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Cureus | Accuracy of the Different Materials Used to Fabricate a Verification Jig of Implant-Supported Fixed Complete Dental Prostheses: An In Vitro Study
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implant passive fit, framework fit, biological and mechanical complication, 3d assessment, implant framework, implant master cast, verification jig
Mawaddah S. Aljohani, Heba A. Bukhari, Mayar Alshehri, Ahmed Alamoudi
Cite this article as: Aljohani M S, Bukhari H A, Alshehri M, et al. (September 30, 2022) Accuracy of the Different Materials Used to Fabricate a Verification Jig of Implant-Supported Fixed Complete Dental Prostheses: An In Vitro Study. Cureus 14(9): e29794. doi:10.7759/cureus.29794
The passive fit of a full arch implant-supported prosthesis is one of the elements influencing implant success. Achieving the passive fit of a prosthesis requires verification of the master cast before the fabrication of the framework. A verification jig is a common way to verify the implant master cast and ensure the accuracy of the implant impression and produced cast. Different materials can be used to fabricate verification jigs, and each material exhibits different dimensional changes. In this study, we compared the accuracies of verification jig materials by 3D assessment.
A type IV stone cast with four implant analogs was constructed and used as a control. Verification jigs were constructed from five different materials, and test casts were made from these jigs and poured using low expansion stone (type IV), resulting in five groups (n=5). All test casts and the control cast were scanned using a lab scanner. The scans of test casts were superimposed on that of the control cast for 3D accuracy assessment. The distortion of the implant analogs was recorded using Geomagic Design X and Geomagic Control X software (3D Systems, Rock Hill, South Carolina, USA). Statistical differences in the 3D accuracies between the five groups were analyzed using the Kruskal-Wallis test.
The photopolymerizable resin group had a mean value of 23.16 (± 0.88) µm; the composite group had a mean value of 46.72 (± 2.122) µm; the GC pattern group had a mean of 23.51 (± 0.736) µm; the type III stone group had a mean of 19.84 (± 1.017) µm; the type IV stone group had a mean of 18.72 (± 0.819) µm. The Kruskal-Wallis test indicated that there were statistically significant differences between groups 2 (composite), 4 (type III stone), and 5 (type IV stone).
The most accurate cast was produced by type IV stone, followed by type III stone, photopolymerizable resin, GC pattern, and composite in order of decreasing accuracy. Within the limitations of the study, a material with low distortion and high accuracy is recommended when fabricating verification jigs of implant-supported complete dental prostheses.
Full-mouth rehabilitation with implant-supported fixed complete dental prostheses has become a routine procedure that has gained popularity among clinicians [1]. The framework of prostheses must fit precisely and passively on dental implants to prevent unfavorable stresses on the implant components and surrounding bone [2]. Any degree of prostheses misfit will lead to mechanical and biological complications [3-5]. Mechanical problems include screw-loosening of the prosthesis and its abutments, fracture of the framework, and chipping of the veneer material [5-7]. Biological consequences of a misfit include unfavorable tissue reactions, tissue soreness, bone loss, pain, and loss of osseointegration [5]. Therefore, an accurate impression is required to fabricate an accurate master cast and passive framework [8].
There is no clear definition of acceptable passive fit. It was defined by several authors hypothetically: Branemark proposed that passive fit at a level of 10 μm is acceptable [9]. Jemt considered a 150-μm gap or less between the abutment and prosthesis an acceptable passive fit of an implant framework [10]. Karl et al. described passive fit as the absence of strain on the implant when the framework is in place [11]. Patterson defined a passive fit as when there is no gap between the abutment and framework and no strain or stress after screw-torquing [12].
There are several clinical methods of evaluating implant-framework fit, such as alternating finger pressure, radiographs, the one-screw test, the screw-resistance test, direct visual inspection, disclosing media, and tactile sensation; however, there is no reliable standard test to assess passive fit [8]. One common technique to assess the accuracy of a produced cast and ensure the accuracy of the implant impression prior to framework fabrication is the verification jig [13-15]. In addition, verification jigs can be used if the master cast is not accurate by correcting the position of the implant analogs in the cast [13-15]. Verification jigs can be fabricated from different materials, such as light-polymerizing acrylic resin, auto-polymerizing acrylic resin, composite resin, and dental stone [13,16,17]. Each material has different dimensional accuracy, which can affect the accuracy of the jigs and result in an inaccurate cast and framework [18].
