Long-term cost-effectiveness of weight management in primary care.

BACKGROUND: As obesity prevalence and health-care costs increase, Health Care providers must prevent and manage obesity cost-effectively. METHODS: Using the 2006 NICE obesity health economic model, a primary care weight management programme (Counterweight) was analysed, evaluating costs and outcomes associated with weight gain for three obesity-related conditions (type 2 diabetes, coronary heart disease, colon cancer). Sensitivity analyses examined different scenarios of weight loss and background (untreated) weight gain. RESULTS: Mean weight changes in Counterweight attenders was -3 kg and -2.3 kg at 12 and 24 months, both 4 kg below the expected 1 kg/year background weight gain. Counterweight delivery cost was pound59.83 per patient entered. Even assuming drop-outs/non-attenders at 12 months (55%) lost no weight and gained at the background rate, Counterweight was 'dominant' (cost-saving) under 'base-case scenario', where 12-month achieved weight loss was entirely regained over the next 2 years, returning to the expected background weight gain of 1 kg/year. Quality-adjusted Life-Year cost was pound2017 where background weight gain was limited to 0.5 kg/year, and pound2651 at 0.3 kg/year. Under a 'best-case scenario', where weights of 12-month-attenders were assumed thereafter to rise at the background rate, 4 kg below non-intervention trajectory (very close to the observed weight change), Counterweight remained 'dominant' with background weight gains 1 kg, 0.5 kg or 0.3 kg/year. CONCLUSION: Weight management for obesity in primary care is highly cost-effective even considering only three clinical consequences. Reduced healthcare resources use could offset the total cost of providing the Counterweight Programme, as well as bringing multiple health and Quality of Life benefits.

The Counterweight programme: Prevalence of CVD risk factors by body mass index and the impact of 10% weight change.

OBJECTIVES: To examine relationships between body mass index (BMI), prevalence of physician-recorded cardiovascular disease (CVD) risk factors in primary care, and changes in risk with 10% weight change. METHODS: The Counterweight Project conducted a baseline cross-sectional survey of medical records of 6150 obese (BMI ≥ 30 kg/m(2)), 1150 age- and sex-matched overweight (BMI 25 to <30 kg/m(2)), and 1150 age- and sex-matched normal weight (BMI 18.5 to <25 kg/m(2)) controls, in primary care. Data were collected for the previous 18 months to examine BMI and disease prevalence, and then modelled to show the potential effect of 10% weight loss or gain on risk. RESULTS: Obese patients develop more CVD risk factors than normal weight controls. BMI ≥ 40 kg/m(2) exhibits increased prevalence of type 2 diabetes mellitus (DM), odds ratio (OR) men: 6.16 (p < 0.001); women: 7.82 (p < 0.001) and hypertension OR men: 5.51 (p < 0.001); women: 4.16 (p < 0.001). Dyslipidaemia peaked around BMI 35 to <37.5 kg/m(2), OR men: 3.26 (p < 0.001); women 3.76 (p < 0.001) and CVD at BMI 37.5 to <40 kg/m(2) in men, OR 4.48 (p < 0.001) and BMI ≥ 40 kg/m(2) in women, OR 3.98 (p < 0.001). A 10% weight loss from the sample mean of 32.5 kg/m(2) reduced the OR for type 2 DM by 30% and CVD by 20%, while 10% weight gain increased type 2 DM risk by more than 35% and CVD by 20%. CONCLUSION: Obesity plays a fundamental role in CVD risk, which is reduced with weight loss. Weight management intervention strategies should be a public health priority to reduce the burden of disease in the population.

Empowering primary care to tackle the obesity epidemic: the Counterweight Programme.

OBJECTIVE: To improve the management of obese adults (18-75 y) in primary care. DESIGN: Cohort study. SETTINGS: UK primary care. SUBJECTS: Obese patients (body mass index > or =30 kg/m(2)) or BMI> or =28 kg/m(2) with obesity-related comorbidities in 80 general practices. INTERVENTION: The model consists of four phases: (1) audit and project development, (2) practice training and support, (3) nurse-led patient intervention, and (4) evaluation. The intervention programme used evidence-based pathways, which included strategies to empower clinicians and patients. Weight Management Advisers who are specialist obesity dietitians facilitated programme implementation. MAIN OUTCOME MEASURES: Proportion of practices trained and recruiting patients, and weight change at 12 months. RESULTS: By March 2004, 58 of the 62 (93.5%) intervention practices had been trained, 47 (75.8%) practices were active in implementing the model and 1549 patients had been recruited. At 12 months, 33% of patients achieved a clinically meaningful weight loss of 5% or more. A total of 49% of patients were classed as 'completers' in that they attended the requisite number of appointments in 3, 6 and 12 months. 'Completers' achieved more successful weight loss with 40% achieving a weight loss of 5% or more at 12 months. CONCLUSION: The Counterweight programme provides a promising model to improve the management of obesity in primary care.

Analysis of genomic sequences by Chaos Game Representation.

MOTIVATION: Chaos Game Representation (CGR) is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to find the coordinates for their position in a continuous space. This distribution of positions has two properties: it is unique, and the source sequence can be recovered from the coordinates such that distance between positions measures similarity between the corresponding sequences. The possibility of using the latter property to identify succession schemes have been entirely overlooked in previous studies which raises the possibility that CGR may be upgraded from a mere representation technique to a sequence modeling tool. RESULTS: The distribution of positions in the CGR plane were shown to be a generalization of Markov chain probability tables that accommodates non-integer orders. Therefore, Markov models are particular cases of CGR models rather than the reverse, as currently accepted. In addition, the CGR generalization has both practical (computational efficiency) and fundamental (scale independence) advantages. These results are illustrated by using Escherichia coli K-12 as a test data-set, in particular, the genes thrA, thrB and thrC of the threonine operon.

Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities.

The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C. ) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, and Branchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.

Hypothetical model for monitoring microbial growth by using capacitance measurements--a minireview.

Microbiological impedance devices are used routinely by food and manufacturing industries, and public health agencies to measure microbial growth and metabolism. In this paper a hypothetical model explaining the effects of microbial growth and metabolism on capacitance at electrode-medium interfaces, that can be supported by fundamental theories and principles of electrochemistry, is presented. This model provides a framework to interpret changes in capacitance during microbial growth and metabolism and can be used to generate and test hypotheses on factors (i.e., temperature, microbial cell density, microbial growth and medium conductivity) contributing to increases or decreases in capacitance.

Factors influencing capacitance-based monitoring of microbial growth.

Microbiological impedance devices are routinely used by food and manufacturing industries, and public health agencies to measure microbiological growth. Factors contributing to increases and decreases in capacitance at the culture medium-electrode interface are poorly understood. To objectively evaluate the effects of temperature, cell density and medium conductivity on capacitance, admittance values from an impedance device were standardized; capacitance was converted to susceptance to allow unit comparisons with conductance. Although increases in temperature increased susceptance, a linear relationship could not be established between the change of susceptance with temperature and conductance of the medium. Cell density by itself had no measureable effect on susceptance or conductance, indicating that cells did not impede the movement of ions in the medium or around the electrode. In a low conductivity medium, increases in conductance by the addition of ions resulted in a concomitant increase of susceptance values. However, in a high conductivity medium, increases in conductance resulted in little or no increase of susceptance values because ions saturated the electrode surface. Susceptance increased when Escherichia coli, Pseudomonas aeruginosa, Alcaligenes faecalis and Staphylococcus aureus were grown in high conductivity media because protons produced by metabolically active bacteria balance more charge on the electrode than other ions. Increases in susceptance due to bacterial growth and metabolism in low conductivity media were attributed to both increases in protons and ionic metabolites. These results indicate that capacitance may provide a better measure of microbial growth and metabolism than conductance.

Tetranucleotide frequencies in microbial genomes.

A computational strategy for determining the variability of long DNA sequences in microbial genomes is described. Composite portraits of bacterial genomes were obtained by computing tetranucleotide frequencies of sections of genomic DNA, converting the frequencies to color images and arranging the images according to their genetic position. The resulting images revealed that the tetranucleotide frequencies of genomic DNA sequences are highly conserved. Sections that were visibly different from those of the rest of the genome contained ribosomal RNA, bacteriophage, or undefined coding regions and had corresponding differences in the variances of tetranucleotide frequencies and GC content. Comparison of nine completely sequenced bacterial genomes showed that there was a nonlinear relationship between variances of the tetranucleotide frequencies and GC content, with the highest variances occurring in DNA sequences with low GC contents (less than 0.30 mol). High variances were also observed in DNA sequences having high GC contents (greater than 0.60 mol), but to a much lesser extent than DNA sequences having low GC contents. Differences in the tetranucleotide frequencies may be due to the mechanisms of intercellular genetic exchange and/or processes involved in maintaining intracellular genetic stability. Identification of sections that were different from those of the rest of the genome may provide information on the evolution and plasticity of bacterial genomes.

