What are some of the challenges that you might face when implementing a wireless network

This is the what I have to write about for my post

For this Discussion, respond to the following:

· In this unit, you have reviewed the components that form the infrastructure of your network. Discuss at least three devices and the network media that is used within your home network or you organization’s network. Explain in detail!

· What are some of the challenges that you might face when implementing a wireless network and how do you plan to overcome these challenges? Explain your answer

These are the post I have to response to there’s no amount of words the response must be.

Post #1

In my home, the network is both wired and wireless. My internet service provider is Verizon and I have yet to be disappointed by their service. Now, I say my network is both wired and wireless because for some devices, I prefer them to be connected to the router with an Ethernet cable and others to simply be wireless devices. Devices such as my main desktop and video game console are always connected with an Ethernet cable so that I don’t have to worry about disconnection. As long as the router has a strong connection to the modem, there shouldn’t be any problems with wired connections. Wired connections, when configured correctly, are more stable, reliable, and secure. Any other devices such as smartphones, laptops, and printers are always going to be connected wirelessly for convenience and mobility.

· Desktop PC: My desktop PC is the main machine that I do my everyday general computer activities that also happens to hold all of my important personal data that I would like to keep secure.

· Firewall: Thank to the firewall I have implemented into my home network, I never have to worry about my personal data being exposed. The firewall I have is an Anonabox Pro, found at: https://www.anonabox.com/buy-anonabox-pro.html (Links to an external site.)Links to an external site.. This firewall is a small device with a whole lot of might. I have the firewall sitting between the modem and the router to filter and secure all incoming and outgoing data. One thing I love about the Anonabox is that it comes with a VPN (Virtual Private Network) which makes me feel as if I am completely safe from the evils of the internet.

· Router: A router is the most common piece of hardware in a network that allows for the communication between a home network and the internet. A router is usually the first line of defense when it comes to security, but in my case, data hits my firewall before my router.

Some challenges faced when implementing a new wireless network can be coverage, security, or performance. Sometimes router’s wireless signals cannot reach every room in a house or it is at least a weak signal. In most cases, consumers add an extra wireless access point to carry the original wireless signal further though the house. Another challenge can be security. A huge mistake that consumers make is buying a basic router that has little to no security which puts important data at risk. A simple fix to this problem would be to do some research on better routers or contact the internet service provider and see if there are any recommendations of good router that are easily compatible. Lastly, performance can be a struggle. Not getting the most out of a wireless network can be aggravating when you need to get stuff done. This challenge kind of goes along with upgrading the router. Sometimes a cheap router cannot handle high speed internet and will not output the correct amount of megabytes leaving you hanging wondering why you pay so much for slow internet.

Post #2

End devices- which is a source of destination device in a networked system. For example, an user’s PC is an end device, and so is a server Network switches, routers, and other equipment work in between to enable messages to travel from one end device to the other.

The second device I choose is Intermediate devices. These devices provide connectivity and work behind the scenes to ensure that data flows across the network. Lastly, a network media is the actual path over which an electrical signal travels as it moves from one component to another. It discusses completely to the communication channels used to interconnect device on a computer communications network.

What are some of the challenges that you might face when implementing a wireless network and how do you plan to overcome these challenges? Explain your answer.

They are many challenges that I might face when implementing a wireless network. I think the most common error is location placed of the wireless router. For example, if you decided to place the wireless router next to a speaker this will cause poor signal delivery to devices like PC, smart, phone, etc. The speakers make the signal of the wireless router bounce or deprive it form reach it out to devices. The best way to overcome these challenges is to place the wireless router in a wide-open area make sure to not have any obstruction of other devices that omit signal as well.

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Epidemiology And Statistics

Exercise 5.3: Journal Article Analysis – 65 Points

Getting Started

Directions:   Read the article. Then, respond to each question in one or two sentences (5   points each –  0 if skipped, 3 if   reasonable attempt but inaccurate or missed the point of the question; 4 if   basically correct but with something erroneous, 5 if fully correct)

Question &   Answer

Points

1. What public health problem is the study described in   this article attempting to address?  

/5

2. What is the research hypothesis that this study aims   to test?

/5

3. What type of epidemiologic study is this?

/5

4. What is the independent variable in this study?

/5

5. What is the (primary) dependent variable in this   study?

/5

6. How were cases of brain cancer ascertained in this   study?

/5

7. What broader population do you believe the sample of   cases   represents? (this will determine the   population to which the results of the study can ultimately be generalized)

/5

8. What exclusion criteria were used?

/5

9. What kind of comparison group was used in this   study? How were they selected? Of what   broader population is this group intended to be   representative?

/5

10. What measure of association was calculated in this   study? What statistical test was used   to test its significance?

/5

11. What did the study find?

/5

12. What practical application do the results of this   study have for clinical care of patients?

/5

13. What practical application do the results of this   study have for public health policy in Canadian provinces? 

/5

Total

/65

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International Journal of Epidemiology 2002;31:210–217

The aetiology of brain tumours is not well understood. Ionizing radiation and a genetic predisposition have been implicated as risk factors, however, they are thought to account for a small proportion of all such tumours.1 Positive associations between brain cancer and other occupational exposures such as vinyl chlorides,2–4 pesticides5 and electromagnetic fields6 have been observed in some studies, but, taken as a whole, the results are inconclusive. Efforts to clarify the role of these factors are needed, particularly in light of the extremely poor prognosis for patients diagnosed with these neoplasms.7

During the past decade, a number of studies have examined the relationship between occupational exposure to magnetic fields and the occurrence of brain tumours. Several of these studies were performed within electric utility industry workers and incorporated detailed exposure assessments obtained using either personal monitoring devices or other sampled measures

© International Epidemiological Association 2002 Printed in Great Britain

Brain cancer and occupational exposure to magnetic fields among men: results from a Canadian population-based case-control study Paul J Villeneuve,a,b David A Agnew,c Kenneth C Johnson,a Yang Maoa and the Canadian Cancer Registries Epidemiology Research Groupd

Background The relationship between occupational exposure to magnetic fields and brain cancer in men was investigated using population-based case-control data collected in eight Canadian provinces. Emphasis was placed on examining the variations in risk across different histological types.