Photopolymerizable resin is a light-curing acrylic resin material. It is used mainly for fabricating custom impression trays, base plates, and bite blocks in prosthodontics treatment. It is composed of acrylates/methacrylates and glass with no monomer causing less polymerization shrinkage [19]. Composite resin has been used as a restorative material in modern dentistry and has several clinical applications with numerous desirable qualities such as esthetic appearance, and adequate physical and mechanical properties. One of the limitations of these white-colored materials is the polymerization shrinkage which ranges from 1.35% to 7.1% during curing and can be reduced by incremental technique [20]. Pattern resin is a self-curing acrylic resin that is used by dentists and laboratory technicians for making copings, custom-made post and core, bars, prosthetic attachments, clasps, telescopic crowns, as well as in some soldering techniques. It sets quickly and can be modulated easily and is the ideal material for dentists and laboratories. As an acrylic resin GC pattern resin has some polymerization shrinkage and 80% of the shrinkage occurs within 17 minutes, insignificant changes happen after 24 hours [18]. Gypsum is a commonly used material in cast and die fabrication. There are five types of gypsum products - type I: plaster impression; type II: plaster model; type III: dental stone; type IV: high strength, low expansion dental stone; type V: high strength, high expansion dental stone. Gypsum products have remarkable properties such as high strength, high compressive strength, hardness, and minimal expansion. One drawback of gypsum material is that it displays some linear expansion after setting and leads to dimensional changes varying from 0.08% to 0.3% [21].
Papaspyridakos et al. compared three verification jig materials: GC pattern resin, Fixpeed resin, and Triad gel. They found no significant differences between these materials when tissue-level implants were parallel [16]. Other materials have not been investigated, and there is a lack of data about which material is best for constructing verification jigs. Therefore, the purpose of this in vitro study was to compare the accuracy of different materials used to fabricate verification jigs by comparing the generated fabricated casts.
A stone dental cast with four analogs of internal implant connections (bone-level conical connection; Nobel Biocare, Zurich, Switzerland) was constructed using type IV stone (extra-hard natural stone; Protechno, Girona, Spain), which represented the edentulous mandible and served as a control (Figure 1). Two implant analogs were placed anteriorly, and two implant analogs were placed posteriorly, resembling a clinical scenario. The two implants in the anterior region were parallel to each other, as well as the two implants in the posterior region.
Verification jigs using non-engaging temporary abutments (Nobel Biocare) and five different materials were created on the control stone model to fabricate five stone casts from each group with a total of 25 casts. Each verification jig material was grouped as follows.
Stone cast fabricated using photopolymerizable resin verification jig (n=5): Four non-engaging temporary abutments were tightened on the implant analogs of the control cast and splinted together using photopolymerizable resin (ECO-TRAY LUZ, Protechno) creating five verification jigs (Figure 2a and Figure 3a). Once the photopolymerizable resin jig was created, it was cured for three minutes using a Triad 2000 VLC light curing unit (Dentsply Sirona, Charlotte, North Carolina, USA). The jig was then sectioned into three segments, and a small amount of material was used to fill the gaps to compensate for polymerization shrinkage, before being cured again for an additional three minutes. To construct the test cast, the jig was unscrewed and removed from the cast. The temporary abutments were then connected to the implant analogs (Figure 2b), and placed in a silicone mold to create the test cast (Figure 2c). The test casts were poured with low expansion type IV stone (extra-hard natural stone, Protechno) (Figure 2d). After the stone was set, the temporary abutments were untightened from the cast and were ready for 3D assessment. This method was performed five times to produce five test casts.
a) Temporary abutments were tightened to the implant analogs on the control cast
b) Attached temporary abutments to verification jig after they were untightened from the implant analogs of the control cast and connected to another implant analogs
c) Connected temporary abutments to implant analogs were placed in a silicone mould
d) Low-expansion type IV stone was poured to construct the test cast. This method was used to construct all test casts from the different materials used in this study.
Group 2: Stone cast fabricated using composite verification jig (n=5): Five verification jigs were prepared using light-cured nanohybrid composite resin (DiaFil, Guidonia, Rome, Italy) using the same steps as described above to fabricate the test casts. The curing light (Light Curing Unit iLED Plus; Guilin Woodpecker Medical Instrument Co. Ltd., Guangxi, China) was held at a distance of 1 cm distance, and each side of the jig was cured for 20 seconds for a total of 60 seconds (Figure 3b).
Group 3: Stone cast fabricated using GC pattern verification jig (n=5): Five verification jigs were prepared using auto-polymerizing resin (GC pattern resin). The jigs were left for 24 hours to allow complete polymerization. Then the jig was sectioned, and resin was used to fill the gaps between the separated segments (Figure 3c). The non-engaging temporary abutments were untightened after 15 minutes to make the test casts following the same procedures described above.
Group 4: Stone cast fabricated using type III dental stone verification jig (n=5): Five verification jigs were prepared using type III dental stone (hard natural stone, Protechno) (Figure 3d). The water:powder ratio used was recommended by the manufacturer. A mould made of boxing wax (Coltene, Altstätten, Switzerland) was used to hold the stone (Figure 4). The same procedures described above were used to fabricate the test casts.
a) Angled view, b) Top view of wax mould
Group 5: Stone cast fabricated using type IV dental stone verification jig (n=5): Five verification jigs were prepared using high strength, low expansion (extra-hard natural stone, Protechno) type IV stone (Figure 3e). The water:powder ratio used was recommended by the manufacturer. A mould of boxing wax (Coltene) was used to hold the stone (Figure 4). After the stone had set, the same procedures described above were followed to fabricate the test casts.