Natural Microbial Community Compositions Compared by a Back-Propagating Neural Network and Cluster Analysis of 5S rRNA.

The community compositions of free-living and particle-associated bacteria in the Chesapeake Bay estuary were analyzed by comparing banding patterns of stable low-molecular-weight RNA (SLMW RNA) which include 5S rRNA and tRNA molecules. By analyzing images of autoradiographs of SLMW RNAs on polyacrylamide gels, band intensities of 5S rRNA were converted to binary format for transmission to a back-propagating neural network (NN). The NN was trained to relate binary input to sample stations, collection times, positions in the water column, and sample types (e.g., particle-associated versus free-living communities). Dendrograms produced by using Euclidean distance and average and Ward's linkage methods on data of three independently trained NNs yielded the following results. (i) Community compositions of Chesapeake Bay water samples varied both seasonally and spatially. (ii) Although there was no difference in the compositions of free-living and particle-associated bacteria in the summer, these community types differed significantly in the winter. (iii) In the summer, most bay samples had a common 121-nucleotide 5S rRNA molecule. Although this band occurred in the top water of midbay samples, it did not occur in particle-associated communities of bottom-water samples. (iv) Regardless of the season, midbay samples had the greatest variety of 5S rRNA sizes. The utility of NNs for interpreting complex banding patterns in electrophoresis gels was demonstrated.

Development of bacterial biofilms on silastic catheter materials in peritoneal dialysis fluid.

A modified Robbins' device was used to monitor the growth of bacteria associated with clinical peritonitis in peritoneal dialysis fluid. To simulate bacterial colonization and biofilm formation on peritoneal catheters, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Escherichia coli were allowed to adhere to silastic disks and were then grown in fresh or used dialysis fluid. Adherent bacteria formed microcolonies and biofilms on silastics within 4 to 24 hours. Our data showed that colonization of the silastic disks was related to the quantity of bacteria and there were significant differences between the growth of adherent bacteria in fresh and used dialysis fluid. Adherent S epidermidis and P aeruginosa grew better in dialysis fluids than adherent E coli. These results suggest that S epidermidis and P aeruginosa are more likely to colonize silastic catheters and to cause catheter-related peritonitis in peritoneal dialysis patients than E coli.

Clarification of the structure of the ampicillin-resistance plasmid RSF0885 from Haemophilus influenzae HR-885 serotype b.

The nonconjugative ampicillin-resistance plasmid RSF0885 has been reported to be as small as 2.9 MDa and as large as 4.1 MDa with at least two restriction enzyme maps reported. In addition, the source of the original plasmid has been reported to be Haemophilus influenzae and Haemophilus parainfluenzae. Characterization of the source strains and sequencing data of the plasmids revealed that H. influenzae serotype b was the original source strain and that IS1-K in the larger plasmid was presumably acquired when the smaller plasmid was maintained in Escherichia coli in S. Falkow's laboratory during the late 1970s.

Heterotrophic plate counts of surface water samples by using impedance methods.

Membrane filtration, spread plating, and pour plating are conventional methods used to determine the heterotrophic plate counts of water samples. Impedance methods were investigated as an alternative to conventional methods, since sample dilution is not required and the bacterial count can be estimated within 24 h. Comparisons of impedance signals obtained with different water samples revealed that capacitance produced faster detection times than conductance. Moreover, the correlation between heterotrophic plate count and detection time was highest (r = 0.966) when capacitance was used. Linear and quadratic regressions of heterotrophic plate count and impedance detection time were affected by incubation temperatures. Regressions between heterotrophic plate counts based on conventional methods and detection times of water samples incubated at less than or equal to 25 degrees C had R2 values of 0.878 to 0.933. However, regressions using detection times of water samples incubated at greater than or equal to 30 degrees C had lower R2 values, even though water samples produced faster detection times. Comparisons between broth-based versions of R2A medium and plate count agar revealed that the latter correlated highly with heterotrophic plate count, provided that water samples were incubated at 25 degrees C and impedance measurements were conducted with the capacitance signal (r = 0.966). When the linear regression of this relationship was tested with 100 water samples, the correlation between predicted and actual log10 CFU milliliter-1 was 0.869. These results indicate that impedance methods provide a suitable alternative to conventional methods.