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R E S E A R C H A R T I C L E Open Access
Mapping subnational HIV mortality in six
Latin American countries with incomplete
vital registration systems
Local Burden of Disease HIV Collaborators
Abstract
Background: Human immunodeficiency virus (HIV) remains a public health priority in Latin America. While the
burden of HIV is historically concentrated in urban areas and high-risk groups, subnational estimates that cover
multiple countries and years are missing. This paucity is partially due to incomplete vital registration (VR) systems
and statistical challenges related to estimating mortality rates in areas with low numbers of HIV deaths. In this
analysis, we address this gap and provide novel estimates of the HIV mortality rate and the number of HIV deaths
by age group, sex, and municipality in Brazil, Colombia, Costa Rica, Ecuador, Guatemala, and Mexico.
Methods: We performed an ecological study using VR data ranging from 2000 to 2017, dependent on individual
country data availability. We modeled HIV mortality using a Bayesian spatially explicit mixed-effects regression
model that incorporates prior information on VR completeness. We calibrated our results to the Global Burden of
Disease Study 2017.
Results: All countries displayed over a 40-fold difference in HIV mortality between municipalities with the highest
and lowest age-standardized HIV mortality rate in the last year of study for men, and over a 20-fold difference for
women. Despite decreases in national HIV mortality in all countries—apart from Ecuador—across the period of
study, we found broad variation in relative changes in HIV mortality at the municipality level and increasing relative
inequality over time in all countries. In all six countries included in this analysis, 50% or more HIV deaths were
concentrated in fewer than 10% of municipalities in the latest year of study. In addition, national age patterns
reflected shifts in mortality to older age groups—the median age group among decedents ranged from 30 to 45
years of age at the municipality level in Brazil, Colombia, and Mexico in 2017.
Conclusions: Our subnational estimates of HIV mortality revealed significant spatial variation and diverging local
trends in HIV mortality over time and by age. This analysis provides a framework for incorporating data and
uncertainty from incomplete VR systems and can help guide more geographically precise public health intervention
to support HIV-related care and reduce HIV-related deaths.
Keywords: HIV/AIDS, Latin America, HIV mortality, Vital registration, Small area estimation, Mapping, Spatial statistics
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Correspondence: ladwyer@uw.edu
Institute for Health Metrics and Evaluation, University of Washington, Seattle,
WA, USA
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4
https://doi.org/10.1186/s12916-020-01876-4
Background
Human immunodeficiency virus (HIV) continues to be a
large contributor to morbidity and mortality across the
globe [1,2]. While the burden of the HIV epidemic is
most concentrated in sub-Saharan Africa, HIV remains a
public health priority in Latin America. In 2017, the
Global Burden of Disease Study (GBD) estimated over
30,000 deaths from HIV/AIDS-related causes in the re-
gion [1,2]. To combat the epidemic, the Joint United
Nations Program on HIV/AIDS (UNAIDS) Fast-Track
strategy emphasizes the need to reduce HIV-related
deaths to less than 500,000 deaths worldwide by 2020—a
75% reduction from deaths in 2010 [3]. UNAIDS further
targets a 90% reduction in HIV-related deaths by 2030
[3]. Despite increased access to antiretroviral therapy in
many countries in Latin America [3,4], few countries
show substantial reduction in HIV mortality since 2000
[1,2]. Continued efforts are needed to track progress
towards meeting the UNAIDS Fast-Track goals with
respect to HIV mortality.
Country-level estimates of HIV mortality in Latin
America are available from a variety of sources [1,5]
and estimates of mortality exist at the state level in select
countries such as Brazil and Mexico [1]. Beyond this,
however, detailed subnational estimates at the second
administrative level in many countries in Latin America
are absent. This lack of subnational estimates is alarming
given the localized burden of HIV in urban areas and
among high-risk subgroups such as people who inject
drugs, sex workers, and men who have sex with men
(MSM) [6–10]. Additionally, inequalities in HIV burden
across local geographies likely occur given that many
underlying drivers of HIV infection and death—such as
poverty, incarceration, undernutrition, distribution of
health practitioners, and access to health services—vary
across geographic areas and through time [11–14]. Pre-
vious studies have confirmed substantial within-country
variation in mortality rates but are limited to select
countries, areas, and years [15–18].
The paucity of evidence on subnational HIV mortality
is likely due to several methodological challenges associ-
ated with a granular spatial modeling of HIV mortality
in Latin American countries. Deaths attributable to HIV
are inherently small in number in areas with small popu-
lations, adding stochastic noise to direct estimates [19].
Past approaches have used Bayesian models that apply
small area methods that borrow strength across age,
time, and space to produce stable estimates of mortality
rates in areas with a small number of deaths [15,20–22].
An additional complication in HIV mortality estimation
is that—due to stigma or misdiagnosis—HIV deaths may
be misclassified and coded to other underlying causes of
death such as tuberculosis, endocrine disorders, menin-
gitis, or encephalitis [23–25]. Moreover, vital registration
(VR) systems in many countries in Latin America are in-
complete and not all deaths are recorded in official sta-
tistics [23,25–27]. While a variety of methods have been
proposed to estimate the completeness of death registra-
tion at the country level [28,29], standard methods rely
on stable population and sex pattern assumptions that
often do not hold in small subnational areas [29,30]. To
resolve the difficulties associated with small numbers of
deaths and VR completeness, Schmertmann and
Gonzaga proposed a Bayesian model framework for
small area life expectancy estimation in countries with
incomplete VR systems [31]. This method incorporates a
novel functional form for mortality that is informed by
prior distributions for VR completeness coverage based
on empirical evidence.
In this analysis, we address these challenges by utiliz-
ing comprehensive cause of death assignment and apply-
ing a small area estimation framework that incorporates
prior information on VR completeness to produce
estimates of HIV mortality and deaths due to HIV by
age and sex at the municipality level in six countries in
Latin America: Brazil, Mexico, Guatemala, Costa Rica,
Colombia, and Ecuador. Our modeling approach lever-
ages information from neighboring areas across space
and time to produce estimates across all years of avail-
able data. These six countries were selected based on
availability of VR data, but not all contained the same
range of available years: our analysis extends from 2000
to 2017 in Brazil, Colombia, and Mexico; from 2009 to
2017 in Guatemala; from 2004 to 2014 in Ecuador; and
from 2014 to 2016 in Costa Rica.
Methods
Overview
This analysis complied with the Guidelines for Accur-
ate and Transparent Health Estimates Reporting
(GATHER) [32]. Our ecological study estimated the
HIV mortality rate and the number of HIV deaths by
age group and sex at the municipality level for all years
of available VR (Additional file 1:FigureS1).Allana-
lyses were carried out at the second administrative unit
level, which we refer to as municipalities for conveni-
ence unless referencing country-specific results, where
we use the appropriate national nomenclature for the
administrative subdivision (municipality for Brazil,
Colombia, Guatemala, Mexico; canton for Costa Rica
and Ecuador). Municipalities were combined as needed
to create stable units of analysis over the study period,
reducing the total number of areas analyzed in select
countries (Table 1; Additional file 1:TableS1).Inthe
results, all presented rates are age-standardized for
comparison between countries, unless otherwise stated.
We used standard age weights produced by GBD 2017
for age standardization [1].
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 2 of 25
Data
Vital registration data
Vital registration (VR) mortality data consisted of anon-
ymized individual-level records from all deaths reported
in each country’s VR system occurring between the years
of study (Additional file 1: Table S2). These records were
tabulated by municipality of residence, age group (0–4,
5–9, 10–14, …,75–79, and ≥80 years), sex, and the
underlying cause of death according to the tenth revision
of the International Classification of Diseases (ICD-10)
[33]. We standardized VR data using methods developed
for the GBD [1]. This process requires all deaths to be
attributed to a single underlying cause of death following
ICD guidelines and fits within a hierarchy of mutually
exclusive and collectively exhaustive causes. Deaths that
were coded with ICD-10 codes that could not be an
underlying cause of death, as well as deaths that were
coded to non-specific causes of death, were redistributed
to most detailed causes of death by age, sex, municipal-
ity, and year according to a framework developed by
Naghavi et al. [34] and updated for GBD 2017 [1]. This
includes an HIV correction step that rectifies deaths
assigned to comorbidities such as tuberculosis, endo-
crine disorders, meningitis, or encephalitis that diverge
from locations without HIV epidemics [1].
Location hierarchy
We created country-specific location hierarchies that list
all subnational administrative units for each year in the
specified time period and match each corresponding
death from the VR system to the municipality level
(Additional file 1: Figure S2). For each country, munici-
palities were geo-matched to shapefiles provided by the
Global Administrative Unit Layers (GAUL) [35] (Brazil,
Costa Rica, Guatemala) or the Humanitarian Data Ex-
change [36] (Colombia, Ecuador, Mexico). In all selected
countries, municipality boundaries changed over time,
reflecting new boundary designations across the years of
study (Table 1). Municipalities that underwent a bound-
ary change during the period of the analysis were
merged to create a stable unit across the period of ob-
servation, and the municipality-level shapefiles were
manually edited to match the split hierarchy using
ArcMap version 10.6 [37]. Merged units that included
multiple municipalities were modeled as one area, and
in the results share the same estimates of HIV mortality
rate. Details of these shifts are provided in Add-
itional file 1: Table S1.
Covariates and population
We included several available covariates to help in-
form estimates of HIV mortality: population density
[38], night-time light brightness [39], urbanicity [40],
and travel time to the nearest settlement of more
than 50,000 inhabitants [41](Additionalfile1:Table
S3). These covariates were selected because they are
factors or proxies for factors previously identified in
the literature as associated (not necessarily causally)
with HIV mortality. Specifically, these four variables
were included as measures or proxies for connected-
ness and urbanicity as HIV historically spread among
high-risk groups in urban areas [6,42] and is typically
found to be higher in more urban compared to more
rural locations. Each covariate was obtained in a
raster format at a 5 × 5-km resolution and required
aggregation to the modeled municipality level for in-
clusion in our modeling framework. This aggregation
was done fractionally: raster cells that crossed munici-
pality borders were fractionally allocated to munici-
palities in proportion to the covered area.
We created age- and sex-specific populations for each
municipality unit by aggregating the WorldPop [38]
raster to the modified shapefile, utilizing the same frac-
tional aggregation process. The age- and sex-specific
populations for municipalities were then scaled to the
national population estimates derived from the GBD [1].
To do so, for each country, sex, age group, and year, we
defined a population raking factor as the ratio of the
GBD population estimate for that same sex, age group,
and year to the sum of the WorldPop population for all
municipalities within the country, and then multiplied
the WorldPop population estimates for each municipal-
ity within the country by this raking factor. This resulted
in age- and sex-specific population estimates for each
Table 1 Data availability and administrative characteristics by country
Country Years of available VR data Name of second
administrative
subdivision
Total number of second
administrative subdivisions
Number of modeled units
in this analysis
Brazil 2000–2017 Municipality 5570 5477
Colombia 2000–2017 Municipality 1122 1115
Costa Rica 2014–2016 Canton 81 81
Ecuador 2004–2014 Canton 224 222
Guatemala 2009–2017 Municipality 340 333
Mexico 2000–2017 Municipality 2458 2441
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 3 of 25
municipality which aligned with the GBD national popu-
lation sizes and structures.
Statistical model
Vital registration completeness
Expanding on previous literature [31], we used a Bayesian
framework that bypasses a lack of identifiability between
the mortality rate and completeness estimate by incorpor-
ating an informed prior on VR completeness. In this ana-
lysis, we incorporated information from GBD [1]on
subnational (for Brazil and Mexico) and national VR com-
pleteness (for remaining countries) as well as geographic
patterns in under-5 VR completeness from past analyses
[43] to generate priors on municipality-level VR coverage
by two age groups (< 15 years and 15+ years) and year
(Additional file 1: Figure S3). We selected these two age
groups based on the available national VR completeness
estimated in GBD and established literature and expert
opinion [31,44]. The supplemental methods outlined in
Additional file 1summarize our process for generating in-
formed priors on VR completeness in greater detail.
We did not model VR completeness for adults if na-
tional GBD completeness estimates exceeded 95% in all
years of available VR data (Costa Rica and Colombia).
Similarly, we did not model under-15 VR completeness if
GBD estimates of child completeness were greater than
90% in all years of available VR data (Costa Rica,
Guatemala, Mexico). We therefore modeled adult com-
pleteness in Ecuador, Guatemala, Mexico, and Brazil, and
child completeness in Ecuador, Colombia, and Brazil.
Modeling framework
We estimated HIV mortality separately by sex using a
small area estimation framework built upon a model de-
veloped in prior modeling studies [15,45]. This Bayesian
hierarchical generalized linear model used a Poisson data
likelihood to model the number of HIV deaths in a mu-
nicipality, year, and age group (supplemental methods in
Additional file 1). The Poisson distribution was charac-
terized by a parameter that multiplied the mortality rate,
and population by municipality and VR completeness by
municipality (Colombia, Ecuador, and Guatemala) or
state (Brazil and Mexico). Completeness priors added
probabilistic information about VR coverage that
allowed estimation of mortality rates given counts of
registered deaths. We modeled the log of the mortality
rate as a linear combination of terms including random
effects with conditional autoregressive distributions to
smooth over age, year, and municipality. We also in-
cluded covariates as fixed effects (see supplemental
methods in Additional file 1).