Four scan bodies (Nobel Biocare Nobelactive Scanbody (Ti) RP 4.3, Smart Implant Solution, Nobel Biocare, Kloten, Switzerland) were placed on the four implant analogs for assessment of their 3D accuracy (Figure 5). A 3D scanner (KaVo ARCTICA Autoscan, KaVo Dental, Berlin, Germany) was utilized to capture the 3D orientation and position of the implants. This scanner uses very sensitive 3D sensors in conjunction with striped light projection to gather detailed images and convert them into a 3D model. The scanner does not require powder. All the scanning was performed by one operator. The scan of the control cast was used as the reference. The same scan bodies were placed on the same implant analogs in each cast for all 3D digital scans. Following scanning of the casts of the test groups and the control cast, Standard Tessellation Language (STL) files were obtained. Each scan of the test groups was superimposed over the control scan. Geomagic Design X and Geomagic Control X software (3D Systems, Rock Hill, South Carolina, USA) were used to measure 3D differences between the control STL file and the 25 test STL files.
Descriptive statistics were calculated for the five groups in this study (photopolymerizable resin, composite, GC pattern, type III stone, and type IV stone). A test for normality was done for the data of this study and it showed uneven distribution. The Kruskal-Wallis test was used to analyze the differences in 3D accuracy between the five groups and the control. The result was considered statistically significant if p<0.001.
A 3D assessment was performed after superposition of the control cast scan and test cast scans using Geomagic Design X and Geomagic control software as presented as seen in Figure 6. Color gradient-map of superimposition of control cast and photopolymerizable resin casts scans showed green color with few areas of yellow and blue colors over the implant scan bodies indicating minor deviations (Figure 6a). Colorimetric map of the control cast and composite group scans displayed yellow, blue, and red colors more than green indicating a significant deviation between the position of the implants in the control cast and produced casts from the composite verification jigs (Figure 6b). The superimposition of control cast and GC pattern group scans color-gradient map showed mainly green color with few surface areas of blue and yellow colors suggesting minimal 3D implant deviation (Figure 6c). Colorimetric map of control cast scan and type III and IV scans revealed primarily green color with few areas of blue and yellow colors over the implant scan bodies indicating high accuracy with minimum implant deviation (Figure 6d, 6e).
The mean ± SD, minimum and maximum values of the 3D deviations are shown in Table 1. The mean ± SD value of Group 1 (photopolymerizable resin) was 23.16 ± 0.88 μm; Group 2 (composite) was 46.72 ± 2.122 μm; Group 3 (GC pattern) was 23.51 ± 0.736 μm; Group 4 (type III stone) was 19.84 ± 1.017 μm; Group 5 (type IV stone) was 18.72 ± 0.819 μm.
Type IV stone (Group 5) showed the lowest deviation value by 3D assessment, followed by type III stone (Group 4), Triad (Group 1), GC pattern (Group 3), and composite (Group 2), which showed the highest deviation value (Figure 7).
The Kruskal-Wallis test indicated statistically significant differences between Groups 2 (composite), 4 (type III stone), and 5 (type IV stone) (p< 0.001). However, there were no significant differences between the other groups. These results suggest low accuracy of composite verification jigs causing significant 3D implant deviation in the produced casts compared to type III and IV stone groups which may affect the clinical outcomes when composite verification jig is used. Photopolymerizable resin and GC pattern groups displayed comparable accuracy with type III and IV stone groups.
To our knowledge, only one study was conducted to compare the accuracy of different verification jig materials and there is not enough data to determine which is the best accurate material to use for verification jig. Papaspyridakos et al. compared three different materials: GC pattern resin, Fixpeed resin, and Triad gel which are frequently used for making verification jigs. Their results showed no significant differences between these materials when tissue-level parallel implants were used [16]. However, other available materials that can be used for making verification jigs have not been investigated yet. In this study, the accuracy of different verification jig materials was investigated by 3D assessment of the generated fabricated casts constructed from the verification jigs.
Fabrication of full arch implant-supported fixed prostheses necessitates accurate working casts; the implant position in the cast must be the same as in the mouth to produce an accurate framework [2]. Any discrepancy in the cast will lead to a non-passive prosthesis, which may cause unfavorable strain on the implants and prosthetic components [6,7,22].
Numerous factors influence the accuracy of the implant master cast and final implant-prosthesis fits, such as impression technique, impression material, cast material, the skills and experience of the clinician and technician, framework/prosthesis material, and fabrication technique [23-31]. A systematic review by Lee et al. has shown that there was no difference between open-tray and closed-tray impression techniques with three or fewer implants. However, with four or more implants, open-tray impressions were more accurate in producing precise implant master casts than closed-tray impressions [26]. Another study by Vigolo et al. showed that a splinted open-tray impression technique with an acrylic resin splint produced more accurate casts than a non-splinted technique using polyether material [17].
Using a verification jig is an important step to determine if there is any discrepancy before fabricating the final framework/prosthesis. Moreover, a retrospective study by Ercoli et al. has shown that all frameworks fit passively in all cases when verification jigs were used. However, most frameworks did not achieve a passive fit when verification jigs were not used [31].
Five different verification jig materials were investigated in this study to determine which material produces the most accurate cast. Light-cure resin and GC pattern are common materials used by dentists to fabricate verification jigs. Composite has also been advocated as a novel material for use in verification jig fabrication. All acrylic resin materials (photopolymerizable resin, GC pattern, and composite) exhibit some polymerization shrinkage, which may impact the accuracy of the verification jigs. To minimize the effect of polymerization shrinkage, all verification jigs were sectioned in three places, separating the jig into six sections. Minimal amounts of material were applied in the sectioned gaps. Thus, only dimensional changes of the additional material connecting the sectioned parts of the jig will affect jig accuracy. Additionally, dental stone has been proposed as an accurate method of verifying the position accuracy of implant analogs in the implant master cast. It is considered one of the most accurate materials for verification jigs due to its low levels of dimensional changes, which are total distortions of 0.08% to 0.2% [21].
The 3D analysis showed that the test casts formed from type IV stone verification jigs more precisely duplicated the exact position of the implant analogs than those constructed of acrylic resin or composite. Also, type III stone demonstrated greater accuracy compared with acrylic resin and composite, but it was not superior to type IV stone. The acrylic resins Triad and GC pattern showed acceptable results, but the composite did not.
The clinical implication that can be concluded from this in vitro study is that all materials used in the study except composite showed promising results with minimum distortion in the produced casts indicating that these materials could be used to create verification jigs. Additionally, type III and IV stone verification jigs exhibited excellent results and using them as a verification jig may improve the framework's passive fit.
One limitation of this study is that it was conducted in vitro, which differs from clinical situations. The difference in the accuracy of different materials used to fabricate verification jigs in this study may or may not be clinically significant. Jemt defined passive fit as a level that did not cause any long-term clinical consequences, and misfits less than 150 μm were considered acceptable [10]. Other studies suggested that clinically acceptable fits of implant-supported full-arch prostheses vary from 59 to 72 μm and from 91 to 111 μm [10,16]. Therefore, an in vivo comparative study of different materials is required to assess the level of a clinically tolerable misfit.
Additionally, in our study, the implants were parallel to each other, which is an ideal scenario and different from most clinical cases. It is advisable to conduct a study with tilted implants to determine if the angulation affects the accuracy of the casts produced by the verification jigs.
With advancements in digital technology, computer-aided design/computer-aided manufacturing (CAD/CAM)-milled verification jigs were proposed as an efficient way to confirm the accuracy of the implant's position [32]. It would be interesting to include them in a future study to compare the accuracy of digitally manufactured verification jigs with those fabricated from commonly used materials.
Full-arch rehabilitation is a complex restorative procedure influenced by many factors. The passive fit of an implant-supported prosthesis is a critical factor in its success and durability. The present in vitro study concluded that the best verification-jig material for producing the most accurate cast with the best 3D accuracy was type IV stone, followed by type III stone, Triad, GC pattern, and lastly, composite. There were statistically significant differences between Groups 4 (type III stone), 5 (type IV stone), and 2 (composite). Under the limitations of this research, the use of a low-distortion material is recommended for the fabrication of verification jigs for implant-supported fixed dental prostheses to achieve precise results and a passive framework and prosthesis. This will positively impact the quality of the clinical outcome and minimize biological and mechanical complications. Future research to test these materials clinically and assess a tolerable level of prosthesis misfit is recommended.
General Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, SAU
Oral Biology, Faculty of Dentistry, King Abdulaziz Univeristy, Jeddah, SAU
Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.
Mawaddah S Aljohani and Heba A Bukhari contributed equally to the work and should be considered co-first authors.
