|Year : 2019 | Volume
| Issue : 2 | Page : 107-113
Burden of dengue in Kerala using disability-adjusted life years from 2006 to 2016
Zinia T Nujum1, K Vijayakumar2, V Meenakshy3, M Saboora Beegum4
1 Associate Professor, Department of Community Medicine, Government Medical College, Kollam, India
2 Professor and Head, Department of Community Medicine, Amrita Institute of Medical Sciences, Kochi, India
3 Additional Director, Public Health, Directorate of Health Services, Thiruvananthapuram, Kerala, India
4 Professor and Head, Department of Biochemistry, Government Medical College, Thiruvananthapuram, Kerala, India
|Date of Web Publication||18-Jun-2019|
Zinia T Nujum
Department of Community Medicine, Government Medical College, Kollam; TC 9/1044(9), Thekkadathu, Kallampally, Sreekaryam, Thiruvananthapuram, Kerala
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: State-specific disability-adjusted life years (DALYs) are seldom calculated. Understanding the health and disease trends in groups of states is useful for finding the heterogeneity of disease epidemiology in the country. Objective: The objective of the study was to assess dengue burden in Kerala state, using DALY. Methods: DALY was computed using the DALY package of R. Incidence was derived using reported and estimated dengue cases for 2006–2016. Mortality was calculated using reported deaths. We computed DALYs using the mortality estimates for the South-East Asia region (SEAR) also. Sensitivity and scenario analyses were done. Results: The highest estimated relative DALY for dengue is 7.22 (95% confidence interval [CI]: 6.66–7.72) per lakh population. The DALY obtained using the mortality rates of SEAR ranged from 19.89 (95% CI: 8.44–29.45) per lakh population to 28.56 (95% CI: 17.04–38.05). We observed a cyclical pattern of increase in DALY every 2–3 years. The DALY in lower age groups is lesser. DALY in females was higher than males. Conclusions: The dengue-related DALY for the state of Kerala is lower than that of the global burden of disease due to lower mortality rates. Mortality reduction becomes the key to reducing burden, especially in areas of low incidence. The study also forms the benchmark for evaluating and implementing cost-effective measures for dengue control in the state.
Keywords: Absolute disability-adjusted life year, dengue, disability-adjusted life year package in R, Kerala, relative disability-adjusted life year
|How to cite this article:|
Nujum ZT, Vijayakumar K, Meenakshy V, Beegum M S. Burden of dengue in Kerala using disability-adjusted life years from 2006 to 2016. Indian J Public Health 2019;63:107-13
|How to cite this URL:|
Nujum ZT, Vijayakumar K, Meenakshy V, Beegum M S. Burden of dengue in Kerala using disability-adjusted life years from 2006 to 2016. Indian J Public Health [serial online] 2019 [cited 2020 May 30];63:107-13. Available from: http://www.ijph.in/text.asp?2019/63/2/107/260595
| Introduction|| |
Dengue is a serious and rapidly emerging tropical mosquito-borne disease. Its burden is 465,000 disability-adjusted life years (DALYs) across the globe. India is heading to be a major hyperendemic niche for dengue infection. Kerala is hyperendemic for dengue.
Quantifying the burden of dengue is the key to effective disease control. Burden of dengue is generally measured using a set of epidemiological indicators. All of them can be combined into a single health indicator, such as quality-adjusted life years or DALYs. Internationally, DALYs are most often used. The DALY is a summary measure of population health used widely in disease burden assessment and cost–utility analyses., Meaningful comparison with other health problems of differing severity and duration can be done using DALY. Although the dengue burden using DALY is available at the global and country levels, state-specific figures are seldom done. Understanding the health and disease trends in groups of states is useful for finding the heterogeneity of disease epidemiology in the country.
The burden of dengue in Kerala in terms of DALY has not been assessed. The objective of this study was to assess dengue burden using DALY. We also looked at the trends for the years, 2006–2016.
| Materials and Methods|| |
Absolute DALY was computed using the DALY package, accessible in the R statistical program. A graphical user interface is available for calculating DALYs. Relative DALY (RD) was calculated using the population size as denominator and expressed per lakh for comparison with other areas.
The age–sex distribution of Kerala population and the life expectancy (LE) used in the analysis are presented as [Supplementary Table 1] and [Supplementary Table 2], respectively. LE for Kerala (2011–2015) gives values only up to 85+, LE at 90, and 95 years were retained as in LE table of R and used in the package.
