Objectives. Prevalence of household & individual non-communicable disease (NCD) risk factors and outcomes in rural populations

Transcription

1 Objectives Prevalence of household & individual non-communicable disease (NCD) risk factors and outcomes in rural populations Feasibility of community-based interviews, point-of-care diagnostics and electronic data capture Evaluate clustering of NCD s in households, effects of NCD s on expenditures, HH members & health care decision-making 1

2 Results Cigarettes/Beedi Tobacco Alcohol n/a n/a Matlab (n=1143) Carambolim (n=1212) Sirudhavur (n=940) Men Women Men Women Men Women Age, yrs (SD) 30.5 (22.0) 30.0 (19.2) 29.0 (18.4) 33.6 (19.0) 28.2 (17.1) 30.5 (18.1) Education Illiterate Primary school Secondary school Occupation At home, unemployed, student Unskilled manual, farming Skilled manual Semi-/Professional

3 Results Cigarettes/Beedi Tobacco Alcohol n/a n/a Matlab (n=1143) Carambolim (n=1212) Sirudhavur (n=940) Men Women Men Women Men Women Age, yrs (SD) 30.5 (22.0) 30.0 (19.2) 29.0 (18.4) 33.6 (19.0) 28.2 (17.1) 30.5 (18.1) Education Illiterate Primary school Secondary school Occupation At home, unemployed, student Unskilled manual, farming Skilled manual Semi-/Professional

4 Results Matlab (n=1143) Carambolim (n=1212) Sirudhavur (n=940) Men Women Men Women Men Women High fasting glucose (Glucose > 126 mg/dl) Hypertension (SBP>140 or DBP>90 mmhg or BP meds) Body Mass Index 23+ kg/m kg/m Depression Any (PHQ>5) Disability (WHO-DAS II> 5) Airflow obstruction (Obs. Vs.pred < 0.7)

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6 Comments position, unable to complete, unable to understand, refused to cooperate, etc. Lung function data Spirometry 5 blows (FEV1, FVC, Predicted) Exclusion criteria: - Surgeries in past 3 months (eg, eye, heart) - Heart attack in past 3 mos, suffers from heart ailment - Pulse > 120 beats/min - Blood pressure greater than 180 (SBP)/ 100 (DBP) - Epilepsy, Pregnant, breast feeding Re-schedule if: - Respiratory infection, bronchodilators, smoking

7 Spirometry A method for assessing lung function by measuring the volume of air a patient can expel from the lungs after a maximal inspiration It is then compared with predicted normal values based on age, height, ethnicity, gender to gauge airway obstruction Uses of Spirometry: - Gold standard (other clinical-based measures) - Variations in technical abilities, interpretations - Used to distinguish asthma vs. COPD - Management of respiratory disease - Epilepsy, Pregnant, breast feeding

8 Airway obstruction Spirometry data: FVC : Forced Vital Capacity total volume of air patient can forcibly exhale in one breath (litres) - FEV1: Forced Expiratory Volume in 1 second- volume of air patient can exhale in the 1 st second of exhalation (litres) - FEV1/FVC: Ratio expressed as a fraction Interpreting the data: - Normal FEV1/FVC: Airway obstruction < 0.7 (COPD post-bronchodilator) - Caution with 70+ years (overdx; 0.65 threshold OK) - Flow-volume measurement: traces flow rate against rate of air exhaled to produce a flow-volume curve

9 Spirometry curve * GOLD: 3 blows that are consistent and within 5% of each other is ideal Normal: Volume-time curve rises rapidly & smoothly & plateaus within 3-4 seconds

10 Flow-vol curves

11 Concluding points Comparison with national/other data - Similarities: tobacco use, hypertension, depression - Differences: alcohol abuse - First time: physical activity, disability Gender differences - Health awareness - Tobacco & alcohol use - Depression Potential Intra-/inter-household NCD pathways & effects Challenges Recruitment to clinics, male migrants, blood donation, spirometry in women

12 Report from working group #3

13 Multi-centre Household Chronic Disease Risk Factor (CDRF) Study Preet Dhillon, Dilip Jha Dewan Alam, Amit Dias, Joseph Williams Shah Ebrahim Project period: Jan Jan Funded by the Wellcome Trust, UK 13

14 Methods Design: Cross-sectional, community-based Sample size: 250 households x 3 partner sites 3000 total Study population: Adults Children 2+ years Locations: Matlab, Bangladesh Carambolim, Goa Sirudhavur, Chennai 14

15 Data collection Household-level data - Cooking fuel exposure - Salt, sugar, oil - Household expenditures, insurance Individual-level data Questionnaires: - Tobacco, alcohol, physical activity, diet, medicine - Disabilities, pain, falls, urinary - Mental health, neighbourhood, networks Physical Measurements: - Anthropometrics, body fat - Lung function, visual acuity, grip strength - Blood pressure, fasting glucose, 24-hr urine

16 Scientific questions What is the effect of indoor exposure to biomass fuel on respiratory (e.g. lung function) and on cognitive outcomes (e.g. depression score)? (exposure-response) What is the causal effect of installing electricity and natural gas as both primary and secondary sources of fuel on health outcomes? (causal effect of an intervention)

