were extracted for the set of health
conditions likely benefiting from physical rehabilitation. Previously, these were systematically
determined and tested for robustness (i.e., similar patterns of results were found for a sub-set of
conditions) [25]. Table 1, left column, details the set of conditions deemed as likely benefiting from
physical rehabilitation.
Among the GBD “measures”, we extract data only for Years Lived with Disability (YLDs), due
its exclusive focus on non-fatal health losses. YLDs consist of the years lived with any short-term or
long-term health loss weighted for severity by disability weights. For stroke, for example, disability
weights vary from 0.019 for mild consequences to 0.588 for severe consequences plus cognition
problems. Details on how YLDs and disability weights are determined, and the disability weights for
all conditions, are available elsewhere [48,50].
Int. J. Environ. Res. Public Health 2020, 17, 4139 4 of 18
Table 1. How Years Lived with Disability from health conditions of the Global Burden of Disease
Study 2017 are grouped, referring to type of impairments or type physical rehabilitation service level.
Due to its specificity, we do not aggregated YLDs from neoplasm and HIV/AIDS.
Underlying Health Conditions (from the Global
Burden of Disease Study)
Intermediate
Aggregation
Main Groups
(Condition Types)
Low back pain
Pain
Musculoskeletal &
Pain
Neck pain
Tension-type headaches
Injuries (all selected except Spinal & Brain Injuries;
Asphyxiation; and Severe Chest Injuries)
Musculoskeletal
Trauma
Osteoarthritis
Musculoskeletal
disorders
Rheumatoid arthritis
Gout & Other musculoskeletal disorders
Leprosy
Chronic Respiratory Diseases
Pulmonary
Severe Chest Injuries
Cardiovascular Diseases (excluding Stroke)
Cardiovascular
Cardiotoracic
Heart Failure (resulting from all the non-considered
health conditions)
Stroke
Neurological Disorders
(non-communicable)
Neurological
Multiple sclerosis; Parkinson’s; Alzheimer’s & Other
Dementias; Motor neuron disease; Other neurological
disorders; Neoplasm–brain & nervous system
Infectious–affecting the nervous system: Encephalitis;
Meningitis; Tetanus; ZIKA virus Neurological–Infectious
*
Syndrome: Guillain-Barré (resulting from non-
considered health conditions)
Spinal Cord Injury
Neurological Trauma
Traumatic Brain Injury; Asphyxiation
Congenital birth defects (digestive & urogenital disorders
excluded) - Pediatric **
Neonatal
Autism Spectrum Disorder
All Neoplasms (not nervous system) - Neoplasm (not
nervous system)
HIV/AIDS
-
HIV-related
Legend: * Conditions that may have early onsets and hence rather require pediatric physical
rehabilitation. Zika virus can led to both neurological and musculoskeletal sequalae and physical
rehabilitation interventions. ** When leading to long-term impairments, pediatric conditions may also
require adult physical rehabilitation services.
For “years”, data were extracted for every year between 1990 and 2017, for a more precise
determination of the evolving trend. For “location”, YLDs were extracted at the national level for
each of the BRICS. No sub-national data were extracted, as we focused on nation-wide and cross-
national analyses.
As for “metrics”, we extracted YLDs data for prevalent number (i.e., YLD counts), rate (i.e., YLDs
per 100,000 people), and percentage (i.e., percentage of YLDs from the selected conditions relative to
the total amount of YLDs).
Regarding “age”, we extracted YLDs both for all ages and age-standardized rates, the latter used
to determine age-standardized YLD Rates (i.e., physical rehabilitation needs adjusted for both
population size and ageing).
All the selected data were imported from the webtool to Excel spreadsheets for data storage,
management, and analysis.
Int. J. Environ. Res. Public Health 2020, 17, 4139 5 of 18
In the Excel spreadsheets, we summed YLDs within each of the five “locations”, four “metrics”,
and 28 “years”, computed any percent changes from 1990 to 2017, plotted the entire time series [1990–
2017] of the combined YLD values, and then determined which type of simple regression model (i.e.,
linear, exponential, or logarithmic) best fit the plotted data. We used visualization and r
2
values for
that. Given negligible differences in r
2
values (<0.02 between the models), we retained the linear
regression option.
To assess yearly changes of YLDs between 1990 and 2017, ANOVA was applied. This test
considers the data on every year between 1990 and 2017, which increases preciseness. The
significance level for the analysis was set at two subsequent levels: p = 0.05 and p = 0.01, the latter
accounts for a Bonferroni correction (0.05/5 = 0.01) considering the analyses are made for five
countries within each item/metric under study. The respective confidence intervals (CIs) in turn were
used to analyze whether yearly changes for each BRICS nation significantly differed (i.e., did not
overlap) from those of the 4 other BRICS countries or from the global benchmarks that we extracted
from the literature–as the same methods were used.[25,49]
Finally, using the analytical procedures above, we performed a subgroup analysis on the
physical rehabilitation needs, stratified per six major groups of conditions–detailed in the Table 1′s
right column. For that analysis, we only use YLD Rates as a metric, either through actual YLD Rates
or through those transformed into percent values, e.g., percentage of the YLD Rates related to
physical rehabilitation that came specifically from neurological conditions.
3. Results
We provide below the results for the two study questions:
3.1. Total Physical Rehabilitation Needs
Table 2 shows a significant increase in total physical rehabilitation needs from 1990 to 2017
across the five countries in all the metrics analyzed (p < 0.01); the exception being Age-Standardized
YLD Rates for both India and Russia, with no significant changes since 1990 (p > 0.05).
Per metric, Table 2 (see Supplementary Material 1, pages 1 to 4, for a visual representation of the
data) shows the following trends:
Table 2. YLDs (Years Lived with Disability), in four metrics, from all conditions likely benefiting from
physical rehabilitation–i.e., all conditions combined. YLD values are provided for each of the five
countries analyzed, as well as for global benchmarks.
