Tag: mortality rate

Lav Agarwal, Health Ministry’s Face for Initial Media Briefings, Tests Positive for COVID-19

Health ministry media briefings are now addressed by Union Health Secretary Rajesh Bhushan. Agarwal is also present at them.

New Delhi: Joint Secretary in the health ministry Lav Agarwal, who addressed daily media briefings on the COVID-19 outbreak when it was picking up pace in the country, has tested positive for COVID-19.

The 48-year-old tweeted that he will be isolating at home.


A 1996-batch IAS officer of the Andhra Pradesh cadre, Agarwal was the central government’s official spokesperson at the national media briefings on the COVID-19 situation in the country throughout April and May.

The briefings were first held daily and were open to journalists of all media outlets.

Gradually, they became sporadic as India’s COVID-19 numbers and growth rates grew. Restrictions were also placed on questions from the media, with several reports alleging that the panel only took questions from news agency ANI and the public service broadcaster DD.

Agarwal’s role in the briefings had come under the spotlight especially as he, in his addresses, had repeatedly sought to highlight the role of the Tablighi Jamaat in the initial spread of the coronavirus in India, despite his own ministry’s guidelines against stigmatisation of a particular sect or person over carrying COVID-19.

In his briefings, Agarwal had also focused on the comparatively ‘low fatality rate’ in India, which most experts feel is neither an indicator of suitable handling of the crisis, nor a reflection that it has not spread.

Health ministry media briefings are now addressed by Union Health Secretary Rajesh Bhushan. Agarwal is also present at them.

Agarwal had also been part of the Central teams that had been constituted to look into containment measures in states with high coronavirus caseloads, reported Hindustan Times.

‘India Probably Has Tens of Millions of COVID-19 Infections Already’

Transmission is happening within the community, the challenge is to organise our healthcare system to deal with the consequences of hundreds of millions of infections, says Ramanan Laxminarayan, director of the Washington-based Center for Disease Dynamics.

New Delhi: Citing numbers that could come as a shock to many people, Ramanan Laxminarayan, director of the Washington-based Center for Disease Dynamics, Economics & Policy has said that India probably already has tens of millions of COVID-19 infected people. He says the nationwide part of the ICMR’s recent serological survey which suggests that 0.73% of the population has been exposed to the virus corroborates this. Laxminarayan says he believes India could have 200 million COVID infected people by September.

In an interview to The Wire, Laxminarayan said there is no real difference between COVID-19 infections and COVID-19 cases except for the fact that what we call ‘cases’ are officially recorded infections whereas other infections have either not been detected or recorded. The majority of these will be asymptomatic people although a few might have mild infections which they have not bothered about.

However, Laxminarayan refused to relate his forecast of 200 million infections by September to a corresponding figure for the number of deaths.

He said that whilst the phrase ‘community transmission’ may not be a technical phrase, its meaning in common parlance is clearly understood. It means that people are getting infected from within the community without knowing from whom or how. In other words, the source of the infection cannot be traced to someone who has come from abroad or been in touch with such a person. In that sense, he said, there was no doubt that community transmission was happening in India.

Laxminarayan said he was certain that the return of millions of migrants  to villages in Bengal, UP, Bihar, Orissa, Chhattisgarh, Jharkhand, etc. will lead to a surge of infections in these rural hinterlands. However, he was reluctant to accept reports in several newspapers that India would see its peak only in mid-November. Instead, he said India would see several regional peaks as individual states hit their peak at different times. He said in overall terms, the picture or graph is of a high plateau stretching for several weeks or months with different states or regions peaking at different times.

Questioned about the second part of the ICMR serological survey – which has not been officially released but has been widely leaked – which suggests that in hotspots and containment zones up to 30% of the population could be infected, Laxminarayan said it seems difficult to believe the percentage could be so large because such a sizeable number have not been infected so quickly in any other country. However, if true, it would certainly be good news because it would take these containment zones and hotspots a lot closer to achieving herd immunity which, at an R-O of 2, needs 50% infection levels.

However, Laxminarayan pointed out that even if the leaked percentage figure of 30% is correct, a lot depends upon the size of these hotspots and containment zones. It would be meaningful if a containment zone comprises an entire city of the size of Delhi or Mumbai. It would be a lot less significant if containment zones are just a few streets or a small locality in a city.

