Analysis of Relapse in Leukemia Patients With Missing Data Using an Extension of the EM Algorithm

Introduction: Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) are two types of acute leukemia. When complete remission of leukemia has not been achieved or the disease refracts to its stage in initial chemotherapy, relapse occurs. Leukemia has poor prognosis for patients with relapse. This sample included AML and ALL patients. The purpose of this study was to use and extension of the Expectation-Maximization (EM) algorithm in order to find significant factors that affect the occurrence of relapse in leukemia patients with missing data. Methods: The EM logistic model consists of three steps. First, the initial logistic regression intercept and coefficients are estimated using the complete data, the data with relapse outcome present. Next, the predicted probability of relapse is calculated based on the number of patients relapsed from the complete data. This probability is then used to determine relapse outcome (Y = 0 or 1) in the patients with missing data in the expectation step. During the expectation step, predicted probabilities are produced based on the logistic regression model. We proposed using the mean of the predicted probabilities as the cut-off point for determining whether the missing binary values are set equal to 1 or 0 during imputation. In the maximization step, the logistic regression intercept and coefficients are updated until the estimates converge. Results: The results indicate that there are a number of significant variables associated with leukemia relapse including the sex of the donor, patient cytomegalovirus status, and FAB (French-American-British classification of AML) grade. Conclusion/Discussion: This study used the EM algorithm and the mean predicted probabilities as the cut-off point during imputation to predict which factors can affect relapse outcome in leukemia patients who had received a bone marrow transplant. Public health significance: The EM algorithm can improve leukemia treatment outcomes. By using the EM algorithm in conjunction with selecting optimal cut points for imputation of a dependent categorical variable using the AUC, public health professionals can find which factors are associated with relapse in order to prevent relapse by controlling for these factors. They can also use the EM algorithm to predict relapse and recommend treatment plans for different patients

Urban Versus Rural Differences in Insurance Coverage and Impact on Employment Among Families Caring for a Child With Cerebral Palsy

Background: The purpose of this study was to examine urban vs. rural differences on the relationship between family contextual variables and adequacy of insurance coverage and impact on employment for among families with a child with Cerebral Palsy from a nationally representative sample. Methods: A retrospective, observational study was carried out using data from the National Survey of Children with Special Healthcare Needs. Results: A total of 744 participants reported as having a child with a diagnosis of Cerebral Palsy and were included in the sample. Logistic regression analyses, adjusting for urban and rural setting revealed different predictors of adequacy of insurance coverage and impact on employment. Among urban respondents, three variables with odds ratios ranging from 1.33 to 1.58 served as protective factors, increasing the likelihood of adequate insurance coverage. Four variables with odds ratios ranging from 1.41 to 1.79 decreased the likelihood of negatively impacting employment. Among rural families, there was only one significant protective factor for adequacy of insurance coverage (odds ratio 1.80) and one for decreasing the chances of impact on employment (odds ratio 2.53). Conclusion: Families in rural areas caring for a child with CP have few protective factors for adequate insurance coverage and impact on familial employment

Chinese Social Media Reaction to the MERS-Cov and Avian Influenza A (H7N9) Outbreaks

Background: As internet and social media use have skyrocketed, epidemiologists have begun to use online data such as Google query data and Twitter trends to track the activity levels of influenza and other infectious diseases. In China, Weibo is an extremely popular microblogging site that is equivalent to Twitter. Capitalizing on the wealth of public opinion data contained in posts on Weibo, this study used Weibo as a measure of the Chinese people’s reactions to two different outbreaks: the 2012 Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreak, and the 2013 outbreak of human infection of avian influenza A(H7N9) in China. Methods: Keyword searches were performed in Weibo data collected by The University of Hong Kong’s Weiboscope project. Baseline values were determined for each keyword and reaction values per million posts in the days after outbreak information was released to the public. Results: The results show that the Chinese people reacted significantly to both outbreaks online, where their social media reaction was two orders of magnitude stronger to the H7N9 influenza outbreak that happened in China than the MERS-CoV outbreak that was far away from China. Conclusions: These results demonstrate that social media could be a useful measure of public awareness and reaction to disease outbreak information released by health authorities

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Joint Confidence Region Estimation on Predictive Values

For evaluating diagnostic accuracy of inherently continuous diagnostic tests/biomarkers, sensitivity and specificity are well-known measures both of which depend on a diagnostic cut-off, which is usually estimated. Sensitivity (specificity) is the conditional probability of testing positive (negative) given the true disease status. However, a more relevant question is “what is the probability of having (not having) a disease if a test is positive (negative)?”. Such post-test probabilities are denoted as positive predictive value (PPV) and negative predictive value (NPV). The PPV and NPV at the same estimated cut-off are correlated, hence it is desirable to make the joint inference on PPV and NPV to account for such correlation. Existing inference methods for PPV and NPV focus on the individual confidence intervals and they were developed under binomial distribution assuming binary instead of continuous test results. Several approaches are proposed to estimate the joint confidence region as well as the individual confidence intervals of PPV and NPV. Simulation results indicate the proposed approaches perform well with satisfactory coverage probabilities for normal and non-normal data and, additionally, outperform existing methods with improved coverage as well as narrower confidence intervals for PPV and NPV. The Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) data set is used to illustrate the proposed approaches and compare them with the existing methods

Adequacy of Insurance Coverage and Impact on Employment Among Families Caring for a Child With Cerebral Palsy