Methods A list of occupations was compiled for 543 cases and 543 controls that were individually matched by age. Occupations were categorized according to their aver- age magnetic field exposure through blinded expert review (,0.3, 0.3–,0.6, and >0.6 µT). In total, 133 cases (14%) and 123 controls (12%) were estimated to have at least one occupation whereby magnetic field exposures exceeded 0.3 µT. Odds ratios (OR) were generated using conditional logistic regression, and were adjusted for suspected occupational risk factors for brain cancer.

Results A non-significantly increased risk of brain cancer was observed among men who had ever held a job with an average magnetic field exposure .0.6 µT relative to those with exposures ,0.3 µT (OR = 1.33, 95% CI : 0.75–2.36). A more pro- nounced risk was observed among men diagnosed with glioblastoma multiforme (OR = 5.36, 95% CI : 1.16–24.78). Moreover, a cumulative time weighted index score of magnetic field exposure was significantly related to glioblastoma multi- forme (P = 0.02). In contrast, magnetic field exposures were not associated with astrocytoma or other brain cancers.

Conclusions Our findings support the hypothesis that occupational magnetic field exposure increases the risk of glioblastoma multiforme.

Keywords Magnetic fields, brain cancer, occupation

Accepted 2 August 2001

a Environmental Risk Assessment and Case Surveillance Division, Labora- tory Centre for Disease Control, Health Canada, Ottawa, Ontario, Canada K1A 0L2.

b Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, Canada K1H 8M5.

c Department of Public Health Sciences, University of Toronto, 12 Queen’s Park Crescent, Toronto, Ontario, Canada M5S 1A8.

d The Canadian Cancer Registries Epidemiology Research Group comprises a Principal Investigator from each of the Provincial Cancer Registries involved in the National Enhanced Cancer Surveillance System: Bertha Paulse, Newfoundland Cancer Foundation; Ron Dewar, Nova Scotia Cancer Registry; Dagny Dryer, Prince Edward Island Cancer Registry; Nancy Kreiger, Cancer Care Ontario; Erich Kliewer, Cancer Care Manitoba; Diane Robson, Saskatchewan Cancer Foundation; Shirley Fincham, Division of Epidemiology, Prevention and Screening, Alberta Cancer Board; and Nhu Le, British Columbia Cancer Agency.

Correspondence: Dr Paul Villeneuve, Department of Epidemiology and Com- munity Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, Canada K1H 8M5. E-mail: pvillene@uottawa.ca

210

BRAIN CANCER AND OCCUPATIONAL EXPOSURE TO MAGNETIC FIELDS 211

taken from relevant work-sites.8–15 Despite elaborate efforts to characterize exposure to 50/60 Hz power frequency magnetic fields, the findings of these studies have been equivocal. This may partly be due to the size of the cohorts that have typically yielded a small number of cases, and consequently, limited the power of the study to detect effects. Inconsistent results have also been obtained from a series of population-based case- control studies that investigated the association between occupational magnetic field exposures and brain cancer.16–21

Many of these studies did present risk estimates across different histological types of brain cancer. However, several were limited by either small sample sizes,8,10,12,17 crude assessment of ex- posure,16,18,22,23 incomplete occupational history,19 lack of data on potential occupational confounders,11,17,23 and the use of decedent rather than incident cases.13,16,18,24,25 The identifica- tion of brain cancer cases using death certificates is particularly problematic as such tumours may represent a metastatic spread from a cancer that originated at another anatomical site.26

The results from both in vivo and in vitro studies suggest that if exposures to 60 Hz magnetic fields increase the risk of cancer, it is through the promotional stage of the carcinogenic process. In the traditional multistage model, tumour promotion is regarded as an extended process that requires prolonged or repeated exposure to the promoting agent.27 Continued exposure to promoting or co-promoting agents after tumour development may cause the tumour to evolve with increased metastatic properties.28 Tumour promoters are characterized by the existence of a threshold, prolonged exposure and reversibility of effects.29 The present study was undertaken to explore the relationship between occupational magnetic field exposures and different histological types of brain cancer. Elevated risks for more aggressive subtypes of brain cancer would support the hypothesis that magnetic fields act as tumour promoter.

Using data collected through the Canadian National Enhanced Cancer Surveillance System (NECSS), we examined the relation- ship between occupational exposure to magnetic fields and brain cancer using several different exposure indices. Occupational magnetic field exposure assessment was performed by an expert review that was blinded to the case-control status of the subjects. An important strength of this study is the ability to derive magnetic field exposures indices that take into account the complete occupational history of each subject. Perhaps more importantly, the size of the study is sufficiently large to perform risk assessment across different histological types of brain cancer.

Subjects and Methods The NECSS was designed to investigate environmental causes of cancer using population-based data. The case-control component of the NECSS collected data between January 1994 to August 1997 in eight Canadian provinces (Newfoundland, Prince Edward Island, Nova Scotia, Ontario, Manitoba, Alberta, Saskatchewan and British Columbia). The collection of data was conducted through the co-operation of Health Canada and the provincial cancer registries. All brain cancer cases included in the NECSS were confirmed histologically and cases were defined according to the International Classification of Diseases, Ninth Revision (ICD-9) rubric 191.30 Benign brain tumours were not included in these analyses. Analyses are based on a total of 543 brain cancer cases that were categorized by histological type using the

International Classification of Diseases for Oncology (ICD-O)31

using the codes shown in Table 1. The participating provinces attempted to identify eligible

brain cancer cases as early as possible in the registration process in order to minimize the loss of subjects due to severe illness or death. Of these eligible cases, data were not collected among those who had died (23%) or for whom physician consent was not granted (10.2%). Among those cases that were sent ques- tionnaires 63% were completed, while the corresponding response rate from the control population was approximately 65%.

