Estimating cause-specific mortality in Madagascar: an evaluation of death notification data from the capital city

Survival within 12months after stroke in Madagascar

The data on the long-term prognosis of stroke are scarce in Madagascar. Our objective was to determine survival within 12months after a stroke event. A longitudinal study was carried out on a hospital cohort of subjects with stroke in Mahajanga in western Madagascar. We included in the study all subjects admitted to adult emergencies at Mahajanga University Hospitals during the year 2019 and diagnosed with stroke. A follow-up by telephone call or by descent at the home of the patients was carried out after at least 12months from the onset of the disease. We analyzed in-hospital mortality and survival within 12months after the stroke. At the end of the study period, 144 stroke cases were retained. Strokes accounted for 5.07% of emergency admission causes. Male gender accounted for 51.4% of the population. The average age of the subjects was 60.7years. In-hospital mortality was 32.6%. Survival at 1month was 50%, at 3months 48.4%, and at 12months 43%. High blood pressure was found as a risk factor for stroke in 79.9% of patients, 76.5% of whom were undertreated. Stroke mortality was high in our population. Most of the deaths occurred during the first month. Improved prevention and care are needed in Madagascar.

Excess mortality associated with the COVID-19 pandemic during the 2020 and 2021 waves in Antananarivo, Madagascar

INTRODUCTION

COVID-19-associated mortality remains difficult to estimate in sub-Saharan Africa because of the lack of comprehensive systems of death registration. Based on death registers referring to the capital city of Madagascar, we sought to estimate the excess mortality during the COVID-19 pandemic and calculate the loss of life expectancy.

METHODS

Death records between 2016 and 2021 were used to estimate weekly excess mortality during the pandemic period. To infer its synchrony with circulation of SARS-CoV-2, a cross-wavelet analysis was performed. Life expectancy loss due to the COVID-19 pandemic was calculated by projecting mortality rates using the Lee and Carter model and extrapolating the prepandemic trends (1990-2019). Differences in life expectancy at birth were disaggregated by cause of death.

RESULTS

Peaks of excess mortality in 2020-21 were associated with waves of COVID-19. Estimates of all-cause excess mortality were 38.5 and 64.9 per 100 000 inhabitants in 2020 and 2021, respectively, with excess mortality reaching ≥50% over 6 weeks. In 2021, we quantified a drop of 0.8 and 1.0 years in the life expectancy for men and women, respectively attributable to increased risks of death beyond the age of 60 years.

CONCLUSION

We observed high excess mortality during the pandemic period, in particular around the peaks of SARS-CoV-2 circulation in Antananarivo. Our study highlights the need to implement death registration systems in low-income countries to document true toll of a pandemic.

National, regional, and global causes of mortality in 5-19-year-olds from 2000 to 2019: a systematic analysis

BACKGROUND

Investments in the survival of older children and adolescents (aged 5-19 years) bring triple dividends for now, their future, and the next generation. However, 1·5 million deaths occurred in this age group globally in 2019, nearly all from preventable causes. To better focus the attention of the global community on improving survival of children and adolescents and to guide effective policy and programmes, sound and timely cause of death data are crucial, but often scarce.

METHODS

In this systematic analysis, we provide updated time-series for 2000-19 of national, regional, and global cause of death estimates for 5-19-year-olds with age-sex disaggregation. We estimated separately for countries with high versus low mortality, by data availability, and for four age-sex groups (5-9-year-olds [both sexes], 10-14-year-olds [both sexes], 15-19-year-old females, and 15-19-year-old males). Only studies reporting at least two causes of death were included in our analysis. We obtained empirical cause of death data through systematic review, known investigator tracing, and acquisition of known national and subnational cause of death studies. We adapted the Bayesian Least Absolute Shrinkage and Selection Operator approach to address data scarcity, enhance covariate selection, produce more robust estimates, offer increased flexibility, allow country random effects, propagate coherent uncertainty, and improve model stability. We harmonised all-cause mortality estimates with the UN Inter-agency Group for Child Mortality Estimation and systematically integrated single cause estimates as needed from WHO and UNAIDS.

FINDINGS

In 2019, the global leading specific causes of death were road traffic injuries (115 843 [95% uncertainty interval 110 672-125 054] deaths; 7·8% [7·5-8·1]); neoplasms (95 401 [90 744-104 812]; 6·4% [6·1-6·8]); malaria (81 516 [72 150-94 477]; 5·5% [4·9-6·2]); drowning (77 460 [72 474-85 952]; 5·2% [4·9-5·5]); and diarrhoea (72 679 [66 599-82 002], 4·9% [4·5-5·3]). The leading causes varied substantially across regions. The contribution of communicable, maternal, perinatal, and nutritional conditions declined with age, whereas the number of deaths associated with injuries increased. The leading causes of death were diarrhoea (51 630 [47 206-56 235] deaths; 10·0% [9·5-10·5]) in 5-9-year-olds; malaria (31 587 [23 940-43 116]; 8·6% [6·6-10·4]) in 10-14-year-olds; self-harm (32 646 [29 530-36 416]; 13·4% [12·6-14·3]) in 15-19-year-old females; and road traffic injuries (48 757 [45 692-52 625]; 13·9% [13·3-14·3]) in 15-19-year-old males. Widespread declines in cause-specific mortality were estimated across age-sex groups and geographies in 2000-19, with few exceptions like collective violence.

INTERPRETATION

Child and adolescent survival needs focused attention. To translate the vision into actions, more investments in the health information infrastructure for cause of death and in the related life-saving interventions are needed.

FUNDING

Bill & Melinda Gates Foundation and WHO.

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

Motivations and barriers to death registration in Dakar, Senegal

Strengthening civil registration systems requires a better understanding of motivations and barriers related to the registration of deaths. We used data from the 2013 Senegalese census to identify deaths that are more likely to be registered in the Dakar region, where the completeness of death registration is higher than 80%. We also interviewed relatives of the deceased whose death had been registered to collect data on reasons for registration and sources of information about the process. The likelihood of death registration was positively associated with age at death and household wealth. Death registration was also more likely in households whose head was older, had attended school, and had a birth certificate. At the borough commune level, the geographical accessibility of civil registration centres and population density were both positively associated with completeness of death registration. The main motivations for registering deaths were compliance with the legal obligation to do so and willingness to obtain a burial permit and a death certificate. Families, health facilities, and friends were the primary sources of information about death registration. Further research is needed to identify effective interventions to increase death registration completeness in Dakar, particularly amongst the poorest households and neighbourhoods on the outskirts of the city.

Effects of human variation on foot and ankle pain in rural Madagascar

OBJECTIVES

Foot and ankle dysfunction in barefoot/minimally shod populations remains understudied. Although factors affecting musculoskeletal pain in Western populations are well-studied, little is known about how types of work, gender, and body shape influence bone and joint health in non-Western and minimally shod communities. This study examines the effect of human variation on locomotor disability in an agrarian community in Madagascar.

MATERIALS AND METHODS

Foot measurements were collected along with height, weight, age, and self-report data on daily activity and foot and ankle pain from 41 male and 48 female adults. A short form revised foot function index (FFI-R), that measures functional disability related to foot pain, was calculated. Raw and normalized foot measurements were compared by gender and used in a multiple linear regression model to determine predictors of FFI-R.

RESULTS

Compared to men, women reported higher FFI-R scores (p = 0.014), spent more time on their feet (p = 0.019), and had higher BMIs (p = 0.0001). For their weight, women had significantly smaller and narrower feet than men. Bimalleolar breadth (p = 0.0005) and foot length (p = 0.0223) standardized by height, time spent on feet (p = 0.0102), ankle circumference standardized by weight (p = 0.0316), and age (p = 0.0090) were significant predictors of FFI-R score.

DISCUSSION

Our findings suggest that human variation in anatomical and behavioral patterns serve as significant explanations for increased foot and ankle pain in women in this non-Western rural population. Foot and ankle pain were prevalent at similar levels to those in industrialized populations, indicating that research should continue to examine its effect on similar barefoot/minimally shod communities.