Aljohani M S, Bukhari H A, Alshehri M, et al. (September 30, 2022) Accuracy of the Different Materials Used to Fabricate a Verification Jig of Implant-Supported Fixed Complete Dental Prostheses: An In Vitro Study. Cureus 14(9): e29794. doi:10.7759/cureus.29794
Peer review began: September 12, 2022 Peer review concluded: September 28, 2022 Published: September 30, 2022
© Copyright 2022 Aljohani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Attributable healthcare utilization and cost of pneumoniae due to drug-resistant Streptococcus pneumoniae: a cost analysis
Courtney A Reynolds1,
Jonathan A Finkelstein2,3,
G Thomas Ray4,
Matthew R Moore5 &
Susan S Huang1
Antimicrobial Resistance and Infection Control volume 3, Article number: 16 (2014) Cite this article
The burden of disease due to S. pneumoniae (pneumococcus), particularly pneumonia, remains high despite the widespread use of vaccines. Drug resistant strains complicate clinical treatment and may increase costs. We estimated the annual burden and incremental costs attributable to antibiotic resistance in pneumococcal pneumonia.
We derived estimates of healthcare utilization and cost (in 2012 dollars) attributable to penicillin, erythromycin and fluoroquinolone resistance by taking the estimate of disease burden from a previously described decision tree model of pneumococcal pneumonia in the U.S. We analyzed model outputs assuming only the existence of susceptible strains and calculating the resulting differences in cost and utilization. We modeled the cost of resistance from delayed resolution of illness and the resulting additional health services.
Our model estimated that non-susceptibility to penicillin, erythromycin and fluoroquinolones directly caused 32,398 additional outpatient visits and 19,336 hospitalizations for pneumococcal pneumonia. The incremental cost of antibiotic resistance was estimated to account for 4% ($91 million) of direct medical costs and 5% ($233 million) of total costs including work and productivity loss. Most of the incremental medical cost ($82 million) was related to hospitalizations resulting from erythromycin non-susceptibility. Among patients under age 18 years, erythromycin non-susceptibility was estimated to cause 17% of hospitalizations for pneumonia and $38 million in costs, or 39% of pneumococcal pneumonia costs attributable to resistance.
We estimate that antibiotic resistance in pneumococcal pneumonia leads to substantial healthcare utilization and cost, with more than one-third driven by macrolide resistance in children. With 5% of total pneumococcal costs directly attributable to resistance, strategies to reduce antibiotic resistance or improve antibiotic selection could lead to substantial savings.
Streptococcus pneumoniae (pneumococcus) causes a variety of clinical syndromes, including acute otitis media, pneumonia, and meningitis. Although several pneumococcal vaccines have been developed (including pneumococcal polysaccharide vaccine 23 (PPSV23) in 1983, pneumococcal conjugate vaccine 7 (PCV7) in 2000, and pneumococcal conjugate vaccine 13 (PCV13) in 2010), the burden of pneumococcal disease remains high across age groups, in the United States and abroad [1–10]. Our previously published model of pneumococcal burden in the United States estimated that pneumococcus was responsible for 4 million disease episodes in 2004, with acute otitis media in children accounting for the majority of cases [11]. Vaccination has led to large reductions for invasive pneumococcal diseases, yet Active Bacterial Core surveillance (ABCs) identified approximately 36,850 cases of invasive pneumococcal disease in the US in 2011 [12]. This continued high incidence of invasive pneumococcal disease is attributable to disease caused by non-vaccine serotypes [13, 14].
A major concern for treatment of pneumococcal disease is the increasing frequency of antibiotic resistance [15]. These strains are primarily resistant to penicillin, macrolides and, to a lesser extent, fluoroquinolones [16]. Among the 36,850 invasive pneumococcal disease cases identified by ABCs in 2011, 9% were caused by penicillin non-susceptible strains and 26% were caused by erythromycin non-susceptible strains [12]. In addition, despite overall reductions in the incidence of resistant IPD, the prevalence of some multi-drug resistant strains, such as serotype 19A in the United States, increased due to serotype replacement and capsular switching following the introduction of PCV7 in 2000 [15, 17, 18]. The financial impact of antibiotic-resistant pneumococcus has not been evaluated and is likely to depend upon many factors including whether antibiotic treatment is concordant or discordant with the susceptibility profile of the infecting strain. Discordant treatment may result in delayed cure, multiple outpatient visits or hospitalization, and increases in morbidity, mortality and cost.
We used a previously developed decision tree-based model of U.S. pneumococcal disease burden to estimate the impact of antibiotic resistance upon healthcare utilization and cost [11]. We focused our analysis on pneumonia, as our prior study found that pneumococcal pneumonia accounted for 866,000 annual cases (22% of pneumococcal disease burden) and $4.9 billion dollars, or 72%, of total healthcare and time-related costs attributable to pneumococcal disease.