The reported cases of dengue and deaths for the years 2006–2016 were obtained from the state surveillance unit. The line list of cases with age- and sex-specific details was obtained for the years 2014 and 2015. Instead of using multiplication factor, we used the data from sentinel surveillance and fever surveillance to compute the highest (20% of fever cases may be applicable to Thiruvananthapuram only during epidemic period) and lowest possible (2.8% of fever may be applicable to the whole state) dengue estimates. These estimates of dengue in Kerala have been published earlier. It was used for sensitivity analysis and for obtaining the range of possible estimates for DALY. The incidence/1000 and mortality/1000 population was calculated for the years 2006–2016 and then imputed in R for DALY calculation. The age–sex-specific incidence/1000 was calculated only for the years 2014 and 2015. The age–sex profile of dengue cases [Supplementary Table 3], mean age of onset in each age group, [Supplementary Table 4] and mean age of mortality for 2014 and 2015 were computed in SPSS 11, IBM, Armonk, NY, United States of America. These data were used for DALY calculation for the corresponding years. For the rest of the years, the mean age of onset and the mean age of mortality of 2015 were used.
We assumed that deaths due to dengue may not be missed to reporting. Therefore, the incidence of deaths/1000 was not changed during calculations of DALY yearly for the highest and lowest possible burdens estimated. It was taken as reported deaths. We also computed DALYs using the mortality estimates for the South-East Asia region (SEAR) at 0.008/1000.
There was a wide variability in disability weights (DWs) for dengue used [Supplementary Table 5]. For the base case analysis, DW of 1990 (the weighted average DWs of 2004 and 2010 comes close to 0.2, which is similar to 1990 figures) was used, since it has age-specific values. The 2004 global burden of disease (GBD) DWs for dengue was 0.197 (95% confidence interval [CI]: 0.172–0.211), and for dengue hemorrhagic fever, it was 0.545 (95% CI: 0.475–0.483). In 2010 (the DWs were not specific for dengue but for acute infections), the DWs were 0.005 (95% CI: 0.002–0.011) for mild dengue, 0.053 (95% CI: 0.033–0.081) for moderate, and 0.21 (95% CI: 0.033–0.081) for severe dengue. In 2015, the revised DW was 0.051 (95% CI: 0.032–0.074) for moderate dengue and 0.133 (95% CI: 0.088–0.19) for severe dengue. A Korean study has used a DW of 0.428 (95% CI: 0.331–0.530). The analysis was also done using the weighted average of DWs of GBD 2015 (0.0583) and a DW of 0.8, which was used in most studies.,,,,
Since severity of disease was not given in the line list of cases reported, it was accounted in the duration of disease and DWs. From a 5-year evaluation of dengue cases, it was seen that 90% of cases are not severe and only 10% are severe. The duration disease for classic dengue is 4 days (range: 2–6), moderate dengue is 10 days (range: 8–12), and severe dengue is 14 days (range: 10–18 days). For the base case analysis, we took duration of disease as 7 days, based on a weighted proportion of disease severity. Others have used a duration of 5 days, 14 days for hospitalized patients, and 4.5 days for ambulatory cases. Variation in duration was considered in the sensitivity analysis (4–14 days). Proportion of treated cases was taken as 90%, because in Kerala, treatment seeking is high.
The sensitivity analysis was performed because of the variability in input parameters. Variability in incidence and age of onset was used. DWs were assigned as minimum 0.05, and the mode value was given as 0.2 and maximum as 0.8. No variability was assigned to mortality. The variability in treatment range was given as follows: mode 0.9 (since most patients seek treatment in Kerala) and minimum was assigned as 0.5 and maximum as 1. Regarding the variability in duration of disease, mode was taken as 7 days (0.019), minimum as 4 days (0. 0101), and maximum as 14 days (0.038). The variability in age of death was taken as minimum 4.5 years, maximum 65 years, and mode 38. Full age weighting was given in the base case analysis and varied in the scenario analysis. The discount rate used in the base analysis was 3%. We also performed a scenario analysis, varying the discount rates and age weighting. Three more scenarios were assessed namely, with no age weighting and discount rate as 0, no age weighting and discount rate as 3%, and age weighting and discount rate as 2%.
