Given an individual’s serum haemoglobin level, anaemia status can be classified as either not present, mild, moderate, or severe. The serum haemoglobin cut-offs for classification of anaemia status are different for specific population groups as shown in the table below.
| Population | Mild | Moderate | Severe |
|---|---|---|---|
| Children under 5 years of age | 100 - 109 | 70 - 99 | < 70 |
| Children 5-11 years of age | 110 - 114 | 80 - 109 | < 80 |
| Children 12-14 years of age | 110 - 119 | 80 - 109 | < 80 |
| Non-pregnant women 15 years and above | 110 - 119 | 80 - 109 | < 80 |
| Pregnant women | 100 - 109 | 70 - 99 | < 70 |
| Men 15 years and above | 110 - 129 | 80 - 109 | < 80 |
The functions in the detect_anaemia function set use
these serum haemoglobin cut-offs to classify individuals from specific
population groups as either having no anaemia,
mild anaemia, moderate anaemia, or
severe anaemia. For example, a three year old child
with a serum haemoglobin level of 105 g/L can be classified as
follows:
detect_anaemia_u5(hb = 105)
#> [1] "mild anaemia"In this example, we use the detect_anaemia_u5() function
which is specific for the under 5 years of age population group. Other
functions for specific population groups are:
detect_anaemia_5to11()- for children 5 to 11 years of age;detect_anaemia_12to14()- for children 12 to 14 years of age;detect_anaemia_np_women()- for women 15 years and older who are not pregnant;detect_anaemia_pregnant()- for pregnant women; and,detect_anaemia_men()- for men 15 years and older.
The more general detect_anaemia() function can also be
used in this case:
detect_anaemia(hb = 105, group = "u5")
#> [1] "mild anaemia"In this case, we just need to specify the population group that the case corresponds to, which in this example is the under 5 years of age group (specified as “u5”). The different population age group can be specified as “5to11” for children 5 to 11 years of age, “12to14” for children 12 to 14 years of age, “np_women” for women 15 years and older who are not pregnant, “pregnant” for pregnant women, or “men” for men 15 years and older.
Correcting serum haemoglobin
Serum haemoglobin levels are affected by the altitude of where an individual lives and by an individual’s smoking status. Altitudes of 1000 metres or more increase serum haemoglobin levels. A smoker would have elevated serum haemoglobin levels and the greater the number of packs of cigarette an individual smokes in a day further increases serum haemoglobin levels.
It is recommended that serum haemoglobin be adjusted accordingly depending on the altitude of where an individuals lives and/or by an individual’s smoking status.
The recommended adjustment of serum haemoglobin based on altitude is shown in the table below:
| Altitude (metres above sea level) | Measured haemoglobin adjustment (g/L) |
|---|---|
| < 1000 | 0 |
| 1000 | -2 |
| 1500 | -5 |
| 2000 | -8 |
| 2500 | -13 |
| 3000 | -19 |
| 3500 | -27 |
| 4000 | -35 |
| 4500 | -45 |
The function get_altitude_correction() provides the
appropriate haemoglobin correction factor given altitude in metres. For
example, the haemoglobin correction factor for a person who lives in a
place at 1400 metres above sea level is determined as follows:
get_altitude_correction(alt = 1400)
#> [1] -2An adjustment of -2 g/L to measured haemoglobin at 1400 metres above sea level will be needed.
The recommended adjustment of serum haemoglobin based on smoking status is shown in the table below:
| Smoking status | Measured haemoglobin adjustment (g/L) |
|---|---|
| Non-smoker | 0 |
| Smoker (all) | -0.3 |
| ½ -1 packet/day | -0.3 |
| 1-2 packets/day | -0.5 |
| ≥ 2 packets/day | -0.7 |
The function get_smoking_correction() provides the
appropriate haemoglobin correction factor given smoking status. For
example, the haemoglobin correction factor for a person who smokes 1.5
packs a day is determined as follows:
get_smoking_correction(smoke = 1.5)
#> [1] -0.5An adjustment of -0.5 g/L to measured haemoglobin of an individual who smokes 1.5 packs of cigarette per day will be needed.
The function correct_hb() provides adjusted haemoglobin
values given altitude above sea level and smoking status. For example,
an individual with a haemoglobin of 105 g/L who is a smoker at 1.5 packs
a day and lives 1400 metres above sea level will have an adjusted
haemoglobin of:
correct_hb(hb = 105, alt = 1400, smoke = 1.5)
#> [1] 102.5This individual will have a corrected haemoglobin of 102.5 g/L.