Weight Loss, Thinness, Malnutrition
Conditions
Keywords
Hypocholesterolemia, Frail Elderly, Aged, Aged, 80 and over, Cholesterol, Clinical Laboratory Information Systems, Hemoglobin A, Glycosylated, diet therapy, Anticholesteremic Agents, Health Services for the Aged, Hypoglycemic Agents, Hypoglycemia
Brief summary
The purpose of this study is to determine whether alerting primary care providers by email about low values of BMI, HbA1c% or cholesterol will affect treatment and improve overall survival and other health indexes of people older than 75 years.
Detailed description
Scientific background Interventions aimed to ameliorate malnutrition are important for elderly health and include dietary counseling and discontinuing unnecessary medicines. Emailing an alert regarding low BMI was found to improve dietary counseling numbers. Correlation between death and HbA1c% is U-shaped, with increased mortality under a 6.5% level in patients taking two anti-diabetic medicines. Sending an email alert regarding an over-tight control of diabetes was followed by a reduction in mortality. Death and cholesterol correlation is also U-shaped, with increased mortality and morbidity under 160 mg%. The investigator found no interventional study for this situation. Objectives To check whether alerting the primary care providers by email, about low values of BMI, HbA1c% or cholesterol will affect treatment and improve health indexes of people older than 75 years. Working hypotheses During a year, and relative to the control group, intervention emails may result in the following: * A decrease in mortality. * An increase in dietary counseling percentage and a decrease in prescribing anti-diabetic and cholesterol-lowering medicines. * A decrease in medical expenses and in other morbidity indexes. Type of research and methods of data collection This randomized controlled trial will be conducted entirely through the existing computer system. The participants (patients) will be assigned to the two Arms/Groups Intervention Email and Control. It has three separate interventions: a. Alerting about a significant drop in BMI. b. Alerting about a low HbA1c% level in patients taking anti-diabetics. c. Alerting about a low cholesterol level in patients taking cholesterol-lowering medicines. The alerts will be sent to the primary clinicians. Method(s) of data analysis Differences between intervention groups and control groups will be analyzed using Chi-square test (or Fishers' exact test) for categorical variables and using T-test (or Two-sample Wilcoxon test) for continuous variables. Uniqueness and relevance Health service policy regarding signs of malnutrition and excessive medicinal treatment needs a relevant scientific knowledge base. Nutritional counseling and revision of medicinal treatment may dramatically affect health. This research deals with questions that have no commercial interest, but are important to the public.
Interventions
OTHEREmail
Automated Email to the primary doctor and nurse, with the details of the patient, the condition found and the relevant measures to consider.
Sponsors
Clalit Health Services
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
75 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* All found by computerized search in the data base of Clalit Health Services North and South districts: * 1\. A drop in BMI of 2 Kg/m\^2 or more during previous two years AND * BMI less than 23 Kg/m\^2 AND * No dietitian counseling during previous year * OR * 2\. Last HbA1c% level of 6.5% or less AND * dispensing anti-diabetic medicines during previous 2 months * OR * 3\. Last total cholesterol less than 160 mg/dL AND * dispensing cholesterol-lowering medicines during previous 2 months
Exclusion criteria
* Patients whose their primary doctor and nurse email address is unobtainable * For criterion 3: Patients diagnosed to have had a myocardial infarction, an ischemic heart disease, a transient ischemic attack or an ischemic stroke.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Death From Any Cause | 1 year | Impact on overall-survival |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Impact on Evaluation Rate | 1 year | Percentage of patients evaluated by a nurse and counseled by a dietitian |
| Impact on Medical Costs | 1 year | Medical expenses to the medical insurer, including hospitalizations, consultations, examinations, devices and medicines. |
| Impact on a Composite Measure of Medical Treatment | 1 year | A composite measure of doses of prescribed anti-diabetic and cholesterol-lowering medicines - According to relevant alert by email. |
Participant flow
Pre-assignment details
Some of the participants may fit more than one criterion, as explained in the Eligibility section. Hence 7 subgroups are created: A) Fit only alert 1. B) Fit only alert 2. C) Fit only alert 3. D) Fit both alert 1. and alert 2. E) Fit both alert 1. and alert 3. F) Fit both alert 2. and alert 3. G) Fit all alerts: 1. , 2. and 3.
Participants by arm
| Arm | Count |
|---|---|
| Intervention Email An email is sent, alerting the primary care providers about low values of BMI, HbA1c% or cholesterol and advising to consider appropriate dietary and medical revision.
Email: Automated Email to the primary doctor and nurse, with the details of the patient, the condition found and the relevant measures to consider. | 4,269 |
| Control No email is sent. | 4,218 |
| Total | 8,487 |
Withdrawals & dropouts
| Period | Reason | FG000 | FG001 |
|---|---|---|---|
| Overall Study | Died before Email was sent (<9.11.2015) | 35 | 43 |
| Overall Study | Lost to Follow-up | 6 | 13 |
Baseline characteristics
| Characteristic | Intervention Email | Total | Control |
|---|---|---|---|
| Age, Continuous | 80.6 years STANDARD_DEVIATION 4.7 | 80.7 years STANDARD_DEVIATION 4.8 | 80.8 years STANDARD_DEVIATION 4.9 |
| Body Mass Index (BMI) | 28.7 kg/m^2 STANDARD_DEVIATION 5.9 | 28.8 kg/m^2 STANDARD_DEVIATION 5.9 | 28.8 kg/m^2 STANDARD_DEVIATION 5.9 |
| HbA1c% Mean (Standard Deviation) | 6.51 % STANDARD_DEVIATION 1.05 | 6.49 % STANDARD_DEVIATION 1.02 | 6.46 % STANDARD_DEVIATION 1 |
| Race and Ethnicity Not Collected | — | 0 Participants | — |
| Region of Enrollment Israel | 4269 participants | 8487 participants | 4218 participants |
| Sex: Female, Male Female | 2484 Participants | 4929 Participants | 2445 Participants |
| Sex: Female, Male Male | 1785 Participants | 3558 Participants | 1773 Participants |
| Total Cholesterol Mean (Standard Deviation) | 154.61 mg/dL STANDARD_DEVIATION 33.88 | 154.42 mg/dL STANDARD_DEVIATION 33.74 | 154.23 mg/dL STANDARD_DEVIATION 33.6 |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 285 / 4,269 | 245 / 4,218 |
| other Total, other adverse events | 0 / 4,269 | 0 / 4,218 |
| serious Total, serious adverse events | 0 / 4,269 | 0 / 4,218 |
Outcome results
Primary
Death From Any Cause
Impact on overall-survival
Time frame: 1 year
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention Email | Death From Any Cause | 285 Participants |
| Control | Death From Any Cause | 245 Participants |
Secondary
Impact on a Composite Measure of Medical Treatment
A composite measure of doses of prescribed anti-diabetic and cholesterol-lowering medicines - According to relevant alert by email.
Time frame: 1 year
Population: Data were not collected.
Secondary
Impact on Evaluation Rate
Percentage of patients evaluated by a nurse and counseled by a dietitian
Time frame: 1 year
| Arm | Measure | Value (COUNT_OF_PARTICIPANTS) |
|---|---|---|
| Intervention Email | Impact on Evaluation Rate | 3387 Participants |
| Control | Impact on Evaluation Rate | 3337 Participants |
Secondary
Impact on Medical Costs
Medical expenses to the medical insurer, including hospitalizations, consultations, examinations, devices and medicines.
Time frame: 1 year
Population: Data were not collected