By Malingi, Timothy KoeMSc IPH

Background and classic LQAS MC-LQAS Application to malaria control within the

research activity con

LQAS today is a statistical quality control method

Developed in the 1920’s attributable to Dodge and Romig’s work. Mainly to control quality of industrially produced goods on the principle that:◦ Supervisor inspects a lot of goods from a

production unit or assembly line◦ If number of defective goods exceeds a pre-

determined allowable number, then the lot is rejected; otherwise classified as acceptable quality

◦ Number of allowable defective goods is based on a production standard and statistically determined sample size

Transitioned into health systems to assess health care services, health behaviors and disease burden.

Production standard is a predetermined population coverage target set by managers

Lot consists of a supervision area e.g. a community or health facility catchment

LQAS data collected at multiple time points can be used to measure the spatial variation or behavior change

Robertson and Valadez (2006)

Implemented as part of stratified random sampling design

Uses small samples often 19 per strata or lot

Sample determines whether coverage by a health intervention reaches a specific target by using a statistically determined decision rule(DR)

DR is the minimum number of individuals in the sample that should have received an intervention

Classic LQAS uses one decision rule, sample size 19 and 2 threshold values, that define lower and upper regions.

Each lot is then classified as ‘Acceptable’ or ‘Unacceptable’ against the target.

Since LQAS is based on rigorous random sampling, results from the catchment area can be aggregated for provincial or national level coverage.

Statistical underpinning is the operating characteristic (OC)curve

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Supervision Area Coverage

Operating Characteristic Curve for

Sample of 19 and Decion Rule of 13

Popular tool◦ Ease of use◦ Straight forward implementation◦ Rapidity of results◦ Sound statistical underpinning

Valadez 2011

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Reached Target

Below the TargetOr Below Average

Valadez 2011

Identify the reasons for program problems

Develop targeted solutions

Maintain the program at the current level

Identify Supervisors and Health Workers that can help other HealthWorkers improve their performance

Reached Target

Below the TargetOr Below Average

Control of misclassification (α-alpha error & β-beta error)

Requirement for finer classification in disease control and treatment recommendations e.g. WHO treatment guidelines for schistosomiasis are linked to three way classification of prevalence of infection

Inevitable extension to LQAS

Focuses into three classification of ‘low’, ‘middle’, ‘high’

Defines two decision rules e.g. (d1 and d2 ) to yield least misclassification error for a given sample size (n)

Probability of correct classification remains high at upper and lower thresholds

On analysis, classify ‘low’ if the successes x from total n observations is less than or equal to d1 ;classify ‘high’ if x is greater than d2; otherwise, classify ‘middle’

Uses sample size of n=28, decision rules d1=2 and d2=10

With d2=10, elicits grey region around the upper threshold of 40% favouring classification of category 3( high) over category 2 (Middle).

Thus, grey regions ranging from 0.06 to 0.15 and 0.30 to 0.45 respectively. That is a better trade-off , on divides of producer and consumer risks

Sample of 28, if 2 or fewer of these observation are malaria RDT+, then the area is classified as category 1, termed ‘low’.

For 10 or more counts malaria RDT+, area is classified as ‘category 3 termed ’high’

Counts between 3 and 9 classify area as category 2 termed ‘Middle’.

Design gives 80% chance of correctly classifying a given locale at each of the listed thresholds. A double sample size of 56 increases the power but often obtain similar results

Malaria prevalence threshold values are set at PfPR of 10% and 40%.

Locale with below 10% is of low prevalence, 10%-40% is moderate prevalence, above 40% is high prevalence

MC-LQAS methodology classifies areas into these three categories using RDTs for PfPR.

MC-LQAS measures malaria intervention indicators and classify locale.

MC-LQAS data maps locale malaria prevalence

Classifications of ‘low’, ‘middle’ and ‘high’ for link interventions to the prevalence detected

Category 3(high) is targeted for complete set of malaria interventions(IPT, ITNs, case management and IRS)

Category 2 (middle) receive ITNs, IPT and case management

Category 3 (low) maintain strategies towards elimination agenda.

The reverse is true for performance indicators measured in terms of achieving set targets.

• Reliable malaria density data is lacking in most programs at levels where management decisions are made.

• Research contributes to M&E of the malaria control program’s impact on the prevalence at sub-district or lower levels (parish), classifying these areas to target cost-effective control interventions.

• Test MC-LQAS for malaria control (1st Time Use)

Aim : To assess malaria prevalence for priority cost-effective and targeted interventions

Objectives1. To classify and map malaria prevalence at the

parish level within the district.2. To validate the utility of Multiple Classification

LQAS (MC-LQAS) during the survey. 3. To measure malaria control performance

indicators and coverage within the sub- counties and parishes.

4. To disseminate findings as evidence for decisions to prioritize malaria intervention strategies.

Ethical application completed and community assent sought

Trained research assistants Data collection through questionnaires and

blood samples for malaria test and Hb estimation

Sampling conducted to identify eligible child of ages 6months to 9 years.

Analyzed 448 cases, 6 months to 9 years◦ Malaria prevalence◦ Malaria outcome indicators

Demonstrated high prevalence of malaria & anemia, low coverage of interventions and their performance, all with marked variations

Such variations is often masked from aggregate measures reported in large country surveys

MC-LQAS is effective to monitor malaria endemicity and control interventions providing reliable data and classification that can aid target interventions.

It can be replicated

Use data generated as baseline and re-define targets to monitor progress

Draw attention of the malaria situation to the malaria control program

Replicate the studies

CCM management and staff CCM Executive Director, Filippo Spagnuolo

& Head of Programs, Valeria Pecchioni Professor Joseph Valadez, LSTM Dr Olives, University of Washington Professor Feiko ter Kuile, LSTM ChildFund International, Uganda MSH Uganda Uganda Christian University, Mukono Family support