This study was part of a larger study funded by the World Health Organization (WHO) Training in Tropical Disease. Ethics committee clearance was obtained from the WHO (protocol ID B40138 last approved on 27/4/2016) and institution (Government Medical College, Thiruvananthapuram-IEC No: 02/42/MCT dated 14/02/2014).
| Results|| |
The cases reported from 2006 to 2016 annually ranged from 657 in 2007 to 7938 in 2013. Incidence/1000 population was accordingly found to be in the range of 0.0197–0.2379. Mortality/1000 ranged from 0.000,009 in 2008 to 0.000,869 in 2013 and 2015. The reported fever cases were maximum in 2009, followed by 2007 and 2013. Therefore, the estimated dengue fevers were highest for the same years. The estimated highest incidence of dengue was 21.43/1000 and the lowest was 1.36/1000 [Table 1]. Information on the age–sex distribution of mortality was analyzed for 2014 and 2015. Nine deaths were reported in 2014 and 29 in 2015. The mean age of mortality was 38.06 years (standard deviation [SD]: 22.52) in 2014 which decreased to 34.87 years (SD: 18.43) in 2015. The lowest age of mortality was 1 year and 77 was the highest. 77.8% and 62.1% of the deaths were male for the years 2014 and 2015, respectively. Around 15% of cases were <15 years. The age–sex-specific incidences for 2014 and 2015 showed the highest incidence in 15–59 years. The highest estimate of incidence was for males in the age group of 15–44 years at 28/1000 population. Based on the reported cases also, the highest burden was in males of the same age group (0.13/1000 and 0.21/1000 for 2014 and 2015, respectively). The mean age of onset of dengue was also similar for 2 years in both sexes. In the age group of 15–44 years, the mean age of onset of dengue was significantly higher for females. In other groups, the mean age of onset was higher in males. The age–sex-wise incidence was imputed for DALY calculation for the years 2014 and 2015.
Absolute DALY estimates based on reported cases varied from 78 in 2008 (although reported cases were lower in 2007, deaths were higher in 2008) to 753 in 2013. Rather than a linear increase in trend, we observed a cyclical pattern of increase in DALY every 2–3 years [Figure 1]. The absolute DALY values obtained using the highest estimates of dengue ranged from a1718 to 3154 and the lowest ranged from 335 to 1064. 2013 consistently recorded the highest DALY [Table 2]. The average DALYs for the 11 years based on reported cases, highest estimate, and lowest estimate were 357 (95% CI: 162–521), 2412 (95% CI: 2223–2575), and 630 (95% CI: 437–790), respectively.
|Table 2: Burden of dengue in Kerala in disability-adjusted life years from 2006 to 2016|
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The contribution of years lost due to disability (YLD) and years of life lost (YLL) to total DALYs is also shown in the table. When DALYs were computed using the reported cases, the contribution of YLL was in the range of 96%–97%. However, estimated figures show that contribution by YLD is much higher compared to YLL.
The RDs were 1.07 (95% CI: 0.48–1.56), 7.22 (95% CI: 6.66–7.72), and 1.88 (95% CI: 1.31–2.36) per lakh population based on reported cases, highest estimate, and lowest estimate, respectively. Hence, the highest estimated dengue burden is 7.22/lakh population.
Mortality estimates of SEAR of 0.008 were applied, and DALYs were recalculated [Table 3] for the years with least and highest incidence. The DALY thus obtained ranged from 19.89 (95% CI: 8.44–29.45) per lakh population to 28.56 (95% CI: 17.04–38.05). The average RD for the 11 years based on the highest estimate was 26.17/lakh.
|Table 3: Disability-adjusted life years recalculated using mortality estimates of South-East Asia Region|
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The DWs were varied, and the analysis was repeated for the years 2014 and 2015. There was not much variation in DALY when the DWs were changed to 0.05 and 0.8. The DALYs obtained were, respectively, 326 (95% CI: 180–468) and 368 (95% CI: 220–511) for the reported cases in 2014. For the year 2015, the values were 730 (95% CI: 315–1075) and 797 (95% CI: 381–1142).
Age-sex-specific DALYs were computed for the years 2014 and 2015. Reported cases and DALY were highest for the 15–44 years' age group [Table 4]. The disease burden in lower age groups (<15 years) is low compared to adults. DALY in females was higher than males although cases and mortality were higher in males.
|Table 4: Dengue burden in absolute disability-adjusted life years in distributed by age and sex|
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During sensitivity analysis, the variable that contributed to maximum uncertainty was the incidence of disease in the age group of 15–44 years and the duration of the disease [Figure 2]. Scenario analysis yielded the highest estimates for no age weighting and no discounting [Supplementary Table 6]. The DALYs were 537 and 1217 for 2014 and 2015 in this scenario, respectively [Figure 3].
