Microsoft word - kipsongo clinic 2011 v2.doc

Analysis of patient data
from the Global Health
Partnership mobile clinic at
Kipsongo, Kitale, Kenya on
27 July 2011

Consultant Statistician
Development and Health Informatics Ltd

This report is a brief analysis of patient data collected at the Global Health Partnership mobile clinic
held in Kipsongo, Kitale, Kenya on 27 July 2011. An estimated 504 patients were seen at the clinic, but
this analysis is based on only the 307 of those for whom it was possible to collect data.
The Kipsongo clinic was one of three held during the July 2011 mobile clinics project in the Kitale area.
The second clinic was held at St Raphael’s dispensary, Kiminini and had a larger attendance. Similar
information was collected but had not been analysed at time of writing. The third was a special clinic
providing follow-up care to members of the orphans’ programme in Kiminini.

Patient records were kept in a temporary manner. A blank slip of paper approximately A5 in size was
started for each patient. The information was added by each member of the team in sequence as
shown in Box 1.
Box 1
Recording the patient record

Team member
Information recorded
Weight, blood pressure, pulse rate, temperature Symptoms, diagnosis, medicines prescribed, referral to hospital Information for this report was abstracted from the patient record at the end of the clinic process. The information was recorded manually, based on the clinicians’ written notes, inspection of the medications dispensed, and verbal clarification from the patients themselves when necessary. The items recorded are listed in Box 2. The accuracy of dispensing by the pharmacy team was checked in the process, and a number of errors and omissions in dispensing were corrected. Box 2
Data used in this report

Information abstracted
It was not possible to record details from all patients attending the clinic, because a substantial number left immediately after receiving their medication. The total number of patients treated can be estimated on the basis of the duration of the clinic, number of clinical teams active and average length of consultation, to be around 500 (see Box 3). It is not possible to determine whether there was any systematic difference between those patients who were and were not recorded, but it is assumed for the purpose of this report that the percentage distributions found are representative of all patients seen. For some results, estimated figures are reported based on a total attendance of 504, by grossing-up using a factor of 1.65 (504/307). Box 3
Calculation of total attendance

Factors taken into account
(a) Duration of clinic time (allowing for setting up time and lunch break) (b) Average number of clinical teams active (c) Average clinical team consultation time per patient (d) Patients seen per team per hour: 60/(c) (e) Total patients seen per hour (b)x(d) (f) Estimated total patients seen (a)x(e)


Details of 307 patients were recorded. This would represent 61% of an estimated total of 504
attendances. Of the 307, 200 (65%) were female and 106 (35%) were male (1 sex not recorded).
Overall, 53% of the patients were children under 15 years of age (Table 1 and Figure 1). Children aged
1-4 years were the biggest age group, with 61 patients, making up 20% of all patients.
There was a large sex difference in the ages of those recorded, with children making up 74% of males
but only 42% of females. Half of all females attending were women between the ages of 15 and 44,
reflecting the prevalence of mothers bringing one or more children among the patient population.
The majority of patients (78%) came as part of a family group, usually consisting of an adult woman and
one or more children. Membership of a family group was estimated retrospectively based on the
sequence of patients recorded, age and place of residence, rather than recorded on site. At future
clinics, it would be useful to record family membership and structure on the patient records or in a
separate survey, so as to gain an accurate picture of the groups of people attending and be able to
compare them to the demographic patterns of the area.
Patients by sex and age group
Age group
Place of residence was recorded for all patients, with 1 missing. The large majority (63%) said they were from Matisi (Table 2). Based on 2009 population figures (provided by colleagues at Kitale General Hospital) that figure represents some 8.6 attendances per 1,000 population of Matisi, or an average of 2.7 attendances per 100 households. Grossed up to the estimated total of 504 attendances, the figures would be 14.2 attendances per 1,000 population of Matisi and an average of 4.4 attendances per 100 households. However, these figures are very broad approximations – proper estimates cannot be made without knowing the representativeness of the attenders in respect of family size and structure. Patients by place of residence
Place of residence


Although weight was recorded, height was not. Consequently it was not possible to calculate Body
Mass Index (BMI). Table 3 shows the average weight for each age group, divided by sex, along with the
minimum, maximum and standard deviation within each group. The data available do not suggest the
presence of severe malnutrition in this population.
Weight (kilograms) by age group and sex
Age group
* Number of cases too small to calculate standard deviation
Blood pressure (BP) was recorded by the nurses using a variety of instruments. If a patient’s BP was
unusually high, a second reading was taken. Table 4 shows the average BP for each age group,
divided by sex, along with the minimum, maximum and standard deviation within each group. A small
number of patients were identified with clear hypertension and were treated accordingly.
Table 4
Patients by age group, sex and blood pressure (systolic and diastolic)
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+
* Number of cases too small to calculate standard deviation

