Epidemiology: H1N1 Virus - Essay Example

? Epidemiology Introduction H1N1 virus has been one of the most studied in Australia, this is partly because of the risk that it poses to the citizens and the levels of hospitalization associated with the disease especially among people who had other illnesses and medical complications. This essay will study research that has been done in Sydney, Australia about the risk factors associated with hospitalization of H1N1 patients. In addition, it will look at the findings of the study and biases which may have compromised the results. Qn 1 This study was conducted in Sydney Australia to identify risk factors that are related to hospitalization from the pandemic of (H1N1) 2009 virus infection among people above 16 years of age (Ward, Spokes & McAnulty, 2011). The study was carried out after the emergence of H1NI virus in 2009 associated with the large increase in people who required hospitalization due to severe influenza ailments in all parts of the world (Real-Time, 2009). Before the vaccine for the disease was available in June 2009, there was widespread community transmission in Australia where the public health authorities were more concerned about protecting the people who were at greatest risk of sever illness; these included, expectant mothers, individuals suffering from obesity; people with chronic medical conditions such as heart, lung and kidney diseases; and indigenous people (Dominguez-Cherit et al, 2009; Morgan et al, 2010). In addition, people with blood, metabolic or neurologic conditions, those with low immunity and asthma were also put in the category of individuals who were at risk of sever forms of the illness (Ward, Spokes & McAnulty, 2011). Qn 2 The objective of this study was to identify the risk factors that could be associated with hospitalization from pandemic H1N1 2009 virus infections among persons above 16 years of age in Sydney, Australia (Fraser et al 2009; Kumar et al 2009)). The risk factors that the study aimed to test included pregnancy, immune suppression, pre-existing lung disease, asthma requiring regular preventive medication, heart diseases, diabetes and the tendency to smoke (both current and previous smokers) (Perez-Padilla et al, 2009; Chen et al, 2010) . The hypothesis that the researchers used is not clear in this article, this can be contributed by the fact that it was a case study that was aimed at understanding the risk factors associated with H1N1 (Ward, Spokes & McAnulty, 2011). Qn 3 The study design that was used in this research was a case control study, which is an observational study and involved a standard questionnaire to patients suffering from H1N1 and the control group (Ward, Spokes & McAnulty, 2011). The questionnaire asked the sample questions about influenza symptoms, pregnancy and delivery within the previous 28 days, weight and height, smoking history, current and previous medicines and medical procedures administered (Ward, Spokes & McAnulty, 2011). In addition, the questionnaire also sought to gather information regarding general health conditions such as Asthma, heart and lung diseases, diabetes and other metabolic disorders, kidney ailments, mental conditions, blood related complications and immunosuppressive conditions. Since this research was a case control study, using questionnaires on the sample was the most effective method to collect data as it ensured that accurate information was collected from the patients while at the same time avoiding the risk of the researchers being infected with the disease due to close contact with the patients (Ward, Spokes & McAnulty, 2011). Since the choice of the sample group was done in a thorough way ensuring only those people who were relevant to the study were selected, the study design captured the outcome and exposure in the best way possible (Ward, Spokes & McAnulty, 2011). Qn 4 In this case control study, there were more than one primary exposures of interest that included the risk factors associated with the hospitalization of H1N1 pandemic (Kelly & Grant, 2009). These included pregnancy, immune suppression, pre-existing lung disease, asthma requiring regular preventive medication, heart diseases, diabetes, smoking tendencies ( both current and previous) and obesity (Kark, Lebiush & Rannon, 2006). These exposures were got through use of questionnaires on the sample size where the sample group was required to respond on whether they had any of the conditions/exposure (Ward, Spokes & McAnulty, 2011). The