Frequency matching was employed by the investigators of the NECSS to select population-based controls so as to achieve a similar age and sex distribution to all cancer cases. There were subtle differences in the methods that were used to select controls in each participating province. In Prince Edward Island, Nova Scotia, Manitoba, Saskatchewan and British Columbia, provincial health insurance plans were used to obtain a random age- and sex-stratified sample of the provincial population. In each of these provinces, more than 95% of residents are covered by these public plans; those excluded include current military personnel and their families and indigenous peoples who are covered by other plans. Newfoundland and Alberta used random digit-dialling to recruit controls while Ontario used Ministry of Finance data to create a stratified random sample.

Mailed questionnaires were used to obtain information on subjects’ residential and occupational histories and on other risk factors for cancer. When necessary, telephone follow-up was used to clarify responses. The NECSS questionnaire was designed to collect data on ethnicity, education, income, smoking, height, weight, exposure to specific occupational carcinogens, physical activity, diet two years before interview (60-item food- frequency questionnaire) and general changes in diet compared with 20 years ago. Subjects were asked whether they had ever been occupationally exposed to 17 different agents. Of these, the following exposures have been identified as possible risk factors for brain cancer: pesticides, herbicides, radiation sources, and vinyl chlorides.

Each subject was asked to report on all the jobs they had held for at least one year and all Canadian residences that they had lived in for at least one year. For each job, subjects were asked to describe their job-title, company name, work location, duties, the starting and ending calendar year of employment, and information on exposure to workplace odours and tobacco smoke. Residential data that was collected included address, the

Table 1 Brain cancers ascertained among men in the National Enhanced Cancer Surveillance System (NECSS) case-control study, by histological type, 1994–1997

Histological type ICD-O Codes 1991a No. of cases

Astrocytoma 9384, 9400–9421 214

Glioblastoma multiforme 9440–9442 198

Other 9380, 9382, 9391, 9392, 115 9424, 9430, 9450, 9451,

9460, 9470, 9473

Unknown 8000, 8010, 8900, 16 9150, 9505, 9990

Total 543

a ICD-O codes given by the World Health Organization.31

occupancy period and the source of water, the type of heating used and the number of smokers they lived with.

Although the NECSS also collected data among women, we decided to restrict magnetic field exposure assessment and analysis to men for several reasons. First, because most occupational studies of electromagnetic fields have been conducted using male workers, there was limited data to characterize occupational exposures to magnetic fields for women. Second, restricting analyses among men facilitated comparisons with previously published studies. Finally, as the median (or mean) age of the female brain cancer cases was 52 years, it was anticipated that as a whole, there would be little variation in occupational exposure to magnetic fields as few women in this population- based study would have been employed in occupations charac- terized by jobs with high magnetic field exposures in their distant past.

It was determined a priori that occupational magnetic field exposure would not be assigned by using occupational coding, but rather through a manual inspection for each subject of several key variables through expert review. Controls were individually matched to cases because it would have been quite onerous to code all occupations held by the entire control population, and the matching procedure ensured that the age distributions of the case and control populations were similar. Specifically, one control was randomly selected for each case and matched within a single year of age. In total, 543 controls were chosen in this manner from the pool of 4823 NECSS controls with completed questionnaire data.

A list of all the occupations held was compiled for the cases and matched controls. Each occupation was assigned an exposure value based on a time-weighted average magnetic flux density for full-time workers. This exposure assessment also incorporated questionnaire data that were collected on the job duties and the employment location. The categories of average exposure were: ,0.3, 0.3–,0.6, and >0.6 µT. The lower cut- point of 0.3 µT was chosen to provide reasonable assurance that occupational exposures in the upper two categories were greater than background exposure levels that workers receive at home. Information about the distribution of residential exposures was obtained from a Canadian study of residential magnetic field exposures and childhood leukaemia.32 It has been estimated that the cutpoint of 0.3 µT corresponded to the 82th percentile for adult exposures in the same homes.33 The occupational exposure categorizations were derived through expert review (D Agnew) of the employment variables described above and were performed blinded to case-control status. There were a total of 3808 unique character string job title descriptors. The assign- ment of exposure relied on results from published reports9,34–37

and consultations with occupational hygienists specializing in the area of electromagnetic fields. For some occupations that could not be readily classified, field measurements were performed using a Drexel Corporation Magnum 310 magnetic field monitor. The upper 0.6 µT limit was chosen as it was double the lower cutpoint, and split the job titles with .3 mG into two groups with number of job titles in the ratio of 2:1. Examples of highly exposed occupations (>0.6 µT) included: sheet metal work- ers, telephone cable splicer, projectionists (motion pictures), welders, electricians, electronic assemblers, and electric utility workers. Incomplete questionnaire data prevented us from classi- fying 42 (1.3%) of the occupations held by the study subjects.

Odds ratios (OR) were estimated using conditional logistic regression which took into account the matched design of the study. Five different magnetic field exposure indices were modelled. These included the highest average occupational exposure to magnetic fields (,0.3, >0.3, >0.6 µT) and the mag- netic field exposure received in the job held the longest (,0.3, 0.3–,0.6 and >0.6 µT). To evaluate the effect of magnetic field exposures received early or later on in life, we calculated the risk of brain cancer based on exposure categorizations for sub- jects’ first and last held jobs. The last index we examined was a cumulative time-weighted occupational magnetic field exposure score that was calculated by taking into account exposure at each job (E), the duration of employment (D) and whether the work was full-time (F). Mathematically, the cumulative index score was calculated as follows:

MFindex = i=1 ∑ i= j

Ei × Di × Fi

where E = 0 for jobs with average occupational exposures of ,0.3 µT

= 1 for jobs with average occupational exposure 0.3– ,0.6 µT

= 2 for jobs with average occupational exposure >0.6 µT j = the total number of jobs held

D = duration of employment (in years) and F = 1 for full-time employment

= 0.5 for part-time or seasonal employment.

Several variables were evaluated to determine whether they confounded the results. These included self-reported occu- pational exposures to vinyl chloride, herbicides, pesticides and radiation sources. Similar to the assignment of magnetic field exposures, an index of exposure to ionizing radiation was also constructed through a manual review of the occupational vari- ables for each job. Cases and controls were classified as having an annual exposure ,1 or >1 mSv (milliSievert).

