Dataset: 11.1K articles from the COVID-19 Open Research Dataset (PMC Open Access subset)
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Deep Learning Technology: Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers and Alexander Löser. Learning Contextualized Document Representations for Healthcare Answer Retrieval. The Web Conference 2020 (WWW'20)
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In 2018, the MOHW allocated USD 435.1 million for major R&D funds, corresponding to about 3% of the government's 2018 regular budget. Leaving out the non-disease portion of the budget, the MOHW allocated 35.9% of the remainder of its R&D budget to communicable diseases, 64.1% to non-communicable diseases, and 0% to injuries and violence. Fig. 2 shows the distribution of the 2018 MOHW fund allocations across the diseases categories. In this figure, R&D funds and the burden of disease in Korea were not correlated. For example, much part of the MOHW R&D funds were allocated to upgrading the national system for responding to communicable diseases, focusing on prevention of variant infectious disease inflow and spread and on-site responses. However, communicable diseases in Korea only accounted for 2.8% of DALYs in 2015.7 Non-communicable diseases, on the other hand, accounted for more than 87% of Korean DALYs but were only allocated 64.1% of the MOHW R&D funds. The most salient findings, though, were in the injuries and violence disease group. There was no MOHW funding whatsoever for this disease group. However, injuries and violence such as self-harm, interpersonal injury, or transport injury accounted for 10.1% of the total 2015 DALYs in the KNBD study, even higher than those of the communicable disease category.6
To further investigate whether the economic burden and funds allocation are aligned, we also compared the differences between them. We noted that the funds were also skewed when compared to economic burden. The economic burden of injury and violence was 9.1%, but injury and violence did not receive any portion of the MOHW total funding (Fig. 2).
We undertook sub-analysis to investigate the relationship between DALYs for level 2 disease groups and MOHW allocation. Budget which could not be allocated to each level 2 disease group is not sub classified. Table 2 shows the R&D groups that correspond to the DALYs of each level 2 disease group. Within these groups, only parts of groups had their own R&D budgets. R&D funds for non-communicable disease were sorted into neoplasms, neurological disorders, and mental and behavioral disorders (and ranked in that order). This differs from the pattern of DALYs in the KNBD study. For example, DALYs from musculoskeletal disorders were the highest, followed by cardiovascular and circulatory diseases. However, the allocation of R&D funding for neoplasms was more than nine times that of musculoskeletal disorders. Neurological disorders, which include Alzheimer's dementia, were ranked second after neoplasm in the MOHW R&D funds.
In prioritizing health resources, risk factors should also be considered.4 Some of the R&D budget was directly targeted for risk factors in 2018. Fig. 3 shows the R&D budget for risk factors. We compared four risk categories in the DALYs of the 2013 KNBD study (behavioral, socio-economic, environmental, and metabolic) with R&D budgetary allocations. As a result, mismatches between R&D budget allocations and DALY distributions were found. For example, it is difficult to locate the fund for socioeconomic risk in the MOHW R&D budget. In the behavioral risk category, even though tobacco smoking was the most dangerous risk factor, only “alcohol use” had its own funding.
This study was approved by the Institutional Review Board (IRB) of Korea University (IRB No. KU-IRB-18-EX-51-A-1). Informed consent was waived by the board.
This study ranked diseases based on the DALYs per 100,000 population by gender and age. We ranked a total of 242 diseases by excluding 18 residual categories (e.g., other musculoskeletal disorders, other chronic respiratory diseases, other infectious diseases, etc.) that were not classified into a certain disease category during analysis.8
The studied communities were located within 95 km (severe neurological infectious disease) and 62 km (fatal respiratory infectious disease) of a surveillance hospital. In these communities, 76 of 426 severe neurological disease cases (18%, 95% CI 14%–22%) and 234 of 1,630 fatal respiratory disease cases (14%, 95% CI 13%–16%) attended a surveillance hospital. Adjusting for distance, the case detection probability was nearly twice as high among severe neurological disease cases than among fatal respiratory disease cases (risk ratio 1.8, 95% CI 1.4–2.3; p < 0.001). At 10 km distance, an estimated 26% (95% CI 18%–33%) of severe neurological disease cases and 18% (95% CI 16%–21%) of fatal respiratory disease cases were detected by the hospital-based surveillance. The detection probability decreased with distance from the surveillance hospital, and the decline was faster for fatal respiratory disease than for severe neurological disease. A 10 km distance increase resulted in a 12% (95% CI 4%–19%; p = 0.003) relative reduction in case detection probability for severe neurological disease but a 36% (95% CI 29%–43%; p < 0.001) relative reduction for fatal respiratory disease (Fig 2C). Including more complex functional forms of distance in the log-binomial regression models did not improve model fit based on AIC (Table A and Figs. B and C in S1 Text).
The probability of detecting an outbreak of exactly three cases (if a single detected case was considered an outbreak) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease (Fig 3A). Fig 3B and 3C show the minimum number of cases required for surveillance to detect outbreaks with a probability of ≥90% if different outbreak thresholds are applied. For outbreaks defined as detection of at least one case, we found that an outbreak of fatal respiratory disease required 12 cases (95% CI 11–13) to be detected with 90% probability at 10 km from a surveillance hospital, but 30 cases (95% CI 24–39) to be detected at 30 km. In contrast, the impact of distance on the outbreak size requirement was much more limited for severe neurological disease: eight cases (95% CI 6–12) at 10 km and 11 cases (95% CI 9–14) at 30 km. For outbreaks defined as detection of at least two cases, 14 severe neurological disease cases (95% CI 11–20) and 20 fatal respiratory disease cases (95% CI 18–23) would be necessary for an outbreak to be detected at 10 km distance, and 19 severe neurological disease cases (95% CI 15–24) and 51 fatal respiratory disease cases (95% CI 41–66) at 30 km. The necessary outbreak sizes increased further when a five-case threshold was applied, so that 28 severe neurological disease cases (95% CI 21–39) and 39 fatal respiratory disease cases (95% CI 35–44) would need to occur for an outbreak to be detected at 10 km distance, and 36 (95% CI 30–46) and 97 (95% CI 79–128), respectively, cases at 30 km.
