National Statistics in China

The official statistics can be obtained from the website, National Bureau of Statistics of China (中華人民共和國國家統計局). Website: http://www.stats.gov.cn/english/. All the database is still at the trial state. I don't think we have the access to the actual data yet, especially for the English Version. It's pretty hard to find the National Database focus on Chinese Health related perspective. Therefore, It could be difficult to study the trend of the disease prevalence at the Chinese population level, nor to say compare between different ethnicity.

China Today has summarized some statistics in the website (http://www.chinatoday.com/data/data.htm). They included the official and unofficial statistics, for reference use only.

They have published some basic descriptive statistics on their website. There are no public available data to download for detail comparison or analysis.

They generate some comparison between US and China,

A Statistical Comparison  Between China and United States

Development Indicators	China	United States
Population	1.31 billion	301 million
GDP	$2.7 trillion ($2,054 per person)	$13.2 trillion ($43,950 per person)
Taxes Collected	$486 billion ($370 per person)	2.5 trillion ($8,297 per person)
Balance of Trade	$177.5 billion (surplus)	$225 billion (deficit)
Cell-phone Users	461 million (35 per 100 people)	219 million (73 per 100 people)
Cable TV Subscribers	139 million (11 per 100 people)	110 million (37 per 100 people)
Airline Passengers	160 million	658 million
Foreign Visitors	22 million (9% from USA)	51 million (1% from China)
Private Cars	11.5 million (9 per 1000 people)	136.4 million (450 per 1000 people)
Deaths in Traffic Accidents	89,445	48,433
Practicing Doctors	1.97 million (15 per 10,000 people)	745,000 (25 per 10,000 people)
Feature Films Produced	330	699

All $ US Currency/  Source  TIME Mar. 19. 2007

Population statistics in very general level about China could be,

Population

Most recent census shows that China has 1.37 billion people (click for information of the most recent census and related figures) China has 37 million more males than females Population hits 1.30756 bln by 2005: Survey.

Some Health/Medical related statistics could be,

Illegal Drug Related Statistics

Illegal Drug Users: 1.14 million (2004)

Health and Related Statistics

Smoking Population in China: More than one quarter of China's population (about 300 million adults - smok, and tobacco kills one million Chinese people every year. The number of new HIV/AIDS infections in China was about 70,000 in 2005, with 25,000 deaths reported across the country.

Some other statistics at the bottom of the page,

Health and Medical: The total number of hospitals and clinics: 320,000, the total number of doctors: 1.39 million, nurses and technicans: 1,05 million. About AIDS in China: First case found in 1985, and by now 173 had died, and HIV infections: 400,000, two third of them are regular drug users (July 1999 data).

High Blood Pressure (Hypertension) population in China: 100 million.

Nearsightedness (Myopia) According to the most recent survey, about 50% Chinese teenagers are suffered from nearsightedeness compared with 15% in 1970's. (Source: www.cnd.org Feb. 25, 2000)

Smoking Population: 350 million (2003 data), female share about 10% of the total smoking population. (compared with 1% in 1978 and 4% in 1996).

Smoking: (based on data collected in January 2000, by China Consumers Association) Smoking population in China: 350 million (about 50 million smokers are teen-agers), shared about 1/4 of total smoking population in the world. 62% Chinese male and 3.8% Chinese female smoking. 37.6% of total Chinese population smoking.

For those smokers in China, 16 cigarettes on average per day; and the expense for smoking shared 15% of their income.The average age of first smoking in China is 25 years old, 3 years earlier than that of 1984.

The total smoking population in China increased 3.5% compared with the statisitcs in the year of 1984 (Health Ministry of PRC Nov. 99 data.)

Population of Drug Addict: 791,000. (data of 2005 ). New drug addict population increased 22,000 in 2004.

Suicide & Suicide Rate: 2002 statistic shows there are 287,000 people commit suicide in China every year (about 22 per 100 thousand population), which is 42% of total suicide in the world. (Data of 2001)

We are looking forward more national statistics/data related to general population health could be released. We may study the trend for certain disease and better estimate/compare prevalence for the disease. This may help on allocation of the facilities, e.g. what kind of utility/medicine/health care should be borrowed from  developed countries.

National Available Data Source

In this article, we will introduce some national data source. You may face the situation of having the research questions, but not the data source. In this case, a secondary use of the current available data source would be better idea. Someone already take the effort to collect the data with the appropriate questionable/instruments. The only left thing for you to do, is to link the right data source with your research.