Results A total of 543 brain cancer cases (ICD-9: 191) formed the basis of this analysis (Table 1). Of these cases, 214 were astrocytomas, 198 were glioblastoma multiforme, 115 were classified into the ‘other category’. Sixteen cases could not be categorized because they were lacking histological data.

The frequency distribution of several key variables is pre- sented for both cases and controls in Table 2. The matched design of the study ensured identical age distributions in the case and control series; 64% of the cases were >45 years of age. The average number of jobs held by each subject and the length of employment were similar between cases and controls. Likewise, the total number of subjects with reported workplace exposures to pesticides, herbicides, radiation sources and vinyl chloride did not differ appreciably by case-control status. Eighty-six per cent (86%) of jobs held by cases were determined to have an average magnetic field exposure of ,0.3 µT. Among controls, the corres- ponding percentage was 88%. Based on our three-level expos- ure categorization, 845 subjects (78%) did not experience a change in the average level of exposure to magnetic fields based on their lifetime occupational history. Of those that did

212 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

experience such a change, 12% of the subjects experienced one change while the remaining 10% experienced two or more changes during their occupational history. A greater number of occupations among cases (n = 32) relative to controls (n = 10) could not be categorized according to the average level of magnetic field exposure. We were unable to classify these

occupations because subjects did not provide data that described either their job-title or duties.

A statistically not significant increased risk of brain cancer was observed among those subjects who had ever held a job having average magnetic field exposures .0.6 µT relative to those whose highest level was ,0.3 µT (OR = 1.33, 95% CI : 0.75–2.36) (Table 3). When analyses were restricted to those cases diagnosed with a glioblastoma multiforme, the resulting risk estimate was considerably higher (OR = 5.36, 95% CI : 1.16– 24.78). No significant differences in risk were observed based on the highest level of occupational magnetic field exposure ever received were for those diagnosed with astrocytomas or other brain cancers. Similar results were obtained when risk assess- ment was performed using the average occupational magnetic field exposure received in the longest held job (results not shown). Specifically, among those subjects diagnosed with glioblastoma multiforme, the risk estimates were most pronounced among subjects whose longest held job had an average exposure that exceeded 0.6 µT when compared to those with exposures ,0.3 µT (OR = 3.70, 95% CI : 0.96–1.20); the corresponding OR for all brain cancers combined was 1.27 (95% CI : 0.64–2.53).

The results obtained from modelling the relationship between the incidence of brain cancer and the constructed index that represents a cumulative lifetime occupational magnetic field exposure score is presented in Table 4. Consistent with our previous findings, this continuous index of magnetic field ex- posure was not significantly related to the incidence of all brain cancers, astrocytomas nor other brain cancers. However, for those diagnosed with glioblastoma multiforme, this exposure index was significantly related to case-control status as indicated by the Wald χ2 statistic (P = 0.02), and upon categorization, those subjects that had an index score >8 had an OR of 2.58 (95% CI : 1.15–5.82) relative to those with a score of zero (results not shown).

BRAIN CANCER AND OCCUPATIONAL EXPOSURE TO MAGNETIC FIELDS 213

Table 2 Characteristics of study subjects, by case-control status

Variable Cases Controls

Age at interview (years)

,35 88 94

35–44 108 112

45–54 129 131

55–64 122 120

65–74 93 86

75 3 3

Average number of jobs held 3.6 (SD = 2.2) 3.5 (SD = 2.1)

Average length of time spent in each job (in years) 8.1 (SD = 9.3) 8.3 (SD = 9.5)

Subjects who worked with the following for more than one year

Pesticides 77 80

Herbicides 65 65

Radiation sources 32 36

Vinyl chloride 7 9

Total no. of jobs held according to average exposure to magnetic fields

,0.3 µT 1690 1654 0.3–,0.6 µT 164 162 >0.6 µT 71 53 Exposure could not be assigned 32 10

Total subjects 543 543

Table 3 The risk of brain cancer according to the highest average level of occupational magnetic field exposure ever received, by histological type, Canadian National Enhanced Cancer Surveillance System (NECSS), male participants, 1994–1997

Highest average occupational exposure magnetic fields ever received Cases Controls Odds ratioa 95% CI Odds ratiob 95% CI

All brain cancers

,0.3 µTc 410 420 1.0 1.0 >0.3 µT 133 123 1.11 0.84–1.48 1.12 0.83–1.51 >0.6 µT 42 29 1.38 0.79–2.42 1.33 0.75–2.36

Astrocytomas

,0.3 µT 163 160 1.0 1.0 >0.3 µT 51 54 0.93 0.60–1.44 0.93 0.59–1.47 >0.6 µT 12 16 0.61 0.26–1.49 0.59 0.24–1.45

Glioblastoma multiforme

,0.3 µT 143 156 1.0 1.0 >0.3 µT 55 42 1.50 0.91–2.46 1.48 0.89–2.47 >0.6 µT 18 6 5.50 1.22–24.8 5.36 1.16–24.78

Other

,0.3 µT 92 94 1.0 1.0 >0.3 µT 23 21 1.11 0.59–2.10 1.10 0.58–2.09 >0.6 µT 9 7 1.50 0.53–4.21 1.58 0.56–4.50

a Unadjusted odds ratio obtained from the conditional logistic model. b The odds ratio was adjusted for occupational exposure to ionizing radiation and vinyl chloride. c Referent group.

The risk of brain cancer based on the average field exposure of the subject’s first or last held job is presented in Table 5. Among subjects diagnosed with glioblastoma, the OR among those with averages exposures .0.6 µT relative to those with exposures ,0.3 µT were 4.81 (95% CI : 0.94–24.71) and 12.59 (95% CI : 1.50–150) for the first and last held job respectively. However, differences between these two risk estimates should be interpreted cautiously as only one control had an average exposure >0.6 µT in the last held job.

Discussion We found that as a whole, brain cancer was not significantly related to occupational exposure to magnetic fields. However, when the analyses were restricted by histological type, four indices of occupational magnetic fields (highest exposure received, exposure during first job, exposure during last job, exposure during longest held job, and cumulative exposure) were positively associated with glioblastoma multiforme. In

contrast, no significant associations were observed with astro- cytomas or other brain cancers. The large variation in risk between astrocytomas and glioblastoma multiforme requires comment. These two cancers account for approximately 80% of all gliomas.26 It is generally accepted that astrocytic gliomas that are classified as grades 1 and 2 are classified as astrocytomas and the more aggressive forms (grades 3 and 4) are classified as glioblastomas.26 Indeed, cases of glioblastoma multiforme often evolve from less malignant forms of astrocytoma, although some cases rise de novo.26 The results from in vivo and in vitro work suggest that if magnetic fields influence carcinogenesis, it is through a promoting effect.27,34,38 For example, 60 Hz magnetic field exposures were recently shown to increase the rate of proliferation in astrocytoma cells and potentiate the effect of the phrobol ester PMA.39 Continued exposure to promoting or co-promoting agents after tumour development may cause the tumour to evolve with increased invasive and metastatic properties.28 Although the underlying mechanisms of carcino- genesis continue to be widely debated, the increased risk due to

214 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Table 4 Parameter estimates obtained by modelling the relationship between brain cancer and a cumulative index of occupational magnetic field exposure using conditional logistic regression, by histological type, Canadian National Enhanced Cancer Surveillance System (NECSS), male participants, 1994–1997

Parameter estimatea for cumulative Histological type of cancer index of magnetic field exposure Standard error Odds ratiob P-valuec

All brain cancers 0.0173 0.0107 1.02 0.10

Astrocytomas –0.0096 0.0192 0.98 0.62

Glioblastoma multiforme 0.0415 0.0177 1.04 0.02

Other –0.0169 0.0335 0.98 0.61

a The parameter estimate was adjusted for exposure to ionizing radiation and vinyl chloride. b This odds ratio represents the change in risk of cancer per unit increase in the cumulative index of magnetic field exposure. c The P-value was calculated using the Wald χ2 test statistic.

Table 5 The risk of brain cancer according to the occupational magnetic field exposure received in the first and last held job, by histological type, Canadian National Enhanced Cancer Surveillance System (NECSS), male participants, 1994–1997

Average occupational exposure Earliest held job Last held job

to magnetic fields Cases Controls Odds ratioa 95% CI Cases Controls Odds ratiob 95% CI

All brain cancers

,0.3 µTc 458 474 1.0 475 490 1.0 >0.3–,0.6 µT 43 48 0.89 0.57–1.37 46 41 1.13 0.72–1.79 >0.6 µT 21 12 1.72 0.80–3.66 16 11 1.50 0.69–3.28

Astrocytomas

,0.3 µT 180 187 1,0 186 187 1.0 >0.3–,0.6 µT 19 20 0.81 0.43–1.53 21 20 0.98 0.50–1.92 >0.6 µT 7 4 1.51 0.45–5.38 5 7 0.71 1.22–2.27

Glioblastoma multiforme

,0.3 µT 163 174 1.0 171 184 1.0 >0.3–,0.6 µT 15 16 1.21 0.55–2.66 17 12 1.99 0.83–4.81 >0.6 µT 10 3 4.81 0.94–24.71 8 1 12.59 1.50–105.6

Other

,0.3 µT 101 100 1.0 105 105 1.0 >0.3–,0.6 µT 8 7 1.11 0.37–3.33 6 7 0.83 0.25–2.70 >0.6 µT 3 5 0.65 0.15–2.77 2 3 0.62 0.10–3.76

a Unadjusted odds ratio obtained from the conditional logistic model. b The odds ratio was adjusted for occupational exposure to ionizing radiation and vinyl chloride. c Referent group.

BRAIN CANCER AND OCCUPATIONAL EXPOSURE TO MAGNETIC FIELDS 215

exposure to magnetic fields that was found for more aggressive malignancies (i.e. glioblastoma multiforme) is consistent with the hypothesis that magnetic fields act at the promotional stage.

It is possible that our results may be biased due to non- response in the case and control series. Since questionnaires were not mailed out to cases known to be deceased, our analyses does not include aggressive forms of brain cancer that were rapidly fatal. To the extent that physician consent was not given due to the poor health of the cancer patient, additional cases of advanced disease will also be excluded. In total, almost one- third of eligible cases were excluded either because the subject had died, or consent was not given by the physician to approach patients diagnosed with brain cancer. Of the remaining cases, 63% participated in the study. Therefore, if magnetic field exposures act as a promoter of brain cancer, our risk estimates would be attenuated because the risk profiles of less aggressive brain cancer cases may be more similar to the profiles in the controls.

A large Tri-Utility study that employed personal monitoring to construct a job-exposure matrix of magnetic field exposures found an elevated risk of brain cancer among those with high cumulative exposures.9 The Tri-Utility study has a considerable number of strengths including a relatively large sample (n = 250), and workplace exposures that were inferred using personal moni- toring worn by a sample of current workers. The investigators found that those workers having a cumulative exposure to mag- netic fields that exceeded the median exposure (3.15 µT-years) had a twofold increase in brain cancer risk (OR = 2.0, 95% CI : 0.98–3.9). Contrary to our findings, the increased brain cancer risk in the Tri-Utility study was observed among those cases diag- nosed with an astrocytoma. Their findings should be interpreted with caution as there were only five cases in the exposed popu- lation, and there were differences in the follow-up procedures of workers from the Ontario, Quebec and French utilities. Further- more, electric utility workers represent a select subset of indi- viduals that are likely to exhibit less variation with respect to magnetic fields exposures, demographic characteristics and other occupational exposure than encountered in our population- based sample of individuals.

Many occupations with greater than background levels of exposure to magnetic fields are also associated with higher ex- posure to electric fields. A re-analysis of the French component of the Tri-Utility study observed a positive relationship between occupational exposure to electric fields and the incidence of brain cancer and benign tumours.40 In particular, subjects having exposures in the 90th percentile had an OR of 3.1 (95% CI : 1.1–8.7) relative to the baseline group. On the other hand, a re-analysis of the Ontario data found no association between cumulative electric field exposure and the incidence of brain tumours.8 Occupational data for electric field exposures were not assembled for the subjects that we analysed, and therefore, our risk estimates were unable to be adjusted for the potential confounding influence of these exposures.