Surveillance hospital attendance among community cases varied by case characteristics, leading sometimes to biased disease statistics among surveillance cases (Table B in S1 Text). For severe neurological disease, individuals aged <5 y represented 48% of community cases but only 29% of surveillance cases (p < 0.001). Additionally, the proportion of cases in the lowest socioeconomic group was lower among surveillance cases than among community cases (43% versus 57%; p = 0.012), while the proportion of individuals aged 15–59 y was higher (43% versus 29%; p = 0.005) (Fig 4A). For fatal respiratory disease, the proportion of individuals aged ≥60 y (47% versus 62%; p < 0.001) was lower among surveillance cases than among community cases, while the proportion of individuals aged <5 y (24% versus 18%; p = 0.020), individuals aged 15–59 y (27% versus 18%; p < 0.001), and cases in the highest socioeconomic group (43% versus 37%; p = 0.022) was higher (Fig 4B). We observed a slight difference in the proportion of females for fatal respiratory disease (34% among surveillance cases versus 38% among community cases; p = 0.108), but not for severe neurological disease (39% versus 40%; p = 0.861). Results were consistent in sensitivity analyses with age as a continuous variable and socioeconomic status classified into quintiles (Figs. D and E in S1 Text).
A substantial proportion of cases (severe neurological disease 42% [95% CI 38%–47%]; fatal respiratory disease 26% [95% CI 24%–28%]) visited multiple healthcare providers during their illness. Forty-eight percent (95% CI 44%–53%) of severe neurological disease cases and 31% (95% CI 29%–34%) of fatal respiratory disease cases attended any hospital, including surveillance hospitals (Fig 5). Including other hospitals that were attended by cases in the surveillance system could have increased the overall case detection probability by 31% (absolute increase) for severe neurological disease cases and 17% for fatal respiratory disease cases. The capacity to detect outbreaks would have increased, so that outbreaks containing four severe neurological or eight fatal respiratory disease cases would have been detected with ≥90% probability for any distance in the range 0–40 km from the original surveillance hospital, compared to 13 and 47 cases, respectively, with the current system (Fig. F in S1 Text). However, since individuals who attended any hospital had similar characteristics in terms of sex, age, and socioeconomic status as those attending surveillance hospitals (Fig. G in S1 Text), this expansion would not have increased disease detection in key groups such as the lowest socioeconomic group. Only with the informal sector incorporated in the surveillance system would cases in such groups be detected.
This study was approved by the Institutional Review Board (IRB) of Korea University (IRB No. KU-IRB-16-EX-51-A-1). Because this study used MOHW R&D budget data which is open and published data, informed consent was not acquired.
In terms of burden of congenital infections in the newborns, almost all the burden (97%) was attributable to toxoplasmosis, listeriosis and rubella infections (Table 3).
The diseases with the highest number of DALYs per case, which represents the individual burden and to a certain extent the severity of the disease, were rabies and variant Creutzfeldt–Jakob disease, which are ultimately fatal conditions. HIV/AIDS, invasive meningococcal disease, listeriosis, TB, IHID, Legionnaires’ disease, HBV infection, IPD, congenital toxoplasmosis, tetanus and diphtheria followed, with DALYs per case ranging from 6.03 to 1.16. Diseases determined to have a high individual and population burden were Legionnaires’ disease, IPD, HIV/AIDS and TB, while influenza was determined to have a low individual but high population burden (Figure 4).
Associations between HDI, health workforce, international travel, IHR scores and disease control outcomes are shown in Table 3. Regarding analysis with IHR score in 2016 for all cases, HDI, international travel, total health expenditure and IHR average scores were significantly associated with disease control outcomes. Cases occurring in high HDI (OR = 2.23) and low HDI countries had higher risk (OR = 1.84) of having bad disease control outcomes than very high HDI countries. Cases occurring in high international travel volume countries had twice the risk of having bad disease control outcomes than cases occurring in low international travel volume countries (OR = 2.19). Cases occurring in low total health expenditure countries had nearly four times risk of having bad disease control outcomes than countries with high health expenditure (OR = 3.99). And cases occurring in low IHR average scores countries had 5 times the risk (OR = 7.83) of having bad disease control outcomes than in countries with high IHR average scores.
For only human cases, associations among HDI, total health expenditure and IHR average scores in 2016 and disease control outcomes were statistically significant. Cases occurring in middle to low HDI countries had twice as high a risk of having bad disease control outcomes than those in very high HDI countries (OR = 2.65). Cases occurring in low total health expenditure countries had two times risk of having bad disease control outcome than countries with high health expenditure (OR = 2.84). Cases occurring in low IHR average scores countries had an 11 times higher risk (OR = 11.16) of having bad disease control outcomes than countries with high IHR average scores.
Regarding analysis with IHR score in 2017 for all cases, HDI, international travel, health workforce density, total health expenditure and IHR average scores were all significantly associated with disease control outcomes. Cases occurring in high HDI (OR = 4.71), middle-low HDI (OR = 2.29) and low HDI countries had higher risk (OR = 3.59) of having bad disease control outcomes than very high HDI countries. Cases occurring in high international travel volume countries had twice the risk of having bad disease control outcomes than cases occurring in low international travel volume countries (OR = 2.97). Cases occurring in middle health workforce density countries had two times risk of having bad disease outcomes than countries with high health workforce countries (OR = 2.59). Cases occurring in low total health expenditure countries had two times risk of having bad disease control outcomes than countries with high health expenditure (OR = 2.79). And cases occurring in low IHR average scores countries had 2 times the risk (OR = 2.23) of having bad disease control outcomes than in countries with high IHR average scores.
Similarly, for only human cases, associations among HDI, international travel, health workforce density, total health expenditure and IHR average scores and disease control outcomes were all statistically significant. Cases occurring in low IHR average scores countries had 3 times the risk (OR = 3.45) of having bad disease control outcomes than in countries with high IHR average scores.
The Odds Ratio of IHR 2017 is lower than the Odds Ratio of IHR 2016.
The total DALYs of communicable diseases were 445 per 100,000 in 2012. Overall, 29.0% of DALYs were from YLLs (129 per 100,000) and 71.0% of DALYs were from YLDs (316 per 100,000). The total DALYs in men were 468 per 100,000 and in women were 422 per 100,000. The proportion of YLLs in men was 34.2%, which was higher than that seen in women (23.5%) (Table 1).