Models were estimated separately for each country and
sex and fit using the TMB package [46]. One thousand
draws were sampled from the approximated posterior
distributions of each modeled parameter and used to con-
struct 1000 draws ofHIVmortality(m
j,t,a
) for each muni-
cipality j, year t,andagegroupa. We calculated point
estimates from the mean of these draws, and the lower and
upper bounds of the 95% uncertainty interval from the
2.5th and 97.5th percentiles, respectively, for each age, sex,
year, and municipality. Municipality-level estimates for each
age, year, and sex were aggregated to the state and national
level using a population-weighted average. In Brazil and
Mexico, estimates were calibrated to GBD at the state level
and estimates were calibrated to national HIV mortality es-
timates for the remaining four countries. To accomplish
this, we calculated the ratio of the national- or state-level
estimate from GBD to the mean national estimate derived
from population-weighting m
j,t,a
, and multiplied all draws
of m
j,t,a
by this ratio. We generated the number of HIV
deaths for each age-sex-year-municipalitybymultiplying
the mean, lower, and upper bounds of our mortality esti-
mates by the corresponding WorldPop population estimate.
We quantified the relative inequalityasthemortalityrate
ratios for municipalities in the 90th percentile versus those
in the 10th percentile of mortality rate by year. We calcu-
lated the absolute inequality asthedifferenceinmortality
between municipalities within a country in the 90th per-
centile and those in the 10th percentile in terms of mortal-
ity rate by year. Throughout our analysis, we qualify
statements as statistically significant if the posterior prob-
ability of that statement exceeds 95%. We completed our
analysis using R version 3.6.3 [47].
Model assessment
To assess if including VR completeness in our statistical
framework improved model estimates, for the five coun-
tries where we applied completeness priors to either
children under-15 or adults we also fit a model where
the completeness term in the statistical model, πk;t;a,
was removed. We then compared the ratio of annual na-
tional HIV mortality in children under 15 and adults
from GBD to 1000 draws of national estimates of annual
HIV mortality from the standard and completeness
models. This ratio, known as the raking factor, is plotted
in Additional file 1: Figure S4–S9. A raking factor closer
to 1 indicates better alignment with GBD, and inclusion
of completeness priors generally resulted in closer align-
ment with national GBD mortality estimates.
Results
Geographic patterns in HIV mortality rate and notable
time trends
Brazil
In Brazil, the estimated national HIV mortality rate for
both sexes combined in 2017 was 6.5 (95% uncertainty
interval 6.4–6.7) deaths per 100,000, 8.7 [8.5–8.9] deaths
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 4 of 25
among men, and 4.5 [4.4–4.6] deaths among women
(Additional file 1: Table S4). Estimated HIV mortality
for men in 2017 varied over 53-fold among municipal-
ities: from 0.9 (0.2–2.9) deaths per 100,000 in the Jordão
municipality, Acre state to 47.8 (36.4–61.3) deaths per
100,000 in the Tramandaí municipality, Rio Grande do
Sul state (Fig. 1and Additional file 1: Figure S10). Esti-
mated female HIV mortality in 2017 ranged from 0.8
(0.3–1.7) deaths per 100,000 in the Maraã municipality,
Amazonas state to 28.6 (20.7–37.7) deaths per 100,000
in the Tramandaí municipality, Rio Grande do Sul state.
Between 2000 and 2017, estimated national HIV mortal-
ity decreased by 25.3% (from 11.7 [11.4–11.9] deaths per
100,000 in 2000) among men and 14.9% (from 5.3 [5.1–
5.4] deaths per 100,000 in 2000) among women. These
national decreases hide substantial variation at the
Fig. 1 HIV mortality among men and women in Brazil by municipality, 2017. HIV mortality per 100,000 by municipality in Brazil in 2017 among
men (a) and women (b). Relative change in HIV mortality between 2000 and 2017 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 5 of 25
municipality level. Estimated male HIV mortality de-
creased in 3389 (60.8%) municipalities, and 91 [1.6%]
municipalities had a statistically significant decrease in
HIV mortality. Estimated female HIV mortality de-
creased in 3000 (53.9%) municipalities and 37 [0.7%]
municipalities had a statistically significant decrease in
HIV mortality. Changes in estimated male HIV mortality
at the municipality level ranged from a 248.1% increase
in Bacabal municipality, Maranhão state (from 7.2 [4.9–
10.5] deaths per 100,000 in 2000 to 25.1 [18.9–33.2]
deaths per 100,000 in 2017) to a 70.9% decrease in
Ribeirao Preto municipality, São Paulo state (from 32.6
[28.6–36.9] deaths per 100,000 in 2000 to 9.5 [8.1–11.2]
deaths per 100,000 in 2017). Changes in estimated HIV
mortality among women at the municipality level varied
from a 233.9% increase in Novo Hamburgo municipality,
Rio Grande do Sul state (from 5.2 [3.9–6.9] deaths per
100,000 in 2000 to 17.4 [13.8–21.7] deaths per 100,000
in 2017) to a 68.8% decrease in Jundiaí municipality, São
Paulo state (from 5.7 [4.4–7.4] deaths per 100,000 in
2000 to 1.8 [1.2–2.5] deaths per 100,000 in 2017).
Colombia
In 2017, Colombia’s estimated national HIV mortality
rate for both sexes combined was 5.0 (4.8–5.3) deaths
per 100,000 (7.8 [7.5–8.1] deaths for men, and 2.5 [2.3–
2.7] deaths for women). Estimated male HIV mortality
in 2017 varied over 75-fold: from 0.5 (0.2–0.9) deaths
per 100,000 in the La Calera municipality, Cundina-
marca department to 37.6 (26.4–50.9) deaths in the
Chinchiná municipality, Caldas department (Fig. 2and
Additional file 1: Figure S11). Among women, estimated
mortality varied over 86-fold, fluctuating from 0.2 (0.1–
0.5) deaths per 100,000 in the La Calera municipality,
Cundinamarca department to 17.2 (10.6–27.4) deaths in
the La Virginia municipality, Risaralda department. Be-
tween 2000 and 2017, changes in national HIV mortality
diverged by sex: estimated HIV mortality decreased
among men by 19.8% (from 9.8 [9.3–10.2] deaths per
100,000 in 2000) but increased among women by 19.5%
(from 2.1 [1.9–2.3] deaths per 100,000 in 2000). While
national estimated HIV prevalence decreased among
men and not women, HIV mortality increased in a ma-
jority of municipalities from 2000 to 2017 for both men
and women. Estimated male HIV mortality increased in
594 (60.8%) municipalities, and five (1.6%) municipalities
had a statistically significant increase in HIV mortality.
Estimated female HIV mortality increased in 1081
(96.3%) municipalities and five (0.4%) municipalities had
a statistically significant increase in HIV mortality. There
was large variation in subnational changes in HIV mor-
tality: estimated male HIV mortality ranged from a
361.7% increase in San Andrés de Tumaco municipality,
Nariño department (from 2.8 [1.7–4.3] deaths per 100,
000 in 2000 to 12.8 [9.4–17.1] deaths per 100,000 in
2017) to a 59.7% decrease in Barbosa municipality, Anti-
oquia department (from 15.5 [9.4–24.5] deaths per 100,
000 in 2000 to 6.2 [3.2–10.5] deaths per 100,000 in
2017). Among women, estimated relative change in HIV
mortality at the municipality level varied from a 170%
increase in Puerto Colombia municipality, Atlántico de-
partment (from 3 [1.5–5.4] deaths per 100,000 in 2000
to 8.2 [3.9–16.1] deaths per 100,000 in 2017) to a 31.2%
decrease in Bogotá, D.C. municipality, Bogotá, D.C. de-
partment (from 1.6 [1.4–1.9] deaths per 100,000 in 2000
to 1.1 [0.9–1.3] deaths per 100,000 in 2017).
Costa Rica
Among the six countries considered, Costa Rica had the
lowest HIV mortality rates in the latest year of study,
with an estimated national HIV mortality rate for both
sexes combined in 2016 of 3.2 (2.5–3.9) deaths per 100,
000 (4.9 [4.2–5.8] deaths for men, and 1.5 [1.1–2.3]
deaths for women). At the canton level, we estimated
male HIV mortality varied over 104-fold in 2016, from
0.5 (0.01–2.9) deaths per 100,000 in the Aserrí canton to
52.1 (42.1–63.2) deaths in the San José canton, San José
province (Fig. 3and Additional file 1: Figure S12). For
women, mortality ranged over 40-fold, from 0.2 (0.0–
1.5) deaths per 100,000 in the Atenas canton, Alajuela
province, to 11.9 (7.8–17.4) deaths per 100,000 in the
San José canton, San José province. Between 2014 and
2016, estimated national HIV mortality decreased by
18.5% among men (from 6.0 [5.1–7.1] deaths per 100,
000 in 2014) and by 20% among women (from 1.9 [1.4–
2.7] deaths per 100,000 in 2014). Estimated HIV mortal-
ity decreased in 80 (98.8%) municipalities for men and
all 81 municipalities for women, though no canton for
either sex registered a statistically significant decrease in
HIV mortality. National temporal decreases were largely
driven by the San José canton, San José province, which
had the largest decrease in estimated male HIV mortal-
ity: 21.1%, from 65.9 [53.6–81.5] deaths per 100,000 in
2014 to 52.1 [42.1–63.2] deaths per 100,000 in 2016.
From 2014 to 2016, estimated HIV mortality in San José
canton, San José province, decreased among women by
22.7% (from 15.3 [10.4–22.9] deaths per 100,000 in 2014
to 11.9 [7.8–17.4] deaths per 100,000 in 2016).
Ecuador
In 2014, Ecuador had the highest estimated national
HIV mortality rate for both sexes combined among the
six countries investigated: 7.0 (6.5–7.6) deaths per 100,
000 (10.9 [10.1–11.7] deaths for men, and 3.4 [3.0–3.8]
deaths for women). Estimated male HIV mortality varied
among cantons from 1.1 (0.4–2.4) deaths per 100,000 in
the Tulcán canton, Carchi province, to 50.7 (29.0–81.6)
deaths per 100,000 in the Palestina canton (Fig. 4and
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 6 of 25
Additional file 1: Figure S16). For women, estimated
HIV mortality ranged from 0.6 (0.2–1.3) deaths per 100,
000 in the Tulcán canton, Carchi province, to 13.0 (4.3–
29.8) deaths per 100,000 in the San Lorenzo canton,
Esmeraldas province. Between 2004 and 2014, estimated
national HIV mortality increased by 28.3% among men
(from 8.5 [7.8–9.2] deaths per 100,000 in 2004) and in-
creased by 63.2% among women (from 2.1 [1.8–2.4]
deaths per 100,000 in 2004). Estimated male HIV mor-
tality increased in 216 (96.4%) cantons, and three [1.3%]
municipalities had a significant increase in HIV mortal-
ity. Estimated female HIV mortality increased in 223
(99.6%) municipalities and one [0.4%] canton had a sta-
tistically significant increase in HIV mortality. Among
men, estimated change in HIV mortality at the canton
level ranged from a 239.7% increase in Río Verde can-
ton, Esmeraldas province (from 9.1 [2.9–24.4] deaths per
100,000 in 2004 to 31 [9.5–79.1] deaths per 100,000 in
2014), to a 42.7% decrease in Huaquillas canton, El Oro
province (from 8.3 [3.9–15.9] deaths per 100,000 in 2004
Fig. 2 HIV mortality among men and women in Colombia by municipality, 2017. HIV mortality per 100,000 by municipality in Colombia in 2017
among men (a) and women (b). Relative change in HIV mortality between 2000 and 2017 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 7 of 25
to 4.7 [1.8–10] deaths per 100,000 in 2014). Among women,
estimated relative change in HIV mortality at the canton
level varied from a 199.9% increase in Río Verde canton,
Esmeraldas province (from 3.4 [0.9–10.2] deaths per
100,000 in 2004 to 10.2 [1.7–34.6] deaths per 100,000
in 2014), to essentially no change in Latacunga canton,
Cotopaxi province (0.6 [0.3–1.2] deaths per 100,000 in
2004 compared to 0.6 [0.3–1.3] deaths per 100,000 in
2014).
Guatemala
In Guatemala, estimated national HIV mortality for both
sexes combined in 2017 was 4.6 (4.1–5.1) deaths per
100,000 (6.8 [6.2–7.4] deaths for men and 2.8 [2.4–3.1]
deaths for women). At the municipality level, estimated
HIV mortality for men varied from 0.8 (0.2–2.1) deaths
per 100,000 in the Santa Cruz Barillas municipality,
Huehuetenango department to 38.6 (24.5–57.5) deaths
per 100,000 in the San José municipality, Escuintla
Fig. 3 HIV mortality among men and women in Costa Rica by canton, 2016. HIV mortality per 100,000 by canton in Costa Rica in 2016 among
men (a) and women (b). Relative change in HIV mortality between 2014 and 2016 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 8 of 25
department (Fig. 5and Additional file 1: Figure S11). For
women, estimated HIV mortality ranged from 0.7 (0.2–
1.5) deaths per 100,000 in the Chiantla municipality,
Huehuetenango department to 20.3 (12.3–31.8) deaths
per 100,000 in the San José municipality, Escuintla de-
partment. Between 2009 and 2017, estimated national
HIV mortality decreased by 36.9% among men (from
10.8 [9.9–11.7] deaths per 100,000 in 2009) and by
33.5% among women (from 4.1 [3.7–4.6] deaths per 100,
000 in 2009). Unlike in the other countries considered,
estimated HIV mortality decreased in all 340 municipal-
ities for both men and women though only two [0.6%]
municipalities for men and no municipalities for women
had a statistically significant decrease in HIV mortality.