1990 2017
% Change
[1990–
2017]
Regression Model
Type r
2
b
Coefficient
95% CI 99% CI
YLD Counts, Millions
Brazil 4.94 9.02 82.6% Linear 1 0.16 * 0.15–0.16 0.15–0.16
China 39.8 67.0 68.1% Linear 0.98 1.01 * 0.94–1.07 0.92–1.10
India 28.8 55.1 91.6% Linear 0.98 0.98 * 0.93–1.03 0.91–1.05
Russia 8.69 9.35 7.6% Linear 0.76 0.03 * 0.02–0.04 0.02–0.04
South Africa 1.25 2.68 114.1% Linear 0.94 0.06 * 0.06–0.07 0.05–0.07
World 206.4 342.9 66.2% Linear 0.99 5.10 * 4.88–5.32 4.80–5.40
High-income 57.5 79.0 37.4% Linear 0.99 0.81 * 0.77–0.84 0.76–0.86
Upper Middle-Income 75.9 123.0 62.1% Linear 0.99 1.78 * 1.69–1.87 1.66–1.90
Lower Middle-Income 62.3 118.8 90.4% Linear 0.99 2.10 * 2.02–2.19 1.99–2.22
Low-Income 9.81 20.8 111.5% Linear 1 0.39 * 0.38–0.40 0.38–0.41
Int. J. Environ. Res. Public Health 2020, 17, 4139 6 of 18
Table 2. Cont.
YLD Rates (per 100,000 inhabitants)
Brazil 3306 4528 28.8% Linear 1 37.2 * 36.3–38.1 36.0–38.4
China 3329 4743 42.5% Linear 0.95 54.3 * 49.3–59.2 47.6–60.9
India 3300 3990 20.9% Linear 0.92 25.2 * 22.2–28.2 21.1–29.3
Russia 5741 6393 11.4% Linear 0.91 31.6 * 27.6–35.6 26.2–37.0
South Africa 3399 4803 43.3% Linear 0.85 73.4 * 61.1–85.7 56.8–90.0
World 3825 4488 17.3% Linear 0.96 25.7 * 23.7–27.7 23.0–28.4
High-income 5748 6643 15.6% Linear 0.98 33.1 * 31.1–35.1 30.4–35.8
Upper Middle-Income 3594 4669 29.9% Linear 0.96 42.6 * 39.3–46.0 38.1–47.2
Lower Middle-Income 3233 3806 17.7% Linear 0.96 21.6 * 19.9–23.2 19.3–23.8
Low-Income 2977 3112 4.5% Logarithmic 0.50 2.5 ** 0.40–4.55 −0.33–5.28
Age-standardized YLD Rates
Brazil 3993 4010 0.44% Linear 0.69 2.9 * 2.08–3.63 1.81–3.90
China 3795 3898 2.71% Linear 0.24 5.4 * 1.47–9.02 0.14–10.4
India 4361 4368 0.16% Logarithmic 0.09 - 0.2 −2.62–2.22 −3.47–3.08
Russia 5156 4991 −3.20% Logarithmic 0.13 - 0.1 −4.02–3.77 −5.40–5.14
South Africa 4415 5131 16.2% Logarithmic 0.72 47.8 * 32.7–62.8 27.5–68.1
World 4377 4334 −1.0% Logarithmic 0.22 −0.62 −2.13–0.89 −2.66–1.42
High-income 5007 4872 −2.7% Logarithmic 0.86 −5.36 * −6.76–(−3.96) −7.26–(−3.47)
Upper Middle-Income 4106 4080 −0.6% Linear 0.04 1.34 −1.38–4.06 −2.33–5.02
Lower Middle-Income 4262 4314 1.2% Linear 0.46 2.33 * 1.26–3.40 0.89–3.78
Low-Income 4189 4276 2.1% Logarithmic 0.15 0.29 −3.29–3.87 −4.55–5.14
% of YLDs Benefiting from Physical Rehabilitation (among total YLDs)
Brazil 60.1% 66.2% 10.2% Linear 0.96 0.26 * 0.24–0.28 0.23–0.29
China 37.8% 44.8% 18.7% Linear 0.97 0.26 * 0.24–0.28 0.24–0.29
India 29.9% 35.9% 20.1% Linear 0.93 0.23 * 0.21–0.26 0.20–0.27
Russia 45.4% 46.6% 2.6% Linear 0.46 0.07 * 0.04–0.09 0.03–0.11
South Africa 34.8% 43.8% 25.8% Linear 0.87 0.43 * 0.37–0.50 0.34–0.52
World 36.7% 40.2% 9.5% Linear 0.97 0.14 * 0.13–0.15 0.12–0.15
High-income 47.6% 48.6% 2.2% Linear 0.87 0.03 * 0.03–0.04 0.02–0.04
Upper Middle-Income 37.9% 42.2% 11.4% Linear .97 0.17 * 0.16–0.18 0.15–0.19
Lower Middle-Income 30.7% 35.9% 16.8% Linear 0.98 0.20 * 0.19–0.21 0.19–0.22
Low-Income 27.8% 32.1% 15.4% Linear 0.97 0.16 * 0.15–0.17 0.14–0.17
Data obtained from: http://ghdx.healthdata.org/gbd-results-tool. Abbreviations: YLD–Year Lived
with Disability. Legend: * p < 0.01; ** p < 0.05. While possibly obtained through the same source, data
for the global benchmarks, including computed values, were extracted from: Jesus TS, Landry MD,
Hoenig H. Global need for physical rehabilitation: systematic analysis from the Global Burden of
Disease Study 2017. Int J Environ Res Public Health. 2019, 16: 980; Notes: The “b coefficient” refers to
the annual change within a linear regression model. Different population structures apply to countries
with varying income levels; so, cross-location comparisons are not valid for the metric YLD Counts,
except for the variable “% change [1990–2017]”.
In YLD Counts (i.e., absolute YLD values), South Africa more than doubled their physical
rehabilitation needs from 1990 to 2017 (i.e., 114.1% growth), similarly to low-income countries (i.e.,
111.5% growth). India, Brazil, and China had a 91.6%, 82.6%, and 68.1% growth, respectively. Russia,
in turn, had the lowest percentage growth in YLD counts (7.6%), substantially lower than any global
benchmark: i.e., the lowest being 37.4% for high-income countries.