Because of the different professional backgrounds, places of training and native languages of the team
members, there were limitations in the clarity and consistency of the recording of symptoms and
diagnoses. However, it was possible to assign one or more main health problems to the majority of
Table 5 groups the number of patients by broad age group and diagnosis. The figure below illustrates
the proportional distribution of diagnoses, for all ages together. More than one presenting problem
and/or diagnosis was recorded for many of the patients. The most frequent problems were upper
respiratory tract infections, including miscellaneous coughs and similar symptoms (13% of all problems
recorded); and various forms of dermatitis of the trunk or limbs (12%). Both of these were most
common in children, and their frequency in the patient group as a whole reflects the high proportion of
attenders under age 15. The most common presenting problems among adults were chest pains,
headaches and dizziness.
Patient diagnoses (all ages)
Upper respiratory infection, misc. symptoms Diarrhoea, misc. gastrointestinal symptoms Dermatitis, misc. skin problems, head or face Asthma, bronchitis, misc. respiratory problems Table 5
Patients by age group and diagnosis

Age group (years)
Upper respiratory infection, misc. symptoms Diarrhoea, misc. gastrointestinal symptoms Dermatitis, misc. skin problems, head or face Asthma, bronchitis, misc. respiratory problems Allergic symptoms, respiratory or unspecified Out of 307 patients, 55 (18%) had a potentially serious or life-threatening condition, defined as an upper
respiratory infection or diarrhoea and/or vomiting at age <5; malaria or unspecified fever at age <14;
chest pain at age 30+; pneumonia or lower respiratory infection, hypertension, sickle cell disease or
cancer at all ages.

A relatively small number of attenders (4 per cent) were referred to local health facilities for further
investigations or follow-up. This proportion would translate into about 20 people out of the total clinic
attenders. However, the data collection is likely to have underestimated this number, as some of those
who were referred to hospital were sent there directly after the clinical consultation; moreover, others
who were referred may have left taking their record sheets with them to give to the receiving health
professional, reducing the chances of their details being recorded for analysis.
Table 6 shows the specialties to which patients were referred, and Table 7 shows the breakdown of
those referred by age group. Not all age groups are represented due to the relatively small total number
of referrals.
Referrals to hospital by specialty
Referrals to hospital by age group
Age group


A total of 679 courses of medication were prescribed to the 307 patients for whom details were
available. This would equate to approximately 1,120 courses of medication altogether. Table 8 lists the
number of prescriptions by type, drug and age group. A wide variety of proprietary names and different
spellings were used, and these have been consolidated in the table as accurately as possible.
Differences in dosage or formulation are not recorded here, for example ASAQ was dispensed in three
versions (infant, child, adult), paracetamol was prescribed in syrup form for children in some cases.
Table 9 lists the most commonly prescribed drugs in order of frequency. The most frequently prescribed
drug was Artesunate/amodiaquine (ASAQ) (13% of all prescriptions), followed by amoxicillin (11%),
paracetamol (11%) and ibuprofen (8%). Overall, 36% of patients received a broad spectrum antibiotic
(amoxicillin, ampicillin or erythromycin), 49% received a more specific antibiotic, and 28% were
prescribed ASAQ.
These figures may suggest that both ASAQ and broad-spectrum antibiotics are being prescribed more
than is necessary. This risk of resistance to antimalarials as well as antibiotics is now a well recognised
problem. In the case of ASAQ, this is likely to be because the clinician thought it best to treat for malaria
even when the symptoms were equivocal; this is understandable especially given the high proportion of
younger children among those presenting with malaria-like symptoms. In the case of broad-spectrum antibiotics, it is likely to be because of a lack of laboratory facilities to identify the specific infective organisms. In both cases, access to appropriate rapid diagnostic facilities would alleviate the problems. Medications by age group and drug type/name
Age group
Medication type
Medications by age group in order of frequency – most commonly prescribed only

Age group
* Percent of all prescriptions
In an experiment in clinic design, three separate pharmacy stations were set up in different rooms so as
to reduce the crowding at a single pharmacy. This experiment proved unsuccessful due to the lack of
central stock control; on a number of occasions medications had run out in one pharmacy when they
were still in stock in another, leading to a small number of patients potentially being denied medications

The clinic provided care for around 500 people in one day, the great majority of whom were from the
local area. Just over half of all attenders were children, and most came in family groups. Very few adult
men attended.
Most of those seen had complaints such as upper respiratory infections or dermatitis, which are
relatively common in childhood. However, some potentially severe conditions were treated or referred to
Around1,100 courses of medication were prescribed. There is some concern that broad-spectrum
antibiotics and ASAQ may have been used more than necessary; this would be prevented by the
availability of on-site lab testing.
Overall, the clinic reported here and the subsequent clinics during the 2011 project provided primary
care for at least 1,000 people in the Kitale area who did not otherwise have access to healthcare.



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