Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Comparative Study of COVID-19 Pandemic Progressions in 175 Regions in Australia, Canada, Italy, Japan, Spain, U.K. and USA Using a Novel Model That Considers Testing Capacity and Deficiency in Confirming Infected Cases

Not identified as being exposed or infected, the group of asymptomatic and presymptomatic patients has become the key source of infectious hosts for the COVID-19 pandemic, triggering the re-emergence of outbreaks. Acknowledging the impacts of movement of unidentified patients and the limited testing capacity on understanding the spread of the virus, an augmented Susceptible-Exposed-Infectious-Confirmed-Recovered (SEICR) model integrating intercity migration data and testing capacity is developed to probe into the number of unidentified COVID-19 infected patients. This model allows evaluation of the effectiveness of active interventions, and more accurate prediction of the pandemic progression in a country, region or city. A pseudo-coevolutionary algorithm is adopted in the model fitting to provide an effective estimation of high-dimensional unknown parameter sets using a limited amount of historical data. The model is applied to 175 regions in Australia, Canada, Italy, Japan, Spain, the UK and USA to estimate the number of unconfirmed cases using limited historical data. Results showed that the actual number of infected cases could be 4.309 times as many as the official confirmed number. By implementing mass COVID-19 testing, the number of infected cases could be reduced by about 50%.

Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

Advanced Algorithms for Local Routing Strategy on Complex Networks

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets R_c increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.

Traffic congestion in interconnected complex networks

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

A complex systems analysis of stick-slip dynamics of a laboratory fault

We study the stick-slip behavior of a granular bed of photoelastic disks sheared by a rough slider pulled along the surface. Time series of a proxy for granular friction are examined using complex systems methods to characterize the observed stick-slip dynamics of this laboratory fault. Nonlinear surrogate time series methods show that the stick-slip behavior appears more complex than a periodic dynamics description. Phase space embedding methods show that the dynamics can be locally captured within a four to six dimensional subspace. These slider time series also provide an experimental test for recent complex network methods. Phase space networks, constructed by connecting nearby phase space points, proved useful in capturing the key features of the dynamics. In particular, network communities could be associated to slip events and the ranking of small network subgraphs exhibited a heretofore unreported ordering.

An efficient and secure medical image protection scheme based on chaotic maps

Recently, the increasing demand for telemedicine services has raised interest in the use of medical image protection technology. Conventional block ciphers are poorly suited to image protection due to the size of image data and increasing demand for real-time teleradiology and other online telehealth applications. To meet this challenge, this paper presents a novel chaos-based medical image encryption scheme. To address the efficiency problem encountered by many existing permutation-substitution type image ciphers, the proposed scheme introduces a substitution mechanism in the permutation process through a bit-level shuffling algorithm. As the pixel value mixing effect is contributed by both the improved permutation process and the original substitution process, the same level of security can be achieved in a fewer number of overall rounds. The results indicate that the proposed approach provides an efficient method for real-time secure medical image transmission over public networks.

Characterizing chaotic dynamics from simulations of large strain behavior of a granular material under biaxial compression

For a given observed time series, it is still a rather difficult problem to provide a useful and compelling description of the underlying dynamics. The approach we take here, and the general philosophy adopted elsewhere, is to reconstruct the (assumed) attractor from the observed time series. From this attractor, we then use a black-box modelling algorithm to estimate the underlying evolution operator. We assume that what cannot be modeled by this algorithm is best treated as a combination of dynamic and observational noise. As a final step, we apply an ensemble of techniques to quantify the dynamics described in each model and show that certain types of dynamics provide a better match to the original data. Using this approach, we not only build a model but also verify the performance of that model. The methodology is applied to simulations of a granular assembly under compression. In particular, we choose a single time series recording of bulk measurements of the stress ratio in a biaxial compression test of a densely packed granular assembly-observed during the large strain or so-called critical state regime in the presence of a fully developed shear band. We show that the observed behavior may best be modeled by structures capable of exhibiting (hyper-) chaotic dynamics.

Attack resilience of the evolving scientific collaboration network

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local ("microscopic") level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level--fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.

Analysis, design, and control of a transcutaneous power regulator for artificial hearts

Based on a generic transcutaneous transformer model, a remote power supply using a resonant topology for use in artificial hearts is analyzed and designed for easy controllability and high efficiency. The primary and secondary windings of the transcutaneous transformer are positioned outside and inside the human body, respectively. In such a transformer, the alignment and gap may change with external positioning. As a result, the coupling coefficient of the transcutaneous transformer is also varying, and so are the two large leakage inductances and the mutual inductance. Resonant-tank circuits with varying resonant-frequency are formed from the transformer inductors and external capacitors. For a given range of coupling coefficients, an operating frequency corresponding to a particular coupling coefficient can be found, for which the voltage transfer function is insensitive to load. Prior works have used frequency modulation to regulate the output voltage under varying load and transformer coupling. The use of frequency modulation may require a wide control frequency range which may extend well above the load insensitive frequency. In this paper, study of the input-to-output voltage transfer function is carried out, and a control method is proposed to lock the switching frequency at just above the load insensitive frequency for optimized efficiency at heavy loads. Specifically, operation at above resonant of the resonant circuits is maintained under varying coupling-coefficient. Using a digital-phase-lock-loop (PLL), zero-voltage switching is achieved in a full-bridge converter which is also programmed to provide output voltage regulation via pulsewidth modulation (PWM). A prototype transcutaneous power regulator is built and found to to perform excellently with high efficiency and tight regulation under variations of the alignment or gap of the transcutaneous transformer, load and input voltage.