National available data source is amazing by its generalizability and rich data source. From the website of United Nations Statistics Devision: http://unstats.un.org/unsd/methods/inter-natlinks/sd_natstat.asp, the available data source for the US as,

United States	Fedstats
	Bureau of the Census Bureau of Economic Analysis Bureau of Justice Statistics Bureau of Labor Statistics Bureau of Transportation Statistics Department of Commerce (STAT-USA) Office of Energy Statistics National Center for Health Statistics The National Center for Education Statistics United States Department of Agriculture, Economic Research Service

For health purpose, we use the data from NCHS very often, which include the survey data,

National Health and Nutrition Examination Survey (NHANES)

National Health Care Surveys

National Health Interview Survey (NHIS)

National Immunization Survey (NIS)

National Survey of Family Growth (NSFG)

National Vital Statistics System (NVSS)

Longitudinal Studies of Aging (LSOA)

State and Local Area Integrated Telephone Survey (SLAITS)

you can read here for detail about the available data within US, at national level statistics, http://www.cdc.gov/nchs/Default.htm

Within California:
There are some very good data source too, if you don't need the national data level. OSHPD (website:
http://www.oshpd.ca.gov/) usually provides a lot of data.

Here is some data provided by California State of Public Health (website: http://www.cdph.ca.gov/data/statistics/Pages/default.aspx).

RAND has some statistics about California too (website: http://ca.rand.org/stats/statistics.html)

Which data source is best for you depends on your research questions. Sometimes, there are no available data source that include all the variables you would like. In such case, you will need to consider taking the effort to collect data yourself, which is really time consuming and can be very expensive. You may sometimes balance between your exact research questions or optimal data source.

Estimating the Prevalence of Limb Loss in the United States

How to estimate the prevalence of Limb Loss in US? Most of the current Statistics are from the paper [1], There are nearly 2 million people living with limb loss in the United States. In the paper, the author projected by the year 2050, there will be 3.6 million people with the loss of a limb. Based on the limitation of the data on the first amputations, the estimation may have great uncertainty.

Among those living with limb loss, main causes are vascular disease (54%) and Trauma (45%). The number of person that undergo amputation of an upper or lower limb each year is 185,000. But little is known about the number of people currently living with the loss of a limb. The trend of increasing number prevalence will trigger the demand of prosthetic device. If the prevalence is underestimate, there will be unmet need for the limb loss survivors.

We currently don not have a national surveillance system for monitoring limb loss. The NHANES system does not contain the questions related to limb loss, but the 2011  NATIONAL HEALTH INTERVIEW SURVEY contains some questions related to limb loss as (Adults 18+),


Question ID:  AHS.200_00.000 
What condition or health problem causes you to have difficulty with {names of up to 3 specified activities/these activities}?

Missing or amputated limb/finger/digit causes difficulty with activity
1  Mentioned --- 70 mentioned
2  Not mentioned
7  Refused
8  Not ascertained
9  Don't know


Question ID:  AHS.318_01.000 RECODE  
How long have you had missing limbs (fingers, toes, or digits)?

Duration of missing limbs (fingers, toes, or digits); amputation: Number of units
01-94  1-94
95  95+
96  Since birth
97  Refused
98  Not ascertained
99  Don't know

Question ID:  AHS.318_02.000 RECODE
How long have you had missing limbs (fingers, toes, or digits)?

Duration of missing limbs (fingers, toes, or digits); amputation: Time unit
1  Day(s)
2  Week(s)
3  Month(s)
4  Year(s)
6  Since  birth
7  Refused
8  Not ascertained
9  Don't know





Duration (in years) of missing limbs (fingers, toes, or digits); amputation, recode 1
00  Less than 1 year --- 6 less than a year
01-84  01-84 years --- 62 1-84 years
85  85+ years
96  Unknown number of years
97  Refused
98  Not ascertained
99  Don't know


Duration of missing limbs (fingers, toes, or digits); amputation, recode 2
1  Less than 3 months --- 3 less 3 month
2  3-5 months
3  6-12 months --- 6 within 6-12 months
4  More than 1 year --- 59 more than a year
7  Refused
8  Not ascertained
9  Don't know




Missing limbs (fingers, toes, or digits); amputation condition status recode
1  Chronic --- 70 Chronic
2  Not chronic
9  Unknown if chronic

The new available national statistics, may help us address some new issues about the trend.




