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If you are is using excel 2010 or older you will not be able to view this report as it has features from Office 365 that causes the report to appear corrupt in the older versions of Excel. If you do have this issue we advise you use a web-based spreadsheet viewer or upgrade to Office 365.
This sheet has a summary of the information throughout the report. Note the numbers in this spreadsheet do not match the IPIF report - this is a clinical report that is not constrained by the indicator definitions. There are several tables each detailed below.
Total Patients Aged over 15 - This is the number of patients who are aged over 15 enrolled in your practice
Current Smoker - This is the number and percentage of patients over 15 currently recorded as current smokers
Ex Smoker - This is the number and percentage of of patients over 15 currently recorded as ex-smokers
Non Smokers - This is the number and percentage of of patients over 15 currently recorded as non-smokers
Not Recorded - This is the number and percentage of of patients over 15 who don't have their smoking status recorded
Total Recorded - This is the number and percentage of of patients over 15 who have their smoking status recorded
Total Patients Aged over 15 - This is the number of patients who are aged over 15 enrolled in your practice
Diabetes Recorded - This is the number and percentage of patients aged over 15 who are recorded as having diabetes
Get Checked in last 12 Months - This is the number of patients who are recorded as having diabetes and have had a DAR in the last 12 months and the percentage of patients who are recorded as having diabetes who have received a DAR in the last twelve months.
Total Female Patients aged 20-69 - This is the number of Female Patients aged 20-69 on your current register.
Screening complete or exempt - This is the number and percentage of female patients aged 20-69 who have been screened or marked exempt in the PMS
Recorded as Refused - This is the number and percentage of female patients 20-69 who have been recording as refusing a cervical screen
Not Recorded - This is the number and percentage of female patients 20-69 who haven't received a screening or aren't recorded as either exempt or refused
Total Female Patients aged 45-69 - This is the number of female patients aged 45-69 on your current register.
Screening complete or exempt - This is the number and percentage of female patients aged 45-69 who have been screened or marked exempt in the PMS
Recorded as Refused - This is the number and percentage of female patients aged 45-69 who have been recording as refusing a mammogram screen
Not Recorded - This is the number and percentage of female patients aged 45-69 who haven't received a screening or aren't recorded as either exempt or refused
Total Patients aged over 15 - This is the number of patients who are aged over 15 on your current register.
Ischaemic CVD Recorded - This is the number and percentage of patients who have Ischaemic CVD recorded
This sheet has 4 tables; these tables show a pattern of smoking across the practicee.g. higher percentages of 25-29 year old males or higher percentages of Maori who are smokers. This information is important when considering how to target smoking cessation information.
Each table shows the breakdown of the total enrolled patients and their smoking status. Each table has the following columns:
Current Smoker - The number of patients recorded as current smokers
Ex-Smoker - The number of patients recorded as Ex-smokers
Non-Smoker - The number of patients recorded as Non-smokers
Not Recorded - The number of patients who don't have a smoking status recorded.
Patients - Total number of registered patients
% Recorded - The percentage of Registered Patients who have a smoking status recorded
There will usually be more people with a smoking status not recorded in the 15-19 age group. Generally the highest current smokers are those aged 20 - 29 years. However when combining the ex smoker and current smoking it is higher in the older age groups, decreasing after 65 (potentially show the impact of smoking on life expectancy)
This sheet has a graph of the by Gender and Age Group table on the Smoking Ever Recorded sheet. These rates reflect those of the age group graph but the rate of smoking for women relative to men is generally lower during child bearing age groups.
Gender Count Table
This table has a breakdown of patients who have diabetes by gender, please note that patients with unknown gender are not included in this table.
Gender Percentage Table
This table shows what percentage of the total population have Diabetes recorded. This table is broken down in the same columns and rows as the Gender Count Table.
Ethnicity DAR in last 12 Months Count Table
This table has a breakdown of the patients who have diabetes by ethnicity.
PI Age Group and each Ethnicity who have received a DAR in the last 12 months.
Ethnicity Percentage Table
This table shows what percentage of Diabetics who have received a DAR in the last 12 months. This table is broken down in the same columns and rows as the Ethnicity DAR in last 12 Months Count Table.
Gender DAR last in last 12 Months Count Table
This table has a breakdown of patients who have received a DAR in the last 12 Months by Gender, please note that patients with unknown gender are not included in this table.
Gender Percentage Table
This table shows what percentage of Diabetics who have received a DAR in the last 12 months. This table is broken down in the same columns and rows as the Gender DAR last in last 12 Months Count Table
By Practice table
This table shows the total cvdra eligible population, total population over 15 years old and percentage of the total population over 15 who are CVDRA eligible. It has this information breakdown by practice.
This sheet has 3 tables the data is sourced from the PMS, however it has to be noted that not all practice management systems export the result of the screening so if you are using one of those then this information will not be adequately broken down
I'm sorry we don't have the Ron vs I/O voltage in the older datasheets and appreciate your honest feedback. In our newer signal switches we include this graph as standard practice like the TS5A21366 datasheet.
It is possible to parallel the 2 channels of the SN74CB3Q3305 to decrease the amount of current that flows through the signal path but I don't think you will need to do this with your application and TI's large portfolio.
In economics, the Lorenz curve is a graphical representation of the distribution of income or of wealth. It was developed by Max O. Lorenz in 1905 for representing inequality of the wealth distribution.
The curve is a graph showing the proportion of overall income or wealth assumed by the bottom x% of the people, although this is not rigorously true for a finite population (see below). It is often used to represent income distribution, where it shows for the bottom x% of households, what percentage (y%) of the total income they have. The percentage of households is plotted on the x-axis, the percentage of income on the y-axis. It can also be used to show distribution of assets. In such use, many economists consider it to be a measure of social inequality.
The concept is useful in describing inequality among the size of individuals in ecology[1] and in studies of biodiversity, where the cumulative proportion of species is plotted against the cumulative proportion of individuals.[2] It is also useful in business modeling: e.g., in consumer finance, to measure the actual percentage y% of delinquencies attributable to the x% of people with worst risk scores. Lorenz curves were also applied to epidemiology and public health, e.g., to measure pandemic inequality as the distribution of national cumulative incidence (y%) generated by the population residing in areas (x%) ranked with respect to their local epidemic attack rate.[3]
The Lorenz curve for a probability distribution is a continuous function. However, Lorenz curves representing discontinuous functions can be constructed as the limit of Lorenz curves of probability distributions, the line of perfect inequality being an example.
The PGPF chart pack illustrates that budget-making involves many competing priorities, limited resources, and complex issues. In this set of charts, we aim to frame the financial condition and fiscal outlook of the U.S. government within a broad economic, political, and demographic context.
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