Scale-free user-network approach to telephone network traffic analysis

The effect of the user network on the telephone network traffic is studied in this paper. Unlike classical traffic analysis, where users are assumed to be connected uniformly, our proposed method employs a scale-free network to model the behavior of telephone users. Each user has a fixed set of acquaintances with whom the user may communicate, and the number of acquaintances follows a power-law distribution. We show that compared to conventional analysis based upon a fully connected user network, the network traffic is significantly different when the user network assumes a scale-free property. Specifically, network blocking (call failure) is generally more severe in the case of a scale-free user network. It is also shown that the carried traffic is practically limited by the scale-free property of the user network, rather than by the network capacity.

Clustering model for transmission of the SARS virus: application to epidemic control and risk assessment

We propose a new four state model for disease transmission and illustrate the model with data from the 2003 SARS epidemic in Hong Kong. The critical feature of this model is that the community is modelled as a small-world network of interconnected nodes. Each node is linked to a fixed number of immediate neighbors and a random number of geographically remote nodes. Transmission can only propagate between linked nodes. This model exhibits two features typical of SARS transmission: geographically localized outbreaks and "super-spreaders". Neither of these features are evident in standard susceptible-infected-removed models of disease transmission. Our analysis indicates that "super-spreaders" may occur even if the infectiousness of all infected individuals is constant. Moreover, we find that nosocomial transmission in Hong Kong directly contributed to the severity of the outbreak and that by limiting individual exposure time to 3-5 days the extent of the SARS epidemic would have been minimal.

Minimum description length neural networks for time series prediction

Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (neurons) each with several linear and nonlinear parameters that are fitted to data through a computationally intensive training process. Longer training results in a closer fit to the data, but excessive training will lead to overfitting. We propose an alternative scheme that has previously been described for radial basis functions (RBF). We show that fundamental differences between ANN and RBF make application of this scheme to ANN nontrivial. Under this scheme, the training process is replaced by an optimal fitting routine, and overfitting is avoided by controlling the number of neurons in the network. We show that for time series modeling and prediction, this procedure leads to small models (few neurons) that mimic the underlying dynamics of the system well and do not overfit the data. We apply this algorithm to several computational and real systems including chaotic differential equations, the annual sunspot count, and experimental data obtained from a chaotic laser. Our experiments indicate that the structural differences between ANN and RBF make ANN particularly well suited to modeling chaotic time series data.

Polymorphisms in the DNA repair gene XRCC1 and breast cancer

X-ray repair cross complementing group 1 (XRCC1) encodes a protein involved in base excision repair. We examined the association of polymorphisms in XRCC1 (codon 194 Arg-->Trp and codon 399 Arg-->Gln) and breast cancer in the Carolina Breast Cancer Study, a population-based case-control study in North Carolina. No association was observed between XRCC1 codon 194 genotype and breast cancer, and odds ratios (ORs) were not modified by smoking or radiation exposure. A positive association for XRCC1 codon 399 Arg/Gln or Gln/Gln genotypes compared with Arg/Arg was found among African Americans (253 cases, 266 controls; OR = 1.7, 95% confidence interval, 1.1-2.4) but not whites (386 cases, 381 controls; OR =1.0, 95% confidence interval, 0.8-1.4). Among African-American women, ORs for the duration of smoking were elevated among women with XRCC1 codon 399 Arg/Arg genotype (trend test; P < 0.001) but not Arg/Gln or Gln/Gln (P = 0.23). There was no difference in OR for smoking according to XRCC1 codon 399 genotype in white women. ORs for occupational exposure to ionizing radiation were stronger for African-American and white women with codon 399 Arg/Arg genotype. High-dose radiation to the chest was more strongly associated with breast cancer among white women with XRCC1 codon 399 Arg/Arg genotype. Our results suggest that XRRC1 codon 399 genotype may influence breast cancer risk, perhaps by modifying the effects of environmental exposures. However, interpretation of our results is limited by incomplete knowledge regarding the biological function of XRCC1 alleles.

On-line adaptive chaotic demodulator based on radial-basis-function neural networks

Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel.

Plasma 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) levels and immune response

For determination of whether plasma 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) pesticide levels (< or =1-32 ppb) are associated with immune suppression or DNA damage in lymphocytes, 302 individuals residing in Moore County, North Carolina, in 1994-1996 provided a blood specimen, underwent a skin test, and answered a questionnaire concerning factors affecting plasma organochlorine pesticide levels and the immune system. The blood specimens were analyzed for levels of plasma DDE (a metabolite of 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane), numbers and types of blood cells, immunoglobulin levels, mitogen-induced lymphoproliferative activity, and lymphocyte micronuclei. When DDE levels were categorized as 1 or less, more than 1 to 2, more than 2 to 4.3, more than 4.3 to 7.6, and more than 7.6 ppb, individuals with higher plasma DDE levels had lowered mitogen-induced lymphoproliferative activity (concanavalin A, range: 74,218 dropping to 55,880 counts per minute, p = 0.03) and modestly increased total lymphocytes (range: 2.0-2.3 x 10(3)/microl, p = 0.05) and immunoglobulin A levels (range: 210-252 mg/dl, p = 0.04). There were no consistent differences in response to the skin tests by plasma DDE levels. Plasma DDE levels were not associated with a higher frequency of micronuclei. The authors conclude that relatively low levels of plasma DDE are associated with statistically significant changes in immune markers, although the magnitude of the effects are of uncertain clinical importance.

