The Efficacy of Primary and Tertiary Specialist Care Collaboration and Mobile Complications Screening for Improving the Management of Diabetes at Primary Health Care Level in Tshwane District - A Cluster Randomized Trial
The Efficacy of Primary and Tertiary Specialist Care Collaboration and Mobile Complications Screening for Improving the Management of Diabetes at Primary Health Care Level in Tshwane District - A Cluster Randomized Trial
A pragmatic cluster randomised controlled trial will be done where the intervention will be a mobile screening team visiting selected PHC facilities in Tshwane district. It will provide education and screening for diabetic complications (foot, kidney, cardiac and retinal complications). Six clinics will receive the intervention and six clinics will serve as controls. Six hundred patients will be recruited (2 x 300).
The screening results will be evaluated by an expert panel at tertiary care level and an individualised patient management plan will be compiled. This plan will be communicated to the family physician and integration team at the clinic for further management or referral of the patients. Laser therapy will be available on the mobile clinic for patients that require it (as assessed by an Ophthalmologist who will review the retinal photos).
A baseline evaluation (including HbA1c, serum creatinine, lipogram and urine albumin-creatinine ratio) will be done to determine current disease management at patient and health facility level, followed by the intervention and a follow-up visit a year later. The main outcome measures are glucose, lipid and blood pressure control as well as the percentage of patients screened and referred for diabetes complications.
A cost effectiveness analysis will be done to estimate the added cost per added complication prevented or referred.
The potential implications for improving diabetes care and preventing long term complications are extremely important. The study results will be used to help plan future health care services for people with diabetes mellitus in the region.
Diabetes Mellitus (DM) is a major metabolic disease that is characterised by a reduced or impaired capability of the body to regulate its blood glucose levels. The disease is now reaching pandemic levels. The global prevalence for DM (across all age groups) was estimated to be 2,8% in 2000, and it will rise to 4,4% by 2030. In real numbers this implies from 171 million people to 366 million people will be affected globally. This rise will certainly have an impact of the health care system of many countries. Most of the DM patients will be managed at primary health care level.
It has become clear that the difficulties in achieving a satisfactory level of health care delivery for DM patients are not related to the availability of knowledge of the disease, nor the best diagnostic and therapeutic procedures, but rather to system and organisational problems. Many of the complications of diabetes are potentially preventable or can be minimized if earlier action has been taken. The chronic nature of the disease and the need for the empowerment of the patient and the health workers in understanding the disease to ensure optimal glucose control makes DM an ideal disease to test new models of health care delivery which can include patient empowerment, health worker empowerment and the use of telemedicine and E-Health .
It is believed that the current primary health care approach is not allowing for sufficient time and personnel to provide patients with optimal care and to screen for diabetic complications. Currently only the blood pressure and random blood glucose measures are taken at Primary Health Care (PHC) facilities, even though the national policy for the management of diabetes prescribes the monitoring of complications at least once a year.
This study investigates the benefits of a comprehensive mobile screening effort to identify and to improve the early detection of diabetes-related complications. With the expected improvement in disease management and patient specific outcomes, this study will contribute to policy changes for diabetes management at primary health care level.
This study differs from other studies that evaluated complications in isolation. It aims to evaluate the efficacy of a complex intervention that provides an interface for Specialist from Family Medicine and Internal Medicine to collaborate and includes a mobile comprehensive complication screening service. The study also provides opportunity for training of clinic staff as well exposure and training of Clinical Associates regarding diabetes care and complication screening and management. It is envisaged that the structured screening and review programme will provide evidence for ways to ensure future collaboration enhancing diabetes patient outcomes.
The aim of this pragmatic study is to investigate the effectiveness of a mobile complication screening team and expert review and collaboration between tertiary care specialist and primary care generalist for improving optimal, comprehensive diabetic care at the primary health care level in one of the districts in South Africa, using a cluster randomized control design.
STUDY DESIGN A two arm Pragmatic Cluster Randomised Controlled Trial (C-RCT) with an active arm consisting of a mobile screening service and a control arm which receives standard care.
2. A cross sectional descriptive study of patients not enrolled in the trial but attending the screening visit (in the clinics known as the intervention clinics).
SETTING Primary Health Care (PHC) Clinics that are situated in the Tshwane Health District, Gauteng Province, South Africa.
PATIENT/RESEARCH OBJECT SELECTION All patients with type 2 DM and type 1 DM with minimum of five years duration will be considered for both the intervention and the comparison arm.
Only patients over the age of 18 years will be enrolled into the study (Children with diabetes are not managed at Primary Care level).
The first 50 patients meeting selection criteria will be enrolled in the study (maximum 5 type 1 patients per clinic). The rest of the diabetics at the clinic will also be invited onto the mobile clinic for the purposes of screening for complications, but their data will be kept separate from the study population's data. It will also be analysed separately.
The study budget only allows for laboratory testing of the 600 study participants.
Sample Size:
Prior to randomization all eligible clinics will be stratified according to the following criteria and intervention and control clinics will be matched accordingly:
The investigators have no reason to believe that quality of care indicators or HbA1c will differ between clinics.
A restricted randomization procedure using a random number table will be carried out by an independent researcher to ensure equal numbers of clinics in each group.
The sample size is based on a cluster sampling estimate done in STATA Intercooled Version 10.
Primary outcome 1: Lowering of HbA1c from 9.5% to 8.5% (thus 1% reduction) using ANCOVA adjusting for baseline HbA1c; SD=2.4 &r=0.65,ICC=0.05 for continuous variable; Alpha-value= 0.05 and Power of 90% = 198 patients in each arm in 12 clusters are required.
Primary outcome 2: DM Complications Detection Proportion improvement from 10% to at least 30% (thus 20% improvement); ICC=0.02 for binary variable; Alpha=0.05 and power=90% giving an estimated sample size of 275 in each arm.
In order to ensure adequate randomization and compensate for possible loss to follow up 300 patients will be evaluated in each arm (at baseline and at final evaluation).
DATA ANALYSIS:
The unit of analysis in Cluster RCT's can change, depending on the level of inference:
Baseline and end of study process measures and outcome measures:
Data will be entered twice (and validated) in Epidata (V3.1). Analysis will be done in Stata Intercooled Version 11. The analysis would use ANCOVA, linear regression and logistic regression adjusting for baseline values and the effect of clustering and/or mixed effects modelling.
Descriptive analysis of the study population will be done, before between cluster- and within cluster analysis will be performed.
Inclusion Criteria:
Exclusion Criteria: