The expressions developed for Model 3 were as follows:. Model 3 — data :. The actual, fitted, and predicted HFRS cases during the — and — epochs are showed in Figs 4 and 5 , respectively. The adjusted predictive R 2 values for Model 2 were higher than those for other two models in both epochs, indicating that Model 2 had the best predictive ability of the three models Table 5.
The adjusted R 2 values were lower in the early epoch than in the recent epoch for all three models. Although HFRS epidemic prediction models have been constructed for many geographic areas [ 9 , 10 , 12 ], they are difficult to apply elsewhere, because the contingencies affecting HFRS outbreak levels vary regionally. Although relative humidity was found to be a risk factor in both Heilongjiang [ 9 ] and Inner Mongolia [ 12 ], the correlation coefficients and parameters of the prediction models differed between these two areas.
In the present study, we construct an HFRS prediction model optimized for Hu County based on the relationship between the local epidemiological history of HFRS cases before and after the implementation of vaccinations and concurrent meteorological factors. Redundancy cased by multi-collinearity between meteorological variables in the GLM was minimized by applying the stepwise least square method, while the backfitting method was used in the GAM, and principal components were extracted from the PCRM in the model-fitting process.
Both the fitted and predicted residuals of the GAM were white noise in both time periods. GAMs are flexible in that they allow non-parametric fits with relaxed assumptions on the relationship between response and predictor variables. This characteristic has made GAMs useful for studies examining the risk factors of respiratory diseases [ 23 ], coronary heart disease mortality [ 24 ], and autistic disorder [ 25 ], among other conditions.
The GAM may be more robust than the GLM for interpreting relationships between response and predictor variables [ 17 , 26 , 27 ]. The predictor variables included in our GAM for Hu County may not be maintained in GAMs for other locations with differing rodent host population, environmental characteristics and socio-economic factors.
Nevertheless, the flexibility of the GAM in terms of setting predictor variables should be help yield high predictive accuracy. Consequently, epidemiologists should be able to use data describing ongoing fluctuations in these variables to predict future HFRS surges in Hu County accurately. Such predictions will enable targeted countermeasures to be prepared ahead of time and thus implemented promptly, which should improve the effectiveness of HFRS surveillance and control programs. This study provides practical evidence for the usefulness of the GAM in HFRS prediction, with the optimal predictive meteorological variables being determined for each particular locality.
It should be noted that the adjusted R 2 values of the three models in the post-vaccination time period were less than those in the preceding period, which indicates that the predictive power of all three models declined after Thus, there may be other factors that became more influential after the introduction of vaccines. It has been reported that increasing vaccination compliance plays an important role in reducing HFRS incidence in Hu County [ 8 ]. The vaccination factor was not included in our models as an explanatory variable because annual vaccination compliance data were tracked in Hu County after , whereas monthly numbers of HFRS cases and meteorological data have been recorded consistently since If the adjusted R 2 values demonstrate further decreases, then some other methods should be pursued to adjust the model with respect to vaccination compliance data.
This study had a couple limitations which should be noted. Firstly, in the construction of our prediction model, we considered only meteorological factors and HFRS cases numbers in previous months. We did not account for other potential influencing factors such as rodent density, changes in land-use, and fluctuations in the non-immunized population, which may have decreased the predictive ability of the model. However, relative to data describing other environmental factors, meteorological data are highly objective, accurate, and contiguous, and are readily available.
Thus a model constructed based on meteorological data will be more applicable than if it had been based on relatively subjective, inaccurate, discontinuous, or difficult to obtain data. Moreover, the goodness and stability of fit and the predictive capacity of the GAM in this study demonstrated that meteorological factors inform the HFRS epidemic pattern to a great extent and can be used to predict HFRS outbreaks accurately in Hu County. Secondly, the diagnostic criteria for HFRS changed in However, because the clinical diagnostic criteria did not change and the clinical symptoms of HFRS are easy to identify, most clinically-diagnosed HFRS cases were laboratory-confirmed after Therefore, we are not concerned that the diagnostic policy change of affects the suitability of the present GAM models.