In YLD Rates (i.e., YLDs per 100,000 people), Russia had the highest value in 2017 (6393), but
South Africa and China had the highest yearly growths (99% CIs: 56.8−90.0 and 47.6−60.9,
respectively), each of them significantly higher (i.e., greater, non-overlapping 99% CIs) than those of
the 3 other BRICS nations or any global benchmark.
In Age-standardized YLD Rates, (i.e., YLDs adjusted for both population size and ageing), South
Africa had the highest value in 2017 (5131), and significantly greater yearly increases (i.e., non-
overlapping 99% CIs) than all comparators; for example high-income countries had a significant
decrease. Russia and India had non-significant yearly changes in this metric.
Int. J. Environ. Res. Public Health 2020, 17, 4139 7 of 18
Finally, in the percentage of YLDs likely benefiting from physical rehabilitation among total
YLDs, Brazil stands out with nearly two-thirds of their YLDs coming from rehabilitation-sensitive
conditions in 2017 (66.2%). For the other BRICS countries or global benchmarks, values were all below
50%; in India little more than one-third (35.9%). In South Africa, the yearly growths in the YLDs
percentage were significantly greater while in Russia significantly lower than in any other BRICS
nations. Indeed, the 99% CIs of the Russia’s yearly growth was rather aligned (i.e., partly
overlapping) with that of high-income nations.
3.2. Needs by Condition Types
Table 3 (see Supplementary Material 2, pages 1 to 6, for a visual representation of the data) shows
significant increases in rehabilitation-sensitive YLD Rates from 1990 to 2017 across the BRICS
countries, for all the condition groups (p < 0.01). The exceptions are the South African’s YLD rates
coming from pediatric and from neoplasm conditions (99% CIs: −1.60−1.53 and −0.06−0.62,
respectively), although the latter had a significant increase within the 95% CI (0.03−0.53).
The highest yearly increases in YLD Rates came from: (1) HIV-related conditions in South Africa
(b = 63.8), yet with a logarithmic growth (i.e., greater growth rate in the earlier years); (2)
musculoskeletal & pain conditions in both Brazil and China (b = 25.4 and b = 25.0, respectively); (3)
neurological conditions in China (b = 15.6), within exponential type of growth (i.e., greater growth
rate in the more recent years), and (4) pediatric conditions in India (b = 7.18). In each of these cases,
the growths were significantly greater (i.e., higher, non-overlapping 95% CIs) than those of any
comparators for the same condition group. Finally, Russia stands out with the highest YLD Rates for
neurological conditions in 2017, e.g., over than 3 times that of Brazil, India, or South Africa.
Table 3.
How
YLD Rates (i.e., Years Lived with Disability per 100,000 people) likely benefiting from
physical rehabilitation are distributed per major groups of conditions across the five countries
analyzed, and how values have evolved over time [1990–2017].
1990 2017 % Change
[1990–2017]
Regression
Model Type r
2
b
Coefficient 95% CI 99% CI
Musculoskeletal & Pain
Brazil
1901
2551
34.2%
Linear
1
25.4 *
24.9–25.9
24.8–26.0
China 1646 2258 37.2% Linear 0.99 25.0 * 23.8–26.2 23.4–26.6
India 1765 2048 16.0% Linear 0.91 10.1 * 8.9–11.4 8.4–11.9
Russia
3271
3435
5.0%
Linear
0.88
11.6 *
9.8–13.3
9.2–13.9
South Africa 1605 1731 7.9% Linear 0.93 5 .7 * 5.1–6.4 4.9–6.6
World
2071
2363
14.1%
Linear
0.98
11.4 *
10.7−12.1
10.5−12.4
High-Income
3359
3835
14.2%
Linear
0.99
16.8 *
16.1−17.5
15.9−17.7
Upper Middle-Income
1875
2369
26.3%
Linear
0.98
20.5 *
19.4−21.5
19.0−21.9
Lower Middle-Income
1724
1983
15.1%
Linear
0.96
9.4 *
8.6−10.3
8.3−10.5
Low-Income 1486 1491 0.4% Linear 0.09 −0.6 −1.3
−0.1
−1.5
−0.4
Neurological
Brazil 263 396 50.4% Linear 0.96 5.2 * 4.7–5.6 4.6–5.7
China 410 870 112.5% Exponential 0.97 15.6 * 13.8–17.3 13.2–17.9
India 247 323 30.5% Linear 0.94 2.8 * 2.5–3.1 2.4–3.2
Russia 922 1207 30.9% Linear 0.83 11.4 * 9.3–13.4 8.6–14.1
South Africa 330 356 7.9% Linear 0.57 0.7 * 0.5–1.0 0.4–1.1
World
441
578
31.1%
Linear
0.89
4.8 *
4.1−5.5
3.9−5.7
High-Income
750
929
23.7%
Linear
0.90
6.1 *
5.3−6.9
5.0−7.2
Upper Middle-Income
441
736
66.9%
Exponential
0.95
10.4 *
9.1−11.6
8.7−12.0
Lower Middle-Income
301
364
21.2%
Linear
0.86
2.3 *
1.9−2.7
1.8−2.8
Low-Income
321
314
−2.1%
Linear
0.53
−0.5 *
−0.7−(−0.3)
−0.8−(−0.2)
Cardiotoracic
Brazil 624 685 9.8% Linear 0.88 2.65 * 2.3–3.1 2.11–3.19
China 759 857 12.9% Linear 0.36 4.95 * 2.3–7.6 1.35–8.56
India 746 857 14.8% Linear 0.49 4.02 * 2.4–5.7 1.78–6.26
Russia 802 856 6.7% Linear 0.94 2.21 * 2.0–2.4 1.90–2.52
South Africa
851
918
7.9%
Logarithmic
0.73
2.89 *
1.7–4.1
1.23–4.56
World 733 807 10.1% Linear 0.63 3.5 * 2.4
−4.5
2.0
−4.9
High-Income
956
1103
15.4%
Linear
0.94
7.0 *
6.3−7.7
6.0−8.0
Upper Middle-Income
719
810
12.7%
Linear
0.55
4.3 *
2.7−5.9
2.2−6.4
Lower Middle-Income
662
745
12.5%
Linear
0.67
3.4 *
2.5−4.4
2.1−4.7
Low-Income 557 549 −1.5% Linear 0.06 −0.3 −0.8
−0.2
−1.0
−0.3
Int. J. Environ. Res. Public Health 2020, 17, 4139 8 of 18
Table 3. Cont.