We assessed the attributable burden of resistance using a previously published decision-tree model of pneumococcal disease burden in the United States in 2004 [11]. Model inputs and assumptions for an example pediatric (0- < 5 yrs) and adult (50- < 65 yrs) age group are provided in Table 1. All uses of the term "resistance" denote the combination of intermediate or high level resistance. Since minimum inhibitory concentration (MIC) breakpoints for penicillin were changed in 2008, we performed most analyses using both old and new sets of breakpoints. We assumed that the effects of antibiotic resistance occurred only in the presence of discordance between the antibiotic chosen for treatment and the susceptibility of the infecting organism. From expert opinion, we estimated what fraction of patients with discordant therapy would have delayed cure, resulting in additional outpatient visits, courses of antibiotics, or hospitalization. We further assumed that inpatient antibiotic coverage was sufficiently broad that treatment failure resulting from discordant therapy only occurred in the outpatient setting. This study was exempted from human research oversight by the Harvard Pilgrim Health Care Institutional Review Board.
Table 1 Inputs and assumptions for outpatient pneumococcal pneumonia for sample pediatric and adult age groups: 0- < 5 and 50- < 65 years*
The incidence of outpatient pneumonia cases was obtained from the 2004–2005 National Ambulatory Medical Care Survey and 2004–2005 National Hospital Ambulatory Medical Care Survey using ICD-9 codes (all primary and secondary diagnoses) by age. We restricted outpatient cases to those visits during which an antibiotic was prescribed. Using expert panel consensus described elsewhere, we corrected for age-specific over-diagnosis among outpatient cases (16% for pneumonia) [11]. Also using expert opinion, we estimated the frequency of relevant treatment outcomes, based upon antibiotic susceptibility profiles. The distribution of agents prescribed was obtained from the National Ambulatory Medical Care Survey. The incidence of inpatient pneumonia cases was obtained from the 2004 National Hospital Discharge Survey and the 2004 National Inpatient Sample using ICD-9 codes (primary diagnosis only) by age. For cases requiring hospital admission, we estimated the likelihood of follow-up outpatient visits, nursing home stays and death from expert opinion and literature review. We used estimates of the number of inpatient and outpatient pneumonia cases in 2004 involving pneumococcal isolates resistant to penicillin, erythromycin and fluoroquinolones by age group from Active Bacterial Core Surveillance data (inpatient) and primary literature (outpatient). In our previous analyses using this model we assumed current prevalence of antibiotic resistance [11].
Cost associated with total pneumococcal disease burden (both sensitive and resistant strains) was estimated in our previously published paper using a combination of sources including the Federal Register, and public and private payor rates. We examined four categories of cost: 1) direct costs (including medical care), 2) costs from adverse outcomes, 3) work-loss costs, and 4) cost from lost wages. We conducted several sensitivity analyses related to cost in this manuscript, including an analysis for cost per hospital day. Further details on estimations of cost are provided in Appendices C-E of that manuscript [11]. To derive an estimate of healthcare utilization and costs due to medical visits and work loss (in 2012 dollars) attributable solely to resistance to penicillin, erythromycin and fluoroquinolones, we analyzed model outputs assuming only the existence of susceptible strains and calculating the resulting differences in cost and utilization. Resistant disease was assumed to be more likely to result in delayed cure, and, if not treated with concordant antibiotics, to incur additional medical and work loss costs. For outpatient cases with delayed cure, cost attributable to resistance reflected additional outpatient visits, courses of antibiotics, or hospitalization. No changes in initial antibiotic therapy were assumed.
Finally, we performed sensitivity analyses to model the projected attributable costs resulting from increases, from present levels, in resistance to penicillin, erythromycin and fluoroquinolones. We assumed antibiotic resistant strains would replace sensitive strains, resulting in stable overall prevalence of pneumococcal disease; this assumption is partly supported by several studies in children finding that overall carriage rates of pneumococcus in the nasopharynx remained stable despite serotype replacement with resistant strains after conjugate vaccination [19–21]. We further assumed that the amount of disease and frequency of treatment failure due to resistant strains would remain similar.
Overall inpatient pneumococcal pneumonia burden was estimated to be highest among those over age 65, while outpatient visits for pneumonia were most common among children less than 5 years old (Table 2). Using current MIC breakpoints, 23% (93,853 of 401,282) of inpatient cases and 34% (156,496 of 464,523) of outpatient cases exhibited resistance to at least one antibiotic. Resistance occurred most frequently to macrolides, with 20% of inpatient cases (80,932 of 401,282) and 30% of outpatient cases (139,499 of 464,523) exhibiting resistance.
Table 2 Estimated pneumonia burden due to antibiotic-resistant Streptococcus pneumoniae isolates
To provide perspective, we also calculated resistance profiles using pre-2008 MIC breakpoints and estimated that 30% (121,503 of 401,282) of inpatient pneumococcal cases and 42% (197,680 of 464,523) of outpatient cases of pneumococcal pneumonia involved resistance to one or more of the three antibiotics studied. Using pre-2008 breakpoints, resistance would have occurred most frequently to penicillin, with nearly 50% (59,551 of 121,503) of resistant inpatient isolates and 60% (117,204 of 197,680) of resistant outpatient isolates exhibiting resistance to multiple antibiotics.
