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Research problem statement on diabetes mellitus

Metrics details Abstract The burden of an epidemic is often characterized by death counts, but this can be misleading as it fails to acknowledge the age of the deceased patients. Years of life lost is therefore widely used as a more relevant metric, however, such calculations in the context of COVID are all biased upwards: patients dying from COVID are typically multimorbid, having far worse life expectation than the general population.

These questions are quantitatively investigated using a unique Hungarian dataset that contains individual patient level data on comorbidities for all COVID research problem statement on diabetes mellitus in the country. To account for the comorbidities of the patients, a parametric survival model using 11 important long-term conditions was used to estimate a more realistic years of life lost.

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The usual calculation indicates The expected number of years lost implied by the life table, reflecting the mortality of a developed country just before the pandemic is Further research is warranted on how to optimally integrate this information into epidemiologic risk assessments during a pandemic.

Perhaps the most widely used indicator of burden is the mortality associated with the epidemic [ 7 ]. This can be directly measured, it is considered to be very relevant and often correlated with other—more difficult to measure—indicators [ 8 ].

Simply calculating the number of deaths due to COVID to measure the burden is associated with two inherent problems [ 910 ]. The first is the definition of dying from the disease: how deaths are attributed in a multimorbid patient is not necessarily unambiguously defined, procedures might be different between countries or change over time. In research problem statement on diabetes mellitus to this uncertainty, deaths may be undercounted if patients are not tested for COVID even post mortem.

Diabetes mellitus endokrinológiai fórum

This is the reason why the application of excess deaths, that is, the number of deaths minus the number of baseline value, i. Excess death calculation however relies on a baseline, a predicted value, which is always an extrapolation based on historical data, and as such, its reliability necessarily gets worse over time as we are further from the data from which it is predicted. In addition to this, excess death calculation cannot discern the—positive or negative—indirect effects from the direct effects of the epidemic.

These problems will not be addressed in the present paper. The second problem, that will be investigated in detail is that raw deaths counts ignore the age of the deceased patient: this calculation gives equal research problem statement on diabetes mellitus to the death of a multimorbid, year-old patient, who only had a few years of life expectancy even without the infection, to the death of a healthy year-old patient, who had several decades of life expectancy.

Thus, years of life lost YLLfirst used by Haenszel [ 12 ] is widely used to more accurately tea a cukorbetegség kezelésében the burden of a disease [ 131415 ]. YLL calculations either assume a fixed target age, to which years lost is measured, or—more typically—use a life table to calculate the expected remaining time i.

They are usually multimorbid, often having several long-term conditions [ 1718 ], which are themselves associated with reduced life expectancy. The correct calculation should take this into account, by subtracting the age at death from a lower expectation, resulting in a lower number of lost years of life.

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Calculation of the appropriate expectation is however not straightforward, as life table for patients with comorbidities—and especially for their arbitrary combinations—are not usually available, and are not feasible to produce.

A further important but often overlooked problem about YLL is the appropriate interpretation of its numerical value. This is not the case however. YLL is based on losing the remaining life expectancy, but this is never zero, so it is mathematically impossible to have a YLL of zero—even a patient dying at years still loses some remaining life 1.

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More precisely, YLL is calculated for a hypothetical cohort undergoing exactly the mortality specified by the life table and the resulting number of years lost is divided by the number of deaths [ 1920 ]. In this sense, the norm serves as a reference point, to which actual YLLs can be compared, as the norm signifies what loss is to be expected lacking any special mortality-modifying circumstance.

How this should be integrated into the calculation i.

Different approaches to quantify years of life lost from COVID-19

Uniquely, individual patient level data on comorbidities is publicly available in Hungary for every reported death. Methods Hungarian life table was obtained from the Human Mortality Database for the latest available [ 21 ].

Life table norm was calculated from this life table by anémia-kezelés cukorbetegséggel the product of the number of deaths from a fixed starting cohort with a size ofand expected remaining years for each age group and dividing it withEleven comorbidities investigated by Hanlon et al.

Search strings are given in Table 1. Table 1 Regular expressions used for pattern matching comorbidities in the unstructured list of comorbidities Full size table Case is ignored everywhere except for the regular expression after OR in case of diabetes and IHD, where case is matched.

Hungarian diacritical marks were removed by transliteration to the corresponding non-diacritic equivalent in the original database and the search strings were formed accordingly to reduce the impact of typographical errors.

Although it would be possible to decode any other comorbidity in the above fashion, these 11 were selected, as survival information were available for these from the literature.

In more detail, survival models based on age and comorbidities were obtained from Hanlon et al. Covariates were the 11 comorbidities and their interactions with age, and were assumed to govern the location parameter research problem statement on diabetes mellitus.

Foltok a lábak viszkető diabéteszében

Using the estimated parameters from Hanlon et al. Separate parameter sets were used for males and females. To account for the overall difference in the survival of the Hungarian population and the data—based on the population of Wales—from Hanlon et al.

These survival models are only available for ages above 50 years, so the entire present analysis will be restricted to this age group. Confidence interval for the prevalence estimates was calculated with Clopper-Pearson exact method [ 25 ]. Age-specific prevalence estimates were calculated using a spline-regression to smoothly model the effect of age without assuming any parametric functional form [ 26 ].

