1
|
Evans NJ, Arakkal AT, Cavanaugh JE, Newland JG, Polgreen PM, Miller AC. The incidence, duration, risk factors, and age-based variation of missed opportunities to diagnose pertussis: A population-based cohort study. Infect Control Hosp Epidemiol 2023; 44:1629-1636. [PMID: 36919206 PMCID: PMC10587384 DOI: 10.1017/ice.2023.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 03/16/2023]
Abstract
OBJECTIVE To estimate the incidence, duration and risk factors for diagnostic delays associated with pertussis. DESIGN We used longitudinal retrospective insurance claims from the Marketscan Commercial Claims and Encounters, Medicare Supplemental (2001-2020), and Multi-State Medicaid (2014-2018) databases. SETTING Inpatient, emergency department, and outpatient visits. PATIENTS The study included patients diagnosed with pertussis (International Classification of Diseases [ICD] codes) and receipt of macrolide antibiotic treatment. METHODS We estimated the number of visits with pertussis-related symptoms before diagnosis beyond that expected in the absence of diagnostic delays. Using a bootstrapping approach, we estimated the number of visits representing a delay, the number of missed diagnostic opportunities per patient, and the duration of delays. Results were stratified by age groups. We also used a logistic regression model to evaluate potential factors associated with delay. RESULTS We identified 20,828 patients meeting inclusion criteria. On average, patients had almost 2 missed opportunities prior to diagnosis, and delay duration was 12 days. Across age groups, the percentage of patients experiencing a delay ranged from 29.7% to 37.6%. The duration of delays increased considerably with age from an average of 5.6 days for patients aged <2 years to 13.8 days for patients aged ≥18 years. Factors associated with increased risk of delays included emergency department visits, telehealth visits, and recent prescriptions for antibiotics not effective against pertussis. CONCLUSIONS Diagnostic delays for pertussis are frequent. More work is needed to decrease diagnostic delays, especially among adults. Earlier case identification may play an important role in the response to outbreaks by facilitating treatment, isolation, and improved contact tracing.
Collapse
Affiliation(s)
- Nicholas J. Evans
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa
| | - Alan T. Arakkal
- Department of Biostatistics, University of Iowa, Iowa City, Iowa
| | | | - Jason G. Newland
- Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri
| | | | - Aaron C. Miller
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa
| |
Collapse
|
2
|
Whitfield E, White B, Denaxas S, Lyratzopoulos G. Diagnostic windows in non-neoplastic diseases: a systematic review. Br J Gen Pract 2023; 73:e702-e709. [PMID: 37308303 PMCID: PMC10285689 DOI: 10.3399/bjgp.2023.0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/28/2023] [Accepted: 04/04/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Investigating changes in prediagnostic healthcare utilisation can help identify how much earlier conditions could be diagnosed. Such 'diagnostic windows' are established for cancer but remain relatively unexplored for non-neoplastic conditions. AIM To extract evidence on the presence and length of diagnostic windows for non-neoplastic conditions. DESIGN AND SETTING A systematic review of studies of prediagnostic healthcare utilisation was carried out. METHOD A search strategy was developed to identify relevant studies from PubMed and Connected Papers. Data were extracted on prediagnostic healthcare use, and evidence of diagnostic window presence and length was assessed. RESULTS Of 4340 studies screened, 27 were included, covering 17 non-neoplastic conditions, including both chronic (for example, Parkinson's disease) and acute conditions (for example, stroke). Prediagnostic healthcare events included primary care encounters and presentations with relevant symptoms. For 10 conditions, sufficient evidence to determine diagnostic window presence and length was available, ranging from 28 days (herpes simplex encephalitis) to 9 years (ulcerative colitis). For the remaining conditions, diagnostic windows were likely to be present, but insufficient study duration was often a barrier to robustly determining their length, meaning that diagnostic window length may exceed 10 years for coeliac disease, for example. CONCLUSION Evidence of changing healthcare use before diagnosis exists for many non-neoplastic conditions, establishing that early diagnosis is possible, in principle. In particular, some conditions may be detectable many years earlier than they are currently diagnosed. Further research is required to accurately estimate diagnostic windows and to determine how much earlier diagnosis may be possible, and how this might be achieved.