Our results are consistent with findings from a Swedish case- control study of occupational and residential exposure to mag- netic fields11 that observed a significant relationship between

magnetic field exposure and the incidence of astrocytomas grades III and IV (or glioblastoma multiforme). A non- significantly increased risk of astrocytoma grades III and IV was observed among those having both residential and occupational exposure .0.2 µT (OR = 2.2, 95% CI : 0.6–8.5). The precision of this estimate was limited by the fact that only three cases had high exposures to both residential and occupational magnetic fields. Unlike the Swedish study which only took into account one occupation held by the subject (based on census data), our analyses considered all occupations held. Although we were unable to model residential magnetic field exposures, in general, the weak correlation between home and workplace exposures41

reduces the likelihood that our results will be confounded. More recently, it has been suggested that the failure to

consider magnetic field frequencies ,20 Hz that emanate from radial tyres may compromise risk estimates obtained from epidemiological studies.42 If exposures ,20 Hz are relevant to the biological mechanisms associated with the development of brain tumours then our risk estimates may be understated due to increased exposure misclassification.

We also evaluated the relationship between the total number of years spent in occupations with exposures of (1) 0.3–0.6 µT and (2) .0.6 µT. However, the precision of the parameter estimates that were derived for these two continuous measures of exposures was limited by the small number of subjects that had such exposure. For example, only 4.1% of subjects had average occupational magnetic fields that were >0.6 µT, while 16.1% had exposures that were 0.3–,0.6 µT. For this reason, we have presented results based on the cumulative measure of magnetic field exposure that combines information across the three possible job exposure categories. Comparative analyses of the risk of glioblastoma multiforme between the first and last held jobs revealed a more pronounced risk for those jobs held more recently. However, caution should be exercised when interpreting this finding due to the small number of subjects with exposure >0.6 µT and the width of the accompanying confidence intervals.

The results of this study support the hypothesis that occu- pational magnetic field exposures play a role in the aetiology of brain cancers. Despite a sample size that is considerably larger than most studies of brain cancer and magnetic field exposure, these findings must still be interpreted cautiously due to a smaller number of cases within each histological grouping and the unavailability of direct sampled measures of field exposure. Nonetheless, the elevated risk of glioblastoma multiforme is of significance and replication of this study result should be pursued in another population.

Acknowledgements The authors are grateful to Long On of the Environmental Risk Assessment and Case Surveillance Division of Health Canada for preparing the NECSS analysis files and to helpful comments provided by Erich Kliewer on an early draft of this manuscript. We also thank the reviewers for their thoughtful comments on the original submission of this manuscript.

216 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

References 1 Preston-Martin S. Epidemiology of primary CNS neoplasms. Neurol

Clin 1996;14:273–90. 2 IARC monographs on the evaluation of the carcinogenic risk of

chemicals to humans. Overall evaluations of carcinogenicity: an updating of IARC monographs volumes 1 to 42. Supplement 7. Lyon, France: International Agency for Research on Cancer, 1987.

3 Moulin JJ, Portefaix P, Wild P, Mur JM, Smagghe G, Mantout B. Mortality study among workers producing ferroalloys and stainless steel in France. Br J Ind Med 1990;47:537–43.

4 Waxweiler RJ, Stringer W, Wagoner JK, Jones J, Falk H, Carter C. Neoplastic risk among workers exposed to vinyl chloride. Ann NY Acad Sci 1976;271:40–48.

5 Bohnen NI, Kurland LT. Brain tumor and exposure to pesticides in humans: a review of the epidemiologic data. J Neurol Sci 1995;132: 110–21.

6 Miller RD, Neuberger JS, Gerald KB. Brain cancer and leukemia and exposure to power-frequency (50- to 60-Hz) electric and magnetic fields. Epidemiol Rev 1997;19:273–93.

7 National Cancer Institute of Canada. Canadian Cancer Statistics 1995. Toronto, 1995.

8 Miller AB, To T, Agnew DA, Wall C, Green LM. Leukemia following occupational exposure to 60-Hz electric and magnetic fields among Ontario electric utility workers [see comments]. Am J Epidemiol 1996; 144:150–60.

9 Thériault G, Goldberg M, Miller AB et al. Cancer risks associated with occupational exposure to magnetic fields among electric utility workers in Ontario and Quebec, Canada, and France: 1970–1989 [published erratum appears in Am J Epidemiol 1994;139:1053] [see comments]. Am J Epidemiol 1994;139:550–72.

10 Harrington JM, McBride DI, Sorahan T, Paddle GM, van Tongeren M. Occupational exposure to magnetic fields in relation to mortality from brain cancer among electricity generation and transmission workers. Occup Environ Med 1997;54:7–13.

11 Feychting M, Forssen U, Floderus B. Occupational and residential magnetic field exposure and leukemia and central nervous system tumors. Epidemiology 1997;8:384–89.

12 Sahl JD, Kelsh MA, Greenland S. Cohort and nested case-control studies of hematopoietic cancers and brain cancer among electric utility workers. Epidemiology 1993;4:104–14.

13 Savitz DA, Loomis DP. Magnetic field exposure in relation to leukemia and brain cancer mortality among electric utility workers [published erratum appears in Am J Epidemiol 1996;144:205]. Am J Epidemiol 1995;141:123–34.

14 Tynes T, Reitan JB, Andersen A. Incidence of cancer among workers in Norwegian hydroelectric power companies. Scand J Work Environ Health 1994;20:339–44.

15 Loomis DP, Savitz DA. Mortality from brain cancer and leukaemia among electrical workers. Br J Ind Med 1990;47:633–38.

16 Lin RS, Dischinger PC, Conde J, Farrell KP. Occupational exposure to electromagnetic fields and the occurrence of brain tumors. An analysis of possible associations. J Occup Med 1985;27:413–19.

