METHODS: In this cross-sectional study, the 15-item MAR-Scale was administered to 665 patients with hypertension who attended one of the four government primary healthcare clinics in the Hulu Langat and Klang districts of Selangor, Malaysia, between early December 2012 and end-March 2013. The construct validity was examined in two phases. Phase I consisted of translation of the MAR-Scale from English to Malay, a content validity check by an expert panel, a face validity check via a small preliminary test among patients with hypertension, and exploratory factor analysis (EFA). Phase II involved internal consistency reliability calculations and confirmatory factor analysis (CFA).
RESULTS: EFA verified five existing factors that were previously identified (i.e. issues with medication management, multiple medications, belief in medication, medication availability, and the patient's forgetfulness and convenience), while CFA extracted four factors (medication availability issues were not extracted). The final modified MAR-Scale model, which had 11 items and a four-factor structure, provided good evidence of convergent and discriminant validities. Cronbach's alpha coefficient was > 0.7, indicating good internal consistency of the items in the construct. The results suggest that the modified MAR-Scale has good internal consistencies and construct validity.
CONCLUSION: The validated modified MAR-Scale (Malaysian version) was found to be suitable for use among patients with hypertension receiving treatment in primary healthcare settings. However, the comprehensive measurement of other factors that can also lead to non-adherence requires further exploration.
METHODS: In 14 Central England general practices, a novel case-finding tool (Familial Hypercholetserolaemia Case Ascertainment Tool, FAMCAT1) was applied to the electronic health records of 86 219 patients with cholesterol readings (44.5% of total practices' population), identifying 3375 at increased risk of FH. Of these, a cohort of 336 consenting to completing Family History Questionnaire and detailed review of their clinical data, were offered FH genetic testing in primary care.
RESULTS: Genetic testing was completed by 283 patients, newly identifying 16 with genetically confirmed FH and 10 with variants of unknown significance. All 26 (9%) were recommended for referral and 19 attended specialist assessment. In a further 153 (54%) patients, the test suggested polygenic hypercholesterolaemia who were managed in primary care. Total cholesterol and low-density lipoprotein-cholesterol levels were higher in those patients with FH-causing variants than those with other genetic test results (p=0.010 and p=0.002).
CONCLUSION: Electronic case-finding and genetic testing in primary care could improve identification of FH; and the better targeting of patients for specialist assessment. A significant proportion of patients identified at risk of FH are likely to have polygenic hypercholesterolaemia. There needs to be a clearer management plan for these individuals in primary care.
TRIAL REGISTRATION NUMBER: NCT03934320.
DESIGN: Cross-sectional survey conducted between April and May 2017.
SETTING: Forty public clinics in Malaysia.
PARTICIPANTS: A total of 956 adult patients with T2D and/or hypertension were interviewed.
MAIN OUTCOME MEASURES: Patient experience on SMS was evaluated using a structured questionnaire of the short version Patient Assessment of Chronic Illness Care instrument, PACIC-M11. Linear regression analysis adjusting for complex survey design was used to determine the association of patient and clinic factors with PACIC-M11 scores.
RESULTS: The overall PACIC-M11 mean was 2.3(SD,0.8) out of maximum of 5. The subscales' mean scores were lowest for patient activation (2.1(SD,1.1)) and highest for delivery system design/decision support (2.9(SD,0.9)). Overall PACIC-M11 score was associated with age, educational level and ethnicity. Higher overall PACIC-M11 ratings was observed with increasing difference between actual and expected consultation duration [β = 0.01; 95% CI (0.001, 0.03)]. Better scores were also observed among patients who would recommend the clinic to friends and family [β = 0.19; 95% CI (0.03, 0.36)], when health providers were able to explain things in ways that were easy to understand [β = 0.34; 95% CI (0.10, 0.59)] and knew about patients' living conditions [β = 0.31; 95% CI (0.15, 0.47)].
CONCLUSIONS: Our findings indicated patients received low levels of SMS. PACIC-M11 ratings were associated with age, ethnicity, educational level, difference between actual and expected consultation length, willingness to recommend the clinic and provider communication skills.
DESIGN: Retrospective study SETTING: A primary care clinic in a university hospital in Malaysia.
PARTICIPANTS: Random sampling of 1403 patients aged 30 years and above without any CV event at baseline.
OUTCOMES MEASURES: The effect of the number of BP measurement for calculation of long-term visit-to-visit BPV in predicting 10-year CV risk. CV events were defined as fatal and non-fatal coronary heart disease, fatal and non-fatal stroke, heart failure and peripheral vascular disease.
RESULTS: The mean 10-year SD of systolic blood pressure (SBP) for this cohort was 13.8±3.5 mm Hg. The intraclass correlation coefficient (ICC) for the SD of SBP based on the first eight and second eight measurements was 0.38 (p<0.001). In a primary care setting, visit-to-visit BPV (SD of SBP calculated from 20 BP measurements) was significantly associated with CV events (adjusted OR 1.07, 95% CI 1.02 to 1.13, p=0.009). Using SD of SBP from 20 measurement as reference, SD of SBP from 6 measurements (median time 1.75 years) has high reliability (ICC 0.74, p<0.001), with a mean difference of 0.6 mm Hg. Hence, a minimum of six BP measurements is needed for reliably estimating intraindividual BPV for CV outcome prediction.
CONCLUSION: Long-term visit-to-visit BPV is reproducible in clinical practice. We suggest a minimum of six BP measurements for calculation of intraindividual visit-to-visit BPV. The number and duration of BP readings to derive BPV should be taken into consideration in predicting long-term CV risk.