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

Neighbourhood-level housing quality indices for health assessment in Dakar, Senegal

In sub-Saharan African cities, the dearth of accurate and detailed data is a major problem in the study of health and socioeconomic changes driven by rapid urbanization. Data on both health determinants and health outcomes are often lacking or are of poor quality. Proxies associated with socioeconomic differences are needed to compensate the lack of data. One of the most straightforward proxies is housing quality, which is a multidimensional concept including characteristics of both the built and natural environments. In this work, we combined the 2013 census data with remotely sensed land cover and land use data at a very high resolution in order to develop an integrated housing quality-based typology of the neighbourhoods in Dakar, Senegal. Principal component analysis and hierarchical classification were used to derive neighbourhood housing quality indices and four neighbourhood profiles. Paired tests revealed significant variations in the censusderived mortality rates between profile 1, associated with the lowest housing quality, and the three other profiles. These findings demonstrate the importance of housing quality as an important health risk factor. From a public health perspective, it should be a useful contribution for geographically targeted planning health policies, at the neighbourhood spatial level, which is the most appropriate administrative level for interventions.

Long-term trends in seasonality of mortality in urban Madagascar: the role of the epidemiological transition

Background: Seasonal patterns of mortality have been identified in Sub-Saharan Africa but their changes over time are not well documented.Objective: Based on death notification data from Antananarivo, the capital city of Madagascar, this study assesses seasonal patterns of all-cause and cause-specific mortality by age groups and evaluates how these patterns changed over the period 1976-2015.Methods: Monthly numbers of deaths by cause were obtained from death registers maintained by the Municipal Hygiene Office in charge of verifying deaths before the issuance of burial permits. Generalized Additive Mixed regression models (GAMM) were used to test for seasonality in mortality and its changes over the last four decades, controlling for long-term trends in mortality.Results: Among children, risks of dying were the highest during the hot and rainy season, but seasonality in child mortality has significantly declined since the mid-1970s, as a result of declines in the burden of infectious diseases and nutritional deficiencies. In adults aged 60 and above, all-cause mortality rates are the highest in the dry and cold season, due to peaks in cardiovascular diseases, with little change over time. Overall, changes in the seasonality of all-cause mortality have been driven by shifts in the hierarchy of causes of death, while changes in the seasonality within broad categories of causes of death have been modest.Conclusion: Shifts in disease patterns brought about by the epidemiological transition, rather than changes in seasonal variation in cause-specific mortality, are the main drivers of trends in the seasonality of all-cause mortality.

Monitoring for outbreak-associated excess mortality in an African city: Detection limits in Antananarivo, Madagascar

OBJECTIVES

Quantitative estimates of the impact of infectious disease outbreaks are required to develop measured policy responses. In many low- and middle-income countries, inadequate surveillance and incompleteness of death registration are important barriers.

DESIGN

Here, we characterize how large an impact on mortality would have to be for being detectable using the uniquely detailed mortality notification data from the city of Antananarivo, Madagascar, with application to a recent measles outbreak.

RESULTS

The weekly mortality rate of children during the 2018-2019 measles outbreak was 161% above the expected value at its peak, and the signal can be detected earlier in children than in the general population. This approach to detect anomalies from expected baseline mortality allows us to delineate the prevalence of COVID-19 at which excess mortality would be detectable with the existing death notification system in Antananarivo.

CONCLUSIONS

Given current age-specific estimates of the COVID-19 fatality ratio and the age structure of the population in Antananarivo, we estimate that as few as 11 deaths per week in the 60-70 years age group (corresponding to an infection rate of approximately 1%) would detectably exceed the baseline. Data from 2020 will undergo necessary processing and quality control in the coming months. Our results provide a baseline for interpreting this information.

Inequalities in cause-specific mortality in children and adolescents in the Moramanga health survey, Madagascar

OBJECTIVES

One child or young adolescent dies every 10 min in Madagascar and large disparities in survival persist. We estimated cause-specific mortality in a cohort of children aged 0-14 in the Moramanga district and explored how causes of death shape these inequalities.

METHODS

Children were followed prospectively between 2012 and 2017. Causes of death were established based on verbal autopsies. Incidence rate ratios were estimated in Poisson regression models.

RESULTS

The risk of dying before age 15 was 68.1 per thousand live births. Risks of dying were highest in the first year of life (31.2‰) and lowest in children aged 10-14 (6.4‰). The male-to-female sex ratios of mortality increased with age and reached 2.3 among adolescents aged 10-14. Communicable, nutritional and neonatal causes accounted for 79.5% of deaths below age 5 and 47.0% above age 5. Mortality was positively associated with household poverty, lack of education of the household head, and rural residence.

CONCLUSIONS

Interventions should be designed with an equity lens to reduce large disparities in survival and be tailored to the needs of each age-group.