Background: Children with cerebral palsy (CP) require care from both healthcare providers and the family. The purpose of this study is to examine factors related to adequate insurance coverage and impact on family employment among families with a child with CP. Methods: Data from a survey with a nationally representative sample examining children with special healthcare needs was analyzed. A total of 744 of participants reported having a child with CP. Logistic regression modeled the probability of adequate insurance coverage and impact on family employment. Results: Families whose child missed fewer school days, had no unmet needs, no financial burden, or early screening were less likely to have employment negatively affected (adjusted OR [odds ration] = \u3e 1, 95% CI [confidence interval], p = \u3c 0.05). Whereas families who spent additional time caring for the child, and did not have a medical home were more likely to be negatively affected (OR \u3c 1, 95% CI, p \u3c 0.05). Families without unmet needs, fewer out-of-pocket expenses, or no financial burden, were more likely to have adequate coverage (adjusted OR = \u3e 1, 95% CI, p = \u3c 0.05). Families of children who missed fewer school days, did have not access to community-based services, did not engage in shared decision-making, and without a medical home were less likely to have adequate coverage (OR \u3c 1, 95% CI, p \u3c 0.05). Conclusion: Complex factors affect families caring for a child with CP and policy changes may improve their quality of life

Urban vs. rural differences in insurance coverage and impact on employment among families caring for a child with cerebral palsy

Background: The purpose of this study was to examine urban vs. rural differences on the relationship between family contextual variables and adequacy of insurance coverage and impact on employment for among families with a child with Cerebral Palsy from a nationally representative sample. Methods: A retrospective, observational study was carried out using data from the National Survey of Children with Special Healthcare Needs. Results: A total of 744 participants reported as having a child with a diagnosis of Cerebral Palsy and were included in the sample. Logistic regression analyses, adjusting for urban and rural setting revealed different predictors of adequacy of insurance coverage and impact on employment. Among urban respondents, three variables with odds ratios ranging from 1.33 to 1.58 served as protective factors, increasing the likelihood of adequate insurance coverage. Four variables with odds ratios ranging from 1.41 to 1.79 decreased the likelihood of negatively impacting employment. Among rural families, there was only one significant protective factor for adequacy of insurance coverage (odds ratio 1.80) and one for decreasing the chances of impact on employment (odds ratio 2.53). Conclusion: Families in rural areas caring for a child with CP have few protective factors for adequate insurance coverage and impact on familial employment

Urban Versus Rural Differences in the Effects of Providing Care to Children With Cerebral Palsy on Family Member\u27s Employment

Background and Objective(s): Context (urban vs rural) can mediate the impact on the family among other childhood populations with special healthcare needs. To date, few studies have examined the role of context in caring for a child with CP; therefore, we analyzed data from the National Survey of Children with Special Health Care Needs (NS-CSHCN). Study Design: Cross-sectional. Study Participants & Setting: Participants were parents of children (\u3c 18y old) with CP. Children\u27s mean age of rural respondents was 10.17 (SD=4.69), while 73% were white, 13.5% Hispanic, 11% black, and 2.5% other (non-Hispanic). Among rural families, 33.05% were living in households with incomes below the Federal Poverty Level (FPL). For those respondents living in urban areas, children\u27s mean age was 9.74 (SD=4.50) and 63% were white, 13% Hispanic, 16% black, and 8% other (non-Hispanic). Overall 21.1% of urban families lived in households with incomes below the FPL. Materials/Methods: The NS-CSHCN was designed to examine state- and national-level estimates of CSHCN. A national random-digit-dial sample of US households were screened for children with special healthcare needs aged 0–17 years. Households reporting a CSHCN participated in an interview for one randomly selected child with a special healthcare need. Of 40 242 completed interviews from 2009 to 2011, 744 reported as having a child with a diagnosis of CP and were included in the sample. We performed logistic regression analyses, in the context of multiply imputed data to address missing data concerns, modeling the probability that family member\u27s employments were unaffected by the child\u27s health. Results: Our analysis indicated significant differences between families with children with CP living in an urban environment and those living in a rural setting concerning the impact that the child\u27s health had on family member\u27s work lives. There were six significant variables impacting the employment of family members for families living in an urban setting categories relating to missed school days, financial burden, and access to services (see Table 1). However, only a low financial burden and less time spent caring for the child were significant with regards to the impact on employment among rural families. Conclusions/Significance: Despite a higher percentage of families living in poverty, urban families had more protective factors to prevent the diagnosis of a child from impacting the employment of the family

An Examination of Caregiver Frustration Among Families Caring for Children with Special Health Care Needs (CSHCN)

Children with special health care needs (CSHCN) require access to healthcare providers with additional expertise. Context can affect access to services. While barriers to access have been explored, some outcomes related to urban and rural families\u27 experiences with care for CSHCN remain unknown. The purpose of this study was to examine associations between shared decision-making, care coordination, and caregiver frustration. We examined data from the latest version of the National Survey of Children with Special Health Care Needs (NS-CSHCN). The NS-CSHCN utilized a random sample of US households of CSHCN aged 0-17 years, resulting in 40,242 participants. Chi square and logistic regression analyses examined frustration among rural and urban respondents. Both rural (X2 = 159.03, N =5973, p \u3c .0001) and urban (X2 = 819.56, N = 21492, p \u3c .0001) families who did not meet the criteria for decision-making were more likely to experience frustration. The odds of frustration were the same in both contexts (OR = 0.26, CI = 0.24, 0.29). Frustration was also more likely when families experienced a lack of coordinated care in both rural and urban areas (X2 = 180.79, N = 5849, p \u3c .0001; X2 = 943.53, N = 20988, p \u3c .0001). Rural families were less likely to be frustrated when care was not coordinated (OR = 0.21, CI = 0.16, 0.27) than urban families (OR = 0.12, CI, 0.11, 0.15). Healthcare partnerships between families and providers are vital when meeting the needs of these families regardless of context