1. Socioeconomic inequities in treatment and prevention of malaria in Tanga district, Tanzania Presenter: Fred Matovu Inaugural AfHEA Conference 10-12 th March, 2009 Accra, Ghana

2. DeMTAP study site Study site

3. Background Malaria situation in Tanzania Accounts for > 39% of the national disease burden Accounts for about 48% of under5 mortality (facility –based data, 2005) Leading diagnosis for outpatient visits Major cause of mortality in Tanga (Lusingu, et al 2004). Malaria prevalence higher among the poor

4. Effective remedies ITNs Reduce Under5s death by 20%, saving 6 lives for every 1000 under5 in SSA. Reduce maternal mortality, anaemia & low birth weight Cost per DALY averted <$50 >40% protective efficacy against clinical malaria (Ter Kuile, et al 2003) ACTs Effective in malaria treatment

5. Accessibility to ITNs and ACTs The poor are less likely to use preventive measures (Webster et al, 2005; Worrall et al, 2007, 2005; Thwing et al, 2008 etc). RBM initiative emphasises improving ITN and ACT access for the poor The poor cannot afford ACTs without a subsidy (Wiseman, et al 2005; Whitty et al 2008)

7. Bednets in Tanga By time of survey 2003-2005; Nets were available from drug stores, pharmacies and retail shops A net cost about 3000/=Ts( US$ 3) Insecticide for net treatment cost ~ 0.20US$ No subsidised nets (only a few distributed by Tanga Rotary club (very occasional)

8. Bednets in Tanga cont.. After survey –Subsidised nets for pregnant women were introduced mid-2006 Discounted voucher scheme of 75% of cost of ITN –Subsidised nets distributed in an integrated child health campaign (CHC) Mass free distribution of nets to under-5s –Net re-treatment campaigns under CHC

9. Malaria treatment By survey time: Sulfadoxine-pyrimethamine (SP) was 1 st line treatment –Retail price ranging 0.30-0.50 US per adult dose Other antimalarials included: quinine; amodiaquine, artesunate, artemether- lumefantrine ( ALU) Post-Survey ALU is 1 st line treatment (since 2006)

10. Study objectives To analyse socioeconomic inequalities in: 1.Ownership and utilisation of bednets 2.Obtaining AMs for reported fever

11. Sampling Simple random sampling was used to select wards, villages/streets and sub-villages 32 streets and 40 sub-villages were selected 1603 households interviewed: (863 in rural and 740 in urban areas), Sept.03 - July 05 16 FGDs: (8-mothers & 8- male household heads), Dec 2006

12. Typical urban homestead

13. Typical rural homestead

14. Data collection process

15. Measurement: SES Education class: formal schooling of household head 1.None 2.Lower primary (1-4 yrs) 3.Upper primary (5-7yrs) 4.Secondary (8-11 yrs) 5.Post-secondary (12+ yrs) Asset-based wealth index (McKenzie, 2003) –PCA score for 14 household items (e.g. iron roof, bicycle, iron bed, mattress etc)

16. Distribution of HH by education.

17. Distribution of HH by wealth index

18. Measurement 1: Net Ownership and Utilisation Household level – at least one net –Assumed all households in same “need” Individual level – slept under a net night before the survey (HH roster) –Assumed Under5s are in greater “need” ITNs: nets treated in past six months

19. Measurement 2: Utilisation of AMs Obtaining an AM at health provider visited –Perceived severe fevers and Under5s were considered in greater need Health providers considered were: –Hospital –Health centre –Dispensary –Drug shop

20. Result1 : Distribution of at least one net by wealth quintiles

21. Result 2: Distribution of at least one ITNs by wealth quintiles

22. Result 3: Concentration curves for utilisationof ITNs

23. Result 4: Inequalities in bednet utilisation at household level Intervention Inequality measureRuralUrbanOverall Utilisation of ITNsEquity ratio: Wealth10.73.48.3 Education class8.56.312.4 Conc. Index: Wealth0.368 (3.67)*0.093 (1.95)0.276 (2.60)* Education class0.276 (2.93)*0.117(1.70)0.234 (2.01)* Utilisation of all netsEquity ratio: Wealth2.91.62.4 Education class2.31.22.0 Conc. Index: Wealth0.169 (3.98)*0.027 (0.96)0.138 (2.30)* Education class0.108 (2.21)*0.028 (2.36)*0.089 (2.07)*

