Global, regional, and national trends of dengue and its relationship with dietary iron deficiency: estimates for 204 countries and territories from 1990 to 2019

Global, regional, and national trends of dengue and its relationship with dietary iron deficiency: estimates for 204 countries and territories from 1990 to 2019 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global, regional, and national trends of dengue and its relationship with dietary iron deficiency: estimates for 204 countries and territories from 1990 to 2019 Yun Liang, Siyu Zou, Ziwei He, Jiarui Zhang, Yibin Zhu, Gong Cheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6154986/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Dengue is one of the most prevalent infectious diseases and has caused significant public health problems worldwide. Experimental studies suggest that dietary iron deficiency is associated with an increased risk of dengue. This study aimed to investigate the association between dietary iron deficiency and dengue prevalence over the past 30 years using standardized epidemiological data. Methods We conducted a comprehensive analysis using data from the GBD 2019 study, which included age-standardized prevalence rates of dengue and dietary iron deficiency in 204 countries and territories from 1990 to 2019. The primary measure used in this study was the age-standardized prevalence rate (ASPR), expressed as cases per 100,000 population and their 95% uncertainty intervals (UIs). We evaluated the annual percentage change (APC) and quantified the trend over thirty years by using joinpoint regression analysis. Linear mixed models were conducted to investigate the association between iron deficiency and dengue, adjusting for key covariates such as the Human Development Index (HDI), population density, and temperature. Results The global age-standardized prevalence rate (ASPR) of dengue increased over the 30 years, from 33.2 (95% UI 14.2 to 71.9) cases per 100,000 population in 1990 to 44.2 (28.1 to 79.2) cases per 100,000 population in 2019. Geographically, the highest prevalence rate of dengue was observed in South Asia (101.76 [37.84 to 247.73]) and lower-middle-income countries (77.94 [37.33 to 166.33]) in 2019. The prevalence rate of dietary iron deficiency decreased by 0.5% per year (APC − 0.5 [-0.6 to -0.5]) between 1999 and 2019. The prevalence rate of iron deficiency was highest in South Asia (26860.7 [26274.4 to 27405.7]) and in lower-middle-income countries (22271.9 [21838.5 to 22675.3]) in 2019. Our analysis revealed a positive association between the prevalence of dietary iron deficiency and dengue, with an adjusted coefficient of 21.5 (95% CI, 19.6 to 23.4). The association was more significant in females, with an adjusted coefficient of 19.8 (95% CI, 17.9 to 21.6) than in males, with an adjusted coefficient of 18.4 (95% CI, 6.6 to 20.3). Additionally, the association between dietary iron deficiency and dengue was more significant among adults older than 70 years (55.3, 95% CI 51.7 to 59.0) than in other age groups. Conclusions This study provides epidemiological evidence of the association between iron deficiencies and dengue prevalence. The highest burden of dengue and the strongest association with dietary iron deficiency were found in middle-income countries, and dengue endemic regions like South Asia. More importantly, our study may suggest iron supplementation as a potential dengue prevention strategy, which should be targeted for vulnerable populations, including females and older adults. Dietary iron deficiency dengue endemic regions prevalence Figures Figure 1 Figure 2 Introduction Dengue is an acute systemic viral infection which is transmitted between humans by mosquitoes. 1 Dengue virus maintains its lifecycle between humans and Aedes mosquito species. 1 For some patients, dengue is a life-threatening illness that has established itself globally in both endemic and epidemic transmission cycles. Although dengue virus infection in humans is often inapparent, it can lead to a wide range of clinical manifestations, from mild fever to potentially fatal dengue shock syndrome. 2 The lifelong immunity developed after infection with one of the four virus types is type-specific. 1 Progression to more serious disease is frequently, but not exclusively, associated with secondary infection by heterologous types. 2 Unfortunately, no effective antiviral agents exist to treat dengue infection, therefore, treatment remains supportive. 2 Moreover, no licensed vaccine against dengue infection is available, and the most advanced dengue vaccine candidate did not meet expectations in a recent large trial. 3 – 4 At present, the measures to curb the spread of the dengue virus mainly focus on the control of mosquito vectors, such as the control of breeding sites for Aedes mosquitoes through biochemical means. 2 However, these control measures have failed to prevent the increase in dengue prevalence and incidence rates and the expansion of the geographical scope of local transmission. 5 Some laboratory studies have shown that iron is an essential nutrient for maintaining a variety of cellular, immune, and metabolic activities. 6 It regulates many viral infections in humans. The pleiotropic role of iron in the pathogenesis of dengue fever, malaria and other infectious diseases has been noted. The acquisition of dengue virus by Aedes aegypti was negatively correlated with the serum iron concentration of the donor. In laboratory studies, Iron supplementation reduced the prevalence and viral load of dengue virus, while serum iron neutralization promoted dengue virus infection in Ae. aegypti mosquitoes. 7 Mosquitoes that feed on iron deficient hosts exhibit higher dengue virus prevalence, while reversing host iron deficiency significantly reduces dengue virus acquisition in mosquitoes. 8 It has been shown that the iron metabolism pathway of mosquitoes utilizes serum iron instead of heme binding iron to increase the activity of reactive oxygen species in the intestinal epithelium, thereby inhibiting dengue virus infection. 7 These studies suggest that iron deficiency in humans may contribute to vector transmission of the dengue virus, thus facilitating its transmission by mosquitoes. 9 Dengue spreads more easily due to poor sanitary conditions, overcrowding, dirty water resources, and limited medical resources. 10 The poor living environment may aggravate the spread of infectious diseases and dengue fever is an infectious disease closely related to climate. 11 Poor environmental conditions and a lack of proper nutrition can lead to iron deficiency, which is more common among vulnerable groups, including low-income people, refugees, and immigrants from low-income and middle-income countries. 12 However, there is still a lack of epidemiological evidence to explore the association between iron deficiency and dengue based on large population studies. To investigate the potential association of dietary iron deficiency with dengue prevalence at population level, we analyzed the data from the Global Burden of Disease Study 2019 (GBD 2019) to provide updated estimates of the prevalence of dengue related to dietary iron deficiency at global, regional, and national levels across 204 countries and territories from 1990 to 2019. We also examined the relationship between dietary iron deficiency and dengue prevalence by different age groups, sex, levels of economic status, and geographical location, while considering key dimensions of human development, population density, and temperature. Methods Data sources Data on dengue and dietary iron deficiency from 1990 to 2019 were obtained from the Institute for Health Metrics and Evaluation (IHME), a data-sharing center for the GBD (available from http://ghdx.healthdata.org/gbd-results-tool ) (accessed on Dec 10, 2022). The GBD 2019 utilized standardized and replicable methods to provide a variety of relevant indicators to measure population health loss from hundreds of diseases, injuries, and risk factors, and a total of 204 countries and territories were included in the final dataset for analysis. Details of the GBD 2019 study design and methods used to estimate the incidence and prevalence of causes of death and disease have been described previously. 13 Briefly, prevalence rates of dengue and dietary iron deficiency were modelled from multiple relevant data sources by using the disease model-Bayesian meta-regression (DisMod-MR) tool, including disease registry data, censuses, epidemiological surveillance data, and other sources. The uncertainty of prevalence rates was estimated by 95% uncertainty intervals (UIs). 95% UI was defined as the 2·5th and 97·5th ordered values of the 1000 draw-level estimates. The prevalence rates and their 95% UIs from 1990 to 2019 were directly downloaded from the Global Health Data Exchange (GHDx) GBD Results Tool. 14 Due to the missing value of the incidence rates of dietary iron deficiency, we selected the prevalence rates of dengue and dietary iron deficiency. 13 The prevalence rates per 100,000 population in both sexes were obtained and compared at the global, regional, and national levels. Measures Dengue and dietary iron deficiency The primary measure used in this study was the age-standardized prevalence rate (ASPR) of dengue and dietary iron deficiency, expressed as the number of cases per 100,000 population. The estimates for dengue represent cases of clinically significant diseases (i.e. dengue fever, dengue haemorrhagic fever, dengue shock syndrome, and resulting chronic fatigue syndrome). These estimates do not represent asymptomatic dengue virus infection. Dietary iron deficiency in the GBD 2019 referred to the iron deficiency due to insufficient dietary iron intake, rather than absolute or functional iron deficiency due to other causes. The 204 countries and territories were divided into higher and lower dengue endemic region, based on the prevalence of dengue. Endemic areas were those where the dengue prevalence rate was higher than the global median prevalence rate. Geographical location and economic status The 204 countries and territories included in the study were divided into seven regions based on their geographical locations as defined by the World Bank. These regions were Sub-Saharan Africa, Middle East & North Africa, Latin America & Caribbean, East Asia & Pacific, South Asia, North America, and Europe & Central Asia. Additionally, the countries were classified based on their economic status: including low-income countries, lower middle-income countries, upper-middle-income countries, and high-income countries as per World Bank definition. Population density Population density (people per sq. km of land area) from 1990 to 2019 in each country was also obtained from The World Bank ( https://data.worldbank.org ). Population density was calculated by dividing mid-year population by land area in square kilometers. The population count included all residents regardless of legal status or citizenship of their country of origin, while land area referred to a country's total area. Temperature The year-specific temperature index for every country was used in this study. The land-ocean temperature index for every country from 1990 to 2019 was obtained from the National Aeronautics and Space Administration (NASA) global climate change website ( https://climate.nasa.gov/ ). The data was credited by NASA's Goddard Institute for Space Studies (GISS). Human Development Index (HDI) Human Development Index (HDI) is a composite index used to measure the average achievement in three basic dimensions of human development: a long and healthy life, knowledge and a decent standard of living. The Human Development Index (HDI) for every country from 1990 to 2019 was obtained from the United Nations Development Programme. The HDI is a composite index used to measure average achievement in three key dimensions of human development: a long and healthy life, access to knowledge, and a decent standard of living. The HDI is the geometric mean of the three-dimensional indexes (see Supplementary Table 1). Detailed methods for calculating the HDI have been described previously. 