data were then run in a logistic regression model at 95% confidence interval to produce accurate results (Ward, Spokes & McAnulty, 2011). Qn 5 The primary outcome of interest for the researchers involved in this study was the number of people who were hospitalised as a result of the H1N1 pandemic due to the risk factors that had been included in the study. The cases were defined as having been hospitalised as for H1N1 virus and had influenza like illnesses and had been admitted for at least overnight stay in one of Sydney’s metropolitan hospitals from July 1 to august 31 2009 and whose laboratory tests had confirmed the presence of H1N1 virus (Ward, Spokes & McAnulty, 2011). Qn 6 The study population for this research were persons above 16 years of age living in metropolitan Sydney between July 1 and august 31 2009. The people who were eligible to be included as cases were those that had spent at least a night in one of Sydney’s metropolitan hospitals and whose laboratory tests had confirmed the presence of H1N1 pandemic (Ward, Spokes & McAnulty, 2011). Patients who were below 16 years of age or those residing outside Sydney were excluded from this study because it was assumed that the threshold for hospital admissions for people below 16 years was different and also for those in regional and rural areas (Ward, Spokes & McAnulty, 2011).402 patients had been identified to take part in the survey, however, only 302 of those identified took part in the actual study (Ward, Spokes & McAnulty, 2011). In addition, a total of 1252 people had been identified as potential control population but only 603 took part in the actual study. From these figures, the ration of the individuals who were exposed to the H1N1 virus against the control group is 1:2 (Ward, Spokes & McAnulty, 2011). Qn 7 & 8 This study used odds ratio (OR), and logistic regression model as measure of association between exposure and hospitalisation (Ward, Spokes & McAnulty, 2011). Since the study did not have a definite hypothesis which was being tested, it was therefore likely that it did not have much need for the measures of testing statistical stability since there was no comparisons between hypothesised results and the actual results (Ward, Spokes & McAnulty, 2011). Qn 9 A major weakness in this study in assessing whether hospitalisation was caused by the risk factors being tested or it might have been caused by chance rather than by the risk factors that have been highlighted. (Ward, Spokes & McAnulty, 2011).Qn 10 During the selection of study subjects, there was likelihood that there could have been bias (Hewagama et al, 2010). This was because first, the sample was only from Sydney therefore did not cover other places in the country, therefore the sample size was not representative of the whole country, and the likelihood of hospitalization may have been caused by other factors outside the risk factors that have been identified (Louie et al, 2009). Secondly, the selection was also biased toward women since the sample size had a large number of women who participated in the study as compared to men, this therefore means that the final results may have been influenced by certain traits that are inherent in women and which were not captured in the risk factors (Carcione et al, 2010). This bias was mitigated by the researchers since they adjusted their analysis for gender (Mashonganyika et al, 2009). The other bias in this study was the exclusion of those who died from the cases. This selection bias removes those who had severe diseases due to H1N1 infection and can cause underestimation of the association between exposure and hospitalisation. (Ward, Spokes & McAnulty, 2011) Qn 11 Since collection of information was mainly through questionnaires, there is a likelihood that the individuals responding to the questions may have failed to capture some undiagnosed medical conditions that may have largely contributed to the increased chance of them being hospitalised (Ward, Spokes & McAnulty, 2011). This effect means that there was overestimation of the contribution of H1N1 to the patient being hospitalised. Another bias that was likely to have happened during collection of the data is due to the different threshold that nurses or health institutions in Sydney required to in order to admit pregnant women. This would cause discrepancies in the collected data where some of the doctors had a lower admission threshold than those in other health institutions (Mashonganyika et al, 2009). Another data