17 Exposure to biomass fuel Household-level questionnaire 2.1 Does the house have electricity? 1=No, 2=Yes 2.2 Fuel for cooking 1=Kerosene 5=Wood 9=Animal Dung/cake 2=Charcoal 6=Agriculture/crop 10=Shrub/Grass 3=Coal 7=Gobar Gas/bio gas 11=Other 4=Gas 8=Electricity 12=None 2.3 Where is the cooking for the household done? 1=Inside the house 2=Inside the house in a separate kitchen 3=Outside the house 4=Both inside and outside 2.4 Does the inside cooking area have the following? 1=No 2=Yes, 3=Not Applicable Primary Fuel Secondary Fuel Window Chimney 2.5 On average, how many months per year do you cook inside? (0-12 months, 99=unknown) Exhaust Primary Fuel Secondary Fuel 2.6 On average, how many months per year do you cook outside? (0-12 months, 99=unknown) Primary Fuel Secondary Fuel

18 Overview How do we define exposure to biomass fuel from the questionnaire? 1. Howard and Lindsay exposure index 2. Ashis and Neeraj exposure index Visualizing exposure (Hemangi) Considerations about the pulmonary outcomes (Roopa and Kapil) Estimating the association between exposure index and health outcomes using a regression model (Lindsay and Howard) Estimating the causal effects of using clean sources of fuel for indoor cooking on health outcomes (Francesca)

19 Hemangi (graphical representation of the exposure distribution) Almost 20% households do not have access to electricity

20 Type of cooking fuel Wood is used by more than 50% households for primary and nearly 20% households for secondary cooking.

21 More than 80% households do their cooking inside the house either totally or partially.

22 Kitchen Facilities More than 20% households do not have any ventilation facility. Less than 10% households have sophisticated facilities like Chimney and Exhaust fan.

23 Inside cooking More than 35% households do their primary cooking and more than 15% households do their secondary cooking inside the house through out the year.

24 Outside cooking

25 Type of fuel Vs BP

26 Ashis and Neeraj Exposure Index Exposure Indices Separately for Adult M/F and Child M/F with corresponding weights / discounting factors relative to Adult Female Indoor Exposure = [{# months per year (py) cooking}*{# months py primary unclean cooking(a)} + {# months py secondary unclean cooking(b)}*] *(#years cooking) * Discount factor for windows*discount factor for outdoor cooking with unclean fuel (A) and (B) to be obtained thru weights computed from time spent within a month for the corresponding activity.

27 Outcome analysis (Roopa and Kapil) Depression Lung function

28 Lung function (N=1459) Forced vital capacity in liters Up to five measures Forced expiratory volume (1 st second) in liters FVC (Liters) FEV1/FVC ( %) Children (n=301) Adult men (n=554) Adultwomen (n= 604) Mean (SD) Range 2.0 (0.9) Mean (SD) ( 9.4) 3.6 (1.3) (10.6) Range (0.8) (9.8)

29 Depression (N=2772, only adults) Patient Health Questionnaire -9 data Density phqsumm

30 Depression and gender Depress ion Men Women Total NO Mild Modera te Severe

31 Howard and Lindsay Exposure Score: Conceptualization Primary Fuel Score x2 Total Fuel Score Secondary Fuel Score

32 Exposure Index: Some Issues Missing data on number of months spent cooking outside or inside for primary and secondary fuel sources for 1/3 site Could not calculate proportion of time spent cooking inside for those participants that cook inside Measures are at the household level Assumptions required to assign exposure to individuals (women, men, and children)

33 Sco re Fuel Source Primary Fuel Score Location of Cooking Ventilation: window, chimney, or exhaust 0 Clean N/A N/A Unclean Outside N/A Unclean Sometimes outside or inside but in separate room 3 Unclean Sometimes outside or inside but in separate room 4 Unclean Sometimes outside or inside but in separate room 2 or None Unclean Inside 2 or Unclean Inside Unclean Inside None 69 NA 24 Total Count

34 Primary Fuel Score

35 Secondary & Total Fuel Score Secondary Total

36 Francesca This is a cross sectional study of approx 3000 households in 3 locations in rural areas of India (slides with her description of the data sets are attached) The exposure (indoor exposure to biomass) is at the household level (and categorical), but the individual level outcomes and risk factors more than 100) are at the individual level The key slide is #9 where are summarized the key questions regarding the exposure to biomass. From this slide we need to define the intervention variable as clean" versus "non clean" cooking.

37 Francesca We define clean" cooking if a household uses "gas or electricity" as a primary AND as secondary OR "gas and electricity" as a primary and non secondary source. We define no clean cooking all the other options. We could estimate the causal effect of the intervention of clean" cooking on several outcomes, some are continuous (pulmonary functions) and some are categorial (depression score). We could do a matched analyses, propensity scores, or anything else you like!

38 Saanvi 1.She is 49 2.She lives in Sangath 3.She is cooking indoor 4.High intake of sugar 5.Her husband smoke 6.No physical activity 7.3 children 8.She has hypertension (outcome) 9.She does only indoor cooking with woods (exposure) Question: what would have been Saanvi s health outcome, if she had a gas stove? (counterfactual) Obviously we would never know, but we can estimate the counterfactual by taking the outcome for the women that are as similar as possible to Saanvi in terms of all the measured confounders but that they use ONLY clean sources of indoor cooking (indoor and gas)