| Discussion|| |
Accurate estimates of dengue using measures such as DALY are indispensable for controlling the disease. The formulas for calculating the YLLs and YLDs are presented by Murray and Murray and Acharya. It has been assimilated in a spreadsheet prepared by the WHO for the GBD project. However, we preferred to use R because of the stochastic nature of the data. The reliability of the final DALY result depends largely on the quality of the epidemiological data. We used data from routine data surveillance systems and data from studies conducted in the same setting. These estimates and data sources have an inherent level of uncertainty. Monte Carlo simulations have been suggested as a suitable technique to incorporate this uncertainty in the final DALY result. The DALY package used in R gives us this kind of assessment. Another reason for not using the WHO template was that the mortality rate in our area is very low and its variability could not be captured while entering into the WHO template.
Age-specific DWs used in our study are lower than that used in most other studies., The DWs have a major influence on DALY, are arbitrary, and are not transparently stated. Due to these issues, Fox-Rushby and Hanson suggested a set of minimum reporting criteria. The treated proportion in our study was taken as 90%. Other studies state that 70% do not choose to take treatment or go for self-treatment.
An expansion factor (EF) is generally used for DALY calculation. In SEAR, an EF of 7.6 is reported. Only 13.2% of symptomatic dengue episodes are reported to surveillance systems. In Kerala also, only 10% of the cases are reported to the health system. In the GBD study, the EF used was 12.3. They assumed that 8% of cases are reported. Using cartographic approaches, Bhatt et al. have estimated the dengue infections at 360 million and apparently symptomatic dengue at 96 million globally annually. This estimate gives an incidence of 13.9/1000, which is close to our highest estimated incidence. It is also comparable with that of the GBD. For the year 2013, our estimates are in the range of 254–1815/lakh as compared to 327–1691/lakh according to GBD. The total highest estimated cases for the period 2006–2016 are 169 times more than the reported cases. The lowest estimates are 23 times that of reported cases. In a study on economic burden in India, the EF used was 282, based on a case study and expert Delphi panel.,
The RD estimate by GBD in 2013 is 15.8/lakh (95% CI: 10.06–27.38). The GBD 2015 estimate for dengue is 25.5/lakh (95% CI: 17.1–39.5). Using the reported mortality, our highest estimates of DALY are 7.22/lakh (95% CI: 6.66–7.72). In spite of having a comparable estimate of incidence, this difference could be due to the very low mortality rate, DW, and difference in duration of disease assignment. The global burden of dengue estimation assumed that 8·5% will develop postdengue chronic fatigue. A DW for “infectious disease, postacute consequences,” with a mean duration of 6 months, has been assigned to this. It was not accounted in this study. The mortality estimate for 2013 in the global burden of dengue assessment is 1.27/million (0.00127/1000). The mortality rate reported worldwide in 2010 is 0.002/1000 (14,000 deaths out of 6.9 billion population). These figures are ten times higher than the mortality rates used in this study. The most cited figure is 20,000 annual deaths which again are more than ten times higher. However, there is evidence from literature, showing that the mortality due to dengue is declining. Global burden of dengue estimation has used the Cause of Death Ensemble model for finding the mortality rates. When we used the mortality estimates of SEAR, i.e., 0.008/1000, our estimates of DALY were higher than the global estimates of 2013 (15.8/lakh) and 2015 (25.5/lakh). Our estimated DALY using the mortality rate of SEAR for 2013 was 27.2/lakh (95% CI: 15.7–36.7). This figure is higher than the South Asia region estimates in the global burden of dengue estimates (19.5 [95% CI: 10.8–37.3]/million). Our entire estimates lie in the range of 19.8–28.5/lakh population. It is at par with estimates for East and Central Asia and Cambodia but lesser than estimates of Puerto Rico and several other studies [Supplementary Table 7].
In general, DALY is more in lower age groups. In our study, DALY was lower in 0–4 and 5–14 years' age groups compared to 15–44 years' age group probably because mortality was not reported in the lower age groups. Dengue-related DALY for females is higher than males. In our study also' this is reflected, despite higher case reporting and mortality in males. This could be due to the higher LE for females in Kerala.
The trend in DALY from 1990 to 2015 shows a 53.5% increase, from 16.6 in 1990 to 25.5 in 2015. The global estimates of DALY indicate a 58% increase in 2013 compared to 1990. In our data of highest estimated cases, we also see an increase in burden of 27.11% in 2016 compared to 2006 (7.5–5.9/5.9 * 100).