Collapse
Affiliation(s)
- Emma Whitfield
- ECHO (Epidemiology of Cancer Healthcare & Outcomes), Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London (UCL), London, and Institute of Health Informatics, UCL, London
| | - Becky White
- ECHO (Epidemiology of Cancer Healthcare & Outcomes), Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London (UCL), London
| | - Spiros Denaxas
- Institute of Health Informatics, UCL, London; associate director, British Heart Foundation Data Science Centre, London; Health Data Research UK, London, and UCL Hospitals Biomedical Research Centre, London
| | - Georgios Lyratzopoulos
- ECHO (Epidemiology of Cancer Healthcare & Outcomes), Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London (UCL), London
| |
Collapse
|
3
|
Liberman AL, Wang Z, Zhu Y, Hassoon A, Choi J, Austin JM, Johansen MC, Newman-Toker DE. Optimizing measurement of misdiagnosis-related harms using symptom-disease pair analysis of diagnostic error (SPADE): comparison groups to maximize SPADE validity. Diagnosis (Berl) 2023; 10:225-234. [PMID: 37018487 PMCID: PMC10659025 DOI: 10.1515/dx-2022-0130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/06/2023] [Indexed: 04/07/2023]
Abstract
Diagnostic errors in medicine represent a significant public health problem but continue to be challenging to measure accurately, reliably, and efficiently. The recently developed Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) approach measures misdiagnosis related harms using electronic health records or administrative claims data. The approach is clinically valid, methodologically sound, statistically robust, and operationally viable without the requirement for manual chart review. This paper clarifies aspects of the SPADE analysis to assure that researchers apply this method to yield valid results with a particular emphasis on defining appropriate comparator groups and analytical strategies for balancing differences between these groups. We discuss four distinct types of comparators (intra-group and inter-group for both look-back and look-forward analyses), detailing the rationale for choosing one over the other and inferences that can be drawn from these comparative analyses. Our aim is that these additional analytical practices will improve the validity of SPADE and related approaches to quantify diagnostic error in medicine.
Collapse
Affiliation(s)
- Ava L. Liberman
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine
| | - Zheyu Wang
- The Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Division of Biostatistics and Bioinformatics
- The Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
| | - Yuxin Zhu
- The Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Division of Biostatistics and Bioinformatics
- The Johns Hopkins University School of Medicine, Department of Neurology and the Armstrong Institute Center for Diagnostic Excellence
| | - Ahmed Hassoon
- The Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
| | - Justin Choi
- Department of Internal Medicine, Weill Cornell Medicine
| | - J. Matthew Austin
- The Johns Hopkins University School of Medicine, Department of Anesthesiology and Critical Care Medicine and the Armstrong Institute Center for Diagnostic Excellence
| | - Michelle C. Johansen
- The Johns Hopkins University School of Medicine, Department of Neurology and the Armstrong Institute Center for Diagnostic Excellence
| | - David E. Newman-Toker
- The Johns Hopkins University School of Medicine, Department of Neurology and the Armstrong Institute Center for Diagnostic Excellence
- The Johns Hopkins Bloomberg School of Public Health, Departments of Epidemiology and Health Policy & Management
| |
Collapse
|
4
|
Miller AC, Cavanaugh JE, Arakkal AT, Koeneman SH, Polgreen PM. A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods. BMC Med Inform Decis Mak 2023; 23:68. [PMID: 37060037 PMCID: PMC10103428 DOI: 10.1186/s12911-023-02148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/15/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND The incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different diseases or settings. Administrative and other real-world data sources may offer the ability to better identify and study diagnostic delays for a range of diseases. METHODS We propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We provide a conceptual model of the disease-diagnostic, data-generating process. We then propose a bootstrapping method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach identifies diagnostic opportunities based on signs and symptoms occurring prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different bootstrapping algorithms are described along with estimation procedures to implement the resampling. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke to estimate the frequency and duration of diagnostic delays for these diseases. RESULTS Using the IBM MarketScan Research databases from 2001 to 2017, we identified 2,073 cases of tuberculosis, 359,625 cases of AMI, and 367,768 cases of stroke. Depending on the simulation approach that was used, we estimated that 6.9-8.3% of patients with stroke, 16.0-21.3% of patients with AMI and 63.9-82.3% of patients with tuberculosis experienced a missed diagnostic opportunity. Similarly, we estimated that, on average, diagnostic delays lasted 6.7-7.6 days for stroke, 6.7-8.2 days for AMI, and 34.3-44.5 days for tuberculosis. Estimates for each of these measures was consistent with prior literature; however, specific estimates varied across the different simulation algorithms considered. CONCLUSIONS Our approach can be easily applied to study diagnostic delays using longitudinal administrative data sources. Moreover, this general approach can be customized to fit a range of diseases to account for specific clinical characteristics of a given disease. We summarize how the choice of simulation algorithm may impact the resulting estimates and provide guidance on the statistical considerations for applying our approach to future studies.
Collapse
Affiliation(s)
- Aaron C Miller
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.
| | - Joseph E Cavanaugh
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, 52242, USA
| | - Alan T Arakkal
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, 52242, USA
| | - Scott H Koeneman
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, 52242, USA
| | - Philip M Polgreen
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA
| |
Collapse
|
5
|
Granillo A, Le Maréchal M, Diaz-Arias L, Probasco J, Venkatesan A, Hasbun R. Development and Validation of a Risk Score to Differentiate Viral and Autoimmune Encephalitis in Adults. Clin Infect Dis 2023; 76:e1294-e1301. [PMID: 36053949 DOI: 10.1093/cid/ciac711] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/10/2022] [Accepted: 08/29/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Encephalitis represents a challenging condition to diagnose and treat. To assist physicians in considering autoimmune encephalitis (AE) sooner, we developed and validated a risk score. METHODS The study was conducted as a retrospective cohort of patients with a diagnosis of definite viral encephalitis (VE) and AE from February 2005 to December 2019. Clinically relevant and statistically significant features between cases of AE and VE were explored in a bivariate logistic regression model and results were used to identify variables for inclusion in the risk score. A multivariable logistic model was used to generate risk score values and predict risk for AE. Results were externally validated. RESULTS A total of 1310 patients were screened. Of the 279 enrolled, 36 patients met criteria for definite AE and 88 criteria for definite VE. Patients with AE compared with VE were more likely to have a subacute to chronic presentation (odds ratio [OR] = 22.36; 95% confidence interval [CI], 2.05-243.7), Charlson comorbidity index <2 (OR = 6.62; 95% CI, 1.05-41.4), psychiatric and/or memory complaints (OR = 203.0; 95% CI, 7.57-5445), and absence of robust inflammation in the cerebrospinal fluid defined as <50 white blood cells/µL and protein <50 mg/dL (OR = 0.06; 95% CI, .005-0.50). Using these 4 variables, patients were classified into 3 risk categories for AE: low (0-1), intermediate (2-3), and high (4). Results were externally validated and the performance of the score achieved an area under the curve of 0.918 (95% CI, .871-.966). DISCUSSION This risk score allows clinicians to estimate the probability of AE in patients presenting with encephalitis and may assist with earlier diagnosis and treatment.
Collapse
Affiliation(s)
- Alejandro Granillo
- Department of Infectious Diseases, UT Health McGovern Medical School, Houston, Texas, USA
| | - Marion Le Maréchal
- Johns Hopkins Encephalitis Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Luisa Diaz-Arias
- Johns Hopkins Encephalitis Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - John Probasco
- Johns Hopkins Encephalitis Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Arun Venkatesan
- Johns Hopkins Encephalitis Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rodrigo Hasbun
- Department of Infectious Diseases, UT Health McGovern Medical School, Houston, Texas, USA.,Department of Internal Medicine, UT Health McGovern Medical School, Houston, Texas, USA
| |
Collapse
|
6
|
Miller AC, Arakkal AT, Koeneman SH, Cavanaugh JE, Polgreen PM. A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities. Diagnosis (Berl) 2023; 10:43-53. [PMID: 36127310 PMCID: PMC9934811 DOI: 10.1515/dx-2022-0044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/26/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis. METHODS We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases. RESULTS For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified. CONCLUSIONS Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.
Collapse
Affiliation(s)
- Aaron C Miller
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Alan T Arakkal
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Scott H Koeneman
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Joseph E Cavanaugh
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Philip M Polgreen
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| |
Collapse
|
7
|
Erickson BA, Miller AC, Warner HL, Drobish JN, Koeneman SH, Cavanaugh JE, Polgreen PM. Understanding the Prodromal Period of Necrotizing Soft Tissue Infections of the Genitalia (Fournier's Gangrene) and the Incidence, Duration, and Risk Factors Associated With Potential Missed Opportunities for an Earlier Diagnosis: A Population-based Longitudinal Study. J Urol 2022; 208:1259-1267. [PMID: 36006046 PMCID: PMC11005462 DOI: 10.1097/ju.0000000000002920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 07/22/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE The purpose of this paper was to investigate patterns of health care utilization leading up to diagnosis of necrotizing soft tissue infections of the genitalia and to identify risk factors associated with potential diagnostic delay. MATERIALS AND METHODS IBM MarketScan Research Databases (2001-2020) were used to identify index cases of necrotizing soft tissue infections of the genitalia. We identified health care visits for symptomatically similar diagnoses (eg, penile swelling, cellulitis) that occurred prior to necrotizing soft tissue infections of the genitalia diagnosis. A change-point analysis identified the window before diagnosis where diagnostic opportunities first appeared. A simulation model estimated the likelihood symptomatically similar diagnosis visits represented a missed opportunity for earlier diagnosis. Patient and provider characteristics were evaluated for their associations with delay. RESULTS We identified 8,098 patients with necrotizing soft tissue infections of the genitalia, in which 4,032 (50%) had a symptomatically similar diagnosis visit in the 21-day diagnostic window, most commonly for "non-infectious urologic abnormalities" (eg, genital swelling; 64%): 46% received antibiotics; 16% saw a urologist. Models estimated that 5,096 of the symptomatically similar diagnosis visits (63%) represented diagnostic delay (mean duration 6.2 days; mean missed opportunities 1.8). Risk factors for delay included urinary tract infection history (OR 2.1) and morbid obesity (OR 1.6). Visits to more than 1 health care provider/location in a 24-hour period significantly decreased delay risk. CONCLUSIONS Nearly 50% of insured patients who undergo debridement for, or die from, necrotizing soft tissue infections of the genitalia will present to a medical provider with a symptomatically similar diagnosis suggestive of early disease development. Many of these visits likely represent diagnostic delay. Efforts to minimize logistic and cognitive biases in this rare condition may lead to improved outcomes if they lead to earlier interventions.
Collapse
Affiliation(s)
- Bradley A. Erickson
- Department of Urology, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Aaron C. Miller
- Department of Internal Medicine, Division of Infectious Diseases, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Hayden L. Warner
- Department of Urology, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Justin N. Drobish
- Department of Urology, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Scott H. Koeneman
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa
| | - Joseph E. Cavanaugh
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa
| | - Philip M. Polgreen
- Department of Internal Medicine, Division of Infectious Diseases, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| |
Collapse
|
8
|
Miller AC, Arakkal AT, Koeneman SH, Cavanaugh JE, Thompson GR, Baddley JW, Polgreen PM. Frequency and Duration of, and Risk Factors for, Diagnostic Delays Associated with Histoplasmosis. J Fungi (Basel) 2022; 8:jof8050438. [PMID: 35628693 PMCID: PMC9143509 DOI: 10.3390/jof8050438] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 02/07/2023] Open
Abstract
Histoplasmosis is often confused with other diseases leading to diagnostic delays. We estimated the incidence, length of, and risk factors for, diagnostic delays associated with histoplasmosis. Using data from IBM Marketscan, 2001–2017, we found all patients with a histoplasmosis diagnosis. We calculated the number of visits that occurred prior to the histoplasmosis diagnosis and the number of visits with symptomatically similar diagnoses (SSDs). Next, we estimated the number of visits that represented a delay using a simulation-based approach. We also computed the number of potential opportunities for diagnosis that were missed for each patient and the length of time between the first opportunity and the diagnosis. Finally, we identified risk factors for diagnostic delays using a logistic regression model. The number of SSD-related visits increased significantly in the 97 days prior to the histoplasmosis diagnosis. During this period, 97.4% of patients had a visit, and 90.1% had at least one SSD visit. We estimate that 82.9% of patients with histoplasmosis experienced at least one missed diagnostic opportunity. The average delay was 39.5 days with an average of 4.0 missed opportunities. Risk factors for diagnostic delays included prior antibiotic use, history of other pulmonary diseases, and emergency department and outpatient visits, especially during weekends. New diagnostic approaches for histoplasmosis are needed.
Collapse
Affiliation(s)
- Aaron C. Miller
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA;
| | - Alan T. Arakkal
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA; (A.T.A.); (S.H.K.); (J.E.C.)
| | - Scott H. Koeneman
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA; (A.T.A.); (S.H.K.); (J.E.C.)
| | - Joseph E. Cavanaugh
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA; (A.T.A.); (S.H.K.); (J.E.C.)
| | | | - John W. Baddley
- Department of Medicine, University of Maryland, Baltimore, MD 21201, USA;
| | - Philip M. Polgreen
- Departments of Internal Medicine and Epidemiology, University of Iowa, Iowa City, IA 52242, USA
- Correspondence: ; Tel.: +1-319-384-6194
| |
Collapse
|