Among men, estimated relative change in HIV mortality
at the municipality level ranged from a 14.4% decrease
in San José municipality, Escuintla department (from 45
[30.2–65.5] deaths per 100,000 in 2009 to 38.6 [24.5–
57.5] deaths per 100,000 in 2017) to a 54% decrease in
Siquinalá municipality, Escuintla department (from 25.4
[14.3–41.3] deaths per 100,000 in 2009 to 11.7 [6.5–
19.7] deaths per 100,000 in 2017). Estimated change in
female HIV mortality at the municipality level varied
from an 18.5% decrease in San José municipality, Escuin-
tla department (from 24.9 [15.9–38.6] deaths per 100,
000 in 2009 to 20.3 [12.3–31.8] deaths per 100,000 in
2017) to a 39.9% decrease in Coatepeque municipality,
Quetzaltenango department (from 6.9 [4.4–10.3] deaths
Fig. 4 HIV mortality among men and women in Ecuador by canton, 2014. HIV mortality per 100,000 by canton in Ecuador in 2014 among men
(a) and women (b). Relative change in HIV mortality between 2004 and 2014 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 9 of 25
per 100,000 in 2009 to 4.1 [2.6–6.6] deaths per 100,000
in 2017).
Mexico
In Mexico, the estimated national HIV mortality rate for
both sexes combined in 2017 was 4.3 (4.2–4.4) deaths
per 100,000 (6.9 [6.7–7.1] deaths per 100,000 for men,
and 1.9 [1.8–2.0] for women). At the municipality level,
estimated HIV mortality for men in 2017 varied over 53-
fold: from 0.8 (0.4–1.3) deaths per 100,000 in the San
Salvador Atenco municipality, Mexico state to 42.6
(29.1–61.4) deaths in the Tlacotalpan municipality,
Veracruz state (Fig. 6and Additional file 1: Figure S12).
For women, estimated HIV mortality ranged over 47-
fold from 0.3 (0.2–0.6) deaths per 100,000 in the
Texcoco municipality, Mexico state to 14.2 (8.8–22.0)
deaths per 100,000 in the San Juan Cancuc municipality,
Chiapas state. Between 2000 and 2017, estimated na-
tional HIV mortality decreased by 23.5% among men
(from 9.0 [8.8–9.3] deaths per 100,000 in 2000) and by
5.2% among women (from 2.0 [1.9–2.1] deaths per 100,
000 in 2000). Estimated male HIV mortality decreased in
2048 (83.3%) municipalities, and 32 [1.3%] municipalities
had a statistically significant decrease in HIV mortality.
Fig. 5 HIV mortality among men and women in Guatemala by municipality, 2017. HIV mortality per 100,000 by municipality in Guatemala in 2017
among men (a) and women (b). Relative change in HIV mortality between 2009 and 2017 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 10 of 25
Estimated female HIV mortality decreased in 1683
(68.5%) municipalities and 4 [0.2%] municipalities had a
significant decrease in HIV mortality. Among men, esti-
mated change in HIV mortality from 2000 to 2017
ranged from a 162.4% increase in the Solidaridad and
Tulum municipalities, Quintana Roo state (from 6.8
[4.9–9.3] deaths per 100,000 in 2000 to 17.9, [14.5–21.6]
deaths per 100,000 in 2017) to a 61.9% decrease in
Playas de Rosarito municipality, Baja California state
(from 19.3 [13.6–27.2] deaths per 100,000 in 2000 to 7.4
[5.1–10.2] deaths per 100,000 in 2017). For women, esti-
mated relative change in HIV mortality at the municipal-
ity level varied from a 110.3% increase in Coatzacoalcos
municipality, Veracruz state (from 4.5 [3.5–5.8] deaths
per 100,000 in 2000 to 9.5 [7.5–11.8] deaths per 100,000
in 2017) to a 54.9% decrease in Zapopan municipality,
Jalisco state (from 2.5 [1.9–3.3] deaths per 100,000 in
2000 to 1.1 [0.8–1.5] deaths per 100,000 in 2017).
Concentrated deaths due to HIV
We estimated that a large proportion of HIV deaths
were concentrated in a small number of geographical
areas with large populations. In all countries, over half of
the HIV deaths were located in less than 10% of munici-
palities in the latest year of study (Fig. 7). In Colombia,
over half of all HIV deaths in 2017 were located in just
1.2% (14 of 1122) of municipalities that contain 37.0% of
the total population, and in Guatemala in 2017, over half
the HIV deaths were spread out over 9.4% (32 of 340) of
municipalities that contain 33.4% of the population.
Several countries contained single municipalities that
contributed a large proportion of national HIV
deaths: for example, in Costa Rica in 2016, 45.9% of
all HIV deaths were located in San José canton, San
José province, compared to 4.6% of the total popula-
tion. Mexico had a greater spread of HIV deaths
across municipalities; in 2017 the area with the
Fig. 6 HIV mortality among men and women in Mexico by municipality, 2017. HIV mortality per 100,000 by municipality in Mexico in 2017
among men (a) and women (b). Relative change in HIV mortality between 2000 and 2017 among men (c) and women (d)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 11 of 25
highest proportion of HIV deaths was Tijuana muni-
cipality, Baja California state, which amounted to
3.2% of the total deaths compared to 1.5% of the
population. There was higher HIV mortality among
men as compared to women in all countries, ranging
from 64.7% of all deaths concentrated among men in
Brazil in 2017, to 77.6% of all deaths concentrated
among men in Mexico in 2017.
Fig. 7 Number of HIV deaths in latest year of study, by municipality. Estimated number of HIV deaths by municipality in the latest year of study:
2017 in Brazil (a), 2017 in Colombia (b), 2016 in Costa Rica (c), 2014 in Ecuador (d), 2017 in Guatemala (e), 2017 in Mexico (f). Color and size are
proportional to estimated HIV deaths
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 12 of 25
Absolute and relative inequality over time
Relative inequality—the mortality rate ratio for municipal-
ities in the 90th percentile versus those in the 10th per-
centile—varied from 5.0 (4.9–5.2) and 4.8 (4.6–5.0) among
men and women in Brazil in 2017, to 63.4 (31.6–113.0)
and 167.6 (54.6–403.5) among men and women in Costa
Rica in 2016 (Fig. 8). The estimated relative geographic in-
equality in HIV mortality increased in all countries from
the first to the last year of study, and this increase was
statistically significant in all countries barring Guatemala
and Costa Rica. The largest percent increase in relative in-
equality for each sex over the study period was 49.0% in
Colombian men (from 6.5 [6.0–7.0] in 2000 to 9.6 [8.8–
10.5] in 2017) and 55.9% in Ecuadorian women (from 4.7
[4.0–5.5] in 2004 to 7.4 [6.1–8.8] in 2014).
Absolute inequality—the difference between the mor-
tality rate in the 90th percentile and the 10th percent-
ile—showed less temporal variation in Brazil, Mexico,
Fig. 8 Relative and absolute inequality among municipalities in HIV mortality. aRelative inequality, defined as the ratio of estimated HIV mortality
for municipalities in the 90th percentile versus 10th percentile, by year with 95% uncertainty intervals. Costa Rica is omitted from this panel
because its estimated relative inequality was > 50. bAbsolute inequality, defined as the difference in HIV mortality rates for municipalities in the
90th versus 10th percentile, by year with 95% uncertainty intervals. Selected countries are differentiated by color and line type
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 13 of 25
and Costa Rica: the difference in estimated absolute in-
equality between the first and last year of study was less
than 1.5 deaths per 100,000 among both men and
women. Male absolute inequality increased in Colombia
by 40.9% (from 6.9 [6.4–7.4] in 2000 to 9.7 [9.1–10.4] in
2017) and in Ecuador by 74.1% (from 13.5 [11.8–15.4] in
2004 to 23.6 [21.0–26.4] in 2014), while male absolute
inequality decreased in Guatemala by 48.2% (from 17.8
[15.8–19.8] in 2009 to 9.2 [8.1–10.4] in 2017). Female
absolute inequality increased in Colombia by 50.8%
(from 2.2 [2.0–2.4] in 2000 to 3.3 [3.0–3.6] in 2017) and
Ecuador by 123.9% (from 3.6 [3.0–4.3] in 2004 to 8.1
[6.8–9.6] in 2014), while female absolute inequality
decreased in Guatemala by 43.5% (from 6.2 [5.4–7.2] in
2009 to 3.5 [3.0–4.1] in 2017). For both men and
women, increases in absolute relative inequality in
Colombia and Ecuador, as well as decreases in absolute
inequality in Guatemala, were statistically significant.
Local disparities in median age group among those who
died from HIV
The estimated median age group among men who died
varied substantially at the municipality level in the latest
year of study: by 15 years in Brazil, Ecuador, and Mexico,
and by 10 years in Colombia, Costa Rica, and Guatemala
(Fig. 9). Among women, estimated median age group
Fig. 9 Estimated median age group among those who died from HIV, by municipality. Estimated median age of death among men (a) and
women (b) who died from HIV in the last year of study in selected countries: 2017 in Brazil, Colombia, Guatemala, and Mexico, 2016 in Costa Rica,
and 2014 in Ecuador. Estimated difference in median age of death among men (c) and women (d) who died from HIV from first year to last year
of study in selected countries (2000–2017 in Brazil, Colombia, and Mexico, 2009–2017 in Guatemala, 2014–2016 in Costa Rica, and 2004–2014
in Ecuador)
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 14 of 25
among those who died in the latest year of study varied
at the municipality level by 15 years in Brazil,
Guatemala, and Mexico, by 10 years in Colombia, and
by only 5 years in Costa Rica and Ecuador. Differences
in median age group among those who died also shifted
over time. In Brazil, the estimated median age group
among male decedents rose in 99.6% of municipalities
from 2000 to 2017, while in Guatemala only 21.5% of
municipalities saw an increase in estimated median age
group among male decedents from 2009 to 2017. An
increase in estimated median age was also observed among
women: in Mexico, Ecuador, Colombia, and Brazil, the
median age group among female decedents rose in > 97%
of all municipalities in each country. Additional file 1:
Figure S16-S21 show estimated HIV mortality by age group
and sex for each country in the last year of study.
Discussion
There are few past analyses that assess HIV mortality in
Latin America using VR data at a subnational level,
largely due to the statistical challenges of incorporating
incomplete VR systems and estimating mortality in areas
with small populations and small numbers of deaths.
The few analyses that integrate estimates of VR com-
pleteness into their modeling framework are often done
at the national or state level or are limited to a single
country and year [15–18]. In this analysis, we expand on
previously described methods [31] that include prior es-
timates of VR completeness and demonstrate the utility
of estimates that combine uncertainty from both incom-
plete registration systems and from statistical methods
designed to leverage information across space, time,
and age to inform mortality rates in areas with small
numbers of HIV deaths.
Our estimates revealed large-scale spatial heterogen-
eity in HIV mortality across the six Latin American
countries considered in our analysis. We also reveal
divergent national trends in HIV mortality in the six
countries across the study period, and variable relative
change within countries at the municipality level. From
the first to the last year of study, HIV mortality de-
creased among men and women in all countries, with
the exception of women in Colombia from 2000 to 2017
and both men and women in Ecuador from 2004 to
2014. Despite the progress in reducing HIV mortality
among both sexes at the national level in Brazil,
Guatemala, Costa Rica, and Mexico, inequalities in
municipality-level HIV mortality persist and relative in-
equality increased over time in all countries. This ana-
lysis highlights uneven progress towards reducing HIV
mortality and reaching UNAIDS Fast-Track goals, and
emphasizes an alarming trend in Ecuador, where over
95% of cantons experienced increases in estimated mor-
tality among both sexes from 2004 to 2014. Nonetheless,
it also underlines stories of success: all countries con-
tained municipalities with an estimated decrease in HIV
mortality. Further evaluating municipalities with the
greatest decreases in HIV mortality within a country
may help decision-makers recognize successful strategies
that could be implemented in municipalities experien-
cing increases or slower declines.
There are likely a multitude of factors that contribute
to the spatial and temporal patterns in HIV mortality
observed in our analysis. Consistent with past analyses,
we found higher rates of HIV mortality among men
compared to women and slightly different spatial pat-
terns by sex [15,48,49]. The consistently elevated levels
of mortality among men is likely partially because men
who have sex with men (MSM) continue to be one of
the populations with the highest prevalence throughout
Latin America, and a group that suffers a higher level of
stigma and discrimination [48,50–52]. These trends
may also reflect prevalent gender norms [49], unequal
access to timely diagnosis and treatment [48], or differ-
ences in disease burden from comorbidities between
genders [1]. The spatial distribution of high-risk groups
possibly also contributes to the HIV epidemic remaining
concentrated in large urban centres [6]. For example,
one important driver of spatial differences in HIV mor-
tality is prison populations, where HIV transmission is
high due to overcrowding, violence, and lack of informa-
tion on the risk of HIV acquisition [53]. In Brazil, there
is evidence of low adherence to antiretroviral therapy
(ART) and a higher proportion of primary and second-
ary resistance among prison populations, which are pre-
dominantly male [54]. Municipalities with large prison
populations—such as several in the state of Sao Paulo,
Brazil—show higher levels of HIV mortality rates and
number of deaths due to HIV. Another potential driver
of spatial heterogeneity is population migration. Political
conflicts and economic hardships across the region, not-
ably in Venezuela and Central America, have fostered
waves of migration that can affect HIV prevention, treat-
ment, and care program [55]. Furthermore, difficulties in
acquiring HIV treatment and ART shortages spurs re-
gional migration that can differentially impact HIV care
and control programs in bordering countries [55,56].