15 Other covariates We divided the age groups into six categories: <5 years old, 5–9 years old, 10–24 years old, 25–49 years old, 50–69 years old, and ≥ 70 years old. We also classified the participants by sex, dividing them into male and female categories. Statistical analysis ASPR and 95% UI of dengue and dietary iron deficiency were reported to show the trends over 30 years at the global and regional levels. We conducted joinpoint regression analysis to examine changes in prevalence rates of dengue and iron deficiencies. This regression model is useful for identifying significant changes in linear trend slopes. We computed the estimated annual percentage change (APC, %) for each trend by fitting a regression line to the natural logarithm of the rates within each period or phase. At the country and region level, we estimated the average annual change in percentage per year and 95% confidence interval (CI) using a linear regression model, with prevalence rates of dengue and iron deficiency as the dependent variable and year as the independent variable. Furthermore, we conducted generalized linear mixed models to investigate the association between iron deficiency and dengue. These models were controlled for region-fixed effect and time random effect. We considered a total of four models in this study: Model 1, which was a univariate mixed model; Model 2, which was adjusted for HDI; Model 3, which was adjusted for HDI, and population density; Model 4, which was adjusted for HDI, population density, and temperature. We reported Odds Ratios (ORs) and 95% CIs for these models. In addition, subgroup analysis was conducted to investigate the association between dietary iron deficiency and dengue in different economic statuses, geographical locations, and prevalence of dengue, while adjusting for all covariates mentioned above. The data were analyzed in R version 4·2·1 using the lme4 package, and Joinpoint version 4·9·1·0· The Jointpoint software, developed by the Surveillance Research Program of the United States National Cancer Institute, was used for this purpose. Results Global trends for dengue prevalence In 1990 and 2019, the ASPR of dengue globally was 33·2 (95% UI 14·2 to 71·9) and 44·2 (95% UI 28.1 to 79.2) per 100,000 population, respectively (Table 1 ). The global ASPR increased over the 30 years, with estimated average annual percentage changes (AAPCs) of 1·0 (95% CI, 0·9 to 1·1). However, the trends did not consistently increase over time and had two different phases worldwide (Table 1 , Fig. 1 ). During the first phase, the prevalence of dengue slowly and steadily increased from 1990 to 2010 (APC 1·7 [95% CI 1·6 to 1·8]), with a total increase of 38%. In the second phase, the prevalence rate of dengue decreased between 2010 and 2019, with a total decrease of 4% (APC − 0·4 [95% CI -0·7 to -0·2]). Table 1 Age-standardized prevalence rate (ASPR) for dengue in 1990 and 2019 and estimated annual percentage changes (APC) by region ASPR in 1990 ASPR in 2019 Average APC between 1990 and 2019 Trend 1 Trend 2 Rate (95% UI) Rate (95% UI) AAPC (95% CI) P-value Years APC (95% CI) Years APC (95% CI) Global 33·2 (14·2 to 71·9) 44·2 (28·1 to 79·2) 1·0 (0·9 to 1·1) < 0·001 1990–2010 1·7 (1·6 to 1·8)* 2010–2019 -0·4 (-0·7 to -0·2)* Economic status Low income 28·57 (15·34 to 43·78) 25·45 (8·76 to 44·45) -0·5 (-0·8 to -0·1) 0·005 1990–2010 1·4 (1·2 to 1·7)* 2010–2019 -4·6 (-5·5 to -3·7)* Lower middle income 73·08 (23·31 to 179·50) 77·94 (37·33 to 166·33) 0·3 (0·2 to 0·3) < 0·001 1990–2010 0·7 (0·6 to 0·8)* 2010–2019 -0·7 (-0·9 to -0·5)* Upper middle income 14·13 (11·56 to 16·70) 27·36 (17·30 to 41·69) 2·4 (2·3 to 2·5) < 0·001 1990–2010 3·5 (3·4 to 3·6)* 2009–2019 0·3 (0·1 to 0·5)* High-income 2·88 (2·04 to 3·82) 4·93 (3·30 to 7·61) 2·0 (1·8 to 2·1) < 0·001 1990–2008 3·2 (3·0 to 3·3)* 2008–2019 0·0 (-0·3 to 0·4) Geographical location Sub-Saharan Africa 31·96 (18·53 to 46·38) 27·66 (9·40 to 47·76) -0·5 (-0·8 to -0·2) 0·004 1990–2010 1·2 (0·9 to 1·5) 2010–2019 -4·2 (-5·0 to -3·3) Middle East & North Africa 6·15 (1·25 to 13·71) 6·47 (0·74 to 15·12) 0·2 (-0·1 to 0·4) 0·198 1990–2011 1·0 (0·8 to 1·2)* 2011–2019 -2·0 (-2·7 to -1·3)* Latin America & Caribbean 37·54 (29·23 to 45·93) 45·89 (41·80 to 50·35) 0·5 (0·1 to 0·8) 0·010 1990–2010 2·7 (2·3 to 3·0)* 2010–2019 -4·2 (-5·2 to -3·2)* East Asia & Pacific 21·01 (13·37 to 46·50) 39·94 (25·83 to 59·37) 2·3 (2·0 to 2·7) < 0·001 1990–1996 0·4 (-1·1 to 1·8) 1996–2019 2·9 (2·7 to 3·0)* South Asia 97·19 (25·41 to 252·85) 101·76 (37·84 to 247·73) 0·2 (0·2 to 0·2) < 0·001 1990–2009 0·6 (0·6 to 0·6)* 2009–2019 -0·6 (-0·6 to -0·5)* North America 0·30 (0·19 to 0·42) 0·38 (0·26 to 0·53) 1·9 (0·7 to 3·2) 0·002 1990–2000 -2·1 (-5·1 to 1·0) 2000–2019 4·1 (2·9 to 5·4)* Europe & Central Asia 0 0 - - - - - - *Indicates a P-value less than 0·05. ASPR indicates age-standardized prevalence rate (Total cases per 100 000 population); APC, annual percentage change; AAPC, average annual percentage change; UI, uncertainty interval; CI, confidence interval. Regarding geographical location, the ASPR of dengue per 100,000 population was highest in South Asia (101·76 [95% UI 37·84 to 247·73]), followed by Latin America & Caribbean (45.89 [95% UI 41.80 to 50.35]), East Asia & Pacific (39.94 [95% UI 25.83 to 59.37]), Sub-Saharan Africa (27.66 [95% UI 9.40 to 47.76]) and Middle East & North Africa (6.47 [95% UI 0.74 to 15.12]) in 2019 (Table 1 , Fig. 1 ). Concerning economic status, the ASPR of dengue per 100,000 population was highest in lower-middle-income countries (77·94 [95% UI 37·33 to 166·33]), followed by upper-middle-income countries (27·36 [95% UI 17·30 to 41·69]) in 2019 (Table 1 , Supplemental Fig. 1). Global trend for dietary iron deficiency The ASPR of dietary iron deficiency globally decreased from 16 252·6 (95% UI 15948·8 to 16509·4) per 100 000 population to 14 106·4 (95% UI 14342·1 to 13850·7) over the past 30 years (Table 2 ). Globally, the prevalence of dietary iron deficiency decreased by 0.5% per year (APC − 0·5 [-0·6 to -0·5]) between 1999 and 2019. At the geographical level, the ASPR of dietary iron deficiency per 100 000 population was highest in South Asia (26 860·7 [95% UI 26 274·4 to 27 405·7]), followed by Sub-Saharan Africa (18 845·0 [95% UI 18376·6 to 19 300·7]) in 2019. In terms of economic status, the ASPR of dietary iron deficiency was highest in lower-middle-income countries (22 271·9 [95% UI 21 838·5 to 22 675·3]), followed by low-income countries (18 932·3 [95% UI 18 485·9 to 19 373·6]) in 2019 (Table 2 , Supplemental Fig. 2). The decreasing trends of dietary iron deficiency in different geographical locations and economic statuses were generally consistent with the global level (Supplemental Fig. 2). Table 2 Age-standardized prevalence rate (ASPR) for dietary iron deficiency in 1990 and 2019 and estimated annual percentage changes (APC) by region ASPR in 1990 ASPR in 2019 Average APC between 1990 and 2019 Trend 1 Trend 2 Rate (95% UI) Rate (95% UI) AAPC (95% CI) P-value Years APC (95% CI) Years APC (95% CI) Dietary iron deficiency Global 16252·6 (15948·8 to 16509·4) 14106·4 (14342·1 to 13850·7) -0·5 (-0·6 to -0·5) < 0·001 1990–2010 -0·7 (-0·7 to -0·6)* 2010–2019 -0·2 (-0·3 to -0·1)* Economic status Low income 20261·0 (19834·0 to 20685·5) 18932·3 (18485·9 to 19373·6) -0·2 (-0·3 to -0·2) < 0·001 1990–2010 -0·3 (-0·3 to -0·3)* 2010–2019 -0·1 (-0·2 to 0·0) Lower middle income 24740·4 (24279·9 to 25175·4) 22271·9 (21838·5 to 22675·3) -0·4 (-0·4 to -0·3) < 0·001 1990–1999 -0·1 (-0·2 to 0·0) 1999–2019 -0·5 (-0·5 to -0·5)* Upper middle income 12878·9 (12474·3 to 13250·5) 6818·1 (6515·9 to 7108·4) -2·3 (-2·5 to -2·2) < 0·001 1990–2012 -2·6 (-2·7 to -2·5)* 2012–2019 -1·4 (-2·0 to -0·8)* High-income 6769·8 (6523·5 to 7040·0) 4584·6 (4336·5 to 4839·4) -1·3 (-1·4 to -1·3) < 0·001 1990–2004 -2·5 (-2·5 to -2·4)* 2004–2019 -0·3 (-0·3 to -0·3)* Geographical location Sub-Saharan Africa 19074·4 (18606·9 to 19515·9) 18845·0 (18376·6 to 19300·7) 0·0 (0·0 to 0·0) 0·763 1990–2007 -0·2 (-0·2 to -0·1)* 2007–2019 0·2 (0·1 to 0·3)* Middle East & North Africa 13373·6 (12792·1 to 13958·8) 9479·4 (8949·4 to 10024·8) -1·1 (-1·2 to -1·1) < 0·001 1999–2002 -1·5 (-1·6 to -1·4)* 2002–2019 -0·9 (-1·0 to -0·8)* Latin America & Caribbean 13806·7 (13226·7 to 14393·8) 9781·9 (9301·2 to 10271·4) -1·2 (-1·2 to -1·1) < 0·001 1990–2006 -1·5 (-1·6 to -1·5)* 2006–2019 -0·7 (-0·8 to -0·7)* East Asia & Pacific 13951·1 (13487·8 to 14378·9) 7294·3 (6970·2 to 7616·3) -2·4 (-2·6 to -2·2) < 0·001 1990–1996 -1·5 (-1·7 to -1·3)* 1996–2019 -3·0 (-3·1 to -3·0)* South Asia 29763·9 (29159·0 to 30272·5) 26860·7 (26274·4 to 27405·7) -0·3 (-0·4 to -0·3) < 0·001 1990–2000 0·1 (0·0 to 0·1)* 2000–2019 -0·6 (-0·6 to -0·5)* North America 4126·8 (3715·3 to 4572·4) 4028·2 (3504·5 to 4599·0) 0·0 (-0·2 to 0·2) 0·993 1990–2002 -2·9 (-3·3 to -2·5)* 2002–2019 2·1 (1·9 to 2·3)* Europe & Central Asia 9144·3 (8800·7 to 9520·8) 6747·6 (6440·7 to 7082·5) -1·1 (-1·3 to -1·0) < 0·001 1990–1994 0·1 (-0·9 to 1·1) 1994–2019 -1·3 (-1·4 to -1·3)* *Indicates a P-value less than 0·05. ASPR indicates age-standardized prevalence rate (Total cases per 100 000 population); APC, annual percentage change; AAPC, average annual percentage change; UI, uncertainty interval; CI, confidence interval. Association of dietary iron deficiency with dengue prevalence Table 3 shows the linear associations between dietary iron deficiency and dengue in the last 30 years. The prevalence of dietary iron deficiency was positively associated with the prevalence of dengue, with the crude coefficient of 19·3 (95% CI, 17·8 to 20·9). The association remained statistically significant even after adjusting for the HDI, population density, and temperature, and taking into account the systematic and random variation over 30 years (adj. coeff, 21·5, 95% CI 19·6 to 23·4). Table 3 Associations of dietary iron deficiency with dengue, from 1990 to 2019 in 204 countries and territories, overall and stratified by sex and age groups Prevalence Dengue (per 100k) Model 1 Model 2 Model 3 Model 4 Crude coeff·(95%CI) Adj. coeff·(95%CI) Adj. coeff·(95%CI) Adj. coeff·(95%CI) Total Dietary iron deficiency 19·3 (17·8, 20·9) *** 31·3 (29·2, 33·3) *** 31·0 (29·0, 33·1) *** 21·5 (19·6, 23·4) *** Sex Female 22·7 (20·9, 24·4) *** 29·5 (27·6, 31·5) *** 29·3 (27·3, 31·3) *** 19·8 (17·9, 21·6) *** Male 14·9 (13·5, 16·3) *** 25·4 (23·4, 27·5) *** 25·5 (23·4, 27·6) *** 18·4 (16·6, 20·3) *** Age category < 5 years 8·0 (7·2, 8·8) *** 9·8 (8·6, 11·0) *** 9·7 (8·5, 10·9) *** 3·4 (2·3, 4·5) *** 5–9 years 8·4 (6·7, 10·0) *** 11·9 (9·5, 14·3) *** 11·4 (9·0, 13·9) *** 1·5 (-0·7, 3·7) *** 10–24 years 14·3 (12·4, 16·2) *** 17·9 (15·6, 20·2) *** 17·5 (15·1, 19·8) *** 9·8 (7·6, 12·0) *** 25–49 years 24·2 (22·2, 26·1) *** 23·6 (21·5, 25·6) *** 23·6 (21·6, 25·7) *** 18·4 (16·5, 20·3) *** >=70 years 36·7 (33·2, 40·1) *** 64·0 (60·4, 67·5) *** 64·6 (61·0, 68·2) *** 55·3 (51·7, 59·0) *** **p < 0·05; ***p < 0·01 1-SD standardized coefficient, all models are mixed-effects model that treating the region as a fixed effect and time as a random effect. Adj. coeff, adjusted coefficient. Model 1: unadjusted Model 2: adjusted for Human Development Index (HDI) Model 3: adjusted for HDI, and population density Model 4: adjusted for HDI, population density, and temperature Dietary iron deficiency was also positively associated with dengue in females and males, with an adjusted coefficient of 19·8 (95% CI, 17·9 to 21·6) and 18·4 (95% CI, 16·6 to 20·3), respectively, as shown by the sex-stratified analyses (Table 3 , Supplementary Table 2). In addition, when stratified by age, the positive associations between dietary iron deficiency and dengue persisted across all age groups, especially in adults older than 70 years (adj. coeff, 55·3, 95% CI 51·7 to 59·0) (Table 3 , Supplementary Table 3).When stratified by economic status and adjusted for HDI, population density, and temperature, the strongest association between dietary iron deficiency and dengue was found in upper-middle-income countries, with an adjusted coefficient of 31·3 (95% CI, 27·2 to 35·5) (Fig. 2 ). For geographical location, the strongest association between dietary iron deficiency and dengue prevalence was found in the East Asia & Pacific region, with an adjusted coefficient of 59·8 (95% CI, 49·8 to 69·9). When stratified by the prevalence of dengue, areas where dengue is endemic observed the strongest association between iron deficiency and dengue, with an adjusted coefficient of 20·2 (95% CI, 17·0 to 23·3) (Fig. 2 ). Discussion This study presents the first observation of the association between dietary iron deficiency and dengue prevalence in 204 countries and territories from 1990 to 2019. The results highlighted the need to support the potential of iron to mitigate the dengue pandemic. Our findings reported the potential pathways underlying dietary iron deficiency and dengue. When considering geographical location and income status, a more significant association between dengue and dietary iron deficiency was found in Asia, particularly in countries with middle-income status. Additionally, there is a more significant association between dietary iron deficiency and dengue virus infection among females compared to males, and a more significant association among the elderly compared to other age groups. Our study provided epidemiological evidence to support the potential pathways underlying dietary iron deficiency and dengue. Firstly, the nutritional status of the host is a strong predictor of immunity. 