collection bias in this study was that it did not collect the main reason for admission of patients to the hospital; it did not ascertain whether it was a result of H1N1 or other illnesses since the patients were only required to meet the H1N1 infection threshold (Ward, Spokes & McAnulty, 2011). The likelihood of these biases in data collection was present since the researchers did not put in place any measures to mitigate the effect that this bias may have had on the results (Ward, Spokes & McAnulty, 2011). Qn 12 &13 Due to the biases that were present in the process of data collection and sample selection, the authors put in place several measures to ensure that these biases do not affect the results (Eastwood, Durrheim, Butler & Jones, 2010). To avoid distortion of data due to the large number of women present in the sample size, the researchers conducted a separate analysis on both genders and found no significant differences in the results as a result of gender(Ward, Spokes & McAnulty, 2011). In respect to the dead patients, the researcher collected data that was available for them before their death from their physicians. However, the other limitations and data collection biases were not controlled for (Ward, Spokes & McAnulty, 2011). Despite the measures that were taken to control for bias, more could have been done to improve on the quality of data that was collected (Jain et al, 2009, Miller et al, 2010). For instance, the researchers could have tried to collect data on the initial reasons why the patients who qualified to be included in the sample had been admitted in the health institutions. Secondly, to avoid the bias due to gender they could have put the characteristics that are present in both genders and do away with those characteristics that are only present in one gender, for instance, pregnancy. Qn 14 From the data that they collected from the sample, the following were the results: the median age for the patients that were interviewed was 45 years as the range of the people interviewed was between 16 and 88 years. 23 per cent of the total respondents were admitted in high dependency unit with 12 per cent requiring mechanical ventilation. The reported risk factors among the patients interviewed included history of asthma as 38 per cent with 29 per cent requiring regular medication, 19 per cent had lung complications, 19 per cent had diabetes, 18 per cent had mental health complications those who had heart diseases were 14 per cent (Ward, Spokes & McAnulty, 2011). In addition, pregnant women were 13 per cent, patients with obstructive sleep apnea were 12 per cent, those with immune suppression were 10 per cent neurologic condition patients were 8 per cent, liver diseases were 8 per cent, kidney diseases were 3 per cent, blood disorders were 5 per cent and those with metabolic conditions were 1 per cent. In the case control study, there were no significant discrepancies in the patients and controls regarding their residence. In univariate analysis, the sample patients were more likely to be male as compared to the control group, in addition, they were also likely to be between 16 and 35 years of age and 46 to 55 years, and have a higher body mass index (Riu, & Rius, 2003). In addition, this group was also likely to have suffered from history of asthma, heart complications, mental health disorders, immune suppression, obstructive sleep apnea, lung diseases, diabetes, liver complications, blood disorders, pregnancy and smoking. When the researchers carried out the logistic regression model, they found out that age, gender, asthma that requires treatment, smoking, heart complications, immune suppression, diabetes, lung diseases and pregnancy were independently associated with hospitalization for sever pandemic H1N1 2009 infection (Hosmer & Lemeshow, 2008; Peduzzi et al, 2006). The risk factor that was strongly associated with hospitalization was pregnancy, followed by lung complications, immune suppression and age in that order (Ward, Spokes & McAnulty, 2011). In univariate analysis, cases who required mechanical ventilation were more likely to develop lung complications, asthma that required medical intervention, higher body mass index, pregnancy and likely to have been vaccinated against influenza in the last 12 months (Wenzel & Edmond, 2009). Qn 15 A major and probably the only influence of the bias in data collection and sample selection is the strong probability that the chance of hospitalization is linked to pregnancy. This is linked to