This is the unadjusted lower quartile house priced for residential property sales (transactions) in the area for a 12 month period with April in the middle (year-ending September). These figures have been produced by the ONS (Office for National Statistics) using the Land Registry (LR) Price Paid data on residential dwelling transactions.

The LR Price Paid data are comprehensive in that they capture changes of ownership for individual residential properties which have sold for full market value and covers both cash sales and those involving a mortgage.

The lower quartile is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls three quarters of the way down the list, such that 75Percentage of transactions lie above and 25Percentage lie below that value. These are particularly useful for assessing housing affordability when viewed alongside average and lower quartile income for given areas.

Note that a transaction occurs when a change of freeholder or leaseholder takes place regardless of the amount of money involved and a property can transact more than once in the time period.

The LR records the actual price for which the property changed hands. This will usually be an accurate reflection of the market value for the individual property, but it is not always the case. In order to generate statistics that more accurately reflect market values, the LR has excluded records of houses that were not sold at market value from the dataset. The remaining data are considered a good reflection of market values at the time of the transaction. For full details of exclusions and more information on the methodology used to produce these statistics please see http://www.ons.gov.uk/peoplepopulationandcommunity/housing/qmis/housepricestatisticsforsmallareasqmi

The LR Price Paid data are not adjusted to reflect the mix of houses in a given area. Fluctuations in the types of house that are sold in that area can cause differences between the lower quartile transactional value of houses and the overall market value of houses.

If, for a given year, for house type and area there were fewer than 5 sales records in the LR Price Paid data, the house price statistics are not reported." Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

The ranges of the plasma lactate quartiles were determined using specimens from the weighted random cohort sample.†Represents the maximum number of participants in each category. Actual number may vary due to missing data.‡Plasma lactate mg/dL may be converted to mmol/L by multiplying by 0.111.§P-trend evaluated with linear or logistic regression using the median lactate value for each quartile as an ordinal variable.∧There were no participants with coronary heart disease in quartile 1. SE not calculated due to small sample size.*Represents geometric mean and interquartile range.Note: LDL represents low density lipoprotein. HDL represents high density lipoprotein.

Ratio of lower quartile workplace earnings to lower quartile house prices. The statistics used are workplace based full-time individual earnings. The ""lower quartile"" property price/income is determined by ranking all property prices/incomes in ascending order. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile." Source: Land Registry/Annual Survey of Hours and Earnings Publisher: Communities and Local Government (CLG) Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 1997 to 2009 Type of data: Survey

Baseline characteristics of the physical activity program participants by quartiles of change in accelerometer-determined physical activity (minutes per week above 760 counts per minute change between baseline and 24 months).

Annual descriptive price statistics for each calendar year 2005 – 2024 for 462 electoral wards within 11 Local Government Districts.

The statistics include:

• Minimum sale price

• Lower quartile sale price

• Median sale price

• Simple Mean sale price

• Upper Quartile sale price

• Maximum sale price

• Number of verified sales

Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded:

• Non Arms-Length sales

• sales of properties where the habitable space are less than 30m2 or greater than 1000m2

• sales less than £20,000.

Annual median or simple mean prices should not be used to calculate the property price change over time.
The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

*Values after common logarithmic transformation of the number of microparticles (per µl).†related to larger infarct size in patients with atrial fibrillation.

s(P)RR denotes soluble (pro)renin receptor, SGA: small for gestational age, OR: odds ratio, CI: confidence interval*All multivariate models were adjusted for maternal age, conception by in vitro fertilization, drinking during current pregnancy, family history of diabetes mellitus, preexisting hypertension, preexisting asthma, gestational age at delivery, gestational weight gain, hypertensive disorders during pregnancy, gestational diabetes, placental weight and neonatal sex

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

Administrative disposable income is a third pillar of the income statistics that Statbel publishes, alongside "\2" and poverty indicators based on "\2", and allows answering other types of questions than SILC and tax statistics.

SILC uses "\2" at the household level as a concept of income, cumulating the incomes of all household members. In the next step, this disposable income is converted into equivalised disposable income to take into account the composition of the household. Based on the SILC, at-risk-of-poverty figures are published up to the provincial level. However, the sample size does not allow for analyses at a more detailed geographical level. However, statistics based on tax revenues are available up to the level of the statistical sector, but are limited to taxable income in the context of personal income tax returns. Non-taxable income is not taken into account and there is also no correction according to the composition of the household.

