References (.csv) ================= :download:`Download original .csv file from GitHub ` ==== ============ =========================================================================================================================================================================================================================================================================================================================================== .. Unnamed: 0 Unnamed: 1 ==== ============ =========================================================================================================================================================================================================================================================================================================================================== 0 1 “Life(style)” characteristics 2 3 4 urban - rural 5 6 baseline in 2018: 7 8 17% urban 9 83% rural 10 11 assume this is time fixed and is allocated at birth to new births, with same status as mother 12 13 source: DHS 2015 14 15 Comment: Malawi is urbanising I think, so perhaps increase urban and reduce rural by 1% per year? We could use the 2018 census data – should be available end of this year / early next year to see what it is in 2018 and compare with 2008 census and 2010 and 2015 DHS to see trend i.e. whether 1% increase per year seems about right. 16 Response: according to world bank the recent proportion urban is increasing by just 0.25% per year. I’m happy for us tp include this. https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS?locations=MW 17 18 wealth level 19 (since recorded in DHS I suggest we use this instead of socio-economic status) 20 21 (wealth level is initially based on wealth quintile from 2015 DHS but distribution will not remain uniform if fertility and death rate differ by wealth level) 22 23 baseline in 2018 (assuming not changed since 2015 DHS): 24 25 if urban = 1 then 26 75% wealth\_level = 1 27 16% wealth\_level = 2 28 5% wealth\_level = 3 29 2% wealth\_level = 4 30 2% wealth\_level = 5 31 32 if urban = 0 then 33 11% wealth\_level = 1 34 21% wealth\_level = 2 35 23% wealth\_level = 3 36 23% wealth\_level = 4 37 23% wealth\_level = 5 38 39 source: DHS 2015 40 41 assume this is time fixed and allocated at birth with same status as mother. 42 43 44 tobacco use 45 (much tobacco use is not in fact cigarettes) 46 47 baseline in 2018: 48 49 if age < 20 and male then percent using tobacco = 1% x wealth\_level 50 (i.e. 5 times higher in lowest level) 51 if 20 <= age < 40 and male then percent using tobacco = 4% x wealth\_level 52 if 40 <= age and male then percent using tobacco = 6% x wealth\_level 53 if female then percent using tobacco = 0.2% x wealth\_level 54 55 source: DHS 2015 56 57 assume time varying with value allocted age 18 (1% x wealth level) but rate of initiating smoking for those assigned as non-smokers and rate of stopping for those assigned as tobacco users to initially be set at zero. 58 59 60 excess alcohol 61 62 baseline in 2018: 63 64 if age > 18 and male 15% drink excess\_alcohol 65 if age > 18 and female 1% drink excess\_alcohol 66 67 source: WHO 2014 report http://www.who.int/substance\_abuse/publications/global\_alcohol\_report/msb\_gsr\_2014\_2.pdf?ua=1 68 69 no impact of urban / rural or wealth level 70 (if we can contact authors we may be able to see if wealth level has an independent influence) 71 72 Msyamboza et al; 2012; WHO STEPS 73 74 assume time varying with value allocted age 18 (15% for men, 1% for women) but rate of initiating alcohol for those assigned as non-excess-drinkers and rate of stopping for those assigned as excess alcohol drinkers to initially be set at zero. 75 76 77 low exercise 78 (I suggest we create this variable for people aged 18 and over only) 79 80 baseline in 2018: 81 82 if urban and male 32% have low exercise 83 if urban and female 18% have low exercise 84 if rural and male 11% have low exercise 85 if rural and female 7% have low exercise 86 87 Msyamboza et al; 2011; WHO STEPS 88 89 (if we can contact authors we may be able to see if wealth level has an independent influence) 90 91 assume time varying with value allocted age 18 (25% for men, 9% for women) but rate of becoming low exercise for those assigned as having exercise and rate of starting exercise for those assigned as low exercise to initially be set at zero. 92 93 94 BMI 95 96 Informed by Price et al 2018 Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi Lancet Diabetes Endocrinol 97 98 99 marital status 100 DHS 2015/2016 chapter 4 101 102 103 education 104 DHS 2015/2016 chapter 2 p 14-17 ==== ============ ===========================================================================================================================================================================================================================================================================================================================================