Laxminarayan was dismissive of attempts to compare India’s mortality rate, whether per thousand or as a percentage, with that of other countries to suggest that we are doing better. Not only are mortality rates calculated differently in different countries and thus not comparable but, furthermore, he illustrated his scepticism by citing the example of setting fire to two forests, one of which is 10 acres and the other 100 acres. He said both will burn at the same rate and the destruction in the burnt areas will be equivalent but, obviously, the bigger forest will take longer because of its size.

Laxminarayan also seemed to suggest that there are many questions that can be asked about the accuracy and reliability of India’s mortality rate. Are all COVID-19 deaths being counted as COVID deaths? The recent controversy in Delhi and, now, in Mumbai as well suggests that a fair number of COVID deaths are not being taken into account.

He said the biggest challenge facing the government as it gears up to handle a possible 200 million infections by September is organising the healthcare system to respond adequately. He said the answer is to move medical staff and facilities from an area where the virus is under relative control to another where it is peaking. There is no way that in the limited time available, India can actually build up a sufficient number of doctors or trained medical staff. He was particularly dismissive of government plans to rapidly train doctors to become intensivists and to adopt telemedicine handling of ICUs.

Asked whether the decision of the Tamil Nadu government to impose a stringent lockdown on Chennai and three other districts from June 19 is correct, Laxminarayan said it is not necessary. He said the situation in Chennai was under control and this second lockdown is not needed.

Finally, Laxminarayan explained that the fact nearly 50% of COVID-19 deaths in India are of people under 60 compared to just 4% in Italy and 5% in the whole of Europe does not refute the belief that  India’s strength against the virus is the fact that it has a young population where 90% are under 60. He said there are many more people in India under the age of 60 than in Italy, which is why it is to be expected that more people of this age group would die in India than in Italy. But, he forcefully argued, that does not undermine the argument that with a young population, India’s ability to withstand the virus is greater.

We’re Focusing on National Data on COVID-19 When We Should Be Looking at State-Level Trends

Given the variation in doubling times and effective reproduction numbers across states, the pandemic is much more likely to peak at quite different times in different states.

There is enormous variation across Indian states in the magnitude, severity and stage of the COVID-19 pandemic. That is why national data is of limited use in either understanding the progression, or in formulating policies for containment and mitigation of the pandemic. 

In recent research we have documented state-level variation of the COVID-19 outbreak in India in a comprehensive manner (a pre-print version of our paper can be found here). To generate a relatively complete picture of across-state variations, we have looked at three crucial aspects: rapidity with which the pandemic is spreading; adequacy of testing; and impact of the pandemic on mortality. 

To track growth of the pandemic, we look at the effective reproduction number (the average number of persons that will be infected by an infected person) and the doubling time of total cases; to assess adequacy of testing, we look at the test positive rate (positives per person tested); and to ascertain the impact of the pandemic on mortality, we look at the case fatality rates (deaths per reported case).

Using seven-day average values of various metrics, we classify and colour-code states into three groups: states that are doing well (green); states that are not doing well and need improvement (red); and, states which fall in between (grey). We collect together this information in an easy-to-interpret fashion in a summary dashboard, which is updated daily, in our app hosted at COV-IND-19 Study Group (see the “Metrics” tab). The dashboard with data up to June 4 is presented in Figure 1.   

Figure 1: COVID-19 in India Dashboard. (a) Case fatality rate is the number of COVID-19 deaths for every reported case. (b) Doubling time is the number of days it takes for total reported case counts to double. (c) Effective reproduction number is the average number of persons an already-infected person will infect. (d) Test positive rate is the number of positives per test.

Growth of the Pandemic

On March 24, a day before the start of the national lockdown, the doubling time of total cases at the all-India level was 3.56 days. In the week ending on June 4, the all-India doubling time had increased to 15.19 days – a significant improvement.

But this all-India number hides enormous variation (panel b, Figure 1). Four states – Assam, Uttarakhand, Haryana and Jharkhand – had doubling times of lower than 10 days; and, five states – Andhra Pradesh, Rajasthan, Madhya Pradesh, Gujarat and Punjab – had doubling times of over 20 days. 

Also read: How We Can Use Technology to Help Identify Higher Risk COVID-19 Patients Early

The all-India effective reproduction number has declined from 3.36 on March 24 to 1.27 in the week ending on June 4. While this is a significant decline, it is important to keep in mind that the pandemic will only start slowing down when the effective reproduction number falls and stays below, 1.

India has not yet reached that stage.

In fact, the steady decline in the effective reproduction number that was witnessed over the month of April has been halted since early May. There has been no significant decline in the effective reproduction number between May 1 (1.22) and June 6 (1.27) (see plot for Time-varying R under the “National Forecast” tab in our app at COV-IND-19 Study Group).  

Also read: Has Window of Opportunity Opened by Dip in Daily Growth Rates Closed?

The slowing down of the decline in the effective reproduction number at the all-India level should also be seen in the context of state-level variations (panel c, Figure 1). While five states –  Madhya Pradesh, Gujarat, Rajasthan, Bihar and Maharashtra – have attained effective reproduction numbers close to 1, only in Madhya Pradesh has it fallen below 1. At the other end of the spectrum, there are five states with effective reproduction numbers of greater than 1.75, and two among them – Haryana and Uttarakhand – have effective reproduction numbers even larger than 2.

Adequacy of testing

A relatively accurate picture of the pandemic only emerges when there is adequate testing. In fact, all the metrics that are commonly used for studying various aspects of the pandemic rely on adequacy of testing. In our study, we use test positive rates (positives per person tested) to assess adequacy of testing. If the test positive rate is high and/or rising, it means that the ambit of testing is too narrow. This provides evidence that testing is inadequate.

Also read: India’s COVID-19 Testing Conundrum: Why the Govt and Its Critics Are Both Right

At the all-India level, the test positive rate was 4.3% in the week ending on June 4. While this is low by international standards, it is worrisome that the test positive rate has been slowly rising since May 01 – with a sharp rise on June 05 (see Temporal pattern of testing in India under the “Testing” tab in our app at COV-IND-19 Study Group). 

When we look across states, we see (from panel d in Figure 1) three key states, which together accounted for more than 50% of total cases in early June, with relatively high test positive rates: Maharashtra (14.22%), Delhi (9.6%), Gujarat (7.9%). Moreover, the temporal pattern of the test positive rate in these states display an upward trajectory – rapidly rising in Maharashtra and Delhi, and slowly rising in Gujarat.  

The implication of this is important to spell out. States which together accounted for more than half of all the reported cases in the country have grossly inadequate testing. Thus, there is likely to be significant underestimation of the true number of cases in these states, and by implication, in the case count for the country as a whole.

COVID-19 mortality

In the week ending on June 4, the case fatality rate of COVID-19 at the all-India level was below 3%. Compared to many countries in the world where the pandemic has taken hold, this is a relatively low figure.

Also read: Why the ‘Gujarat Model of Development’ Has Seen the Highest COVID-19 Fatality Rate

But like other metrics, there is substantial variation in case fatality rates across states (see panel a in Figure 1). Seven states have case fatality rates below 1%: Assam, Odisha, Bihar, Jharkhand, Haryana, Tamil Nadu, Uttarakhand and Kerala. These states have so far managed to limit the impact of the pandemic in terms of lives lost.

At the other end of the spectrum are states with relatively high case fatality rates, i.e. states in the ‘red’ group (see panel a in Figure 1). According to the 7-day average, there are 5 states in the red group: Gujarat, West Bengal, Madhya Pradesh, Maharashtra and Telangana. These states have so far been least successful in limiting the mortality impact of the pandemic. 

Take-home message

Our analysis has a simple take-home message for analysts and policy makers. The COVID-19 outbreak in India, as in any other large country, displays large state-level variations.

The concept of a peak of the pandemic at the all-India level is at best ambiguous. Given the variation in doubling times and effective reproduction numbers across states, the pandemic is much more likely to peak at quite different times in different states.

Given the spatial and temporal pattern of the pandemic’s spread, it is extremely important to prioritise policies. Resources must be mobilised to help one cluster of states and then move to the next cluster.