We are grateful to the anonymous reviewers for their helpful comments, valuable suggestions, and critical reviews of the manuscript. Conceived and designed the experiments: DX ZX. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: The raw data included monthly records of HFRS cases, annual population data, and monthly meteorological data for Hu County.
Introduction Hemorrhagic fever with renal syndrome HFRS is a rodent-borne zoonosis caused by hantaviruses.
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Cross-correlation analysis and autocorrelation analysis A cross-correlation analysis between monthly HFRS cases and meteorological index sequences were conducted with a lag time of six months to enable detection of the dominant meteorological factors influencing HFRS infections. Model construction We constructed and compared three models using meteorological factors as explanatory variables. Model evaluation The actual, fitted, and predicted values of monthly HFRS cases during each epoch of the three models were plotted with different colored lines.
Download: PPT. Cross-correlation between monthly HFRS cases and meteorological factors The number of HFRS cases reported each month correlated significantly with meteorological factors from the current and the previous month in both epochs Table 1.
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Table 1. Cross correlation between monthly hemorrhagic fever with renal syndrome cases and meteorological factors in Hu County, China during the — and — time periods. Autocorrelation of monthly HFRS cases The monthly numbers of HFRS cases correlated significantly with the numbers of cases in the first and twelfth lagged months in both epochs, pointing to a clear short-term temporal persistence effect and yearly trend Fig 2.
Multi-collinearity among meteorological variables There were 17 variables with variance inflation factors greater than 10 and corresponding tolerance values less than 0. Table 2. Collinearity diagnostics of preliminary predictor variables during the — and — epochs. Table 3. Table 4. The expressions developed for Model 2 were as follows: Model 2 — data : Model 2 — data : Plots of the smooth functions of each predictor variable in Model 2 are shown in Fig 3.
Fig 3. Curves of the smooth functions of each predictor variable in the GAM. Model evaluation The actual, fitted, and predicted HFRS cases during the — and — epochs are showed in Figs 4 and 5 , respectively. Fig 4. Actual, fitted, and predicted HFRS cases for the three models during the — epoch. Fig 5. Table 5. The adjusted fitting and predictive R 2 , Ljung-Box Q statistics and p values of three models.
Discussion Although HFRS epidemic prediction models have been constructed for many geographic areas [ 9 , 10 , 12 ], they are difficult to apply elsewhere, because the contingencies affecting HFRS outbreak levels vary regionally. Supporting Information. S1 Table. The annual population in Hu County, China during — Acknowledgments We are grateful to the anonymous reviewers for their helpful comments, valuable suggestions, and critical reviews of the manuscript.
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References 1. Hantavirus infections for the clinician: from case presentation to diagnosis and treatment. Crit Rev Microbiol. Epidemiology of Hantavirus infections in humans: A comprehensive, global overview. The current epidemic situation and surveillance regarding hemorrhagic fever with renal syndrome in China, Ministry of Health of China.
Analysis of epidemic situation of hemorrhagic fever with renal syndrome in Hu county, Xi'an, China from to Environmental variability and the transmission of haemorrhagic fever with renal syndrome in Changsha, People's Republic of China. Epidemiol Infect. Environmental risk factors for haemorrhagic fever with renal syndrome in a French new epidemic area. The impact of the vaccination program for hemorrhagic fever with renal syndrome in Hu County, China.
Am J Trop Med Hyg. Atmospheric moisture variability and transmission of hemorrhagic fever with renal syndrome in Changsha City, Mainland China, — Air pollution and hemorrhagic fever with renal syndrome in South Korea: an ecological correlation study. BMC Public Health. Climate variability and hemorrhagic fever with renal syndrome transmission in Northeastern China.