Pediatric
Brazil 468 505 7.8% Linear 0.99 1.48 * 1.42–1.54 1.40–1.57
China 467 613 31.4% Linear 0.97 5.46 * 5.04–5.87 4.90–6.02
India 519 713 37.3% Linear 0.99 7.18 * 6.92–7.43 6.83–7.52
Russia 636 658 3.6% Linear 0.50 1.89 * 1.13–2.66 0.86–2.93
South Africa 558 596 6.8% Logarithmic 0.06 −0.03 −1.19–1.12 −1.60–1.53
World
498 588 18.0% Linear 0.99 3.5 * 3.4−3.6 3.3−3.7
High-Income
493 486 −1.5% Logarithmic 0.91 −0.2 * −0.3−(−0.2) −0.3−(−0.1)
Upper Middle-Income
505 594 17.7% Linear 0.97 3.4 * 3.2
−3.6
3.1
−3.7
Lower Middle-Income
507 624 23.0% Linear 0.99 4.6 * 4.4
−4.8
4.4
−4.8
Low-Income
424 579 36.5% Linear 0.97 6.4 * 5.9−6.8 5.8−6.9
Neoplasm
Brazil 38 75 99% Linear 1 1.32 * 1.30–1.35 1.29–1.36
China 47 136 189% Exponential 0.94 3.00 * 2.51–3.49 2.34–3.66
India
22
34
58%
Linear
0.81
0.40 *
0.32–0.47
0.29–0.05
Russia 106 174 64% Linear 0.93 2.68 * 2.38–2.98 2.27–3.08
South Africa 38 49 30% Logarithmic 0.39 0.28 ** 0.03–0.53 −0.06–0.62
World
62
100
62.3%
Linear
0.95
1.3 *
1.1−1.4
1.1−1.4
High-Income
173
271
56.6%
Linear
0.99
3.4 *
3.3−3.5
3.2−3.6
Upper Middle-Income 50 115 130% Exponential 0.95 2.3 * 1.9
−2.6
1.8
−2.7
Lower Middle-Income
27
38
40.1%
Linear
0.76
0.4 *
0.3−0.5
0.3−0.5
Low-Income
27
26
−1.9%
Linear
0.55
−0.1 *
−0.2−(−0.1)
−0.2−(−0.1)
HIV-related
Brazil 12.4 45.2 163% Linear 0.94 1.1 * 1.0–1.2 0.09–1.3
China
1.2
7.4
528%
Linear
0.98
0.2 *
0.2–0.2
0.2–0.3
India 0. 8 15.2 1905% Logarith mic 0.64 0.7 * 0.3–1.0 0.2–1.1
Russia 4.5 61.7 1277% Exponential 1 1.8 * 1.5–2.1 1.4–2.2
South Africa 17.3 1219 6469% Logarithmic 0.83 63.8 * 51.0–76.5 46.5–81.0
World
17
54
207%
Logarithmic
0.79
1.28 *
0.79−1.77
0.62−1.94
High-Income
14
19
37%
Linear
0.04
0.04 *
−0.2−0.11
−0.6−0.13
Upper Middle-Income 4 48 1255% Linear 0.89 1.89 * 1.62
−2.15
1.53
−2.24
Lower Middle-Income
23
50
113%
Logarithmic
0.71
1.39 *
0.85−1.93
0.66−2.12
Low-Income
160
178
12%
Linear
0.09
−2.29
−5.15−0.58
−6.16−1.59
Data obtained from: http://ghdx.healthdata.org/gbd-results-tool. Abbreviations: YLD–Year Lived
with Disability. Legend: * p < 0.01; ** p < 0.05. While possibly obtained through the same source, data
for the global benchmarks, including computed values, were extracted from: Jesus TS, Landry MD,
Brooks D, Hoenig H. Physical rehabilitation needs per condition type: Results from the Global Burden
of Disease study 2017. Arch Phys Med Rehabill. doi: 10.1016/j.apmr.2019.12.020; Notes: The “b
coefficient” refers to the annual change within a linear regression model. HIV/AIDs include YLDs
from resultant tuberculosis.
Figure 1 shows that Musculoskeletal & Pain conditions contributed the most to physical
rehabilitation needs in 2017 across the five countries, ranging from 36% of South African’s physical
rehabilitation needs to 60% of Brazilian’s - greater than any global benchmark. In India and Brazil,
cardiothoracic conditions were the second most represented (22% and 16%, respectively). In China,
both neurological and cardiothoracic conditions hold that second rank (18% each). In Russia,
neurological conditions were the second most represented (19%). Finally, in South Africa, HIV-
related conditions were the second most representative (25%): in no other BRICS country did HIV-
related conditions accounted for more than 1% of physical rehabilitation needs, and the maximum
global benchmark was 6% for low-income countries.
Finally, Table 4 shows how the distribution of physical rehabilitation needs evolved per
condition groups from 1990 to 2017. In South Africa, physical rehabilitation needs coming from HIV-
related conditions increased massively, yet logarithmically: from 1% to 25% of South African’s
physical rehabilitation needs (p < 0.01). In China, the percentage of physical rehabilitation needs
coming from both neurological conditions and neoplasms increased significantly (from 12.3% to
18.4%, and from 1.42% to 2.86%, respectively: p < 0.01), both with an exponential type of growth. In
India, the percentage of physical rehabilitation needs coming from pediatric conditions increased
significantly (from 15.7% to 17.8%: p < 0.01). In Russia, the percentage of physical rehabilitation needs
that came from neoplasms has grown by 47% (p < 0.01). Finally, only in Brazil did the percentage of
physical rehabilitation needs coming from musculoskeletal & pain conditions increased significantly
(from 57.5% to 59.9%; p < 0.01), becoming greater than that in high-income countries (57.7% in 2017).