The 40-49 years old age group had the highest number of DALYs and the ≥ 80 years old age group had the lowest number of DALYs. On the other hand, the ≥ 80 years old age group had the highest proportion of DALYs per 100,000 at 1,249 DALYs per 100,000, followed by the 70-79 years old age group at 799 DALYs per 100,000, the 0-9 years old age group at 552 DALYs per 100,000, and the 60-69 years old age group at 488 DALYs per 100,000. The proportion of YLLs and YLDs in DALYs varied by age group. With increasing age, the proportion of YLLs increased for all groups except for the 0-9 years old age group (Table 1, Fig. 1).
We classified communicable diseases into 4 categories according to GBD 2010 study (2): (i) HIV/AIDS and tuberculosis, (ii) diarrhea, lower respiratory infections, meningitis, and other common infectious diseases, (iii) neglected tropical diseases and malaria, and (iv) other communicable disorders. The largest proportion of DALYs was for diarrhea, lower respiratory infections, meningitis, and other common infectious diseases, comprising 59.7% of the total DALYs, followed by HIV/AIDS and tuberculosis at 33.8%, other communicable disorders at 6.1%, and neglected tropical diseases and malaria at 0.4% (Table 2).
In 2012, lower respiratory infections were responsible for the highest proportion of DALYs at 143 DALYs per 100,000, followed by tuberculosis at 121 DALYs per 100,000, upper respiratory infections at 69 DALYs per 100,000, HIV/AIDS at 30 DALYs per 100,000, and hepatitis at 22 DALYs per 100,000. The top five communicable diseases accounted for approximately 86% of the total DALYs (Table 3).
Tuberculosis was responsible for the highest proportion of DALYs in men (148 DALYs per 100,000), followed by lower respiratory infections, upper respiratory infections, HIV/AIDS, and hepatitis. In women, lower respiratory infections were responsible for the highest proportion of DALYs (154 DALYs per 100,000) followed by tuberculosis, upper respiratory infections, hepatitis, and otitis media (Table 4).
Lower respiratory infections were responsible for the highest proportion of DALYs in the 0-19 years old age group, the 30-39 years old age group, and the over 80 years old age group. On the other hand, tuberculosis was responsible for the highest proportion of DALYs in the other age groups. HIV/AIDS ranked fourth in all age groups between the ages of 20-69 years old. Otitis media was the third leading cause of DALYs in the 0-9 years old age group and the fourth leading cause of DALYs in the 10-19 years old age group (Fig. 2). In addition, as age increased, DALYs per 100,000 for otitis media decreased. The DALYs per 100,000 for upper respiratory infections also decreased as age increased. In the case of diarrheal disease, the DALYs per 100,000 increased rapidly in the over 70 years old age group (Fig. 3).
The disease with the highest YLLs was tuberculosis at 43 per 100,000, followed by lower respiratory infections, HIV/AIDS, diarrheal disease and meningitis. On the other hand, the disease with the highest YLDs was lower respiratory infections at 104 per 100,000 followed by tuberculosis, upper respiratory infections, hepatitis, and otitis media (Table 5).
Table 1 presents the summary profile characteristics and univariate analysis of the categorical variables in the dataset and age. 959 MERS cases were recorded in KSA during the study period with 317 (33%) deaths while 67 (7%) had contact with camels or camel products, 126 (13%) were health-care workers and 52.7% had some kind of comorbidity (Table 1). Similarly, out of the 630 male patients, 28% died as a result of MERS-CoV while only 36% of the females died from the disease (Table 1). The median age for males was 53.5 years (interquartile range 39-66) while the median age for females was 48 years (interquartile range 32-63).
Not all of the comorbidities were equally prevalent. While most of the patients in this study had some kind of underlying comorbidities (52.7% have at least one comorbidities), around 38% of all patients had more than one comorbidities with the most common being obesity, diabetes and hypertension (which occurred in more than 50% of those with any underlying comorbidity) (Table 1). Others comorbidities were heart disease, respiratory disease, pneumonia, renal/kidney disease and asthma.
Pearson’s chi-square test of health outcomes between subgroups shows significant difference in gender, comorbidity, health-care worker, clinical outcome, contact type and secondary contact (Table 1). About 3 out of every 10 males died of MERS disease, compared to 28% of the females. The percentage of health-care workers that died of MERS (8.73%) were much less than non-health care workers (36.5%), while 46.14% of persons with comorbidity died of MERS compared with 17.05% of those without comorbidity. Similarly, there effect of comorbidity on mortality from MERS-CoV was significant; patients who died of the disease were more likely to have one or more comorbidities with an odd ratios of 3.4 and 4.7 respectively.
Fig 1 shows the study area and the distribution of the number of infected people and the number of people who died of the disease in the 13 provinces of the KSA. Most of the MERS cases occurred in Ar Riyad (38%) and Makkah (34%) provinces. Fig 2 shows the pyramids of the distribution of the mortality status for the 13 regions based on comorbidity status (upper part) and whether or not the individual was a health worker (lower part). From the pyramids, it is clear that the highest number of cases occurred in Ar Riyad followed by Makkah. The incidence of comorbidities was significantly higher among patients in Ar Riyad, Makkah and Ash Sharqiyah (about half of the cases of comorbidities occurred in these three regions). Al Bahah had the least cases of infected individuals. Similarly, Ar Riyad, Makkah and Ash Sharqiyah recorded the highest number of infected health-care works (Fig 2 bottom). The proportion of health-care workers who died of MERS-CoV were smaller than the proportion of non health-care works who died of the disease.
Social and demographic contexts can significantly influence the transmission of infectious disease, while also creating increased vulnerabilities for some population subgroups. The elderly are at greater risk of many infectious diseases, and the ageing trend in many high-income countries could increase the challenges related to nosocomial (hospital-acquired) and nursing-home acquired infections. An additional challenge related to population ageing is that the share of employed workers in a country decreases. The combination of more people to care for and fewer tax-related revenues may challenge publicly financed public health and disease control programmes (7).
