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

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Study Justification:
This study aims to assess the seasonal patterns of mortality in Antananarivo, the capital city of Madagascar, and evaluate how these patterns have changed over the period 1976-2015. Understanding the trends in seasonal mortality is important for public health planning and resource allocation. By analyzing death notification data, the study provides insights into the impact of the epidemiological transition on seasonal mortality patterns.
Highlights:
1. Seasonal patterns of mortality in Antananarivo have been identified, with different age groups experiencing different risks of dying during specific seasons.
2. Among children, the highest risks of dying were observed during the hot and rainy season. However, there has been a significant decline in seasonality in child mortality since the mid-1970s, attributed to reductions in infectious diseases and nutritional deficiencies.
3. In adults aged 60 and above, all-cause mortality rates are highest in the dry and cold season, primarily due to peaks in cardiovascular diseases. The seasonality of adult mortality has shown little change over time.
4. Changes in the seasonality of all-cause mortality have been driven by shifts in the hierarchy of causes of death, rather than changes in seasonal variation within broad categories of causes.
5. The epidemiological transition, characterized by changes in disease patterns and risk factors, is the main driver of trends in the seasonality of all-cause mortality.
Recommendations:
1. Public health interventions should continue to focus on reducing infectious diseases and nutritional deficiencies in children, particularly during the hot and rainy season.
2. Efforts to prevent and manage cardiovascular diseases in older adults should be prioritized, especially during the dry and cold season.
3. Monitoring and surveillance systems should be strengthened to track changes in seasonal mortality patterns and inform timely interventions.
4. Further research is needed to explore the underlying factors contributing to the shifts in the hierarchy of causes of death and their impact on seasonal mortality.
Key Role Players:
1. Municipal Hygiene Office: Responsible for maintaining death registers and verifying deaths before the issuance of burial permits.
2. Physicians: Certify causes of death and assign International Classification of Diseases (ICD) codes.
3. Public Health Authorities: Responsible for implementing public health interventions and policies based on the study findings.
4. Researchers and Academics: Conduct further research to deepen the understanding of seasonal mortality patterns and their implications.
Cost Items for Planning Recommendations:
1. Strengthening surveillance systems: Includes investment in data collection, analysis, and reporting infrastructure.
2. Public health interventions: Budget for implementing programs targeting infectious diseases, nutritional deficiencies, and cardiovascular diseases.
3. Research funding: Allocate resources for conducting further studies on seasonal mortality patterns and their determinants.
4. Capacity building: Provide training and education for healthcare professionals and researchers to enhance their skills in data analysis and public health interventions.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, but there are some areas for improvement. The study uses death notification data from Antananarivo, the capital city of Madagascar, over a period of four decades to assess seasonal patterns of mortality. The study employs Generalized Additive Mixed regression models (GAMM) to analyze the data and control for long-term trends in mortality. The findings show that seasonality in child mortality has significantly declined since the mid-1970s, while seasonality in all-cause mortality rates for adults aged 60 and above remains relatively stable. The study concludes that shifts in disease patterns brought about by the epidemiological transition are the main drivers of trends in the seasonality of all-cause mortality. The evidence is supported by the use of statistical models and a large dataset. However, the abstract could be improved by providing more specific information about the sample size, the statistical significance of the findings, and any limitations of the study. Additionally, it would be helpful to include a brief summary of the key actionable steps that could be taken to improve the evidence, such as conducting further research to explore the underlying factors driving the changes in seasonality and considering the implications of these findings for public health interventions.

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.

Antananarivo is located in the central highlands of Madagascar and culminates at an altitude of 1280 m. It has a subtropical climate with a cold and dry season from May to October (with average minimal temperature around 11°C) and a hot and rainy season from November to April (with average maximal temperature around 27°C) (Figure 1). December, January and February are the 3 months with the highest rainfall and they also correspond to the lean season. Rice is by far most consumed staple food in Madagascar; it furnishes more than 50% of the average calorie ration of the country. The cropping calendar varies greatly according to rice species and climate conditions of the regions, but about 70% of the rice produced in the country is harvested between April and June. Because of its predominance in the agriculture and diet, the seasonal production of rice drives seasonal movements in food prices and overall food consumption [25]. Monthly means of daily maximum/minimum temperatures and rainfall in Ivato station (over the period 1976–2015). Sc: DGHCN/daily Since the 1950s, climate change has led to a rise in temperatures in Madagascar, particularly in the dry season [26]. The rainy season is also being delayed [27]. The frequency of extreme events such as cyclones, floods and droughts is increasing. All these changes are likely to modify seasonal patterns of mortality. Antananarivo has undergone a major epidemiological transition in the last decades, only interrupted by a mortality crisis in the mid-1980s caused by the combination of the resurgence of malaria and food shortages [28,29]. Life expectancy first declined from 56 in 1976 to 47 in 1986, before increasing steadily to reach 64 years in 2015 (Figure S1). This progress was mostly driven by a decline in under-five mortality, which fell to 34 deaths per thousand live births in 2015, from 116‰ in 1976 (a 70% decline). By comparison, there has been virtually no improvement in survival changes in adults: the risk of dying between ages 15 and 60 was 280 deaths per thousand in 1976, peaked at 473‰ in 1986, at the height of the crisis, and declined again to 283‰ in 2015. The contrast between child and adult mortality suggests that survival gains were for the most part achieved through public health interventions targeting diarrheal and vaccine-preventable diseases. Demographic and Health Surveys show increases in the percentage of children who received all 8 basic vaccinations (BCG, DPT1-3, Polio 1–3 and measles), from 43% in 1992 to 62% in 2008–2009 in the country [30]. Chronic malnutrition has been slightly reduced; the prevalence of stunting in children under age 5 declined from 60% in 1992 to 50% in 2008–2009 (47% in the capital). In contrast, there has been little progress in skilled attendance at birth, access to improved water sources and sanitation. According to the 2008–2009 DHS, only 14% of the population of the capital lived in households with improved, non-shared toilet facilities [30]. Seventy percent of the population had access to water from public taps or standpipes. Overall, the country’s health situation remains exceptionally fragile, as illustrated by recent outbreaks of plague (in 2014 and 2017) and measles (in 2019). Madagascar has one of the lowest levels of per capita health spending in the world, and more than three quarters of the population live in extreme poverty [31]. Still, the epidemiological transition is well underway. As a result of population ageing and changes in risk factors, the distribution of causes of death has changed considerably. In the period 1976–1980, 54% of deaths registered in Antananarivo were due to communicable, maternal, neonatal and nutritional conditions, but this proportion had dropped to 21% in 2011–2015 (Figures S2 and S3). This study is based on data on 249 421 residents of Antananarivo-city who died between 1976 and 2015. This corresponds to the central administrative sector of Antananarivo-Renivohitra, with a population estimated at 1.28 million inhabitants in 2018 (5% of the national population) [32]. It was not possible to reconstitute an individual database for the period before 1976 because the registers are lost. All deaths that occur within this area should be reported to the BMH. About 60% of deaths occur at home. Relatives of the deceased contact the BMH and a physician is sent to the house of the deceased to establish a cause, based on the information provided by the family on the symptoms and circumstances preceding the death, as well as available medical documents. This is equivalent to medical certification and is different from verbal autopsy methods that have been primarily developed to identify the probable cause of death in the absence of a physician. For facility deaths, the reports are filled in by medical personnel and transmitted to the BMH by relatives. The completeness of reporting of deaths among adults (with a physician-certified cause of death) was higher than 90% in the intercensal period 1975–1993. Estimates of completeness will be updated when the detailed population counts from the 2018 census become available. In recent years, under-five mortality rates inferred from the BMH are aligned with trends derived from Demographic and Health Surveys [24]. Cause-specific mortality fractions derived from the registers are also consistent with epidemiological models [24]. The team in charge of certifying deaths currently consists of eight physicians, all of whom have been trained on the application of the International Classification of Diseases (ICD). For home deaths, an ICD-10 code is assigned based on the cause noted in plain text in the information sheet used in post-mortem interviews. For health facility deaths, the ICD code is based on the cause mentioned in the death certificate, which can come in various formats (directly with an ICD-10 code, a code from a previous revision of the ICD or reported in plain text). In the past, not all deaths in the registers had an ICD code but one physician with special training ensured that all deaths were coded in ICD-9 when registers were digitalized. To group causes of deaths in broad categories, we used the hierarchical cause-of-death list established by the Global Burden of Disease (GBD) Study 2016 [33]. This list has four levels. The first level distinguishes between (a) communicable, maternal, neonatal, and nutritional diseases, (b) non-communicable diseases, and (c) injuries. The second level refers to 21 broad categories, such as, for example, diarrhea, lower respiratory and other common infectious diseases among one group of causes. The third and fourth levels refer to more detailed causes of death, such as, for example, intestinal infectious diseases (level 3) and typhoid fever (level 4). For this study, we considered only the second level of the GBD hierarchy. All ICD 9 codes were mapped to a GBD cause of death. Some ICD codes were considered ‘garbage codes’. This refers not only to causes identified as ‘undefined’ in the specific ICD chapters, but also deaths attributed to causes which should not be considered as initial causes, such as dehydration or septicemia. We used a simplified redistribution algorithm to map these codes to acceptable GBD causes. For example, ill-defined cardiovascular diseases were redistributed to ischemic heart disease and other cardiovascular and circulatory diseases. The redistribution is summarized in the Appendix and described in detail elsewhere [24]. We conducted a sensitivity analysis using ICD-9 chapters before any redistribution of garbage codes, and results were similar to those obtained with the GBD cause categories (Appendix). Mortality rate ratios associated with months are obtained from a Generalized Additive Mixed Model using a Negative Binomial distribution, a generalization of the Poisson distribution that accounts for overdispersion. The model includes a penalized regression spline [34] to model long-term trends in mortality and avoid over-fitting, month as a random effect to model seasonality and year as a random slope to assess any change in seasonality, after stratifying by age groups. Age groups consisted of infants (less than 1 year old), young children (1 to 4 years old), older children and young adults (5- to 59-year-olds) and people aged 60 or above. We used the Bayesian (BIC) Information Criteria to choose the best model among a model with penalized splines for the trend only (model 1); a model with random intercepts for months (model 2); and a model with random slopes and random intercepts (model 3). Retaining models that minimized the BIC with a difference of more than 10 [35] allowed us to characterize first, if seasonality was present (model 2) and second if it was changing over time (model 3). Because of the unequal number of days in a month, we multiplied each monthly death count by 30.4 and divided by the number of days in each month. The full model (model 3) can be expressed as follows: where yt are monthly counts of deaths, s() is a penalized spline, j ∈ {Jan., Feb., …, Dec.}, Yeari∈−20,−19,…0,1,…19,20 where 0 represents 1996, the middle of the analysis period and t ∈ {1, 2, …, 480} is a continuous variable reflecting the count of month. We evaluated goodness-of-fit by visual inspection of the deviance residuals, considering that a good fit was obtained if 95% of the deviance residuals were between −2 and 2 standard deviations and no large outliers were present. All analyses were conducted using R statistical software.

Based on the provided information, it seems that the focus is on analyzing seasonal patterns of mortality in Antananarivo, the capital city of Madagascar, and understanding how these patterns have changed over time. The study highlights the impact of the epidemiological transition on the seasonality of mortality, with shifts in disease patterns being the main drivers of trends in all-cause mortality.

To improve access to maternal health in this context, the following innovations could be considered:

1. Mobile Health (mHealth) Solutions: Implementing mobile health technologies, such as text messaging and mobile apps, to provide pregnant women with important health information, reminders for prenatal visits, and access to teleconsultations with healthcare providers.

2. Community Health Workers: Training and deploying community health workers to provide maternal health education, antenatal care, and postnatal support to women in remote or underserved areas.

3. Telemedicine: Establishing telemedicine services to enable pregnant women in remote areas to consult with obstetricians and receive prenatal care remotely, reducing the need for travel and improving access to specialized care.

4. Maternal Health Vouchers: Introducing a voucher system that provides pregnant women with financial assistance to cover the costs of maternal health services, including prenatal care, delivery, and postnatal care.

5. Maternal Waiting Homes: Establishing maternal waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to stay closer to the facility during the final weeks of pregnancy, ensuring timely access to skilled birth attendants.

6. Transportation Support: Providing transportation support, such as ambulances or transportation vouchers, to pregnant women in remote areas to facilitate their access to healthcare facilities for prenatal visits and delivery.

7. Strengthening Health Infrastructure: Investing in improving healthcare infrastructure, including the construction and renovation of maternal health facilities, to ensure that pregnant women have access to quality care.

