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How to investigate Adherence to Antiretroviral Treatment ... - INRUD

How to investigate Adherence to Antiretroviral Treatment ... - INRUD

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<strong>How</strong> <strong>to</strong> Investigate <strong>Adherence</strong> <strong>to</strong> <strong>Antiretroviral</strong> <strong>Treatment</strong> in Health Facilities: <strong>Adherence</strong> Indica<strong>to</strong>rs<br />

There is a difference between having a series of small gaps and one long gap in terms of<br />

adherence. For this reason the other indica<strong>to</strong>r measures the rate of patients with gaps in<br />

treatment of 30 days or more. Between the four countries in the feasibility trial, this varied<br />

from 2 percent <strong>to</strong> 18 percent of patients, but in the worst facility 60 percent of patients had<br />

such a gap. This may be because they dropped out permanently through defaulting or death,<br />

or that they remained in treatment. This would be an important follow-up question.<br />

Indica<strong>to</strong>r 3. Percentage of patients who experienced a gap in ARV availability of more<br />

than 30 days in a row during a defined period.<br />

Rationale<br />

Source of data<br />

Data collection<br />

Computation<br />

Comments<br />

Pitfalls<br />

A gap in medicine supply of more than 30 days has serious implications for<br />

resistance and treatment failure<br />

Pharmacy records<br />

Based on the same sample of 100 patients and the same data on dates of<br />

dispensing and number of days dispensed since index visit<br />

For individual patient: Discontinuation—If patient ever experiences a gap of<br />

> 30 days between the end of days’ supply in one dispensing (or end of <strong>to</strong>tal<br />

days’ supply available if ARVs remain from previous dispensings) and date<br />

of the next dispensing<br />

For facility: Percent discontinuation (number of patients experiencing a gap<br />

in ARV treatment > 30 days/number of patients) × 100<br />

This can be calculated au<strong>to</strong>matically using the spreadsheet analysis <strong>to</strong>ol<br />

As with the previous indica<strong>to</strong>r, identifying reliable patient-specific<br />

longitudinal records may be a problem in some systems, if one visit is not<br />

recorded then the patient will appear <strong>to</strong> have a gap of 30 days, so a low<br />

measure on the indica<strong>to</strong>r may reflect poor record keeping.<br />

Patient Attendance and Defaulting Measure<br />

A missed appointment should trigger programme action <strong>to</strong> reach out <strong>to</strong> patients at risk of<br />

defaulting on their treatment. <strong>How</strong>ever, because the patient may have had extra days of<br />

medicine, attendance failure within three days of an appointment can also be a trigger point.<br />

The two core performance indica<strong>to</strong>rs related <strong>to</strong> attendance are—<br />

4. Percentage of patients who attend on or before the day of their appointment<br />

5. Percentage of patients who attend within three days of their appointment<br />

The purpose is <strong>to</strong> look at a visit the patient made, note when the next appointment was made<br />

for, and then see if the patient kept the appointment. Because some programmes give certain<br />

patients three months of medicine, it is necessary <strong>to</strong> review the records <strong>to</strong> see the patient’s<br />

attendance four months before, see the date of the next appointment, and then note whether<br />

the patient’s next visit was on or before that date (indica<strong>to</strong>r 4), or within three days of that<br />

date (indica<strong>to</strong>r 5).<br />

When filling in the dispensing data collection form, alternate attendance indica<strong>to</strong>rs can be<br />

calculated easily. This means that we have two alternative attendance indica<strong>to</strong>rs—<br />

10

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