Reference:
1. Ziegler‐Graham K, MacKenzie EJ, Ephraim PL, Travison TG, Brookmeyer R. Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation2008;89(3):422‐9. link: http://www.sciencedirect.com/science/article/pii/S0003999307017480#

2. NHIS questions, link: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2011/samadult_layout.pdf

3. NHIS frequency, link: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2011/samadult_freq.pdf

Statistics Job Opportunities Statistics in LA and Top Companies

I just noticed recently, Sterling-Hoffman Life Science posted some job information about the statistics on different levels. The job announcements can be found at: http://www.simplyhired.com/a/jobs/list/q-statistician/l-Los+Angeles%2C+CA/mi-5/fcn-Sterling-hoffman+Executive+Search

The levels include the entry level Statistical SAS Programmer, medium level Senior Statistical Programmer, Biostatistician, as well as high level Director of Statistical Operations.

I found some useful information, from their official website about the top companies in the industry field as below. I include the links for each top companies, for your convenience to check and view their information.


Top Biotech Companies

Amgen Genentech Biogen Idec Genzyme Chiron Corp. Gilead Sciences MedImmune Cephalon Millennium Pharmaceuticals Genencor International

ImClone Systems Celgene MGI Pharma (Eisai buys) QLT Nabi Biopharmaceuticals AEterna Zentaris Regeneron Pharmaceuticals Enzon Pharmaceuticals Ligand Pharmaceuticals InterMune

Top Medical Device Companies

Johnson and Johnson GE Healthcare Siemens Healthcare Medtronic Cardinal Health Baxter International Philips Healthcare Boston Scientific Covidien Becton, Dickinson & Co.

Abbott Laboratories Stryker B. Braun 3M Healthcare Zimmer Holdings St. Jude Medical Hospira Smith & Nephew Danaher Synthes

Top Pharmaceutical Companies

Pfizer Sanofi-Aventis Novartis GlaxoSmithKline AstraZeneca Merck & Co. Johnson & Johnson Eli Lilly & Co. Bristol-Myers Squibb Abbott Laboratories

Takeda Chem. Ind. Boehringer-Ingelheim Teva Pharma Bayer Schering Astellas Daiichi-Sankyo Eisai Otsuka Gilead Sciences Mylan

Measurement Errors

The measures play an important role in statistics. We don't have the access to the true values, therefore, we will need the estimation on the true value. We can measure the variables we are able to observe with error. In literature, we model/predict the ideal outcomes based on the observed measure with a normally distributed error.

The measurement with error usually includes the error from the device, the error from the adherence to the device, the error from the observer.

Each type of device has its own limitation. They may not possible target the true value, instead it may get the true value plus or minus some tolerate errors. This sometimes called the systematic error. It could hardly being avoided, unless the technology being improved.

The error from the behavior of using the device inappropriately can usually being avoidable. It can be verified by validity the measures and by taking average of several observations. However, sometimes it is hard to always get the valid measure, depending on the nature of the data. For example the non-adherence to the device may be common in some study, and will result the missing values mixed with the error in measurements. In this situation, it could be hard to differentiate the measures.

The error from the observer, is frequently studied by discover the demographic nature of the observer. Usually the conclusion could be, certain kind of observers may have large amount of error. This can be done by comparing the errors contributed by different types of observers.

How the study can be designed to valid the measures and distinguish between the types of measurement is really need some investigations. Usually will need the design nature of the study to take this into account, other than at the end of the study to discuss about the issue. The investigators will need to take more effort on the measurement errors before collecting the data.

Turn Research Topic to Poster Presentation

I have some experience working with health professions on their research poster presentations. Most of them have not much experience on how the research getting started and how the statistics getting involved. There are some essential steps for present your research in a poster.

1. Research Topic.

This is the key part for the presentation. The research topic can be measuring the disease severity, as well as testing a hypothesis that certain behavior could be associated with increase/decrease the risk of the disease. It could be a question that need to answer. This may required some background knowledge and some literature review. In this process, the health professionals can know what related research has already been done. They can determine the direction for the on going research and modify their topics.

2. Data source.

After set up the research topic, there would be some important factors/variables. The important outcome variable, the most correlated independent variable, and some other related factors that we may need to control for. We will need to identify if we have to collect the data ourselves or we can use some publicly available data source. For example, most of the health behavior or health outcome related research topic can be assessed by the NHANES (The National Health and Nutrition Examination Survey http://www.cdc.gov/nchs/nhanes.htm/) data one line.

