Month: December 2015

Stage, Expression, and Causal Model of Diseases

December 29, 2015 Epidemiology, Therapeutics No comments , , , , , , , , ,

Natural History of Disease

Stage of Disease

Screen Shot 2015-12-26 at 8.46.43 PMThe natural history of disease refers to the progression of a disease in an individual over time. This includes all relevant phenomena from before initiation of the disease (the stage of susceptibility) until its resolution. In the period following exposure to the causal factor, the individual enters a stage of subclinical disease (also called the preclinical phase). For infectious agents, this corresponds to the incubation period during which the agent multiplies within the body but has not yet produced discernible signs or symptoms. For noninfectious diseases, this corresponds to the induction period between a causal action and disease initiation.

The stage of clinical disease begins with a patient's first symptoms and ends with resolution of the disease. Be aware that the onset of symptoms marks the beginning of this stage, not the time of diagnosis. The time-lag between the onset of symptoms and diagnosis of disease can be considerable. Resolution of the disease may come by means of recovery or death. When recovery is incomplete the individual may be left with disability.

Incubation periods of infectious diseases vary considerably. Some infectious diseases are characterized by short incubation periods. Others are characterized by intermediate incubation periods. Still others are characterized by extended incubation periods. Note that even for a given infectious disease, the incubation period may vary considerably. For example, the incubation period for human immunodeficiency virus (HIV) and AIDS ranges from 3 to more than 20 years.

Induction periods for noninfectious diseases also exhibit a range. For example, the induction period for leukemia following exposure to fallout from the atomic bomb blast in Hiroshima ranged from 2 to more than 12 years. Variability in incubation is due to differences in host resistance, pathogenicity of the agent, the exposure dose, and the prevalence and availability of cofactors responsible for disease.

Understanding the natural history of a disease is essential when studying its epidemiology. For example, the epidemiology of HIV/AIDS can only be understood after identify its multifarious stages. Exposure to HIV is followed by an acute response that may be accompanied by unrecognized flu-like symptoms. During this acute viremic phase, prospective cases do not exhibit detectable antibodies in their serum, yet may still transmit the agent. During a lengthy induction, CD4+ lymphocyte counts decline while the patient is still free from symptoms. The risk of developing AIDS is low during these initial years, but increase over time as the immune response is progressively destroyed, after which AIDS then may express itself in different forms (e.g., opportunistic infections, encephalitis, Kaposi's sarcoma, dementia, wasting syndrome).

Screen Shot 2015-12-26 at 9.29.53 PMA slightly more sophisticated view of the natural history of disease divides the subclinical stage of disease into an induction period and a latent period. Induction occurs in the interval between a causal action and the point at which the occurrence of the disease becomes inevitable. A latent period follows after the disease becomes inevitable but before clinical signs arise. During this latent phase, various causal factors may promote or retard the progression of the disease. The induction and promotion stages combined are referred to as the empirical induction period. This more sophisticated view better suits the consideration of multi-facored disease, where multiple factors must act together to result in a cause.

Stage of Prevention

Disease prevention efforts are classifed according to the stage of disease at which they occur. Primary prevention is directed toward the stage of susceptibility. The goal of primary prevention is to prevent the disease from occuring in the first place. Examples of primary preventiion include needle-exchange programs to prevent the spread of HIV, vaccination programs, and smoking prevention programs.

Secondary prevention is directed toward the subclinical stage of disease, after which the individual is exposed to the causal factor. The goal of secondary prevention is to prevent the disease from emerging or delay its emergence by extending the induction period. It also aims to reduce the severity of the disease once it emerges. Treating asymptomatic HIV-positive patients with antiretroviral agents to delay the onset of AIDS is a form of secondary prevention.

Tertiary prevention is directed toward the clinical stage of disease. The aim of tertiary prevention is to prevent or minimize the progression of the disease or its sequelae. For example, screening and treating diabetics for diabetic retinopathy to avert progression to blindness is a form of tertiary prevention.


Variability in The Expression of Disease

Spectrum of Disease

Diseases often display a broad range of manifestations and severities. This is referred to as the spectrum of disease. Both infectious and noninfectious diseases exhibit spectrums. When considering infectious diseases, there is a gradient of infection. As an example, HIV infection ranges from inapparent, to mild (e.g., AIDS-related complex), to severe (e.g., wasting syndrome). As an example of a noninfectious disease's spectrum, consider that coronary artery disease exists in as asymptomatic form (atherosclerosis), transient myocardial ischemia, and myocardial infarctions of various severities.