17 Rodvall Y, Ahlbom A, Stenlund C, Preston-Martin S, Lindh T, Spannare B. Occupational exposure to magnetic fields and brain tumours in central Sweden. Eur J Epidemiol 1998;14:563–69.

18 Speers MA, Dobbins JG, Miller VS. Occupational exposures and brain cancer mortality: a preliminary study of east Texas residents. Am J Ind Med 1988;13:629–38.

19 Floderus B, Persson T, Stenlund C, Wennberg A, Ost A, Knave B. Occupational exposure to electromagnetic fields in relation to leukemia and brain tumors: a case-control study in Sweden. Cancer Causes Control 1993;4:465–76.

20 Guenel P, Raskmark P, Andersen JB, Lynge E. Incidence of cancer in persons with occupational exposure to electromagnetic fields in Denmark. Br J Ind Med 1993;50:758–64.

21 Preston-Martin S, Mack W, Henderson BE. Risk factors for gliomas and meningiomas in males in Los Angeles County. Cancer Res 1989;49: 6137–43.

22 Johansen C, Olsen JH. Risk of cancer among Danish utility workers— a nationwide cohort study. Am J Epidemiol 1998;147:548–55.

23 Tynes T, Andersen A, Langmark F. Incidence of cancer in Norwegian workers potentially exposed to electromagnetic fields. Am J Epidemiol 1992;136:81–88.

24 Savitz DA, Cai J, van Wijngaarden E et al. Case-cohort analysis of brain cancer and leukemia in electric utility workers using a refined magnetic field job-exposure matrix [In Process Citation]. Am J Ind Med 2000;38:417–25.

25 Baris D, Armstrong BG, Deadman J, Theriault G. A mortality study of electrical utility workers in Quebec. Occup Environ Med 1996;53:25–31.

26 Inskip PD, Linet MS, Heineman EF. Etiology of brain tumors in adults. Epidemiol Rev 1995;17:382–414.

27 Cridland N. Effects of power frequency EMF exposures at the cellular level. Radiation Protection Dosimetry 1997;72:279–90.

28 Loscher W, Mevissen M. Animal studies on the role of 50/60-Hertz magnetic fields in carcinogenesis. Life Sci 1994;54:1531–43.

29 Pitot HC, Dragan YP. Facts and theories concerning the mechanisms of carcinogenesis. FASEB J 1991;5:2280–86.

30 World Health Organization. International Classification of Diseases, 9th Revision. Geneva: WHO, 1985.

31 World Health Organization. International Classification of Diseases for Oncology. Geneva: WHO, 1976.

32 Green LM, Miller AB, Agnew DA et al. Childhood leukemia and personal monitoring of residential exposures to electric and magnetic fields in Ontario, Canada. Cancer Causes Control 1999;10:233–43.

33 Ontario Hydro. Summary of Electric and Magnetic Field Measurements to June 16th. Toronto: Ontario Hydro, 1989.

34 NIEHS. Assessment of Health Effects from Exposure to Power-line Frequency Electric and Magnetic Fields. Research Triangle Park, NC, US: National Institute of Health, 1998.

35 Floderus B, Persson T, Stenlund C. Magnetic-field exposures in the workplace: reference distribution and exposures in occupational groups. Int J Occup Environ Health 1996;2:226–38.

KEY MESSAGES

• This study examined the relationship between occupational exposure to magnetic fields and the incidence of brain cancer in a population-based sample of Canadians.

• A positive and statistically significant relationship was found between average levels of occupational magnetic field exposure and the incidence of glioblastoma multiforme, which is a more aggressive subtype of brain cancer.

• These results support the hypothesis than magnetic fields play a role in the development of brain tumours, and that they may exert an influence as tumour promoters.

BRAIN CANCER AND OCCUPATIONAL EXPOSURE TO MAGNETIC FIELDS 217

36 London SJ, Bowman JD, Sobel E et al. Exposure to magnetic fields among electrical workers in relation to leukemia risk in Los Angeles County. Am J Ind Med 1994;26:47–60.

37 Bowman JD, Garabrant DH, Sobel E, Peters JM. Exposures to extremely low frequency (ELF) electromagnetic fields in occupations with elevated leukemia rates. Appl Ind Hyg 1988;3:189–94.

38 Hester, GL. Electric and magnetic fields: managing an uncertain environment. Environment 1992;34:7–31.

39 Wei M, Guizzetti M, Yost M, Costa LG. Exposure to 60-Hz magnetic fields and proliferation of human astrocytoma cells in vitro. Toxicol Appl Pharmacol 2000;162:166–76.

40 Guenel P, Nicolau J, Imbernon E, Chevalier A, Goldberg M. Exposure to 50-Hz electric field and incidence of leukemia, brain tumors, and other cancers among French electric utility workers [see comments]. Am J Epidemiol 1996:144:1107–21.

41 Knave, B. Electric and magnetic fields and health outcomes—an overview. Scand J Work Environ Health 1994;20:78–89.

42 Milham S, Hatfield JB, Tell R. Magnetic fields from steel-belted radial tires: implications for epidemiologic studies. Bioelectromagnetics 1999; 20:440–45.

Copyright of International Journal of Epidemiology is the property of Oxford University Press / UK and its

content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s

express written permission. However, users may print, download, or email articles for individual use.

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Develop a formula using the PV function to determine the outstanding balance of the campground mortgage at the end of the current year using the parameters below

Shelly Cashman Excel 2016 | Module 4: SAM Project 1a

C:UsersakellerbeeDocumentsSAM DevelopmentDesignPicturesg11731.png Shelly Cashman Excel 2016 | Module 4: SAM Project 1a

Camp Millowski

Financial Functions, Data Tables, and Amortization Schedules

GETTING STARTED
Open the file SC_EX16_4a_FirstLastName_1.xlsx, available for download from the SAM website.

Save the file as SC_EX16_4a_FirstLastName_2.xlsx by changing the “1” to a “2”.

1. If you do not see the .xlsx file extension in the Save As dialog box, do not type it. The program will add the file extension for you automatically.

With the file SC_EX16_4a_FirstLastName_2.xlsx still open, ensure that your first and last name is displayed in cell B6 of the Documentation sheet.