Environ Health Perspect. Wang L, Liu Q. Bayesian Network Inference Based research on transmission mechanism of hemorrhagic fever with renal syndrom in China. Foreign Medical Sciences Section of Medgeography. View Article Google Scholar Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. BMC Infect Dis. Meteorological factors are associated with hemorrhagic fever with renal syndrome in Jiaonan County, China, — Int J Biometeorol. Time-series analysis of the risk factors for haemorrhagic fever with renal syndrome: comparison of statistical models.
Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. Generalized additive models. London: Chapman and Hall; The central people's government of the people's republic of China website. The national kidney syndrome hemorrhagic fever monitoring programme. Accessed Jun Zhang L, Wilson DP.
Trends in notifiable infectious diseases in China: implications for surveillance and population health policy. Hantavirus infections in humans and animals, China. At the time data collection began, San Diego County had a population of roughly 2. Persons were excluded if there was no recorded diagnosis, no information about any service use in fiscal year —, missing information regarding their use of acute mental health services emergency psychiatric unit EPU , psychiatric emergency response team PERT , inpatient psychiatric hospital, and crisis residential during any of these four years or missing selected baseline characteristics.
Among 15, subjects on whom there were data for four fiscal years, 10, subjects had sufficient data to be included in the final sample for the analyses. Information on age, gender, ethnicity, living situation, insurance coverage, and presence of a co-morbid substance use disorder was derived from the San Diego County MIS database for AOAMHS for the index year — Living situation was recorded at admission to and discharge from each type of service and coded into the following categories: living independently or with family or others, residing in a board-and-care assisted living facility, being incarcerated, being homeless, or other.
We used the modal living situation for the index year — defined as the most frequent living situation recorded for the year to define residential status.
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As appropriate use of mental health services includes access to and use of outpatient care, we compared the number of outpatient visits according to the following three categories: three or fewer, four to six, and greater than six visits per year. It could be argued that outpatient visits could be a representation of need for services rather than an enabling factor influencing the use of services.
We chose to categorize outpatient visits as an enabling factor reasoning that more outpatient care may be associated with less utilization of acute services, a pattern of service use that is usually more optimal for both the individual and the system than excessive use of costly acute services. Substance use disorder diagnoses were categorized as the presence or absence of any substance use diagnosis assigned in any care setting for the fiscal year — Individuals were classified into five diagnostic groups based on the diagnosis recorded in the index year: schizophrenia, bipolar disorder, major depressive disorder, other psychotic disorders, and other.
If more than one diagnosis was recorded, we assigned diagnostic categories based on a hierarchical algorithm used in previous studies in which the most severe diagnosis given i. The use of this algorithm is supported by our previous finding that the diagnosis of schizophrenia is more reliable than a diagnosis of depression in this database Folsom et al. Each client was considered to have received service in a particular category if at least one billable encounter in that service category for the fiscal year — was recorded. For each service group, a variable was created by aggregating episodic visits for each client into a dichotomous presence or absence of the service category.
EPU treatment included service in the AOAMHS-operated psychiatric emergency unit, but did not include emergency psychiatric treatment in any of the other emergency departments in the county. We also included visits made by the Psychiatric Emergency Response Team PERT , which consists of police officers and mental health clinicians who respond to calls involving persons with psychiatric disorders.
Inpatient psychiatric hospitalization included admission to 11 fee-for-service psychiatric hospital programs providing acute inpatient care under contract with AOAMHS or admission to the San Diego County Psychiatric Hospital. Crisis residential treatment included admission to any of a network of six crisis residential programs regionally located throughout San Diego County.
These programs provide short-term acute residential treatment services as an alternative to voluntary hospitalization. Three types of outpatient visits were considered: appointments for medication management, psychotherapy, and case management. Outpatient use was categorized into three groups: up to three outpatient visits, four to six outpatient visits, and more than six outpatient visits base on the assumption that the minimum standard of care is quarterly medication management visits.