The epidemiologic iceberg

The bulk of a health problem in a population may be hidden from view. This phenomenon, referred to as the "epidemiologic iceberg", applies to infectious, noninfectious, acute, and chronic diseases alike.

Uncovering disease that might otherwise be "below sea level" by screening and better detection often allows for better control of health problems. Consider that for every successful suicide attempt there are dozens of unsuccessful attempts and a still larger number of people with depressive illness that might be severe enough to have them wish to end their lives. With appropriate treatment, individuals with suicidal tendencies would be less likely to have suicidal ideation and be less likely to attempt suicide. As another example: reported cases of AIDS represent only the tip of HIV infections. With proper antiretroviral therapy, clinical illness may be delayed and transmission averted.


Causal Models

Definition of Cause

A cause of a disease event is an event, condition or characteristic that preceded a disease without which the disease event either would not have occurred at all or would not have occurred until some later time. On a population basis, we expect that an increase in the level of a causal factor in inhabitants will be accompanied by an increase in the incidence of disease in that population, caeteris parabus (all other things being equal). We also expect that if the causal factor can be eliminated or diminished, the frequency of disease or its severity will decline.

Component cause model (causal pies)

Most diseases are caused by the cumulative effect of multiple causal components acting ("interacting") together. Thus, a causal interaction occurs when two or more causal factors act together to bring about an effect. Causal interactons apply to both infectious and noninfectious diseases and explains, for example, why two people exposed to the same cold virus will not necessarily experience the same outcome: one person may develop a cold while the other person may experience no ill effects.

Rothman's causal pies helps to clarify the contribution of causal components in disease etiology. Figure 2.6 displays two causal mechanisms for a disease. Wedges of each pie represent components of each causal mechanism, corresponding to risk factors we hope to identify. Each pie represents a sufficient causal mechanism, defined as a set of factors that in combination makes disease occurrence inevitable. Each casual componet (wedge) plays an essential role in a given causal mechanism (pie); a specific disease may result from a number of different causal combination mechanisms.Screen Shot 2015-12-28 at 6.19.20 PM

A causal factor is said to be necessary when it is a component cause member of every sufficient mechanism. In other words, the component cause is necessary if the disease cannot occur in its absence. In Figure 2.6, Component A is a necessary cause, since it is evident in all possible mechanisms – the disease cannot occur in its absence. Causal components that do not occur in every sufficient mechanism yet are still essential in some cases are said to be contributing component causes. In Figure 2.6, B, C, and D are nonnecessary contributing causal components. Component causes that complete a given causal mechanism (pie) are said to be causal complements. In Figure 2.6, for example, the causal complements of factor A in Mechanism 1 is (B + C). In mechanism 2, the causal complement of factor A is D. Factors that work together to form sufficient causal mechanism are said to interact causally.

Causal interactions have direct health relevance. For example, when a person develops an infectious disease, the causal agent must interact with the causal complement known as "susceptibility" to cause the disease. When considering hip fractures in elderly patients, the necessary element of trauma interacts with the causal complement of osteoporosis to cause the hip fracture. In similar veins, smoking interacts with genetic susceptibility and other environmental factors in causing lung cancer, and dietary excess interact with lack of exercise, genetic susceptibility, atherosclerosis and various clotting factors to cause heart attacks. Causal factors rarely act alone.

Causal pies demonstrate that individual risk is an all-or-none phenomenon. In a given individual, either a causal mechanism is or is not completed. This makes it impossible to directly estimate individual risk. In contrast, the notion of average risk is a different matter. Average risk can be estimated directly as the proportion of individuals regarded as a member of a recognizable group that develops a particular condition. For example, if one in ten smokers develop lung cancer over their lifetime, we can say that this population has a lifetime risk for this outcome of one in ten (10%). The effects of a given cause in a population depend on the prevalence of causal complements in that population. The effect of phenylketanines, for instance, depends not only on the prevalence of an inborn error of metabolism marked by the absence of phenylalanine hydroxylase, but depends also on the environmental prevalence of foods high in phenylalanine. Simiarly, the effects of falls in the elderly depend not only on the opportunity for falling, but also on the prevalence of osteoporosis. The population-wide effects of a pathological factor cannot be predicted without knowledge of the prevalence of its causal complements in the population.