Effects on the immune system associated with living near a pesticide dump site

In this paper, we report results of the second phase of a larger study designed to evaluate the effects on the immune system of living near a Superfund site containing organochlorine pesticides, volatile organic compounds, and metals. Phase II was conducted to determine whether living near the site, consisting of six locations in Aberdeen, North Carolina, is associated with higher plasma organochlorine levels, immune suppression, or DNA damage. Each of 302 residents of Aberdeen and neighboring communities provided a blood specimen, underwent a skin test, and answered a questionnaire. Blood specimens were analyzed for organochlorine pesticides, immune markers, and micronuclei. Of 20 organochlorines tested, only DDE was detected in the blood of participants (except for one individual). Age-adjusted mean plasma DDE levels were 4.05 ppb for Aberdeen residents and 2.95 ppb (p = 0.01) for residents of neighboring communities. Residents of 40-59 years of age who lived within a mile of any site, but particularly the Farm Chemicals site, had higher plasma DDE levels than residents who lived farther away. Residents who lived near the Farm Chemicals site before versus after 1985 also had higher plasma DDE levels. Overall, there were few differences in immune markers between residents of Aberdeen and the neighboring communities. However, residents who lived closer to the dump sites had statistically significantly lower mitogen-induced lymphoproliferative activity than residents who lived farther away (p < 0.05). Residential location was not consistently associated with frequency of micronuclei or skin test responses. Although some statistically significant differences in immune markers were noted in association with residential location, the magnitude of effects are of uncertain clinical importance.

Dichlorodiphenyldichloroethene, polychlorinated biphenyls, and breast cancer among African-American and white women in North Carolina

We examined plasma dichlorodiphenyldichloroethene (DDE) and total polychlorinated biphenyl (PCB) levels in relation to breast cancer in a population-based, case-control study of African-American women (292 cases and 270 controls) and white women (456 cases and 389 controls) in North Carolina. Adjusted odds ratios (ORs) for breast cancer comparing the highest to lowest third of DDE were 1.41 [95% confidence interval (CI), 0.87-2.29] in African-American women and 0.98 (95% CI, 0.67-1.43) in white women. ORs comparing the highest to lowest third of total PCBs were 1.74 (95% CI, 1.00-3.01) in African-American women and 1.03 (95% CI, 0.68-1.56) in white women. Among African-Americans, the OR for total PCBs was highest for obese women (body mass index 234.2; OR, 4.92; 95% CI, 1.63-14.83). In contrast, the OR for DDE was highest for the leanest African-American women (body mass index, <25; OR, 3.84; 95% CI, 0.98-15.08). ORs for DDE were not elevated among women who lived or worked on farms or elevated among farming women who reported exposure to pesticides. Our results suggest absence of a strong effect for DDE or total PCBs in breast cancer but lend support for associations among subgroups of women. In our study, factors such as income, parity, breastfeeding, race/ethnicity, and body mass index influenced the relationship of organochlorines and breast cancer. Differing distributions of such factors may explain some of the inconsistencies across previous studies.

Glutathione S-transferases M1, T1, and P1 and breast cancer

We examined associations for glutathione S-transferases M1 (GSTM1), T1 (GSTT1), and P1 (GSTP1) genotypes and breast cancer in the Carolina Breast Cancer Study, a population-based, case-control study in North Carolina. Odds ratios were close to the null value for each GST locus among African-American women (278 cases and 271 controls) and white women (410 cases and 392 controls), as well as pre- and postmenopausal women. For women with a history of breast cancer in one or more first-degree relatives, odds ratios were 2.1 (95% confidence interval, 1.0-4.2) for GSTM1 null and 1.9 (0.8-4.6) for GSTT1 null genotypes. Among women with a family history, age at diagnosis was significantly earlier for those with the GSTM1 null genotype. We did not observe strong evidence for modification of odds ratios for smoking according to GST genotypes. There was no evidence for combined effects of GSTM1, GSTT1, and GSTP1 genotypes, and there were no combined effects for GST genotypes and the catechol O-methyltransferase genotype. We conclude that GSTM1, GSTT1, and GSTP1 genotypes do not play a strong role in susceptibility to breast cancer. However, the role of GST genotypes in age at onset and risk of breast cancer among women with a family history merits further investigation.

Participation rates in a case-control study: the impact of age, race, and race of interviewer

PURPOSE

Despite concerns about declining participation rates in epidemiologic studies in recent years, relatively few papers have discussed obstacles to recruiting study participants or strategies for optimizing response rates. This report describes factors associated with nonparticipation in a population-based, case-control study of breast cancer and discusses ways to overcome barriers to participation.