the large number of women and the inevitable occurrence that whether a pregnant woman is suffering from H1N1 or not, she has to be admitted during delivery (Ward, Spokes & McAnulty, 2011) Qn 16 The discussion of the results in this article has mainly dwelt on the limitations of the study and how the researchers attempted to avoid these biases from showing in the results. However, despite these efforts, some of the biases were not captured. For instance, selection of pregnancy as one of the risk factors yet the sample size was of mixed gender would make the results appear biased; this could be true since in the logistic regression analysis shows that pregnancy was the leading factor in relation to hospitalization (Dowse, et al, 2011; Vaillant, La Ruche, Tarantola & Barboza, 2009)). Another limitation of the study that was not captured in the discussion is the coverage of the study since this study was only conducted among people who were living in Sydney between July 1 2009 and August 31 2009 (Ward, Spokes & McAnulty, 2011). In addition, it also captured only those who attended health institutions within Sydney, this means that the information did not capture patients from other parts of Australia or patients from Sydney who sought medical attention from other parts of the country (Blyth et al, 2010). Qn 17 The main conclusion from this article is that based on the results from the research, there is evidence to support the continuation of efforts to prevent spread of influenza which includes vaccination of people with lung complications, immune suppression, asthma, diabetes and heart complications (Ward, Spokes & McAnulty, 2011). Qn 18 & 19 Since the research was a case control study in an area with special features, generalisation of the results require utmost care since the conditions of one area may be different to another, therefore, they can only be generalised in areas with similar characteristics (Bishop, Murnane & Owen, 2009; Looker, Carville, Grant & Kelly, 2010). The information got from this study goes to add on to the already available knowledge of the disease and to support the need to continue with preventive measures among those at risk (Van Kerkhove, et al, 2011) Conclusion Hospitalization due H1N1 virus in Sydney, Australia is associated with several risk factors that include acute asthma that requires medication, lung complications and diabetes among other medical complications, pregnancy was also found to be a risk factor in H1N1 which contributed to hospitalization. These results were however found to have several biases which may compromise their accuracy and applicability in other areas, for instance, they were only conducted in one area and they main reason for hospitalization of the patients. References Bishop, J. F., Murnane, M. P., & Owen, R. (2009). Australia's winter with the 2009 pandemic influenza A (H1N1) virus. New England journal of medicine, 361(27), 2591-2594. Bishop, J. F., Murnane, M. P., & Owen, R. (2009). Australia's winter with the 2009 pandemic influenza A (H1N1) virus. New England journal of medicine, 361(27), 2591-2594. Bland, J. M., & Altman, D. G. (2000). Statistics notes: the odds ratio. BMJ: British Medical Journal, 320(7247), 1468. Blyth, C. C., Kelso, A., McPhie, K. A., Ratnamohan, V. M., Catton, M., Druce, J. D., ... & Dwyer, D. E. (2010). The impact of the pandemic influenza A (H1N1) 2009 virus on seasonal influenza A viruses in the southern hemisphere, 2009. Euro Surveill, 15, 31. Carcione, D., Giele, C., Dowse, G. K., Mak, D. B., Goggin, L., Kwan, K., ... & Effler, P. (2010). Comparison of pandemic (H1N1) 2009 and seasonal influenza, Western Australia, 2009. Emerging infectious diseases, 16(9), 1388. Chen, M. I., Lee, V. J., Lim, W. Y., Barr, I. G., Lin, R. T., Koh, G. C., ... & Leo, Y. S. (2010). 2009 influenza A (H1N1) seroconversion rates and risk factors among distinct adult cohorts in Singapore. JAMA: the Journal of the American Medical Association, 303(14), 1383-1391. Dominguez-Cherit, G., Lapinsky, S. E., Macias, A. E., Pinto, R., Espinosa-Perez, L., de la Torre, A., ... & Fowler, R. A. (2009). Critically ill patients with 2009 influenza A (H1N1) in Mexico. JAMA: the journal of the American Medical Association, 302(17), 1880-1887. Dowse, G. K., Smith, D. W., Kelly, H., Barr, I., Laurie, K. L., Jones, A. R., ... & Effler, P. (2011). Incidence of pandemic (H1N1) 2009 influenza infection in children and pregnant women during the 2009 influenza season in Western Australia—a seroprevalence study. Medical