A key driver of temporal trends in HIV mortality is
the implementation of ART treatment programs, which
have been incorporated to varying degrees in all Latin
American countries and generally led to increases in
ART coverage [55]. ART treatment is often a central
focus of national HIV programs, but differences in prior-
ity and ability to commit resources have likely impacted
progress in reducing HIV mortality. Access to HIV treat-
ment for people living with HIV is country-dependent
and has shifted over time. Brazil was the first middle-
income country to offer free ART treatment to people
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 15 of 25
living with HIV (PLHIV) in 1996 [57], with Costa Rica
following soon after in 1998. Within the ensuing decade,
Mexico [58] and Colombia [59] adopted similar policies
of universal treatment, which may have contributed to
the observed reduction in national HIV mortality as
these programs matured. In recent years, Guatemala has
also expanded ART treatment options through joint
support from the national government and The Global
Fund to Fight AIDS, Tuberculosis, and Malaria [60].
Given the concentration of HIV in urban high-risk
groups, many national programs—such as those in Costa
Rica and Guatemala—have emphasized a combination
prevention strategy that focus on HIV testing, STI diag-
nosis, and linkage to care in vulnerable populations [61].
In Ecuador, the only country in our analysis where we
estimated increases in national HIV mortality among
men and women over the study period, there has histor-
ically been a paucity of information available on research
findings concerning HIV/AIDS burden [62]. Notably,
our analysis in Ecuador only extends to 2014 and pat-
terns of HIV mortality may have changed with the re-
cent emphasis on community testing and treatment in
metropolitan areas [63]. While all countries have ex-
panded access to treatment and documented increases
in ART coverage, albeit at different time periods, persist-
ent disparities in access to quality health services and ad-
herence to ART remain [6,64,65] and may contribute to
differences in HIV mortality declines observed in this ana-
lysis. Further, in all countries in our analysis, communities
with socioeconomic and health disadvantages—such
as indigenous communities, sex workers, and trans-
gender populations—often have unequal access to treat-
ment and are an emerging or establish public health
priority [66,67].
Our analysis also revealed substantial variation in me-
dian age group among those who died from HIV at the
municipality level, and an increase over time in median
age group among those who died of HIV. These results
agree with past research that demonstrates an accelerat-
ing growth in the number of people living with HIV that
are above 50 years of age [68,69]. Several countries, not-
ably Brazil and Mexico, contained municipalities with a
15-year difference in median age groups among men
and women who died from HIV in the latest year of
study. While attributing these trends to specific drivers
is outside the scope of this analysis, there are several fac-
tors that could influence an increase in median age of
death, including changes in HIV incidence in specific
age groups [70], increases in life expectancy among
people living with HIV [70], access to ART [71], migra-
tion, and the age distribution of the population across
the study period.
This analysis provides novel subnational estimates of
HIV mortality that convey important information to
policymakers and could inform future action. Knowledge
of local differences in HIV mortality can help guide
scale-up of ART where mortality might reflect subopti-
mal coverage. Our estimates highlight how deaths due
to HIV are concentrated in a low proportion of munici-
palities. In the longer term, HIV mortality measures
could be used to highlight areas that might benefit from
programmatic interventions that target HIV prevention
such as pre-exposure prophylaxis (PrEP). As countries in
Latin America consider expanding access to PrEP,
studies have demonstrated that prioritization of PrEP
to those at highest risk could save money and lives
[72,73]. Cost-effective interventions are especially im-
portant in Guatemala, Ecuador, Colombia, and Costa
Rica, where HIV programs depend on donor funding
[74]. Furthermore, subnational differences in HIV
burden have already been used to develop localized
strategies for HIV prevention and elimination in sub-
Saharan Africa [75,76].
Limitations
This analysis is subject to a number of limitations. First,
the VR data that inform our estimates are subject to
misclassification biases. HIV is generally under-reported
as a cause of death [23]. While the HIV correction
methodology from the GBD employed in this analysis
corrects for biases in HIV deaths classified to other
underlying causes of death, there may be additional
country-specific biases not addressed by our methodo-
logical approach. While we matched decedents to their
municipality of residence as provided by the VR mortal-
ity databases, these data could overrepresent urban areas
with larger health care facilities where individuals may
have died after seeking treatment. It is possible that in
some cases these individuals may have been mistakenly
recorded as residing in that municipality if their munici-
pality of residence was not available. Second, our
method for correcting for incomplete VR across space
and time makes several crucial assumptions. Our ana-
lysis incorporates estimates of VR completeness for chil-
dren under 15 and adults 15 and over based on previous
analyses, but VR completeness may vary within these
age ranges. Further, we use the geographic variation in
completeness identified by comparing reported under-5
all-cause deaths to previous estimates, and the variation
in under-5 mortality may not be comparable to patterns
in adult VR incompleteness. Additionally, variation in
all-cause completeness may differ from patterns in HIV-
specific VR completeness. Third, we use population esti-
mates from WorldPop in this analysis that are subject to
error, especially in sparsely populated areas. While
WorldPop estimates include census data as inputs [77],
depending on timing and data accessibility, estimates
may differ from the underlying census measures and
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 16 of 25
may not utilize the most recent census or the most de-
tailed tabulations. Fourth, population migration in re-
sponse to political conflict or economic instability in the
region, including Central America and Venezuela, may
not be properly captured in WorldPop estimates or re-
corded in vital registration systems. Fifth, our analysis is
subject to large uncertainty. This reflects uncertainty
both due to the small number of HIV deaths at the mu-
nicipality level and the need to estimate completeness.
While we believe this method better captures major
sources of uncertainty, care must be taken when inter-
preting results. Sixth, we use custom shapefiles that are
matched to country-level administrative subdivisions,
and differences in administrative divisions between
GAUL [35] or the Humanitarian Data Exchange [36]
and an individual country’s designation of administrative
areas may affect the accuracy of results, especially in our
estimates of the number of HIV deaths by municipality.
Seventh, our small area estimation models smooth over
space and time by making assumptions about the tem-
poral and spatial structure of HIV mortality that may
not always hold. Eighth, VR data availability varied
across the countries selected in our analysis, and com-
parison between temporal trends in HIV mortality may
be difficult to assess for countries with different years of
data availability. Finally, it is difficult to directly assess
violations of our modeling assumptions or quality issues
in the underlying data sources given that VR complete-
ness cannot be verified. Nonetheless, comparisons to
GBD national estimates (Additional file 1: Figure S4-S9)
provide reassurance in overall country trends.
Future directions
There is considerable opportunity to expand this ana-
lysis. First, access to VR data over more years of study,
or in neighboring countries in Latin America, could pro-
vide valuable benchmarks for more direct comparisons
and allow additional information across space and time
to potentially improve our models. Our current study
uses four available covariates that serve as proxies for
urbanization and development, but in the future,
availability of other drivers of HIV mortality at the
municipality level such as socioeconomic status,
healthcare infrastructure, high-risk group concentra-
tion, and ART treatment availability could improve
our estimates. Further, the technique we used to in-
clude uncertainty and information on subnational VR
completeness could be extended to other countries
where VR systems are not complete. Finally, this
small area estimation framework could be used to
estimate all-cause and cause-specific mortality due to
other causes at local levels in the six modeled Latin
American countries.
Conclusion
Our analysis finds large-scale variation in HIV mortality
among municipalities in six Latin American countries,
both in the latest year of study as well as over the entire
study period. Our estimates demonstrate the need to as-
sess HIV burden at a granular geographic scale in Latin
America, given that the HIV epidemic is concentrated in
high-risk groups and select urban areas. The methods
developed in this analysis provide a framework for in-
corporating prior information on VR completeness into
subnational estimates of HIV burden. This analysis
could be used to identify areas that have successfully re-
duced HIV mortality and areas of high HIV burden, as
well as to inform the rollout of preventive interventions
that are required to help countries progress towards
achieving UNAIDS targets and advance health equity.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12916-020-01876-4.
Additional file 1:. Supplemental methods, GATHER checklist,
Supplemental Figure S1-S21, and Supplemental Tables S1-S4. Figure S1.
Analytical process overview. Figure S2. Analytical process for VR data. Fig-
ure S3. Analytical process overview for VR completeness priors. Figure S4.
Model alignment with GBD, Brazil. Figure S5. Model alignment with GBD,
Colombia. Figure S6. Model alignment with GBD, Costa Rica. Figure S7.
Model alignment with GBD, Ecuador. Figure S8. Model alignment with
GBD, Guatemala. Figure S9. Model alignment with GBD, Mexico. Figure
S10. Mean and uncertainty in estimated HIV mortality in Brazil, 2017. Fig-
ure S11. Mean and uncertainty in estimated HIV mortality in Colombia,
2017. Figure S12. Mean and uncertainty in estimated HIV mortality in
Costa Rica, 2016. Figure S13. Mean and uncertainty in estimated HIV mor-
tality in Ecuador, 2014. Figure S14. Mean and uncertainty in estimated
HIV mortality in Guatemala, 2017. Figure S15. Mean and uncertainty in es-
timated HIV mortality in Mexico, 2017. Figure S16. Estimated HIV mortality
in Brazil by age group, 2017. Figure S17. Estimated HIV mortality in
Colombia by age group, 2017. Figure S18. Estimated HIV mortality in
Costa Rica by age group, 2016. Figure S19. Estimated HIV mortality in
Ecuador by age group, 2014. Figure S20. Estimated HIV mortality in
Guatemala by age group, 2017. Figure S21. Estimated HIV mortality in
Mexico by age group, 2017. Table S1: Merged municipalities by country
to form stable geographical units. Table S2: Vital Registration data. Table
S3: Covariate data sources. Table S4: National HIV mortality rates among
men and womenfwil.