METHODS

Contact and cooperation rates were calculated for participants in the Carolina Breast Cancer Study (CBCS), stratified by case status, age, race, and race of interviewer. Demographic and breast cancer risk factor characteristics of partial and full responders also were compared.

RESULTS

Contact rates and cooperation rates varied by case/control status and demographic characteristics. Contact rates were lower among controls, younger women, and black women. Cooperation rates were lower among controls, older women, and black cases. Cooperation rates were higher among both black and nonblack women when participants and interviewers were concordant on race.

CONCLUSIONS

Obstacles to recruitment seem to differ among race and age subgroups, suggesting that recruitment strategies may need to be tailored to potential participants based upon demographic characteristics. Strategies have been implemented to improve response rates in this and other epidemiologic studies; however, additional research and innovation in this area are needed.

P57 (KIP2) polymorphisms and breast cancer risk

A previous report in this journal has suggested that germline deletions in the proline-alanine-rich (PAPA-repeat) region of P57 (KIP2) are associated with increased risk of a variety of cancers, including breast cancer. We have analyzed the association of P57 PAPA-repeat deletion polymorphisms and breast cancer risk as part of a population-based case-control study of breast cancer. We have not observed an association between the presence of one or two copies of deletion polymorphisms in P57 and breast cancer risk (adjusted odds ratio: 1.1, 95% confidence interval: 0.6-2.0). Further investigation is necessary to determine the functional significance of P57 deletion polymorphisms and their potential relationship with disease.

Catechol-O-methyltransferase and breast cancer risk

Recent studies suggest that a polymorphism in catechol-O-methyltransferase (COMT) is associated with increased risk of breast cancer. Methylation by COMT is the principal pathway for inactivation of catechol estrogens, which are hypothesized to participate in estrogen-induced carcinogenesis. We examined the association of COMT genotype and breast cancer risk in a population-based, case-control study of invasive breast cancer in North Carolina. The study population consisted of 654 cases and 642 controls, with approximately equal numbers of African-American and white women and women under the age of 50 and aged 50 or over. Contrary to previous reports, we did not observe an association between one or more copies of the low activity COMT allele (COMT-L) and breast cancer risk. Multivariate relative risks (RRs) were 0.8 (95% confidence interval: 0.6-1.1) for COMT-HL and 0.8 (0.6-1.1) for COMT-LL, compared with the COMT-HH genotype. RRs for COMT did not differ among African-American and white women and we did not observe strong modification of RR estimates by menopausal status, body mass index, physical activity or other covariates. Our results suggest that COMT genotype is not related to breast cancer risk.

Cigarette smoking, N-acetyltransferases 1 and 2, and breast cancer risk

To examine the effects of smoking and N-acetylation genetics on breast cancer risk, we analyzed data from an ongoing, population-based, case-control study of invasive breast cancer in North Carolina. The study population consisted of 498 cases and 473 controls, with approximately equal numbers of African-American and white women, and women under the age of 50 and age 50 years or older. Among premenopausal women, there was no association between current smoking [odds ratio (OR), 0.9; 95% confidence interval (CI), 0.5-1.5] or past smoking (OR, 1.0; 95% CI, 0.6-1.6) and breast cancer risk. Among postmenopausal women, there was also no association with current smoking (OR, 1.2; 95% CI, 0.7-2.0); however, a small increase in risk was observed for past smoking (OR, 1.5; 95% CI, 1.0-2.4). For postmenopausal women who smoked in the past, ORs and 95% CIs were 3.4 (1.4-8.1) for smoking within the past 3 years, 3.0 (1.3-6.7) for smoking 4-9 years ago, and 0.6 (0.3-1.4) for smoking 10-19 years ago. Neither N-acetyltransferase 1 (NAT1) nor N-acetyltransferase 2 (NAT2) genotype alone was associated with increased breast cancer risk. There was little evidence for modification of smoking effects according to genotype, except among postmenopausal women. Among postmenopausal women, ORs for smoking within the past 3 years were greater for women with the NAT1*10 genotype (OR, 9.0; 95% CI, 1.9-41.8) than NAT1-non*10 (OR, 2.5; 95% CI, 0.9-7.2) and greater for NAT2-rapid genotype (OR, 7.4; 95% CI, 1.6-32.6) than NAT2-slow (OR, 2.8; 95% CI, 0.4-8.0). Future studies of NAT genotypes and breast cancer should investigate the effects of environmental tobacco smoke, diet, and other exposures.

Electrical occupations and neurodegenerative disease: analysis of U.S. mortality data

Investigators have hypothesized that occupations involving electric and magnetic field exposure are associated with a variety of health problems, including neurological disease. The authors conducted a case-control study, and they used U.S. death certificates with occupational coding to compare male cases of Alzheimer's disease (n = 256), Parkinson's disease (n = 168), and amyotrophic lateral sclerosis (n = 114) with controls matched for age and calendar time. The authors selected controls in a 3:1 ratio to cases from persons who died of causes other than leukemia, brain cancer, and breast cancer. Overall associations with electrical occupations were modest (i.e., adjusted odds ratios of 1.2, 1.1, and 1.3 for Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis, respectively). Individual electrical occupations were associated more strongly with disease than overall electrical occupations, particularly amyotrophic lateral sclerosis, for which relative risks ranged from 2 to 5 across several job categories. The largest associations with all three diseases occurred for power plant operators.