8. Health Education and Awareness Campaigns: Conducting targeted health education and awareness campaigns to promote maternal health practices, including the importance of prenatal care, nutrition, and hygiene during pregnancy.

It is important to note that the specific needs and challenges of the local context should be taken into consideration when implementing these innovations. Collaboration between government agencies, healthcare providers, and community organizations is crucial for the successful implementation and sustainability of these initiatives.
AI Innovations Description
The provided information is a research study on the seasonal patterns of mortality in Antananarivo, the capital city of Madagascar, from 1976 to 2015. The study examines the changes in all-cause and cause-specific mortality by age groups and evaluates how these patterns have evolved over time.

To improve access to maternal health based on this research, the following recommendation can be developed into an innovation:

1. Enhance healthcare services during the hot and rainy season: The study found that the risks of child mortality were highest during the hot and rainy season. To address this, innovative approaches can be developed to ensure that maternal healthcare services are readily available and accessible during this period. This could include mobile clinics, telemedicine services, or community health workers equipped with necessary resources to provide maternal healthcare in remote areas.

2. Strengthen public health interventions targeting infectious diseases and nutritional deficiencies: The study highlights that the decline in child mortality seasonality is attributed to improvements in the burden of infectious diseases and nutritional deficiencies. Innovations can focus on strengthening public health interventions such as vaccination campaigns, nutritional programs, and education on hygiene practices to further reduce the impact of these factors on maternal health.

3. Improve access to skilled attendance at birth: The study mentions that there has been little progress in skilled attendance at birth. Innovations can be developed to improve access to skilled healthcare professionals during childbirth, especially in rural and underserved areas. This could involve training and deploying midwives, implementing telemedicine platforms for remote consultations, or establishing birthing centers in areas with limited healthcare facilities.

4. Utilize technology for data collection and analysis: The study relied on death registers and manual data collection methods. Innovations can leverage technology to improve data collection and analysis, allowing for real-time monitoring of maternal health indicators. This could involve the use of electronic health records, mobile applications for data collection, and data analytics tools to identify trends and patterns in maternal health.

Overall, the recommendation is to develop innovative solutions that address the specific challenges identified in the research study, such as improving access to maternal healthcare during high-risk seasons, strengthening interventions for infectious diseases and nutritional deficiencies, enhancing skilled attendance at birth, and utilizing technology for data collection and analysis.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening healthcare infrastructure: Invest in improving healthcare facilities, including hospitals, clinics, and maternity centers, particularly in rural areas where access to quality maternal healthcare is limited.

2. Enhancing transportation services: Develop and improve transportation systems to ensure that pregnant women can easily access healthcare facilities, especially in remote areas. This could involve providing ambulances or other means of transportation for pregnant women in need.

3. Increasing community awareness and education: Implement community-based programs to raise awareness about the importance of maternal health and educate communities on prenatal care, safe delivery practices, and postnatal care. This can be done through health campaigns, workshops, and partnerships with local organizations.

4. Training and capacity building: Provide training and capacity building programs for healthcare professionals, including midwives, nurses, and doctors, to enhance their skills in providing quality maternal healthcare services. This can include training on emergency obstetric care, prenatal and postnatal care, and family planning.

5. Improving access to essential medicines and supplies: Ensure that healthcare facilities have an adequate supply of essential medicines, equipment, and supplies needed for safe delivery and postnatal care. This includes access to contraceptives, prenatal vitamins, and emergency obstetric care supplies.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of pregnant women accessing healthcare facilities, the percentage of skilled birth attendants present during deliveries, and the maternal mortality rate.

2. Collect baseline data: Gather data on the current status of maternal health in the target area, including the number of healthcare facilities, transportation infrastructure, community awareness levels, and healthcare provider capacity.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the selected indicators. This model should consider factors such as population demographics, geographical distribution, and existing healthcare infrastructure.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of the recommendations. Adjust the parameters of the model to reflect different scenarios and analyze the outcomes.

5. Evaluate results: Analyze the results of the simulations to determine the potential impact of the recommendations on improving access to maternal health. Assess the changes in the selected indicators and compare them to the baseline data.

6. Refine and iterate: Based on the evaluation of the simulation results, refine the recommendations and the simulation model if necessary. Repeat the simulation process to further optimize the proposed interventions.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on implementing the most effective interventions.

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