Int. J. Environ. Res. Public Health 2020, 17, 4139 9 of 18
Figure 1. How YLD Rates (per 100,000 people) likely benefiting from physical rehabilitation are distributed per major groups of conditions in 2017 across the five nations
analyzed, and how these values compare to global benchmarks.
Brazil China India Russia South Africa World High-Income
Upper
Middle-
Income
Lower
Middle-
Income
Low-Income
HIV/AIDs 1% 0% 0% 1% 25% 1% 0% 1% 1% 6%
Neoplasms 2% 3% 1% 3% 1% 2% 4% 2% 1% 1%
Pediatric 12% 13% 18% 10% 12% 13% 7% 13% 16% 18%
Neurological 9% 18% 8% 19% 7% 13% 14% 16% 10% 10%
Cardiothoracic 16% 18% 22% 13% 19% 18% 17% 17% 20% 18%
Musculoskeletal & Pain 60% 48% 51% 54% 36% 53% 58% 51% 52% 48%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Int. J. Environ. Res. Public Health 2020, 17, 4139 10 of 18
Table 4.
How YLD Rates (Years Lived with Disability per 100,000 people) likely benefiting from
physical rehabilitation are distributed per major groups of conditions across the five countries
analyzed, and how those percent values have evolved over time [1990–2017].
#
1990
#
2017
% Change
[1990–2017]
Regression
Model Type r
2
b Coefficient 95% CI 99% CI
Musculoskeletal & Pain
Brazil 57.5% 59.9% 4.2% Linear 0.97 0.09 * 0.09–0.10 0.09–0.10
China 49.4% 47.6% −3.7% Linear 0.09 −0.03 −0.08–(−0.01) −0.08–0.01
India 53.5% 51.3% −4.0% Linear 0.91 −0.09 −0.10–(−0.08) −0.10–(−0.07)
Russia 57.0% 53.7% −5. 7% Linear 0.67 −0.10 −0.13–(−0.07) −0.14–(−0.06)
South Africa 47.2% 35.6% −24.7% L ogarithmic 0.87 −0.54 * −0.66–(−0.41) −0.70–(−0.37)
World 54.1% 52.6% −2.8% Linear 0.87 −0.06 * −0.06−( −0.05) −0.07−(−0.03)
High–Income 58.4% 57.7% −1. 2% Linear 0.72 −0.04 * −0.05−(−0.03) −0.05−(−0.03)
Upper Middle–Income 52.2% 50.7% −2.8% Linear 0.29 −0.04 * −0.06
−(
−0.01) −0.07
−(
−0.01)
Lower Middle–Income 53.3% 52.1% −2.3% Linear 0.97 −0.06 * −0.06
−(
−0.05) −0.06
−(
−0.05)
Low–Income 49.9% 41.7% −4.0% Logarithmic 0.62 −0.0 6 * −0.09
−(
−0.02) −0.11
−(
−0.01)
Neurological
Brazil 8.0% 9.3% 16.8% Linear 0.86 0.05 * 0.04–0.06 0. 04–0.06
China 12.3% 18.4% 49.2% Exponential 0. 96 0.19 * 0.17–0.21 0.16–0.22
India 7.5% 8. 1% 8.0% Linear 0.95 0.02 * 0.02–0.03 0. 02–0.03
Russia 16.1% 18.9% 17. 6% Linear 0.74 0.10 * 0.08–0.12 0. 07–0.13
South Africa 9.7% 7.3% −24.7% Logarithmic 0.81 −0.12 * −0.15–(−0.09) −0.16–(−0.08)
World 11.5% 12.9% 11.7% Linear 0.72 0.04 * 0.03
−0.05
0.03
−0.05
High–Income 13.1% 14.0% 7.1% Linear 0.58 0.03 * 0.02
−0.04
0.02
−0.04
Upper Middle–Income
12.3%
15.8%
28.4%
Exponential
0.90
0.11 *
0.10−0.13
0.10−0.13
Lower Middle–Income
9.3%
9.6%
2.9%
Linear
0.23
0.01 *
0.002–0.02
0.002–0.02
Low–Income
10.8%
10.1%
−6.3%
Logarithmic
0.86
0.02 *
−0.03−(−0.02)
−0.03−(−0.02)
Cardiothoracic
Brazil 18.9% 16.1% −14.7% Linear 0. 96 −0.10 * −0.11–(−0.09) −0.11–(−0.09)
China 22.8% 18.1% −20. 8% L ogarithmic 0.71 −0.16 * −0.21–(−0.11) −0.23–(−0.09)
India 22.6% 21.5% −5.0% Logarithmic 0.46 −0.04 ** −0.07–(−0.01) −0.07–0.002
Russia 14.0% 13.4% −4. 2% Linear 0.79 −0.04 * −0.04–( −0.03) −0.05–(−0.03)
South Africa 25.0% 18.9% −24.7% Linear 0. 89 −0.30 * −0.34–(−0.26) −0.36–(−0.24)
World 19.2% 18.0% −6.2% Logarithmic 0. 57 −0.03 * −0.05−(−0.01) −0.06−(−0.01)
High–Income 16.6% 16.6% −0. 1% Linear 0.56 0.03 * 0.02−0.03 0.01−0.04
Upper Middle–Income 20.0% 17.4% −13.2% L ogarithmic 0.74 −0.09 * −0.12
−(
−0.06) −0.13
−(
−0.05)
Lower Middle–Income 20.5% 19.6% −4.4% L ogarithmic 0.48 −0.02 ** −0.04
−(
−0.005) −0. 05
−0.002
Low–Income 18.7% 17.6% −5.7% Logarithmic 0.43 −0.02 −0.05
−0.001
−0.06
−0.01
Pediatric
Brazil 14.2% 11.9% −16.3% Linear 1 −0.09 * −0.09–(−0.09) −0.09–(−0.09)
China 14.0% 12.9% −7.8% Linear 0.39 −0.05 * −0.08–(−0.03) −0.09–(−0.02)
India 15.7% 17.9% 13.6% Linear 0.84 0.08 * 0.07–0.10 0.06–0.10
Russia 11.1% 10.3% −7. 0% Logarithmic 0. 92 −0.02 * −0.09–(−0.09) −0.09–(−0.09)
South Africa 16.4% 12.3% −25.5% L ogarithmic 0.80 −0.23 * −0.29–(−0.16) −0.31–(−0.14)
World 13.0% 13.1% 0.6% Logarithmic 0. 15 0.003 −0.004
−0.011
−0.007
−0.013
High–Income 8.6% 7.3% −14.8% Linear 0.99 −0.05 * −0.048
−(
−0.045) −0.048
−(
−0.044)
Upper Middle–Income
14.0%
12.7%
−9.4%
Linear
0.73
−0.06 *
−0.073−(−0.044)
−0.078−(−0.039)
Lower Middle–Income