We chose this definition of HU for the following reasons: 1 It was consistent with other definitions in the literature Sullivan et al. To further verify the use of three acute visits for the definition of HU, we conducted a sensitivity analysis using a cut-off of four visits and found similar results as with a cut-off of three but with inadequate samples sizes per cell for some analyses. Chi-square tests were used to study differences in categorical baseline predisposing, enabling, and need characteristics among the three mental health service utilization groups, and Kruskal—Wallis tests were used to compare the number of episodes of EPU, inpatient, crisis residential and PERT among the three mental health service utilization groups in the index year.
Pairwise comparisons with Hochberg adjustment for p -values were performed for all significant tests. We next conducted two sets of analyses. First, we utilized logistic regression to compare the associations between baseline predisposing, enabling, and need factors and ever being a HU comprised of one-time HU and multiple-year HU.
Second, we used multinomial logistic regression to study the association between the three mental health service groups never being a HU, one-time HU and multiple-year HU and baseline predisposing, enabling, and need factors. Since excluding the influential observations did not change the results significantly, the analysis results with all observations included were reported. Statistical analyses were performed using the open source statistical package version 2. Predisposing, enabling, and need characteristics percentages for overall and groups of public mental health service utilizers.
Mean standard deviations of number of visits to acute mental health services in the index year for groups of service utilizers. Because the results from multinomial regression analyses of the non-HU, one-time HU, and multiple-year HU differed almost exclusively in magnitude and not in pattern from the results of logistic regression analysis of the non-HU and ever HU data available upon request , we report only the results of the logistic regression analyses that assess the relationship of the baseline characteristics and the likelihood of ever being in the HU group includes single-year HU and multiple-year HU.
Associations between mental health service utilization and predisposing, enabling, and need characteristics using logistic regression. Individuals with a schizophrenia diagnosis had odds of being categorized an HU that were three times the odds for those with depression. Subjects with a concomitant substance use disorder had three times the odds of being categorized as a HU compared to those without a concomitant substance use disorder.
The majority of individuals receiving mental health services from a public system were not classified as HU, that is, as having used three or more acute inpatient, crisis residential, EPU, or PERT services in a year. The factors most strongly associated with being a HU versus a non-HU, controlling for other variables, were enabling factors of homelessness, medical insurance, and minimal use of outpatient services, as well as need factors, including a substance use disorder and a diagnosis of schizophrenia, bipolar or other psychotic disorder.
Factors from all categories of the Andersen model—predisposing, enabling, and need—were significantly associated with a higher probability of ever having been classified as HU: younger age, female gender, homelessness, substance use disorder, medical insurance, and diagnosis of schizophrenia, bipolar disorder or other psychotic disorder. Several factors were significantly related to a lower likelihood of being a HU. In the s, Quinliven and others reported on the characteristics of high utilizers in San Diego County Quinlivan et al.
Consistent with most previous reports in the literature, we found that a small proportion of individuals in the public mental health system were identified as high use consumers Sullivan et al. This finding is robust in that it is seen across many studies that examine different service systems and use different definitions of high service utilization. Therefore, identifying characteristics of such a group or groups of individuals is crucial to the development and implementation of solutions to optimize service use for the benefit of both the consumer and the service delivery system.
Many of the predisposing characteristics found in other studies to be associated with high mental health service utilization were observed to have significant relationships with being categorized as a HU in our study. As we predicted, and as others have reported Sullivan et al. Female gender was significantly associated with an increased likelihood of being classified as an ever HU. Most previous research has failed to find a gender difference in service utilization Arfken et al.
Our finding that female gender was associated with higher likelihood of being defined as a HU may reflect gender differences in treatment seeking. Other literature has reported that women are more likely to use mental health services Mackenzie et al. Alternatively, there could be gender differences in the systems for which men and women receive care.
For example, men who are high use consumers of mental health services due to the severity of illness may be more likely to get services in the justice system if they exhibit dangerous behaviors toward others and are subsequently incarcerated. Our finding that the enabling factor, homelessness, is associated with high use of mental health services has been noted by others Arfken et al. In our study, those residing in residential settings and jail were less likely than those living independently to be classified as HU.