Hogben's example of yellow shank disease in chickens provides a memorable example of how population effects of a given causal agent cannot be separated from the prevalence of its causal complements. The trait of yellow shank in poultry is a condition expressed only in certain genetic strains of fowl when fed yellow corn. A farmer with a susceptible flock who switches from white corn to yellow corn will perceive the disease to be caused by yellow corn. A farmer who feeds only yellow corn to a flock with mulltiple strains of chickens, some of which are susceptible to the yellow shank condition, will perceive the condition to be caused by genetics. In fact, the effects of yellow corn cannot be separated from the genetic makeup of the flock, and the effect of the genetic makeup of the flock cannot be separated from the presence of yellow corn in the environment. To ask whether yellow shank disease is environmental or genetic is like asking whether the sound of a faraway drum is caused by the drum or the drummer – one does not act without the other. This is what we mean by causal interaction.

Agent, Host, and Environment

Causal components can be classified as agent, host, or environmental factors. Agent are biological, physical, and chemical factors whose presence, absence, or relative amount (too much or too little) are necessary for disease to occur. Host factors include personal characteristics and behaviros, genetic predispositions, and immunologic and other susceptibility-related factors that influence the likelihoood or severity of disease. Host factors can be physiological, anatomical, genetic, behavioral, occupational, or constitutional. Environmental factors are external conditions other than the agent that contribute to the disease process. Environmental factors can be physical, biological, social, economic, or political in nature.

Medical Statistics – Charting Continuous Metric Data

December 23, 2015 Uncategorized No comments

Screen Shot 2015-12-23 at 7.30.00 PMThe Histogram

A continuous metric variable can take a very large number of values, so it is usually impractical to plot them without first grouping the values. The grouped data is plotted using a frequency histogram, which has frequency plotted on the vertical axis and group size on the horizontal axis.

A histogram looks like a bar chart but without any gaps between adjacent bars. This emphasises the continuous nature of the underlying variable. If the groups in the frequency table are all of the same width, then the heights of the bars in the histogram will be proportional to their frequency.

One limitation of the histogram is that it can represent only one variable at a time (as in the case of the pie chart), and this can make comparisons between two histograms difficult because if you try to plot more than one histogram on the same axes, invariably parts of one chart will overlap the other.

The Box (and Whisker) Plot

Figure 3.17 shows an example of what is known as a box plot, or more precisely, a box and whisker plot. This form of chart can be used with either ordinal data or metric data, but it is more common with the latter, as in this example, which shows sperm concentration among survivors of childhood cancer and a control (non-cancer) group.Screen Shot 2015-12-23 at 7.42.10 PM

The bottom and top of the box mark what are called the 25th and 75th percentiles, respectively. The 25th percentile is the value below which 25 percent of the values in the sample lie (and thus 75 percent exceed this value) – about 50×106/mL for the control group. The 75th percentile is the value above which 25 percent of the sample lie (and 75 percent below) – about 120×106/mL. The line across the inside of the box (not necessarily in the middle) marks the value which divides the sample into two equal numbers of values – 50 percent below this value and 50 percent above it, about 85×106/mL here, is the 50th percentile. The bottom and top of each whisker mark the smallest and the largest values in the sample, respectively.

Red Blood Cell Analytic Parameters

December 14, 2015 Hematology, Laboratory Medicine No comments , , , , , ,

blood_transfusionRBCs are defined by three quantitative values: the volume of packed red cells or hematocrit (Hct), the amount of hemoglobin (Hb), and the red cell concentration per unit volume. Three additional indices describing average qualitative characteristics of the red cell polupation are also collected. These are mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC).