15.7%
16.4%
4.5%
Logarithmic
0.74
0.03 *
0.023−0.042
0.020−0.045
Low–Income
14.2%
18.6%
30.6%
Linear
0.92
0.19 *
0.171−0.216
0.163−0.224
Neoplasms
Brazil 1.14% 1.75% 54% Linear 0.98 0.021 * 0. 020–0.022 0.019–0.022
China 1.41% 2.86% 103% Exponential 0.91 0.048 * 0.040–0.056 0.037–0.059
India 0.66% 0.86% 30% Linear 0.71 0. 006 * 0.004–0.007 0.004–0.008
Russia 1.84% 2.72% 47% Linear 0.87 0.032 * 0.027–0.032 0.025–0.039
South Africa 1.10% 1.00% −10% Linear 0.56 −0.011 * −0.015–(−0.007) −0.016–(−0.006)
World 1.6% 2.2% 38.3% Linear 0.94 0.02 * 0. 02−0.02 0.02−0.02
High–Income 3.0% 4.1% 35.5% Logarithmic 0.97 0.04 * 0.03
−0.04
0.03
−0.04
Upper Middle–Income 1.4% 2.5% 77.0% Exponential 0.92 0.04 * 0.03
−0.04
0.03
−0.04
Lower Middle–Income 0. 8% 1.0% 19.0% Linear 0.53 0.01 * 0.004
−0.01
0.003
−0.01
Low–Income 0.9% 0.8% −6.1% Linear 0.59 −0.01 * −0.01
−(
−0.004) −0.01
−(
−0.003)
HIV–related
Brazil 0.38% 1.06% 182% Linear 0.89 0.022 * 0.019–0.025 0.018–0.027
China 0.04% 0.16% 341% Linear 0.99 0.005 * 0.005–0.005 0.004–0.005
India 0.02% 0.38% 1558% Linear 0. 55 0.016 * 0.006–0.026 0.003–0.030
Russia 0.08% 0.97% 1137% Exponential 1 0.028 * 0.024–0.033 0.023–0.034
South Africa 0.51% 25.0% 4831% Logarithmic 0.85 1.193 * 0. 939–1.448 0.849–1.538
World
0.5%
1.2%
162%
Linear
0.89
0.02 *
0.012−0.037
0.007−0.042
High–Income
0.2%
0.3%
19%
Linear
0.99
0.001
−0.002−0.001
−0.003−0.001
Upper Middle–Income
0.1%
1.0%
943%
Logarithmic
0.55
0.04 *
0.032−0.048
0.029−0.050
Lower Middle–Income 0. 7% 1.3% 81% Exponential 1.0 0.03 * 0.016−0.050 0.010−0.056
Low–Income 5.4% 5.7% 7% Logarithmic 0.85 −0.08 −0.166−0.009 −0.197−0.040
Data obtained from: http://ghdx.healthdata.org/gbd-results-tool. Abbreviations: YLD–Year Lived
with Disability. Legend: * p < 0.01; ** p < 0.05. While possibly obtained through the same source, data
for the global benchmarks, including computed values, were extracted from: Jesus TS, Landry MD,
Brooks D, Hoenig H. Physical rehabilitation needs per condition type: Results from the Global Burden
of Disease study 2017. Arch Phys Med Rehabill. doi: 10.1016/j.apmr.2019.12.020;
Notes:
Notes: The “b
coefficient” refers to the annual change within a linear regression model and is set in percent values.
Different population structures apply to countries with varying income levels; so, cross-location
comparisons are not valid for the metric YLD Counts.
Int. J. Environ. Res. Public Health 2020, 17, 4139 11 of 18
4. Discussion
In each of the BRICS countries, total physical rehabilitation needs have increased significantly
from 1990 to 2017 in absolute values, per-capita, and in percentage of all YLDs. This means that, in
each of these countries, physical rehabilitation needs have increased beyond the population growth,
and that physical rehabilitation could be helpful for a greater portion of non-fatal health losses.
Apart from common trends across the BRICS nations (e.g., growth of total physical rehabilitation
needs per capita with no decrease or even an increase in age-standardized needs), we found
important country-specific differences across the BRICS countries in the amount, typology, and
evolution of their physical rehabilitation needs.
For the overall age-standardized YLD Rates germane to physical rehabilitation, we did not
observe significant changes for India and Russia. This means that for these countries the aging of the
population (and the subsequent higher disability rates [23,26,28]) has been a key driver of their
increased physical rehabilitation needs, including in YLDs Rates. Yet, in Brazil, China, and South
Africa we did observe a significant growth in the age-standardized YLD Rates, which means that
variables other than those related to the population ageing might have contributed to the overall
growth of their physical rehabilitation needs. Only in high-income nations did we observe a
significant decrease in the rehabilitation-related age-standardized YLD Rates. Possibly a more
developed rehabilitation infrastructure or health care systems in high-income nations than in the
BRICS nations contributed their reduction in age-standardized YLD-rates for rehabilitation-related
conditions.