The variable "administrative equivalised disposable income" responds to a growing demand for income and poverty figures at the communal level. It uses an income concept based on administrative sources that tries to correspond as much as possible to that of SILC. For the population as a whole, both taxable and non-taxable income are taken into account. They are added together for all members of the household in order to obtain an administrative disposable income for the household. After adjusting for the composition of the household, the variable "administrative equivalised disposable income" is established. This can be used to calculate income and poverty figures at the communal level.

Indicators are not disseminated for an entity and a category when there are at least 15% of people whose equivalent administrative disposable income is missing or when there are less than 100 people with a valid income.

More information on the page "\2" of Statbel

This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough.

The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings.

Land Registry housing data are for the first half of the year only, so that they comparable to the ASHE data which are as at April.
Prior to 2006 data are not available for Inner and Outer London.

The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile.
The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order.
The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median.

Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data for 2014 has been calculated by the GLA.

Link to DCLG Live Tables

Annual descriptive price statistics for each calendar year 2005 – 2023 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Annual descriptive price statistics for each calendar year 2005 – 2024 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Earnings data is taken from the Annual Survey of Hours and Earnings (ASHE). This is based on a 1 per cent sample of employee jobs. Information on earnings is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings. HM Land Registry house price data is for the first half of the year only, so it is comparable to the ASHE data which is as at April. The 'lower quartile' property price/income is determined by ranking all property prices/incomes in ascending order. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile. The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median.

This instructional activity introduces students to the application of statistical tools for analyzing biological data, with a focus on measures of center (mean, median, mode) and measures of spread (range, quartiles, standard deviation). Using real-world biological contexts. students learn how to summarize datasets, identify trends, and evaluate variability. The activity integrates the use of MS Excel and TI-84 Plus graphing calculators to calculate descriptive statistics and interpret results. By engaging with authentic biological data, students develop quantitative reasoning skills that enhance their ability to detect patterns, recognize variability, and draw meaningful conclusions about biological systems

BackgroundNon-alcoholic fatty liver disease (NAFLD) is independently associated with atrial fibrillation (AF) risk. The uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) has been shown to be closely associated with cardiovascular disease (CVD) and NAFLD. The aim of this study is to clarify whether elevated UHR is associated with the occurrence of AF in patients with NAFLD and to determine whether UHR predicted AF.MethodsPatients diagnosed with NAFLD in the Department of Cardiovascular Medicine of the Second Hospital of Shanxi Medical University from January 1, 2020, to December 31, 2021, were retrospectively enrolled in this study. The study subjects were categorized into AF group and non-AF group based on the presence or absence of combined AF. Logistic regression was performed to evaluate the correlation between UHR and AF. Sensitivity analysis and subgroup interaction analysis were performed to verify the robustness of the study results. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff value for UHR to predict the development of AF in patients with NAFLD.ResultsA total of 421 patients with NAFLD were included, including 171 in the AF group and 250 in the non-AF group. In the univariate regression analysis, NAFLD patients with higher UHR were more likely to experience AF, and the risk of AF persisted after confounding factors were adjusted for (OR: 1.010, 95%CI: 1.007–1.013, P<0.001). AF risk increased with increasing UHR quartile (P for trend < 0.001). Despite normal serum UA and HDL-C, UHR was still connected with AF in patients with NAFLD. All subgroup variables did not interact significantly with UHR in the subgroup analysis. The ROC curve analysis showed that the areas under the curve for UA, HDL-C, and UHR were 0.702, 0.606, and 0.720, respectively, suggesting that UHR has a higher predictive value for AF occurrence in NAFLD patients compared to HDL-C or UA alone.ConclusionIncreased UHR level was independently correlated with a high risk of AF in NAFLD patients.