· If cell B6 does not display your name, delete the file and download a new copy from the SAM website.

PROJECT STEP
Justin and Kaleen Millowski have always dreamed of purchasing and running a campground. Kaleen wants to be ready when a campground becomes available, so she decides to start calculating how a mortgage will impact her family’s budget on a monthly basis and over the life of the loan. She also wants to consider how different mortgage interest rates will impact the total cost of the campground.

Switch to the Campground Mortgage worksheet.

In cell D5, create a formula using the PMT function to determine the monthly payments for the anticipated Campground mortgage, using the defined names Rate, Term_Years, and Loan_Amount as the rate, nper, and pv arguments in the formula.

a. Put a negative sign before the PMT function to make the formula return a positive value.

b. In the function, Rate should be divided by 12 to calculate the monthly interest rate, and Term_Years should be multiplied by 12 to calculate the total number of monthly payments.

Kaleen calculated the anticipated total cost of the campground using the mortgage interest rate she expects to qualify for. She now wants to determine how different interest rates could impact the total cost of the campground.

Select the range A12:A26 and fill it with a percent series based on the values in range A12:A13. These values are the interest rates that Kaleen will analyze in the Varying Interest Rate Schedule.

Create a single variable data table to determine the impact that the variable interest rates (in the range A12:A22) will have on the total cost of the campground.

c. In cell B11, create a formula without using a function that references cell D5 (the monthly payments).

d. In cell C11, create a formula without using a function that references cell D6 (the total interest paid on the loan).

e. In cell D11, create a formula without using a function that references cell D7 (the total cost of the mortgage).

f. Select the range A11:D26 and create a single-variable data table, using an absolute reference to cell D3 (the mortgage interest rate) as the Column input cell.

To help Kaleen identify how each rate in her Variable Interest Rate Schedule compares to the interest rate she anticipates on her mortgage, she decides to highlight the matching interest rate in the schedule with a conditional formatting rule.

Apply a Highlight Cells conditional formatting rule to the range A12:A26 that formats any cell in the range that is equal to the value in cell D3 (using an absolute reference to cell D3) with Green Fill with Dark Green Text.

Kaleen now wishes to finalize the Amortization schedule.

In cell J4, create a formula without using a function that subtracts the value in cell I4 from the value in cell H4 to determine how much of the mortgage principal is being paid off each year.

Copy the formula in cell J4 to the range J5:J18.

In cell K4, create a formula using the IF function to calculate the interest paid on the mortgage (or the difference between the total payments made each year and the total amount of mortgage principal paid each year).

g. The formula should first check if the value in cell H4 (the balance remaining on the loan each year) is greater than 0.

h. If the value in cell H4 is greater than 0, the formula should return the value in J4 subtracted from the value in cell D5 multiplied by 12. Use a relative cell reference to cell J4 and an absolute cell reference to cell D5. (Hint: Use 12*$D$5-J4 as the is_true argument value in the formula.)

i. If the value in cell H4 is not greater than 0, the formula should return a value of 0.

Copy the formula from cell K4 into the range K5:K18.

Apply the Accounting number format with two decimal places and $ as the symbol to the range K4:K18.

In cell K20, create a formula without using a function that references the defined name Down_Payment.

Kaleen decides to add custom cell borders to the amortization schedule to make it easier to read.

Apply custom cell borders with a Green, Accent 6, Darker 50% (10th column, 6th row in the Theme Colors palette) line color as described below:

j. Add an Outline border with a Medium border style (2nd column, 5th row) to the range G3:K21.

k. Add a Vertical Line border with a Light border style (1st column, 7th row) to the range G3:K21.

l. Add a Top border with a Light border style (1st column, 7th row) to the range G4:K4.

m. Add a Bottom border with a Light border style (1st column, 7th row) to the range G18:K18.

To make the various elements of the Campground Mortgage worksheet easier to select and print, Kaleen wants to add custom names to ranges in the worksheet.

n. Apply the custom name Mortgage_Payment to the range A2:D7.

o. Apply the custom name Interest_Rate_Schedule to the range A9:D26.

p. Apply the custom name Amortization_Schedule to the range G2:K21.

Assign names to the cells in the range D5:D7 by selecting the range C5:D7 and creating names from the selection using the values in the Left column as the defined names.

Kaleen wishes to protect the worksheet, so that she doesn’t make any accidental changes to the values. However, since her assumptions about the price of the campground, the down payment, and the mortgage interest rate may be incorrect, she wants to be able to update these values in the protected worksheet.

q. Select and unlock the range B5:B6.

r. Select and unlock cell D3.

s. Protect the Campground Mortgage worksheet without a password.

Kaleen had previously hidden a worksheet containing data on other recently purchased campgrounds in New Hampshire. Now she wants to compare the data in that worksheet with the data she just calculated.

Unhide the Campground Research worksheet.

Switch to the Campground Research worksheet.

In cell B8, create a formula without using a function that determines the total interest associated with the mortgage. First multiply the value in cell B6 (the number of terms) by the value in cell B7 (the number of monthly payments) and by 12 (to convert the yearly terms to monthly terms), and then subtract the value in cell B4 (the total loan amount).

Copy the formula in cell B8 into the range C8:E8.

Kaleen would like to be able to see the remaining balance of the campground mortgage at the end of the current year.

In cell B11, create a formula using the PV function to determine the outstanding balance of the campground mortgage at the end of the current year using the parameters below:

t. For the rate parameter, use the value in cell B5 (the yearly interest rate of the mortgage) divided by 12.

u. For the nper parameter, subtract the value in cell B10 (the current year of the mortgage) from the value in cell B6 (the total number of years of the mortgage), and multiply that by 12.

v. For the pmt parameter, use the value in cell B7 (the monthly payments), putting a negative sign before this value to make the outcome of the PV function positive.

Copy the formula from cell B11 to the range C11:E11.

Your workbook should look like the Final Figures below. Save your changes, close the workbook, and then exit Excel. Follow the directions on the SAM website to submit your completed project.

Final Figure 1: Campground Mortgage Worksheet

Final Figure 2: Campground Research Worksheet

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