16 Iron is required for proper immune function as it promotes the growth and differentiation of various immune cells. Iron deficiency has been found to reduce mitogen reactivity, NK cell activity, lymphocyte bactericidal activity, and neutrophil phagocytic activity, while also affecting cytokine activity at each stage of the immune response to dengue virus infection. 17 Secondly, experiments have shown iron inhibited Influenza A virus, HIV virus, Zika virus, and Enterovirus 71 (EV71) infections. Mechanistically, iron inhibited viral infection through inducing viral fusion and blocking endosomal viral release. 18 Finally, regarding the transmission route, the infection of Ae. aegypti with dengue virus was negatively correlated with the concentration of iron in human hosts. Iron deficiency in human populations may lead to vector tolerance of the dengue virus, thus promoting its transmission through mosquitoes. 7 A significant positive association between dengue and dietary iron deficiency was found in Asia, particularly in countries with middle-income status. Both the prevalence of dietary iron deficiency and dengue were high in this region. South Asian countries such as India, Pakistan, and Nepal are known to be endemic for dengue fever. 19 Dengue episodes have also occurred in China, Japan, and Korea. 20 Rapid urbanization and ineffective vector control measures in Asia contribute to the spread of dengue. 21 Despite these challenges, many people in Asian countries still live in poverty and suffer from dietary iron deficiency. 22 Iron is essential for immune function and iron deficiency can impair the body's ability to fight off dengue virus infections. 17 Possible factors leading to the high prevalence of both iron deficiency and dengue in middle-income countries could be strengthened surveillance systems along with higher detection rates and insufficient intervention in these countries. Moreover, disadvantaged subpopulations including those from middle-income countries are more likely to suffer from dietary iron deficiency and dengue prevalence, as reported in previous studies. 23 To summarize, all these different factors may act together and thereby contribute to remarkable association between the high prevalence of dengue and dietary iron deficiency in Asia countries with middle-income status. There is a more significant positive association between dietary iron deficiency and dengue virus infection among females compared to males. Studies have shown that the prevalence of dietary iron deficiency is higher in females than in males, 24 and this sex difference might be explained by differences in dietary intake of iron-rich foods. Additionally, females lose iron through menstrual blood (Each mL of blood contains 0·4–0·5 mg of iron) which can cause a negative iron balance. 25 Biologically, iron status can affect immune function and may impact the risk of dengue virus infection since iron promotes the growth and differentiation of various immune cells. 17 Furthermore, iron in human blood can affect mosquitoes infected with the dengue virus. Mosquitoes can use serum iron to activate the activity of reactive oxygen species in the intestinal epithelium, thereby inhibiting dengue virus infection. 7 Furthermore, there is a more significant positive association among the elderly compared to other age groups. This is partially due to their high risk of iron deficiency anemia and low immunity. 26 A study found that the elderly are more likely to suffer underlying diseases such as myelodysplastic syndromes, other blood cell disorders, cancer, chronic kidney diseases or certain gastrointestinal diseases, which contribute to the development of anemias in older age. Treating and managing anemias in older individuals remains a clinical challenge. 27 Successful vector control and surveillance programs lead to lower seroprevalence and herd immunity in adults, which may contribute to disease acquisition at a later stage in life. 28 With the WHO’s Integrated Vector Management strategy push in all its member states in high dengue prevalence regions, this shifting epidemiological trend is expected to persist, with the age of disease incidence steadily increasing over time. 29 Thus, the association among the elderly is more significant. The current dengue prevention and control situation is far away from the WHO’s global aim, established to reduce morbidity from dengue by at least 25% in 2020 compared to 2010. 30 Considering that low-cost dietary iron supplements can improve treatment success in patients with dengue, it is surprising that research in this area has been limited. Researchers should also evaluate the possibility of nutritional status being a predictor of the acquisition of DENV infection in endemic areas. Strengths and limitations This ecological analysis provides the most up-to-date estimates of dengue prevalence in correlation with dietary iron deficiency in 204 countries and territories from 1990 to 2019. Although the dengue prevalence is associated with some other risk factors, such as sex, population density and temperature. The analysis provided thus far the comprehensive examination of the association of iron deficiency with dengue at population level, while taking into consideration the differences in sex, age, geographical and economic regions, and considering the impact of population density, temperature, and human development. In addition, this study described the latest trend of dietary iron deficiency and dengue prevalence from 1990 to 2019. Also, we used robust analytical approach and various sensitivity analysis to examine the association between iron deficiency and dengue. For example, all models are mixed-effects model that treating the region as a fixed effect and time as a random effect. We provided continuous time series estimates of the association between iron deficiency and dengue with sufficiently long periods at the global and regional levels. Additionally, we conducted multiple subgroup analyses, including geographical and economic regions, dengue endemic status, sex, and age groups, to explore the association in different levels of sociodemographic status. This study has several limitations that should be considered when interpreting the findings. First, as an ecological study, our analysis was conducted at the population level rather than the individual level. While we observed a positive association between dietary iron deficiency and dengue prevalence, this does not necessarily imply causation. Ecological studies are prone to the ecological fallacy, where group-level associations may not hold at the individual level. Future research using longitudinal cohort studies and randomized controlled trials (RCTs) is needed to establish a causal relationship. Second, our study relies on data from the Global Burden of Disease (GBD) database, which, while comprehensive, has inherent limitations. The quality and availability of dengue prevalence data vary across countries, particularly in low-income and middle-income countries (LMICs) in sub-Saharan Africa, Asia, and Latin America & the Caribbean. Underreporting due to limited surveillance systems, inadequate medical facilities, and misdiagnosis may lead to an underestimation of the true burden of dengue. Additionally, the absence of direct incidence data in the GBD dataset meant that we could not examine the association between dietary iron deficiency and the incidence of dengue, but only its prevalence. Future studies should incorporate more granular epidemiological data to enhance the accuracy of the findings. Third, although we adjusted for key confounders such as Human Development Index (HDI), population density, and temperature, other potential confounding factors were not accounted for. For example, other micronutrient deficiencies, such as vitamin A and zinc, may also influence immune responses and susceptibility to dengue. Moreover, country-level variations in dengue control policies, vaccination programs, and vector control measures were not included in our models, which could impact the observed associations. Future studies should consider a broader range of confounders to refine the estimates. Despite these limitations, this study provides novel insights into the global trends of dengue and dietary iron deficiency and highlights the need for further research to explore potential intervention strategies. Conclusion In summary, this study has contributed to an improved understanding of the association between iron deficiency and dengue at a global and regional levels. Consistent with the laboratory studies, our results support the potential of dietary iron deficiency and increased risk of dengue transmission in large populations. This association was evident in both females and males, and across the life course. The highest burden of dengue and the strongest association between iron deficiency were found in middle-income countries, and dengue-endemic regions like East Asia & Pacific. More importantly, our study may indicate iron supplementation as a potential dengue prevention strategy, which should be targeted for vulnerable populations, including females and older adults. Future studies of the association between iron deficiency and dengue should consider using longitudinal or experimental study design to establish its causal relationship. Abbreviations GBD Global Burden of Disease Study IHME Institute for Health Metrics and Evaluation APC Annual percentage change UIs Uncertainty intervals HDI Human Development Index NASA National Aeronautics and Space Administration GISS Goddard Institute for Space Studies Declarations Acknowledgements We are thankful to the IHME, NASA, and United Nations Development Programme for providing the data for this article. Author contributions YL conceived the study with support from SYZ, YBZ, GC and KT. YL, SYZ, ZWH conducted main literature review. SYZ and ZWH contributed to data extraction and cleaning, and data analysis. YL, SYZ, ZWH, RJZ writing the first draft of the manuscript, and critically reviewed the manuscript for intellectual content. KT, GC and YBZ provided critical feedback on data sources, methods and results. All the authors (YL, SYZ, ZWH, RJZ, YBZ, GC and KT) contributed to the study design, data interpretation, and revisions to the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 20201301033) and the Independent Research Project of Tsinghua University, Vanke School of Public Health (Grant No. 2021PY009). Data availability All GBD 2019 data are publicly available online at the Global Burden of Disease Results Tool (http://ghdx.healthdata.org/gbd-results-tool). The year-specific land-ocean temperature data are obtained from the NASA global climate change website (https://climate.nasa.gov). The HDI data are obtained online from the United Nations Development Programme (https://hdr.undp.org/data-center/human-development-index#/indicies/HDI). Ethics approval and consent to participate As the study is drawn from publicly available data of multiple sources, a waiver for ethical approval was obtained from the Tsinghua University’s Medical Ethical Review Committee. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Simmons CP, Farrar JJ, Nguyen van VC, Wills B. Dengue. N Engl J Med. 2012; 366 :1423–32. World Health Organization. Dengue guidelines for diagnosis, treatment, prevention and control : new edition [Internet]. World Health Organization ; 2009. Report No.: WHO/HTM/NTD/DEN/2009.1. Available from: https://apps.who.int/iris/handle/10665/44188 Sabchareon A, Wallace D, Sirivichayakul C, Limkittikul K, Chanthavanich P, Suvannadabba S, et al. Protective efficacy of the recombinant, live-attenuated, CYD tetravalent dengue vaccine in Thai schoolchildren: a randomised, controlled phase 2b trial. Lancet . 