When persons from regions with high endemicity of a given disease move to ones with lower endemicity, new challenges for public health are created. In addition migrant communities can be highly vulnerable to certain infectious diseases. In the EU, for example, approximately 37% of HIV cases reported in 2011 were among people born abroad, and the equivalent number of cases for tuberculosis was 25% (40). Similarly, migrants suffer from a higher burden of chronic hepatitis B infections (41).
It is widely established that socially and economically disadvantaged groups suffer disproportionally from disease (42). This is applicable to infectious disease burdens in both high- and low-income settings (43, 44). Income inequalities are generally widening globally, and this appears to be have been exacerbated in many countries due to the global economic crisis (45). Rising unemployment and the prospect of public health budget cuts can increase the risk of infectious disease transmission (44, 46), with a prominent example being an outbreak of HIV among people who inject drugs (PWID) in Greece (see ‘Measles among Roma in Bulgaria and HIV among PWID in Greece: the impact of socioeconomic contexts’ section) (47, 48). In a similar fashion, it has been speculated that tuberculosis rates could rise in some countries in Central and Eastern Europe (49).
Social trends and behaviours can also play a significant role in infectious disease transmission. The most notable example would be vaccine hesitancy, the phenomenon through which vaccination coverage rates remain suboptimal due to the varying and complicated reasons that individuals may have for not getting vaccinated (50, 51). In some cases, this might be related to misconceptions about the safety or efficacy of vaccines (50, 52), whereas in others this may be related to religious or cultural beliefs (53).
We present a new journal selection to survey articles of infectious disease research. The 100 selected journals contribute to quantitative survey of research articles in not only international, but also regional and non-English journals, with little bias among countries and regions. We suggest that surveying these 100 journals is more beneficial than the SCI Infectious Disease Category, because it identifies more research articles and avoids underestimation of the numbers of articles in regional and non-English journals. Our survey method may require further development; nevertheless, the method provides an effective tool for grasping overall trends in infectious disease research around the world.
SaTScan for local cluster detection detects the area of Al Qasim as primary cluster with high rates after adjusting for all explanatory variables (Relative risk(RR) = 1.83, p − value < 0.0001) and the area of Aseer and Jizan as primary cluster for low rates (RR = 0.093, p − value < 0.0001) while Al Jawf, Riyadh and Hail were secondary cluster for low rates (RR = 0.51, p − value < 0.0001). The Wang’s q-statistics for global stratified spatial heterogeneity was 0.2285 using the geographical detector method [30, 31]. The spatial stratified heterogeneity analysis indicated no significant stratified spatial heterogeneity of the district MERS incidence (q = 0.2285, p − value = 0.9444).
The estimated posterior odds ratio of mortality from MERS disease and corresponding 95% credibility intervals are shown in Table 2. The results reveal that individuals with comorbidities were twice as likely to have died from MERS-CoV compared with those without comorbidities (OR = 2.071; CI: 1.307, 3.263). Estimates for those individuals that had animal or camel contact, those with secondary contact and results based on gender were not significant. However, individuals who were health-care workers were significantly less likely to have died from the disease compared with non-health workers (OR = 0.372, CI: 0.151, 0.827). Compared with patients who had fatal clinical experience, those with clinical and subclinical experiences were equally less likely to have died from the disease.
Fig 3 shows the estimated effects of age (a) and the estimated effects of comorbidity as it varies smoothly over age (interaction between comorbidity and age). Individuals aged 25 years or younger who suffered from MERS-CoV were less likely to have suffered mortality. Nevertheless, the odds of dying from the disease tended to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.
Results of the estimated total spatial variation in mortality due to MERS-CoV are presented in Fig 4. From Fig 4, individuals from provinces with red shading were less likely to have suffered mortality due to MERS-CoV but mortality was higher as the shading moves towards green colour. This implies evidence of significant geographical variation and clustering of mortality from MERS-CoV with lower risk (after adjusting for other variables) occurring in Riyadh, Ar’ar, Al Jawf and Jizan, and higher risk in Al Qasim.
The financing, provision, and quality of healthcare systems; the availability of vaccines, antivirals, and antibiotics medicines, and appropriate compliance to treatment protocols are all important determinants of infectious disease transmission. Although the correlation between healthcare system financing and efficacy is not perfect, recent budget cuts to healthcare are an important consideration when anticipating infectious disease risk. In part related to the global economic crisis, it has been reported that many high-income governments have introduced policies to lower spending through cutting the prices of medical products and, for example, through budget restrictions and wage cuts in hospitals (54). There are many indirect and direct pathways through which budget cuts could affect disease transmission; to provide just one example, it has been estimated that 20–30% of healthcare-associated infections are preventable with intensive hygiene and control programmes2 – should investments in this area diminish, then healthcare-acquired infections could become an even more problematic issue. There are currently roughly 4.1 million healthcare-associated infections each year in the EU alone.3
A broader issue related to healthcare provision is population mobility for both healthcare professionals and patients who might increasingly seek work or healthcare in other countries – the provision of cross-border healthcare and the mitigation of cross-border health threats will necessitate collaboration across borders (55, 56) and solutions for the brain-drain of medical personnel from resource-poor countries (57). Also related to the healthcare provision and practice is the over-prescription or overuse of antibiotics. In combination with a lag in pharmaceutical innovation, rapid transmission, and poor infection control measures, this has driven resistance of organisms such as methicillin-resistant Staphylococcus aureus, or extended-spectrum beta-lactamases, and carbapenemase-producing gram-negatives such as Klebsiella pneumoniae carbapenemase (KPC) (58). Antimicrobial resistance is currently one of the major health risks facing society (59).
Food production systems remain a persistent source for human infectious diseases. Attempts are underway to estimate the global burden of food-borne disease (60), which is likely substantial. Many factors in food production affect human health. A vast range of familiar human pathogens can be acquired through the consumption of animal products and other disease drivers, such as global travel, further provoke this (61). In addition to farmed animals, the hunting and slaughtering of wild animals has led to the emergence of more exotic pathogens: SARS originated in wildlife markets and restaurants in southern China (62) and HIV and Ebola have both been linked to the hunting or slaughtering of primates and other wild animals (33, 63, 64). The density and health of livestock, meanwhile, have been linked to disease in humans (65, 66). Although inconclusive, there is some evidence to suggest that livestock production may lead to increased antibiotic resistance in human pathogens. There are certainly many pathways by which drug resistant pathogens could transmit from livestock to humans, including environmental contamination by excreted veterinary antibiotics (33, 67, 68).