It might be useful for the central government and Indian Council of Medical Research (ICMR) to classify states in terms of the phases of the epidemic. Even as the worst-hit states are being addressed, the next set could be put on high alert. A dynamic policy intervention will be required to deal effectively with the cascading pattern of the pandemic across Indian states.

The authors are researchers across fields.

COVID-19 Data in South Asia Shows India is Doing Worse than Its Neighbours

In terms of the spread of the disease, Sri Lanka has the best position and India is the worst hit. Forty days into the epidemic, India has consistently recorded the highest cumulative and daily case count and highest death rate among the four largest SAARC countries.

This is the first of a three-part analysis looking at the comparative performance of major South Asian nations in terms of managing the public health and economic fallout of the novel coronavirus.

In recent days, comparisons of India’s COVID-19 situation with countries in Europe and North America have been often used by the Indian government to highlight the apparent success of its pandemic strategy.

These comparisons are not fair. Hence, the conclusions are largely misleading.

The spread of the pandemic has been very different in Europe and North America from most other countries in the world, including India. This has puzzled scientists and public health experts. Many hypotheses explain this puzzle, like climate, geography, genetic make-up, age-structure of population, BCG vaccination, are currently under investigation.

No matter which of these factors turn out to be important, the fact of the difference in the shape of the pandemic between Europe and North America, on the one hand, and the rest of the world, on the other, remains salient.

Also Read: Tracking COVID-19 in India: The BCG Hypothesis

The effectiveness, or otherwise, of India’s pandemic strategy (at the Central level) can be better assessed through a comparison with its neighbours. In addition to being similar in terms of economic structure and average income, India’s neighbouring countries are also similar in all these respects that might account for differences in the pandemic’s spread – climate, geography, genetic make-up, age structure, BCG vaccination. Therefore, variation in the spread of the pandemic in these countries can be largely attributed to country-specific responses.

Motivated with this understanding, we offer a comparative analysis of the spread of, and government responses to, the COVID-19 pandemic in four South Asian countries: Bangladesh, India, Pakistan and Sri Lanka.

The spread of COVID-19

The first case of COVID-19 was reported at very different times in the four countries we have studied. The first to report a case was Sri Lanka – its first case of COVID-19 was reported on 27 January, 2020. Within three days, India reported its first case – on 30 January. It would be another four weeks before Pakistan reported its first case on 26 February while Bangladesh reported its first case on 8 March, a week and a half after Pakistan.

According to the Worldometers website, on May 8, the cumulative case count in the four countries are as follows: Bangladesh has 13,134 cases; India has 59,693 cases; Pakistan has 26,435 cases; and Sri Lanka has recorded a total of 824 cases.

Part of the variation in the cumulative case counts comes from the different duration for which the pandemic has been active in each country. To get a better comparative picture of the rapidity of the spread across the four countries, we therefore start counting cumulative cases in each country since it first reported 50 or more COVID-19 cases.

Figure 1. Logarithm of cumulative case count of COVID-19 till May 08, 2020 in four South Asian countries. A steeper line indicates faster growth of total case counts. Source: The data are taken from the Our World in Data website.

We summarise the comparative picture of the COVID-19 outbreak in the four countries using several charts. Figure 1 presents cumulative case counts since they first reported their 50th case; and Figure 2 presents the daily case counts since they first reported their 50th case count.

The two charts together highlight some important differences across these four countries regarding the spread of COVID-19. Sri Lanka is the clear outlier and the best performer here. It has had the outbreak for the longest duration but has managed to limit the case count the most. Not only is its total case count several fold lower than in India and Pakistan (Figure 1), but its daily growth rate has also been declining over the past several days (Figure 2).

Figure 2. Daily case count of COVID-19 till May 08, 2020 in four South Asian countries. Source: The data are taken from the Our World in Data website

India and Pakistan have recorded the highest number of cases (Figure 1), with India having about 11,499 more cases than Pakistan on the 53rd day of the outbreak (since each country first reported 50 or more cases). When we look at the number of daily cases, we see that not only are they high but have also been rising for the past several days (Figure 2).