Volume of Packed Red Cells (Hematocrit)

The hematocrit is the proportion of the volume of a blood sample that is occupied by red cells. Hct may be determined manually by centrifugation of blood at a given speed and time in a standardized glass tube with a uniform bore. The height of the column of red cells after centrifugation compared with total blood sample volume yields the Hct. However, several sources of error are inherent in the manual methods of measuring Hct technique. The spun Hct measures the red cell concentration, not red cell mass. Therefore, patients in shock or with volume depletion may have normal or high Hct measurements due to hemoconcentration despite a decreased red cell mass. In addition, technical sources of error in manual Hct determinations usually arise from inappropriate concentrations of anticoagulants, poor mixing of samples, or insufficient centrigugation. Another inherent error   in manual Hct determinations arises from trapping of plasma in the red cell column. This may account for 1% to 3% of the volume in microcapillary tube methods, with macrotube methods trapping relative more plasma. It should be noted that abnormal red cells (e.g., sickle cells, microcytic cells, macrocytic cells, or spherocytes) often trap higher volumes of plasma due to increased cellular rigidity, possibly accounting for up to 6% of the red cell volume. Very high Hcts, as in polycythemia, may also have excess plasma trapping. Manual Hct methods typically have a precision coefficient of variation (CV) of approximately 2%.

Automated analyzers do not depend on centrifugation techniques to determine Hct, but instead calculate Hct by direct measurements of red cell number and red cell volume (Hct = red cell number X mean red cell volume). Automated Hct values closely parallel manually obtained measurements, and the manual Hct is used as the reference method for hematology analyzers (wtih correction for the error induced by plasma trapping). Errors of automated Hct calculation are more common in patients with polycythemia or abnormal plasma osmotic pressure. Manual methods of Hct determination may be preferable in these cases. The precision of most automated Hcts is <1% (CV). 

Hemoglobin Concentration

Hemoglobin (Hb) is an intensely colored protein, allowing its measurement by spectrophotometric techniques. Hemoglobin is found in the blood in a variety of forms, including oxyhemoglobin, carboxyhemoglobin, methemoglobin, and other minor components. These may be converted to a single stable compound, cyanmethemoglobin, by mixing blood with Drabkin solution. Sulfhemoglobin is not converted but is rarely present in significant amounts. The main errors in measurement arise from dilution errors of increased sampel turbidity due to improperly lysed red cells, leukocytosis, or increased levels of lipid or protein in the plasma. With automated methods the precision for hemoglobin determinations is <1% (CV).

Red Cell Count

Manual methods for counting red cells have proven to be very inaccurate, and automated counters provide a much more accurate reflection of red cell numbers. Both erythrocytes and leukocytes are counted after whole blood dilution in an isotonic solution. As the number of red cells greatly exceeds the number of white cells, the error introduced by counting both cell types is negligible. However, when marked keukocytosis is present, red cell counts and volume determinations may be erroneous unless corrected for white cells. The observed precision for RBC counts using automated hematology analyzers is <1% (CV) compared with a minimum estimated value of 11% with manual methods.

Mean Corpuscular Volume

The MCV is usually measured directly with automated instruments but may also be calculated from the erythrocyte count and the Hct by means of the following formula:

MCV = Hct (L/L) X 1,000/red cell count (1012/L)

The MCV is measured in femtoliters (fl, or 10-15 L). Using automated methods, this value is derived by dividing the summation of the red cell volumes by the erythrocyte count. The CV in most automated system is approximately 1%, compared to ~10% for manual method. Agglutination of cells, as in cold agglutinin disease or paraproteinemia, may result in a falsely elevated MCV. Most automated analyzers gate out MCV values above 360 fl, thereyby excluding most cell clumps, although this may falsely lower Hct determinations. In addition, severe hyperglycemia (glucose >600 mg/dL) may cause osmotic swelling of the red cells, leading to a falsely elevated MCV.

Mean Corpuscular Hemoglobin

MCH is a measure of the average hemoglobin content per red cell. It may be calcuated manually or by automated methods using the following formula:

MCH = hemoglobin (g/L)/red cell count (1012/L)

MCH is expressed in picograms (pg, or 10-12 g). Thus, the MCH is a reflection of hemoglobin mass. MCH measurements may be falsely elevated by hyperlipidemia, as increased plasma turbidity will erroneously elevate hemoglobin measurement. Centrifugaton of the blood sample to eliminate the turbidity followed by manual hemoglobin determination allows correction of the MCH value. Leukocytosis may also spuriously elevate MCH values. The CV for automated analysis of MCH is <1% in most modern analyzers, compared with approximately 10% for manual methods.