Acknowledgements
Local Burden of Disease HIV Collaborators:
Michael A Cork
1
, Nathaniel J Henry
1,2
, Stefanie Watson
1
, Andrew J
Croneberger
1
, Mathew Baumann
1
, Ian D Letourneau
1
, Mingyou Yang
1
,
Audrey L Serfes
1
, Jaffar Abbas
3
, Nooshin Abbasi
4
, Hedayat Abbastabar
5
, Lucas
G Abreu
6
, Eman Abu-Gharbieh
7
, Basavaprabhu Achappa
8
, Maryam Adabi
9
,
Tadele G Adal
10
, Adeyinka E Adegbosin
11
, Victor Adekanmbi
12
, Olatunji O
Adetokunboh
13,14
, Marcela Agudelo-Botero
15
, Bright O Ahinkorah
16
, Keivan
Ahmadi
17
, Muktar B Ahmed
18,19
, Robert K Alhassan
20
, Vahid Alipour
21,22
, Amir
Almasi-Hashiani
23
, Nelson Alvis-Guzman
24,25
, Robert Ancuceanu
26
, Tudorel
Andrei
27
, Davood Anvari
28,29
, Muhammad Aqeel
30
, Jalal Arabloo
21
, Olatunde
Aremu
31
, Malke Asaad
32
, Desta D Atnafu
33
, Alok Atreya
34
, Beatriz Paulina
Ayala Quintanilla
35
, Samad Azari
21
, Darshan B B
36
, Atif A Baig
37
, Maciej
Banach
38,39
, Simachew A Bante
40
, Miguel A Barboza
41,42
, Sanjay Basu
43,44
,
Neeraj Bedi
45,46
, Diana F Bejarano Ramirez
47,48
, Isabela M Bensenor
49
, Fenta-
hun Y Beyene
40
, Yihienew M Bezabih
50,51
, Akshaya S Bhagavathula
52,53
, Nikha
Bhardwaj
54
, Pankaj Bhardwaj
55,56
, Krittika Bhattacharyya
57,58
, Zulfiqar A
Bhutta
59,60
, Ali Bijani
61
, Sait M Birlik
62,63
, Zebenay W Bitew
64
, Somayeh
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 17 of 25
Bohlouli
65
, Archith Boloor
8
, Andre R Brunoni
49,66
, Zahid A Butt
67,68
, Rosario
Cárdenas
69
, Felix Carvalho
70
, Joao Mauricio Castaldelli-Maia
66
, Carlos A Casta-
ñeda-Orjuela
71,72
, Jaykaran Charan
73
, Souranshu Chatterjee
74
, Vijay Kumar
Chattu
75
, Soosanna Kumary Chattu
76
, Mohiuddin Ahsanul Kabir Chowdh-
ury
77,78
, Devasahayam J Christopher
79
, Dinh-Toi Chu
80
, Aubrey J Cook
1
, Nat-
alie M Cormier
1
, Saad M A Dahlawi
81
, Farah Daoud
1
, Claudio A Dávila-
Cervantes
82
, Nicole Davis Weaver
1
, Fernando P De la Hoz
83
, Feleke M
Demeke
84
, Edgar Denova-Gutiérrez
85
, Kebede Deribe
86,87
, Keshab Deuba
88,89
,
Samath D Dharmaratne
1,90,91
, Govinda P Dhungana
92
, Daniel Diaz
93,94
, Shirin
Djalalinia
95
, Andre R Duraes
96,97
, Arielle W Eagan
98,99
, Lucas Earl
1
, Andem
Effiong
100
, Maysaa El Sayed Zaki
101
, Maha El Tantawi
102
, Rajesh Elayedath
103
,
Shaimaa I El-Jaafary
104
, Emerito Jose A Faraon
105
, Andre Faro
106
, Nazir Fat-
tahi
107
, Nelsensius K Fauk
108
, Eduarda Fernandes
109
, Irina Filip
110,111
, Florian Fi-
scher
112
, Nataliya A Foigt
113
, Masoud Foroutan
114
, Takeshi Fukumoto
115
,
Mohamed M Gad
116,117
, Tesfay B B Gebremariam
118
, Ketema B Gebremed-
hin
119
, Gebreamlak G Gebremeskel
120,121
, Hailay A Gesesew
108,122
, Keyghobad
Ghadiri
123,124
, Ahmad Ghashghaee
21,125
, Syed Amir Gilani
126
, Mahaveer
Golechha
127
, Ugo Gori
128
, Alessandra C Goulart
49,129
, Bárbara N G Goulart
130
,
Harish C Gugnani
131,132
, Mark D C Guimaraes
133
, Rafael A Guimarães
134
, Yum-
ing Guo
135,136
, Rahul Gupta
137,138
, Emily Haeuser
1
, Mohammad Rifat Haider
139
,
Teklehaimanot G Haile
120
, Arvin Haj-Mirzaian
140,141
, Arya Haj-Mirzaian
142
, Asif
Hanif
143
, Arief Hargono
144
, Ninuk Hariyani
145,146
, Soheil Hassanipour
147,148
,
Hadi Hassankhani
149
, Khezar Hayat
150,151
, Claudiu Herteliu
27,152
, Hung Chak
Ho
153
, Ramesh Holla
154
, Mehdi Hosseinzadeh
155,156
, Mowafa Househ
157
, Bing-
Fang Hwang
158
, Charles U Ibeneme
159,160
, Segun E Ibitoye
161
, Olayinka S Ile-
sanmi
162,163
, Milena D Ilic
164
, Irena M Ilic
165
, Usman Iqbal
166
, Deepa Jahagir-
dar
1
, Vardhmaan Jain
167
, Mihajlo Jakovljevic
168,169
, Ravi P Jha
170,171
, Kimberly
B Johnson
1
, Nitin Joseph
36
, Farahnaz Joukar
147,148
, Leila R Kalankesh
172
, Rohol-
lah Kalhor
173,174
, Tanuj Kanchan
175
, Behzad Karami Matin
107
, André Karch
176
,
Salah Eddin Karimi
177
, Getinet Kassahun
178
, Gbenga A Kayode
179,180
, Ali
Kazemi Karyani
107
, Maryam Keramati
181
, Nauman Khalid
182
, Ejaz A Khan
183
,
Gulfaraz Khan
184
, Md Nuruzzaman N Khan
185,186
, Khaled Khatab
187,188
, Neda
Kianipour
189
, Yun Jin Kim
190
, Sezer Kisa
191
, Adnan Kisa
192,193
, Soewarta
Kosen
194
, Sindhura Lakshmi Koulmane Laxminarayana
195
, Ai Koyanagi
196
,
Kewal Krishan
197
, Barthelemy Kuate Defo
198,199
, Ricardo S Kuchenbecker
130,200
,
Vaman Kulkarni
36
, Nithin Kumar
36
, Manasi Kumar
201,202
, Om P Kurmi
203,204
,
Dian Kusuma
205,206
, Carlo La Vecchia
207
, Dharmesh K Lal
208
, Iván Landires
209
,
Savita Lasrado
210
, Paul H Lee
211
, Kate E LeGrand
1
, Bingyu Li
212
, Shanshan
Li
213
, Xuefeng Liu
214
, Hawraz I M. Amin
215,216
, Daiane B Machado
217,218
, Dee-
pak Madi
8
, Carlos Magis-Rodriguez
219
, Deborah C Malta
220
, Mohammad Ali
Mansournia
221
, Md Dilshad Manzar
222
, Carlos A Marrugo Arnedo
24,223
, Fran-
cisco R Martins-Melo
224
, Seyedeh Zahra Masoumi
225
, Benjamin K Mayala
1,226
,
Carlo E Medina-Solís
227
, Ziad A Memish
228,229
, Walter Mendoza
230
, Ritesh G
Menezes
231
, Tomislav Mestrovic
232,233
, Andreea Mirica
234
, Babak Moazen
235,236
,
Yousef Mohammad
237
, Naser Mohammad Gholi Mezerji
238
, Abdollah
Mohammadian-Hafshejani
239
, Reza Mohammadpourhodki
240
, Shafiu Moham-
med
235,241
, Ali H Mokdad
1,91
, Mohammad Ali Moni
242
, Masoud Moradi
107
,
Yousef Moradi
243
, Rahmatollah Moradzadeh
23
, Paula Moraga
244
, Amin Mou-
savi Khaneghah
245
, Ghulam Mustafa
246,247
, Lillian Mwanri
108
, Ravishankar
Nagaraja
248
, Ahamarshan J Nagarajan
249,250
, Mukhammad David Naim-
zada
251,252
, Bruno R Nascimento
253,254
, Dr M Naveed
255
, Vinod C Nayak
256
,
Javad Nazari
257
, Hadush Negash
258
, Ionut Negoi
259,260
, Samata Nepal
261
,
Georges Nguefack-Tsague
262
, Cuong T Nguyen
263
, Huong L T Nguyen
263
,
Rajan Nikbakhsh
141
, Jean Jacques Noubiap
264
, Virginia Nunez-Samudio
265,266
,
Bogdan Oancea
267
, Felix A Ogbo
268
, Andrew T Olagunju
269,270
, Nikita Otstav-
nov
251
, Mahesh P A
271
, Jagadish Rao Padubidri
256
, Seithikurippu R Pandi-
Perumal
272
, Ana M Pardo-Montaño
273
, Urvish K Patel
274
, Shrikant Pawar
275
,
Emmanuel K Peprah
276
, Alexandre Pereira
277,278
, Samantha Perkins
1
, Julia M
Pescarini
279
, Khem N Pokhrel
280
, Maarten J Postma
281,282
, Faheem H Pot-
too
283
, Sergio I Prada
284,285
, Liliana Preotescu
286,287
, Dimas R A Pribadi
288
,
Amir Radfar
289
, Fakher Rahim
290,291
, Mohammad Hifz Ur Rahman
292
, Amir
Masoud Rahmani
155,293
, Kiana Ramezanzadeh
294
, Juwel Rana
295,296
, Chhabi L
Ranabhat
297,298
, Sowmya J Rao
299
, Priya Rathi
154
, Salman Rawaf
300,301
, David L
Rawaf
302,303
, Reza Rawassizadeh
304
, Vishnu Renjith
305
, Nima Rezaei
306,307
, Aziz
Rezapour
21
, Ana Isabel Ribeiro
308
, Leonardo Roever
309
, Enrico Rubagotti
310
,
Susan F Rumisha
311,312
, Godfrey M Rwegerera
313
, Rajesh Sagar
314
, S. Moham-
mad Sajadi
315,316
, Marwa R Salem
317
, Abdallah M Samy
318
, Rodrigo
Sarmiento-Suárez
319,320
, Brijesh Sathian
321,322
, Lauren E Schaeffer
1
, Ione J C
Schneider
323
, Abdul-Aziz Seidu
324,325
, Feng Sha
326
, Masood A Shaikh
327
, Kio-
mars Sharafi
107
, Aziz Sheikh
328,329
, Kenji Shibuya
330
, Jae Il Shin
331
, Diego A S
Silva
332
, Jasvinder A Singh
333,334
, Valentin Y Skryabin
335
, Anna A Skryabina
336
,
Amber Sligar
1
, Amin Soheili
337
, Krista M Steuben
1
,Mu’awiyyah B Sufiyan
338
,
Eyayou G Tadesse
339
, Ayenew K T Tesema
340
, Fisaha H Tesfay
341,342
, Rekha
Thapar
36
, Robert L Thompson
1
, Marcos R Tovani-Palone
343,344
, Bach X Tran
345
,
Gebiyaw W Tsegaye
346
, Chukwuma D Umeokonkwo
347
, Bhaskaran Unnikrish-
nan
154
, Yasser Vasseghian
155
, Francesco S Violante
348,349
, Bay Vo
350
, Giang T
Vu
351
, Yasir Waheed
352
, Yuan-Pang Wang
66
, Yanzhong Wang
353
, Paul Ward
108
,
Fissaha T Welay
354
, Ronny Westerman
355
, Nuwan D Wickramasinghe
356
, Sanni
Yaya
357,358
, Paul Yip
359,360
, Naohiro Yonemoto
361,362
, Chuanhua Yu
363,364
,
Deniz Yuce
365
, Hasan Yusefzadeh
366
, Maryam Zamanian
23
, Mikhail S Zastroz-
hin
367,368
, Zhi-Jiang Zhang
369
, Yunquan Zhang
370,371
, Arash Ziapour
372
, Simon
I Hay
1,91
*, Laura Dwyer-Lindgren
1,91
*.
*Joint senior authors
1. Institute for Health Metrics and Evaluation, University of Washington,
Seattle, WA, USA.
2. Big Data Institute, University of Oxford, Oxford, UK.
3. Antai College of Economics, Shanghai Jiao Tong University,
Shanghai, China.
4. Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
5. Advanced Diagnostic and Interventional Radiology Research Center,
Tehran University of Medical Sciences, Tehran, Iran.
6. Department of Pediatric Dentistry, Federal University of Minas Gerais, Belo
Horizonte, Brazil.
7. Department of Clinical Sciences, University of Sharjah, Sharjah, United Arab
Emirates.
8. Department of Internal Medicine, Manipal Academy of Higher
Education, Mangalore, India.
9. Hamadan University of Medical Sciences, Hamadan, Iran.
10. Department of Public Health, Wolkite University, Wolkite, Ethiopia.
11. School of Medicine, Griffith University, Gold Coast, QLD, Australia.
12. Department of Population Health Sciences, King’s College London,
London, England.
13. Centre of Excellence for Epidemiological Modeling and Analysis,
Stellenbosch University, Stellenbosch, South Africa.
14. Department of Global Health, Stellenbosch University, Cape Town, South Africa.
15. Center for Policy, Population & Health Research, National Autonomous
University of Mexico, Mexico City, Mexico.
16. The Australian Centre for Public and Population Health Research (ACPP
HR), University of Technology Sydney, Sydney, NSW, Australia.
17. Lincoln Medical School, Universities of Nottingham & Lincoln, Lincoln, UK.
18. Department of Epidemiology, Jimma University, Jimma, Ethiopia.
19. Australian Center for Precision Health, University of South Australia,
Adelaide, SA, Australia.
20. Institute of Health Research, University of Health and Allied Sciences,
Ho, Ghana.
21. Health Management and Economics Research Center, Iran University of
Medical Sciences, Tehran, Iran.
22. Health Economics Department, Iran University of Medical Sciences,
Tehran, Iran.
23. Department of Epidemiology, Arak University of Medical Sciences,
Arak, Iran.
24. Research Group in Health Economics, University of Cartagena,
Cartagena, Colombia.
25. Research Group in Hospital Management and Health Policies, ALZAK
Foundation, Cartagena, Colombia.
26. Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.
27. Statistics and Econometrics Department, Bucharest University of
Economic Studies, Bucharest, Romania.
28. Department of Parasitology, Mazandaran University of Medical Sciences,
Sari, Iran.
29. Department of Parasitology, Iranshahr University of Medical Sciences,
Iranshahr, Iran.
30. Department of Psychology, Foundation University Islamabad,
Rawalpandi, Pakistan.
31. Department of Public Health, Birmingham City University,
Birmingham, UK.
32. Department of Plastic Surgery, University of Texas, Houston, TX, USA.
33. Department of Health System and Health Economics, Bahir Dar
University, Bahir Dar, Ethiopia.
34. Department of Forensic Medicine, Lumbini Medical College, Palpa, Nepal.
35. The Judith Lumley Centre, La Trobe University, Melbourne, VIC, Australia.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 18 of 25
36. Department of Community Medicine, Manipal Academy of Higher
Education, Manipal, India.
37. Unit of Biochemistry, Sultan Zainal Abidin University, Kuala
Terengganu, Malaysia.
38. Department of Hypertension, Medical University of Lodz, Lodz, Poland.
39. Polish Mothers’Memorial Hospital Research Institute, Lodz, Poland.
40. Department of Midwifery, Bahir Dar University, Bahir Dar, Ethiopia.
41. Department of Neurosciences, Costa Rican Department of Social Security,
San Jose, Costa Rica.
42. School of Medicine, University of Costa Rica, San Pedro, Costa Rica.
43. Center for Primary Care, Harvard University, Boston, MA, USA.
44. School of Public Health, Imperial College London, London, UK.
45. Department of Community Medicine, Gandhi Medical College Bhopal,
Bhopal, India.
46. Jazan University, Jazan, Saudi Arabia.
47. Department of Medicine, El Bosque University, Bogota, Colombia.
48. Transplant Service, University Hospital Foundation Santa Fe de Bogotá,
Bogota, Colombia.
49. Department of Internal Medicine, University of São Paulo, São
Paulo, Brazil.
50. Department of Internal Medicine, Bahir Dar University, Bahir Dar, Ethiopia.
51. One Health, University of Nantes, Nantes, France.
52. Institute of Public Health, United Arab Emirates University, Hradec
Kralova, United Arab Emirates.
53. Department of Social and Clinical Pharmacy, Charles University, Al
Ain, Czech Republic.
54. Department of Anatomy, Government Medical College Pali, Pali, India.
55. Department of Community Medicine and Family Medicine, All India
Institute of Medical Sciences, Jodhpur, India.
56. School of Public Health, All India Institute of Medical Sciences,
Jodhpur, India.