Male and female occupation in relation to miscarriage and preterm delivery in central North Carolina

PURPOSE

This study was undertaken to evaluate the role of parental occupation in miscarriage and preterm delivery. Previous studies raise the possibility that both male and female exposures could affect pregnancy.

METHODS

Data from a population-based study of miscarriage and preterm delivery in central North Carolina were used to examine potential associations with male and female occupation. Medically treated miscarriage cases (n = 418), preterm delivery cases identified through hospital record review (n = 582), and term, normal birth weight controls (n = 787) were sought for telephone interview. The interview included information on jobs the woman held before and during the pregnancy, reports of her partner's job around the time of pregnancy, and information on potential confounding factors.

RESULTS

Female employment overall, or in specific jobs, around the time of conception or early pregnancy was not associated with the risk of miscarriage, whereas working during pregnancy, especially in the seventh month, was inversely associated with risk of preterm delivery. Male employment in several industrial occupations was weakly associated with miscarriage (adjusted odds ratios (OR) of 1.6 to 1.8), and somewhat more strongly associated with preterm delivery, particularly for chemists and sheet metal workers (adjusted OR over 3). Restriction to married men strengthened the associations.

CONCLUSIONS

Our results are limited by nonresponse, imprecision, incomplete identification of miscarriages, and lack of detailed occupational exposure information. Nonetheless, we found greater support for further examination of male compared to female jobs in relation to pregnancy outcome.

Male and female employment in the textile industry in relation to miscarriage and preterm delivery

To address potential reproductive hazards in textile manufacturing, we conducted a community-based case-control study in central North Carolina. Miscarriage cases were identified from medical records (280 interviewed cases): preterm delivery cases and term, normal birth weight controls (454 and 605, respectively) were identified from area hospitals. Exposures were based on job title, an interview concerning textile-related exposures, expert imputation of exposure based on job titles and interviews, and self-reported exposures by women. Relative to women and men working in nonhazardous occupations, workers in the textile industry were not at increased risk of miscarriage or preterm delivery, with the possible exception of preterm delivery among women and men employed in sectors other than knitting and yarn mills and men employed in yarn mills. Inferred exposures to specific agents were also not associated with adverse pregnancy outcome. Subject to uncertainty in exposure assessment and nonresponse, these data indicate an absence of adverse effects of the textile workplace environment on these pregnancy outcomes.

Cigarette smoking and semen quality

OBJECTIVE

To determine whether cotinine levels provide stronger evidence for an association between smoking and semen quality than the number of cigarettes smoked per day or years smoked controlling for potential confounders and effect modifiers.

DESIGN

Cross-sectional study.

SETTING

Male volunteers at the Reproductive Endocrinology-Fertility Laboratory.

PARTICIPANTS

Eighty-eight men (ages 18 to 35 years) provided a semen, urine, and blood specimen and completed a self-administered questionnaire concerning smoking and demographic information as well as caffeine and alcohol consumption. Urine, blood, and semen cotinine levels were analyzed via RIA.

MAIN OUTCOME MEASURE

Standard clinical semen analysis.

RESULTS

Number of cigarettes smoked per day, years smoked, and log-transformed cotinine levels were associated negatively with semen quality (density, total count, and motility). The association was evident among men age > or = 22 years. For example, the correlation coefficient for the overall association between logged urine cotinine and logged sperm density was -0.23; those stratified by age were 0. 13 (age < 22 years) and - 0. 39 (age > or = 22 years). Potential confounders included in regression models did not diminish the associations.

CONCLUSIONS

Smoking is associated with lowered semen quality.

Anxiety and depressive symptom identification using the Duke Health Profile

Duke Health Profile (DUKE) subscales were compared for their ability to identify anxiety and depressive symptoms as measured by the State Anxiety Inventory (SAI) and the Center for Epidemiologic Studies Depression Scale (CES-D) in 413 primary care patients. The seven-item Duke Anxiety-Depression Scale (DUKE-AD) was the best symptom identifier, with sensitivities and specificities greater than 70% for high scores on both the SAI and CES-D. Also, baseline DUKE-AD scores predicted five clinical outcomes during an 18-month follow-up period, with receiver operating characteristic (ROC) curve areas ranging from 57.1 to 58.7%. Patients shown by DUKE-AD scores to be at high risk (>30, scale 0-100) for symptoms of anxiety and/or depression were more often women, less well-educated, not working, and with lower socioeconomic status. The severity of illness was higher than that of low-risk patients. Although the providers did not know which patients were at high risk, they made a clinical diagnosis of anxiety or depression more often in high-risk patients.

Perceived family stress as a predictor of health-related outcomes

OBJECTIVE

To measure the predictive effect of patient-perceived family stress for health-related outcomes.

DESIGN

Prospective study of patients whose social stress and support were measured by self-report at baseline with the Duke Social Support and Stress Scale and used as predictors of outcomes during an 18-month follow-up period.