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Notes: The SIR reports are not league tables. The ranking parameter –the scientific output of institutions- should be understood as a default rank, not our ranking proposal. The only goal of this report is to characterize research outcomes of organizations so as to provide useful scientometric information to institutions, policymakers and research manager so they are able to analyze, evaluate and improve their research results. If someone uses this report to rank institutions or to build a league table with any purpose, he/she will do it under his/her own responsibility.Output - Total number of documents published in scholarly journals indexed in Scopus (Romo-Fernández, et al., 2011).International Collaboration - Institution's output ratio produced in collaboration with foreign institutions. The values are computed by analyzing an institution's output whose affiliations include more than one country address (Guerrero-Bote, Olmeda-Gómez and Moya-Anegón, 2013; Lancho-Barrantes, Guerrero-Bote and Moya-Anegón, 2013; Lancho-Barrantes, et al., 2013; Chinchilla-Rodríguez, et al., 2012)Normalized Impact - Normalized Impact is computed using the methodology established by the Karolinska Intitutet in Sweden where it is named "Item oriented field normalized citation score average". The normalization of the citation values is done on an individual article level. The values (in %) show the relationship between an institution's average scientific impact and the world average set to a score of 1, --i.e. a NI score of 0.8 means the institution is cited 20% below world average and 1.3 means the institution is cited 30% above average (Rehn and Kronman, 2008; González-Pereira, Guerrero-Bote and Moya- Anegón, 2011).High Quality Publications - Ratio of publications that an institution publishes in the most influential scholarly journals of the world, those ranked in the first quartile (25%) in their categories as ordered by SCImago Journal Rank (SJRII) indicator (Miguel, Chinchilla-Rodríguez and Moya-Anegón, 2011).Specialization Index - The Specialization Index indicates the extent of thematic concentration /dispersion of an institution’s scientific output. Values range between 0 and 1, indicating generalist vs. specialized institutions respectively. This indicator is computed according to the Gini Index used in Economy (Moed, et. al., 2011; López-Illescas, Moya-Anegón and Moed, 2011; Arencibia-Jorge et al., 2012). In this indicator, when the value is 0 it means that the data are not sufficient to calculate.Excellence Rate - Excellence rate indicates the amount (in %) of an institution’s scientific output that is included into the set of the 10% of the most cited papers in their respective scientific fields. It is a measure of high quality output of research institutions (SCImago Lab, 2011; Bornmann, Moya-Anegón and Leydesdorff, 2012; Guerrero-Bote and Moya-Anegón, 2012).Scientific Leadership - Leadership indicates an institution’s “output as main contributor”, that is the number of papers in which the corresponding author belongs to the institution (Moya-Anegón, 2012; Moya-Anegón et. al, 2013; Moya-Anegón, et al., forthcoming) Excellence with Leadership - Excellence with Leadership indicates the amount of documents in the Excellence rate in which the institution is the main contributor (Moya-Anegón, et al., 2013).

The Index of Household Advantage and Disadvantage (IHAD) provides a summary measure of relative socio-economic advantage and disadvantage for households, based on the characteristics of dwellings and the people living within them, using 2021 Census data.

All in-scope households are ordered from lowest to highest score. A low score indicates relatively greater disadvantage and a lack of advantage in general. A high score indicates a relative lack of disadvantage and greater advantage in general.

This dataset presents IHAD data in quartiles. The lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of households as it depends on the distribution of the IHAD scores. The data is grouped by Statistical Area Level 2 (SA2 2021). SA2s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3.

Key Attributes:

      Field alias
      Field name
      Description


      Statistical Areas Level 2 2021 code
      SA2_CODE_2021
      2021 Statistical Areas Level 2 (SA2) codes from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


        Statistical Areas Level 2 2021 name
      SA2_NAME_2021
      2021 Statistical Areas Level 2 (SA2) names from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


      Area in square kilometres
      AREA_ALBERS_SQKM
      The area of a region in square kilometres, based on the Albers equal area conic projection.


      Uniform Resource Identifier
      ASGS_LOCI_URI_2021
      A uniform resource identifier can be used in web linked applications for data integration. 


      IHAD quartile 1
      IHAD_QUARTILE1
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 1, indicating relatively greater disadvantage and a lack of advantage in general.


      IHAD quartile 2
      IHAD_QUARTILE2
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 2.


      IHAD quartile 3
      IHAD_QUARTILE3
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 3.


      IHAD quartile 4
      IHAD_QUARTILE4
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 4, indicating a relative lack of disadvantage and greater advantage in general.


      Occupied private dwellings
      OPD_2021
      Dwellings in-scope of the IHAD i.e. classifiable occupied private dwellings.


      SEIFA IRSAD quartile
      IRSAD_QUARTILE
      Index of Relative Socio-economic Advantage and Disadvantage quartile. All SA2s are ordered from lowest to highest score, the lowest 25% of SA2s are given a quartile number of 1, the next lowest 25% of SA2s are given a quartile number of 2 and so on, up to the highest 25% of SA2s which are given a quartile number of 4. This means that SA2s are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of SA2s as it depends on the distribution of SEIFA scores.


      Usual resident population
      URP_2021
      Population counts in this column are based on place of usual residence as reported on Census Night. These include persons out of scope of the IHAD.