The picture for Bangladesh is probably somewhere in between, on the one hand, Sri Lanka, and on the other, India and Pakistan. The total case count in Bangladesh was relatively low on May 8 only because the epidemic started late in that country. But if we look at Figure 1, we see that its cumulative case count has been comparable to India’s and Pakistan’s on similar days since it first reported its 50th case. In Figure 2 we also see that the daily case count is rising in Bangladesh. But the rate of increase in case counts seems to be lower in Bangladesh: in Figure 1, the total case count trajectory seems less steep for Bangladesh than for India and Pakistan.

Death rate

In Figure 3, we look at the death rate of COVID-19 (deaths per 100 confirmed cases) in the four countries. This gives an indication of how fatal the pandemic has been in each country. Bangladesh started out with a very high death rate, but it has managed to reduce it drastically to below 2% within a month of the outbreak. In Sri Lanka, there was an initial period when the death rate was rising. But from day 20 onwards, the death rate in Sri Lanka has fallen steadily and is now close to 1%. In contrast, Pakistan and India have both seen the death rate rise over time. In terms of levels, the death rate at present is highest in India – at about 3.52%.

Figure 3. Death rate of COVID-19 (deaths per 100 confirmed cases) in four South Asian countries till May 08, 2020. Source: The data are taken from the Our World in Data website.

Conclusion

In terms of the spread of COVID-19, Sri Lanka has the best position and India is the worst hit. After about 40 days into the epidemic (which, in our analysis, starts when a country first reports 50 or more cases), India has consistently recorded the highest cumulative and daily case count. Its daily case count has kept increasing, and it has recorded by far the highest death rate among these four countries.

To be continued.

(Deepankar Basu is Associate Professor in the Department of Economics, University of Massachusetts Amherst; Priyanka Srivastava is Associate Professor in the Department of History, University of Massachusetts Amherst.)

Fund Transfer Delay Common Cause for Concern in States’ Health Performance 

Why are there such jarring variations in states’ overall performance?

The Health Index, titled Healthy States, Progressive India, published some time ago, presents an assessment of states and Union Territories, based on their health performance. 

The second edition of the report was an outcome of NITI Aayog’s resolute optimism that a ranking exercise would encourage a competitive approach for potentially better outcomes, and would therefore be crucial for meeting sustainable development goals (SDGs). It takes the period 2017-18 as the reference year, and 2015-16 as the base year.  

The Index evaluates the status of health in each state at two levels: overall performance and its incremental improvement.

Overall performance is an aggregate measure based on indicators in three domains: health outcomes, governance and key inputs and processes. It is judged on the basis of the composite index score, and accordingly categorises the states as ‘Front-runners’, ‘Achievers’ and ‘Aspirants’. 

The incremental progress, on the other hand, is calculated as the change in composite Index scores as compared to the base year. It is thus an indication of the levels of momentum in states to realise health gains over the period from the base year to reference year. 

Enormous degree of variation

There is significant variation in states’ overall performance. The composite index score of ‘Front runners’ is higher than 58.88, while that of ‘Aspirants’ has dwindled to below 43.74. The ‘Achievers’ hovered between 43.74 and 58.88.

The score for Kerala, ranked as the best performer for the second time, stands at 74.01, and Uttar Pradesh at the bottom end of the list has scored 28.61.

Andhra Pradesh (65.13), Maharashtra (63.99), Gujarat (63.52) and Punjab (63.01) are the other top performing states.

Also read: Why the Boost to Healthcare in Budget 2019 Should be Viewed With Caution

Together with Uttar Pradesh, Bihar (32.11), Odisha (35.97), Madhya Pradesh (38.39), and Uttarakhand (40.20) emerged as the least performing states.      

There is wide disparity on the incremental progress too. States showing nil or even negative incremental change fared as ‘Not Improved’, those with 0.01 to 2.0 points increase as ‘Least Improved‘, an increase of  2.01 to 4.0 points marked them as ‘Moderately Improved’.

The ‘Most Improved’ ones reflected more than a four point increase. Among the larger states, twelve states displayed a positive incremental change in the Index score. But only seven out of these twelve states made significant incremental progress leading to improvement in the overall performance position.  

Haryana was judged as the ‘Most Improved’ state as its health score increased the maximum, from 46.77 to 53.51. Rajasthan and Jharkhand too registered significantly high incremental performance.

Haryana qualified for this position not only on account of progress in most health outcome indicators like NMR (neonatal mortality rate), U5MR (Under 5 Mortality Rate), LBW (low birth weight in newborns) and SRB (sex ratio at birth), improvement in process related indicators too.