3. Statistical Methods.

When both the research topic and data source are determined, you can start to talk to a statistician about the research question. Let him/her know your research topic, your data source, your study design (how do you choose your eligible sample, e.g. age criteria; which are the related variables, e.g. gender, ethnicity, education, etc;), your definition of the variables. In your field, people may have some categorical suggestions. For example, we always defined age into groups for adults as, 18-34, 45-64 and 65+.

4. Statistical Analysis.

You may help the statisticians/biostatisticians to determine the analysis. Usually, the set of analysis can be summarized as Univariate Analysis (the distribution of your dependent/outcome variable, independent variable - the variable you care most, covariates - other variables you would like to adjust for), Bivariate Analysis (the bivariate distribution for each related variable - test if all the variables you identified are actually related to your outcome variable), Multivariate Analysis (the actual model that will help answer your research question).

5. Interpret the Results.

You will have to discuss with the statistician about the results after the analysis. After discuss the results, you have some idea if the interpretation are conflict with what already known in your field. You may help the statisticians to identify whether this is the error in the analysis or the limit of the data source. Some time, this process could be back and forth, until we have the results answering the research question and consistent with current literature. 

6. Present your Findings

There are two ways to present your findings, orally present as a talk (you will need the power point slides) or present your results in a poster. Here I will focus on how to present the research in a poster. You may want to include some figures to illustrate your results, which always quickly orient the audience. You will need the key parts on your poster

• Title
• Author/Co-author
• Abstract
• Background/Introduction
• Methods
• Results
• Discussion/Conclusion
• Acknowledgment 
• Reference

7. Print your Poster

Many free poster presentation pre-designed templates are available for putting together your findings into a fixed size page for large print. For example, 

http://www.posterpresentations.com/ (recommend)

http://www.makesigns.com/SciPosters_Templates.aspx

http://www.writing.engr.psu.edu/posters.html

http://www.postersession.com/templates.php

To summarize, presentation experience is important. More practice, can make a perfect. Reading others' poster presentation and getting feedback from your audience can improve your own skills significantly. 

Good luck on your poster presentation.

Prepare for the Large Data

How large the data could be?

The recent  August <Significance> brought the spacial data in physics to its audience in the first published article. Millions of millions of millions ... data, counted by size on the hard drive, not by the number of observations would become at hand day by day.

The DNA, Microarray data, as the following picture, from Stanford as an example, would be ready for analysis for large amount of people, in order to find the associated module/gene to the disease.



Currently, most of the patients' data would be electronically, i.e. EMR. The current MEDSCAPE call for the attention of all the clinicians - are you ready for the electronic medical records or electronic health records (EHRs)? The health reform could make the larger medical data even larger, with quickly growing population, and large insured population.

Are you ready for the associated analysis and complicated data management ?!

The International Year of Statistics (Statistics2013)

Official Website: http://statistics2013.org/

The International Year of Statistics ("Statistics2013") is a worldwide celebration and recognition of the contributions of statistical science. Through the combined energies of organizations worldwide, Statistics2013 will promote the importance of Statistics to the broader scientific community, business and government data users, the media, policy makers, employers, students, and the general public. The goal of the year:

• increasing public awareness of the power and impact of Statistics on all aspects of society;
• nurturing Statistics as a profession, especially among young people; and
• promoting creativity and development in the sciences of Probability and Statistics

Of course, your organization might choose to have special activities during 2013, or to shine a special light on Statistics through some of your regularly scheduled activities.

We hope many groups will place special emphasis on activities to engage students and potential students of statistics, helping them to become more aware of statistics as a key scientific discipline. Some organizations might consider special foci, such as a broad statistical literacy outreach or promoting the ways in which statistics advances science and improves the human condition.

Activities for the first quarter of 2013:

October 17-18, 2012 — "Statistics and Its Applications", National University of Uzbekistan (NUUz), Dept. of Probability Theory & Mathematical Statistics

January 1-9, 2013 — Workshop and Conference on Limit Theorems in Probability, Virtual Institute for Mathematical and Statistical Sciences, Bangalore, India

January 2-5, 2013 — International Indian Statistical Association meeting, India

January 6-10, 2013 — ISBA Regional Meeting and International Workshop/Conference on Bayesian Theory and Applications, Banaras Hindu University, India

February 7-8, 2013 — 5th Biennial Statistical Society of Australia Young Statisticians Conference, Trinity College, University of Melbourne

February 21-23, 2013 — ASA Conference on Statistical Practice, New Orleans, LA, USA