Mean Corpuscular Hemoglobin Concentration

The average concentration of hemoglobin in a given red cell volume or MCHC may be calcualted by the following formula:

MCHC = hemoglobin (g/dL)/Hct (L/L)

The MCHC is expressed in grams of hemoglobin per deciliter of packed RBCs, representing the ratio of hemoglobin mass to the volume of red cells. With the exception of hereditar spherocytosis and some cases of homozygous sickle cell or hemoglobin C disease, MCHC values will not exceed 37 g/dL. This level is close to the solubility value for hemoglobin, and further increases in Hb may lead to crystallization. Factors that alert the accuracy of both Hct and hemoglobin can affect the precision of MCHC.

Red Cell Distribution Width

The red cell distribution width (RDW) is a red cell measurement that quantitates cellular volume heterogeneity reflecting the range of red cell sizes within a sample.

Reticulocyte Counts

Determination of the numbers of reticulocytes or immature, non-nucleated RBCs that still retain RNA provides useful information about the bone marrow's capacity to synthesize and release red cells in response to a physiologic challenge, such as anemia. In the past, reticulocyte counts were performed manually using supravital staining with methylene blue that will stain precipitated RNA as a dark blue meshwork or granules (at least two per cell), allowing retriculocytes to be identified and enumerated manually. Because there are relatively low numbers of reticulocytes, the CV for reticulocyte counting is relatively large (10% to 20%).

To increase accuracy of reticulocyte counting, automated detection methods to detect staining allow for many more cells to be analyzed, thereby increasing accuracy and precision of counts. Most of the newest automated hematology analyzers have automated reticulocyte counts to be included with routine complete blood count parameters. Reticulocytes are detected by a fluorescent dye that binds to RNA. Comparisons of stand-alone instruments and integrated hematology analyzers demonstrate superior accuracy when compared to manual counting methods, with CVs of 5% to 8%.


Update on Aug 2nd 2017

Ontogeny of Hemoglobin

The hemoglobin composition of the erythrocyte depends on when in gestation or postnatal development it is measured. This is a result of sequential activation and inactivation (i.e., switching) among genes within the alpha- and non-alpha-globin gene clusters. What controls these switches in globin gene transcription is not understood. The two early embryonic hemoglobins consist of ζ- and ε-globin chains (Hb Gower-1) and α- and ε-globin chains (Hb Gower-2). The ζ-globin gene is akin to the α-globin genes but is expressed only during early embryogenesis. The ε-embryonic globin chain is a β-like element. The combination of ζ- and γ-globin chains forms hemoglobin Portland. These early hemoglobins are made primarily in yolk-sac erythroblasts and are detectable only during the very earliest stages of embryogenesis except in certain pathologic states, in which they may persist until gestation is complete. The major hemoglobin of intrauterine life is HbF, which consists of two α- and γ-globin chains. Expression of the γ-globin gene begins early in embryogenesis, peaks during midgestation, and begins a rapid decline just before birth. By 6 months of age in normal infants, only a remnant of prior γ-globin gene expression remains. The level of HbF in the blood declines rapidly thereafter to less than 1% of the total. Expression of the α-globin gene starts early in the first trimester, peaks quickly, and is sustained for life. Expression of the β-globin gene also commences early in gestation and reaches its zenith within a few months after birth. The combination of α-globin with β-globin cahins forms hemoglobin A (HbA), the predominant hemoglobin of postnatal life. Adult cells also contain HbA2. The δ-globin gene, which directs synthesis of the non-α-globin chain of HbA2, is very inefficiently expressed. Only low levels of HbA2 are present; defects in the δ-globin gene are of no clinical consequence. In adult blood, HbF is not evenly distribbuted among erythrocytes and is present in only a very small number of RBCs, called F cells. HbA2 is present in all RBCs, albeit at levels less than 3.5% of the total hemoglobin in adult life.

Medical Statistics – Types of Data

December 7, 2015 Medical Statistics No comments , , , , ,

national-center-for-health-statisticsCategorical Data

Nominal Categorical Data

Nominal data are "nominal" because it usually relates to named things, such as occupation, blood type, or ethnicity. It is particularly not numeric. It is "categorical" because we allocate each value to a specific category. Therefore, for example, we allocate each M value to the category Male and each F value to the category Female. Notice two things about this data, which is typical of all nominal data:

  • The data do not have any units of measurement.
  • The ordering of the categories is arbitary. In other words, the categories cannot be ordered in any meaningful way.