24. Result 5: Utilisation of any nets by age group

25. Result 6: Utilisation of ITNs by age group

26. Regression results for net use at HH level Explanatory variable All Nets ITNs Marginal/Average effects p-value Marginal/Average effects p-value Family size0.0010.973-0.0020.656 Male-0.0320.555-0.010.859 Urban0.228<0.001*0.205<0.001* Married0.0070.8870.0430.408 Sambaa0.0720.2140.0310.496 Digo-0.0850.108-0.1020.012* Bondei0.0790.248-0.0510.314 Other ethnic group0.0060.908-0.0780.048* Age-0.0020.097-0.0010.133 Using other prevention measures-0.232<0.001*-0.0060.81 Education0.0130.008*0.02<0.001* Wealth0.11<0.001*0.053<0.001* Education-squared-0.0020.028*-0.0010.073 Wealth -squared-0.008<0.001*-0.004<0.001* Poor Road0.0120.698-0.070.085 Market centre-0.0160.764-0.0080.906 Constant----

27. Regression results for net use: Indv level Explanatory variable All NetsITNs Marginal/Average effects p-valueMarginal/Average effects p-value Family size-0.274<0.001*-0.008<0.001* Urban0.315<0.001*0.192<0.001* Education class0.018<0.001*0.02<0.001* Married0.0310.051*0.04<0.001* Poor road0.0050.785-0.075<0.001* Market centre-0.0720.009*-0.0330.235 Under50.157<0.001*0.06<0.001* Male-0.056<0.001*-0.020.023* Using other prevention measure-0.182<0.001*-0.0040.684 Sambaa-0.0870.003*0.0110.546 Digo-0.1760.001*-0.0860.001* Bondei-0.050.15-0.0560.005* Other ethnic group-0.090.001*-0.056<0.001* Wealth0.0910.001*0.043<0.001* Wealth -squared-0.0070.001*-0.004<0.001* Constant----

28. Sources of treatment for reported fever Treatment sourceUnder5sOver5sTotalp-value* (n=339)(n=739)(n=1078) Any treatment†331(98%)681(93%)1012 (94%)<0.0001* of which: Government facility185 (57%)264 (40%)449 (45%)<0.0001* Private facility26 (11%)84 (14%)110 (13%)0.1632 Drug store34 (13%)156 (26%)190 (22%)0.0002* General shop76 (18%)182 (19%)258 (19%)0.4681 Traditional healer13 (3%)5 (0.5%)18 (1%)0.0054* Other6 (2%)10 (1%)16 (1%)0.5856

29. Result 8: Proportion obtaining AMs and reporting severe fever

30. Inequalities in obtaining AMs SES MeasureConcentration index RuralUrbanOverall Wealth0.092 (3.23)0.005(0.33)0.055 (2.62) Education0.085(2.06)0.033(1.39)0.064(1.81)

31. Result 9:Probability of obtaining an AM by treatment source Explanatory variablesMarginal Effects Government Health units Private health unitsDrug shopAll providers Urban0.106*-0.0010.0140.052* Poor road-0.086-0.058*-0.043-0.098* Market centre-0.202*0.010.046-0.062 Education of household head0.0110.0040.0030.021* Married0.0330.0210.0180.043 Sambaa-0.111-0.0160.022-0.131 Digo-0.155*-0.016-0.037-0.150* Bondei-0.175*-0.0080.171*0.012 Other ethnic group-0.170*00.034-0.104* Male-0.064*0.0120.02-0.041 Severe fever0.0580.014-0.0340.009 Wealth-0.0130.011*-0.01-0.001 Under50.180*-0.016-0.078*0.065* Distance to facility0.042*0.004*0.0020.043* Constant----

32. Summary of findings 1: Nets Use of any net was higher in urban (90%) than rural areas (50%) Use of ITNs was higher in urban (48%) than rural (9%) areas A lot of nets in use were not treated SES, urban location, small family size and being under5 positively associated with net use

33. Summary of key findings 2: Nets Pro-rich inequalities in utilisation and ownership of any net and ITNs Inequalities were greater in rural areas Lack of money was major barrier to net use Some evidence of negative perceptions for use of ITNs

34. Summary of findings 3: AMs Inequalities in obtaining AMs were pro-rich overall and in rural areas Drug shops + general shops were a major source of treatment ( >40%) Factors positively associated with obtaining AMs: Living in urban areas; education; short distance to facility; being under5

35. Policy implication Need for community-wide treatment of all nets not treated currently Need to promote greater access of ITNs and ACTs among the poor. For example – Targeted intervention to reduce costs: discounted voucher schemes and mass ITN distribution –Encourage use of LLINs and longer-lasting net treatment –Drug subsidy incl. at drug shops Public campaign to encourage net treatment and mitigate negative perceptions Monitoring equity outcomes on interventions to ensure the poorest of the poor benefit

36. Suggestion for future research Equity analysis in monitoring and evaluation of malaria control interventions ITNs inequality assessment following new strategies: discounted voucher scheme +mass free distribution of ITNs Methodological Using a range of inequality measures Assessment of relevance of SES measure

37. Acknowledgements Gates Malaria Partnership, LSHTM –For funding the DeMTAP study –Training research fellowship AfHEA Secretariat – funding conference DeMTAP field staff, FGDs and survey participants