2012; 380 :1559–67. Halstead SB. Dengue vaccine development: a 75% solution? Lancet. 2012; 380 :1535–6. Gubler DJ. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev. 1998; 11 :480–96. Drakesmith H, Prentice A. Viral infection and iron metabolism. Nat Rev Microbiol . 2008; 6 :541–52. Zhu Y, Tong L, Nie K, Wiwatanaratanabutr I, Sun P, Li Q, et al. Host serum iron modulates dengue virus acquisition by mosquitoes. Nat Microbiol . 2019; 4 :2405–15. Liu J, Liu Y, Nie K, Du S, Qiu J, Pang X, et al. Flavivirus NS1 protein in infected host sera enhances viral acquisition by mosquitoes. Nat Microbiol . 2016; 1 :16087. Liu H, Xu J-W, Bi Y. Malaria burden and treatment targets in Kachin Special Region II, Myanmar from 2008 to 2016: A retrospective analysis. PLoS One . 2018; 13 :e0195032. Spiegel PB, Hering H, Paik E, Schilperoord M. Conflict-affected displaced persons need to benefit more from HIV and malaria national strategic plans and Global Fund grants. Confl Health . 2010; 4 :2. Liu H, Yang H, Tang L, Li X, Huang F, Wang J, et al. In vivo monitoring of dihydroartemisinin-piperaquine sensitivity in Plasmodium falciparum along the China-Myanmar border of Yunnan Province, China from 2007 to 2013. Malar J . 2015; 14 :47. Blas E, Sivasankara Kurup A, Organization WH. Equity, social determinants and public health programmes [Internet]. World Health Organization ; 2010. Available from: https://apps.who.int/iris/handle/10665/44289 GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet . 2020;396:1204–22. IHME. GBD 2019 study [Internet]. Institute for Health Metrics and Evaluation . [cited 2023 May 27]. Available from: https://vizhub.healthdata.org/gbd-results hdr2021-22_technical_notes.pdf [Internet]. [cited 2023 May 27]. Available from: https://hdr.undp.org/sites/default/files/2021-22_HDR/hdr2021-22_technical_notes.pdf Keusch GT. The history of nutrition: malnutrition, infection and immunity. J Nutr . 2003; 133 :336S-340S. Ekiz C, Agaoglu L, Karakas Z, Gurel N, Yalcin I. The effect of iron deficiency anemia on the function of the immune system. Hematol J . 2005; 5 :579–83. Wang H, Li Z, Niu J, Xu Y, Ma L, Lu A, et al. Antiviral effects of ferric ammonium citrate. Cell Discov . 2018; 4 :14. Khadka S, Proshad R, Thapa A, Acharya KP, Kormoker T. Wolbachia: a possible weapon for controlling dengue in Nepal. Trop Med Health . 2020; 48 :50. Lee H, Kim JE, Lee S, Lee CH. Potential effects of climate change on dengue transmission dynamics in Korea. PLOS ONE . 2018; 13 :e0199205. Li Y, Kamara F, Zhou G, Puthiyakunnon S, Li C, Liu Y, et al. Urbanization increases Aedes albopictus larval habitats and accelerates mosquito development and survivorship. PLoS Negl Trop Dis . 2014; 8 :e3301. Nabeyama T, Suzuki Y, Yamamoto K, Sakane M, Sasaki Y, Shindo H, et al. Prevalence of iron deficiency among university kendo practitioners in Japan: an observational cohort study. J Int Soc Sports Nutr . 2020; 17 :62. Tian N, Zheng J-X, Guo Z-Y, Li L-H, Xia S, Lv S, et al. Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019. Trop Med Infect Dis. 2022; 7 :180. Han X, Ding S, Lu J, Li Y. Global, regional, and national burdens of common micronutrient deficiencies from 1990 to 2019: A secondary trend analysis based on the Global Burden of Disease 2019 study. EClinicalMedicine . 2022; 44 :101299. Rushton DH, Barth JH. What is the evidence for gender differences in ferritin and haemoglobin? Crit Rev Oncol Hematol . 2010; 73 :1–9. Wilder-Smith A, Ooi E-E, Horstick O, Wills B. Dengue. The Lancet . 2019; 393 :350–63. Stauder R, Valent P, Theurl I. Anemia at older age: etiologies, clinical implications, and management. Blood . 2018; 131 :505–14. Ooi E-E, Goh K-T, Gubler DJ. Dengue prevention and 35 years of vector control in Singapore. Emerg Infect Dis . 2006; 12 :887–93. Lin RJ, Lee TH, Leo YS. Dengue in the elderly: a review. Expert Rev Anti Infect Ther . 2017; 15 :729–35. World Health Organization. Global Strategy for dengue prevention and control, 2012–2020 [Internet]. Geneva; 2012 p. 49. Available from: https://www.who.int/publications-detail-redirect/9789241504034 Additional Declarations No competing interests reported. Supplementary Files supplementfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6154986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435816383,"identity":"0695d126-8b7d-40d6-bf11-f77c42545e6d","order_by":0,"name":"Yun Liang","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Liang","suffix":""},{"id":435816384,"identity":"8b58b436-266f-4617-a45b-0ffa4fe1b919","order_by":1,"name":"Siyu Zou","email":"","orcid":"","institution":"Johns Hopkins 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13:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6154986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6154986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79578187,"identity":"f52bac71-2b5c-421d-9423-dac2d80951ea","added_by":"auto","created_at":"2025-03-31 11:31:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in age-standardized prevalence rate (ASPR) of dengue and dietary iron deficiency, global and stratified by geographical region in the 204 countries and territories, 1999-2019\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6154986/v1/67317ea62d20cf2f2d04b244.png"},{"id":79580538,"identity":"968d20b7-ab72-4a5b-a6ba-b6afdcb3fea2","added_by":"auto","created_at":"2025-03-31 11:47:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of Dietary iron deficiency and Dengue, stratified by economic status, geographical location, and prevalence of dengue\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6154986/v1/0dc1605f77b08204de0f95f6.png"},{"id":99825854,"identity":"ee38b87b-67a6-4dfc-b310-ce961494d614","added_by":"auto","created_at":"2026-01-08 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1873595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6154986/v1/5db3b11a-b9b6-48d7-bf3d-9e2a911a5b58.pdf"},{"id":79579374,"identity":"e1a8cd70-e398-4cd0-a0e5-ad69a1f455d9","added_by":"auto","created_at":"2025-03-31 11:39:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1667082,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6154986/v1/c7e62669ef8743a6fe049d5b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global, regional, and national trends of dengue and its relationship with dietary iron deficiency: estimates for 204 countries and territories from 1990 to 2019","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDengue is an acute systemic viral infection which is transmitted between humans by mosquitoes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Dengue virus maintains its lifecycle between humans and \u003cem\u003eAedes\u003c/em\u003e mosquito species.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e For some patients, dengue is a life-threatening illness that has established itself globally in both endemic and epidemic transmission cycles. Although dengue virus infection in humans is often inapparent, it can lead to a wide range of clinical manifestations, from mild fever to potentially fatal dengue shock syndrome.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The lifelong immunity developed after infection with one of the four virus types is type-specific.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Progression to more serious disease is frequently, but not exclusively, associated with secondary infection by heterologous types.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Unfortunately, no effective antiviral agents exist to treat dengue infection, therefore, treatment remains supportive.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Moreover, no licensed vaccine against dengue infection is available, and the most advanced dengue vaccine candidate did not meet expectations in a recent large trial.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e At present, the measures to curb the spread of the dengue virus mainly focus on the control of mosquito vectors, such as the control of breeding sites for \u003cem\u003eAedes\u003c/em\u003e mosquitoes through biochemical means.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e However, these control measures have failed to prevent the increase in dengue prevalence and incidence rates and the expansion of the geographical scope of local transmission.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSome laboratory studies have shown that iron is an essential nutrient for maintaining a variety of cellular, immune, and metabolic activities.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e It regulates many viral infections in humans. The pleiotropic role of iron in the pathogenesis of dengue fever, malaria and other infectious diseases has been noted. The acquisition of dengue virus by \u003cem\u003eAedes aegypti\u003c/em\u003e was negatively correlated with the serum iron concentration of the donor. In laboratory studies, Iron supplementation reduced the prevalence and viral load of dengue virus, while serum iron neutralization promoted dengue virus infection in \u003cem\u003eAe. aegypti\u003c/em\u003e mosquitoes.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Mosquitoes that feed on iron deficient hosts exhibit higher dengue virus prevalence, while reversing host iron deficiency significantly reduces dengue virus acquisition in mosquitoes.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e It has been shown that the iron metabolism pathway of mosquitoes utilizes serum iron instead of heme binding iron to increase the activity of reactive oxygen species in the intestinal epithelium, thereby inhibiting dengue virus infection.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These studies suggest that iron deficiency in humans may contribute to vector transmission of the dengue virus, thus facilitating its transmission by mosquitoes.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Dengue spreads more easily due to poor sanitary conditions, overcrowding, dirty water resources, and limited medical resources.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The poor living environment may aggravate the spread of infectious diseases and dengue fever is an infectious disease closely related to climate.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Poor environmental conditions and a lack of proper nutrition can lead to iron deficiency, which is more common among vulnerable groups, including low-income people, refugees, and immigrants from low-income and middle-income countries.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, there is still a lack of epidemiological evidence to explore the association between iron deficiency and dengue based on large population studies.\u003c/p\u003e \u003cp\u003eTo investigate the potential association of dietary iron deficiency with dengue prevalence at population level, we analyzed the data from the Global Burden of Disease Study 2019 (GBD 2019) to provide updated estimates of the prevalence of dengue related to dietary iron deficiency at global, regional, and national levels across 204 countries and territories from 1990 to 2019. We also examined the relationship between dietary iron deficiency and dengue prevalence by different age groups, sex, levels of economic status, and geographical location, while considering key dimensions of human development, population density, and temperature.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eData on dengue and dietary iron deficiency from 1990 to 2019 were obtained from the Institute for Health Metrics and Evaluation (IHME), a data-sharing center for the GBD (available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on Dec 10, 2022). The GBD 2019 utilized standardized and replicable methods to provide a variety of relevant indicators to measure population health loss from hundreds of diseases, injuries, and risk factors, and a total of 204 countries and territories were included in the final dataset for analysis. Details of the GBD 2019 study design and methods used to estimate the incidence and prevalence of causes of death and disease have been described previously.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Briefly, prevalence rates of dengue and dietary iron deficiency were modelled from multiple relevant data sources by using the disease model-Bayesian meta-regression (DisMod-MR) tool, including disease registry data, censuses, epidemiological surveillance data, and other sources. The uncertainty of prevalence rates was estimated by 95% uncertainty intervals (UIs). 