Healthcare continues to be accessible only at extremely high economic costs and social hardship to a vast number of people, Photo: Reuters

These indicators ascertained the proportionate size of vacant staff at the level of nurses, medical officers, institutional delivery, and the pace in which the fund flows from Centre to the spending unit. Bihar, on the other end, displayed the most negative incremental change.  

Need to probe input and process indicators 

The Index does a good job in providing a broad overview of performance of health services. The next step is an analysis of the areas highlighted in the report to examine the fundamental causes behind the problem.

As noted, the composite index discussed above is composed of 23 indicators, representing three domains – health outcomes, governance and information, key inputs and processes. 

This article narrows down its focus on the sub components of the third domain, pertaining to key inputs and processes. Some important indicators of this domain include time taken for National Health Mission (NHM) funds to reach the implementation agencies, number of functional health facilities, proportion of vacant health care provider positions in public health facilities, like nurses, doctors, ANMs, specialists.

Also read: ‘Health for All’ as a Political Question

These indices need to be probed further to understand the causal factors resulting in deficits, and corresponding low scores for many states.  There is a need to identify action areas for states, and apply the right strategies for improving their health outcomes. Only when each state takes this course for maximum incremental improvement, will the nation’s performance on health services go up. 

Delays in fund flow

A Centrally-Sponsored Scheme cannot be implemented efficiently at the ground-level without ensuring that funds from the Centre reach the implementing agencies in a timely manner. 

Data on the status of flow of Central NHM funds highlights huge dissimilarity in states (and UTs) in terms of the average number of days taken to transfer the fund from the Union level to Health Society responsible for expending these funds. A delay in fund transfer has a corresponding adverse effect on its utilisation. 

Telangana has the most efficient system with the transfer happening on the same day. Bihar and Jharkhand are plagued by delays up to 191 and 187 days respectively.

Some others like Odisha, Madhya Pradesh, Tamil Nadu, and some smaller states like Tripura, Meghalaya and Mizoram are affecting the fund transfer relatively sooner. 

Average number of days for transfer of Central NHM fund to implementing agency

NHM is governed by complex administrative procedures. An earlier study shows that the NHM budget has a break-up of over 1000 heads, with a very limited flexibility for utilising funds. The funds can be released by the state government only upon the issuance of a Sanction Order (SO).

Also read: Baghel Government Fails to Tackle Renal Failure in Chhattisgarh Village

This is a major factor contributing to the delays. Bottlenecks are also created by the fact that State Health Societies (SHSs) and the implementing agencies lie outside the administrative purview of the states.

Furthermore, the NHM budget has a break-up of over 1,000 heads, with a very limited flexibility for utilising funds, adding to the complicated architecture of fund release processes.

As a result, a high share of expenditure gets crowded in the last quarter of the financial year compounding the problem of low utilisation. Therefore, urgent efforts are needed to amend the rules and procedures that govern the release of public funds, with a focus to correct these systemic problems as a key strategy. 

Conclusion

It is hoped that the findings of this report lead to more intense discussions on issues of fund flow delays across states, in the policy circles. Fund transfer delay, and associated complexity is an area most states need to focus on. Both the Centre and the States need to take steps to mainstream the sector by addressing these deficiencies.

Interestingly, states who have been investing in nutrition and primary health care have managed to fix the systemic deficiencies, and are the high scorers in the Index. 

Achieving the SDGs on health will be difficult without understanding the causal factors of cracks in health care system. It is high time that the government paid attention to these loopholes that have long impeded progress in such an important sphere.

The impetus of the intensive process of bringing out the Health Index can be used to drive the nature of health services and kick start reforming institutional processes.   

Happy Pant is with the Centre for Budget and Governance Accountability. The views expressed in this piece are those of the author, and don’t necessarily reflect the position of CBGA. You can reach him at happy@cbgaindia.org 

Venezuela’s Soaring Murder Rate Has Plunged the Nation Into a Public Health Crisis

At the beginning of the 1980s, homicides were relatively rare in Venezuela. Now, it’s one of the most dangerous countries in Latin America.