57. Department of Statistical and Computational Genomics, National Institute
of Biomedical Genomics, Kalyani, India.
58. Department of Statistics, University of Calcutta, Kolkata, India.
59. Centre for Global Child Health, University of Toronto, Toronto, ON,
Canada.
60. Centre of Excellence in Women and Child Health, Aga Khan University,
Karachi, Pakistan.
61. Social Determinants of Health Research Center, Babol University of
Medical Sciences, Babol, Iran.
62. University of Bologna, Bologna, Italy.
63. Liaison of Turkey, Guillain-Barre Syndrome/Chronic Inflammatory Demye-
linating Polyneuropathy Foundation International, Conshohocken, PA, USA.
64. Nutrition Department, St. Paul’s Hospital Millennium Medical College,
Addis Ababa, Ethiopia.
65. Department of Veterinary Medicine, Islamic Azad University,
Kermanshah, Iran.
66. Department of Psychiatry, University of São Paulo, São Paulo, Brazil.
67. School of Public Health and Health Systems, University of Waterloo,
Waterloo, ON, Canada.
68. Al Shifa School of Public Health, Al Shifa Trust Eye Hospital,
Rawalpindi, Pakistan.
69. Department of Health Care, Metropolitan Autonomous University, Mexico
City, Mexico.
70. Research Unit on Applied Molecular Biosciences (UCIBIO), University of
Porto, Porto, Portugal.
71. Colombian National Health Observatory, National Institute of Health,
Bogota, Colombia.
72. Epidemiology and Public Health Evaluation Group, National University of
Colombia, Bogota, Colombia.
73. Department of Pharmacology, All India Institute of Medical Sciences,
Jodhpur, India.
74. Department of Microbiology & Infection Control, Medanta Medicity,
Gurugram, India.
75. Department of Medicine, University of Toronto, Toronto, ON, Canada.
76. Department of Public Health, Texila American University,
Georgetown, Guyana.
77. Maternal and Child Health Division, International Centre for Diarrhoeal
Disease Research, Dhaka, Bangladesh.
78. Department of Epidemiology and Biostatistics, University of South
Carolina, Columbia, SC, USA.
79. Department of Pulmonary Medicine, Christian Medical College and
Hospital (CMC), Vellore, India.
80. Hanoi National University of Education, Hanoi, Vietnam.
81. Environmental Health Department, Imam Abdulrahman Bin Faisal
University, Dammam, Saudi Arabia.
82. Department of Population and Development, Latin American Faculty of
Social Sciences Mexico, Mexico City, Mexico.
83. Department of Public Health, National University of Colombia,
Bogota, Colombia.
84. Department of Medical Laboratory Sciences, Bahir Dar University, Bahir
Dar, Ethiopia.
85. Center for Nutrition and Health Research, National Institute of Public
Health, Cuernavaca, Mexico.
86. Wellcome Trust Brighton and Sussex Centre for Global Health Research,
Brighton and Sussex Medical School, Brighton, UK.
87. School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia.
88. National Centre for AIDS and STD Control, Save the Children, Kathmandu,
Nepal.
89. Department of Global Public Health, Karolinska Institute, Stockholm,
Sweden.
90. Department of Community Medicine, University of Peradeniya,
Peradeniya, Sri Lanka.
91. Department of Health Metrics Sciences, School of Medicine, University of
Washington, Seattle, WA.
92. Department of Microbiology, Far Western University, Mahendranagar,
Nepal.
93. Center of Complexity Sciences, National Autonomous University of
Mexico, Mexico City, Mexico.
94. Faculty of Veterinary Medicine and Zootechnics, Autonomous University
of Sinaloa, Culiacán Rosales, Mexico.
95. Development of Research and Technology Center, Ministry of Health and
Medical Education, Tehran, Iran.
96. School of Medicine, Federal University of Bahia, Salvador, Brazil.
97. Department of Internal Medicine, Escola Bahiana de Medicina e Saúde
Pública (Bahiana School of Medicine and Public Health), Salvador, Brazil.
98. Department of Global Health and Social Medicine, Harvard University,
Boston, MA, USA.
99. Department of Social Services, Tufts Medical Center, Boston, MA, USA.
100. Centre for Clinical Epidemiology and Biostatistics, University of
Newcastle, Newcastle, NSW, Australia.
101. Department of Clinical Pathology, Mansoura University, Cairo, Egypt.
102. Pediatric Dentistry and Dental Public Health Department, Alexandria
University, Alexandria, Egypt.
103. School of Behavioral Sciences, Mahatma Gandhi University of Medical
Sciences and Technology, Kottayam, India.
104. Department of Neurology, Cairo University, Cairo, Egypt.
105. Department of Health Policy and Administration, University of the
Philippines Manila, Manila, Philippines.
106. Department of Psychology, Federal University of Sergipe, São Cristóvão,
Brazil.
107. Research Center for Environmental Determinants of Health, Kermanshah
University of Medical Sciences, Kermanshah, Iran.
108. College of Medicine and Public Health, Flinders University, Adelaide, SA,
Australia.
109. Associated Laboratory for Green Chemistry (LAQV), University of Porto,
Porto, Portugal.
110. Psychiatry Department, Kaiser Permanente, Fontana, CA, USA.
111. School of Health Sciences, A.T. Still University, Mesa, AZ, USA.
112. Institute of Gerontological Health Services and Nursing Research,
Ravensburg-Weingarten University of Applied Sciences, Weingarten,
Germany.
113. Institute of Gerontology, National Academy of Medical Sciences of
Ukraine, Kyiv, Ukraine.
114. Department of Medical Parasitology, Abadan Faculty of Medical
Sciences, Abadan, Iran.
115. Department of Dermatology, Kobe University, Kobe, Japan.
116. Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH,
USA.
117. Gillings School of Global Public Health, University of North Carolina
Chapel Hill, Chapel Hill, NC, USA.
118. Department of Human Nutrition, Aksum University, Mekelle, Ethiopia.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 19 of 25
119. Department of Nursing and Midwifery, Addis Ababa University, Addis
Ababa, Ethiopia.
120. Department of Nursing, Aksum University, Aksum, Ethiopia.
121. Department of Nursing, Mekelle University, Mekelle, Ethiopia.
122. Department of Epidemiology, Mekelle University, Mekelle, Ethiopia.
123. Infectious Disease Research Center, Kermanshah University of Medical
Sciences, Kermanshah, Iran.
124. Pediatric Department, Kermanshah University of Medical Sciences,
Kermanshah, Iran.
125. Student Research Committee, Iran University of Medical Sciences,
Tehran, Iran.
126. Faculty of Allied Health Sciences, The University of Lahore, Lahore,
Pakistan.
127. Health Systems and Policy Research, Indian Institute of Public Health
Gandhinagar, Gandhinagar, India.
128. School of Medicine, University of Milan, Milan, Italy.
129. Center for Clinical and Epidemiological Research, University of São
Paulo, Porto Alegre, Brazil.
130. Epidemiology Department, Federal University of Rio Grande do Sul, São
Paulo, Brazil.
131. Department of Microbiology, Saint James School of Medicine, The
Valley, Anguilla.
132. Department of Epidemiology, Saint James School of Medicine, The
Valley, Anguilla.
133. Department of Preventive and Social Medicine, Federal University of
Minas Gerais, Belo Horizonte, Brazil.
134. Institute of Tropical Pathology and Public Health (IPTSP), Federal
University of Goias, Goiânia, Brazil.
135. Department of Epidemiology and Preventive Medicine, Monash
University, Melbourne, VIC, Australia.
136. Department of Epidemiology, Binzhou Medical University, Yantai City,
China.
137. Medical Resources, March of Dimes, Arlington, VA, USA.
138. Health Policy, Management and Leadership, West Virginia University
School of Public Health, Morgantown, WV, USA.
139. Department of Social and Public Health, Ohio University, Athens, OH, USA.
140. Department of Pharmacology, Tehran University of Medical Sciences,
Tehran, Iran.
141. Obesity Research Center, Shahid Beheshti University of Medical Sciences,
Tehran, Iran.
142. Department of Radiology and Radiological Sciences, Johns Hopkins
University, Baltimore, MD, USA.
143. University Institute of Public Health, The University of Lahore, Pakistan.
144. Department of Epidemiology, Universitas Airlangga (Airlangga
University), Surabaya, Indonesia.
145. Department of Dental Public Health, Airlangga University, Surabaya,
Indonesia.
146. Australian Research Centre for Population Oral Health, University of
Adelaide, Adelaide, SA, Australia.
147. Gastrointestinal and Liver Diseases Research Center, Guilan University of
Medical Sciences, Rasht, Iran.
148. Caspian Digestive Disease Research Center, Guilan University of Medical
Sciences, Rasht, Iran.
149. School of Nursing and Midwifery, Tabriz University of Medical Sciences,
Tabriz, Iran.
150. Institute of Pharmaceutical Sciences, University of Veterinary and Animal
Sciences, Lahore, Pakistan.
151. Department of Pharmacy Administration and Clinical Pharmacy, Xian
Jiaotong University, Xian, China.
152. School of Business, London South Bank University, London, UK.
153. Department of Urban Planning and Design, University of Hong Kong,
Hong Kong, China.
154. Kasturba Medical College, Manipal Academy of Higher Education,
Manipal, India.
155. Institute of Research and Development, Duy Tan University, Da Nang,
Vietnam.
156. Department of Computer Science, University of Human Development,
Sulaymaniyah, Iraq.
157. College of Science and Engineering, Hamad Bin Khalifa University, Doha,
Qatar.
158. Department of Occupational Safety and Health, China Medical
University, Taichung, Taiwan.
159. Department of Public Health and Disease Control, Ministry of Health,
Umuahia, Nigeria.
160. Nigerian Field Epidemiology and Laboratory Training Program, African
Field Epidemiology Network, Abuja, Nigeria.
161. Department of Health Promotion and Education, University of Ibadan,
Ibadan, Nigeria.
162. Department of Community Medicine, University of Ibadan, Ibadan,
Nigeria.
163. Department of Community Medicine, University College Hospital,
Ibadan, Ibadan, Nigeria.
164. Department of Epidemiology, University of Kragujevac, Belgrade, Serbia.
165. University of Belgrade, Kragujevac, Serbia.
166. College of Public Health, Taipei Medical University, Taipei, Taiwan.
167. Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA.
168. N. A. Semashko Department of Public Health and Healthcare, I.M.
Sechenov First Moscow State Medical University, Moscow, Russia.
169. Department of Global Health, Economics and Policy, University of
Kragujevac, Kragujevac, Serbia.
170. Department of Community Medicine, Dr. Baba Saheb Ambedkar
Medical College & Hospital, Delhi, India.
171. Department of Community Medicine, Banaras Hindu University, Varanasi,
India.
172. Tabriz University of Medical Sciences, Tabriz, Iran.
173. Institute for Prevention of Non-communicable Diseases, Qazvin Univer-
sity of Medical Sciences, Qazvin, Iran.
174. Health Services Management Department, Qazvin University of Medical
Sciences, Qazvin, Iran.
175. Department of Forensic Medicine and Toxicology, All India Institute of
Medical Sciences, Jodhpur, India.
176. Institute for Epidemiology and Social Medicine, University of Münster,
Münster, Münster, Germany.
177. Social Determinants of Health Research Center, Tabriz University of
Medical Sciences, Tabriz, Iran.
178. Department of Midwifery, Hawassa University, Hawassa, Ethiopia.
179. International Research Center of Excellence, Institute of Human Virology
Nigeria, Abuja, Nigeria.
180. Julius Centre for Health Sciences and Primary Care, Utrecht University,
Utrecht, Netherlands.
181. Mashhad University of Medical Sciences, Mashhad, Iran.
182. School of Food and Agricultural Sciences, University of Management
and Technology, Lahore, Pakistan.
183. Department of Epidemiology and Biostatistics, Health Services Academy,
Mymensingh, Pakistan.
184. Department of Medical Microbiology & Immunology, United Arab
Emirates University, Islamabad, United Arab Emirates.
185. Department of Population Sciences, Jatiya Kabi Kazi Nazrul Islam
University, Al Ain, Bangladesh.
186. Faculty of Health and Medicine, University of Newcastle,
Newcastle, NSW, Australia.
187. Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield,
UK.
188. College of Arts and Sciences, Ohio University, Zanesville, OH, USA.
189. Department of Public Health, Kermanshah University of Medical
Sciences, Kermanshah, Iran.
190. School of Traditional Chinese Medicine, Xiamen University Malaysia,
Sepang, Malaysia.
191. Department of Nursing and Health Promotion, Oslo Metropolitan
University, Oslo, Oslo, Norway.
192. School of Health Sciences, Kristiania University College, Oslo, Norway.
193. Global Community Health and Behavioral Sciences, Tulane University,
New Orleans, LA, USA.
194. Independent Consultant, Jakarta, Indonesia.
195. Kasturba Medical College, Udupi, India.
196. CIBERSAM, San Juan de Dios Sanitary Park, Sant Boi de Llobregat, Spain.
197. Department of Anthropology, Panjab University, Chandigarh, India.
198. Department of Demography, University of Montreal, Montreal, QC,
Canada.
199. Department of Social and Preventive Medicine, University of Montreal,
Montreal, QC, Canada.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 20 of 25
200. Department of Clinical Risk Management, Porto Alegre Clinical Hospital,
Porto Alegre, Brazil.
201. Department of Psychiatry, University of Nairobi, Nairobi, Kenya.
202. Division of Psychology and Language Sciences, University College
London, London, UK.