February 22-23, 2013 — 2013 Spring Undergraduate workshop, SAMSI, Research Triangle Park, NC, USA

March 10-13, 2013 — ENAR Spring Meeting, Orlando, FL USA

March 11-14, 2013 — 10th International Workshop on Operations Research: Making Decisions Under Uncertainty/Modeling Uncertainty, Havana, Cuba

March 12-14, 2013 — NatStats 2013 Conference, Brisbane Convention & Exhibition Centre, Australia

March 14-15, 2013 — Tunisian Association of Statistics and its Applications, Fourth meeting on Statistics and Data Mining (MSDM 2013), Hammamet, Tunisia

Biostatistics and Statistics

A common question being faced by all the Biostatisticians was the difference between the Statistics and Biostatistics. One simple answer would be, Biostatistics is Statistics as applied to the Biological Science.

As mentioned in the book [1], Statistics is a science that deals with the collection, organization, analysis, interpretation, and presentation of information that can be stated numerically. We usually make inferences (scientific evidence) about the population based on the statistics from a sample from the population (usually a simple random sample).  

The logic of Biostatistics relates statistics to medicine when a physician practices medicine: what is observed for a particular patient is incorporated with what has previously been observed for a large group of patients to make a specific decision about that particular patient. Much of the Biostatistics are estimation of population parameters based on the sample statistics, and test the hypothesis about the parameters. We will need the sample represent the population where the sample are taken.

One area of Biostatistics is to search for methods that provide good estimation of the population parameter and to test the hypothesis about the parameters. Despite the obscuring effects of inherent variability and multi-factorial causation, there are many general tendencies that lead to patterns in research data [1]. Biostatisticians will statistically investigate the patterns in the samples of data and will help the health professionals to make inferences about the population, the sample represented, to reduce the chance of disease and to develop and improve disease intervention with the aim of advancing healthy well-beings.

Sound medical research requires a careful synthesis of expertise in many disciplines, including statistics, medicine, epidemiology, etc.

Reference:

1. Elston, Robert C., and William Johnson. Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach. 1st ed. Wiley, 2008.

Biostatistics in Science

Biostatisticians study the application of statistical theory and methods to analyze data collected by Scientists, such as geneticists, epidemiologists, medical doctors, dentists, etc. Such data are collected to further each of the above field study. The book <Basic Biostatistics for Geneticists and Epidemiologists> mentions two good examples to explain the statistical application in the field study.

For example, a genetic study to investigate whether one or more genes might predispose people to an increased risk of developing a specific disease would require an application of statistics to reach a valid conclusion.Another example, an application of statistics is required to reach a valid conclusion when a clinical study is conducted for the purpose of investigating which of the two pharmaceutical treatments is preferred in managing patients with a specific disease.

In the application of statistical theory, it is often but not limit to the responsibility of Biostatisticians to educate and communicate with Scientists about how the statistical methods need to adjust according to the complex study design and/or specific aim of the field study. The application often required some basic knowledge about the science review and background scientific literature review.

We will need to differentiate the two types of reasoning methods. In general, we use deductive reasoning to prove thins with certainty. In scientific method, we use inductive inference and can never prove anything with absolute certainty. 

Deduction Reasoning: General principles are applied to specific situation at hand in order to reach the best decision possible for a particular patient. This is from general to specific.  Most of the basic medical training centers around deductive reasoning, which is based on general scientific laws and what we can deduce from them.

Inductive Reasoning: We conduct experiments and comparative studies to focus on questions that arise in our work. We study a few patients, and from what we observe, we try to make rational inferences about what happens in general. This from specific subjects at hand to general. The Scientific method has the following basic steps:

1. Making observations - that is, collecting data.
2. Generating a hypothesis- the underlying law and order suggested by the data
3. Deciding how to test hypothesis - what critical data are required?
4. Experimenting - this leads to an inference that either rejects or affirms the hypothesis

If the hypothesis is rejected, then we go back to step 2.  If it is affirmed, this does not necessarily mean it is true, only that in light of current knowledge and methods it appears to be so.There are some clinical decisions must be made with variability in mind, such as,

• whether an observation on a patient should be considered normal or abnormal?
• Is a particular observations more typical of a person with disease or of a person without disease?
• Is the observation outside the range typically found in a healthy person?
• If the patient were examined tomorrow, would one obtain essentially the same observations?
• If more observations obtained, would the results be very close to the observations already had?

Inductive inference is a much riskier procedure than deductive inference. Like in mathematics, start with a set of axioms.Refereed to the following book for details,

Elston RC, Johnson W. Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach. 1st ed. Wiley; 2008.