The authors declare that they have no competing interests.
This study was the first national DALY study of all communicable diseases in Korea. The total DALYs from communicable diseases were 445 per 100,000 in 2012, which consisted of 29.0% YLLs and 71.0% YLDs. The total DALYs in men were greater than that seen in women. The largest volume of total DALYs was in the 40-49 years old age group and the lowest was in the ≥ 80 years old age group. However, the ≥ 80 years old age group had the highest DALYs per 100,000, followed by the 70-79 and 0-9 years old age groups. These results indicate that children and elderly people are particularly sensitive to, and at high risk for, communicable diseases. This is consistent with the findings of previous studies in other countries, such as New Zealand, where the communicable disease-related hospitalization rate for patients under 5 years of age was five times greater than that for patients in the 15-29 years old age group; in addition, the communicable disease-related hospitalization rate per 100,000 was highest in the ≥ 70 years old age group (5). In other words, children and elderly people known as vulnerable group were concentrated for communicable disease in high income region. Therefore, we need to additional study for DALY in socioeconomic perspective.
In the four categories of communicable diseases, the largest proportion of DALYs occurred for diarrhea, comprising approximately half of the total DALYs. Fewer DALYs were due to lower respiratory infections, meningitis, and other common infectious diseases, followed by HIV/AIDS and tuberculosis, other communicable disorders, and neglected tropical diseases and malaria. The ranking of the four categories was similar to that seen in the 2010 GBD study in Korea (3).
In case of YLLs and YLDs, 29% of DALYs were from YLLs and 71% were from YLDs. The proportion of YLLs and YLDs due to communicable disease differed from that seen with non-communicable disease and injury. In the case of injury, 38.2% of DALYs were from YLLs and 61.8% were from YLDs. The proportion of YLLs in DALYs was higher with injuries than that seen with communicable diseases. On the other hand, 9.2% of DALYs were from YLLs and 90.8% were from YLDs in non-communicable diseases, with the proportion of YLDs being larger than that seen in communicable diseases (13). These results demonstrate that most of the burden of communicable diseases is due to disability rather than premature death. When compared with non-communicable disease, however, the importance of premature death is higher in communicable disease. Therefore, a strategy focusing on minimizing YLL warrants attention.
In addition, we sub-classified several diseases because of their importance in Korea including herpes genitalia, intestinal infection, and Korean nationally notifiable infectious diseases such as leptospirosis and Tsutsugamushi fever (16). The total DALYs for these diseases was 1,592 (3.13 DALYs per 100,000). Herpes genitalia ranked seventeenth, intestinal infection ranked twenty-second, and Tsutsugamushi fever ranked twenty-third among all the communicable disease DALYs.
In 2012, lower respiratory infections were responsible for the highest communicable disease DALY at 143 DALYs per 100,000 followed by tuberculosis in our study. While lower respiratory infections had the highest DALYs in the 0-19 years old age groups, the 30-39 year old age group, and the ≥ 80 years old age group, in the other age groups tuberculosis had the highest DALYs. Our findings were consistent with the findings of the 2010 and 2013 GBD studies (3).
Because we used data based on claims data, the DALYs of respiratory infections, such as lower respiratory infections including influenza, pneumonia, acute bronchitis, and upper respiratory infections, was high. In addition, upper respiratory infections had the highest total prevalence for all-cause disease in 2012, followed by lower respiratory infections, low back pain, and diabetes mellitus according to claims data (17).
Among diseases, tuberculosis had the second highest DALYs in 2012. In this study, DALYs for tuberculosis in men were 1.6 times higher than that seen in women. While the volume of total DALYs was largest in the 50-59 years old age group, the DALYs per 100,000 were highest in the ≥ 80 years old age group. The mortality and incidence rates of tuberculosis in Korea per 100,000 were the highest among the OECD countries in 2012 and significantly higher than that of the next closest country, Portugal (10). In particular, the YLLs for tuberculosis were the highest and accounted for 33.7% of communicable disease YLLs in Korea. Because mortality due to tuberculosis is considered an avoidable death, appropriate health policy intervention and high quality health care, such as primary diagnostic test for HIV patients and management of drug supplies and timely treatment, could prevent many tuberculosis related deaths (1819). Therefore, a strategy to minimize deaths due to tuberculosis should be undertaken to minimize the burden of tuberculosis. For example, adoption of the WHO Directly Observed Treatment Short-course (DOTS) therapy, a 6 month antituberculosis drug treatment program, would be a cost-effective intervention. For example, the DOTS program had a tuberculosis treatment rate of 86% globally in 2013 (20).
HIV/AIDS was the fourth most common disease responsible for DALYs in Korea in 2012. The DALYs for HIV/AIDS in men were 4.3 times higher than that seen in women. In the DALYs for HIV/AIDS, the 40-49 years old age group represented the largest population in both men and women. While 69.1% of the total DALYs were in the 20-49 years old age groups in men, for women, 69.2% of the total DALYs were in the 40-69 years old age groups. The highest DALYs per 100,000 in men were seen in the 40-49 years old age group at 85 DALYs per 100,000. In women, the 70-79 years old age group was the highest at 25 DALYs per 100,000. In comparison, in the GBD 2010 study HIV/AIDS DALYs per 100,000 were highest in the 35-39 years old age group, and in men the 40-44 years old age group was highest, while the 30-34 years old age group was highest in women (3). Though age distribution presented small differences, HIV/AIDS remains a significant disease and it is important to reduce the burden of HIV/AIDS in Korea as well as in the World.
This study has some limitations. Because we did not consider diseases caused by HPV, HBV, or Helicobacter species, the total DALYs of communicable disease may have been underestimated. Another category not considered was non-communicable diseases caused by infections such as HPV-related disease or HBV-related disease. Considering these diseases, the burden of communicable disease might be greater than in the present estimate. In other words, appropriate interventions for infection such as HPV or HBV, could reduce non-communicable diseases as well as communicable diseases. Additionally, although many previous studies have used national claims data, the accuracy of these data are questionable because they are obtained from insurance claims. Also, we didn’t consider the socioeconomic variables due to limitation of data. Considering the result of this study, the DALYs per 100,000 were concentrated on vulnerable group such as children and elderly people, it is necessary to measure DALYs for communicable disease considering socioeconomic inequalities.