      Dwellings
      DWELLING
      Total dwellings at Census time, including dwellings out of scope of the IHAD e.g. unoccupied private dwellings.

Please note: Proportional totals may equal more than 100% due to rounding and random adjustments made to the data. When calculating proportions, percentages, or ratios from cross-classified or small area tables, the random error introduced can be ignored except when very small cells are involved, in which case the impact on percentages and ratios can be significant. Refer to the Introduced random error / perturbation Census page on the ABS website for more information.

Data and geography references

Source data publication: Index of Household Advantage and Disadvantage Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Index of Household Advantage and Disadvantage methodology, 2021 Source: Australian Bureau of Statistics (ABS)

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The objective was to identify horn fly-susceptible and horn fly-resistant animals in a Sindhi herd by two different methods. The number of horn flies on 25 adult cows from a Sindhi herd was counted every 14 days. As it was an open herd, the trial period was divided into three stages based on cow composition, with the same cows maintained within each period: 2011-2012 (36 biweekly observations); 2012-2013 (26 biweekly observations); and 2013-2014 (22 biweekly observations). Only ten cows were present in the herd throughout the entire period from 2011-2014 (84 biweekly observations). The variables evaluated were the number of horn flies on the cows, the sampling date and a binary variable for rainy or dry season. Descriptive statistics were calculated, including the median, the interquartile range, and the minimum and maximum number of horn flies, for each observation day. For the present analysis, fly-susceptible cows were identified as those for which the infestation of flies appeared in the upper quartile for more than 50% of the weeks and in the lower quartile for less than 20% of the weeks. In contrast, fly-resistant cows were defined as those for which the fly counts appeared in the lower quartile for more than 50% of the weeks and in the upper quartile for less than 20% of the weeks. To identify resistant and susceptible cows for the best linear unbiased predictions analysis, three repeated measures linear mixed models (one for each period) were constructed with cow as a random effect intercept. The response variable was the log ten transformed counts of horn flies per cow, and the explanatory variable were the observation date and season. As the trail took place in a semiarid region with two seasons well stablished the season was evaluated monthly as a binary outcome, considering a rainy season if it rained more or equal than 50mm or dry season if the rain was less than 50mm. The Standardized residuals and the BLUPs of the random effects were obtained and assessed for normality, heteroscedasticity and outlying observations. Each cow’s BLUPs were plotted against the average quantile rank values that were determined as the difference between the number of weeks in the high-risk quartile group and the number of weeks in the low risk quartile group, averaged by the total number of weeks in each of the observation periods. A linear model fit for the values of BLUPS against the average rank values and the correlation between the two methods was tested using Spearman’s correlation coefficient. The animal effect values (BLUPs) were evaluated by percentiles, with 0 representing the lowest counts (or more resistant cows) and 10 representing the highest counts (or more susceptible cows). These BLUPs represented only the effect of cow and not the effect of day, season or other unmeasured counfounders.

1. TUGT, time up and go test2. ORs were determined from logistic regression analyses for the quartiles of grip or TUGT or usual walking speed, comparing participants with pathoglycemia (prediabetes and diabetes) to those with normoglycemia.3. Crude: no adjustment; Model 1: adjusted for age; body mass index (BMI); hypertension; hyperlipidemia; stroke; coronary heart disease (CHD); kidney disease; having 2 or more chronic diseases; whether famer or not; educational level; history of smoking and drinking habits; history of falls; physical activity (IPAQ); creatinine (CRE); blood urea nitrogen (BUN); total cholesterol (TC); triglyceride (TG); Model 2: adjusted for Model 1 variables in addition to the other performance-based assessments.4. Adjusted odds ratio; 95% CI in parenthesesLogistic regression analyses of grip, TUGT and usual walking speed quartiles association with pathoglycemia (prediabetes and diabetes).

Epidemic characteristics at a given school, for a given epidemic year. Performed as individual logistic regression models with subclinical infection as the outcome variable and each epidemic characteristic as a single exposure variable, and incorporating random effects for the individual and the individual's school of attendance.*. Each exposure variable was aggregated across each school and for each year. The median represents the midpoint of these aggregated values.†A one-unit increase for proportions (incidence, proportion DENV-1 etc) was defined as a one-quartile increase in value. Quartiles were calculated based upon the range of values (e.g., if incidence had a range of 0–40%, the upper limit for calculating quartiles of incidence was 40%, not 100%). A one-unit increase in the number of serotypes in circulation compared 2 serotypes in circulation to 1 serotype in circulation, for example.