China stood out with the greatest amount and an exponential type of growth in the physical
rehabilitation needs from neurological and neoplasm conditions. The population ageing, derived
from the previous one-child policy, increased life expectancy,[30] increasing survival rates for those
with neoplasm or other health conditions, along with the huge baby boom cohorts born in 1950s and
1960s entering old ages [39,40,51], can partly account for these findings. As survival rates from health
conditions likely will increase further [52] and life expectancy in China is projected to surpass 80
years by 2040 [53], the rise of physical rehabilitation needs in China being observed is likely to
continue into the future. Moreover, the meeting the rehabilitation needs of older adults in the rural,
underserved regions of China can be particularly challenging as rehabilitation services typically are
distant and/or scant and family support is increasingly absent (e.g., much of the working-age
population has moved to urban, industrialized areas) [24]. Caregiver-delivered, digital-supported,
and nurse-led interventions have been trialed to close the rehabilitation service gap in rural China,
but more work is needed to achieve optimal results [54]. These will be important needs and gaps for
China to address through future research and policy development [24,51,54,55].
In India, another highly populated and emerging economy in Asia, the typology of physical
rehabilitation needs was different than for the other BRICS nations and, in some respects, closer to
that of lower income countries. One such example is the absolute and relative growth of physical
rehabilitation needs arising from pediatric conditions. This pattern may reflect different economic
status from the other BRICS: i.e., although an emergent economy, India is still a lower-middle income
nation per the World Bank classification while the BRICS counterparts are UMICs. Alternatively, the
findings may reflect a different population ageing structure and higher fertility rates [31]. In addition
to the particular rise of pediatric physical rehabilitation needs in India, YLD Rates increased for each
other major groups of conditions responsive to physical rehabilitation. Indeed, the epidemiological
transition for higher rates of non-communicable, chronic and disabling conditions has been
impacting India, although differentially across regions [13,56–58]. All these needs contrast with the
existing systems for rehabilitation and social care in the country. There is an acute shortage of 6.4
million allied health professionals in India [59]. There are no professional bodies that regulate the
practice or practice standards for any health professionals [60]. The national program for tracking of
non-communicable health conditions focuses on early detection and treatment[61]. Furthermore, the
health system’s infrastructure is not architecturally and socially accessible to people with disabilities
[62]. Overall, the health system has not been capable of meeting the growing need for physical
rehabilitation in India [58,63]. Similar to China, technologically-enabled service delivery solutions
Int. J. Environ. Res. Public Health 2020, 17, 4139 12 of 18
have been trialed to meet the growing physical rehabilitation needs in India [64]. Interventions like
these needs to be tested for scalability in combination with existing health and rehabilitation services
in India.
South Africa, a leading UMIC within the African continent, more than doubled their absolute
physical rehabilitation needs, mimicking the trend in low-income countries. This increase in need for
rehabilitation is partly driven by the HIV/AIDS endemic. HIV-related conditions accounted for one-
quarter of South African’s physical rehabilitation needs in 2017, compared to 1% in any of the four
other BRICS countries. South Africa’s successful roll-out of highly active antiretroviral therapy
(HAART) has transformed HIV into a chronic disease, and people with HIV can now achieve normal
life-expectancy.[65] An increasing number of South Africans with HIV live longer, but with either the
potential for or already established impairments in body structure such as muscle weakness, and that
may cause limitations in activities of daily living and restrictions in participation. [65] Despite this,
South Africa’s HIV policies and guidelines do not speak to HIV-related disabilities, as premature
mortality remains a key national health indicator.[66] However, political will to address non-fatal
health loss is rising[67] as an increasing body of literature signals the need to address HIV-related
disability.[68–70] Increased investment in health resources to enhance the quality of life and
functioning in people with HIV will require concerted effort. Access to adequate rehabilitation has
the potential to optimize functioning, employability and could even enhance HAART adherence in
people living with HIV.[70]
Russia had the highest rate of physical rehabilitation needs in 2017 (6393 YLDs per 100,000
inhabitants), but the lowest percent change since 1990; a pattern closest to that of high-income
countries more than other BRICS nations. Similarly, we found that Russia had the highest rates,
although not the highest growth, in physical rehabilitation needs from neurological conditions,
including about the triple of those from Brazil, India and South Africa. The persistently high burden
of rehabilitation-sensitive conditions in Russia may be a result of several factors. First, the incidence
of major chronic non-communicable diseases remains high; national statistics have shown, for
example, that the incidence of coronary heart disease has increased substantially from 495 to 701 per
100,000 inhabitants from 2010 to 2016.[71] That accounts for the high prevalence of risk factors,
primarily of hypertension and overweight/obesity, which is on the rise, especially in men.[72] In
contrast, the rates of smoking and harmful alcohol use are currently decreasing,[73] although
historically high.[14] Secondly, the overall quality of healthcare has increased in Russia, resulting in
increased survival; for example, the age-standardized mortality rates from myocardial infarction
decreased from 47.1 to 42.9 per 100,000 inhabitants [2012–2016].[74] Thirdly, higher survival rates
may also arise from screening and early diagnosis programs such as the national universal health
screening program for cancers and government-led program for screening cardiovascular risk factors
and diseases.[75]
Fourthly, local traditions of ICD 10 codes interpretation may lead to inappropriate
coding of some dementia cases as cardiovascular or neurologic conditions instead of mental
disorders.[76] Finally, the population has aged in Russia, which is not surprising given the growing
per capita income in recent years, and the population aging seen in high-income countries.[77]
Relatedly, the westernization of lifestyle in Russia, with a greater availability of highly processed
foods and environmental problems due to increased car traffic, likely is playing in rendering physical
rehabilitation needs in Russia similar to those high-income nations. To help meet their nation’s high
physical rehabilitation needs, Russia has been actively developing their medical rehabilitation
paradigm[78] and infrastructure.[79,80]
Finally, for Brazil, we found that conditions responsive to physical rehabilitation currently
account for about two-thirds of the nation’s YLD (no other BRICS nation came close to 50%). This
means that physical rehabilitation can address a larger portion of the country’s non-fatal health losses
when compared to the BRICS counterparts. Brazil also stood out with the highest portion of physical
rehabilitation needs coming from musculoskeletal & pain conditions (60%), and with substantial
growth in this percent value over time. Key explanations for that finding may include the prevalence
of interpersonal violence in Brazil,[16] and the high and rising prevalence of road traffic injuries,
especially associated with high consumption of alcohol involving young pedestrian and
Int. J. Environ. Res. Public Health 2020, 17, 4139 13 of 18
motorcyclists within urbanized environments.[81,82] This study emphasizes not only the need of
expanding Brazilian public policies to ameliorate external causes of injury as well as chronic disease
prevention, but also for implementing a rehabilitation infrastructure capable of addressing the
growing burden of physical impairments in Brazil.