Journal of Australia, 194(2), 68. Eastwood, K., Durrheim, D. N., Butler, M., & Jones, A. (2010). Responses to pandemic (H1N1) 2009, Australia. Emerging infectious diseases, 16(8), 1211. Eastwood, K., Durrheim, D. N., Butler, M., & Jones, A. (2010). Responses to pandemic (H1N1) 2009, Australia. Emerging infectious diseases, 16(8), 1211. Fraser, C., Donnelly, C. A., Cauchemez, S., Hanage, W. P., Van Kerkhove, M. D., Hollingsworth, T. D., ... & Roth, C. (2009). Pandemic potential of a strain of influenza A (H1N1): early findings. science, 324(5934), 1557-1561. Hewagama, S., Walker, S. P., Stuart, R. L., Gordon, C., Johnson, P. D., Friedman, N. D., ... & Giles, M. L. (2010). 2009 H1N1 influenza A and pregnancy outcomes in Victoria, Australia. Clinical Infectious Diseases, 50(5), 686-690. Hosmer, D. W., & Lemeshow, S. (2008). Model?Building Strategies and Methods for Logistic Regression. Applied Logistic Regression, Second Edition, 91-142. Jain, S., Kamimoto, L., Bramley, A. M., Schmitz, A. M., Benoit, S. R., Louie, J., ... & Finelli, L. (2009). Hospitalized patients with 2009 H1N1 influenza in the United States, April–June 2009. New England Journal of Medicine, 361(20), 1935-1944. Kark, J. D., Lebiush, M., & Rannon, L. (2006). Cigarette smoking as a risk factor for epidemic a (h1n1) influenza in young men. New England Journal of Medicine, 307(17), 1042-1046. Kelly, H., & Grant, K. (2009). Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination. Euro Surveill, 14(31), 1-5. Kumar, A., Zarychanski, R., Pinto, R., Cook, D. J., Marshall, J., Lacroix, J., ... & Fowler, R. A. (2009). Critically ill patients with 2009 influenza A (H1N1) infection in Canada. JAMA: the journal of the American Medical Association, 302(17), 1872-1879. Looker, C., Carville, K., Grant, K., & Kelly, H. (2010). Influenza A (H1N1) in Victoria, Australia: a community case series and analysis of household transmission. PLoS One, 5(10), e13702. Louie, J. K., Acosta, M., Winter, K., Jean, C., Gavali, S., Schechter, R., ... & Hatch, D. (2009). Factors associated with death or hospitalization due to pandemic 2009 influenza A (H1N1) infection in California. JAMA: the journal of the American Medical Association, 302(17), 1896-1902. Mashonganyika, C., McKee, H., Board, J., Davies, A., Vallance, S., Hatch, F., ... & Cheung, W. (2009). Critical care services and 2009 H1N1 influenza in Australia and New Zealand. N Engl J Med, 361(20), 1925-34. Miller, E., Hoschler, K., Hardelid, P., Stanford, E., Andrews, N., & Zambon, M. (2010). Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. The Lancet, 375(9720), 1100-1108. Morgan, O. W., Bramley, A., Fowlkes, A., Freedman, D. S., Taylor, T. H., Gargiullo, P., ... & Fry, A. M. (2010). Morbid obesity as a risk factor for hospitalization and death due to 2009 pandemic influenza A (H1N1) disease. PloS one, 5(3), e9694. Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (2006). A simulation study of the number of events per variable in logistic regression analysis. Journal of clinical epidemiology, 49(12), 1373-1379. Perez-Padilla, R., De La Rosa-zamboni, D., Ponce de Leon, S., Hernandez, M., Quinones-Falconi, F., Bautista, E., ... & Cordova-Villalobos, J. A. (2009). Pneumonia and respiratory failure from swine-origin influenza A (H1N1) in Mexico. New England Journal of Medicine, 361(7), 680-689. Real-Time, R. (2009). Emergence of a novel swineorigin influenza A (H1N1) virus in humans. N Engl j Med, 360, 2605-15. Riu, J., & Rius, F. X. (2003). Univariate regression models with errors in both axes. Journal of chemometrics, 9(5), 343-362. Vaillant, L., La Ruche, G., Tarantola, A., & Barboza, P. (2009). Epidemiology of fatal cases associated with pandemic H1N1 influenza 2009. Euro surveillance: bulletin europeen sur les maladies transmissibles= European communicable disease bulletin, 14(33). Van Kerkhove, M. D., Vandemaele, K. A., Shinde, V., Jaramillo-Gutierrez, G., Koukounari, A., Donnelly, C. A., ... & Mounts, A. W. (2011). Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis. PLoS medicine, 8(7), e1001053. Ward, K. A., Spokes, P. J., & McAnulty, J. M. (2011). Case–control study of risk factors for hospitalization caused by pandemic (H1N1) 2009. Emerging infectious diseases, 17(8), 1409. Wenzel, R. P., & Edmond, M. B. (2009). Preparing for 2009 H1N1 influenza. New England Journal of Medicine, 361(20), 1991-1993. Zhang, J., & Kai, F. Y. (2008). What's the relative risk?. JAMA: the journal of the American Medical Association, 280(19), 1690-1691. Read More