203. Department of Medicine, McMaster University, Coventry, ON, Canada.
204. Institute of Occupational and Environmental Medicine, University of
Birmingham, Hamilton, UK.
205. Imperial College Business School, Imperial College London, London, UK.
206. Faculty of Public Health, University of Indonesia, Depok, Indonesia.
207. Department of Clinical Sciences and Community Health, University of
Milan, Milan, Italy.
208. Public Health Foundation of India, Gurugram, India.
209. Unit of Genetics and Public Health, Institute of Medical Sciences, Las
Tablas, Panama.
210. Department of Otorhinolaryngology, Father Muller Medical College,
Mangalore, India.
211. School of Nursing, Hong Kong Polytechnic University, Hong Kong,
China.
212. Department of Sociology, Shenzhen University, Shenzhen, China.
213. School of Public Health and Preventive Medicine, Monash University,
Melbourne, VIC, Australia.
214. Department of Systems, Populations, and Leadership, University of
Michigan, Ann Arbor, MI, USA.
215. Department of Pharmaceutical Science, University of Eastern Piedmont,
Novara, Italy.
216. Chemistry Department, Salahaddin University-Erbil, Erbil, Iraq.
217. Center for Integration of Data and Health Knowledge, Oswald Cruz
Foundation (FIOCRUZ), Salvador, Brazil.
218. Centre for Global Mental Health (CGMH), London School of Hygiene &
Tropical Medicine, London, England.
219. National Center for the Prevention and Control of HIV and AIDS,
National Institute of Health, Mexico City, Mexico.
220. Department of Maternal and Child Nursing and Public Health, Federal
University of Minas Gerais, Belo Horizonte, Brazil.
221. Department of Epidemiology and Biostatistics, Tehran University of
Medical Sciences, Tehran, Iran.
222. Department of Nursing, Majmaah University, Majmaah, Saudi Arabia.
223. Research Group, Foundation for the Promotion of Life (Fundovida IPS),
Cartagena, Colombia.
224. Campus Caucaia, Federal Institute of Education, Science and Technology
of Ceará, Caucaia, Brazil.
225. Department of Midwifery, Hamadan University of Medical Sciences,
Hamadan, Iran.
226. ICF International, DHS Program, Rockville, MD, USA.
227. Department of Dentistry, Autonomous University of Hidalgo State,
Pachuca, Mexico.
228. College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
229. Research & Innovation Center, Ministry of Health, Riyadh, Saudi Arabia.
230. Peru Country Office, United Nations Population Fund (UNFPA), Lima,
Peru.
231. Forensic Medicine Division, Imam Abdulrahman Bin Faisal University,
Dammam, Saudi Arabia.
232. Clinical Microbiology and Parasitology Unit, Dr. Zora Profozic Polyclinic,
Zagreb, Croatia.
233. University Centre Varazdin, University North, Varazdin, Croatia.
234. Departament of Statistics and Econometrics, Bucharest University of
Economic Studies, Bucharest, Romania.
235. Heidelberg Institute of Global Health (HIGH), Heidelberg University,
Heidelberg, Germany.
236. Institute of Addiction Research (ISFF), Frankfurt University of Applied
Sciences, Frankfurt, Germany.
237. Internal Medicine Department, King Saud University, Riyadh, Saudi
Arabia.
238. Department of Biostatistics, Hamadan University of Medical Sciences,
Hamadan, Iran.
239. Department of Epidemiology and Biostatistics, Shahrekord University of
Medical Sciences, Shahrekord, Iran.
240. Kashmar Center of Higher Health Education, Mashhad University of
Medical Sciences, Mashhad, Iran.
241. Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria,
Nigeria.
242. University of New South Wales, NSW, Sydney, Australia.
243. School of Public Health, Iran University of Medical Sciences,
Kermanshah, Iran.
244. Computer, Electrical, and Mathematical Sciences and Engineering
Division, King Abdullah University of Science and Technology, Thuwal, Saudi
Arabia.
245. Department of Food Science, University of Campinas (Unicamp),
Campinas, Brazil.
246. Department of Pediatric Medicine, The Children’s Hospital & The
Institute of Child Health, Multan, Pakistan.
247. Department of Pediatrics & Pediatric Pulmonology, Institute of Mother &
Child Care, Multan, Pakistan.
248. Prasanna School of Data Science, Manipal Academy of Higher
Education, Manipal Udupi, India.
249. Research and Analytics Department, Initiative for Financing Health and
Human Development, Chennai, India.
250. Department of Research and Analytics, Bioinsilico Technologies,
Chennai, India.
251. Laboratory of Public Health Indicators Analysis and Health Digitalization,
Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
252. Experimental Surgery and Oncology Laboratory, Kursk State Medical
University, Kursk, Russia.
253. Department of Clinical Medicine, Federal University of Minas Gerais, Belo
Horizonte, Brazil.
254. Clinical Hospital, Federal University of Minas Gerais, Belo Horizonte,
Brazil.
255. Department of Biotechnology, University of Central Punjab, Lahore,
Pakistan.
256. Department of Forensic Medicine and Toxicology, Manipal Academy of
Higher Education, Manipal, India.
257. Department of Pediatrics, Arak University of Medical Sciences, Arak, Iran.
258. Medical Laboratory Sciences, Adigrat University, Adigrat, Ethiopia.
259. Department of General Surgery, Carol Davila University of Medicine and
Pharmacy, Bucharest, Romania.
260. Department of General Surgery, Emergency Hospital of Bucharest,
Bucharest, Romania.
261. Department of Community Medicine, Kathmandu University, Palpa,
Nepal.
262. Department of Public Health, University of Yaoundé I, Yaoundé,
Cameroon.
263. Institute for Global Health Innovations, Duy Tan University, Hanoi,
Vietnam.
264. Centre for Heart Rhythm Disorders, University of Adelaide, Adelaide, WA,
Australia.
265. Unit of Microbiology and Public Health, Institute of Medical Sciences,
Las Tablas, Panama.
266. Department of Public Health, Ministry of Health, Herrera, Panama.
267. Administrative and Economic Sciences Department, University of
Bucharest, Bucharest, Romania.
268. Translational Health Research Institute, Western Sydney University,
Sydney, NSW, Australia.
269. Department of Psychiatry and Behavioral Neurosciences, McMaster
University, Hamilton, ON, Canada.
270. Department of Psychiatry, University of Lagos, Lagos, Nigeria.
271. Department of Respiratory Medicine, Jagadguru Sri Shivarathreeswara
Academy of Health Education and Research, Mysore, India.
272. Corporate, Somnogen Canada Inc., Toronto, ON, Canada.
273. Department of Economic Geography, National Autonomous University
of Mexico, Mexico City, Mexico.
274. Department of Neurology and Public Health, Icahn School of Medicine
at Mount Sinai, New York, NY, USA.
275. Department of Genetics, Yale University, New Haven, CT, USA.
276. School of Global Public Health, New York University, New York, NY, USA.
277. Laboratory of Genetics and Molecular Cardiology, University of São
Paulo, São Paulo, Brazil.
278. Department of Genetics, Harvard University, Boston, MA, USA.
279. Center for Integration of Data and Health Knowledge, Oswaldo Cruz
Foundation, Salvador, Brazil.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 21 of 25
280. HIV and Mental Health Department, Integrated Development
Foundation Nepal, Kathmandu, Nepal.
281. University Medical Center Groningen, University of Groningen,
Groningen, Netherlands.
282. School of Economics and Business, University of Groningen, Groningen,
Netherlands.
283. Department of Pharmacology, Imam Abdulrahman Bin Faisal University,
Dammam, Saudi Arabia.
284. Clinical Research Center, Fundación Valle del Lili, Cali, Colombia.
285. Center for Studies in Social Protection and Health Economics, ICESI
University, Cali, Colombia.
286. National Institute of Infectious Diseases, Bucharest, Romania.
287. Department of Infectious Diseases, Carol Davila University of Medicine
and Pharmacy, Bucharest, Romania.
288. Health Sciences Department, Muhammadiyah University of Surakarta,
Sukoharjo, Indonesia.
289. College of Medicine, University of Central Florida, Orlando, FL, USA.
290. Thalassemia and Hemoglobinopathy Research Center, Ahvaz
Jundishapur University of Medical Sciences, Ahvaz, Iran.
291. Metabolomics and Genomics Research Center, Tehran University of
Medical Sciences, Tehran, Iran.
292. Department of Community Medicine, Maharishi Markandeshwar
Institute of Medical Sciences & Research, Solan, India.
293. Department of Computer Science, Khazar University, Baku, Azerbaijan.
294. Department of Pharmacology, Shahid Beheshti University of Medical
Sciences, Tehran, Iran.
295. Department of Public Health, North South University, Dhaka,
Bangladesh.
296. Department of Biostatistics and Epidemiology, University of
Massachusetts Amherst, Amherst, MA, USA.
297. Research Department, Policy Research Institute, Kathmandu, Nepal.
298. Health and Public Policy Department, Global Center for Research and
Development, Kathmandu, Nepal.
299. Department of Oral Pathology, Srinivas Institute of Dental Sciences,
Mangalore, India.
300. Department of Primary Care and Public Health, Imperial College
London, London, UK.
301. Academic Public Health England, Public Health England, London, UK.
302. WHO Collaborating Centre for Public Health Education and Training,
Imperial College London, London, UK.
303. University College London Hospitals, London, UK.
304. Department of Computer Science, Boston University, Boston, MA, USA.
305. School of Nursing and Midwifery, Royal College of Surgeons in Ireland -
Bahrain, Muharraq Governorate, Bahrain.
306. Research Center for Immunodeficiencies, Tehran University of Medical
Sciences, Tehran, Iran.
307. Network of Immunity in Infection, Malignancy and Autoimmunity (NIIM
A), Universal Scientific Education and Research Network (USERN), Tehran,
Iran.
308. Epidemiology Research Unit Institute of Public Health (EPIUnit-ISPUP),
University of Porto, Porto, Portugal.
309. Department of Clinical Research, Federal University of Uberlândia,
Uberlândia, Brazil.
310. Center for Research in Congenital Anomalies and Rare Diseases, ICESI
University, Cali, Colombia.
311. Malaria Atlas Project, University of Oxford, Oxford, UK.
312. Department of Health Statistics, National Institute for Medical Research,
Dar es Salaam, Tanzania.
313. Department of Internal Medicine, University of Botswana, Gaborone,
Botswana.
314. Department of Psychiatry, All India Institute of Medical Sciences, New
Delhi, India.
315. Department of Phytochemistry, Soran University, Soran, Iraq.
316. Department of Nutrition, Cihan University-Erbil, Erbil, Iraq.
317. Public Health and Community Medicine Department, Cairo University,
Giza, Egypt.
318. Department of Entomology, Ain Shams University, Cairo, Egypt.
319. Department of Health and Society, University of Applied and
Environmental Sciences, Bogota, Colombia.
320. National School of Public Health, Carlos III Health Institute, Madrid,
Spain.
321. Department of Geriatrics and Long Term Care, Hamad Medical
Corporation, Doha, Qatar.
322. Faculty of Health & Social Sciences, Bournemouth University,
Bournemouth, UK.
323. School of Health Sciences, Federal University of Santa Catarina,
Araranguá, Brazil.
324. Department of Population and Health, University of Cape Coast, Cape
Coast, Ghana.
325. College of Public Health, Medical and Veterinary Sciences, James Cook
University, Townsville, QLD, Australia.
326. Center for Biomedical Information Technology, Shenzhen Institutes of
Advanced Technology, Shenzhen, China.
327. Independent Consultant, Karachi, Pakistan.
328. Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK.
329. Division of General Internal Medicine, Harvard University, Boston, MA,
USA.
330. Institute for Population Health, King’s College London, London, UK.
331. College of Medicine, Yonsei University, Seoul, South Korea.
332. Department of Physical Education, Federal University of Santa Catarina,
Florianópolis, Brazil.
333. School of Medicine, University of Alabama at Birmingham, Birmingham,
AL, USA.
334. US Department of Veterans Affairs (VA), Birmingham, AL, USA.
335. Department No.16, Moscow Research and Practical Centre on
Addictions, Moscow, Russia.
336. Therapeutic Department, Balashiha Central Hospital, Balashikha, Russia.
337. Nursing Care Research Center, Semnan University of Medical Sciences,
Semnan, Iran.
338. Department of Community Medicine, Ahmadu Bello University, Zaria,
Nigeria.
339. Department of Biomedical Sciences, Arba Minch University, Arba
Minch, Ethiopia.
340. Health Education and Behavioral Science, University of Gondar,
Gondar, Ethiopia.
341. School of Public Health, Mekelle University, Mekelle, Ethiopia.
342. Southgate Institute for Health and Society, Flinders University,
Adelaide, SA, Australia.
343. Department of Pathology and Legal Medicine, University of São Paulo,
Ribeirão Preto, Brazil.
344. Modestum LTD, London, UK.
345. Department of Health Economics, Hanoi Medical University,
Hanoi, Vietnam.
346. College of Medicine and Health Sciences, Bahir Dar University, Bahir
Dar, Ethiopia.
347. Department of Community Medicine, Alex Ekwueme Federal University
Teaching Hospital Abakaliki, Abakaliki, Nigeria.
348. Department of Medical and Surgical Sciences, University of Bologna,
Bologna, Italy.
349. Occupational Health Unit, Sant’Orsola Malpighi Hospital, Bologna, Italy.
350. Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh
City, Vietnam.
351. Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh
University, Ho Chi Minh City, Vietnam.