Put your Excel data on the map

Source: http://www.techrepublic.com/article/put-your-excel-data-on-the-map/5029707

Start by entering the two-letter abbreviation of the state in one column and enter the value associated with that state in the column beside it. (You must enter the abbreviation in uppercase letters. For a complete list of state abbreviations, click here.) Then select the raw data, including the column headings, as shown in Figure A.

Figure A

To create a sample map, start by selecting the state abbreviations and their associated values.

After selecting your data, click the Standard toolbar’s Map button once. Then click in your worksheet and “drag” to draw the outline for your map. When you release the mouse, Microsoft Map will display a message like the one shown in Figure B. In this case, you can select the United States only (with an Alaska and Hawaii inset), or you can select United States In North America, which will display the entire continent.

Figure B

Microsoft Map recognizes the state abbreviations in our sample data and offers a choice between two U.S. maps.

Figure C shows what our map looks like after we selected the United States (AK & HI Inset) option.

Figure C

Here’s what our map looks like after the program color-coded each state with our data. (No data was available for the states in green.)

Customize your map
You don’t have to settle for the default settings for your map. You can change the color scheme of the map and the data legend and even plot the location of major highways and cities for almost any country.

For instance, to plot the location of all the major cities in the United States, double-click the map to open it, then right-click the map, and choose Add Feature. Select US Major Cities (AK & HI Inset) and click OK. Figure D shows our sample U.S. map after we added the major cities and highways features.

Figure D

We embellished this U.S. map by plotting the locations of major cities and highways.

In addition to the features you can add by right-clicking a map, you can use the Microsoft Map Control dialog box (shown in Figure E) to customize your map even more. (We won’t go into the details here, but using this dialog box effectively takes a bit of practice.)

Figure E
You can further customize your maps by using the Microsoft Map Control dialog box.

 

15 Essential Apps for Clinicians

15 More Smartphone Apps to Improve Your Practice
source: http://www.medscape.com/features/slideshow/apps2?src=ptalk

1. Stay Current With News and Journals: Reeder 
2. Stay Current With News and Journals: Google Reader
3. Stay Current With News and Journals: Instapaper --- This app allows you to save interesting articles for future reading. If you come across a lengthy paper that you don't have time to read, just send it to Instapaper and read it later.
4. Stay Current With News and Journals: Downcast ---Downcast allows you to follow and keep track of Podcasts, including new episodes as they become available. The app supports both audio- and videocasts. Many medical publications these days have weekly or biweekly summary podcasts. For internal medicine, I like to follow Annals of Internal Medicine, JAMA, the New England Journal of Medicine, and Archives of Internal Medicine. For rheumatology news, I subscribe to Medscape Rheumatology.
5. Access Important Files on the Go: Dropbox
6. Access Important Files on the Go: Evernote --- Within a Web browser, you can use the Evernote clipping add-on to save pages being viewed. On your mobile, you can take pictures, write notes, and record sound. Everything gets synced with the cloud.
7. Access Important Files on the Go: Genius Scan --- use an app called Genius Scan in conjunction with Evernote when I want to save a multipage PDF document to Evernote.
8. Access Important Files on the Go: LogMeIn --- LogMeIn allows you to access your computer from your mobile device. I have used LogMeIn to access patient schedules and to review charts and labs.
9. Time Management: Wunderlist ---Wunderlist has all of these features (ability to create folders and a priority hierarchy; set up reminders for date, time, and/or location; voice input for easy entry; a note option for more complex tasks; an easy-to-navigate user interface; and being available in/on multiple platforms and devices ) and is beautifully designed. It has a desktop version that can sync with your mobile.
10. Remember Web Addresses and Passwords: Xmarks ---Xmarks is a cross-platform app that keeps all of your bookmarks synced across different browsers (eg, Chrome, Firefox, IE, Safari) and mobile devices. This app is especially useful on a tablet or small-screen device, where it is hard to type long addresses.
11. Remember Web Addresses and Passwords: LastPass --- The first step in setting up LastPass is to create a master password. This will be the last password you will need to remember. (LastPass -- get it?)
12. Remember Web Addresses and Passwords: Kypass ---This app will not fill in any forms or passwords, but it will keep your passwords safe, sound, and accessible.
13. Reference Resources and Score Calculators: DAS Calc ---This calculator offers multiple ways to calculate DAS, including using ESR, CRP, and/or patient global health. It also includes the CDAI and SDAI assessment tools as a bonus. The app can tell you when a patient is in remission or has high disease burden.
14. Reference Resources and Score Calculators: Lab Gear and Medscape --- Lab Gear is a very useful app that not only shows normal values for many labs (which any EMR should be providing nowadays) but also provides background information on what is being measured, differential diagnosis, and common symptoms when abnormalities are found.
15. Reference Resources and Score Calculators: My Pain Diary ---This app lets you record pain levels on a calendar and describe associated factors, such as triggers, alleviating symptoms, nature of the pain, and location. It automatically adds weather data to the diary as well.
    