Despite these limitations, this study estimated the DALYs of communicable disease for the first time in Korea. These findings should be used to set priorities for evidence-informed policy making to control communicable diseases. This is particularly important since emerging infectious diseases are recognized as a growing threat for public health in Korea.
The National Institutes of Health (NIH) is the largest public funder of biomedical research worldwide,, with a budget that has grown from $11.9 billion in 1996 to $28.5 billion in 2006. In the mid-1990s, Congress and the public raised concerns that disease-specific funding allocations by the NIH failed to adequately reflect burden of disease and incorporate public input. In response, Congress requested that the Institute of Medicine (IOM) assess the NIH funding apportionment processes. In its 1998 report, Scientific Opportunities and Public Needs: Improving Priority Setting and Public Input at the National Institutes of Health, the IOM recommended improved tracking of disease-specific funding and development of a new priority-setting process.
A landmark study comparing disease burden to NIH funding levels was published in 1999. For 29 common conditions, the study examined a variety of measures of societal burden, recognizing that none by itself completely captures relative impacts of diseases. Disease incidence and prevalence were unrelated to funding, while mortality and years of life lost (YLLs) weakly correlated with funding. Disability-adjusted life years (DALYs)—a measure that estimates the equivalent number of healthy years lost due to disability or early death,—were more strongly predictive. Using DALYs as the best single predictor, only 39% of the variance in NIH funding could be explained. The prior analysis was limited to evaluation of univariate predictors, and did not attempt to evaluate whether funding aligned with other measures of disease burden. The NIH Reform Act of 2006 re-emphasized the NIH's role in identifying research to meet public health challenges, and mandated submission of a biennial report to Congress regarding disease-specific funding amounts. There has been no recent comprehensive study of US disease burden and NIH funding, and an analysis of only one of its institutes has been performed.
To determine whether the NIH has developed processes that better align funding with burden, we assessed the correlation between NIH funding and burden of disease, and compared results with those reported 10 years ago. We also considered other potential predictors of funding and assessed the association of NIH funding with estimates of future and global disease burden.
Based on the collected healthcare utilization data, we evaluated how the sensitivity and representativeness of a surveillance system may be improved by integrating other healthcare providers. We classified healthcare providers as (i) surveillance hospitals, (ii) other hospitals (government and private clinics), (iii) qualified private practitioners, and (iv) the informal sector (unqualified practitioners such as traditional healers, village doctors, homeopaths, and pharmacies). We estimated the proportion of cases attending each healthcare provider class, with exact binomial confidence intervals, and estimated outbreak detection probabilities based on proportions attending the surveillance hospital plus (i) other hospitals, (ii) qualified private practitioners, or (iii) informal healthcare providers. Furthermore, we compared the proportion of cases with each characteristic (sex, age, and socioeconomic group) among community cases to the proportion among those attending each healthcare provider class and quantified absolute differences in proportions with 95% CIs and p-values using bootstrapping (2,000 bootstrap iterations).
All statistical analyses and graphics were implemented in the R computing environment; maps were created using QGIS software.
The training cohort consisted of 104 children (overall inclusion percentage 58%, off all patients/asked who were contacted informed consent was given in 77%, see supplemental flowchart) (table 1). Children with severe disease were significantly younger and had more often siblings than patients in the mild and moderate groups. The amount of children below 2 months of age is 5, 12 and 21 in the mild, moderate and severe group, respectively. Duration of hospitalisation significantly increased towards more severe disease. Two per cent of the patients were not hospitalised (2/104), whereas 17% of the hospitalised patients had only mild disease. RSV was detected in the majority of patients (65%), in 43% viral coinfections were present. The highest proportion of RSV mono-infections was seen in children with a severe course of disease (p<0.001), as was previously published by our group.35
In 2006, the total NIH budget was $28.5 billion, with $11.9 billion devoted to the 29 included conditions. Disease funding ranged from $17 million (M) for peptic ulcer disease and otitis media, to $2902 M for AIDS, with a median of $335 M (±standard deviation $537 M; Table 1). Other metrics from the GBD (Table 1); US inpatient, emergency room, and outpatient (Supporting Table S1); and public interest and scientific opportunity (Supporting Table S2) varied by disease.
In the univariate analysis, NIH funding was most strongly associated with burden of disease measured in DALYs (p = 0.001; Table 2). YLLs (p = 0.03), inpatient hospital discharges (p = 0.05), and total hospital days (p = 0.02) were also associated with funding levels.
In standard multivariable analysis, DALYs was the only significant predictor of NIH funding level retained in the final model, so the analysis became univariate. In 2006, the degree of correlation between NIH funding and disease burden as measured by DALYs alone was less than in 1996: Only 33% of NIH funding variance was explained in 2006 compared to 39% in 1996. Differences between actual and expected funding based on burden of disease in DALYs were estimated for 2006 and compared to 1996 funding levels (Table 3; Figure 1). Depression received the least funding compared to expected, and AIDS the most, consistent with findings from 1996. Relative to expected funding, AIDS, diabetes, and perinatal conditions were the three diseases with the largest amounts of funding, while depression, injuries, and COPD received the least funding (Table 3). The largest positive 10-year gains in actual NIH funding relative to expected were AIDS (+$809 M), perinatal conditions (+$420 M), and diabetes (+$193 M); by contrast, injuries (−$578 M), depression (−$541 M), chronic obstructive pulmonary disease (−$512 M), and ischemic heart disease (−$459 M) decreased most sharply (Figure 1).
In standard multivariable regression models including measures of public interest and scientific productivity, the total charity revenue for a given disease in 2006 (p = 0.04) was also predictive of funding in addition to DALYs (p = 0.006). A model including both variables explained 41% of the variation in NIH funding levels.
In multivariable models constrained to require that diseases resulting in no burden of illness receive no NIH funding (equivalent to requiring an intercept of zero-zero in the regression line, expressed in the dashed line of Figure 2), expected funding amounts were generally similar to those found with the unconstrained multivariable model (Table 3; Figure 3).