Our model estimated that resistance to penicillin, erythromycin and fluoroquinolones directly caused 32,398 excess outpatient visits and 19,336 excess hospitalizations for pneumonia (Table 3). Overall, the incremental cost of antibiotic resistance was estimated to account for 4% ($91 million) of annual pneumococcal pneumonia direct medical costs and 5% ($233 million) of total costs (including work and productivity loss). Most of the incremental medical cost ($82 of $91 million) was estimated to be due to hospitalizations resulting from erythromycin resistance. Among the 139,499 cases of outpatient pneumonia resistance to erythromycin, we estimated from expert opinion that 50,285 of these cases (36%) were actually treated with erythromycin. Among these 50,285 cases given discordant treatment, 49% still resulted in simple cure, 11% resulted in delayed outpatient resolution and 40% resulted in hospitalization due to failure to improve.
Table 3 Estimated annual healthcare utilization attributable to antibiotic resistant strains of pneumococcus for all ages*
While pediatric patients contributed to fewer than 15% of inpatient pneumonia cases, they constituted 45% of outpatient cases. Since costs attributable to antibiotic resistance resulted from failure of outpatient cases only, discordant treatment of pediatric cases contributed significantly to overall healthcare utilization and costs related to resistance. In particular, erythromycin resistance among patients under age 18 caused 17% of hospitalizations—that otherwise would not have occurred—and $38 million in costs.
The projected costs of future increases in resistance (vs. baseline costs from 2004 estimates, expressed in 2012 dollars) to specific antibiotics are shown in Figure 1. This figure assumes a linear relationship between the number of resistant cases and the associated cost. Increased resistance to erythromycin was associated with the greatest projected cost.
Projected increased costs associated with increases in resistance to specific antibiotics. Incidence of pneumococcal pneumonia is assumed to be constant. The attributable costs shown are derived from treatment failure of cases presenting as outpatients, which resulted in additional outpatient visits or courses of antibiotics, or led to hospitalizations. PCN resistance is given according to post-2008 MIC breakpoints. Cost is depicted at a given absolute percent resistance; e.g., if 20% of pneumococcal isolates were to be resistant to fluoroquinolones, the total cost attributable to that level of resistance is $127 million dollars. Initial data points for each antibiotic indicate baseline cost and resistance.
We estimated that more than one-third of pneumococcal pneumonia cases involved resistance to penicillin, erythromycin or fluoroquinolones. While antibiotic-resistant strains accounted for a substantial portion of inpatient and outpatient pneumococcal pneumonia cases, resistance itself was responsible for a far lower percentage of total pneumococcal pneumonia medical costs (4%) and total costs (5%). This finding is due in part to the fact that discordant therapy and the resulting treatment failure were assumed to only occur in the outpatient setting. Because empiric therapy recommended by national guidelines address resistant infections, we assumed that inpatient treatment would cover resistant organisms for community-acquired pneumococcal pneumonia [22, 23]. Nevertheless, costs attributable to resistance amount to over $90 million in direct medical and $230 million in total costs. Nearly all costs associated with antibiotic resistance resulted from outpatient discordant therapy that led to delays in cure, ultimately requiring additional courses of antibiotics, additional outpatient visits or hospitalization for resolution.
While resistance to penicillin was the most common, the majority of associated costs were due to erythromycin resistance. In particular, macrolide resistant pneumonia in children comprised 39% of total associated costs due to resistance, primarily resulting from hospitalization of cases initially treated in the outpatient setting. From expert opinion, we estimated that 32% of pediatric outpatient pneumococcal pneumonia cases treated with erythromycin were discordant, resulting in an additional 9700 hospitalizations annually due to delayed resolution. To address the threat of macrolide failure, recent guidelines on community-acquired pneumonia from the Pediatric Infectious Disease Society recommend amoxicillin as first line therapy, with macrolides recommended only if there is suggestion of infection with atypical organisms [22].
Based upon our projections of increased costs due to rising levels of antibiotic resistance, increases in erythromycin resistance are most likely to result in significant increases in cost. This is in agreement with our finding that delayed cure after treatment with erythromycin, predominantly in the pediatric population, is responsible for the majority of costs currently associated with antibiotic resistant pneumococcal infections. Projected increases in resistance are simply reasonable guesses based on current levels of resistance; more dramatic increases in resistance are certainly possible, and would be associated with greater increases in cost.