Over the past three decades, Venezuela has shifted from being a peaceful country, to one of the most violent nations in the world. Decades of poor governance have driven what was once one of Latin America’s most prosperous countries to economic and political ruin.

The violent confrontations between anti-government demonstrators and forces loyal to president Nicolás Maduro in recent days, alongside the systemic breakdown of public services, have plunged Venezuela’s population into a public health crisis.

Most countries in Latin America have increased their life expectancy over the last 50 years. And Venezuela was no exception: the average life expectancy of the population rose by almost four years every decade from 1950 to 1990, thanks to improvements in healthcare, living standards and nutrition. In particular, there were major advances in reducing infant mortality and tackling infectious and parasitic diseases.

Also read: How Latin American Tolerance of Illiberalism Let Venezuela Slide Into Crisis

Had the pattern held, Venezuelan men – whose average life expectancy at birth was about 70 years in 1996 – should have averaged almost 77 years in 2013. Instead, as we report in our new research, men’s life expectancy increased by just one year and six months (from 70 to 71 and a half), while women’s increased by three-and-a-half years (from 76 to 79). The upsurge in violence and murder has stalled further gains.

Rising violence

At the beginning of the 1980s, homicides were relatively rare in Venezuela. At around eight per 100,000 people, it was almost on a par with the most peaceful nations in the region, such as Costa Rica (which had six homicides per 100,000 people in 1980). But the social and economic upheavals of the 1980s unleashed an unstoppable epidemic of violence, which spread across the country.

According to United Nations, the homicides rate in Venezuela was 53.7 murders per 100,000 persons in 2012; that’s higher than figures for many other Latin American countries, including Colombia, which had 30.8 murders per 100,000 people in the same year, while in the grips of an undeclared civil war. In the nation’s interior, the situation is even worse: according to estimations, in the capital city of Caracas, the mortality rate due to violent deaths in 2015 was around 120 homicides per 100,000 people.

Protesters confront trucks of the Bolivarian Armed Forces, in Caracas, Venezuela, April 30, 2019. Credits: Miguel Gutierrez/EPA

Most of the homicides in Venezuela are committed with firearms, which are widely available. While the government and opposition remain locked in turmoil, weapons have flooded onto the black market, where local police and armies have become the main smugglers. Worse, the government has implemented a policy to arm pro-government supporters, in order to contain protesters.

Male life expectancy in Venezuela was curtailed by almost two years, exclusively because of violent deaths between 1996 and 2013. Violence has offset improvements in the nation’s mortality rate, achieved by reducing the risk of death from cardiovascular diseases and other causes of death. Similar findings have been reported for Mexico where the war on drugs held back life expectancy gains in the new century.

Violence has also further negative impacts on the quality of life and psychological well-being of Venezuelan people. Men in Venezuela are dying earlier, but the burden of violence on people goes beyond homicides. Being exposed to a violent environment increases the risk of depression, alcohol abuse, suicidal behaviour, and psychological problems, such as fear – among other detrimental effects on people’s lives.

A bleak outlook

Venezuela’s future does not look promising. Outbreaks of political violence have intensified recently, partly due to the steady militarisation of the police. Random shootings against civilians, tear gas shot straight into homes, extrajudicial killings in military operations against street crime and forced disappearances of political dissidents are increasingly being reported by NGOs such as Human Rights Watch.

What’s more, severe shortages of food and medical supplies, and the total collapse of the public health system, have left Venezuelans unable to feed their families or access to basic healthcare. Infant and maternal mortality has increased again, and infectious and parasitic diseases, such as malaria, measles and diphtheria, have re-emerged.

The acute impacts of political and socioeconomic disintegration on mortality rates since 2013, highlighted by recent deadly demonstrations in Caracas, have yet to be measured. Public institutions in Venezuela have been forced to follow a strict policy of secrecy, and mortality and health data sources have not been updated, nor made publicly available since 2013. The stagnation in life expectancy found up to that year is likely to turn to decline, as this humanitarian crisis worsens.

José Manuel Aburto, PhD Candidate, Interdisciplinary Center on Population Dynamics, University of Southern Denmark and Jenny Garcia, PhD Candidate in Demography, Institut National d’Études Démographiques (INED)

This article is republished from The Conversation under a Creative Commons license. Read the original article.