Calculations were carried out under the R statistical program package version 4. Results As of 12 May,Hungary reported a total of 28, deaths from COVID, of which 27, occurred in patients above 50 years of age 13, females and 14, males.

research problem statement on diabetes mellitus 1 5 órás vércukor érték

Figure 1 shows the distribution of the age of deaths according to repedések sarkú cukorbetegség és a kezelés. The size of the background population is 3, [ 29 ].

This means Prevalence of the 11 investigated comorbidities is shown on Table 2. Prevalence estimates by age and sex are shown on Fig. Overall, The difference is immediately obvious: when taking the comorbidities into account, the life expectancy is markedly lower when using the life table, which represents the general population.

The life table norm of YLL according to the definition of Marshall is This was calculated from the pre-pandemic life table by summing the number of life years lost, the research problem statement on diabetes mellitus of life expectancy and the number of deaths for each age group and then dividing it with the number of deaths, i. Discussion Burden of any disease can be characterized by its impact on the quality of life, and on life expectancy.

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As years of life lost better characterize the true burden of the disease than raw death counts, several studies assessed it for the COVID pandemic. Aroles et al. They use both reported deaths and excess deaths to account for the potential undercounting.

In line with the message of the present paper and that of Hanlon et al. Quast et al.


Mitra el al evaluated the years of life lost in the United States, Italy and Germany using a fixed target age instead of the life table approach [ 32 ]. Rommel et al. Goldstein and Lee estimated The paper of Hanlon et al. However, they had no access to individual patient data on comorbidities and therefore had to rely on an approximate reconstruction of the individual data from aggregate data which is necessarily less efficient and reliable. Briggs et al.


This allowed a more direct analysis. The life years lost are however below the norm as defined by Marshall even without taking the effect of comorbidities into account. This result might seem counterintuitive at first glance, but is actually entirely possible. Simply comparing the YLL to the norm would therefore, at face value, indicate that the activity of a serial killer is beneficial to the public health.

This warns us that how the norm should be research problem statement on diabetes mellitus into the evaluation of a nõkben cukorbetegséggel kezelt vizeletes inkontinencia actual YLL of a specific cause is a complicated question. A few things should be noted about this approach.

First, a national life table was used for the ordinary YLL calculations, instead of using a fixed value, or subnational tables [ 37 ].

Second, the whole period was used, i. This might be relevant, as different population groups could be affected due to changing non-pharmaceutical interventions, or the introduction of a new variant might alter the age-dependent risk for the same group. The major limitation of the present study is the application of an external survival model to calculate the potential life expectancy for the patients who died.

Where is problem statement in research paper

A logical and important research step would be the calculation and application of a survival model for the comorbidities research problem statement on diabetes mellitus the same Hungarian population. Perhaps the most important strength of the present is study is the application of the detailed, individual-level comorbidity database available for more than 27, deaths.

However, the data quality is poor, comorbid diseases are entered research problem statement on diabetes mellitus any form of standardization, with many typographical errors, arbitrary usage of Latin and Hungarian terminologies, arbitrary usage of abbreviations etc.

We tried to overcome these limitations by using carefully selected search expressions to identify the comorbidities, but no formal analysis on the sensitivity or specificity was carried out. We also have no systematic validation on the correctness of the recorded comorbidity data. A third limitation is virsli cukorbetegeknek only 11 comorbidities were used and no information was available on the severity of the comorbidity.

A final limitation is the application of YLL itself. A recent paper of Rubo et al. While strictly speaking we almost never have a perfect such model, this is much less of a problem when the age of death is markedly lower than the age of death in the general population, as this implies that the deaths can be strongly attributed to the investigated factor as other are unlikely to cause death at early age.

Of note, Rubo et al.

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What fraction should be deducted in a situation like the COVID epidemic, and whether the determination of this fraction is a feasible task at all is perhaps the most intriguing future research direction. This is important in two, opposite directions. First, COVID sometimes causes long-lasting sequelae which are detrimental to QoL [ 4142 ], thus a more complete analysis should also consider this, even if the patient survived the disease.

Another, and likely more important consideration is that many of the—typically multimorbid—patients dying from COVID very likely had a reduced QoL even before the infection, so an analysis that adjusts for QoL will reveal an even fewer number of—quality-adjusted—life years lost.

Conclusion Evaluation of the years of life lost is crucial, as it provides a much more relevant insight into the burden of the epidemic than raw death counts. The actual calculation however is not straightforward, and depends on many assumptions which should be carefully assessed.

Ideiglenesen le vagy tiltva

Data Availability and material References 1. New methodology for estimating the burden of infectious diseases in Europe.

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PLoS Med. The burden of infectious diseases in Europe: a pilot study. Article Google Scholar 4.

Diabetes Mellitus Pathophysiology \u0026 Nursing - Diabetes Nursing Lecture NCLEX - Type 1 \u0026 Type 2

Li S, Leader S. Economic burden and absenteeism from influenza-like illness in healthy households with children 5—17 years in the US. Respir Med.