To determine if NIH funding might better correlate with world or future disease burden, we performed sensitivity analyses with global measures and future projections (2015 and 2030), all derived from the GBD project (Supporting Table S3). When restricted to global measures, mortality (p = 0.05) and DALYs (p = 0.001) were predictive of funding in univariate analyses (adjusted R2 values 0.11 and 0.30), but only DALYs were retained in all the multivariable models of both global measures and future predictions. Correlation of funding with disease burden was not improved when data utilizing new NIH accounting methods was used (adjusted R2 = 0.27) compared to prior methods (adjusted R2 = 0.34) applied to 2007 data, the first year for which the new methods were available.
Comparison of HDI, health workforce, international travel, total health expenditure and IHR scores among disease control outcome groups using Chi-square were showed in Table 2. Among all reports, 227 reports concerned avian flu (25%), 152 studied yellow fever (16.8%) and 142 examined Middle East respiratory syndrome coronavirus (MERS-CoV, 15.7%) reports. As for human reports, 186 studies examined avian flu (23.3%), 144 studied yellow fever (18%), and 135 considered MERs-CoV (16.9%) reports.
For all cases, HDI, international travel, health workforce, total health expenditure and IHR average scores all significantly differed among disease control outcome groups. In the good disease control outcome group, cases frequently occurred in very high HDI (56%), high international travel volume (88%), high health workforce (37.20%) and high health expenditure (37.20%) countries. In the normal disease control outcome group, cases often occurred in high international travel volume (59.46%) but low HDI (48.65%), low health workforce (55.41%) and low total health expenditure (54.73%) countries. Concerning the bad disease control outcome group, cases usually occurred in very and high HDI (38.58 and 34.83%), high international travel volume (76.03%) but middle health workforce (37.83%) and middle total health expenditure (53.56%) countries.
Regarding IHR self-reported scores, 33.33% of cases in the good disease control outcome group occurred in countries with high IHR average scores in 2016 while 35.14% cases were found in the normal group and 38.58% cases in the bad group occurred in middle IHR average scores countries. For IHR self-reported scores in 2017, 31.10% of cases in the good disease control outcome group occurred in countries with low IHR average scores while 24.32% in normal group and 22.47 in the bad group occurred in low IHR average scores countries.
Similarly, HDI, international travel, health workforce, total health expenditure and IHR average scores both in 2016 and 2017 all significantly differed among disease control outcome groups for only human case analysis. In the good disease control outcome group, cases frequently occurred in very high HDI (57.66%), high international travel volume (88.31%), high health workforce (36.36%) but middle total health expenditure (37.92%) countries. Regarding IHR self-reported scores, 36.62% of cases in the good disease control outcome group occurred in countries with high IHR average scores in 2016 while 35.14% cases were found in the normal group and 38.58% cases in the bad group occurred in middle IHR average scores countries. For IHR self-reported scores in 2017, 35.84% of cases in the good disease control outcome group occurred in countries with low IHR average scores while 24.32% in normal group and 22.47 in the bad group occurred in low IHR average scores countries.
There are several theories regarding the cause of the first reported case of BSE in the mid-1980s; some insist that the BSE pathogen (PrPSc) formed naturally and others claim that the disease was caused by the cow feed made from sheep infected with scrapie. By an extensive epidemiologic investigation, the main cause for BSE turned out to be a meat and bone meal (MBM) made from the discarded bones and intestines of slaughtered cows and sheep. In the UK, in particular, cow intestines have been used in MBM as a protein supplement since 1972, which accelerated the increase of the occurrence of BSE.
BSE has occurred in European countries that import MBM from the UK; according to statistics from the World Organization for Animal Health (Office International des Epizooties; OIE), there have been 190,628 BSE cases in 25 countries worldwide as of August 30, 2012 (http://www.oie.int). Most reported cases are from the UK, peaking in 1992, and in other countries the epidemic peaked in 2002 or 2003; from then the number started to decrease sharply.
BSE is a chronic degenerative neurological disease in cows; part of the brain becomes sponge like, and exhibits many different kinds of neurotic symptoms and paralysis, eventually leading to death. In BSE, nerve cells and central nerve tissues take on a sponge-like form. After approximately 2–5 years of incubation, the animal dies within approximately 2 weeks to 6 months of development of the disease. Clinical symptoms include extreme sensitivity to external stimuli such as light and sound, neurotic changes (depression and nervousness), positional imbalance, inability to stand straight or move, paralysis in the hind legs, and paralysis of the whole body before death.
At present, BSE is under surveillance by the OIE; in Korea, it is classified as a second category of animal epidemics along with scrapie and CWD. BSE has no effect on the cattle younger than 7 months old; by the time cows reach 24 months of age, there are many variant prions in the body. Most occurrences of BSE are in cows older than 36 months. Therefore, the OIE examines the occurrence of BSE in 24-month-old cows.
In the UK, more than 184,000 cases of BSE have been reported and more than 3 million cows were destroyed to stop the spread of the disease; hence, the UK strictly banned MBM. Owing to their efforts, the occurrence of BSE was dramatically reduced. However, since the 2000s, the disease has been spreading worldwide, including in the USA, Japan, Israel, and various African countries. Determining the precise number of occurrences is challenging, as some infected animals do not exhibit any particular symptoms. Without total inspection and surveillance, it is difficult to research the actual status of the disease.
Therefore, the EU places much emphasis on active monitoring and surveillance systems, such as total inspection, thorough removal of SRM (where 99% of the pathogenic prions exist), banning MBM, and monitoring animal feed. Through such actions, BSE has become manageable, but it is still not eradicated. The USA also started to emphasize the development of an effective animal-monitoring system over concerns for human health.
However, some BSE cases have been reported even after stricter surveillance was put in place, which means that the disease is not controllable by monitoring animal feed alone. Some scientific evidence is given regarding this: pathogenic prions from the feces of TSE-infected animal can be absorbed into the soil, can combine with minerals in the soil, and can become stable. Although BSE does not seem to be transmitted horizontally within species, such findings suggest that more precautionary actions and approaches should be performed in epidemiologic investigations, including studying the possibility of transmission via a contaminated environment.