Ordinal Categorical Data

Let's now consider data from the Glasgow Coma Scale (GCS). As the name suggests, this scale is used to assess the level of consciousness after head injury. A patient's GCS score is judged by the sum of responses in three areas: eye opening response, verbal response, and motor response. Notice particuarly that these responses are assessed rather than measured (as weight, height or temperature would be). The GCS score can vary from 3 (deeply unconscious) to 15 (fully conscious). In other words, there are 13 possible categories of consciousness.

Suppose that we have two motor-cyclists, let us call them Wayne and Kylie, who have been admitted to the Emergency Department with head injuries following a road traffic accident. Wayne has a GCS of 5 and Kylie a GCS of 10. We can say the Wayne's level of consciousness is less than that of Kylie (so we can order the values) but we can't say exactly by how much. We certainly cannot say that Wayne is exactly half as conscious as Kylie. Moreover, the levels of consciousness between adjacent scores are not necessarily the same; for example, the difference in the levels of consciousness between two patients with GCS scores of 10 and 11 may not be the same as that between patients with scores of 11 and 12. It's therefore important to recognise that we cannot quantify these differences.

GCS data is ordinal categorical data. It is ordinal because the values can be meaningfully ordered, and it is categorical because each value is assigned to a specific category. Notice two things about this variable, which is typical of all ordinal variables:

  • The data do not have any units of measurement (so the same as that for nominal variables).
  • The ordering of the categories is not arbitary, as it is with nominal variables.

The seemingly numeric values of ordinal data, such as GCS scores, are not in fact real numbers but only numeric labels which we attach to category values (usually for convenience or for data entry to a computer). The reason is of course that GCS data, and the data generated by most other scales, are not properly measured but assessed in some way by a clinician or a researcher, working with the individual concerned. This is a characteristic of all ordinal data.


Metric Data

Discrete Metric Data

Discrete metric data comes from counting. Counting is a form of measurement – hence the name "metric". The data is "discrete" because the values are in discrete steps; for example, 0, 1, 2, 3 and so on. Parity data comes from counting. Other examples of discrete metric data would include number of deaths, number of pressure sores, number of angina attacks, number of hospital visits and so on. The data produced are real numbers, and in contrast to ordinal data, this means that the difference between parities of 1 and 2 is exactly the same as the difference between parities of 2 and 3, and a parity of 4 is exactly twice a parity of 2. In short:

  • Metric discrete variables can be counted and can have units of measurement – "numbers of things".
  • They produce data which are real numbers and are invariably integers (i.e., whole numbers).

Continuous Metric Data

Birthweight is a metric continuous variable because it can be measured. If we want to know someone's weight, we can use a weighing machine; we don't have to look at the individual and make a guess (which would be approximate) or ask them how heavy they are (very unreliable). Similarly, if we want to know their diastolic blood pressure, we can use a sphygmomanometer. Guessing or asking is not necessary. But, what do we mean by "continuous"? Compare a digital clock with a more old-fashioned analogue clock. With a digital clock, the seconds are indicated in discrete steps: 1, 2, 3 and so on. With the analogue clock, the hand sweeps around the dial in a smooth, continuous movement. In the same way, weight is a continuous variable because the values form a continuum; weight does not increase in steps of 1 g.

Because they can be properly measured, these data are real numbers. In contrast to ordinal values, the difference between any pair of adjacent values, say 4000 g and 4001 g is exactly the same as the difference between 4001 g and 4002 g, and a baby who weighs 4000 g is exactly twice as heavy as a baby of 2000 g. Some other examples of metric continuous data include blood pressure (mm Hg), blood cholesterol (ug/mL), waiting time (minutes), body mass index (kg/m2), peak expiry flow (1 per min) and so on. Notice that all of these variables have units of measurement attached to them. This is characteristic of all metric continuous data. To sum up:

  • Metric continuous data result from measurement and they have units of measurement.
  • The data are real numbers.

How to Distinguish Between Different Types of Datas

The easiest way to tell whether data is metric is to check whether it has units attached to it, such as g, mm, number of pressure sores and number of deaths. If not, it may be ordinal or nominal – the former if values can be put in any meaningful order. Figure 1-6 is an aid to variable-type recognition.Screen Shot 2015-12-07 at 6.25.24 PM