This study has several limitations. We estimated the impact of antibiotic resistance using an existing decision tree model of pneumococcal disease burden from 2004. The burden of antibiotic resistant cases of pneumococcal pneumonia may have changed substantially since then. We also assumed that antibiotic resistance among non-bacteremic cases was similar to bacteremic cases. In addition, the effects of the 2010 PCV13 introduction on disease burden are not reflected in these estimates. For example, emerging antibiotic resistance in replacement non-vaccine serotypes are difficult to predict and are not addressed in this pre-PCV13 model. Our model benefits from incorporation of extensive data from administrative data sources and an expert panel; however the results are highly sensitive to assumptions about the frequency of discordant therapy for which we did not directly collect data. Our model also assumed that the costs of antibiotic resistance were exclusively due to additional treatment failure for cases initially treated in the outpatient setting; this assumption does not account for the possibility of additional delayed cure among inpatients. However, we believe that the use of broad-spectrum antibiotics for inpatient treatment renders delayed cure much less likely in this setting. In our sensitivity analyses, we assumed that rising resistance would have no effect on virulence; however it has been shown that serotypes differ in their ability to cause disease and thus we likely under-estimated the projected cost associated with increased levels of antibiotic resistant strains [24]. Lastly, we assumed that further increases in resistant strains would not result in changes in empirical prescribing practices, although this is likely if levels of resistance substantially rise.
Despite the introduction of pneumococcal vaccines, the burden of pneumococcal pneumonia remains high. While we estimated that antibiotic resistance was only associated with 4% of total pneumococcal healthcare costs, the absolute cost attributed to antibiotic resistance and treatment failure was considerable. Strategies to reduce antibiotic resistance or improve antibiotic selection may prevent a significant number of hospitalizations and outpatient visits and lead to a substantial savings.
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We are grateful to the following expert panel members for their time and expertise provided to this project: Dr. Steven Black, Dr. Ralph Gonzales, Dr. Marie Griffin, Dr. Keith Klugman, Dr. Katherine O'Brien, Dr. Lionel Mandel, and Dr. Daniel Musher. We would also like to thank Dr. Antonio Anzueto, Dr. Peter Lindenhauer, Dr. Lauri Hicks, and Sara Schoenfeld for their contributions, as well as Dr. Mark Messonnier for advice on economic methods. This study was funded by a grant from the CDC (TS-1363, Finkelstein). The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the CDC.
This study was funded by the Centers for Disease Control and Prevention (CDC) (TS-1363, Finkelstein).
Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, 100 Theory Ave, Suite 110, Irvine, CA, 92697, USA
Courtney A Reynolds & Susan S Huang
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, 3rd Floor, Boston, MA, 02215, USA
Jonathan A Finkelstein
Division of General Pediatrics, Boston Children's Hospital, 300 Longwood Ave, Hunnewell G, Boston, MA, 02115, USA
Division of Research, Kaiser Permanente, 2000 Broadway, Oakland, CA, 94612, USA
G Thomas Ray
Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30333, USA
Matthew R Moore
Courtney A Reynolds
Susan S Huang
Correspondence to Courtney A Reynolds.
The author declared that they have no competing interests.
JF, MM and SH determined the study design. GR built the mathematical model and ran all analyses using this model. CR composed the tables and figures and wrote the manuscript. All authors read and approved the final manuscript.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Reynolds, C.A., Finkelstein, J.A., Ray, G.T. et al. Attributable healthcare utilization and cost of pneumoniae due to drug-resistant Streptococcus pneumoniae: a cost analysis. Antimicrob Resist Infect Control 3, 16 (2014). https://doi.org/10.1186/2047-2994-3-16
Healthcare utilization
DRSP

redpajama_set_name: RedPajamaCommonCrawl


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From 1949 to 1959 he worked part-time as an instructor in Institute of Design and from 1959 to 1983 was an assistant professor in the University of Illinois until he died in Chicago, Illinois. From 1972 to 1973 he worked for the Center for Advanced Studies and University of Illinois where he was an associate member.
He was one of the founders of the Society for Photographic Education. In 1966 he was awarded Illinois Arts Council Award and Graham Foundation for Advanced Studies in the Fine Arts Award. In 1969 he became a fellow in Guggenheim and by 1977 became a fellow at the National Endowment for the Arts Photographers'. The Indiana University Art Museum houses the Art Sinsabaugh archive containing over 3,000 photographs, family memorabilia, and Sinsabaugh's personal papers.
Full-time and part-time instructor/head of evening photography program, Institute of Design/IIT, Chicago, 1949-59; assistant professor, associate professor, and professor (head of photography/cinematography department), University of Illinois, Urbana-Champaign, 1959-83; visiting artist at University of Oregon, School of the Art Institute of Chicago, Williams College, University of New Hampshire, and numerous others.
Keith F. Davis, American horizons : the photographs of Art Sinsabaugh, New York : Hudson Hills Press, ca. 2004.
Sherwood Anderson, 6 mid-American chants by Sherwood Anderson. 11 Mid-west photographs by Art Sinsabaugh. A note on Anderson's poetry, by Edward Dahlberg. To Sherwood Anderson in heaven, a poem by Frederick Eckman, Highlands, N.C., J. Williams, 1964.
The New spirit in American photography, Urbana-Champaign : University of Illinois, 1985.