352. Foundation University Medical College, Foundation University
Islamabad, Islamabad, Pakistan
353. School of Population Health & Environmental Sciences, King’s College
London, London, UK.
354. Department of Midwifery, Adigrat University, Adigrat, Ethiopia.
355. Competence Center of Mortality-Follow-Up of the German National Co-
hort, Federal Institute for Population Research, Wiesbaden, Germany.
356. Department of Community Medicine, Rajarata University of Sri Lanka,
Anuradhapura, Sri Lanka.
357. School of International Development and Global Studies, University of
Ottawa, Ottawa, ON, Canada.
358. The George Institute for Global Health, University of Oxford, Oxford, UK.
359. Centre for Suicide Research and Prevention, University of Hong Kong,
Hong Kong, China.
360. Department of Social Work and Social Administration, University of
Hong Kong, Hong Kong, China.
361. Department of Neuropsychopharmacology, National Center of
Neurology and Psychiatry, Kodaira, Japan.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 22 of 25
362. Department of Public Health, Juntendo University, Tokyo, Japan.
363. Department of Epidemiology and Biostatistics, Wuhan University,
Wuhan, China.
364. Global Health Institute, Wuhan University, Wuhan, China.
365. Cancer Institute, Hacettepe University, Ankara, Turkey.
366. Department of Health Care Management and Economics, Urmia
University of Medical Science, Urmia, Iran.
367. Laboratory of Genetics and Genomics, Moscow Research and Practical
Centre on Addictions, Moscow, Russia.
368. Addictology Department, Russian Medical Academy of Continuous
Professional Education, Moscow, Russia.
369. School of Medicine, Wuhan University, Wuhan, China.
370. School of Public Health, Wuhan University of Science and Technology,
Wuhan, China.
371. Hubei Province Key Laboratory of Occupational Hazard Identification
and Control, Wuhan University of Science and Technology, Wuhan, China.
372. Department of Health Education & Promotion, Kermanshah University of
Medical Sciences, Kermanshah, Iran.
Authors’contributions
Providing data or critical feedback on data sources: JA, NA, HA, LGA, EAG, BA,
MaA, AEA, VA, OOA, MA-B, BOA, KA, MBA, RKA, VaA, AAH, NAG, RA, TA, DA,
MuA, JalA, OA, MA, MAJ, AA, BPAQ, SA, DBM, AAB, MacB, SiB, MAB, SaB, MB,
DBB, NeB, DBR, IB, TB, FB, YB, ASB, PB, NB, KB, ZAB, AlB, SMB, ZWB, SB, AB,
ARB, ZB, ACG, RC, FC, JMCM, CCO, JC, SC, SKC, VKC, MAKC, DJC, DTC, AJC,
MC, NC, AC, ACP, SMAD, FD, CDC, NDW, BNDG, FDH, FMD, EDG, KebD, KD,
SDD, GD, DD, SD, AD, LDL, RE, AWE, LE, AE, MESZ, MET, SEJ, EJF, AF, NF, NKF,
EF, PF, IF, FF, NAF, MF, SFR, TF, MMG, GG, HAG, KG, AG, SAG, EG, TG, MG, HG,
MDCG, RAG, YG, RG, EH, MRH, TH, AH-M, AsH, AH, NH, SH, HH, SIH, KH, NJH,
CH, HCH, RH, MH, MoH, BFH, CI, SEI, OI, MI, II, U, DJ, VJ, MJ, AJN, RPJ, KJ, NJ,
FJ, LRK, R, TK, BeKM, AnK, SEK, GK, AKTT, GAK, AKK, MK, NaK, EK, GuK, MNK,
KhK, NK, YJK, SezK, AdK, SK, SLKL, AK, KK, BKD, RK, VK, DNK, ManK, OK, DK,
CLV, DL, HLN, IL, SL, PL, KLG, IDL, ShL, BL, XL, HIMA, DM, CMR, DCM, MoAM,
MDM, CMA, FRMM, SZM, BKM, CEMS, ZAM, WM, RGM, TM, AM, BM, Ymoh,
NM, SMM, AMH, ReM, SM, AHM, MAM, MM, YM, RM, PM, AMK, GM, LM, RavN,
MDN, BN, MN, VCN, JN, HN, IN, SN, GNT, RN, JJN, VNS, BO, FO, ATO, NO, SO,
MPA, JRP, SRPP, APM, UP, EP, SP, JP, KP, MP, FHP, SIP, LLP, DRAP, AR, FR,
MHUR, AMR, KR, JR, DCLR, SJR, PR, SR, DLR, RR, VR, NR, AzR, AIR, LR, ER, GR,
RS, SMS, MS, AMS, RSS, BS, LS, IS, A-AS, ALS, FS, MAS, KiS, AzS, KS, JIS, KHS,
DASS, JS, VS, AAS, AmS, AS, SS, KMS, MBS, CTN, FW, RT, RLT, GTV, MRTP, BT,
GT, CU, BU, JVH, YV, FSV, BV, YasW, YPW, YW, PW, SW, RW, NW, MY, SY, PY,
NY, CY, DY, HY, MZ, MSZ, ZJZ, YZ, AZ. Development of methods or computa-
tional machinery: MBA, AAH, DA, SA, ASB, SB, DJC, MC, AC, FD, LD-L, LE, NJH,
MH, LRK, SEK, NK, AdK, KLG, AHM, JN, RN, DCLR, AMS, JVH, YV, AZ. Providing
critical feedback on methods or results: NA, HA, LGA, EAG, BA, AEA, VA, OOA,
BOA, KA, MBA, RKA, VaA, AAH, NAG, TA, DA, MuA, JalA, OA, AA, BPAQ, SA,
DBM, MacB, SiB, MAB, SB, DBB, DBR, IB, FB, YB, ASB, PB, NB, ZAB, AlB, SMB,
ZWB, SoB, AB, ARB, ZB, ACG, RC, JMCM, JC, SC, SC, VKC, MAKC, DJC, DTC, MC,
AdCP, SMAD, CDC, BNDG, FMD, KebD, KD, DD, SD, LD-L, RE, AWE, AE, MESZ,
EJF, AF, NF, FF, NAF, MF, SFR, TF, MMG, HAG, AG, SAG, EG, TG, MG, HG, YG,
RG, EH, MRH, TH, AsH, NH, SH, SIH, KH, NJH, CH, HCH, RH, MH, MoH, BFH, CI,
SEI, OI, MI, II, UI, VJ, MJ, AJN, RPJ, FJ, LRK, RK, TK, BeKM, AnK, SEK, GK, AyK,
GAK, MK, NaK, EK, GuK, MNK, KhK, YJK, AdK, SezK, SLKL, AK, KK, BKD, RK, VK,
DNK, ManK, OK, DK, CLV, DL, HLN, SL, KLG, ShL, BL, XL, CMR, DCM, MoAM,
MDM, CMA, FRMM, SZM, BKM, CEMS, ZAM, WM, RGM, TM, AM, BM, Ymoh,
NM, AMH, SM, AHM, MAM, RM, AMK, GM, LM, RavN, MDN, BN, MN, JN, HN,
IN, SN, GNT, RN, JJN, BO, FO, ATO, NO, MPA, JRP, SRPP, APM, UP, EP, JP, KP,
MP, FHP, SIP, DRAP, AR, FR, JR, DCLR, SJR, PR, SR, VR, NR, AzR, AIR, LR, GR, RS,
SMS, MS, AMS, RSS, BS, LS, IS, A-AS, FS, MAS, KiS, AzS, KS, JIS, DASS, JS, VS,
AAS, AS, MBS, CTN, FiT, FT, RT, GTV, MRTP, BT, GT, CU, BU, JVH, YV, FSV, YasW,
YPW, YW, PW, RW, NW, SY, PY, NY, CY, DY, HY, MZ, MSZ, YZ, AZ. Drafting the
manuscript or revising it critically for important intellectual content: NA, HA,
LGA, EAG, BA, AEA, VA, OOA, BOA, KA, MBA, RKA, VaA, AAH, NAG, RA, TA, DA,
MuA, JalA, OA, MA, AA, BPAQ, SA, DBM, AAB, MacB, SiB, MAB, SB, DBB, NeB,
DBR, IB, TB, FB, YB, ASB, PB, NB, ZAB, AlB, SMB, ZWB, SoB, AB, ARB, ZB, ACG,
RC, FC, JMCM, CCO, JC, SC, SKC, VKC, MAKC, DJC, DTC, MC, NC, ACP, SMAD,
CDC, NDW, BNDG, FDH, FMD, EDG, KebD, KD, SDD, GD, DD, SD, LD-L, RE,
AWE, AE, MESZ, MET, SEJ, EJF, AF, NF, NKF, PF, FF, NAF, MF, SFR, TF, MMG,
GG, HAG, AG, SAG, EG, TG, MG, HG, MDCG, RAG, YG, RG, EH, MRH, TH, AsH,
AH, NH, SH, SIH, KH, NJH, CH, HCH, RH, MH, MoH, BFH, CI, SEI, OI, MI, II, UI, VJ,
MJ, AJN, RPJ, NJ, FJ, LRK, RK, TK, BeKM, AnK, SEK, GK, AK, GAK, MK, NaK, EK,
GuK, MNK, KhK, YJK, SezK, AdK, SLKL, AK, KK, BKD, RK, VK, DNK, ManK, OK, DK,
CLV, DL, HLN, IL, SL, PL, KLG, ShL, BL, XL, DM, CMR, DCM, MoAM, MDM, CMA,
FRMM, SZM, BKM, CEMS, ZAM, WM, RGM, TM, AM, BM, YMoh, NM, AMH, SM,
AHM, MAM, MM, RM, PM, AMK, GM, LM, RavN, MDN, BN, MN, VCN, JN, HN,
IN, SN, GNT, RN, JJN, VNS, BO, FO, ATO, NO, MPA, JRP, SRPP, APM, UP, ShP,
EP, JP, KP, MP, FHP, SIP, AR, FR, MHUR, JR, DCLR, SJR, PR, SR, VR, NR, AzR, AIR,
LR, GR, RS, SMS, MS, AMS, RSS, BS, LS, IS, A-AS, FS, MAS, KiS, AzS, KS, JIS, DASS,
JS, VS, AAS, AS, MBS, CTN, RT, RLT, GTV, MRTP, BT, GT, CU, BU, JVH, YV, FSV,
YasW, YPW, YW, PW, RW, NW, MY, SY, PY, NY, CY, DY, HY, MZ, MSZ, YZ, AZ
Management of the overall research enterprise (for example, through mem-
bership in the Scientific Council): YV, SB, AHM, MA, LD-L, BN, MPA, MESZ, NR,
RS, SIH, YM, TG, AJC, SP, AS. All authors read and approved the final
manuscript.
Funding
This work was primarily supported by grant OPP1132415 from the Bill &
Melinda Gates Foundation. The funder of the study had no role in study
design, data collection, data analysis, data interpretation, writing of the
report, or decision to publish. The corresponding authors had full access to
all the data in the study and had final responsibility for the decision to
submit for publication.
Availability of data and materials
Our study follows the Guidelines for Accurate and Transparent Health
Estimates Reporting (GATHER). Estimates can be further explored at national,
state, and municipality level by age group, sex, and year through our online
visualization tools (https://vizhub.healthdata.org/lbd/hiv-mort-la). The source
code used to generate estimates, as well as the outputs of the study
(including full sets of estimates at the state and municipality levels), are
publicly available online via the Global Health Data Exchange (http://ghdx.
healthdata.org/record/ihme-data/latin-america-hiv-mortality-estimates-2000-2
017). All maps presented in this study were generated by the authors and
no permissions are required to publish them.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
Dr. Singh reports personal fees from Crealta/Horizon, Medisys, Fidia, UBM
LLC, Trio health, Medscape, WebMD, Clinical Care options, Clearview
healthcare partners, Putnam associates, Focus forward, Navigant consulting,
Spherix, Practice Point communications, the National Institutes of Health and
the American College of Rheumatology, personal fees from Simply Speaking,
other from Amarin, Viking, Moderana and Vaxart pharmaceuticals, non-
financial support from FDA Arthritis Advisory Committee, non-financial sup-
port from Steering committee of OMERACT, an international organization
that develops measures for clinical trials and receives arm’s length funding
from 12 pharmaceutical companies, non-financial support from Veterans Af-
fairs Rheumatology Field Advisory Committee, non-financial support from
Editor and the Director of the UAB Cochrane Musculoskeletal Group Satellite
Center on Network Meta-analysis, outside the submitted work. Dr. Krishan re-
ports grants from UGC Centre of Advanced Study, CAS II, awarded to the De-
partment of Anthropology, Panjab University, Chandigarh, India, outside the
submitted work. Prof. Postma reports grants and personal fees from various
pharmaceutical industries, all outside the submitted work. Prof Postma holds
stocks in Ingress Health and Pharmacoeconomics Advice Groningen (PAG
Ltd) and is advisor to Asc Academics, all pharmacoeconomic consultancy
companies. Dr. Ancuceanu reports he received consultancy and speakers’
fees from various pharmaceutical companies. Walter Mendoza is a Program
Analyst in Population and Development at the United Nations Population
Fund-UNFPA Country Office in Peru, an institution which does not necessar-
ily endorse this study. Dr. Pandi-Perumal reports a non-financial relationship
with Somnogen Canada Inc. and occasional royalities from publishing
houses, outside the submitted work.
Local Burden of Disease HIV Collaborators BMC Medicine (2021) 19:4 Page 23 of 25
Received: 31 August 2020 Accepted: 27 November 2020
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