95% UI was defined as the 2\u0026middot;5th and 97\u0026middot;5th ordered values of the 1000 draw-level estimates. The prevalence rates and their 95% UIs from 1990 to 2019 were directly downloaded from the Global Health Data Exchange (GHDx) GBD Results Tool.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDue to the missing value of the incidence rates of dietary iron deficiency, we selected the prevalence rates of dengue and dietary iron deficiency.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The prevalence rates per 100,000 population in both sexes were obtained and compared at the global, regional, and national levels.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDengue and dietary iron deficiency\u003c/h2\u003e \u003cp\u003eThe primary measure used in this study was the age-standardized prevalence rate (ASPR) of dengue and dietary iron deficiency, expressed as the number of cases per 100,000 population. The estimates for dengue represent cases of clinically significant diseases (i.e. dengue fever, dengue haemorrhagic fever, dengue shock syndrome, and resulting chronic fatigue syndrome). These estimates do not represent asymptomatic dengue virus infection. Dietary iron deficiency in the GBD 2019 referred to the iron deficiency due to insufficient dietary iron intake, rather than absolute or functional iron deficiency due to other causes.\u003c/p\u003e \u003cp\u003eThe 204 countries and territories were divided into higher and lower dengue endemic region, based on the prevalence of dengue. Endemic areas were those where the dengue prevalence rate was higher than the global median prevalence rate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeographical location and economic status\u003c/h3\u003e\n\u003cp\u003eThe 204 countries and territories included in the study were divided into seven regions based on their geographical locations as defined by the World Bank. These regions were Sub-Saharan Africa, Middle East \u0026amp; North Africa, Latin America \u0026amp; Caribbean, East Asia \u0026amp; Pacific, South Asia, North America, and Europe \u0026amp; Central Asia. Additionally, the countries were classified based on their economic status: including low-income countries, lower middle-income countries, upper-middle-income countries, and high-income countries as per World Bank definition.\u003c/p\u003e\n\u003ch3\u003ePopulation density\u003c/h3\u003e\n\u003cp\u003ePopulation density (people per sq. km of land area) from 1990 to 2019 in each country was also obtained from The World Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Population density was calculated by dividing mid-year population by land area in square kilometers. The population count included all residents regardless of legal status or citizenship of their country of origin, while land area referred to a country's total area.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTemperature\u003c/h2\u003e \u003cp\u003eThe year-specific temperature index for every country was used in this study. The land-ocean temperature index for every country from 1990 to 2019 was obtained from the National Aeronautics and Space Administration (NASA) global climate change website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climate.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://climate.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data was credited by NASA's Goddard Institute for Space Studies (GISS).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHuman Development Index (HDI)\u003c/h3\u003e\n\u003cp\u003eHuman Development Index (HDI) is a composite index used to measure the average achievement in three basic dimensions of human development: a long and healthy life, knowledge and a decent standard of living. The Human Development Index (HDI) for every country from 1990 to 2019 was obtained from the United Nations Development Programme. The HDI is a composite index used to measure average achievement in three key dimensions of human development: a long and healthy life, access to knowledge, and a decent standard of living. The HDI is the geometric mean of the three-dimensional indexes (see Supplementary Table\u0026nbsp;1). Detailed methods for calculating the HDI have been described previously.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eOther covariates\u003c/h3\u003e\n\u003cp\u003eWe divided the age groups into six categories: \u0026lt;5 years old, 5\u0026ndash;9 years old, 10\u0026ndash;24 years old, 25\u0026ndash;49 years old, 50\u0026ndash;69 years old, and \u0026ge;\u0026thinsp;70 years old. We also classified the participants by sex, dividing them into male and female categories.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eASPR and 95% UI of dengue and dietary iron deficiency were reported to show the trends over 30 years at the global and regional levels. We conducted joinpoint regression analysis to examine changes in prevalence rates of dengue and iron deficiencies. This regression model is useful for identifying significant changes in linear trend slopes. We computed the estimated annual percentage change (APC, %) for each trend by fitting a regression line to the natural logarithm of the rates within each period or phase. At the country and region level, we estimated the average annual change in percentage per year and 95% confidence interval (CI) using a linear regression model, with prevalence rates of dengue and iron deficiency as the dependent variable and year as the independent variable.\u003c/p\u003e \u003cp\u003eFurthermore, we conducted generalized linear mixed models to investigate the association between iron deficiency and dengue. These models were controlled for region-fixed effect and time random effect. We considered a total of four models in this study: Model 1, which was a univariate mixed model; Model 2, which was adjusted for HDI; Model 3, which was adjusted for HDI, and population density; Model 4, which was adjusted for HDI, population density, and temperature. We reported Odds Ratios (ORs) and 95% CIs for these models. In addition, subgroup analysis was conducted to investigate the association between dietary iron deficiency and dengue in different economic statuses, geographical locations, and prevalence of dengue, while adjusting for all covariates mentioned above. The data were analyzed in R version 4\u0026middot;2\u0026middot;1 using the lme4 package, and Joinpoint version 4\u0026middot;9\u0026middot;1\u0026middot;0\u0026middot; The Jointpoint software, developed by the Surveillance Research Program of the United States National Cancer Institute, was used for this purpose.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGlobal trends for dengue prevalence\u003c/h2\u003e \u003cp\u003eIn 1990 and 2019, the ASPR of dengue globally was 33\u0026middot;2 (95% UI 14\u0026middot;2 to 71\u0026middot;9) and 44\u0026middot;2 (95% UI 28.1 to 79.2) per 100,000 population, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The global ASPR increased over the 30 years, with estimated average annual percentage changes (AAPCs) of 1\u0026middot;0 (95% CI, 0\u0026middot;9 to 1\u0026middot;1). However, the trends did not consistently increase over time and had two different phases worldwide (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During the first phase, the prevalence of dengue slowly and steadily increased from 1990 to 2010 (APC 1\u0026middot;7 [95% CI 1\u0026middot;6 to 1\u0026middot;8]), with a total increase of 38%. In the second phase, the prevalence rate of dengue decreased between 2010 and 2019, with a total decrease of 4% (APC \u0026minus;\u0026thinsp;0\u0026middot;4 [95% CI -0\u0026middot;7 to -0\u0026middot;2]).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-standardized prevalence rate (ASPR) for dengue in 1990 and 2019 and estimated annual percentage changes (APC) by region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASPR in 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASPR in 2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAverage APC between 1990 and 2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTrend 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eTrend 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u0026middot;2 (14\u0026middot;2 to 71\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u0026middot;2 (28\u0026middot;1 to 79\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;0 (0\u0026middot;9 to 1\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;7 (1\u0026middot;6 to 1\u0026middot;8)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;4 (-0\u0026middot;7 to -0\u0026middot;2)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026middot;57 (15\u0026middot;34 to 43\u0026middot;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026middot;45 (8\u0026middot;76 to 44\u0026middot;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;5 (-0\u0026middot;8 to -0\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;4 (1\u0026middot;2 to 1\u0026middot;7)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4\u0026middot;6 (-5\u0026middot;5 to -3\u0026middot;7)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u0026middot;08 (23\u0026middot;31 to 179\u0026middot;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u0026middot;94 (37\u0026middot;33 to 166\u0026middot;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;3 (0\u0026middot;2 to 0\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;7 (0\u0026middot;6 to 0\u0026middot;8)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;7 (-0\u0026middot;9 to -0\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026middot;13 (11\u0026middot;56 to 16\u0026middot;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u0026middot;36 (17\u0026middot;30 to 41\u0026middot;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026middot;4 (2\u0026middot;3 to 2\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u0026middot;5 (3\u0026middot;4 to 3\u0026middot;6)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2009\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u0026middot;3 (0\u0026middot;1 to 0\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026middot;88 (2\u0026middot;04 to 3\u0026middot;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u0026middot;93 (3\u0026middot;30 to 7\u0026middot;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026middot;0 (1\u0026middot;8 to 2\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u0026middot;2 (3\u0026middot;0 to 3\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2008\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u0026middot;0 (-0\u0026middot;3 to 0\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026middot;96 (18\u0026middot;53 to 46\u0026middot;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u0026middot;66 (9\u0026middot;40 to 47\u0026middot;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;5 (-0\u0026middot;8 to -0\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;2 (0\u0026middot;9 to 1\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4\u0026middot;2 (-5\u0026middot;0 to -3\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle East \u0026amp; North Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026middot;15 (1\u0026middot;25 to 13\u0026middot;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026middot;47 (0\u0026middot;74 to 15\u0026middot;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;2 (-0\u0026middot;1 to 0\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;0 (0\u0026middot;8 to 1\u0026middot;2)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2011\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-2\u0026middot;0 (-2\u0026middot;7 to -1\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin America \u0026amp; Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u0026middot;54 (29\u0026middot;23 to 45\u0026middot;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u0026middot;89 (41\u0026middot;80 to 50\u0026middot;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;5 (0\u0026middot;1 to 0\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u0026middot;7 (2\u0026middot;3 to 3\u0026middot;0)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-4\u0026middot;2 (-5\u0026middot;2 to -3\u0026middot;2)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026middot;01 (13\u0026middot;37 to 46\u0026middot;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u0026middot;94 (25\u0026middot;83 to 59\u0026middot;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026middot;3 (2\u0026middot;0 to 2\u0026middot;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;4 (-1\u0026middot;1 to 1\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1996\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;9 (2\u0026middot;7 to 3\u0026middot;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u0026middot;19 (25\u0026middot;41 to 252\u0026middot;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101\u0026middot;76 (37\u0026middot;84 to 247\u0026middot;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;2 (0\u0026middot;2 to 0\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;6 (0\u0026middot;6 to 0\u0026middot;6)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2009\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;6 (-0\u0026middot;6 to -0\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;30 (0\u0026middot;19 to 0\u0026middot;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;38 (0\u0026middot;26 to 0\u0026middot;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;9 (0\u0026middot;7 to 3\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2\u0026middot;1 (-5\u0026middot;1 to 1\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4\u0026middot;1 (2\u0026middot;9 to 5\u0026middot;4)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope \u0026amp; Central Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Indicates a \u003cem\u003eP-value\u003c/em\u003e less than 0\u0026middot;05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eASPR indicates age-standardized prevalence rate (Total cases per 100 000 population); APC, annual percentage change; AAPC, average annual percentage change; UI, uncertainty interval; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding geographical location, the ASPR of dengue per 100,000 population was highest in South Asia (101\u0026middot;76 [95% UI 37\u0026middot;84 to 247\u0026middot;73]), followed by Latin America \u0026amp; Caribbean (45.89 [95% UI 41.80 to 50.35]), East Asia \u0026amp; Pacific (39.94 [95% UI 25.83 to 59.37]), Sub-Saharan Africa (27.66 [95% UI 9.40 to 47.76]) and Middle East \u0026amp; North Africa (6.47 [95% UI 0.74 to 15.12]) in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Concerning economic status, the ASPR of dengue per 100,000 population was highest in lower-middle-income countries (77\u0026middot;94 [95% UI 37\u0026middot;33 to 166\u0026middot;33]), followed by upper-middle-income countries (27\u0026middot;36 [95% UI 17\u0026middot;30 to 41\u0026middot;69]) in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGlobal trend for dietary iron deficiency\u003c/h2\u003e \u003cp\u003eThe ASPR of dietary iron deficiency globally decreased from 16 252\u0026middot;6 (95% UI 15948\u0026middot;8 to 16509\u0026middot;4) per 100 000 population to 14 106\u0026middot;4 (95% UI 14342\u0026middot;1 to 13850\u0026middot;7) over the past 30 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Globally, the prevalence of dietary iron deficiency decreased by 0.5% per year (APC \u0026minus;\u0026thinsp;0\u0026middot;5 [-0\u0026middot;6 to -0\u0026middot;5]) between 1999 and 2019. At the geographical level, the ASPR of dietary iron deficiency per 100 000 population was highest in South Asia (26 860\u0026middot;7 [95% UI 26 274\u0026middot;4 to 27 405\u0026middot;7]), followed by Sub-Saharan Africa (18 845\u0026middot;0 [95% UI 18376\u0026middot;6 to 19 300\u0026middot;7]) in 2019. In terms of economic status, the ASPR of dietary iron deficiency was highest in lower-middle-income countries (22 271\u0026middot;9 [95% UI 21 838\u0026middot;5 to 22 675\u0026middot;3]), followed by low-income countries (18 932\u0026middot;3 [95% UI 18 485\u0026middot;9 to 19 373\u0026middot;6]) in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplemental Fig.\u0026nbsp;2). The decreasing trends of dietary iron deficiency in different geographical locations and economic statuses were generally consistent with the global level (Supplemental Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-standardized prevalence rate (ASPR) for dietary iron deficiency in 1990 and 2019 and estimated annual percentage changes (APC) by region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASPR in 1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASPR in 2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAverage APC between 1990 and 2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTrend 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eTrend 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRate (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAPC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary iron deficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16252\u0026middot;6 (15948\u0026middot;8 to 16509\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14106\u0026middot;4 (14342\u0026middot;1 to 13850\u0026middot;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;5 (-0\u0026middot;6 to -0\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0\u0026middot;7 (-0\u0026middot;7 to -0\u0026middot;6)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;2 (-0\u0026middot;3 to -0\u0026middot;1)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20261\u0026middot;0 (19834\u0026middot;0 to 20685\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18932\u0026middot;3 (18485\u0026middot;9 to 19373\u0026middot;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;2 (-0\u0026middot;3 to -0\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0\u0026middot;3 (-0\u0026middot;3 to -0\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2010\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;1 (-0\u0026middot;2 to 0\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24740\u0026middot;4 (24279\u0026middot;9 to 25175\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22271\u0026middot;9 (21838\u0026middot;5 to 22675\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;4 (-0\u0026middot;4 to -0\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0\u0026middot;1 (-0\u0026middot;2 to 0\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1999\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;5 (-0\u0026middot;5 to -0\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12878\u0026middot;9 (12474\u0026middot;3 to 13250\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6818\u0026middot;1 (6515\u0026middot;9 to 7108\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026middot;3 (-2\u0026middot;5 to -2\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2\u0026middot;6 (-2\u0026middot;7 to -2\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2012\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1\u0026middot;4 (-2\u0026middot;0 to -0\u0026middot;8)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6769\u0026middot;8 (6523\u0026middot;5 to 7040\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4584\u0026middot;6 (4336\u0026middot;5 to 4839\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026middot;3 (-1\u0026middot;4 to -1\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2\u0026middot;5 (-2\u0026middot;5 to -2\u0026middot;4)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2004\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;3 (-0\u0026middot;3 to -0\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeographical location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19074\u0026middot;4 (18606\u0026middot;9 to 19515\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18845\u0026middot;0 (18376\u0026middot;6 to 19300\u0026middot;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;0 (0\u0026middot;0 to 0\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0\u0026middot;2 (-0\u0026middot;2 to -0\u0026middot;1)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2007\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u0026middot;2 (0\u0026middot;1 to 0\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle East \u0026amp; North Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13373\u0026middot;6 (12792\u0026middot;1 to 13958\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9479\u0026middot;4 (8949\u0026middot;4 to 10024\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026middot;1 (-1\u0026middot;2 to -1\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1999\u0026ndash;2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1\u0026middot;5 (-1\u0026middot;6 to -1\u0026middot;4)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2002\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;9 (-1\u0026middot;0 to -0\u0026middot;8)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin America \u0026amp; Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13806\u0026middot;7 (13226\u0026middot;7 to 14393\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9781\u0026middot;9 (9301\u0026middot;2 to 10271\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026middot;2 (-1\u0026middot;2 to -1\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1\u0026middot;5 (-1\u0026middot;6 to -1\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2006\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;7 (-0\u0026middot;8 to -0\u0026middot;7)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asia \u0026amp; Pacific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13951\u0026middot;1 (13487\u0026middot;8 to 14378\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7294\u0026middot;3 (6970\u0026middot;2 to 7616\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026middot;4 (-2\u0026middot;6 to -2\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1\u0026middot;5 (-1\u0026middot;7 to -1\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1996\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-3\u0026middot;0 (-3\u0026middot;1 to -3\u0026middot;0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29763\u0026middot;9 (29159\u0026middot;0 to 30272\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26860\u0026middot;7 (26274\u0026middot;4 to 27405\u0026middot;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0\u0026middot;3 (-0\u0026middot;4 to -0\u0026middot;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;1 (0\u0026middot;0 to 0\u0026middot;1)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0\u0026middot;6 (-0\u0026middot;6 to -0\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4126\u0026middot;8 (3715\u0026middot;3 to 4572\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4028\u0026middot;2 (3504\u0026middot;5 to 4599\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;0 (-0\u0026middot;2 to 0\u0026middot;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2\u0026middot;9 (-3\u0026middot;3 to -2\u0026middot;5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2002\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u0026middot;1 (1\u0026middot;9 to 2\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope \u0026amp; Central Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9144\u0026middot;3 (8800\u0026middot;7 to 9520\u0026middot;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6747\u0026middot;6 (6440\u0026middot;7 to 7082\u0026middot;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026middot;1 (-1\u0026middot;3 to -1\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1990\u0026ndash;1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;1 (-0\u0026middot;9 to 1\u0026middot;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1994\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1\u0026middot;3 (-1\u0026middot;4 to -1\u0026middot;3)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Indicates a \u003cem\u003eP-value\u003c/em\u003e less than 0\u0026middot;05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eASPR indicates age-standardized prevalence rate (Total cases per 100 000 population); APC, annual percentage change; AAPC, average annual percentage change; UI, uncertainty interval; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of dietary iron deficiency with dengue prevalence\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the linear associations between dietary iron deficiency and dengue in the last 30 years. The prevalence of dietary iron