Burden of disease due to dengue is less when compared to malaria and other communicable diseases. Tuberculosis, malaria, HIV, and ischemic heart disease contribute to 1.6%, 2.1%, 2.1%, and 7.1% of DALYs, respectively. Globally, dengue accounts to only 0.07% of total DALY (25.5/34445.7). It is comparable to that of lymphatic filariasis and hepatitis B. From our data, the average highest estimate of DALY using a mortality rate of 0.008 is 26.18/lakh which contributes to 0.09% of total DALY for India. This is comparable to the DALY of ovarian cancer (0.08% – 21.6/27315) and half that of malaria (0.19% – 53.9/27315). Kerala DALY figures are not available for comparison to the best of our knowledge.
| Conclusions|| |
The dengue-related estimated DALY for the state of Kerala is lower than the global burden, despite comparable incidence. It is due to the lower mortality rates in Kerala. These figures are based on estimated incidence and reported mortality. The burden of dengue is also lower in children compared to the world because of lower mortality rates. Mortality reduction becomes a key to reducing burden, especially in areas of reducing incidence.
This study shall form the benchmarking for evaluating and implementing cost-effective measures for dengue control in the state. State-specific DALYs may also be useful for prioritizing among various states of the country with diverse epidemiological profile of a particular disease, for resource allocation. The initiative of the Indian Council of Medical Research in bringing out the State Level Disease Burden in India is a very useful measure in this direction.
This work was supported by the World Health Organization's Tropical Disease Research postgraduate training grants in implementation research 2014; TIMS ID: B40138. We place on record our gratitude to Dr. Edward Kamau for his constant guidance. He has been our contact person with WHO-TDR. Dr. Thomas Mathew, Principal, Government Medical College, Thiruvananthapuram, was a pillar of support. We thank him from the depths of our heart.
Financial support and sponsorship
This work received financial support from TDR, the Special Program for Research and Training in Tropical Diseases, cosponsored by UNICEF, UNDP, the World Bank, and the WHO Postgraduate Training Grants in Implementation Research 2014 (TIMS ID: B40138).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Gubler DJ, Kuno G. Dengue and Dengue Haemorrhagic Fever. New York: CABI Publishing; 1997.
Lall R, Dhanda V. Dengue haemorrhagic fever and the dengue shock syndrome in India. Natl Med J India 1996;9:20-3.
Anoop M, Issac A, Mathew T, Philip S, Kareem NA, Unnikrishnan R, et al.
Genetic characterization of dengue virus serotypes causing concurrent infection in an outbreak in Ernakulam, Kerala, South India. Indian J Exp Biol 2010;48:849-57.
Suaya JA, Shepard DS, Beatty ME. Dengue: Burden Of Disease And Costs Of Illness Working paper for the Scientific Working Group on Dengue Research, Convened by the Special Programme for Research and Training in Tropical Diseases, Geneva, 1-5 October 2006 Full Text Source: Scientific Working Group, Report on Dengue, 1-5 October 2006, Geneva, Switzerland: Copyright © World Health Organization on Behalf of the Special Programme for Research and Training in Tropical Diseases; 2007. Available from: http://www.who.int/tdr/publications/publications/swg_dengue_2.htm
. [Last accessed on 2017 Apr 06].
Zeckhauser R, Shepard D. Where Now for Saving Lives?. Law Contemp Probl 1976;40:5-45.
Murray CJ. Quantifying the burden of disease: The technical basis for disability-adjusted life years. Bull World Health Organ 1994;72:429-45.
Murray CJ, Lopez AD. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries and Risk Factors in 1990 and Projected to 2020. Cambridge: Harvard University Press; 1996.
Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global Burden of Disease and Risk Factors. New York: Oxford University Press; 2006.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: Foundation for Statistical Computing; 2016. Available from: https://www.R-project.org/
. [Last accessed on 2018 Mar 17].
Devleesschauwer B, McDonald S, Haagsma J, Praet N, Havelaar A, Speybroeck N. DALY: The DALY Calculator – Graphical User Interface for Probabilistic DALY Calculation in R. Package Version 1.5.0; 2016. Available from: https://cran.r-project.org/package=DALY
. [Last accessed on 2018 Mar 17].
Nujum ZT, Thomas A, Vijayakumar K, Mathew T, Pillai SS. Estimation of burden using active dengue sentinel surveillance and fever surveillance. Dengue Bull 2016;39:78-68.
Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al.
The global burden of dengue: An analysis from the global burden of disease study 2013. Lancet Infect Dis 2016;16:712-23.