    

Contributor Information

Author
Bruno S. Oliveira, MD, Staff Physician, Billings Clinic, Billings, Montana

Disclosure: Bruno S. Oliveira, MD, has disclosed no relevant financial relationships.

Survey Methodology for Public Health Researchers Journal

Selected Readings from 20 years of Public Opinion Quarterly

Source: http://www.oxfordjournals.org/our_journals/poq/collectionspage.html

1. Evaluations of survey-based data that are used in health-related research and policy

Medicaid Underreporting in the CPS: Results from a Record Check Study
Joanne Pascale, Marc I. Roemer, and Dean Michael Resnick
Public Opinion Quarterly (2009) 73:497-520 Full Text
Adolescents' Inconsistency in Self-Reported Smoking: A Comparison of Reports in School and in Household Settings
Pamela C. Griesler, Denise B. Kandel, Christine Schaffran, Mei-Chen Hu, and Mark Davies
Public Opinion Quarterly (2008) 72:260-290 Full Text
Reaching the U.S. Cell Phone Generation: Comparison of Cell Phone Survey Results with an Ongoing Landline Telephone Survey
Michael W. Link, Michael P. Battaglia, Martin R. Frankel, Larry Osborn, and Ali H. Mokdad
Public Opinion Quarterly (2007) 71:814-839 Full Text
A Comparison of Address-Based Sampling (ABS) Versus Random-Digit Dialing (RDD) for General Population Surveys
Michael W. Link, Michael P. Battaglia, Martin R. Frankel, Larry Osborn, and Ali H. Mokdad
Public Opinion Quarterly (2008) 72:6-27 Full Text

2. Experiments that evaluate the impact of various aspects of survey data collection protocols on data quality

The Association of Survey Setting and Mode with Self-Reported Health Risk Behaviors among High School Students
Nancy D. Brener, Danice K. Eaton, Laura Kann, Jo Anne Grunbaum, Lori A. Gross, Tonja M. Kyle, and James G. Ross
Public Opinion Quarterly (2006) 70:354-374 Full Text
The Influence of Graphical and Symbolic Language Manipulations on Responses to Self-Administered Questions
Leah Melani Christian and Don A. Dillman
Public Opinion Quarterly (2004) 68:57-80 Full Text
Does Conversational Interviewing Reduce Survey Measurement Error?
Michael F. Schober and Frederick G. Conrad
Public Opinion Quarterly (1997) 61:576-602 Full Text PDF
Charities, No; Lotteries, No; Cash, Yes: Main Effects and Interactions In a Canadian Incentives Experiment
Keith Warriner, John Goyder, Heidi Gjertsen, Paula Hohner, and Kathleen McSpurren
Public Opinion Quarterly (1996) 60:542-562 Full Text PDF
Asking Sensitive Questions: The Impact of Data Collection Mode, Question Format, and Question Context
Roger Tourangeau and Tom W. Smith
Public Opinion Quarterly (1996) 60:275-304 Full Text PDF

3. Review articles that summarize the current state of some issue in survey methodology

The State of Surveying Cell Phone Numbers in the United States: 2007 and Beyond
Paul J. Lavrakas, Charles D. Shuttles, Charlotte Steeh, and Howard Fienberg
Public Opinion Quarterly (2007) 71:840-854 Full Text
Nonresponse Rates and Nonresponse Bias in Household Surveys
Robert M. Groves
Public Opinion Quarterly (2006) 70:646-675 Full Text
Research Synthesis: The Practice of Cognitive Interviewing
Paul C. Beatty and Gordon B. Willis
Public Opinion Quarterly (2007) 71:287-311 Full Text
Methods for Testing and Evaluating Survey Questions
Stanley Presser, Mick P. Couper, Judith T. Lessler, Elizabeth Martin, Jean Martin, Jennifer M. Rothgeb, and Eleanor Singer
Public Opinion Quarterly (2004) 68:109-130 Full Text