SETTING

Rural primary care community health clinic.

PATIENTS

Convenience sample of ambulatory adults.

INTERVENTION

None.

MAIN OUTCOME MEASURES

Follow-up (one or more follow-up visits), frequent follow-up (more than six visits), referral and/or hospitalization (one or more), high follow-up severity of illness (upper-tertile mean Duke Severity of Illness Checklist scores), and high follow-up total charges (> or = $268).

RESULTS

There were 413 patients with a mean age of 40.4 years. Of these, 58.6% were women; 47.2%, African American; 52.8%, white; 56.7%, married; 77.2%, wage earners or housekeepers; and 52.3% had more than one health problem. At baseline, patients with high self-reported family stress (upper-tertile Duke Social Support and Stress Scale scores) had lower quality of life, functional health, and social support scores and higher dysfunctional health and social stress scores than other patients. High baseline family stress scores (scale of 0 to 100) predicted follow-up (odds ratio [OR] = 1.014), frequent follow-up (OR = 1.021), referral and/or hospitalization (OR = 1.018), high severity of illness at follow-up (OR = 1.016), and high follow-up charges (OR = 1.018) after controlling for the effects of social support, age, gender, and race. Family stress scores were stronger predictors of these outcomes than the other social stress and support variables.

CONCLUSION

The finding of patient-perceived family stress as a risk factor for unfavorable health-related outcomes suggests the need for early detection and treatment of family stress by family physicians.

Computer-prompted diagnostic codes

BACKGROUND

The purpose of this study was to develop and evaluate a computer system that would translate patient diagnoses noted by a physician into appropriate International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes and maintain a patient-specific up-to-date problem list.

METHODS

The intervention consisted of a computerized list (dictionary) of diagnoses, including practice-specific synonyms and abbreviations, linked to their corresponding ICD-9-CM codes. To record the diagnoses for the office visit before the intervention, physicians used International Classification of Health Problems in Primary Care (ICHPPC-2) codes. After the intervention, physicians used their own words or checked previously identified diagnoses on the computer-generated problem list. The computer then identified the correct ICD-9-CM code. Accuracy of coding was compared before and after the new computerized system was implemented.

RESULTS

Visits in which all diagnoses matched increased from 58% to 76% (P < .001) with use of the computer system. Visits in which no computer diagnoses matched the chart decreased from 22% to 8% (P < .001). Errors of omission declined from 38% to 18% (P < .001). Errors of commission decreased from 19% to 11% (P = .006). Overall accuracy increased from 62% to 82% (P < .001).

CONCLUSIONS

Outpatient medical diagnosis coding can be simplified and accuracy improved by using a computerized dictionary of practice-specific diagnoses and synonyms linked to appropriate ICD-9-CM codes. Such a system provides a computer-generated problem list that accurately reflects the chart and assists with prompted coding on subsequent visits.

Assessing Impact of Weight on Quality of Life

This paper is a preliminary report on the development of a new instrument, the Impact of Weight on Quality of Life (IWQOL) questionnaire, that assesses the effects of weight on various areas of life. We conducted two studies utilizing subjects in treatment for obesity at Duke University Diet and Fitness Center. The first study describes item development, assesses reliability, and compares pre- and post-treatment scores on the IWQOL. In the second study we examined the effects of body mass index (BMI), gender, and age on subjects' perceptions of impact of weight on quality of life. Results indicate adequate psychometric properties with test-retest reliabilities averaging .75 for single items, and .89 for scales. Scale internal consistency averaged .87. Post-treatment scores differed significantly from pre-treatment scores on all scales, indicating that treatment produced positive changes in impact of weight on quality of life. The results of the second study indicate that the impact of weight generally worsened as the patients' size increased. However for women there was no association between BMI and impact of weight on Self-Esteem and Sexual Life. Even at the lowest BMI tertile studied, women reported that weight had a substantial impact in these areas. There were also significant gender differences, with women showing greater impact of weight on Self-Esteem and Sexual Life compared with men. The impact of age was a bit surprising, with some areas showing positive changes and others showing no change.

Health status and severity of illness as predictors of outcomes in primary care

Two measures of health status and severity of illness were tested as indicators of patient case-mix to predict health-related outcomes in a rural primary care community health clinic, using a convenience sample of 413 ambulatory adults (mean age = 40.4 years: 58.6% women, and 47.2% black). At baseline; patients completed the Duke Health Profile, and providers completed the Duke Severity of Illness Checklist. During the 18-month follow-up study, patients experienced the following outcomes: at least one follow-up visit (74.3%), more than six visits (20.6%), at least one referral or hospital admission (17.3%), upper tertile severity scores (24.9%), and upper tertile office charges (24.9%). Baseline physical health, perceived health, and severity scores were statistically significantly predictive of all five outcomes. Predictive accuracy (i.e., area under the receiver operating characteristic curves) for outcome probabilities estimated from a case-mix model of physical health, severity, age, gender, and race was 72.3% for follow-up, 69.7% for frequent follow-up, 70.5% for referral and/or hospital stay, 65.7% for high follow-up severity of illness, and 67.6% for high follow-up charges. These data support health status and severity of illness as case-mix indicators and outcome predictors of follow-up utilization, severity of illness, and cost in the primary care setting.