In contrast, one other study Pasic et al. In regard to those who had some residential services, our results suggest that interventions that include assistance with making and attending appointments and taking medication may lead to decreased use of mental health services. Some studies Martinez and Burt have found that permanent supportive housing reduced emergency room use and episodes of inpatient hospitalizations in a group of individuals with comorbid psychiatric and substance use disorders.
Gilmer and colleagues Gilmer et al. Consistent with the implications of these findings, San Diego County has prioritized supportive housing and developed many new housing resources using funds from the recent enactment of the MHSA.
The association of being enrolled in a medical plan and high utilization of services has been previously reported in the literature Pasic et al. Since we determined Medicaid status from the index year, the association between insurance coverage and use of services is most likely due to the fact that individuals with insurance predominantly Medicaid were more likely to be hospitalized over the course of the study period.
This finding is consistent with a study of enrollment status in a public managed mental health care plan and emergency psychiatric service use that reported that clients with current or previous Medicaid enrollment had more lifetime inpatient hospitalizations than those never enrolled Wingerson et al.
As predicted, we found that need was related to mental health service use. Our findings that the likelihood of being classified as a HU increased with higher need for services i. Some Pasic et al. In contrast to many previous studies, we examined acute mental health services beyond just emergency department use, including EPU, PERT, inpatient, and crisis residential services.
However, a significant limitation of this study is that we did not differentiate between a HU of services who used multiple acute services in one episode of illness e. The characteristics associated with these patterns of use may differ.
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Another limitation includes the lack of length of service and cost data, which could yield important information about patterns of service utilization. Furthermore, the secondary use of an administrative database limited the number and types of factors enabling and need that we could investigate. In addition, the MIS dataset used in the analyses did not include emergency mental health services provided at private fee for service hospital emergency departments which provide services to Medicaid beneficiaries.
Finally, to the extent that the types of acute psychiatric differ from those offered in San Diego County, generalizability may be limited. Despite these limitations, there were several strengths of our study. We analyzed four consecutive years of data from a large public mental health system which allowed comparison of one-time HUs and multiple-year HUs across time.
In summary, many of the enabling and need factors found to be associated with the likelihood of being a high use consumer of mental health services e. In this regard addressing the needs of HUs of mental health services will likely require the involvement of partnerships across service sectors. Therefore, optimal use of public mental services might be achieved by developing and implementing interventions that also attend to other concerns such as homelessness, insurance coverage, and substance use.
The FSP programs are explicitly designed to link persons with mental health concerns to a range of other needed services with the goal of bringing about long-term improvement in mental health status and overall functioning in many facets of life. As the result of the implementation of these programs, we hope to see reductions in the overall numbers of persons characterized as HUs of acute mental health services and the potential for changes in the characteristics associated with HUs.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author s and source are credited. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Open Access. First Online: 01 May Sample At the time data collection began, San Diego County had a population of roughly 2.
Enabling Factors Living situation was recorded at admission to and discharge from each type of service and coded into the following categories: living independently or with family or others, residing in a board-and-care assisted living facility, being incarcerated, being homeless, or other.
Max Qinhong Hu
Need Factors Substance use disorder diagnoses were categorized as the presence or absence of any substance use diagnosis assigned in any care setting for the fiscal year — Statistical Analyses Chi-square tests were used to study differences in categorical baseline predisposing, enabling, and need characteristics among the three mental health service utilization groups, and Kruskal—Wallis tests were used to compare the number of episodes of EPU, inpatient, crisis residential and PERT among the three mental health service utilization groups in the index year.
The use of acute or emergency care services varied among the groups. When we used age as a continuous variable, the results were similar. The race and gender interaction was not statistically significant in any of the models i. Ever HU vs. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author s and source are credited.
R Development Core Team. R: A language and environment for statistical computing. ISBN Andersen, R. Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36 , 1— Societal and individual determinants of medical care utilization in the United States. Milbank Memorial Fund Quarterly, 51 , 95— CrossRef Google Scholar.
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