BSE-infected cows show the possibility of self-mutation of the BSE prion, since the prion gene that causes vCJD in humans, which had some mutations, was found in the brains of affected cows. This implies that a wide range of monitoring systems in DNA and/or protein levels is necessary in addition to a strict animal feed policy. Considering the transmission of BSE to humans, control SRM is the most important step to take. Based on recent findings on the relationship between SRM and the occurrence of the disease, the EU developed some guidelines in April 2008 for its member countries to follow regarding SRM. According to these guidelines, the tonsils, whole intestine, and mesenterium are all vulnerable to prions across all ages; the brains, eyes, spinal cords, and skulls of cows that are older than 12 months are considered to be SRM.
Some older cows have an atypical form of BSE (BASE), which differs from typical BSE with respect to its molecular and biochemical properties; it appears to be a sporadic BSE, although more precise etiological studies need to be performed for confirmation. Recently, attention has been given to BASE [47–49] because of its infectivity and relation to vCJD.
A new cohort of 141 children was tested. Patient characteristics of the original and validation cohort were similar with respect to their distribution of age, gender and number of prematurely born infants (less than 35 weeks). The number of children below 2 months of age is 12, 23 and 22, respectively. In contrast to the training cohort, the presence of siblings was not significantly different between the severity groups in the validation cohort (see online supplementary table 1 and supplementary figure 1).
We ran the algorithm on each outbreak year using only patients who tested negative for all four of influenza, RSV, parainfluenza, and hMPV. Because they were tested, we assume they have an ILI. However, since all of their tests were negative, their diagnoses were indeterminate. That is, they have some kind of ILI but do not have any of the modeled diseases.
Fig 3 shows the (logarithm of the) daily odds of the presence of an unmodeled disease in the monitor window for outbreak year 2014-2015. DUDE begins computing odds on day 93 (September 1). The odds of the presence of an unmodeled disease slowly increased and was greater than 1 on day 106 (September 14, 2014) indicating that it was more likely than not that an unmodeled disease was present. After day 106 the odds of the presence of an unmodeled disease increased dramatically. An examination of records in the monitor window at that time showed a prevalence of patients with wheezing, chest wall retractions, runny nose, respiratory distress, crackles, tachypnea, abnormal breath sounds, headache, stuffy nose, and dyspnea. (These are the findings that were at least 25% more likely to occur in a patient in the monitor window than one in the baseline window and were present in at least 10% of the patients in the monitor window).
During this time period, the CDC identified an outbreak of Enterovirus D68 (EV-D68). In mid-August 2014 hospitals in Missouri and Illinois notified the CDC of an increase in admissions of children with severe respiratory illness. By September 8, 2014 officials at Primary Children’s hospital in Salt Lake City, Utah suspected the presence of EV-D68, and by September 23, 2014 the CDC confirmed the existence of EV-D68 in Utah. Since August 2014, the CDC and states began doing more testing for EV-D68, and have found that EV-D68 was causing severe respiratory illness in almost all states. Symptoms of EV-D68 include wheezing, difficulty breathing, runny nose, sneezing, cough, body aches, and muscle aches. (Severe symptoms of EV-D68 may also include acute flaccid paralysis, but this is not among the symptoms used by DUDE).
Fig 4 shows the results of running the algorithm on patients who tested negative for all four of influenza, RSV, parainfluenza, and hMPV patients for outbreak years 2010-2011 (top) through 2014-2015 (bottom).
PrPSc in infected animals is concentrated in specific areas. These areas are called specific risk material (SRM) and include the brain, eyes, spinal cord, skull, vertebral column, tonsils, and distal ileum; these are the most crucial areas for disease management and control. The disease is transmittable via surgical tools that came into contact with SRM and via blood transfusion. Since blood rarely contains the prion, it was considered safe until a death caused by vCJD from a blood transfusion was reported in the UK, which was alarming to the public. From this case, the UK spent more than £200 million on a preventive process to protect surgical tools against prion transmission. This shows that the occurrence of BSE or vCJD can cause an enormous amount of indirect expenses, although they do not occur frequently.
The species barrier makes it difficult for infectious diseases to be transmitted from one species to another. The value of the species barrier for prion disease transmission between humans and cattle has been estimated to be 4000, based on the study of BSE zoonosis. However, a precautionary principle assumes that there is only one value of species barrier between humans and cattle, and suggests that the same dose that causes the disease in cattle may affect humans in the same way. The experimental disease-inducing amount of SRM in a single administration is 0.001 g injected orally; 10 g can lead to BSE in all administered cattle. The amount required to induce the disease is very small; a scientific report submitted to the British Council in 2001 states that an amount as small as one speck of pepper may cause the disease. Five grams of an oral inoculum with brain homogenate from a BSE-infected cattle in a primate (Cynomolgus macaques) resulted in the development of a vCJD-like neurological disease 60 months after exposure.
Since such a small dose can cause the disease affecting both humans and animals and there is currently no cure, experiments on the PrPSc agent transmissible to humans are supposed to be performed in Biosafety Level 3 in the same manner as biologically strong infectious agents (e.g., anthrax bacterium, Severe acute respiratory syndrome, and the West Nile virus). It is notable that in the study of pathogenic prions, the protein particles have been observed to have many different strains, which are under investigation. In fact, the discovery of new strains is related to the species barrier and has many implications in disease control in relation to the adaptation and progression of TSE.
In that respect, the EU, which has conducted much research on BSE and vCJD, defines any beef that has had contact with any SRM as SRM itself and advises people not to use any cosmetic or food items with SRM, although transmission via cosmetics or food has not been reported yet. In BSE-infected cattle, PrPSc is also found on peripheral nerves. Consequently, the whole body of the infected cattle is disposed of according to the EU regulation.
It is noteworthy that Payer’s patch tissue, which is the most essential factor for PrPSc absorption, is mostly on the ileum in humans; however, similar tissues are predominantly found in the whole intestine including mesentery in cattle. Therefore, the EU defines the whole intestine as SRM, the evidence of which is verified annually. Recently, Switzerland submitted a request (EFSA-Q-2009-00226) to the European Food Safety Authority (EFSA) to reassess the use of bovine intestines for stuffing sausage. The request was declined, which demonstrates a careful approach to the consumption of bovine intestine by global organizations. Similarly, Koreans also need to take precautionary action in consuming bovine intestines.