Global Burden of Disease Study 2015. Global Burden of Disease Study 2015 (GBD 2015) Disability Weights. Seattle, United States: Institute for Health Metrics and Evaluation; 2016.
Meltzer MI, Rigau-Pérez JG, Clark GG, Reiter P, Gubler DJ. Using disability-adjusted life years to assess the economic impact of dengue in Puerto Rico: 1984-1994. Am J Trop Med Hyg 1998;59:265-71.
Shepard DS, Undurraga EA, Halasa YA. Economic and disease burden of dengue in Southeast Asia. PLoS Negl Trop Dis 2013;7:e2055.
Beauté J, Vong S. Cost and disease burden of dengue in Cambodia. BMC Public Health 2010;10:521.
Anderson KB, Chunsuttiwat S, Nisalak A, Mammen MP, Libraty DH, Rothman AL, et al.
Burden of symptomatic dengue infection in children at primary school in Thailand: A prospective study. Lancet 2007;369:1452-9.
Luz PM, Grinsztejn B, Galvani AP. Disability adjusted life years lost to dengue in Brazil. Trop Med Int Health 2009;14:237-46.
Cecilia D. Current status of dengue and Chikungunya in India. WHO South East Asia J Public Health 2014;3:22-6.
Murray CJ, Acharya AK. Understanding DALYs (disability-adjusted life years). J Health Econ 1997;16:703-30.
Mathers CD, Vos T, Lopez AD, Salomon J, Ezzati M. National Burden of Disease Studies: A Practical Guide. 2nd
ed. Global Program on Evidence for Health Policy. Geneva, Switzerland: World Health Organization; 2001.
de Vocht F, Higgerson J, Oliver K, Verma A. Incorporating uncertainty in aggregate burden of disease measures: An example of DALYs-averted by a smoking cessation campaign in the UK. J Epidemiol Community Health 2011;65:751-6.
Global Burden of Disease Study 1990. Global Burden of Disease Study 2015 (GBD 1990) Disability Weights. Seattle, United States: Institute for Health Metrics and Evaluation; 1990.
Ock M, Lee JY, Oh IH, Park H, Yoon SJ, Jo MW, et al.
Disability weights measurement for 228 causes of disease in the Korean burden of disease study 2012. J Korean Med Sci 2016;31 Suppl 2:S129-38.
Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. 4th
ed. Methods for the Economic Evaluation of Health Care Programmes. UK: Oxford University Press; 2015.
Fox-Rushby JA, Hanson K. Calculating and presenting disability adjusted life years (DALYs) in cost-effectiveness analysis. Health Policy Plan 2001;16:326-31.
Shepard DS, Suaya JA, Halstead SB, Nathan MB, Gubler DJ, Mahoney RT, et al.
Cost-effectiveness of a pediatric dengue vaccine. Vaccine 2004;22:1275-80.
Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al.
The global distribution and burden of dengue. Nature 2013;496:504-7.
Undurraga EA, Halasa YA, Shepard DS. Use of expansion factors to estimate the burden of dengue in Southeast Asia: A systematic analysis. PLoS Negl Trop Dis 2013;7:e2056.
Shepard DS, Halasa YA, Tyagi BK, Adhish SV, Nandan D, Karthiga KS, et al.
Economic and disease burden of dengue illness in India. Am J Trop Med Hyg 2014;91:1235-42.
GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: A systematic analysis for the global burden of disease study 2015. Lancet 2016;388:1603-58.
Teixeira Lde A, Lopes JS, Martins AG, Campos FA, Miranzi Sde S, Nascentes GA. Persistence of dengue symptoms in patients in Uberaba, Minas Gerais state, Brazil. Cad Saude Publica 2010;26:624-30.
Clark DV, Mammen MP Jr., Nisalak A, Puthimethee V, Endy TP. Economic impact of dengue fever/dengue hemorrhagic fever in Thailand at the family and population levels. Am J Trop Med Hyg 2005;72:786-91.
Patel V, Chatterji S, Chisholm D, Ebrahim S, Gopalakrishna G, Mathers C, et al.
Chronic diseases and injuries in India. Lancet 2011;377:413-28.
Indian Council of Medical Research, Public Health Foundation of India and Institute for Health Metrics and Evaluation. India: Health of the Nation's States-The India State-Level Disease Burden Initiative. New Delhi: Indian Council of Medical Research, Public Health Foundation of India and Institute for Health Metrics and Evaluation; 2017.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4]