Study Limitations
This study has the following limitations:
First, YLDs from selected health conditions are but proxy indicators of physical rehabilitation
needs, i.e., not a direct functional or impairments-based measure. Nonetheless, YLDs is the
aggregative measure of non-fatal health loss from the prominent GBD study and includes
variables such as the prevalence of conditions, the time lived with sequalae from the
respective conditions, and weighted for the appraised severity of those sequelae.
Second, the set of conditions whose YLDs likely benefit from rehabilitation were replicated
from a previous study which systematically reviewed evidence linking those conditions to
rehabilitation needs;[25] nonetheless, these conditions cannot be considered a fixed standard
as the relevant conditions may change over time with the advancement of rehabilitation
therapies and their scientific support. For example, the recent COVID-19 pandemic has been
boosting new types of rehabilitation need (e.g., for respiratory therapy; for the rehabilitation
for the post-intensive care syndrome) [83–85], which were not reflected in the data up to 2017.
Third, YLD values (extracted from the GBD 2017) are only estimates based on the best-
available evidence, not actual YLDs. The GBD 2017 is the most comprehensive
epidemiological study to date, and the amount of data used to create those estimates is
unprecedented. [25,48] Even so, the quality and the quantity of the underlying data for
computing the GBD estimates vary across locations and in time within the same location,
which in turn affects the precision of the YLD estimates. However, lower precision does not
equate to bias toward over or under-estimation of YLDs for the earlier times or for the
locations in which less or lower-quality data were available. At each cycle, the GBD study
(e.g., the GBD 2017) apply the new data and more advanced estimation methods to re-
calculate YLDs across locations and the entire time series (since 1990), not only the values for
2017.
Fourth, most data obtained for the GBD study (e.g., in India) are from self-reports and hence
many undiagnosed conditions might not be included within this data to represent the true
picture. Hence results of this study could be a gross under-estimation of the problem, at least
in the absolute values.
Fifth, we did not extract or analyze sub-national data (e.g., Brazilian states), although data
are available for that from the GBD 2017 and some important differences exist in both
economic and epidemiological profiles across regions or states of the analyzed
countries.[13,16,39]
Sixth, we do not supplement our analyses of physical rehabilitation ‘needs’ with indicators
of physical rehabilitation ‘supply’ across nations, the other key element in the resources
planning equation. In part, this follows the lack of available data. For example, the World
Confederation of Physical Therapy reports data on the amount of practicing physical
therapists per nations, as locally collected from authoritative sources or estimated by national
associations (i.e., their member organizations) for a total of 89 countries, but unfortunately
not from 3 of the analyzed countries (China, Russia, or India).[86]
5. Conclusions
Physical rehabilitation needs have increased significantly from 1990 to 2017 in each of the BRICS
nations, both in absolute and relative values. However, apart from the common trend in overall
growth, each of the BRICS nations had own patterns for the amount, typology, and evolution of their
physical rehabilitation needs. The BRICS nations and coalition need to address the common challenge
Int. J. Environ. Res. Public Health 2020, 17, 4139 14 of 18
of planning for and deploying the required resources for meeting the growing physical rehabilitation
needs of their population, at the same time they look at country-specific challenges such as the
physical rehabilitation needs coming from HIV/AIDs-related conditions in South Africa, pediatric
conditions in India, musculoskeletal conditions in Brazil, and neurological conditions in Russia and
China. This study shows that physical rehabilitation needs can be determined and compared across
nations, and hence can be used to inform rehabilitation resources and service planning. Most
importantly, this study makes clear that physical rehabilitation needs and growth patterns may not
be assumed equal across nations, irrespective of similarities in income or emerging development.
Supplementary Materials: The following are available online at www.mdpi.com/1660-4601/17/11/4139/s1.
Supplementary Material 1: Total Physical Rehabilitation Needs. Supplementary Material 2: Needs by condition
type.
Author Contributions: Conceptualization, T.S.J., M.D.L., H.H., K.G., Q.A.L.; methodology, T.S.J., QA.L.; formal
analysis, T.S.J.; investigation, T.S.J., Y.Z., S.K., R.R.; N.P., O.S., Q.A.L.; data curation, T.S.J.; writing—original
draft preparation, T.S.J.; writing—review and editing, T.S.J., M.D.L., H.H., Y.Z., S.K., R.R.; N.P., O.S., K.G.,
Q.A.L.; visualization, T.S.J.; supervision, Q.A.L.; project administration, T.S.J., Q.A.L. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding. Raquel R. Britto is funded by CNPq: [Conselho Nacional
de Desenvolvimento Científico e Tecnológico], Brazil. Yi Zeng’s research is funded by the National Key R&D
Program of China (2018YFC2000400), National Natural Sciences Foundation of China (71490732), the U.S.
National Institute of Aging of National Institute of Health (P01AG031719).
Acknowledgments: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest
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