deficiency was positively associated with the prevalence of dengue, with the crude coefficient of 19\u0026middot;3 (95% CI, 17\u0026middot;8 to 20\u0026middot;9). The association remained statistically significant even after adjusting for the HDI, population density, and temperature, and taking into account the systematic and random variation over 30 years (adj. coeff, 21\u0026middot;5, 95% CI 19\u0026middot;6 to 23\u0026middot;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAssociations of dietary iron deficiency with dengue, from 1990 to 2019 in 204 countries and territories, overall and stratified by sex and age groups\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eDengue (per 100k)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude coeff\u0026middot;(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdj. coeff\u0026middot;(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdj. coeff\u0026middot;(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAdj. coeff\u0026middot;(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary iron deficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026middot;3 (17\u0026middot;8, 20\u0026middot;9) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u0026middot;3 (29\u0026middot;2, 33\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u0026middot;0 (29\u0026middot;0, 33\u0026middot;1) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e21\u0026middot;5 (19\u0026middot;6, 23\u0026middot;4) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026middot;7 (20\u0026middot;9, 24\u0026middot;4) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u0026middot;5 (27\u0026middot;6, 31\u0026middot;5) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u0026middot;3 (27\u0026middot;3, 31\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e19\u0026middot;8 (17\u0026middot;9, 21\u0026middot;6) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026middot;9 (13\u0026middot;5, 16\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026middot;4 (23\u0026middot;4, 27\u0026middot;5) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u0026middot;5 (23\u0026middot;4, 27\u0026middot;6) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18\u0026middot;4 (16\u0026middot;6, 20\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026middot;0 (7\u0026middot;2, 8\u0026middot;8) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026middot;8 (8\u0026middot;6, 11\u0026middot;0) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u0026middot;7 (8\u0026middot;5, 10\u0026middot;9) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3\u0026middot;4 (2\u0026middot;3, 4\u0026middot;5) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026middot;4 (6\u0026middot;7, 10\u0026middot;0) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u0026middot;9 (9\u0026middot;5, 14\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u0026middot;4 (9\u0026middot;0, 13\u0026middot;9) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;5 (-0\u0026middot;7, 3\u0026middot;7) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u0026middot;3 (12\u0026middot;4, 16\u0026middot;2) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u0026middot;9 (15\u0026middot;6, 20\u0026middot;2) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u0026middot;5 (15\u0026middot;1, 19\u0026middot;8) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e9\u0026middot;8 (7\u0026middot;6, 12\u0026middot;0) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026middot;2 (22\u0026middot;2, 26\u0026middot;1) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u0026middot;6 (21\u0026middot;5, 25\u0026middot;6) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u0026middot;6 (21\u0026middot;6, 25\u0026middot;7) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18\u0026middot;4 (16\u0026middot;5, 20\u0026middot;3) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026middot;7 (33\u0026middot;2, 40\u0026middot;1) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u0026middot;0 (60\u0026middot;4, 67\u0026middot;5) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u0026middot;6 (61\u0026middot;0, 68\u0026middot;2) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e55\u0026middot;3 (51\u0026middot;7, 59\u0026middot;0) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;01\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e1-SD standardized coefficient, all models are mixed-effects model that treating the region as a fixed effect and time as a random effect.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAdj. coeff, adjusted coefficient.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 1: unadjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 2: adjusted for Human Development Index (HDI)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 3: adjusted for HDI, and population density\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel 4: adjusted for HDI, population density, and temperature\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDietary iron deficiency was also positively associated with dengue in females and males, with an adjusted coefficient of 19\u0026middot;8 (95% CI, 17\u0026middot;9 to 21\u0026middot;6) and 18\u0026middot;4 (95% CI, 16\u0026middot;6 to 20\u0026middot;3), respectively, as shown by the sex-stratified analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;2). In addition, when stratified by age, the positive associations between dietary iron deficiency and dengue persisted across all age groups, especially in adults older than 70 years (adj. coeff, 55\u0026middot;3, 95% CI 51\u0026middot;7 to 59\u0026middot;0) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;3).When stratified by economic status and adjusted for HDI, population density, and temperature, the strongest association between dietary iron deficiency and dengue was found in upper-middle-income countries, with an adjusted coefficient of 31\u0026middot;3 (95% CI, 27\u0026middot;2 to 35\u0026middot;5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For geographical location, the strongest association between dietary iron deficiency and dengue prevalence was found in the East Asia \u0026amp; Pacific region, with an adjusted coefficient of 59\u0026middot;8 (95% CI, 49\u0026middot;8 to 69\u0026middot;9). When stratified by the prevalence of dengue, areas where dengue is endemic observed the strongest association between iron deficiency and dengue, with an adjusted coefficient of 20\u0026middot;2 (95% CI, 17\u0026middot;0 to 23\u0026middot;3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents the first observation of the association between dietary iron deficiency and dengue prevalence in 204 countries and territories from 1990 to 2019. The results highlighted the need to support the potential of iron to mitigate the dengue pandemic. Our findings reported the potential pathways underlying dietary iron deficiency and dengue. When considering geographical location and income status, a more significant association between dengue and dietary iron deficiency was found in Asia, particularly in countries with middle-income status. Additionally, there is a more significant association between dietary iron deficiency and dengue virus infection among females compared to males, and a more significant association among the elderly compared to other age groups.\u003c/p\u003e \u003cp\u003eOur study provided epidemiological evidence to support the potential pathways underlying dietary iron deficiency and dengue. Firstly, the nutritional status of the host is a strong predictor of immunity.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Iron is required for proper immune function as it promotes the growth and differentiation of various immune cells. Iron deficiency has been found to reduce mitogen reactivity, NK cell activity, lymphocyte bactericidal activity, and neutrophil phagocytic activity, while also affecting cytokine activity at each stage of the immune response to dengue virus infection.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Secondly, experiments have shown iron inhibited Influenza A virus, HIV virus, Zika virus, and Enterovirus 71 (EV71) infections. Mechanistically, iron inhibited viral infection through inducing viral fusion and blocking endosomal viral release.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Finally, regarding the transmission route, the infection of \u003cem\u003eAe. aegypti\u003c/em\u003e with dengue virus was negatively correlated with the concentration of iron in human hosts. Iron deficiency in human populations may lead to vector tolerance of the dengue virus, thus promoting its transmission through mosquitoes.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA significant positive association between dengue and dietary iron deficiency was found in Asia, particularly in countries with middle-income status. Both the prevalence of dietary iron deficiency and dengue were high in this region. South Asian countries such as India, Pakistan, and Nepal are known to be endemic for dengue fever.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Dengue episodes have also occurred in China, Japan, and Korea.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Rapid urbanization and ineffective vector control measures in Asia contribute to the spread of dengue.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Despite these challenges, many people in Asian countries still live in poverty and suffer from dietary iron deficiency.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Iron is essential for immune function and iron deficiency can impair the body's ability to fight off dengue virus infections.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Possible factors leading to the high prevalence of both iron deficiency and dengue in middle-income countries could be strengthened surveillance systems along with higher detection rates and insufficient intervention in these countries. Moreover, disadvantaged subpopulations including those from middle-income countries are more likely to suffer from dietary iron deficiency and dengue prevalence, as reported in previous studies.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e To summarize, all these different factors may act together and thereby contribute to remarkable association between the high prevalence of dengue and dietary iron deficiency in Asia countries with middle-income status.\u003c/p\u003e \u003cp\u003eThere is a more significant positive association between dietary iron deficiency and dengue virus infection among females compared to males. Studies have shown that the prevalence of dietary iron deficiency is higher in females than in males,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and this sex difference might be explained by differences in dietary intake of iron-rich foods. Additionally, females lose iron through menstrual blood (Each mL of blood contains 0\u0026middot;4\u0026ndash;0\u0026middot;5 mg of iron) which can cause a negative iron balance.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Biologically, iron status can affect immune function and may impact the risk of dengue virus infection since iron promotes the growth and differentiation of various immune cells.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Furthermore, iron in human blood can affect mosquitoes infected with the dengue virus. Mosquitoes can use serum iron to activate the activity of reactive oxygen species in the intestinal epithelium, thereby inhibiting dengue virus infection.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFurthermore, there is a more significant positive association among the elderly compared to other age groups. This is partially due to their high risk of iron deficiency anemia and low immunity.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e A study found that the elderly are more likely to suffer underlying diseases such as myelodysplastic syndromes, other blood cell disorders, cancer, chronic kidney diseases or certain gastrointestinal diseases, which contribute to the development of anemias in older age. Treating and managing anemias in older individuals remains a clinical challenge.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Successful vector control and surveillance programs lead to lower seroprevalence and herd immunity in adults, which may contribute to disease acquisition at a later stage in life.