4. Articles that address the impact of some aspect of survey question design on the accuracy of the resulting data

Methodological Comparisons Between CATI Event History Calendar and Standardized Conventional Questionnaire Instruments
Robert F. Belli, Lynette M. Smith, Patricia M. Andreski, and Sangeeta Agrawal
Public Opinion Quarterly (2007) 71:603-622 Full Text
Comparing Check-All and Forced-Choice Question Formats in Web Surveys
Jolene D. Smyth, Don A. Dillman, Leah Melani Christian, and Michael J. Stern
Public Opinion Quarterly (2006) 70:66-77 Full Text
The Impact of "No Opinion" Response Options on Data Quality: Non-Attitude Reduction or an Invitation to Satisfice?
Jon A. Krosnick, Allyson L. Holbrook, Matthew K. Berent, Richard T. Carson, W. Michael Hanemann, Raymond J. Kopp, Robert Cameron Mitchell, Stanley Presser, Paul A. Ruud, V. Kerry Smith, Wendy R. Moody, Melanie C. Green, and Michael Conaway
Public Opinion Quarterly (2002) 66:371-403 Full Text PDF
How Unclear Terms Affect Survey Data
Floyd Jackson Fowler, Jr.
Public Opinion Quarterly (1992) 56:218-231 Full Text PDF
A Tale of Two Questions: Benefits of Asking More Than One Question
Elizabeth F. Loftus, Mark R. Klinger, Kyle D. Smith, and Judith Fiedler
Public Opinion Quarterly (1990) 54:330-345 Full Text PDF

IMPACT FACTOR AND RANKING

Year	Impact Factor	Ssi: Communication	Ssi: Political Science	Ssi: Social Sciences, Interdisciplinary
2011	2.247	4 out of 72	3 out of 148	5 out of 89
2010	1.933	4 out of 67	7 out of 139	7 out of 83
2009	1.588	6 out of 54	13 out of 112	8 out of 68
2008	1.972	3 out of 45	6 out of 99	3 out of 61
2007	2.030	1 out of 45	4 out of 93	2 out of 57
2006	1.550	2 out of 44	6 out of 85	4 out of 58

This information is taken from the Journal Citation Reports, published annually as part of the Social Science Citation Index by ISI.

ASA Fellow

The American Statistics Association fellow awards presented in the recent JSM at San Diego was updated by Robert Starbuck in the Amstat News, indicated some changes since 2004 (Link: http://magazine.amstat.org/blog/2012/08/01/fellowsrevisit712/).

Figure 1. Counts of ASA Fellow awards given by employment sector since 2004

Figure 2. Percentages of Fellows awarded by employment sector relative to the percentages of ASA membership by sector

Figure 3. Percentages of successful ASA Fellows nominations by employment sector

Figure 4. Current ASA members by gender

Figure 5. Percentages by gender of ASA Fellow awards in 2004–2012

Figure 6. Percent of successful ASA Fellows nominations by gender

The ASA Fellow award is a significant recognition of contributions to the statistics profession, and one that should reflect the constituency of the ASA membership. If you or others you know are deserving of this award, please participate in and encourage others to participate in the award nomination process.

No shortcuts when collaborating with Statisticians

A new article, August, 2012, in AMStat news addressed a common misunderstanding to believe that statistical support is merely the "labor of plugging data into a computer and pushing a button". Below is from the original article, explained why their request to “please run the analysis” is really not a 2-3 hour job.

Source: http://magazine.amstat.org/blog/2011/08/01/statscienceaug11/

Statistical science is much more than data analysis, and involves the incorporation of statistical methodology at all stages of research, requiring scientific expertise in the field of statistics. Appropriate use of statistical methodology in data analysis means the data should be analyzed in a way that is both scientifically and statistically reasonable.

The statisticians are, themselves, scientists collaborating in research, and are using their statistical expertise in determining and applying the appropriate methodology for rigorously addressing important research questions with excellence. The time invested often requires the following:

• Review of the research for basic understanding of the science
• Review of the data to understand the distributional properties of the variables collected
• Determination of the appropriate methodology to apply in analysis corresponding to the hypothesis and design of the investigation
• Programming of the analysis using appropriate statistical software (specific to the particular data set)
• Review of the analytic results
• Reporting of the results

The time invested for a particular data analysis can take hours, or it can take months. This depends on the research questions, the study design, the properties of data gathered, and the target audience that will need to understand the results.