Disease-specific versus generic measurement of health-related quality of life in insulin-dependent diabetic patients

The health-related quality of life of 170 adult insulin-dependent diabetic patients was measured cross-sectionally to compare a disease-specific instrument, Diabetes Quality of Life (DQOL) questionnaire, and two generic instruments, the Duke Health Profile (DUKE) and the General Health Perceptions Questionnaire (GHP). The generic measures provided as much or more information about health-related quality of life as the disease-specific instrument. This was demonstrated both by comparison of the DQOL with the DUKE and GHP and by comparison of the disease-specific with the generic components of a modified version of the DQOL. Patients with the diabetic complication of nephropathy had increased worry over their health and lower general health perceptions. Neither the duration of diabetes nor the intensity of insulin therapy, however, was found to have a statistically significant effect on any of the health-related quality of life scores. Nondiabetic factors, such as the comorbidity, nondiabetic medications, marital status, social relationships, and family arguments were found to be predictors of health-related quality of life more often than the diabetic factors duration of diabetes, complications, and intensity of insulin therapy. These analyses suggest the clinical value of using generic questionnaires to measure health-related quality of life and psychosocial factors to identify nondiabetic problems that might respond to intervention, thereby potentially enhancing the effect of diabetes-specific therapy.

The Duke Severity of Illness Checklist (DUSOI) for measurement of severity and comorbidity

The Duke Severity of Illness Checklist (DUSOI) was evaluated on 414 primary care adult patients using data collected both by medical providers at the time of the patient visit and later by a chart auditor. Severity scores for individual diagnoses were determined by summing the ratings for four non-disease-specific parameters: symptom level, complications, prognosis without treatment, and expected response to treatment. Mean diagnosis severity scores (scale 0-100) among the 21 most prevalent diagnoses varied from a low of 13.9 for menopausal syndrome to a high of 43.0 for sprains and strains. An overall severity score was calculated by combining diagnosis severity scores and giving highest weights to the most severe diagnoses. Provider-generated overall severity scores (mean = 43.3) and auditor-generated overall severity scores (mean = 38.9) were significantly correlated (coefficient of agreement = 0.59, p < 0.0001). Diagnoses varied in their individual contribution to the overall severity score, from 8.9% for lipid disorder to 90.0% for sprains and strains. Separate comorbidity severity scores were calculated to measure the severity of all of each patient's health problems except the diagnosis under study. For example, patients with menopausal syndrome had co-existing health problems which generated a high mean comorbidity severity score of 43.2, while patients with sprains and strains had a low mean comorbidity score of 4.7. The DUSOI Checklist can be used in the clinical setting by both providers and auditors to produce quantitative severity scores (by diagnosis, overall, and for comorbidity) which are based entirely upon clinical judgment. This method should be useful in controlling for severity of illness in clinical studies and indicating the outcome of medical care in terms of reduction in severity of illness following medical interventions.

Increasing compliance with mammography recommendations: health assessment forms

BACKGROUND

Inexpensive reminder systems are needed to ensure that primary care physicians consistently provide health maintenance services to their patients. The purpose of this study was to determine the effectiveness of a simple, inexpensive health assessment form in place of the standard chart note to increase physician compliance with mammography recommendations.

METHODS

A health assessment form with a reminder for screening mammography was implemented in a family practice in 1987 and was to be used as the official chart record for health maintenance visits. The charts of all women 50 years of age and older with two or more office visits during the years 1985 through 1988 were audited to determine how many mammograms were completed. Results were compared with mammography completion rates at a similar practice that did not use a health assessment form.

RESULTS

The study group showed a significant increase in mammography completion after implementation of the form, with compliance increasing from 7.3% to 32.0% (P < .001). The comparison group had an increase in mammogram completion from 12.0% to 17.8% (P < .001). The difference between the changes in rates of mammography in the two practices was statistically significant (P < .001). Among women in the study group who had a scheduled health maintenance visit during the study period the average rate of mammography completion increased from 21.2% to 65.2% (P < .001).

CONCLUSIONS

The addition of a health assessment form with a mammography reminder at the health maintenance visit is an effective and inexpensive method to increase compliance with mammography.

Quality of life and functional health of primary care patients

Quality of life and functional health were measured cross-sectionally for 314 adult ambulatory primary care patients in a rural clinic and found to be much better for patients with low severity of illness who required no confinement to home because of health problems, than for patients with high severity of illness who required confinement. Severity of illness was the strongest predictor for patient-reported physical health function and for patient quality of life when assessed by the health provider. Confinement was the strongest predictor for patient quality of life when assessed by the patient. There was very little agreement between patient-assessed and provider-assessed quality of life. Family stress was the strongest predictor of function in terms of mental health, social health, general health, self-esteem, anxiety, and depression. These data suggest that clinicians should direct increased attention to patient-assessed quality of life, patient-reported functional health status, and psychosocial factors such as family stress in an effort to improve medical outcomes.