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e With the WHO\u0026rsquo;s Integrated Vector Management strategy push in all its member states in high dengue prevalence regions, this shifting epidemiological trend is expected to persist, with the age of disease incidence steadily increasing over time.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Thus, the association among the elderly is more significant. The current dengue prevention and control situation is far away from the WHO\u0026rsquo;s global aim, established to reduce morbidity from dengue by at least 25% in 2020 compared to 2010.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Considering that low-cost dietary iron supplements can improve treatment success in patients with dengue, it is surprising that research in this area has been limited. Researchers should also evaluate the possibility of nutritional status being a predictor of the acquisition of DENV infection in endemic areas.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis ecological analysis provides the most up-to-date estimates of dengue prevalence in correlation with dietary iron deficiency in 204 countries and territories from 1990 to 2019. Although the dengue prevalence is associated with some other risk factors, such as sex, population density and temperature. The analysis provided thus far the comprehensive examination of the association of iron deficiency with dengue at population level, while taking into consideration the differences in sex, age, geographical and economic regions, and considering the impact of population density, temperature, and human development. In addition, this study described the latest trend of dietary iron deficiency and dengue prevalence from 1990 to 2019. Also, we used robust analytical approach and various sensitivity analysis to examine the association between iron deficiency and dengue. For example, all models are mixed-effects model that treating the region as a fixed effect and time as a random effect. We provided continuous time series estimates of the association between iron deficiency and dengue with sufficiently long periods at the global and regional levels. Additionally, we conducted multiple subgroup analyses, including geographical and economic regions, dengue endemic status, sex, and age groups, to explore the association in different levels of sociodemographic status.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, as an ecological study, our analysis was conducted at the population level rather than the individual level. While we observed a positive association between dietary iron deficiency and dengue prevalence, this does not necessarily imply causation. Ecological studies are prone to the ecological fallacy, where group-level associations may not hold at the individual level. Future research using longitudinal cohort studies and randomized controlled trials (RCTs) is needed to establish a causal relationship. Second, our study relies on data from the Global Burden of Disease (GBD) database, which, while comprehensive, has inherent limitations. The quality and availability of dengue prevalence data vary across countries, particularly in low-income and middle-income countries (LMICs) in sub-Saharan Africa, Asia, and Latin America \u0026amp; the Caribbean. Underreporting due to limited surveillance systems, inadequate medical facilities, and misdiagnosis may lead to an underestimation of the true burden of dengue. Additionally, the absence of direct incidence data in the GBD dataset meant that we could not examine the association between dietary iron deficiency and the incidence of dengue, but only its prevalence. Future studies should incorporate more granular epidemiological data to enhance the accuracy of the findings. Third, although we adjusted for key confounders such as Human Development Index (HDI), population density, and temperature, other potential confounding factors were not accounted for. For example, other micronutrient deficiencies, such as vitamin A and zinc, may also influence immune responses and susceptibility to dengue. Moreover, country-level variations in dengue control policies, vaccination programs, and vector control measures were not included in our models, which could impact the observed associations. Future studies should consider a broader range of confounders to refine the estimates. Despite these limitations, this study provides novel insights into the global trends of dengue and dietary iron deficiency and highlights the need for further research to explore potential intervention strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study has contributed to an improved understanding of the association between iron deficiency and dengue at a global and regional levels. Consistent with the laboratory studies, our results support the potential of dietary iron deficiency and increased risk of dengue transmission in large populations. This association was evident in both females and males, and across the life course. The highest burden of dengue and the strongest association between iron deficiency were found in middle-income countries, and dengue-endemic regions like East Asia \u0026amp; Pacific. More importantly, our study may indicate iron supplementation as a potential dengue prevention strategy, which should be targeted for vulnerable populations, including females and older adults. Future studies of the association between iron deficiency and dengue should consider using longitudinal or experimental study design to establish its causal relationship.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBD \u0026nbsp; \u0026nbsp; Global Burden of Disease Study\u003c/p\u003e\n\u003cp\u003eIHME \u0026nbsp; \u0026nbsp; Institute for Health Metrics and Evaluation\u003c/p\u003e\n\u003cp\u003eAPC \u0026nbsp; \u0026nbsp; Annual percentage change\u003c/p\u003e\n\u003cp\u003eUIs \u0026nbsp; \u0026nbsp; \u0026nbsp; Uncertainty intervals\u003c/p\u003e\n\u003cp\u003eHDI \u0026nbsp; \u0026nbsp; \u0026nbsp;Human Development Index\u003c/p\u003e\n\u003cp\u003eNASA \u0026nbsp; \u0026nbsp;National Aeronautics and Space Administration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGISS \u0026nbsp; \u0026nbsp;Goddard Institute for Space Studies\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to the IHME, NASA, and United Nations Development Programme for providing the data for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL conceived the study with support from SYZ, YBZ, GC and KT. YL, SYZ, ZWH conducted main literature review. SYZ and ZWH contributed to data extraction and cleaning, and data analysis. YL, SYZ, ZWH, RJZ writing the first draft of the manuscript, and critically reviewed the manuscript for intellectual content. KT, GC and YBZ provided critical feedback on data sources, methods and results. All the authors (YL, SYZ, ZWH, RJZ, YBZ, GC and KT) contributed to the study design, data interpretation, and revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 20201301033) and the Independent Research Project of Tsinghua University, Vanke School of Public Health (Grant No. 2021PY009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll GBD 2019 data are publicly available online at the Global Burden of Disease Results Tool (http://ghdx.healthdata.org/gbd-results-tool). The year-specific land-ocean temperature data are obtained from the NASA global climate change website (https://climate.nasa.gov). The HDI data are obtained online from the United Nations Development Programme (https://hdr.undp.org/data-center/human-development-index#/indicies/HDI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the study is drawn from publicly available data of multiple sources, a waiver for ethical approval was obtained from the Tsinghua University\u0026rsquo;s Medical Ethical Review Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSimmons CP, Farrar JJ, Nguyen van VC, Wills B. 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Dengue prevention and 35 years of vector control in Singapore. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e. 2006;\u003cstrong\u003e12\u003c/strong\u003e:887\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eLin RJ, Lee TH, Leo YS. Dengue in the elderly: a review. \u003cem\u003eExpert Rev Anti Infect Ther\u003c/em\u003e. 2017;\u003cstrong\u003e15\u003c/strong\u003e:729\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Strategy for dengue prevention and control, 2012\u0026ndash;2020 [Internet]. Geneva; 2012 p. 49. Available from: https://www.who.int/publications-detail-redirect/9789241504034\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dietary iron deficiency, dengue, endemic regions, prevalence","lastPublishedDoi":"10.21203/rs.3.rs-6154986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6154986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDengue is one of the most prevalent infectious diseases and has caused significant public health problems worldwide. Experimental studies suggest that dietary iron deficiency is associated with an increased risk of dengue. This study aimed to investigate the association between dietary iron deficiency and dengue prevalence over the past 30 years using standardized epidemiological data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive analysis using data from the GBD 2019 study, which included age-standardized prevalence rates of dengue and dietary iron deficiency in 204 countries and territories from 1990 to 2019. The primary measure used in this study was the age-standardized prevalence rate (ASPR), expressed as cases per 100,000 population and their 95% uncertainty intervals (UIs). We evaluated the annual percentage change (APC) and quantified the trend over thirty years by using joinpoint regression analysis. Linear mixed models were conducted to investigate the association between iron deficiency and dengue, adjusting for key covariates such as the Human Development Index (HDI), population density, and temperature.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe global age-standardized prevalence rate (ASPR) of dengue increased over the 30 years, from 33.2 (95% UI 14.2 to 71.9) cases per 100,000 population in 1990 to 44.2 (28.1 to 79.2) cases per 100,000 population in 2019. Geographically, the highest prevalence rate of dengue was observed in South Asia (101.76 [37.84 to 247.73]) and lower-middle-income countries (77.94 [37.33 to 166.33]) in 2019. The prevalence rate of dietary iron deficiency decreased by 0.5% per year (APC \u0026minus;\u0026thinsp;0.5 [-0.6 to -0.5]) between 1999 and 2019. The prevalence rate of iron deficiency was highest in South Asia (26860.7 [26274.4 to 27405.7]) and in lower-middle-income countries (22271.9 [21838.5 to 22675.3]) in 2019. Our analysis revealed a positive association between the prevalence of dietary iron deficiency and dengue, with an adjusted coefficient of 21.5 (95% CI, 19.6 to 23.4). The association was more significant in females, with an adjusted coefficient of 19.8 (95% CI, 17.9 to 21.6) than in males, with an adjusted coefficient of 18.4 (95% CI, 6.6 to 20.3). Additionally, the association between dietary iron deficiency and dengue was more significant among adults older than 70 years (55.3, 95% CI 51.7 to 59.0) than in other age groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides epidemiological evidence of the association between iron deficiencies and dengue prevalence. The highest burden of dengue and the strongest association with dietary iron deficiency were found in middle-income countries, and dengue endemic regions like South Asia. More importantly, our study may suggest iron supplementation as a potential dengue prevention strategy, which should be targeted for vulnerable populations, including females and older adults.\u003c/p\u003e","manuscriptTitle":"Global, regional, and national trends of dengue and its relationship with dietary iron deficiency: estimates for 204 countries and territories from 1990 to 2019","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:31:35","doi":"10.21203/rs.3.rs-6154986/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59111c20-91dc-4bb0-be99-26c0c7443588","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T16:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 11:31:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6154986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6154986","identity":"rs-6154986","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}