Joshua Havumaki

Implementing effective interventions remain a lot of difficulties along all border regions. The emergence of artemisinin resistance of Plasmodium falciparum strains in the Greater Mekong Subregion is a matter of great concern. China has effectively controlled cross-border transmission of malaria and artemisinin resistance of P. falciparum along the China-Myanmar border. A combined quantitative and qualitative study was used to collect data, and then an integrated impact evaluation was conducted to malaria control along the China-Myanmar border during 2007–2013. The parasite prevalence rate (PPR) in the five special regions of Myanmar was decreased from 13.6 % in March 2008 to 1.5 % in November 2013. Compared with the baseline (PPR in March 2008), the risk ratio was only 0.11 [95 % confidence interval (CI), 0.09–0. 14) in November 2013, which is equal to an 89 % reduction in the malaria burden. Annual parasite incidence (API) across 19 Chinese border counties was reduced from 19.6 per 10 000 person-years in 2006 to 0.9 per 10 000 person-years in 2013. Compared with the baseline (API in 2006), the API rate ratio was only 0.05(95 % CI, 0.04–0.05) in 2013, which equates to a reduction of the malaria burden by 95.0 %. Meanwhile, the health service system was strengthened and health inequity of marginalized populations reduced along the international border. The effective collaboration between China, Myanmar and the international non-governmental organization promptly carried out the core interventions through simplified processes. The integrated approaches dramatically decreased malaria burden of Chinese-Myanmar border.

Introduction Michigan has implemented several of the tobacco control policies recommended by the World Health Organization MPOWER goals. We consider the effect of those policies and additional policies consistent with MPOWER goals on smoking prevalence and smoking-attributable deaths (SADs). Methods The SimSmoke tobacco control policy simulation model is used to examine the effect of past policies and a set of additional policies to meet the MPOWER goals. The model is adapted to Michigan using state population, smoking, and policy data starting in 1993. SADs are estimated using standard attribution methods. Upon validating the model, SimSmoke is used to distinguish the effect of policies implemented since 1993 against a counterfactual with policies kept at their 1993 levels. The model is then used to project the effect of implementing stronger policies beginning in 2014. Results SimSmoke predicts smoking prevalence accurately between 1993 and 2010. Since 1993, a relative reduction in smoking rates of 22 % by 2013 and of 30 % by 2054 can be attributed to tobacco control policies. Of the 22 % reduction, 44 % is due to taxes, 28 % to smoke-free air laws, 26 % to cessation treatment policies, and 2 % to youth access. Moreover, 234,000 SADs are projected to be averted by 2054. With additional policies consistent with MPOWER goals, the model projects that, by 2054, smoking prevalence can be further reduced by 17 % with 80,000 deaths averted relative to the absence of those policies. Conclusions Michigan SimSmoke shows that tobacco control policies, including cigarette taxes, smoke-free air laws, and cessation treatment policies, have substantially reduced smoking and SADs. Higher taxes, strong mass media campaigns, and cessation treatment policies would further reduce smoking prevalence and SADs.

Myanmar is one of the 31 highest burden malaria countries worldwide. Scaling up the appropriate use of insecticide-treated nets (ITNs) is a national policy for malaria prevention and control. However, the data on use, influencing factors and maintenance of bed nets is still lack among the population in Kachin Special Region II (KR2), Northeastern Myanmar. The study combined a quantitative household questionnaire survey and qualitative direct observation of households. A Chi-squared test was used to compare the percentages of ownership, coverage, and rates of use of bed nets. Additionally, multivariate logistic regression analysis (MVLRA) was used to analyse factors that influence the use of bed nets. Finally, covariance compared the mean calibrated hole indexes (MCHI) across potential influence variables. The bed net to person ratio was 1:1.96 (i.e., more than one net for every two people). The long-lasting insecticidal net (LLIN) to person ratio was 1: 2.52. Also, the percentage of households that owned at least one bed net was 99.7 % (666/688). Some 3262 (97.3 %) residents slept under bed nets the prior night, 2551 (76.1 %) of which slept under ITNs/LLINs the prior night (SUITNPN). The poorest families, those with thatched roofing, those who use agriculture as their main source of family income, household heads who knew that mosquitoes transmit malaria and those who used bed nets to prevent malaria, were significantly more likely to be in the SUITNPN group. However, residents in lowlands, and foothills were significantly less likely to be SUITNPNs. Finally, head of household attitude towards fixing bed nets influenced MCHI (F = 8.09, P = 0.0046). The coverage and usage rates of bed nets were high, especially among children, and pregnant women. Family wealth index, geographical zones, household roofing, source of family income, household head’s knowledge of malaria transmission and of using bed nets as tools for malaria prevention are all independent factors which influence use of ITNs/LLINs in KR2. Maintaining high coverage, and use rate of bed nets should be a priority for the war-torn population of KR2 to ensure equity and human rights.