Development of the 17-item Duke Health Profile

The 17-item Duke Health Profile (DUKE) was developed as a refined version of the 63-item Duke-UNC Health Profile (DUHP) using a methodology based upon a balanced clinical and statistical rationale. The result is a brief, valid functional health measure with 10 scales that compares well with the MOS Short-form and the COOP Charts. In addition to the five constructs (ambulation, emotional symptoms, activities with friends or relatives, health perception, and pain) which are measured by all three of the instruments, the DUKE quantitates cognition, social self-esteem, confinement, and somatic symptoms other than pain.

Validation of the Duke Social Support and Stress Scale

The Duke Social Support and Stress Scale (DUSOCS) was validated in 249 adult family practice patients using the Family Strengths, the Family Inventory of Life Events, and the Duke Health Profile (DUKE) as comparison instruments. Validity was supported in that the DUSOCS family support measure had the clinically expected positive associations with DUKE health measures (regression coefficients of +7.4 to +18.7) and negative associations with DUKE anxiety and depression measures (-2.0 to -17.2). DUSOCS family stress had negative associations with the health measures (-11.6 to -34.5) and positive associations with anxiety and depression (+18.9 to +32.1). Family and non-family stress contributed more than severity of illness to elevated levels of anxiety and depression and lowered levels of mental health, social health, and self-esteem, while these same stresses contributed only half as much as severity of illness to lowered physical health.

The Duke Health Profile. A 17-item measure of health and dysfunction

The Duke Health Profile (DUKE) is a 17-item generic self-report instrument containing six health measures (physical, mental, social, general, perceived health, and self-esteem), and four dysfunction measures (anxiety, depression, pain, and disability). Items were derived from the 63-item Duke-UNC Health Profile, based upon face validity and item-remainder correlations. The study population included 683 primary care adult patients. Reliability was supported by Cronbach's alphas (0.55 to 0.78) and test-retest correlations (0.30 to 0.78). Convergent and discriminant validity were demonstrated by score correlations between the DUKE and the Sickness Impact Profile, the Tennessee Self-Concept Scale, and the Zung Self-Rating Depression Scale. Clinical validity was supported by differences between the health scores of patients with clinically different health problems. Patients with painful physical problems had a DUKE physical health mean score of 58.1, while patients with only health maintenance problems had a mean score of 83.9 (scale: 0.0 = poorest health and 100.0 = best health). Patients with mental health problems had a DUKE mental health mean score of 49.2, in contrast to 75.7 for patients with painful physical problems and 79.2 for those with health maintenance. The DUKE is presented as a brief technique for measuring health as an outcome of medical intervention and health promotion.

The health status and life satisfaction of first-year medical students

The self-reported health status and life satisfaction of 286 first-year Duke University medical students in four consecutive classes were measured at the beginning and end of the school year and compared statistically with relevant sociodemographic and behavioral factors. Health status, quantitated in terms of Duke Health Profile scores, was generally lower for women than for men. Although there was a definite trend of worsening along all parameters of health and satisfaction during the year for both women and men, the most marked change was the increase in depressive symptoms. The students who were very satisfied with life had fewer symptoms of depression and anxiety; higher self-esteem, better physical, mental, and social health; stronger social ties; more physical activity; more sleep; and fewer stressful life events. Strong social ties was the factor most positively related to better health and life satisfaction.

Acetylcholinesterase antibodies and thyroid autoimmunity

It has been suggested that anti-thyroglobulin antibodies cross-react with acetylcholinesterase (AChE) and that this may explain the pathogenesis of Graves' ophthalmopathy. We have tested this hypothesis using an ELISA. Antibodies to human red blood cell AChE were found in 21% of 47 patients with thyroid autoimmunity. However antibodies to AChE were also detected in one of 25 normal subjects and two of 16 patients with non-organ specific autoimmunity. The anti-AChE antibodies showed no correlation with anti-thyroglobulin antibody levels and they were not associated with the presence of severe ophthalmopathy. Inhibition studies suggested only limited cross reactivity, if any, between anti-Tg and anti-AChE antibodies. Immunoblotting demonstrated antibody binding to at least four human AChE determinants at 130, 55, 32 and 22 kD. Our results demonstrate quite frequent anti-AChE reactivity in sera but no relationship with the development of orbital pathology.

Actions of beta-bungarotoxin on spontaneous release of transmitter at muscle end-plates treated with botulinum toxin

Rat leg muscles were injected subcutaneously with sublethal doses of type A botulinum neurotoxin, and the extensor digitorum longus muscle removed three days later. Intracellular microelectrode recordings were then made of miniature end-plate potentials (mepps). The mepp frequency was reduced by botulinum toxin, while mepp rise times were slowed. Mepp amplitude distributions became characteristically skew. beta-Bungarotoxin (140 nM) was applied to normal muscles in vitro and recordings were made 10-30 min later. The main effect was an increase in mepp frequency during this period. Mepp rise times were unaffected. When beta-bungarotoxin was applied in vitro to muscles treated with botulinum toxin there was also an increase in mepp frequency, although to a value less than in normal muscles. The mepp rise times were speeded up to normal values. The mepp amplitude and rise time distributions showed no obvious evidence for the addition of a second component to the distribution. The data appear to support the hypothesis that the sites for spontaneous release in botulinised muscle may be located at or near the usual release sites at the active zones.